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: 10452456
• 负责人: GAYANI TILLEKERATNE
• 金额: $ 28.36万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10452456
• 关键词: Adult; Anti-Bacterial Agents; Antimicrobial Resistance; Area; Asian population; Bacterial Infections; Biological; Biological Assay; Biological Markers; Blood; Blood specimen; COVID-19; COVID-19 pandemic; COVID-19 patient; Characteristics; Childhood; Clinical; Collection; Consensus; Country; Custom; 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; Microbiology; 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; adjudicate; adjudication; base; biobank; clinical diagnostics; cohort; combat; density; diagnostic platform; improved; 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.

项目概要/摘要 下呼吸道感染 (LRTI) 是全球抗菌药物使用和滥用的常见原因。 当前 LRTI 诊断的局限性是病原体过度使用的主要原因。 基于诊断的敏感性有限，并且不能区分低度或中度定植的感染。 在收入国家（LMIC）中，基于主机的实验室基础设施有限进一步阻碍了 LRTI 诊断。 利用宿主对感染的反应进行诊断，并将感染大致分类为病毒或细菌 病因学可以大大减少 LRTI 的不当抗菌药物使用。 基于外周血的基因表达分类器准确识别细菌与病毒性发热呼吸道疾病 虽然很有希望，但这些分类器需要收集血液样本， 这对于儿科人群或新兴数据有限的中低收入国家可能具有挑战性。 表明鼻咽部的宿主反应也可能有助于识别感染类别。 采样提供了结合病原体和宿主反应的综合诊断的可能性 在单个样本中进行检测，这在中低收入国家环境中特别有吸引力。 应用程序是确定基于 NP 的基因表达分类器的性能特征 区分南亚人群中的病毒性 LRTI 和细菌性 LRTI 提出以下目标：1) 派生基于 NP 的基因表达分类器来区分病毒和细菌 LRTI，以及 2) 转移 基于 NP 的分类器可用于实时聚合酶链式反应 (RT-PCR) 检测，有可能成为 转化为针对呼吸道病毒和病毒的综合微生物学和分子检测的临床平台。 细菌病原体将被判定为患有病毒性 LRTI 和细菌性 LRTI。 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。 分类器将迁移到定制的 TaqMan 低密度阵列 (TLDA) 卡上，RT-PCR 将 将量化基因表达并进行逻辑回归以识别病毒与病毒。 该提案的预期结果是显着提高我们对细菌 LRTI 的了解。 新型基于 NP 的基因表达分类器可识别南亚地区的病毒与细菌 LRTI 成功完成这些目标后，我们计划将基于 NP 的分类器转化为 这项工作的长期目标是制定改进策略。 中低收入国家的抗菌药物使用并帮助应对全球抗菌药物耐药性危机。