Molecular determinants of pathogenicity in viral conjunctivitis

病毒性结膜炎致病性的分子决定因素

基本信息

批准号：
10474498
负责人：
Russell N. Van Gelder
金额：
$ 25.68万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10474498
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 machine learning prediction next generation novel therapeutics ocular surface outcome prediction pathogen predict clinical outcome predictive marker therapy development viral genomics whole genome

项目摘要

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.

抽象的腺病毒性角结膜炎是所有医学中最常见的病症之一。世界范围内发病的常见原因，目前尚无已知的宿主或病原体因素可以预测临床最近一项关于腺病毒相关结膜炎的大型国际临床研究。有限的深度 DNA 测序表明，大约 10% 的患者患有长期视力丧失。在我们之前的 R21 (1R21EY027453) 下进行的 AdV D8 临床样本研究显示出意想不到的结果病毒基因组的序列多样性，87个样本中大约有600个序列变异相同的六邻体定义的分子类型，这些变体分为具有不同倾向的三种亚型。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结果。关键结果——上皮下浸润的发展——仅来自对病毒序列的了解。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。另外 157 个尚未测序的 AdV D8 样本在目标 1 中，我们建议对这些样本进行测序。对这些样品进行腺病毒分析，以便 a.) 进一步表征 AdV D8 的序列多样性和 b.) 验证我们的机器学习方法预测上皮下浸润的发展，而 AdV D8 是在我们的全球研究中，AdV 是 AKC 最常见的原因，出乎意料的是，我们在美国发现 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B3类型的23个样本，D19的9个样本，总共35个样本分布在输入 D53、D56 和 D64 以确定它们的分子多样性，并应用相同的机器学习方法来确定是否可以从病毒序列变异中预测结果这些研究结果将扩大我们对AdVD8的理解。病毒性结膜炎的发病机制，并将提供预测这种情况结果的生物标志物。这些进展将有助于未来开发针对这种常见病症的疗法。