Predicting emergence risk of future zoonotic viruses through computational learning

通过计算学习预测未来人畜共患病毒的出现风险

• 批准号: MR/X019616/1
• 负责人: Liam Brierley
• 金额: $ 188.29万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX019616%2F1
• 关键词: Predicting; emergence; risk; future; zoonotic

Despite substantial research, we have so far failed to successfully predict which viruses would emerge to cause outbreaks with large burdens to public health and economies. Research addressing SARS-CoV-2 (the virus causing the COVID-19 pandemic) has shown that, in retrospect, SARS-like coronaviruses could have been predicted as high risk. To prepare for future pandemics, we need more reliable and specific predictions of which viruses have potential to be 'zoonotic', i.e., capable of transmitting from animals to humans.This research will investigate new ways of making predictions by taking advantage of large contemporary datasets, e.g., genome sequence repositories and text-mined published research. Machine learning will be used as a state-of-the-art computational toolkit that can build models to find patterns in complex information (e.g., images, text, genetic sequences) and apply them to specific tasks (e.g., predicting whether a virus is zoonotic or not). I will model mammal and bird RNA viruses as the most likely sources of emerging infections. By incorporating traditionally neglected data that better captures how viruses interact with host proteins and tissues, models will predict potential of viruses to zoonotically infect and cause disease in humans with improved quality and precision. Although viral sequencing has improved in coverage, different viruses have been sampled unequally. Resulting biases can lead to poor performance or misidentified relationships if data used to train machine learning models is not selected cautiously. Alongside three analytical objectives, I will also innovate new methods to improve model representation of differently sampled viruses based on evolutionary relatedness. Firstly, I will build models using protein sequences to predict which viruses are likely to be zoonotic and from which hosts they will originate. To better represent how viruses interact with host cells, I will build models to use information about their physical and chemical protein properties. Further models will use newer methods that can automatically find important properties straight from raw sequences. These properties can be used to find protein 'hotspots' where important signals for predicting hosts are concentrated. Models will be tested by searching for predicted zoonotic viruses in surveillance data from ongoing hospital sampling.Secondly, I will build models using host tissue and organ data. Data describing which tissues/organs are infected by each virus has already been extracted from scientific literature using text mining methods. Based on this new data, I will model the three-way network of viruses, hosts and their tissues and predict which additional tissues viruses are likely to infect. These predictions can then be tested through experimental in-vitro infection of cells from different tissues and hosts, taking advantage of synthetic viral protein toolkits. Once models are validated, further properties can be built into the network, e.g., disease severity or fatality, to predict which animal viruses have potential to cause severe human disease based on tissue patterns.Finally, I will investigate virus-host interactions in more detail by focusing on host proteins underlying patterns of infection. By combining data on infected tissues and how often those tissues express potential viral-interacting proteins, I will predict which proteins may act as barriers to viral infection and which proteins may act as viral receptors (i.e., structures that directly bind viruses and allow cell entry). Experimental in-vitro infection of cells that do/do not express potential receptor proteins will further support viral interactions identified. The proposed research will generate significant public heath impact by identifying priority viruses for targeted surveillance to prevent disease emergence and priority protein interactions for targeted experiments to develop pre-emptive therapeutics.

尽管进行了大量研究，但迄今为止，我们仍未能成功预测哪些病毒会出现，导致疫情爆发，给公共卫生和经济带来巨大负担。针对SARS-CoV-2（引起COVID-19大流行的病毒）的研究表明，回顾过去，SARS样冠状病毒可以被预测为高风险。为了为未来的大流行做好准备，我们需要更可靠和具体的预测哪些病毒有可能是“人畜共患病”的，即，能够从动物传播到人类。这项研究将研究利用当代大型数据集进行预测的新方法，例如，基因组序列库和文本挖掘发表的研究。机器学习将被用作最先进的计算工具包，可以构建模型来寻找复杂信息中的模式（例如，图像、文本、基因序列）并将它们应用于特定任务（例如，预测病毒是否是人畜共患病的）。我将把哺乳动物和鸟类的RNA病毒作为最有可能的新感染源。通过整合传统上被忽视的数据，更好地捕捉病毒如何与宿主蛋白质和组织相互作用，模型将以更高的质量和精度预测病毒感染人类并导致人类疾病的潜力。尽管病毒测序的覆盖率有所提高，但不同病毒的采样并不平等。如果用于训练机器学习模型的数据没有谨慎选择，那么由此产生的偏见可能会导致性能低下或错误识别的关系。除了三个分析目标外，我还将创新新方法，以改进基于进化相关性的不同样本病毒的模型表示。首先，我将使用蛋白质序列建立模型来预测哪些病毒可能是人畜共患病的，以及它们将起源于哪些宿主。为了更好地表示病毒如何与宿主细胞相互作用，我将建立模型来使用有关其物理和化学蛋白质特性的信息。进一步的模型将使用更新的方法，可以直接从原始序列中自动找到重要的属性。这些特性可用于发现蛋白质的“热点”，其中集中了用于预测宿主的重要信号。模型将通过在正在进行的医院采样的监测数据中搜索预测的人畜共患病病毒来进行测试。其次，我将使用宿主组织和器官数据建立模型。已经使用文本挖掘方法从科学文献中提取了描述每种病毒感染的组织/器官的数据。基于这些新数据，我将对病毒、宿主及其组织的三方网络进行建模，并预测病毒可能感染的其他组织。然后，这些预测可以通过利用合成病毒蛋白工具包对来自不同组织和宿主的细胞进行实验性体外感染来进行测试。一旦模型得到验证，就可以在网络中构建更多的属性，例如，疾病的严重性或致死性，以预测哪些动物病毒有可能导致严重的人类疾病的组织模式的基础上。最后，我将研究病毒-宿主相互作用的更详细的关注宿主蛋白质的感染模式。通过结合感染组织的数据以及这些组织表达潜在病毒相互作用蛋白的频率，我将预测哪些蛋白质可能作为病毒感染的屏障，哪些蛋白质可能作为病毒受体（即，直接结合病毒并允许细胞进入的结构）。对表达/不表达潜在受体蛋白的细胞进行实验性体外感染将进一步支持所鉴定的病毒相互作用。拟议的研究将通过确定优先病毒进行有针对性的监测，以防止疾病的出现和优先蛋白质相互作用的有针对性的实验，以开发先发制人的治疗方法，从而产生重大的公共卫生影响。