title: A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks.
abstract: BACKGROUND
  Complex 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.
  
  
  RESULTS
  Mice 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 NO(2) (-)/NO(3) (-). 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
  These 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.
authors.affiliation: Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Qi
authors.initials: Q
authors.lastname: Mi
authors.forename: Gregory
authors.initials: G
authors.lastname: Constantine
authors.forename: Cordelia
authors.initials: C
authors.lastname: Ziraldo
authors.forename: Alexey
authors.initials: A
authors.lastname: Solovyev
authors.forename: Andres
authors.initials: A
authors.lastname: Torres
authors.forename: Rajaie
authors.initials: R
authors.lastname: Namas
authors.forename: Timothy
authors.initials: T
authors.lastname: Bentley
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Billiar
authors.forename: Ruben
authors.initials: R
authors.lastname: Zamora
authors.forename: Juan Carlos
authors.initials: JC
authors.lastname: Puyana
authors.forename: Yoram
authors.initials: Y
authors.lastname: Vodovotz
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D007249:Inflammation / Q000097:blood* / Q000209:etiology*; D016753:Interleukin-10 / Q000097:blood; D015850:Interleukin-6 / Q000097:blood; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D015999:Multivariate Analysis; D009566:Nitrates / Q000097:blood; D009585:Nitrogen Dioxide / Q000097:blood; D025341:Principal Component Analysis; D012771:Shock, Hemorrhagic / Q000097:blood* / Q000150:complications*; D014409:Tumor Necrosis Factor-alpha / Q000097:blood; D014947:Wounds and Injuries / Q000097:blood* / Q000150:complications*
chemical_list: D015850:Interleukin-6; D009566:Nitrates; D014409:Tumor Necrosis Factor-alpha; D016753:Interleukin-10; D009585:Nitrogen Dioxide