Modeling Brainstem Inflammation's Role in Systemic Dysfunction during Sepsis

脓毒症是伴随着不受控制的炎症反应的全身性感染；一种可以 迅速恶化。早期诊断是生存的关键。心率变异性(HRV)，一种建议的 脓毒症的生物标志物，预测其预后，但太不特异而无法做出诊断。通常，HRV是 由其功率谱、其在频域中的变化性来量化；“高频”分量 反映了迷走神经活动的呼吸调节。脑干的计算确定性模型 心肺控制网络为迷走神经提出了合理的神经机制。 耦合。与HRV相比，动态网络分析(DYNA)和动态贝叶斯网络(DyBN) 模型高度特异性和成功地识别了脓毒症的“临界点”，即当一个对照 炎症反应变得失控，但它的许多变量很难衡量。最近，我们 发现脑干在内毒素血症中发炎。我们假设进步主义 炎症是导致HRV、呼吸模式变异性(VPV)和心肺功能丧失的关键因素 与脓毒症相关的偶联(CRC)。我们建议在基于代理的和 计算建模方法并执行模型驱动的实验，以确定如何进行更改 脓毒症的脑干神经生理学研究限制了生理模式的变异性。我们的初步数据显示， 内毒素血症大鼠CRC进行性丧失与促炎细胞因子表达有关 孤束核(NTS)则位于疑核。此外，与进步一致的 结直肠癌局灶性IL-1β在孤束核内微量注射的缺失对动脉脉压的影响 呼吸使RSA完好无损。具体目标是：1)建立细胞因子的DyNA和DyBN模型 败血症时脑干心肺控制核团的表达 外周炎症模式，2)使这些模型适用于有脓毒症风险的危重患者和 通过在大鼠身上应用治疗性干预来探索该模型的稳健性，以及3)应用我们的对照 为细胞因子对血管内皮细胞功能的影响提出可信和可测试的机制的模型 心肺控制电路。我们的心肺神经控制的计算模型 耦合以及定义组织炎症中细胞因子之间相互作用的模型已经被 成功应用于其他情况(交感-呼吸耦合)或外周组织(细胞因子 表达和互动)。集成这些模型将提供跨尺度的机制解释 在脓毒症期间观察RSA和CVC的丢失，确定用于治疗干预的关键细胞因子， 并将为使用CRC和可变性措施作为补充和 脓毒症的敏感生物标志物。

Sepsis is systemic infection accompanied by an uncontrolled inflammatory response; a condition that can deteriorate rapidly. Early diagnosis is critical for survival. Heart rate variability (HRV), a proposed biomarker for sepsis, predicts its prognosis but is too nonspecific to make a diagnosis. Often HRV is quantified by its power spectra, its variability in the frequency domain; the `high-frequency' component reflects respiratory modulation of vagal nerve activity. Computational deterministic models of the brainstem cardiorespiratory control networks have proposed plausible neural mechanisms for the vago-respiratory coupling. In contrast to HRV, Dynamic Network Analysis (DyNA) and Dynamic Bayesian Network (DyBN) models are highly specific and successful in identifying a `tipping point' in sepsis, i.e. when a controlled inflammatory response becomes uncontrolled but its many variables are hard to measure. Recently, we identified that the brainstem becomes inflamed in endotoxemia. We hypothesize that progressive inflammation is a critical factor in losing HRV, ventilatory pattern variability (VPV), and cardiorespiratory coupling (CRC) associated with sepsis. We propose to build on the strengths of agent-based and computational modeling approaches and perform model-driven experiments to determine how alterations of brainstem neurophysiology in sepsis limit physiologic pattern variability. Our preliminary data show that endotoxemic rats lose CRC progressively in association with proinflammatory cytokines expression first in the nucleus tractus solitarius (nTS) then in the nucleus Ambiguus. Further, consistent with a progressive loss of CRC focal IL-1β microinjections in the nTS uncouples the arterial pulse pressure's influence on respiration leaving RSA intact. The Specific Aims are: 1) to develop DyNa and DyBN models of cytokine expression in brainstem cardiorespiratory control nuclei during septicemia to determine if central and peripheral inflammation patterns, 2) to adapt these models to critically-ill humans at risk for sepsis and probe the robustness of the model by applying therapeutic interventions in rats, and 3) to apply our control model to propose plausible and testable mechanisms for the effects of cytokines on the function of cardiorespiratory control circuitry. Our computational model of the neural control of cardiorespiratory coupling as well as the models defining the interactions among cytokines in tissue inflammation have been applied successfully to other conditions (sympatho-respiratory coupling) or to peripheral tissues (cytokine expression and interaction). Integrating these models will provide cross-scale mechanistic explanations for the loss of RSA and CVC observed during sepsis, identify critical cytokines for therapeutic intervention, and will establish a scientific rationale for using CRC and variability measures as complementary and sensitive biomarkers of sepsis.