Health Data Science CDT

SARS-CoV-2的进化特征是出现高度不同的关注变异体(VOC)，与其他循环谱系相比，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的瓶颈发生，在那里只有少量的病毒颗粒从一个宿主传递到另一个宿主，限制了病毒在新感染中的初始遗传多样性。因此，在急性感染期间积累的多样性很少，这种不同血统的起源大多尚不清楚。一个已经获得支持的假说是，宿主内的进化，特别是在慢性感染的个体中，在双链RNA病毒的进化动力学中发挥着关键作用，这得到了在慢性感染的个体中观察到的加速突变率和谱系定义突变的支持。在预测未来的逃逸变异方面已经取得了很大进展，这种变异携带了使病毒能够逃避免疫反应的突变。这些变异是由宿主内部的选择压力形成的，但很少有人关注宿主内多样性的作用以及它能告诉我们什么关于病毒在这些压力下的适应。项目目标概述本研究旨在提高我们对慢性感染流行和传播动力学的理解，帮助预测逃逸变异和指导变异特异性疫苗的开发。我们可以访问英国国家统计局的新冠肺炎感染调查数据，其中包含超过125,000个SARS-CoV-2序列，包括感染期间多个时间点的序列。这一博士项目旨在开发亚共识水平序列的质量控制方法，以确保与测序制品不同的真实生物宿主内多样性的可靠推断。通过分析病毒序列和常规检测数据，该项目将分析感染期间宿主内病毒多样性的时间动态，根据病毒遗传因素预测感染以来的时间(TSI)。有可能将这一数据集与英国的测试和跟踪应用程序数据联系起来，这可能会增强TSI估计和对感染时间表的了解。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VOCs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，用于在基于群体的研究中从病毒基因组的亚共识水平序列中识别人工微等位基因。无监督聚类算法将整合研究水平的元数据和上下文基因组数据，根据进化过程中预期的模式来区分人工遗传变异和生物遗传变异。目标2：在次共识水平上开发一个框架，用于预测病毒遗传因素感染的时间。这将包括估计与来自大型研究的序列相关的TSI，从病毒遗传数据中挑选一组预测因子(例如宿主内核苷酸多样性、Shannon熵)，校正来自AIM 1的人工位点，并开发用于预测TSI的预测模型。目标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。这可能会考虑到感染的时间因素，这些变异出现在AIM 2中。结论这项拟议的博士项目将利用现有的全面的SARS-CoV-2数据集来弥合我们目前对RNA病毒宿主内进化的重大认识空白，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.