Host response-based diagnostics for identifying bacterial versus viral causes of lower respiratory infection in resource-limited settings

基于宿主反应的诊断，用于识别资源有限环境中下呼吸道感染的细菌与病毒原因

• 批准号: 10615892
• 负责人: GAYANI TILLEKERATNE
• 金额: $ 16.1万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615892
• 关键词: Adult; Anti-Bacterial Agents; Antimicrobial Resistance; Area; Asian population; Bacterial Infections; Biological; Biological Assay; Biological Markers; Blood; Blood specimen; COVID-19 pandemic; COVID-19 patient; Characteristics; Childhood; Classification; Clinical; Collaborations; Collection; Consensus; Country; Data; Detection; Diagnosis; Diagnostic; Enrollment; Epithelial Cells; Etiology; Fever; Funding; Gene Expression; Genes; Genomic medicine; Goals; Immune response; Income; Infection; Infrastructure; Knowledge; Laboratories; Leukocytes; Logistic Regressions; Lower Respiratory Tract Infection; Machine Learning; Minority; Molecular; Nasal Epithelium; Nasopharynx; Outcome; Patients; Performance; Polymerase Chain Reaction; Population; Research; Research Infrastructure; Resource-limited setting; Resources; Sampling; South Asian; Sputum; Sri Lanka; Testing; Time; Translating; Viral; Virus Diseases; Work; adjudication; biobank; clinical diagnostics; cohort; combat; density; diagnostic platform; improved; low and middle-income countries; migration; novel; pathogen; pathogenic bacteria; pathogenic virus; peripheral blood; point of care; precision medicine; procalcitonin; prospective; respiratory; transcriptome sequencing

Project Summary/ Abstract Lower respiratory tract infection (LRTI) is a common reason for antibacterial use and misuse globally. Limitations associated with current LRTI diagnostics are a major driver of antibacterial overuse. Pathogen- based diagnostics have limited sensitivity and do not distinguish infection from colonization. In low- or middle- income countries (LMICs), LRTI diagnosis is further hindered by limited laboratory infrastructure. Host-based diagnostics that leverage the host’s response to infection and broadly classify infection as viral or bacterial in etiology could greatly reduce inappropriate antibacterial use for LRTI. Previously, we showed that novel, peripheral blood-based gene expression classifiers accurately identified bacterial versus viral febrile respiratory illness in a South Asian population. While promising, these classifiers require the collection of a blood sample, which may be challenging in pediatric populations or in LMIC settings with limited resources. Emerging data suggest that the host response in the nasopharynx may also help identify class of infection. Nasopharyngeal sampling offers the possibility of an integrated diagnostic that combines both pathogen and host response detection in a single sample, which would be especially attractive in LMIC settings. The objective of this application is to determine the performance characteristics of NP-based gene expression classifiers at differentiating viral versus bacterial LRTI in a South Asian population. The following aims are proposed 1) to derive NP-based gene expression classifiers to discriminate viral versus bacterial LRTI, and 2) to transfer the NP-based classifier to a real-time polymerase chain reaction (RT-PCR) assay that has potential to be translated to a clinical platform. Comprehensive microbiological and molecular testing for respiratory viral and bacterial pathogens will be completed. Subjects will be adjudicated as having viral versus bacterial LRTI, and RNA sequencing will be performed using NP samples. Machine-learning approaches will identify host gene expression classifiers that discriminate viral versus bacterial LRTI. The genes identified in the NP-based classifier will be migrated onto customized, TaqMan Low-Density Array (TLDA) cards and RT-PCR will be performed. Gene expression will be quantified and logistic regression performed to identify viral versus bacterial LRTI. The expected outcome of this proposal is a significant improvement in our knowledge of how novel NP-based gene expression classifiers perform at identifying viral versus bacterial LRTI in a South Asian population. Following successful completion of these aims, we plan to translate the NP-based classifier to a point-of-care, clinical diagnostic platform. The long-term goal of this work is to develop strategies for improving antibacterial use in LMICs and to help combat the global crisis of antimicrobial resistance.

项目摘要/摘要 下呼吸道感染(LRTI)是全球抗菌药物使用和误用的常见原因。 与当前LRTI诊断相关的限制是抗菌药物过度使用的主要驱动因素。病原体- 基于诊断的方法灵敏度有限，不能区分感染和定植。在低-或中等- 在收入国家中，实验室基础设施有限进一步阻碍了LRTI的诊断。基于主机 利用宿主对感染的反应并将感染大致归类为病毒或细菌感染的诊断 病因学可以大大减少LRTI不适当的抗菌药物使用。之前，我们展示了这部小说， 基于外周血液的基因表达分类器准确识别细菌和病毒发热的呼吸道 南亚人口中的疾病。虽然这些分类器前景看好，但它们需要采集血液样本， 这在儿科人群或在资源有限的LMIC环境中可能是具有挑战性的。新兴数据 提示鼻咽部的宿主反应也可能有助于识别感染的类别。鼻咽部 采样提供了结合病原体和宿主反应的综合诊断的可能性 在单个样品中进行检测，这在LMIC环境中尤其有吸引力。这样做的目的是 应用是确定基于NP的基因表达分类器的性能特征 在南亚人群中区分病毒和细菌的LRTI。提出了以下目标1)以 派生基于NP的基因表达分类器以区分病毒和细菌的LRTI，以及2)将 基于NP的分类器用于实时聚合酶链式反应(RT-PCR)检测，具有潜在的 转化为临床平台。呼吸道病毒和病毒的全面微生物和分子检测 细菌病原体将被完成。受试者将被判定为感染了病毒与细菌的LRTI，以及 RNA测序将使用NP样本进行。机器学习方法将识别宿主基因 区分病毒和细菌LRTI的表达分类器。基于NP的基因识别 分类器将迁移到定制的TaqMan低密度阵列(TLDA)卡上，RT-PCR将 已执行。基因表达将被量化，并进行Logistic回归以识别病毒与 细菌性LRTI。这项提议的预期结果是，我们对如何 基于NP的新型基因表达分类器在南亚地区识别病毒和细菌LRTI的作用 人口。在成功实现这些目标后，我们计划将基于NP的量词转换为 护理点，临床诊断平台。这项工作的长期目标是制定改进战略 在LMIC中使用抗菌药物，并帮助应对全球抗菌素耐药性危机。