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

使用邻近引导宏基因组学和人工智能进行治疗性噬菌体宿主范围预测

基本信息

批准号：
10629378
负责人：
Ivan Liachko
金额：
$ 99.59万
依托单位：
PHASE GENOMICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10629378
关键词：
Antibiotic Therapy Antibiotics Antimicrobial Resistance Artificial Intelligence Bacteria Bacteriophages Basic Science Cells Cellular Structures Clinic Clinical Communities Computing Methodologies DNA Sequence DNA sequencing Data Data Set Databases Democracy Development Disease Donor person Double Stranded DNA Virus Ecosystem Engraftment Environment Failure Family Foundations Future Generations Genome Health Hi-C Human Human Microbiome Human body Infection Infrastructure Intake Knowledge Laboratory culture Libraries Ligation Link Lysogeny Lytic Lytic Phase Machine Learning Metagenomics Methods Microbe Organism Outcome Output Planets Population Process Property Reagent Research Research Personnel Resistance Sampling Shotgun Sequencing Shotguns Specificity Structure Technology Temperature Therapeutic Time Training Treatment outcome Viral Virus antibiotic resistant infections combat cost cost efficient crosslink deep learning deep learning model fecal transplantation fitness genome sequencing gut microbiome improved innovation insight interest knowledge base large-scale database machine learning method machine learning model metagenomic sequencing microbial microbial community microbiome microbiota transplantation multi-drug resistant pathogen novel preservation prevent prospective side effect targeted agent therapeutic target therapy development therapy outcome tool virome virtual whole genome

项目摘要

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.

抽象的人们对噬菌体在抗生素耐药性感染治疗中的治疗应用越来越感兴趣和肠道微生物组相关疾病。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，他们的目标导致很少的脱靶副作用治疗。然而，识别目标生物体的噬菌体并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有趣的是，当试图瞄准难以培养的生物体时，这是一个重大障碍，并引入现有噬菌体宿主知识库存在重大偏差。就像抗生素一样，生物体有可能可以获得对噬菌体转导的抗性，随着时间的推移限制了单个噬菌体治疗感染的效用。由于这些原因，能够识别具有潜在治疗作用的噬菌体将非常有益有效地针对未培养的微生物群体。在此应用中，我们建议开发一个基于机器学习的平台，用于识别和识别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，有效收集直接物理数据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出大规模、高质量的噬菌体-宿主相互作用数据集，用它来训练机器学习模型从现有的WGS数据预测噬菌体-宿主关系，并提供方便的供用户输入宏基因组读数以接收噬菌体宿主信息的平台。这种方法将使鉴定噬菌体和噬菌体组合以同时靶向生物体，否则通过现有和未来的 WGS 数据集的标准临床方法无法处理。