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A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks.

Mi Q, Constantine G, Ziraldo C, Solovyev A, Torres A, Namas R, Bentley T, Billiar TR, Zamora R, Puyana JC, Vodovotz Y - PLoS ONE (2011)

Bottom Line: 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 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.These methods should be applicable to the analysis of other complex biological processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

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.

Methodology/principal findings: 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/significance: 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.

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Dynamic Network Analysis summary for ST ± HS.Mice were subjected to ST ± HS followed by measurement of cytokines, chemokines, and NO2−/NO3− as described in the Materials and Methods. DyNA was carried out using these data as described in the Materials and Methods. Panel A: DyNA for ST, during each of the following four time frames: 0–1 h, 1–2 h, 2–3 h, and 3–4 h. Panel B: DyNA for ST + HS. In both Panels A and B, the most connected inflammatory mediators (nodes) are depicted in blue, and the inflammatory mediators linked directly to each central node are depicted in red. The mediators depicted in green are statistically significantly different from their own baseline values (p<0.05), but not correlated with any other mediators. Panel C: Network density plot for ST and ST + HS during each of the four time frames (0–1 h, 1–2 h, 2–3 h, and 3–4 h).
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pone-0019424-g005: Dynamic Network Analysis summary for ST ± HS.Mice were subjected to ST ± HS followed by measurement of cytokines, chemokines, and NO2−/NO3− as described in the Materials and Methods. DyNA was carried out using these data as described in the Materials and Methods. Panel A: DyNA for ST, during each of the following four time frames: 0–1 h, 1–2 h, 2–3 h, and 3–4 h. Panel B: DyNA for ST + HS. In both Panels A and B, the most connected inflammatory mediators (nodes) are depicted in blue, and the inflammatory mediators linked directly to each central node are depicted in red. The mediators depicted in green are statistically significantly different from their own baseline values (p<0.05), but not correlated with any other mediators. Panel C: Network density plot for ST and ST + HS during each of the four time frames (0–1 h, 1–2 h, 2–3 h, and 3–4 h).

Mentions: To gain a systems perspective on these complex, time-dependent responses to ST ± HS, we carried out univariate analysis, multivariate analysis (MA), hierarchical clustering analysis (Fig. 3), Principal Component Analysis (PCA; Fig. 4), and Dynamic Network Analysis (DyNA; Fig. 5). Initially, we wished to assess the degree of inter-animal and inter-procedure variability between the ST and ST + HS experimental groups. Hierarchical clustering was performed for all the inflammatory analytes in the ST + HS (Fig. 3, samples 1–24) as well as ST (Fig. 3, samples 25–48) groups; the 6 samples from control, untreated mice were omitted from this analysis. Each row of the data matrix corresponds to a sample from a single mouse, and each column corresponds to an inflammatory analyte (21 total: 20 cytokines/chemokines along with NO2−/NO3−). The log-transformed magnitudes of these values are indicated by the colors as shown in the color bar (Fig. 3). The dendrogram on the y-axis shows the similarities among samples. .In agreement with prior studies from our group [19], this analysis suggested that circulating inflammatory mediators could to some degree segregate ST from ST + HS. However, a fair amount of overlap was observed in the inflammatory response to ST alone vs. ST + HS: 93% of Group 1 samples were derived from animals subjected to ST, while 7% were derived from animals subjected to ST + HS. In Group 2, 32% samples were derived from animals subjected to ST and 68% were derived from animals subjected to ST + HS. A Chi-square test with P<0.001 suggested that the distribution of ST vs. ST+HS animals between Groups 1 and 2 was not random.


A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks.

Mi Q, Constantine G, Ziraldo C, Solovyev A, Torres A, Namas R, Bentley T, Billiar TR, Zamora R, Puyana JC, Vodovotz Y - PLoS ONE (2011)

Dynamic Network Analysis summary for ST ± HS.Mice were subjected to ST ± HS followed by measurement of cytokines, chemokines, and NO2−/NO3− as described in the Materials and Methods. DyNA was carried out using these data as described in the Materials and Methods. Panel A: DyNA for ST, during each of the following four time frames: 0–1 h, 1–2 h, 2–3 h, and 3–4 h. Panel B: DyNA for ST + HS. In both Panels A and B, the most connected inflammatory mediators (nodes) are depicted in blue, and the inflammatory mediators linked directly to each central node are depicted in red. The mediators depicted in green are statistically significantly different from their own baseline values (p<0.05), but not correlated with any other mediators. Panel C: Network density plot for ST and ST + HS during each of the four time frames (0–1 h, 1–2 h, 2–3 h, and 3–4 h).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3091861&req=5

pone-0019424-g005: Dynamic Network Analysis summary for ST ± HS.Mice were subjected to ST ± HS followed by measurement of cytokines, chemokines, and NO2−/NO3− as described in the Materials and Methods. DyNA was carried out using these data as described in the Materials and Methods. Panel A: DyNA for ST, during each of the following four time frames: 0–1 h, 1–2 h, 2–3 h, and 3–4 h. Panel B: DyNA for ST + HS. In both Panels A and B, the most connected inflammatory mediators (nodes) are depicted in blue, and the inflammatory mediators linked directly to each central node are depicted in red. The mediators depicted in green are statistically significantly different from their own baseline values (p<0.05), but not correlated with any other mediators. Panel C: Network density plot for ST and ST + HS during each of the four time frames (0–1 h, 1–2 h, 2–3 h, and 3–4 h).
Mentions: To gain a systems perspective on these complex, time-dependent responses to ST ± HS, we carried out univariate analysis, multivariate analysis (MA), hierarchical clustering analysis (Fig. 3), Principal Component Analysis (PCA; Fig. 4), and Dynamic Network Analysis (DyNA; Fig. 5). Initially, we wished to assess the degree of inter-animal and inter-procedure variability between the ST and ST + HS experimental groups. Hierarchical clustering was performed for all the inflammatory analytes in the ST + HS (Fig. 3, samples 1–24) as well as ST (Fig. 3, samples 25–48) groups; the 6 samples from control, untreated mice were omitted from this analysis. Each row of the data matrix corresponds to a sample from a single mouse, and each column corresponds to an inflammatory analyte (21 total: 20 cytokines/chemokines along with NO2−/NO3−). The log-transformed magnitudes of these values are indicated by the colors as shown in the color bar (Fig. 3). The dendrogram on the y-axis shows the similarities among samples. .In agreement with prior studies from our group [19], this analysis suggested that circulating inflammatory mediators could to some degree segregate ST from ST + HS. However, a fair amount of overlap was observed in the inflammatory response to ST alone vs. ST + HS: 93% of Group 1 samples were derived from animals subjected to ST, while 7% were derived from animals subjected to ST + HS. In Group 2, 32% samples were derived from animals subjected to ST and 68% were derived from animals subjected to ST + HS. A Chi-square test with P<0.001 suggested that the distribution of ST vs. ST+HS animals between Groups 1 and 2 was not random.

Bottom Line: 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 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.These methods should be applicable to the analysis of other complex biological processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

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.

Methodology/principal findings: 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/significance: 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.

Show MeSH
Related in: MedlinePlus