Transcriptional Profiling to Discriminate Bacterial and Non-bacterial Respiratory Illnesses

转录谱分析可区分细菌性和非细菌性呼吸道疾病

• 批准号: 10349622
• 负责人: Ann R Falsey
• 金额: $ 11.28万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10349622
• 关键词: Accounting; Acute; Acute bronchitis; Acute respiratory infection; Adult; Affect; Age; Antibiotic Therapy; Antibiotic-resistant organism; Antibiotics; Antimicrobial Resistance; Asthma; Bacteria; Bacterial Infections; Biological Markers; Blood; Chronic; Chronic Obstructive Airway Disease; Chronic lung disease; Clinical; Clinical Medicine; Data; Development; Diagnosis; Diagnostic tests; Etiology; Future; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic; Genetic Transcription; Goals; Hospitals; Immune response; Infection; Integration Host Factors; Life; Link; Lung; Lung diseases; Lung infections; Methods; Microarray Analysis; Microbiology; Modeling; Outpatients; Patient Care; Patients; Pattern; Performance; Persons; Pneumonia; Polymerase Chain Reaction; Population; Public Health; Recording of previous events; Respiratory Signs and Symptoms; Respiratory Tract Infections; Sensitivity and Specificity; Subgroup; Symptoms; Syndrome; Testing; Viral; Virus; Whole Blood; adjudicate; adjudication; age group; base; co-infection; diagnostic accuracy; differential expression; genetic signature; improved; microbial; molecular diagnostics; novel strategies; patient subsets; peripheral blood; personalized diagnostics; point-of-care diagnostics; predictive modeling; rapid diagnosis; respiratory; respiratory virus; tool; transcriptome sequencing

Acute respiratory infections (ARI) occur commonly throughout life and are a leading cause of antibiotic overuse. Antibiotic use is directly linked to spread of antimicrobial resistance, which is now considered to be one of the most urgent threats to global public health. In most cases of ARI, microbial etiology is unknown and antibiotics are administered empirically and often inappropriately. Although sensitive molecular diagnostics such as polymerase chain reaction (PCR) allow rapid diagnosis of a wide variety of respiratory viruses, their impact on patient management and antibiotic prescription has been modest primarily due to concern about bacterial co-infection. Sensitive and specific diagnostic tests for bacterial lung infection are currently lacking. Gene expression profiling of whole blood represents a powerful new approach for analysis of the host response during infection. Preliminary studies using microarrays indicate that viruses and bacteria trigger specific host transcriptional patterns in blood, yielding unique “bio-signatures” that may discriminate viral from bacterial causes of infection. Although encouraging, studies to date have not produced predictive gene sets demonstrating sufficient accuracy required for use in clinical medicine. Importantly, subgroups of patients with underlying conditions, specific clinical syndromes and those with mixed viral-bacterial infections have not been resolved by gene expression signatures. It is likely that the accuracy of diagnostic predictive gene sets can be optimized by analyzing transcriptional profiles while accounting for these host and clinical factors. In contrast to microarray technology, RNA sequencing is an unbiased method and is potentially more sensitive for identifying differentially expressed host genes. This project will evaluate optimal blood predictive gene signatures using RNA sequencing in adults hospitalized with ARI to distinguish bacterial and nonbacterial illness in the presence of preexisting lung disease including asthma and chronic obstructive pulmonary disease as well as for pneumonia vs. non-pneumonic syndromes. From a total of 1950 hospitalized patients with ARI, 680 illnesses that have adjudicated diagnoses of viral alone, bacterial alone or mixed viral-bacterial infection will be selected for RNA sequencing and data used to develop a predictive model to discriminate bacterial and nonbacterial respiratory illness. The goal of this study is to define a limited number of host predictive expression genes that can be developed into a rapid point of care diagnostic and can be used by clinicians to discriminate bacterial and nonbacterial illness to optimally manage patients presenting to the hospital with respiratory symptoms. If successful, this approach could be extended to and validated in outpatients and other age groups in the future for maximal impact on patient care and antibiotic prescription.

急性呼吸道感染（ARI）通常发生在整个生命过程中，是抗生素的主要原因 过度使用抗生素的使用与抗生素耐药性的传播直接相关，现在认为这是一个严重的问题。 全球公共卫生面临的最紧迫威胁之一。在大多数ARI病例中，微生物病因学是未知的， 抗生素的使用是凭经验进行的，而且往往是不适当的。虽然敏感的分子诊断 例如聚合酶链反应（PCR），可以快速诊断多种呼吸道病毒， 对患者管理和抗生素处方影响不大，主要是因为担心 细菌共感染。目前缺乏敏感和特异性的细菌性肺部感染诊断试验。 全血基因表达谱分析是一种强有力的分析宿主的新方法 感染时的反应。使用微阵列的初步研究表明， 血液中特定的宿主转录模式，产生独特的“生物签名”，可以区分病毒和 感染的细菌原因。尽管令人鼓舞，但迄今为止的研究还没有产生预测基因集 证明了在临床医学中使用所需的足够的准确度。重要的是， 基础疾病、特定临床综合征和病毒-细菌混合感染的患者尚未被 通过基因表达特征来解决。诊断预测基因集的准确性可能是 通过分析转录谱同时考虑这些宿主和临床因素来优化。相比之下 与微阵列技术相比，RNA测序是一种无偏的方法，对 鉴定差异表达的宿主基因。本项目将评估最佳血液预测基因 使用RNA测序在ARI住院成人中区分细菌和非细菌的特征 存在既存肺部疾病（包括哮喘和慢性阻塞性肺病）的疾病 以及肺炎与非肺炎综合征。从总共1950名ARI住院患者中， 680种疾病已裁定诊断为病毒单独感染、细菌单独感染或病毒-细菌混合感染 将被选择用于RNA测序，数据用于开发预测模型，以区分细菌和 非细菌性呼吸道疾病本研究的目的是确定一个有限数量的主机预测 表达基因，可以发展成为一种快速的护理点诊断，并可用于临床医生， 区分细菌性和非细菌性疾病，以最佳方式管理到医院就诊的患者 呼吸道症状如果成功，这种方法可以扩展到门诊病人和其他 年龄组在未来对病人护理和抗生素处方的最大影响。