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. 2015 Apr;78(4):671-86.
doi: 10.1097/TA.0000000000000568.

Genomics of injury: The Glue Grant experience

Ronald G Tompkins

Genomics of injury: The Glue Grant experience

Ronald G Tompkins. J Trauma Acute Care Surg. 2015 Apr.
No abstract available

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Figures

Figure 1
Figure 1
Clinical paradigm for Multiple Organ Dysfunction (MOD). Patients initially present with SIRS during resuscitation and an early MOD syndrome with the potential for an early death. A compensatory period of CARS follows for those patients who survive the early inflammatory insult, but they are subject to a later “second hit” from a nosocomial infection, endotoxemia, or the persistence of devitalized tissue. A late, more severe MOD syndrome can develop with a higher risk of death. Substantial contributions to illustrate this concept came from Ron Maier and Linc Moldawer.
Figure 2
Figure 2
Time to recovery and infections associated with severe blunt trauma. (Upper panel) Shown here is time to recovery (TTR) from multiple organ failure (MOF). Heat map of the MOF scores over time after injury up to 28 days for each of the 1,637 massive injured patients. From top to bottom, patients are ordered according to the TTR days. Source: Cuschieri et al.44 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health. (Lower panel) Day of onset and frequency of multiple organ failure (MOF), nosocomial infection (NI), and death. Source: Minei et al.43 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health.
Figure 2
Figure 2
Time to recovery and infections associated with severe blunt trauma. (Upper panel) Shown here is time to recovery (TTR) from multiple organ failure (MOF). Heat map of the MOF scores over time after injury up to 28 days for each of the 1,637 massive injured patients. From top to bottom, patients are ordered according to the TTR days. Source: Cuschieri et al.44 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health. (Lower panel) Day of onset and frequency of multiple organ failure (MOF), nosocomial infection (NI), and death. Source: Minei et al.43 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health.
Figure 3
Figure 3
Mortality. Patients divided into quintiles in Panels B, C, and D based underlying score or injury severity. Panel A shows mortality over the entire study period. Observed (solid lines) versus expected (dashed line) outcome for Panel B) mortality by TRISS (P < 0.001), Panel C) mortality by APACHE II (P < 0.001), and Panel D) mortality by NTDB (P < 0.001). Source: Cuschieri et al.44 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health.
Figure 3
Figure 3
Mortality. Patients divided into quintiles in Panels B, C, and D based underlying score or injury severity. Panel A shows mortality over the entire study period. Observed (solid lines) versus expected (dashed line) outcome for Panel B) mortality by TRISS (P < 0.001), Panel C) mortality by APACHE II (P < 0.001), and Panel D) mortality by NTDB (P < 0.001). Source: Cuschieri et al.44 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health.
Figure 3
Figure 3
Mortality. Patients divided into quintiles in Panels B, C, and D based underlying score or injury severity. Panel A shows mortality over the entire study period. Observed (solid lines) versus expected (dashed line) outcome for Panel B) mortality by TRISS (P < 0.001), Panel C) mortality by APACHE II (P < 0.001), and Panel D) mortality by NTDB (P < 0.001). Source: Cuschieri et al.44 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health.
Figure 3
Figure 3
Mortality. Patients divided into quintiles in Panels B, C, and D based underlying score or injury severity. Panel A shows mortality over the entire study period. Observed (solid lines) versus expected (dashed line) outcome for Panel B) mortality by TRISS (P < 0.001), Panel C) mortality by APACHE II (P < 0.001), and Panel D) mortality by NTDB (P < 0.001). Source: Cuschieri et al.44 © 2012 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health.
Figure 4
Figure 4
Organ injury and genomic changes associated with severe blunt trauma. (A) The presence and severity of organ injury is represented by colors from blue (least severe) to red (most severe). Black indicates death. (B) K-means clustering of the genes into 30 clusters based on patterns of expression over time. Red indicates increased and blue indicates decreased expression relative to the mean (white). 5,136 genes were differentially expressed between patients and controls (ctrl; FDR <0.001 and at least twofold change). (C and D) Summary of the canonical pathways most affected by trauma. The graph shows the −log10 (p value) of the enrichment of the pathway. © 2011 Xiao et al.5 Originally published in Journal of Experimental Medicine.2011 Nov;208(13):2581-2590. doi: 10.1084/jem.20111354.
Figure 5
Figure 5
Correlations of the gene changes among human burns, trauma, and endotoxin and the corresponding mouse models. Scatter plots and Pearson correlations (R2) of the log twofold changes of 4,918 human genes responsive to trauma, burns, or endotoxemia (FDR < 0.001; fold change ≥ 2) and their murine orthologs in the murine models. As shown in the upper left, the genomic responses to human trauma and burns are highly correlated (R2 = 0.91). In contrast, as shown in the lower right, the murine models correlate poorly with each other (R2 = 0.00–0.13) and almost randomly with the corresponding human conditions (R2 = 0.00–0.09). Similar results were seen with rank correlation. © 2013 Seok et al.60 Originally published in Proceedings of the National Academy of Sciences of the United States of America. 2013 Feb;110(9):3507-12. doi: 10.1073/pnas.1222878110.
Figure 6
Figure 6
Comparisons of the time-course changes between complicated and uncomplicated patients. This is a subset of the data from Figure 4B of the K-means clustering of the genes based upon patterns of gene expression over time in the complicated and uncomplicated patient cohorts. To the left are the 55 uncomplicated patients and to the right are the 41 complicated patients. Red indicates increased and blue indicates decreased expression relative to the mean (white).
Figure 7
Figure 7
Differences in gene expression patterns between patients with a complicated and uncomplicated clinical recovery. Heat map of 1,201 genes whose expression was at least twofold different at any time point when compared with controls (CTRL) for patients with a complicated (Comp) or uncomplicated recovery (Uncomp). (A) Cluster analysis of the two cohorts. The brackets to the right of the cluster indicate cluster 2 and 8 shown in B and C, respectively. (B) One cluster of genes whose expression was up-regulated in patients with a complicated recovery. (C) One cluster of genes whose expression was down-regulated in patients with a complicated recovery. © 2011 Xiao et al.5 Originally published in Journal of Experimental Medicine.2011 Nov;208(13):2581-2590. doi: 10.1084/jem.20111354.
Figure 8
Figure 8
Differences in gene expression patterns of the 63 genes. (A) Pearson correlation between the microarray and nanoString™ expression level of the 63 genes found to be differentially regulated between uncomplicated and complicated patient cohorts. Values represent fold change over control expression values. Source: Cuenca et al.35 © 2013 Lippincott Williams & Wilkins. Reproduced with permission from Wolters Kluwer Health. (B) Variance normalized mean expression for all the genes up-regulated and down-regulated differentially between the two cohorts, and fitted curve. The differences in expression between the two cohorts could be primarily explained by the magnitude of the response and the duration.
Figure 9
Figure 9
Expression patterns of selected genes involved in innate and adaptive immunity 152 genes directly associated with either innate or adaptive immune processes were selected, and their expression patterns evaluated in the uncomplicated and complicated patient cohorts. Panel A and B represent cluster analyses of the probe sets involved in innate and adaptive immunity, respectively, while Panels C and D represent a summary of the variance normalized mean gene expression for the individual probe sets. The primary differences in the patterns of gene expression are in the duration of the response, rather than in the magnitude or the timing of the changes.
Figure 10
Figure 10
A genomic storm: refining the immune, inflammatory paradigm in trauma (A) The current paradigm explains complications of severe injury as a result of excessive proinflammatory responses (SIRS) followed temporally by compensatory antiinflammatory responses (CARS) and suppression of adaptive immunity. A second-hit phenomenon results from sequential insults, which leads to more severe, recurrent SIRS and organ dysfunction. (B) The proposed new paradigm involves simultaneous and rapid induction of innate (both pro- and antiinflammatory genes) and suppression of adaptive immunity genes. Complicated recoveries are delayed, resulting in a prolonged, dysregulated immune–inflammatory state. © 2011 Xiao et al.5 Originally published in Journal of Experimental Medicine.2011 Nov;208(13):2581-2590. doi: 10.1084/jem.20111354.
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