Fighting Infection and AMR in broiler farming: AI, omics and smart sensing for diagnostics, treatment selection and gut microbiome improvement

肉鸡养殖中抗击感染和抗菌素耐药性：用于诊断、治疗选择和肠道微生物组改善的人工智能、组学和智能传感

• 批准号: BB/W020424/1
• 负责人: Tania Dottorini
• 金额: $ 25.69万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW020424%2F1
• 关键词: Fighting; Infection; AMR; broiler; farming

The fight against enteric infections while containing the uprise of antimicrobial resistance, represents one of the major challenges in contemporary broiler farming, with repercussions on both bird and consumer's health. Key to future, better solutions for surveillance, diagnostics and treatment selection, is to gain an improved understanding of the bird's gut microbiome, exploring the modifications its population of commensals and opportunistic pathogens undergo as a consequence of infection, treatment and development of resistant traits. In this project, we plan to explore the broiler gut microbiome, focusing on infection and resistance in relation to pathogens typically found in the gastrointestinal tract of the birds: Clostridium perfringens, Enterococcus cecorum, Escherichia coli and Salmonella spp. We cover also scenarios of co-infection with viruses causing dysbiosis of gut microbiome. We consider resistance/susceptibility to 8 classes of antibiotics: tetracyclines, sulphonamides, beta-lactams, fluoroquinolones, polymyxins, macrolides, diaminopyrimidines, aminoglycosides, whose use as therapeutics is diffused in the UK. We plan to collect a large amount of heterogeneous data from farms, feed and birds, covering normal production periods and infection events. Data will include results of microbiological analysis, whole-genome sequencing, shotgun metagenomics and phenotyping performed on faecal samples, on-farm management practices, as well as environmental sensor data and bird imaging. We propose to use machine learning and cloud computing to perform large-scale data mining and ultimately unravel the network of possible interactions amongst the observable variables, following broilers along their life cycle, and capturing episodes of infection, treatment and development of single or multi-drug resistance. Acquired knowledge may provide hints at the selection of observable variables acting as biomarkers, i.e, targetable by future solutions for real-time livestock monitoring, to detect/forecast infection or the presence/insurgence of resistant traits, and to support precision diagnostics and bespoke treatment selection. The results may also suggest routes to improve the birds gut microbiome, for example via feed additives, making it more robust to infection while at the same time inhibiting the development of resistance.

与肠道感染的斗争，同时包含抗菌抗性的起义，这是当代肉鸡养殖中的主要挑战之一，对鸟类和消费者的健康都产生了影响。未来的关键，更好的监测解决方案，诊断和治疗选择的方法是对鸟类的肠道微生物组有了改进的了解，探讨了由于感染，治疗和发展的抗性特征，其共生和机会性病原体的修饰。在这个项目中，我们计划探索肉鸡肠道微生物组，重点是与鸟类胃肠道中通常发现的病原体有关的感染和抗药性：灌注梭状芽胞杆菌，切肠球菌，肠oc骨，大肠杆菌和大肠杆菌和沙门氏菌。我们还涵盖了与导致肠道微生物组营养不良的病毒共同感染的情景。我们考虑对8种抗生素的耐药性/敏感性：四环素，磺酰胺，β-内酰胺，氟喹诺酮类，多聚霉菌素，大环内酯类，二氨基吡啶，氨基糖苷，氨基糖苷，其用作治疗剂的使用在英国。我们计划从农场，饲料和鸟类中收集大量的异质数据，涵盖正常的生产期和感染事件。数据将包括微生物分析，全基因组测序，shot弹枪宏基因组学以及对粪便样品，农场管理实践以及环境传感器数据和鸟类成像进行的表型的结果。我们建议使用机器学习和云计算来执行大规模数据挖掘，并最终揭示可观察到的变量之间可能相互作用的网络，在肉鸡沿着生命周期之后，并捕获了感染，治疗和开发的单个或多种多样的发作耐药性。获得的知识可能会提供有关选择可观察到的生物标志物的可观察变量的提示，即以实时牲畜监测的未来解决方案为目标，以检测/预测感染或抵抗性状的存在/叛乱选择。结果还可能表明改善鸟类肠道微生物组的途径，例如通过饲料添加剂，使感染更加强大，同时抑制了耐药性的发展。