Ancillary to ABC PICU to study MOD in critically ill children

项目摘要/摘要 多器官功能障碍综合征(MODS)常见于危重病种 与死亡风险增加近10倍有关。它是一种“综合征”，而不是一种特定的病理实体。 它的特点是严重的、系统性的和不受控制的炎症过程。几项临床研究 已经报道炎症和凝血的改变在危重病人中很常见，而且可能是 因输注红细胞而恶化。我们已公布和尚未公布的数据表明，较高的血浆 IL-6、IL-8、PAI-1、血栓调节蛋白(TM)、血管生成素-2(Ang-2)水平及PAI-1基因变异 和TM与危重儿童和成人器官功能障碍和死亡率的增加有关。 中性粒细胞胞外陷阱(Net)，由激活的中性粒细胞产生，已被报道在 器官损伤，从输血的红细胞释放的细胞外血红素已知触发产生 篮网。我们的初步研究表明，患有MODS的非幸存者儿童的NETs倾向于增加。 最后，健康受试者的血液中含有大量的细菌基因组物质(微生物组)，而这 循环微生物群有可能导致非感染性炎症，从而促进MODS的发生。 然而，炎症和血栓改变的标志物，以及网络和循环微生物组的作用 尚未在足够数量的患有MODS的危重儿童中进行严格的研究。我们建议 利用ABC PICU研究的基础设施，这是一项正在进行的多中心临床试验，以招募危重患者 接受红细胞输注的儿童在输血前和输血后(第1、3和5天)采集生物样本 表征炎症和凝血相关的生物标志物，并研究它们与血管紧张素转换酶 NPMODS的发生和死亡率。在Aim1中，我们将检测血浆水平和表达谱 选定的标记物(IL-6、IL-8、PAI-1、TM和Ang-2)与输血后血管变化斜率的相关性 用NPMODS和死亡率测量的生物标记物将使用混合效应模型进行测试。在目标2中，我们将 基因标记SNPs和测序靶区，以检测遗传变异并评估它们对 生物标记物的轨迹与NPMODS和死亡率的发展。在Aim 3中，不偏不倚的下一代 在对非人类序列进行比对之后，将使用测序来识别和表征 循环微生物组，我们将测试微生物多样性和负担与循环的关联性 生物标志物和NPMODS。这项研究将表征预后和预测性生物标志物，并提供 在这些标记物和NPMODS的发展之间建立联系的机械性见解。这个 获得的知识和如此定义的分子表型可以确定新的治疗靶点，并将 为未来的临床试验提供信息，并导致针对治疗的精确医学策略的发展 药物适用于具有特定分子表型或生物标志物模式的患者。

PROJECT SUMMARY/ABSTRACT Multiple Organ Dysfunction Syndrome (MODS) occurs frequently in the setting of critical illnesses and is associated with a nearly 10-fold higher mortality risk. It is a “syndrome” rather than a specific pathological entity that is characterized by a severe, systemic, and uncontrolled inflammatory process. Several clinical studies have reported that alterations in inflammation and coagulation are common in critically ill patients and may be worsened by transfusion of RBCs. Our published and yet to be published data suggest that higher plasma levels of IL-6, IL-8, PAI-1, Thrombomodulin (TM), and Angiopoietin -2 (Ang-2), and genetic variants in PAI-1 and TM are associated with increased organ dysfunction and mortality in critically ill children and adults. Neutrophil Extracellular Traps (NETs), produced by activated neutrophils, have been reported to play a role in organ injury, and extracellular heme released from transfused RBCs is known to trigger the production of NETs. Our preliminary studies suggest that NETs tend to be increased in non-survivor children with MODS. Finally, blood from healthy subjects contains a host of bacterial genomic material (microbiome) and this circulating microbiome has the potential to lead to non-infectious inflammation, thereby contributing to MODS. However, markers of altered inflammation and thrombosis, and the role of NETs and circulating microbiome have not yet been studied rigorously in a sufficient number of critically ill children with MODS. We propose to leverage the infrastructure of the ABC PICU study, an ongoing multi-center clinical trial to enroll critically ill children undergoing RBC transfusions and collect biological samples pre- and post-transfusion (day 1, 3 and 5) to characterize inflammation and coagulation related biomarkers and examine their relationship to the development of NPMODS and mortality. In Aim1, we will assay plasma levels and expression profile of selected markers (IL-6, IL-8, PAI-1,TM and Ang-2) and association of post-transfusion slope of change in the measured biomarkers with NPMODS and mortality will be tested using mixed effect models. In Aim 2. we will genotype tag SNPs and sequence target regions to detect genetic variants and assess their effect on biomarker trajectories and development of NPMODS and mortality. In Aim 3, unbiased next generation sequencing followed by alignment of non-human sequences will be used to identify and characterize the circulating microbiome and we will test for association of microbial diversity and burden with circulating biomarkers and NPMODS. This study will characterize prognostic and predictive biomarkers and provide mechanistic insights that establish a link between these markers and the development of NPMODS. The knowledge acquired and molecular phenotypes thus defined may identify novel therapeutic targets, and will inform future clinical trials and lead to development of precision medicine strategies targeting therapeutic agents to patients with specific molecular phenotypes or biomarker patterns.