Transcriptional Profiling to Discriminate Bacterial and Non-bacterial Respiratory Illnesses

急性呼吸道感染（ARI）通常发生在一生中，是抗生素过度使用的主要原因。抗生素的使用与抗生素耐药性的传播直接相关，而抗生素耐药性现在被认为是对全球公共卫生的最紧迫威胁之一。在大多数ARI抗生素的病例中，微生物病因学是未知的，抗生素的使用是经验性的，而且往往是不适当的。尽管灵敏的分子诊断可以快速诊断各种呼吸道病毒，但由于担心细菌合并感染，其对患者管理和抗生素处方的影响不大。目前缺乏敏感和特异性的细菌性肺部感染诊断试验。全血的基因表达谱是一种强大的新方法，用于分析感染期间的宿主反应。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“生物签名”，可以区分病毒和细菌感染。尽管令人鼓舞，但迄今为止的研究尚未产生具有临床医学所需的足够准确性的预测基因集。重要的是，具有基础疾病、特定临床综合征的患者亚组和具有混合病毒-细菌感染的患者亚组尚未通过基因表达特征来解决。可能的是，诊断预测基因集的准确性可以通过分析转录谱同时考虑这些宿主和临床因素来优化。该项目将使用RNA测序在ARI住院的成人中评估最佳血液预测基因特征，以区分既存肺部疾病（包括哮喘和慢性阻塞性肺疾病）以及肺炎与非肺炎综合征的细菌性和非细菌性疾病。将选择已判定诊断为单独病毒、单独细菌或混合病毒-细菌感染的疾病进行RNA测序，并将数据用于开发预测模型，以区分细菌和非细菌呼吸道疾病。本研究的目的是确定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，并可由临床医生用于区分细菌和非细菌疾病，以最佳地管理因呼吸道症状而住院的患者。如果成功，这种方法可以扩展到门诊患者和其他年龄组，并在未来进行验证，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，我们将进行一项短期子研究，应用我们的方法学方法来确定COVID-19患者疾病严重程度的相关性。

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.