Ippolito D.L.,U.S. Army |
Lewis J.A.,U.S. Army |
Yu C.,Biotechnology High Performance Computing Software Applications Institute |
Leon L.R.,U.S. Army |
Stallings J.D.,U.S. Army
BMC Physiology | Year: 2014
Background: Heat illness is a debilitating and potentially life-threatening condition. Limited data are available to identify individuals with heat illness at greatest risk for organ damage. We recently described the transcriptomic and proteomic responses to heat injury and recovery in multiple organs in an in vivo model of conscious rats heated to a maximum core temperature of 41.8°C (Tc,Max). In this study, we examined changes in plasma metabolic networks at Tc,Max, 24, or 48 hours after the heat stress stimulus. Results: Circulating metabolites were identified by gas chromatography/mass spectrometry and liquid chromatography/tandem mass spectrometry. Bioinformatics analysis of the metabolomic data corroborated proteomics and transcriptomics data in the tissue at the pathway level, supporting modulations in metabolic networks including cell death or catabolism (pyrimidine and purine degradation, acetylation, sulfation, redox alterations and glutathione metabolism, and the urea cycle/creatinine metabolism), energetics (stasis in glycolysis and tricarboxylic acid cycle, ß-oxidation), cholesterol and nitric oxide metabolism, and bile acids. Hierarchical clustering identified 15 biochemicals that differentiated animals with histopathological evidence of cardiac injury at 48 hours from uninjured animals. The metabolic networks perturbed in the plasma corroborated the tissue proteomics and transcriptomics pathway data, supporting a model of irreversible cell death and decrements in energetics as key indicators of cardiac damage in response to heat stress. Conclusions: Integrating plasma metabolomics with tissue proteomics and transcriptomics supports a diagnostic approach to assessing individual susceptibility to organ injury and predicting recovery after heat stress. © 2014 Ippolito et al.
Liu J.,Biotechnology High Performance Computing Software Applications Institute |
Khitrov M.Y.,Biotechnology High Performance Computing Software Applications Institute |
Gates J.D.,Burns and Surgical Critical Care |
Odom S.R.,Beth Israel Deaconess Medical Center |
And 7 more authors.
Shock | Year: 2015
Trauma outcomes are improved by protocols for substantial bleeding, typically activated after physician evaluation at a hospital. Previous analysis suggested that prehospital vital signs contained patterns indicating the presence or absence of substantial bleeding. In an observational study of adults (aged Q18 years) transported to level I trauma centers by helicopter, we investigated the diagnostic performance of the Automated Processing of the Physiological Registry for Assessment of Injury Severity (APPRAISE) system, a computational platform for real-time analysis of vital signs, for identification of substantial bleeding in trauma patients with explicitly hemorrhagic injuries. We studied 209 subjects prospectively and 646 retrospectively. In our multivariate analysis, prospective performance was not significantly different from retrospective. The APPRAISE system was 76% sensitive for 24-h packed red blood cells of 9 or more units (95% confidence interval, 59% Y 89%) and significantly more sensitive (P G 0.05) than any prehospital Shock Index of 1.4 or higher; sensitivity, 59%; initial systolic blood pressure (SBP) less than 110 mmHg, 50%; and any prehospital SBP less than 90 mmHg, 50%. The APPRAISE specificity for 24-h packed red blood cells of 0 units was 87% (88% for any Shock Index Q1.4, 88% for initial SBP G110 mmHg, and 90% for any prehospital SBP G90 mmHg). Median APPRAISE hemorrhage notification time was 20 min before arrival at the trauma center. In conclusion, APPRAISE identified bleeding before trauma center arrival. En route, this capability could allow medics to focus on direct patient care rather than the monitor and, via advance radio notification, could expedite hospital interventions for patients with substantial blood loss. © 2015 by the Shock Society.
AbdulHameed M.D.M.,Biotechnology High Performance Computing Software Applications Institute |
Tawa G.J.,Biotechnology High Performance Computing Software Applications Institute |
Kumar K.,Biotechnology High Performance Computing Software Applications Institute |
Ippolito D.L.,U.S. Army |
And 3 more authors.
PLoS ONE | Year: 2014
Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is stillThe elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis differential expression and co-expression analyses, and then used these genes in pathway enrichment andprotein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before associated with liver fibrosis appeared. These results demonstrated that our approach is capable early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarkerand to generally identify disease-relevant pathways. © 2014 PLOS ONE.