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

摘要 噬菌体在治疗抗生素耐药感染方面的应用受到越来越多的关注。 和肠道微生物群相关的疾病。噬菌体疗法具有潜在的极端特异性的优势 与传统抗生素相比，它们的靶点导致的非靶点副作用很小。 心理治疗。然而，以目标生物为靶标的噬菌体的鉴定和确定寄主范围 这仍然是一个技术挑战。噬菌体的寄主分配通常需要在实验室培养该生物体 值得关注的是，当试图瞄准难以培养的生物时，这是一个重大障碍，并引入 对现有的噬菌体宿主知识库存在重大偏见。就像抗生素一样，生物体有可能 可能对噬菌体转导产生抵抗力，随着时间的推移，限制了单一噬菌体治疗感染的效用。 由于这些原因，有能力识别具有潜在治疗作用的噬菌体将是非常有益的 有效地从未培养的微生物种群中进行靶标。 在这个应用中，我们建议开发一个基于机器学习的平台来识别和识别 从全基因组测序(WGS)数据中指定噬菌体及其宿主。我们的方法 利用邻近连接测序的独特特性，或Hi-C，高效地收集直接物理 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来 从人类粪便样本中组装大规模、高质量的噬菌体-宿主相互作用数据集，使用它来训练 机器学习模型能够从现有的WGS数据中预测噬菌体-宿主关系，并提供了一个方便的 用户输入元基因组阅读以接收噬菌体宿主信息的平台。这种方法将使 噬菌体鉴定噬菌体和噬菌体组合的鉴定，以同时针对不同的生物体 从现有和未来的WGS数据集通过标准的临床方法无法处理。

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.