Health Data Science CDT

与其他循环谱系相比，SARS-CoV-2进化的特点是出现了高度分化的关注变体（VOC），具有大量非同义突变。大多数SARS-CoV-2感染是急性的。传播通常通过一个小瓶颈发生，只有少量病毒颗粒从一个宿主传递到另一个宿主，限制了新感染中病毒的初始遗传多样性。因此，在急性感染期间积累的多样性很少，而且这些不同谱系的起源大多仍不清楚。一个已获得关注的假设是，宿主内进化，特别是在慢性感染个体中，在双链RNA病毒的进化动力学中起着至关重要的作用，在慢性感染个体中观察到的加速突变率和定义谱系的突变支持了这一假设。在预测未来的逃逸变异方面已经取得了进展，这些变异携带的突变使病毒能够逃避免疫反应。这些变异是由宿主内的选择压力形成的，但很少有人关注宿主内多样性的作用，以及它能告诉我们在这些压力下病毒的适应性。本研究旨在提高我们对慢性感染流行和传播动态的理解，帮助预测逃逸变异和指导变异特异性疫苗的开发。我们可以访问国家统计局Covid-19感染调查数据，其中包含超过12.5万个SARS-CoV-2序列，包括感染期间多个时间点的序列。该博士项目旨在开发亚共识水平序列的质量控制方法，以确保对宿主内真正生物多样性的可靠推断，不同于测序人工制品。通过分析病毒序列和常规测试数据，该项目将分析宿主内病毒多样性在感染过程中的时间动态，从病毒遗传因素预测感染时间（TSI）。有可能将该数据集与英国的测试和追踪应用程序数据联系起来，这可以提高TSI估计和对感染时间表的理解。最后，该项目将评估低频病毒变异是否可以预测全球流行突变的出现，例如在挥发性有机化合物中看到的突变。该项目属于EPSRC生物信息学研究领域。目的1：建立一种新的方法和工具，用于在基于人群的研究中从亚共识水平的病毒基因组序列中识别人工次要等位基因。无监督聚类算法将整合研究级元数据和上下文基因组数据，根据进化过程中预期的模式区分人工遗传变异和生物遗传变异。目标2：在亚共识水平上建立一个预测病毒遗传因素感染后时间的框架。这将包括估计与大型研究序列相关的TSI，从病毒遗传数据中策划一组预测因子（例如宿主内核苷酸多样性，香农熵），纠正AIM 1中的人工位点，以及开发用于预测TSI的预测模型。目的3：确定个体内宿主内单核苷酸变异的出现是否有助于预测未来的逃逸变异。这可能考虑到感染的时间因素，这些变异出现在AIM 2中。这个拟议的博士项目将利用现有的全面SARS-CoV-2数据集来弥补我们目前对RNA病毒在宿主内进化的认识的重大空白，并提供适用于管理当前和未来RNA病毒爆发的工具。

SARS-CoV-2 evolution is characterised by the emergence of highly divergent variants of concern (VOC) with a large number of non-synonymous mutations compared to the other circulating lineages. Most SARS-CoV-2 infections are acute. Transmission typically occurs through a small bottleneck, where only a small number of viral particles are passed from one host to another, limiting the initial genetic diversity of the virus within a new infection. Hence, little diversity accumulates during acute infections and the origins of such divergent lineages mostly remain unclear. One hypothesis that has gained traction is that, within-host evolution, especially in chronically infected individuals, plays a crucial role in the evolutionary dynamics of the double stranded RNA virus, supported by the accelerated mutation rates and lineage-defining mutations observed in chronically infected individuals. Strides have been made in predicting future escape variants, which carry mutations that allow the virus to evade immune responses. These variants are shaped by the selection pressures within hosts, yet little attention has been paid to the role of within-host diversity and what it can tell us about viral adaptation under these pressures.Overview of the project goalThis research aims to improve our understanding of chronic infection prevalence and transmission dynamics, aiding in predicting escape variants and guiding variant-specific vaccine development. We have access to the Office for National Statistics Covid-19 Infection Survey data, containing over 125,000 SARS-CoV-2 sequences, including sequences across multiple time points during an infection. This doctoral project aims to develop methods for quality control on sub consensus-level sequences to ensure robust inferences of true biological within-host diversity, differing from sequencing artifacts. By analyzing viral sequences and regular testing data, the project will analyze the temporal dynamics of within-host viral diversity throughout an infection, predicting time since infection (TSI) from viral genetic factors. There is a possibility of linking this dataset to the UK's Test and Trace app data which could enhance TSI estimates and understanding of infection timelines. Lastly, the project will assess whether low-frequency viral variants predict the emergence of globally prevalent mutations, such as those seen in VOCs. This project falls within the EPSRC Biological Informatics research area.AimsAIM 1: establishing a new methodology and tool for identifying artifactual minor alleles from sub consensus-level sequences of viral genomes in a population-based study. Unsupervised clustering algorithms will integrate study-level metadata and contextual genomic data to distinguish artifactual from biological genetic variation based on the patterns that would be expected under evolutionary processes.AIM 2: developing a framework for predicting the time since infection from viral genetic factors at the sub consensus level. This will include estimating TSI associated with sequences from a large study, curating a set of predictors (e.g. within-host nucleotide diversity, Shannon entropy) from the viral genetic data, correcting for artifactual sites from AIM 1, and developing a predictive model trained on predicting TSI.AIM 3: determining whether the appearance of intrahost single nucleotide variants within individuals can help predict future escape variants. This may take in consideration the temporal element of an infection at which these variants appear from AIM 2.ConclusionThis proposed doctoral project will leverage an existing comprehensive SARS-CoV-2 datasets to bridge significant gaps in our current knowledge of within-host evolution of RNA viruses and provide tools that are applicable to managing current and future RNA viral outbreaks.