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Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury.

Nemzek JA, Hodges AP, He Y - BMC Res Notes (2015)

Bottom Line: Standard data analysis demonstrated that the lung injury was actually reduced when two insults were involved as compared to one lung injury alone.To our knowledge, these studies are the first to use Bayesian networks together with experimental studies to examine the pathogenesis of sepsis-associated lung injury.Bayesian networks are an effective tool for evaluating complex models of inflammation.

View Article: PubMed Central - PubMed

Affiliation: Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA. jnemzek@umich.edu.

ABSTRACT

Background: Inflammatory disease processes involve complex and interrelated systems of mediators. Determining the causal relationships among these mediators becomes more complicated when two, concurrent inflammatory conditions occur. In those cases, the outcome may also be dependent upon the timing, severity and compartmentalization of the insults. Unfortunately, standard methods of experimentation and analysis of data sets may investigate a single scenario without uncovering many potential associations among mediators. However, Bayesian network analysis is able to model linear, nonlinear, combinatorial, and stochastic relationships among variables to explore complex inflammatory disease systems. In these studies, we modeled the development of acute lung injury from an indirect insult (sepsis induced by cecal ligation and puncture) complicated by a direct lung insult (aspiration). To replicate multiple clinical situations, the aspiration injury was delivered at different severities and at different time intervals relative to the septic insult. For each scenario, we measured numerous inflammatory cell types and cytokines in samples from the local compartments (peritoneal and bronchoalveolar lavage fluids) and the systemic compartment (plasma). We then analyzed these data by Bayesian networks and standard methods.

Results: Standard data analysis demonstrated that the lung injury was actually reduced when two insults were involved as compared to one lung injury alone. Bayesian network analysis determined that both the severity of lung insult and presence of sepsis influenced neutrophil recruitment and the amount of injury to the lung. However, the levels of chemoattractant cytokines responsible for neutrophil recruitment were more strongly linked to the timing and severity of the lung insult compared to the presence of sepsis. This suggests that something other than sepsis-driven exacerbation of chemokine levels was influencing the lung injury, contrary to previous theories.

Conclusions: To our knowledge, these studies are the first to use Bayesian networks together with experimental studies to examine the pathogenesis of sepsis-associated lung injury. Compared to standard statistical analysis and inference, these analyses elucidated more intricate relationships among the mediators, immune cells and insult-related variables (timing, compartmentalization and severity) that cause lung injury. Bayesian networks are an effective tool for evaluating complex models of inflammation.

No MeSH data available.


Related in: MedlinePlus

Consensus Bayesian network obtained for BAL fluid data sets. Mice (n = 10–12/group) were given IT injections of saline, acid, or acid + particles (lung insult) with or without the additional insult of cecal ligation and puncture (CLP). CLP was performed at intervals relative to the aspiration injury (injury interval), either immediately before the IT injection (0 h) or preceding them by 12 or 48 h. There were a total of 12 combinations of CLP, lung insult and insult interval. All mice were euthanized at 6 h post-IT injection. Bronchoalveolar lavage fluid was collected for cell counts and cytokine levels. The data sets were analyzed in Bayesian Networks. When interactions occurred in the same direction in all of the networks, these are represented as directed edges (arrows), whereas those appearing at least once in an opposing direction are represented as undirected edges (no arrowhead). CLP refers to the presence of sepsis. Injury interval is the time interval between the induction of sepsis and the lung insult (none, 0, 12, or 48 h) and Lung Insult refers to the aspiration (saline, acid, or particles)
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Fig5: Consensus Bayesian network obtained for BAL fluid data sets. Mice (n = 10–12/group) were given IT injections of saline, acid, or acid + particles (lung insult) with or without the additional insult of cecal ligation and puncture (CLP). CLP was performed at intervals relative to the aspiration injury (injury interval), either immediately before the IT injection (0 h) or preceding them by 12 or 48 h. There were a total of 12 combinations of CLP, lung insult and insult interval. All mice were euthanized at 6 h post-IT injection. Bronchoalveolar lavage fluid was collected for cell counts and cytokine levels. The data sets were analyzed in Bayesian Networks. When interactions occurred in the same direction in all of the networks, these are represented as directed edges (arrows), whereas those appearing at least once in an opposing direction are represented as undirected edges (no arrowhead). CLP refers to the presence of sepsis. Injury interval is the time interval between the induction of sepsis and the lung insult (none, 0, 12, or 48 h) and Lung Insult refers to the aspiration (saline, acid, or particles)

Mentions: The type of lung insult (saline, acid, or particles), the injury interval (0, 12, 48 h) and the presence of sepsis (CLP) were factored directly into the analysis to determine their effects on the mediators. Separate networks were generated for each compartment. Most striking, it was evident that the two disease processes did not directly influence all of the body compartments. For instance, it appeared that the type of Lung Insult was not directly linked to mediators in the distant compartments, peritoneum (Fig. 3) or blood (Fig. 4). However, both the Type of Lung Insult and CLP were directly related to the inflammation in the lung compartment (Fig. 5). This finding was similar to the conclusions eventually drawn from statistical analysis of BAL fluid. However, the Bayesian network analysis recognized this and designated this relationship independent of inferences by an investigator.Fig. 3


Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury.

Nemzek JA, Hodges AP, He Y - BMC Res Notes (2015)

Consensus Bayesian network obtained for BAL fluid data sets. Mice (n = 10–12/group) were given IT injections of saline, acid, or acid + particles (lung insult) with or without the additional insult of cecal ligation and puncture (CLP). CLP was performed at intervals relative to the aspiration injury (injury interval), either immediately before the IT injection (0 h) or preceding them by 12 or 48 h. There were a total of 12 combinations of CLP, lung insult and insult interval. All mice were euthanized at 6 h post-IT injection. Bronchoalveolar lavage fluid was collected for cell counts and cytokine levels. The data sets were analyzed in Bayesian Networks. When interactions occurred in the same direction in all of the networks, these are represented as directed edges (arrows), whereas those appearing at least once in an opposing direction are represented as undirected edges (no arrowhead). CLP refers to the presence of sepsis. Injury interval is the time interval between the induction of sepsis and the lung insult (none, 0, 12, or 48 h) and Lung Insult refers to the aspiration (saline, acid, or particles)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4589912&req=5

Fig5: Consensus Bayesian network obtained for BAL fluid data sets. Mice (n = 10–12/group) were given IT injections of saline, acid, or acid + particles (lung insult) with or without the additional insult of cecal ligation and puncture (CLP). CLP was performed at intervals relative to the aspiration injury (injury interval), either immediately before the IT injection (0 h) or preceding them by 12 or 48 h. There were a total of 12 combinations of CLP, lung insult and insult interval. All mice were euthanized at 6 h post-IT injection. Bronchoalveolar lavage fluid was collected for cell counts and cytokine levels. The data sets were analyzed in Bayesian Networks. When interactions occurred in the same direction in all of the networks, these are represented as directed edges (arrows), whereas those appearing at least once in an opposing direction are represented as undirected edges (no arrowhead). CLP refers to the presence of sepsis. Injury interval is the time interval between the induction of sepsis and the lung insult (none, 0, 12, or 48 h) and Lung Insult refers to the aspiration (saline, acid, or particles)
Mentions: The type of lung insult (saline, acid, or particles), the injury interval (0, 12, 48 h) and the presence of sepsis (CLP) were factored directly into the analysis to determine their effects on the mediators. Separate networks were generated for each compartment. Most striking, it was evident that the two disease processes did not directly influence all of the body compartments. For instance, it appeared that the type of Lung Insult was not directly linked to mediators in the distant compartments, peritoneum (Fig. 3) or blood (Fig. 4). However, both the Type of Lung Insult and CLP were directly related to the inflammation in the lung compartment (Fig. 5). This finding was similar to the conclusions eventually drawn from statistical analysis of BAL fluid. However, the Bayesian network analysis recognized this and designated this relationship independent of inferences by an investigator.Fig. 3

Bottom Line: Standard data analysis demonstrated that the lung injury was actually reduced when two insults were involved as compared to one lung injury alone.To our knowledge, these studies are the first to use Bayesian networks together with experimental studies to examine the pathogenesis of sepsis-associated lung injury.Bayesian networks are an effective tool for evaluating complex models of inflammation.

View Article: PubMed Central - PubMed

Affiliation: Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA. jnemzek@umich.edu.

ABSTRACT

Background: Inflammatory disease processes involve complex and interrelated systems of mediators. Determining the causal relationships among these mediators becomes more complicated when two, concurrent inflammatory conditions occur. In those cases, the outcome may also be dependent upon the timing, severity and compartmentalization of the insults. Unfortunately, standard methods of experimentation and analysis of data sets may investigate a single scenario without uncovering many potential associations among mediators. However, Bayesian network analysis is able to model linear, nonlinear, combinatorial, and stochastic relationships among variables to explore complex inflammatory disease systems. In these studies, we modeled the development of acute lung injury from an indirect insult (sepsis induced by cecal ligation and puncture) complicated by a direct lung insult (aspiration). To replicate multiple clinical situations, the aspiration injury was delivered at different severities and at different time intervals relative to the septic insult. For each scenario, we measured numerous inflammatory cell types and cytokines in samples from the local compartments (peritoneal and bronchoalveolar lavage fluids) and the systemic compartment (plasma). We then analyzed these data by Bayesian networks and standard methods.

Results: Standard data analysis demonstrated that the lung injury was actually reduced when two insults were involved as compared to one lung injury alone. Bayesian network analysis determined that both the severity of lung insult and presence of sepsis influenced neutrophil recruitment and the amount of injury to the lung. However, the levels of chemoattractant cytokines responsible for neutrophil recruitment were more strongly linked to the timing and severity of the lung insult compared to the presence of sepsis. This suggests that something other than sepsis-driven exacerbation of chemokine levels was influencing the lung injury, contrary to previous theories.

Conclusions: To our knowledge, these studies are the first to use Bayesian networks together with experimental studies to examine the pathogenesis of sepsis-associated lung injury. Compared to standard statistical analysis and inference, these analyses elucidated more intricate relationships among the mediators, immune cells and insult-related variables (timing, compartmentalization and severity) that cause lung injury. Bayesian networks are an effective tool for evaluating complex models of inflammation.

No MeSH data available.


Related in: MedlinePlus