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：
10547653
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/10547653
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 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 Lead 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 base 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.

抽象的对噬菌体治疗抗生素耐药感染的治疗应用的兴趣越来越大和肠道微生物组相关的疾病。噬菌体疗法具有潜在极端特异性的优势与传统的抗生素相比治疗。但是，鉴定针对感兴趣的生物并确定宿主范围的噬菌体仍然是技术挑战。噬菌体的宿主分配通常需要有机体的实验室文化有趣的是，试图靶向难以培养的生物并引入的障碍对现有的噬菌体宿主知识基础有重要的偏见。就像抗生素一样，有机体可能可以获得对噬菌体转导的抗性，从而限制了单个噬菌体的效用，可以随着时间的推移治疗感染。由于这些原因从未培养的微生物种群中有效地靶向。在此应用程序中，我们建议开发一个基于机器学习的平台，以识别和来自元基因组整个基因组测序（WGS）数据的噬菌体及其宿主的分配。我们的方法利用接近连接测序或HI-C的独特属性有效地收集直接的物理混合微生物群落的噬菌体主持协会的证据。我们建议将这项技术用于从人类粪便样品中组装一个大规模的高质量噬菌体 - 主持人交互数据集，使用它来训练机器学习模型可预测现有WGS数据的噬菌体宿主关系，并提供方便用户输入宏基因组学的平台会读取以接收噬菌体 - 宿主信息。这种方法将使噬菌体的鉴定和噬菌体的组合同时靶向否则的生物通过现有和将来的WGS数据集的标准临床方法无法提取。