Molecular determinants of pathogenicity in viral conjunctivitis

摘要 腺病毒性角结膜炎是所有内科疾病中最常见的疾病之一。尽管是一名 在世界范围内，没有已知的宿主或致病因素可以预测临床 在这种情况下的结果。腺病毒相关性结膜炎的近期大型国际临床研究 据透露，大约10%的患者患有长期视力丧失。有限深度DNA测序 在我们之前的R21(1R21EY027453)下进行的对ADV D8临床样本的研究发现了意想不到的 病毒基因组的序列多样性，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。这些变异体分为三种不同倾向的亚型 导致糟糕的结果。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 仅从对病毒序列的了解就可得出关键结果--上皮下浸润性病变的发展。 我们之前的研究对NVC-422临床试验的安慰剂组样本进行了测序。我们有一个 另外157个尚未测序的ADV D8样本。在目标1中，我们建议对 对这些样本进行腺病毒检测，以获得一个。)进一步鉴定Adv D8和b的序列多样性。) 验证我们的机器学习方法在预测ADV D8时上皮下浸润性病变的发展 在我们的全球研究中，AKC最常见的原因是，我们出乎意料地在美国发现ADV 以E4型最多见。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型的23个样本，D19的9个样本，分布在 键入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 方法以确定是否可以从病毒序列变异中预测结果 这些类型与AdVD8相同。这些研究的结果将扩大我们对分子的理解 病毒性结膜炎的发病机制，并将为预测这种情况的结果提供生物标志物。 这些进展将促进未来努力开发这种常见疾病的治疗方法。

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.