Porcine endotoxemia: Multiplexed cytokine analysis and mathematical modeling

Meeting Abstracts for the Society for Complexity in Acute Illness (SCAI) promotes exit from mitosis in mitotic pathways has not been understood well. Methods: In Fig. 1, we propose a general protein interaction network for regulating mitotic exit pathways in budding yeast. Using basic principles of biochemical kinetics, we transform the wiring diagram into a set of ordinary differential equations. Rate constants are estimated so as to explain right characteristics of all mitotic exit mutants. Results: Our model provides a rigorous account of the factors affecting the dual exit pathways, called FEAR (Cdc14 early anaphase release) and MEN (mitotic exit network). The model captures the dynamics of mitotic exit in wild-type and mutant yeast cells, including many details of the physiology, biochemistry, and genetics of the process. Although the model is similar to the recent model of Queralt et al (Cell, 2006), we have added new components to account for all observations on FEAR and MEN networks reflecting the latest knowledge of biology. (i) Cdc5 phosphorylates Net1, directly causing Cdc14 release in any cell cycle stage (Shou et al, BMC Molecular Biology, 2002). Cdc5 is not only part of MEN but also part of FEAR and can induce Cdc14 release even when other FEAR and MEN components are silent. (ii) Net1 has multiple phosphorylation sites. The model incorporates multiphosphorylation of Net1 by protein kinases; Cdk, Cdc5, and the Dbf2/Mob1 kinase in the MEN pathway (because Dbf2/Mob1 is downstream of Cdc15 in MEN and the effect is the same, we consider Cdc15 phosphorylation on Net1 in the model). (iii) Cdc15 acts downstream of Tem1 in MEN network. Even if Tem1 is inactive, overexpressed CDC15 can still make MEN active and sustain Cdc14 release. (iv) Net1 phosphorylation at Cdk consensus sites is an important part of FEAR; however, it is not an essential requirement for mitotic exit events (Azzam et al, Science, 2004). Conclusion: We propose a novel mechanism for multiphosphorylation of Net1 (an inhibitor of Cdc14) by several kinases: Cdk, Cdc5 (Polo), and Dbf2/Mob1 (through activation of Cdc15). The model explains factors affecting the activation and inactivation of FEAR pathway and MEN pathway in a rigorous way. Understanding how Polo-like kinases fit into the exit pathways is important because Polo-like kinases are being actively pursued as therapeutic targets in the treatment of human cancer.

doi:10.1016/j.jcrc.2009.06.023 Porcine endotoxemia: Multiplexed cytokine analysis and mathematical modeling Yoram Vodovotz a, Steve Chang b, Derek Barclay a, Brian Kubiak c, Chris Vieau c, Louis Gatto c, Kris Maier c, Cordelia Ziraldo a, Qi Mi d,e, Ruben Zamora a, Gary Nieman c a Department of Surgery, University of Pittsburgh, Pittsburgh, PA b Immunetrics, Inc., Pittsburgh, PA c Department of Surgery, Upstate Medical University d Department of Sports Medicine and Rehabilitation, University of Pittsburgh e Department of Mathematics, University of Pittsburgh

Objectives: We have previously created a series of mathematical models to address the complexity of acute inflammation in mice, rats, and humans. Swine represent an attractive preclinical species because of their physiologic similarity to humans and because of the

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ease of obtaining serial blood samples as well as continuous physiologic measurements. Herein, we sought to determine the principal drivers of inflammation in porcine endotoxemia and to model the process mathematically. We also sought to determine and model the time evolution of matrix metalloproteinases (MMPs). Methods: Female Yorkshire swine (25-35 kg) were subjected to endotoxemia (100 μg/kg, Escherichia coli 111:B4) delivered intravenously over 1 hour. Serial plasma samples were assayed for inflammatory cytokines (TNF, IL-1β, IL-6, IL-8, and IL-10; Luminex) and NO−2 /NO−3 (nitrate reductase). MMPs were assayed via gel electrophoresis (BioRad). Principal component analysis was carried out to discern principal drivers of inflammation. An equation-based model that describes the interrelationships among inflammatory cytokines, NO−2 /NO−3 , MMP-2, MMP-9, and the tissue inhibitors of metalloproteases was created. Initial calibration of the model used literature data, and the model was refined with the data gathered prospectively in porcine endotoxemia. Results: Temporal changes, similar to those observed for endotoxemic mice (Chow et al, Shock, 2005) and rats (Daun et al, J Theretical Biol, 2008), were observed in all inflammatory analytes. Although TNF was produced to a high degree in all animals, principal component analysis suggested that IL-1β rather than TNF may be a main driver of systemic inflammation in this experimental preparation. The mathematical model was capable of describing the dynamics of inflammatory analytes in swine and predicted qualitative dynamics of MMP-2 and MMP-9. Conclusions: A mathematical model of inflammation in swine has been defined that will aid us in elucidating the pathogenesis of acute inflammation in this clinically relevant preclinical model. doi:10.1016/j.jcrc.2009.06.024 Integer heart rate complexity, mechanical ventilation, and mortality: Effect of pressure and rate in 527 trauma patients William P. Riordan , Patrick R. Norris , Judith M. Jenkins , John A. Morris Jr Division of Trauma and Surgical Critical Care, Vanderbilt University, Nashville, TN, USA

Objective: Reduced heart rate (HR) complexity is associated with trauma patient mortality. We sought to determine how ventilation parameters of positive end expiratory pressure (PEEP), maximum pressure during the breathing cycle (Pmax), and respiration rate (RR) affect integer HR complexity, and the degree to which these relationships modulate associations between HR complexity and mortality. Methods: Five hundred twenty-seven trauma ICU patients had 6 hours or more of continuous (second-by-second) integer heart rate and ventilator data. Data were divided into 5-minute intervals (570 000 total intervals; median, 53 hours of data per patient) and randomly sampled (25%) for additional analysis. HR complexity (Costa's multiscale entropy MSE, sum of scales, m = 2, r = 0.15), and median PEEP, Pmax, and RR was computed for each interval. The correlation between HR complexity and each ventilation parameter was evaluated with multiple linear regression. The simultaneous contribution of HR complexity and ventilation parameters to risk of death was modeled with logistic regression, controlling for probability of survival—a commonly used trauma acuity metric.