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

BMC Res Notes. 2015 Sep 30;8:516. doi: 10.1186/s13104-015-1488-y.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acute Lung Injury / complications
  • Acute Lung Injury / immunology*
  • Acute Lung Injury / pathology
  • Animals
  • Bayes Theorem
  • Bronchoalveolar Lavage Fluid / chemistry
  • Bronchoalveolar Lavage Fluid / cytology
  • Bronchoalveolar Lavage Fluid / immunology
  • Cecum / immunology
  • Cecum / pathology
  • Cytokines / biosynthesis
  • Cytokines / immunology
  • Disease Models, Animal
  • Eosinophils / immunology
  • Eosinophils / pathology
  • Female
  • Immunity, Innate*
  • Inflammation
  • Ligation
  • Lung / immunology
  • Lung / pathology
  • Lymphocytes / immunology
  • Lymphocytes / pathology
  • Mice
  • Mice, Inbred ICR
  • Models, Immunological*
  • Monocytes / immunology
  • Monocytes / pathology
  • Neutrophil Infiltration
  • Neutrophils / immunology*
  • Neutrophils / pathology
  • Pneumonia, Aspiration / complications
  • Pneumonia, Aspiration / immunology*
  • Pneumonia, Aspiration / pathology
  • Sepsis / complications
  • Sepsis / immunology*
  • Sepsis / pathology
  • Severity of Illness Index
  • Time Factors


  • Cytokines