Predicting emergence risk of future zoonotic viruses through computational learning

尽管进行了大量研究，但到目前为止，我们未能成功预测哪些病毒会出现，从而导致对公共卫生和经济造成巨大负担的疫情。针对SARS-CoV-2(导致新冠肺炎大流行的病毒)的研究表明，回过头来看，类似SARS的冠状病毒本可以被预测为高风险。为了为未来的大流行做准备，我们需要对哪些病毒有可能成为人畜共患病的更可靠和具体的预测，即能够从动物传播到人类。这项研究将探索利用当代大型数据集进行预测的新方法，例如基因组序列储存库和文本挖掘的已发表研究。机器学习将被用作一种最先进的计算工具包，它可以建立模型，在复杂信息(例如图像、文本、遗传序列)中找到模式，并将它们应用于特定任务(例如，预测病毒是否具有人畜共患病)。我将把哺乳动物和鸟类的RNA病毒建模为最有可能的新感染源。通过纳入传统上被忽视的数据，更好地捕捉病毒与宿主蛋白质和组织的相互作用，模型将提高质量和精度，预测病毒可能通过人畜共患感染并导致人类疾病。尽管病毒测序的覆盖率有所提高，但不同病毒的样本并不相同。如果没有谨慎地选择用于训练机器学习模型的数据，由此产生的偏差可能会导致较差的性能或错误识别的关系。除了三个分析目标，我还将创新新的方法，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，以预测哪些病毒可能是人畜共患病的，它们将来自哪些宿主。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，使用有关它们的物理和化学蛋白质属性的信息。未来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。这些特性可以用来寻找蛋白质“热点”，这些热点是预测宿主的重要信号集中的地方。将通过在医院正在进行的采样的监测数据中搜索预测的人畜共患病病毒来测试模型。第二，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了哪些组织/器官的数据。基于这一新数据，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哪些额外组织。然后，可以利用合成病毒蛋白工具箱，通过对来自不同组织和宿主的细胞进行体外实验感染来检验这些预测。一旦模型得到验证，就可以在网络中构建更多的属性，例如疾病严重性或致死率，以基于组织模式预测哪些动物病毒可能导致严重的人类疾病。最后，我将通过关注感染模式下的宿主蛋白来更详细地研究病毒与宿主的相互作用。通过结合感染组织的数据和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成为病毒感染的屏障，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的体外实验感染将进一步支持已确定的病毒相互作用。拟议的研究将产生重大的公共卫生影响，方法是确定优先病毒，以进行有针对性的监测，以防止疾病出现，并为有针对性的实验确定优先蛋白质相互作用，以开发预防性治疗方法。

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.