Molecular determinants of pathogenicity in viral conjunctivitis

病毒性结膜炎致病性的分子决定因素

• 批准号: 10313229
• 负责人: Russell N. Van Gelder
• 金额: $ 22.06万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10313229
• 关键词: Adenoviruses; Blindness; Catalogs; Characteristics; Clinical; Clinical Research; Clinical Trials; Code; Complication; Conjunctivitis; DNA; DNA sequencing; Data; Data Set; Development; Disease; Epidemic Keratoconjunctivitis; Future; Genes; Genetic; Genetic Polymorphism; Genome; Heterogeneity; International; Keratoconjunctivitis; Knowledge; Lead; Link; Machine Learning; Medicine; Molecular; Morbidity - disease rate; Outcome; Pathogenesis; Pathogenicity; Patients; Phase; Placebos; Predictive Factor; Prognosis; Public Health; Research Design; Sampling; Sequence Analysis; Shotguns; Swab; TPD52L1 gene; Techniques; Testing; United States; Variant; Viral; Viral Conjunctivitis; Viral Genes; Viral Genome; Viral Pathogenesis; Virus; Visual Acuity; Work; adverse outcome; arm; genome sequencing; improved; insight; machine learning method; next generation; novel therapeutics; ocular surface; outcome prediction; pathogen; predict clinical outcome; predictive marker; predictive modeling; therapy development; viral genomics; whole genome

ABSTRACT Adenoviral keratoconjunctivitis is one of the most common conditions in all of medicine. Despite being a common cause of morbidity world-wide, there are no known host or pathogen factors that predict clinical outcomes in this condition. A recent, large, international clinical study of adenovirus-related conjunctivitis revealed that approximately 10% of patients suffer from long term visual loss. A limited deep DNA sequencing study of AdV D8 clinical samples conducted under our previous R21 (1R21EY027453) revealed unexpected sequence diversity in the viral genome, with approximately 600 sequence variants among 87 samples within the same hexon-defined molecular type. These variants assorted into three subtypes with different propensity to poor outcome. Remarkably, using machine learning approaches, we found we were able to predict one critical outcome – the development of subepithelial infiltrates – from knowledge of the viral sequence alone. Our previous study sequenced samples from the placebo arm of the NVC-422 clinical trial. We have an additional 157 AdV D8 samples that have not been sequenced. In Aim 1 we propose sequencing the adenovirus of these samples in order to a.) further characterize the sequence diversity of AdV D8 and b.) validate our machine learning method for predicting development of subeptithelial infiltrates While AdV D8 was the most prevalent cause of AKC in our study worldwide, unexpectedly we found in the United States that AdV E4 was the most prevalent type. In Aim 2, we propose fully sequencing all 36 samples in the study from type E4, as well as 23 samples from type B3, 9 samples from D19, and a total of 35 samples distributed between type D53, D56, and D64 to determine their molecular diversity, and to apply the same machine learning methods to this set of samples to determine if outcomes can be predicted from viral sequence variants for these types as for AdVD8. The results of these studies will expand our understanding of the molecular pathogenesis of viral conjunctivitis, and will provide biomarkers for predicting outcomes from this condition. These advances will facilitate future efforts toward developing therapies for this common condition.

摘要 腺病毒性角膜结膜炎是所有医学中最常见的疾病之一。尽管是一个 作为世界范围内发病的常见原因，没有已知的宿主或病原体因素可预测临床 在这种情况下的结果。最近一项关于腺病毒相关结膜炎的大型国际临床研究 研究显示，大约10%的患者患有长期视力丧失。有限深度DNA测序 根据我们先前的R21（1 R21 EY 027453）进行的AdV D8临床样本研究显示， 病毒基因组中的序列多样性，在87个样本中有大约600个序列变异， 相同的六邻体定义的分子类型。这些变异体可分为三种亚型， 到可怜的结果。值得注意的是，使用机器学习方法，我们发现我们能够预测一个 关键的结果-上皮下浸润的发展-仅从病毒序列的知识。 我们之前的研究对NVC-422临床试验安慰剂组的样本进行了测序。我们有一个 另外157个AdV D8样品尚未测序。在目标1中，我们建议对 这些样品的腺病毒，以便a.）进一步表征AdV D8和B的序列多样性）。 验证我们的机器学习方法用于预测上皮下浸润的发展，而AdV D8是 AdV是我们在全球范围内研究的AKC最常见的原因，但我们意外地发现，在美国，AdV E4是最常见的类型。在目标2中，我们建议对研究中的所有36个样本进行完全测序， E4，以及来自B3型的23个样品，来自D19的9个样品，以及分布在E4型和E4型之间的总共35个样品。 D53，D56和D 64型，以确定它们的分子多样性，并应用相同的机器学习 方法，以确定是否可以从病毒序列变异预测结果， 这些类型的AdVD 8。这些研究的结果将扩大我们对分子生物学的理解。 本发明的目的是提供一种用于预测病毒性结膜炎的发病机制的生物标志物，并且将提供用于预测这种病症的结果的生物标志物。 这些进展将促进未来为这种常见疾病开发治疗方法的努力。