Nemzek Jean A, Hodges Andrew P, He Yongqun
Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
Center for Computational Medicine and Biology, University of Michigan Medical School, Ann Arbor, MI, USA.
BMC Res Notes. 2015 Sep 30;8:516. doi: 10.1186/s13104-015-1488-y.
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.
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.
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.
炎症性疾病过程涉及复杂且相互关联的介质系统。当两种并发的炎症情况出现时，确定这些介质之间的因果关系会变得更加复杂。在那些情况下，结果可能还取决于损伤的时间、严重程度和部位。不幸的是，标准的实验方法和数据集分析可能只研究单一情况，而无法揭示介质之间许多潜在的关联。然而，贝叶斯网络分析能够对变量之间的线性、非线性、组合和随机关系进行建模，以探索复杂的炎症性疾病系统。在这些研究中，我们对由间接损伤（盲肠结扎和穿刺诱导的脓毒症）并发直接肺损伤（误吸）导致的急性肺损伤的发展进行了建模。为了复制多种临床情况，相对于脓毒症损伤，在不同严重程度和不同时间间隔给予误吸损伤。对于每种情况，我们在来自局部部位（腹腔和支气管肺泡灌洗液）和全身部位（血浆）的样本中测量了多种炎症细胞类型和细胞因子。然后我们通过贝叶斯网络和标准方法分析这些数据。
标准数据分析表明，与单独一种肺损伤相比，当涉及两种损伤时肺损伤实际上有所减轻。贝叶斯网络分析确定，肺损伤的严重程度和脓毒症的存在均影响中性粒细胞募集和肺损伤的程度。然而，与脓毒症的存在相比，负责中性粒细胞募集的趋化因子细胞因子水平与肺损伤的时间和严重程度的联系更为紧密。这表明，与先前的理论相反，除了脓毒症驱动的趋化因子水平升高之外，还有其他因素影响肺损伤。
据我们所知，这些研究首次将贝叶斯网络与实验研究结合起来，以研究脓毒症相关性肺损伤的发病机制。与标准统计分析和推断相比，这些分析阐明了导致肺损伤的介质、免疫细胞和损伤相关变量（时间、部位和严重程度）之间更复杂的关系。贝叶斯网络是评估复杂炎症模型的有效工具。
