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

在抗击肠道感染的同时遏制抗菌素耐药性的上升，是当代肉鸡养殖业的主要挑战之一，对禽类和消费者的健康都有影响。未来更好的监测、诊断和治疗选择解决方案的关键是更好地了解鸟类肠道微生物群，探索由于感染、治疗和抗药性特征的发展而对其共生和机会性病原体种群进行的修改。在这个项目中，我们计划探索肉鸡肠道微生物群，重点是与常见于禽类胃肠道的病原体：产气荚膜梭菌、坏死肠球菌、大肠杆菌和沙门氏菌有关的感染和耐药性。我们还介绍了与病毒合并感染导致肠道微生物群失调的情况。我们考虑了8类抗生素的耐药性/敏感性：四环素类、磺胺类、β-内酰胺类、氟喹诺酮类、多粘菌素、大环内酯类、二氨基嘧啶类、氨基糖苷类，这些药物在英国被广泛用于治疗。我们计划收集大量来自养殖场、饲料和禽类的异质数据，涵盖正常生产期和感染事件。数据将包括对粪便样本进行的微生物分析、全基因组测序、鸟枪式元基因组学和表型分析的结果、农场管理做法以及环境传感器数据和鸟类成像。我们建议使用机器学习和云计算来进行大规模的数据挖掘，并最终揭开可观察变量之间可能相互作用的网络，跟踪肉鸡的生命周期，并捕获感染、治疗和单一或多个耐药的发展过程。所获得的知识可以为选择充当生物标志物的可观察变量提供线索，即，作为未来解决方案的目标，用于实时家畜监测，以检测/预测感染或抗性特征的存在/叛乱，并支持精确诊断和定制治疗选择。这些结果还可能提出改善禽类肠道微生物群的途径，例如通过饲料添加剂，使其对感染更具抵抗力，同时抑制耐药性的发展。

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.