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.

抽象的 腺病毒角膜结膜炎是所有医学中最常见的疾病之一。尽管是 全球发病率的常见原因，没有预测临床的已知宿主或病原体因素 在这种情况下的结果。最近的大型国际临床研究与腺病毒相关结膜炎 显示大约10％的患者患有长期视力丧失。有限的深DNA测序 根据我们以前的R21（1R21EY027453）进行的ADV D8临床样本的研究表明意外 病毒基因组的序列多样性，在87个样品中有大约600个序列变体 相同的己孔定义的分子类型。这些变体分为三个子类型，有不同的承诺 结果不佳。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 批判性结果 - 单独的病毒序列的知识，渗透率的发展。 我们先前的研究对NVC-422临床试验的安慰剂组进行了测序。我们有一个 尚未测序的另外157个ADV D8样品。在AIM 1中，我们提出了测序 这些样品的腺病毒是为了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，以预测Adv D8的次思性浸润的发展 我们在全球研究中最普遍的AKC原因，我们在美国发现 E4是最普遍的类型。在AIM 2中，我们提出了从类型的研究中完全测序的所有36个样本 E4，以及来自B3型的23个样本，来自D19的9个样本，共有35个样本分布在之间 D53，D56和D64类型以确定它们的分子多样性，并应用相同的机器学习 这组样品的方法以确定是否可以从病毒序列变体中预测结果 这些类型与advd8一样。这些研究的结果将扩大我们对分子的理解 病毒结膜炎的发病机理，并将提供生物标志物来预测这种情况的结果。 这些进步将促进未来为这种常见条件开发疗法的努力。