Therapeutic phage host-range prediction using proximity-guided metagenomics and artificial intelligence

摘要 噬菌体在治疗抗药性感染中的应用越来越受到关注 和肠道微生物相关疾病。噬菌体疗法具有潜在的极端特异性的优点， 与传统抗生素相比， 疗法然而，靶向感兴趣的生物体的噬菌体的鉴定和确定宿主范围 仍然是一个技术挑战。噬菌体的宿主分配通常需要生物体的实验室培养 感兴趣的是，当试图靶向难以培养的生物体时， 对现有噬菌体宿主知识库的重大偏见。就像抗生素一样， 可以获得对噬菌体转导的抗性，从而限制了单个噬菌体随时间治疗感染的效用。 由于这些原因，具有鉴定具有潜在治疗性的噬菌体的能力将是非常有益的。 从未培养的微生物群体中有效地靶向。 在这个应用中，我们提出开发一个基于机器学习的平台，用于识别和 根据宏基因组全基因组测序（WGS）数据分配噬菌体及其宿主。我们的方法 利用邻近连接测序（Hi-C）的独特特性， 来自混合微生物群落的噬菌体-宿主关联的证据。我们建议使用这项技术， 从人类粪便样本中组装一个大规模，高质量的噬菌体-宿主相互作用数据集，用它来训练一个 机器学习模型从现有的WGS数据中预测噬菌体-宿主关系，并提供方便的 用于用户输入宏基因组读数以接收噬菌体-宿主信息的平台。这种方法将使 噬菌体和噬菌体组合的鉴定以同时靶向否则 通过现有和未来的WGS数据集的标准临床方法难以处理。

ABSTRACT There is growing interest in the therapeutic application of phage for treatments of antibiotic-resistant infections and gut microbiome-related disorders. Phage therapies have the advantage of potentially extreme specificity for their targets leading to very little in the way of off-target side effects when compared with traditional antibiotic therapy. However, the identification of phage that target an organism of interest and determining host range remains a technical challenge. Host assignment for a phage typically requires laboratory culture of the organism of interest, a significant barrier when trying to target organisms which are difficult to culture, and introducing significant biases into the existing phage-host knowledge base. And like antibiotics, it is possible that organisms can acquire resistance to phage transduction, limiting the utility of a single phage to treat an infection over time. For these reasons it would be highly beneficial to have the ability to identify phage with potentially therapeutic targets efficiently from an uncultured population of microbes. In this application, we propose to develop a machine-learning based platform for the identification and assignment of phage and their hosts from metagenomic whole genome sequencing (WGS) data. Our approach leverages the unique property of proximity ligation sequencing, or Hi-C, to efficiently gather direct physical evidence of phage-host associations from mixed microbial communities. We propose to use this technology to assemble a large-scale, high-quality phage-host interaction dataset from human fecal samples, use it to train a machine learning model to predict phage-host relationships from existing WGS data, and provide a convenient platform for users to input metagenomic reads to receive phage-host information. This approach would enable the identification of phage and combinations of phage to simultaneously target organisms that are otherwise untractable through standard clinical methods from both existing and future WGS data sets.