Merging Temporal Changes in Clinical Informatics, Transcriptomics, and Cytokine Profiles to Understand the Host Response to Bacteremia

合并临床信息学、转录组学和细胞因子谱的时间变化，以了解宿主对菌血症的反应

• 批准号: 10022505
• 负责人: Philip A Verhoef
• 金额: $ 7.9万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10022505
• 关键词: Acute; Address; Affect; Allergic; Award; Bacteremia; Bioinformatics; Biological; Biological Assay; Biology; Blood; Blood specimen; Clinical; Clinical Data; Clinical Informatics; Computerized Medical Record; Consent; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Doctor of Philosophy; Enrollment; Escherichia coli; Functional disorder; Funding; Gene Expression; Goals; Hour; Immune; Immune Targeting; Immune response; Immunologist; Infection; Inflammatory Response; Knowledge; Laboratories; Leukocytes; Life; Machine Learning; Measures; Mediating; Messenger RNA; Microbiology; Modeling; Nature; Organ; Organ failure; Patients; Pharmaceutical Preparations; Physicians; Plasma; Plasma Proteins; Precision therapeutics; Proteins; Research; Research Personnel; Sampling; Scientist; Sepsis; Site; Staphylococcus aureus; Subgroup; Supportive care; Techniques; Technology; Time; Tissues; Training; Transcript; Validation; Viral; Virulence Factors; Work; base; biobank; career; clinical care; clinically significant; comorbidity; cytokine; experience; improved; improved outcome; insight; machine learning method; mortality; novel; novel therapeutics; outcome prediction; pathogen; patient subsets; personalized therapeutic; prospective; response; septic; septic patients; transcriptome sequencing; transcriptomics

As an ICU physician and an immunologist, I have devoted my research career to understanding sepsis, a disease that affects nearly 2 million people annually in the USA. Sepsis is a life-threatening condition that arises when the body's response to infection injures its own tissues. Great strides have been made towards improving the clinical care of the septic patient, but the mortality rate remains >20% for several reasons: First, each sepsis-causing pathogen, be it bacterial, viral, or fungal, carries its own virulence factors that affect the host response, but unfortunately, much research has focused on studying septic patients as a group, rather than distinguishing patients based on microbiologic cause. Second, researchers often study patients once they manifest sepsis-induced organ failure yet many people sustain infections due to common sepsis pathogens, like Staphylococcus aureus, but never develop sepsis; understanding the "appropriate" response to a pathogen is critical to understanding the "inappropriate" response of sepsis. Finally, much sepsis research occurs in silos; clinician researchers focus on the electronic medical record, while basic scientists analyze biologic data. Too often, these groups do not collaborate to share information, even though understanding the biologic significance of clinical data may be of great value. To address these issues, I propose a unique approach to understand the host response to infection that incorporates both biologic data and clinical electronic medical record (EMR) data from patients with S. aureus bacteremia. By limiting analysis of the host response to infections caused by a single pathogen at a single site, we can control for the variability induced by pathogen-specific factors. In addition, by studying all patients with S. aureus bacteremia, and not simply those patients with sepsis, we can understand both the appropriate host response as well as the inappropriate host response that characterizes the development of sepsis. With our bank of samples collected from S. aureus bacteremia patients we will analyze both cellular mRNA transcripts and plasma protein/cytokine levels, collected at different time points from each patient. We will combine the results of these analyses with clinical data found in the EMR to provide a correlation between the biology of the host response and its clinical manifestations. Our per-patient data, then, will have unprecedented granularity which we can then use to apply machine learning techniques to identify multi-faceted endotypes that predict outcomes (such as mortality). Once we have built these endotype models, we will validate them using pilot data collected from newly enrolled patients with either S. aureus or E. coli bacteremia. This approach will allow us to identify factors common to the dysregulated host response across all infections, as well as those that may be specific to the type of infection. Understanding both the appropriate and the inappropriate host response to infection, and understanding which aspects of the host response are pathogen-specific (and which are not) will allow development of novel therapies for this devastating disease.

作为一名重症监护室医生和免疫学家，我致力于了解脓毒症， 这种疾病在美国每年影响近200万人。败血症是一种危及生命的疾病 当身体对感染的反应损伤了自身组织时出现的一种状况。长足进步 已经朝着改善脓毒症患者的临床护理的方向进行了努力，但是死亡率 保持>20%有几个原因：首先，每种引起脓毒症的病原体，无论是细菌，病毒，或 真菌携带自己的毒力因子，影响宿主的反应，但不幸的是， 研究的重点是将脓毒症患者作为一个群体进行研究，而不是区分患者 基于微生物原因。第二，研究人员经常在患者表现出 脓毒症引起的器官衰竭然而许多人由于普通脓毒症而持续感染 病原体，如金黄色葡萄球菌，但从来没有发展脓毒症;了解“适当” 对病原体的反应对于理解病原体的“不适当”反应至关重要。 败血症最后，许多脓毒症研究都是孤立进行的;临床研究人员关注的是 电子医疗记录，而基础科学家分析生物数据。很多时候，这些 群体不合作共享信息，即使了解生物学， 临床数据的意义可能具有很大价值。为了解决这些问题，我提出了一个独特的 了解宿主对感染的反应的方法，包括生物学数据和 临床电子病历（EMR）数据，从患者与S。金黄色菌血症 通过限制分析宿主对单一病原体引起的感染的反应， 位点，我们可以控制由病原体特异性因素引起的变异。此外，本发明还提供了一种方法， 通过研究所有S.金黄色葡萄球菌菌血症，而不仅仅是那些 脓毒症，我们可以理解适当的宿主反应以及不适当的宿主 脓毒症发展的特征性反应。我们从S. 我们将分析金黄色葡萄球菌菌血症患者的细胞mRNA转录物和血浆 蛋白质/细胞因子水平，在不同时间点从每个患者收集。我们将 联合收割机将这些分析的结果与EMR中发现的临床数据相结合，以提供相关性 宿主反应的生物学和临床表现之间的联系。我们的每例患者 然后，数据将具有前所未有的粒度，我们可以使用它来应用机器学习 技术，以确定预测结果（如死亡率）的多方面内型。一旦我们 我们已经建立了这些内型模型，我们将使用新收集的试点数据来验证它们。 入选的患者中，金黄色葡萄球菌E.大肠杆菌菌血症。这种方法将允许 我们要确定所有感染中宿主反应失调的共同因素，以及 这些可能是特定于感染类型的。了解两者 适当和不适当的宿主对感染的反应，并了解 宿主反应的某些方面是病原体特异性的（而某些方面不是）， 针对这种毁灭性疾病的新型疗法。