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）通常发生在整个生命中，并且是抗生素过度使用的主要原因。抗生素使用与抗菌耐药性的传播直接相关，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，微生物病因是未知的，并且抗生素是紧急且经常不适当地给药的。尽管敏感的分子诊断允许快速诊断多种呼吸道病毒，但它们对患者管理和抗生素处方的影响主要是由于对细菌共感染的关注。目前缺乏针对细菌肺部感染的敏感和特定的诊断测试。全血的基因表达分析代表了一种在感染过程中分析宿主反应的强大新方法。使用微阵列的初步研究表明，病毒和细菌引发了血液中特定的宿主转录模式，产生了可能将病毒与细菌感染区分开的独特“生物签名”。尽管令人鼓舞，但迄今为止的研究尚未产生预测基因集，其用于临床医学所需的准确性。重要的是，尚未通过基因表达特征来解决患有潜在疾病，特定临床综合征和患有混合病毒 - 细菌感染患者的患者亚组。在考虑这些宿主和临床因素的同时，可以通过分析的转录曲线来优化诊断预测基因集的准确性。该项目将在患有ARI住院的成年人中使用RNA测序评估最佳的血液预测基因特征，以区分已经存在的肺部疾病，包括哮喘和慢性阻塞性肺疾病以及肺炎与非肺炎综合症。将选择单独调整病毒，单独细菌或混合病毒 - 细菌感染的诊断诊断的疾病进行RNA测序，并用于开发一种预测模型以区分细菌和非细菌呼吸道疾病的数据。这项研究的目的是定义有限数量的宿主预测表达基因，这些基因可以发展为快速的护理诊断点，并可以被临床医生使用来区分细菌和非细菌疾病，以最佳地管理出现呼吸症状的医院的患者。如果成功的话，将来可以将这种方法扩展到门诊病人和其他年龄组中，以最大程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，我们将进行短期子学研究，采用我们的方法学方法来识别COVID19患者专门鉴定疾病严重程度的相关性。

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