A Dynamic View of Trauma/Hemorrhage-Induced Inflammation in Mice: Principal Drivers and Networks

Abstract

BackgroundComplex biological processes such as acute inflammation induced by trauma/hemorrhagic shock/ (T/HS) are dynamic and multi-dimensional. We utilized multiplexing cytokine analysis coupled with data-driven modeling to gain a systems perspective into T/HS.Methodology/Principal FindingsMice were subjected to surgical cannulation trauma (ST) ± hemorrhagic shock (HS; 25 mmHg), and followed for 1, 2, 3, or 4 h in each case. Serum was assayed for 20 cytokines and NO2 −/NO3 −. These data were analyzed using four data-driven methods (Hierarchical Clustering Analysis [HCA], multivariate analysis [MA], Principal Component Analysis [PCA], and Dynamic Network Analysis [DyNA]). Using HCA, animals subjected to ST vs. ST + HS could be partially segregated based on inflammatory mediator profiles, despite a large overlap. Based on MA, interleukin [IL]-12p40/p70 (IL-12.total), monokine induced by interferon-γ (CXCL-9) [MIG], and IP-10 were the best discriminators between ST and ST/HS. PCA suggested that the inflammatory mediators found in the three main principal components in animals subjected to ST were IL-6, IL-10, and IL-13, while the three principal components in ST + HS included a large number of cytokines including IL-6, IL-10, keratinocyte-derived cytokine (CXCL-1) [KC], and tumor necrosis factor-α [TNF-α]. DyNA suggested that the circulating mediators produced in response to ST were characterized by a high degree of interconnection/complexity at all time points; the response to ST + HS consisted of different central nodes, and exhibited zero network density over the first 2 h with lesser connectivity vs. ST at all time points. DyNA also helped link the conclusions from MA and PCA, in that central nodes consisting of IP-10 and IL-12 were seen in ST, while MIG and IL-6 were central nodes in ST + HS.Conclusions/SignificanceThese studies help elucidate the dynamics of T/HS-induced inflammation, complementing other forms of dynamic mechanistic modeling. These methods should be applicable to the analysis of other complex biological processes.

Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure

“…Interestingly, in addition to our prior demonstration of the central role of MCP-1 in stressed hepatocytes 15 , greatly increased MCP-1 levels were observed in livers from patients with fulminant hepatic failure 30 . We demonstrated that IL-6 was elevated but not connected to other inflammatory mediators in dynamic networks in mice undergoing trauma/hemorrhage 14 . In this study, IL-6 was also elevated with very low connectivity in NS, but .…”

“…Accordingly, we employed DyNA 14,15 to discern and compare the interconnections among inflammatory mediators in the PALF subcohorts over defined ranges of time. We hypothesized that patients with worse clinical outcomes would have more robust dynamic networks of inflammation, in line with the concept of pathology driven by self-sustaining inflammation.…”

“…We hypothesized that patients with worse clinical outcomes would have more robust dynamic networks of inflammation, in line with the concept of pathology driven by self-sustaining inflammation. To quantify the robustness of a given dynamic network, we performed DyNA using increasing stringency levels from 0.7 (a correlation value commonly used to characterize trajectories that move in parallel either up or down) 14,15 Fig. 3.…”

“…This concept is especially important given the volume of data needed to define drivers and biomarkers of complex diseases 10,13 . Recently, we utilized computational modeling techniques with measured circulating inflammatory mediators to describe dynamic mediator networks that suggest novel interactions and possible biomarkers in the setting of trauma and sepsis [14][15][16][17] . Using similar techniques, we have also demonstrated that PALF participants who survive with their native liver exhibit inflammatory network structures similar to LTx recipients 18 .…”

Central role for MCP-1/CCL2 in injury-induced inflammation revealed by in vitro, in silico, and clinical studies

“…For example, we have recently applied both mechanistic and data-driven computational modeling to help define the dynamic, multi-dimensional inflammatory response to T/HS in vivo [8][9][10][11][12]. The goal of the present study was to determine if combined in vitro/in silico studies could help elucidate key hepatic inflammatory mediators relevant to human T/HS.…”