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: 10555338
• 负责人: Ann R Falsey
• 金额: $ 70.96万
• 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 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10555338
• 关键词: Acute; Acute bronchitis; Acute respiratory infection; Adult; Affect; Age; Antibiotic Therapy; Antibiotic-resistant organism; Antibiotics; Antimicrobial Resistance; Asthma; Bacteria; Bacterial Infections; Biological; Biological Markers; Biological Testing; Blood; COVID-19 pandemic; COVID-19 patient; Chronic; Chronic Obstructive Pulmonary 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; Hospitalization; Hospitals; Immune response; Infection; Integration Host Factors; Life; Link; Lung; Lung diseases; Lung infections; Methodology; 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; Severity of illness; Subgroup; Symptoms; Syndrome; Testing; Viral; Virus; Whole Blood; adjudication; age group; biosignature; co-infection; diagnostic accuracy; diagnostic strategy; diagnostic tool; 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; severe COVID-19; 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 antibiotics, the microbial etiology is unknown and antibiotics are administered empirically and often inappropriately. Although sensitive molecular diagnostics allow rapid diagnosis of a 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 infection. Although encouraging, studies to date have not produced predictive gene sets with 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. 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. 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. Given the impact of the SARS-CoV-2 pandemic on ARI, we will perform a short-term sub-study applying our methodological approaches to identify correlates of disease severity specifically in COVID19 patients.

急性呼吸道感染(ARI)在一生中普遍发生，是过度使用抗生素的主要原因。抗生素的使用与抗菌素耐药性的传播直接相关，抗菌素耐药性现在被认为是对全球公共卫生最紧迫的威胁之一。在大多数ARI抗生素的情况下，微生物病因未知，抗生素的使用是经验性的，而且往往是不适当的。虽然敏感的分子诊断技术可以快速诊断各种呼吸道病毒，但它们对患者管理和抗生素处方的影响并不大，主要是因为担心细菌的联合感染。目前缺乏对细菌性肺部感染的敏感和特异的诊断试验。全血基因表达谱为分析宿主在感染过程中的反应提供了一种强有力的新方法。使用微阵列的初步研究表明，病毒和细菌在血液中触发特定的宿主转录模式，产生独特的“生物特征”，可能区分病毒和细菌感染。尽管令人鼓舞，但到目前为止，研究还没有产生临床医学所需的足够准确的预测基因集。重要的是，具有潜在疾病、特定临床症状和病毒-细菌混合感染的患者亚群尚未通过基因表达特征得到解决。在考虑宿主和临床因素的同时，通过分析转录图谱，很可能可以优化诊断预测基因集的准确性。该项目将在成人ARI住院患者中使用RNA测序来评估最佳的血液预测基因特征，以区分先前存在的肺部疾病(包括哮喘和慢性阻塞性肺疾病)中的细菌和非细菌疾病，以及肺炎和非肺炎综合征。对病毒、细菌或病毒-细菌混合感染做出诊断的疾病将被选择用于RNA测序和数据，以开发区分细菌性和非细菌性呼吸道疾病的预测模型。这项研究的目标是定义有限数量的宿主预测表达基因，这些基因可以开发成快速的护理点诊断，并可被临床医生用来区分细菌和非细菌疾病，以最佳地管理到医院的有呼吸道症状的患者。如果成功，这种方法可能会扩展到门诊患者和其他年龄段的患者，并在未来得到验证，以最大限度地影响患者护理和抗生素处方。鉴于SARS-CoV-2大流行对ARI的影响，我们将应用我们的方法学方法进行一项短期子研究，以确定疾病严重程度的相关性，特别是在COVID19患者中。

项目成果

Gene Expression Risk Scores for COVID-19 Illness Severity.

• DOI: 10.1093/infdis/jiab568
• 发表时间: 2023-02-01
• 期刊: The Journal of infectious diseases
• 作者: Peterson DR;Baran AM;Bhattacharya S;Branche AR;Croft DP;Corbett AM;Walsh EE;Falsey AR;Mariani TJ
• 通讯作者: Mariani TJ