Modeling Brainstem Inflammation's Role in Systemic Dysfunction during Sepsis

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.

败血症是由不受控制的炎症反应所接受的全身感染。一个可以 迅速恶化。早期诊断对于生存至关重要。心率变异性（HRV），提议 败血症的生物标志物预测其预后，但不是特异性的，无法进行诊断。 HRV通常是 通过其功率谱量化，其在频域中的可变性； “高频”组件 反映迷走神经活性的呼吸调节。脑干的计算确定性模型 心肺控制网络提出了对流浪呼吸系统的合理神经机制 耦合。与HRV相反，动态网络分析（DYNA）和动态贝叶斯网络（DYBN） 模型高度具体且成功地识别败血症中的“临界点”，即当受控的 炎症反应变得不受控制，但其许多变量很难衡量。最近，我们 确定脑干在内毒素血症中发炎。我们假设那个进步 炎症是失去HRV，通风模式变异性（VPV）和心肺的关键因素 与败血症相关的耦合（CRC）。我们建议以基于代理的优势和 计算建模方法并执行模型驱动的实验，以确定如何改变 脓毒症限制生理模式变异性中脑干神经生理学的变异性。我们的初步数据表明 内毒素大鼠与促炎细胞因子表达逐渐失去CRC。 然后，核心核团结核（NTS），然后在歧义核中。此外，与进步的 NTS中CRC局灶性IL-1β微型注射的损失是动脉脉压对动脉脉压的影响 呼吸使RSA完好无损。具体目的是：1）开发细胞因子的dyna和dybn模型 败血症期间脑干心肺控制核中的表达，以确定中央和是否中心 外围炎症模式，2）使这些模型适应脓毒症和败血症风险的严重性人 通过在大鼠中应用治疗干预措施，探测模型的鲁棒性，3）应用我们的控制 模型提出了细胞因子对作用的影响的合理和可检验机制 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相互作用的模型一直是 成功应用于其他疾病（索引呼吸耦合）或周围组织（细胞因子 表达和相互作用）。整合这些模型将为跨尺度机械解释 败血症期间观察到的RSA和CVC的丧失，确定治疗干预的关键细胞因子， 并将建立一个科学理由，用于使用CRC和可变性措施作为完整性和 败血症的敏感生物标志物。