Monitoring the gut microbiome via AI and omics: a new approach to detect infection and AMR and to support novel therapeutics in broiler precision farm

通过人工智能和组学监测肠道微生物组：一种检测感染和抗菌素耐药性并支持肉鸡精准农场新疗法的新方法

• 批准号: BB/X017370/1
• 负责人: Tania Dottorini
• 金额: $ 103.31万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX017370%2F1
• 关键词: Monitoring; gut; microbiome; via; AI

The production of poultry for meat consumption (broilers) is rising globally, the UK being amongst the countries with the highest production. Poultry meat consumption pro capita in the UK is twice more than pork and almost three times more than beef, and growing. Poultry endemic diseases due to bacteria, viruses and parasites are frowned upon, as they can cause considerable economic losses. To save production, the use of broad-spectrum antibiotics at any sign of incipient disease is widespread, even when the source of the disease has not been pinpointed yet (let alone the bacterial origin). The act of administering antibiotics increases the risk of the pathogen developing resistance (antimicrobial resistance - AMR), making it more difficult to fight that pathogen in the future. To reduce the use of broad-spectrum antibiotics, solutions are urgently needed for farms to efficiently monitor livestock, identify infections and the source of infection as soon as possible, and administer more targeted therapeutics.The project aims at developing new surveillance solutions specifically designed for use by the broiler industry. These solutions are designed to be turn-key: operators will periodically upload data acquired within the farm to a cloud-based service where the state of production will be assessed automatically. Warnings and advice will be sent back to the farmers via apps on smartphones/tablets, in case infections, co-infection or increased likelihood of AMR are detected. The project will cover the main pathogens of bacterial, viral and parasitic origin affecting UK broiler farming, as well as AMR to the main classes of antibiotics routinely administered in the country.How will surveillance solutions achieve their predictions, and how will we decide what data to upload? At the core of the project there is a data mining method powered by machine learning, recently perfected by the applicants. The method allows to consider a large amount of heterogeneous information collected from the farm, including historical data of previous infections/AMR events, and allows the development of mathematical models that, based on observing specific patterns in the collected information, estimate the likelihood of infection or resistance manifestations. The method also allows to isolate what farm variables are the most important for each type of prediction (e.g. a specific infection, or AMR trait): these variables are called "biomarkers". Initially, we will consider many variables: sensor data on temperature, humidity, illumination and air composition in the barn, microbiological analysis of samples from feathers, soil, barn floors, water, feed, and operator boots. An important role is reserved to data originating from the analysis of the gut microbiome, i.e. the microbial species living in the broiler gut, whose abundances, genetic traits and metabolic functions, have been proven implicated in numerous aspects of infection and resistance. Co-presence of viruses and parasites will be considered. Thanks to machine learning, for the first time it will be possible to prune such a multitude of variables, isolating the most relevant (biomarkers) to be used in the final prediction models. These models will be turned into software applications running remotely as cloud services. Users (farmers) will periodically upload information (biomarker values) as required, allowing for the models to replicate exactly at any time the state of the real production (models will become "digital twins", being virtual replicas of the real system). Farmers will then receive messages via web-based apps, reporting warnings, alarms, or suggested therapies. The methods for identifying the important variables and developing prediction models have been successful in pilot studies, leading to the identification of promising biomarkers documented in publications. The projected impact of the project on surveillance in broiler farming is expected to be unprecedented.

全球肉类消费家禽（肉鸡）的产量正在增加，英国是产量最高的国家之一。英国的人均禽肉消费量是猪肉的两倍，几乎是牛肉的三倍，而且还在不断增长。由细菌、病毒和寄生虫引起的家禽地方病令人不悦，因为它们会造成相当大的经济损失。为了节省产量，即使疾病的来源尚未查明（更不用说细菌起源），在出现任何疾病初期迹象时，广谱抗生素的使用也很普遍。施用抗生素的行为会增加病原体产生耐药性（抗菌素耐药性 - AMR）的风险，从而使未来对抗该病原体变得更加困难。为了减少广谱抗生素的使用，农场迫切需要解决方案来有效监测牲畜，尽快识别感染和感染源，并进行更有针对性的治疗。该项目旨在开发专门为牲畜设计的新监测解决方案。肉鸡行业使用。这些解决方案被设计为交钥匙解决方案：操作员将定期将农场内获取的数据上传到基于云的服务，在该服务中将自动评估生产状态。如果检测到感染、合并感染或抗菌素耐药性可能性增加，警告和建议将通过智能手机/平板电脑上的应用程序发送给农民。该项目将涵盖影响英国肉鸡养殖的细菌、病毒和寄生虫来源的主要病原体，以及该国常规使用的主要抗生素类别的抗菌素耐药性。监测解决方案将如何实现其预测，以及我们将如何决定哪些数据上传？该项目的核心是一种由机器学习驱动的数据挖掘方法，最近由申请人完善。该方法允许考虑从农场收集的大量异质信息，包括先前感染/AMR事件的历史数据，并允许开发数学模型，基于观察收集的信息中的特定模式，估计感染的可能性或抵抗的表现。该方法还允许隔离哪些农场变量对于每种类型的预测（例如特定感染或 AMR 特征）最重要：这些变量称为“生物标志物”。最初，我们将考虑许多变量：关于谷仓中温度、湿度、照明和空气成分的传感器数据，对羽毛、土壤、谷仓地板、水、饲料和操作员靴子样本的微生物分析。来自肠道微生物组分析的数据发挥着重要作用，即生活在肉鸡肠道中的微生物物种，其丰度、遗传特征和代谢功能已被证明与感染和抵抗力的许多方面有关。将考虑病毒和寄生虫的共存。借助机器学习，首次可以修剪如此众多的变量，分离出最相关的（生物标记）以用于最终的预测模型。这些模型将转变为作为云服务远程运行的软件应用程序。用户（农民）将根据需要定期上传信息（生物标记值），使模型能够随时准确复制真实生产的状态（模型将成为“数字双胞胎”，成为真实系统的虚拟复制品）。然后，农民将通过基于网络的应用程序接收消息，报告警告、警报或建议的治疗方法。用于识别重要变量和开发预测模型的方法在试点研究中取得了成功，从而确定了出版物中记录的有前途的生物标志物。该项目对肉鸡养殖监测的预计影响将是前所未有的。