Modelling for MRSA control: can we harness phage and antibiotics to halt resistance spread?

抗生素耐药性（ABR）是一个主要的全球健康问题，目前在全世界每年造成约70万人死亡，如果不采取关键行动，预计到2050年将导致每年1000万人死亡。当细菌菌株暴露在通常会杀死它们的抗生素水平下并存活下来，它们被允许生长和传播，主宰其他因选择性压力而死亡的细菌时，就会出现ABR。耐甲氧西林金黄色葡萄球菌（MRSA）是一种机会性细菌病原体，是世界各地医院环境中流行的“超级细菌”，在欧盟和欧洲经济区由耐抗生素细菌引起的感染发病率第二高。虽然没有个别菌株获得对所有抗菌素的耐药性，但ABR的威胁表明进一步产生耐药性的严重可能性，这意味着感染可能变得更加困难，甚至不可能治疗，从而造成更多死亡。此外，耐药性的发展威胁到许多其他医疗程序的安全性，如器官移植和化疗，免疫抑制的患者依靠抗生素来对抗身体通常会自行清除的感染。为了解决这一问题，各国正在努力限制不必要地使用抗生素来治疗感染，并研究其他治疗方案，如噬菌体疗法——利用称为噬菌体的病毒来感染和杀死细菌。然而，对耐药基因转移机制的研究很少，包括MRSA中主要的转移机制——广义转导。在这里，噬菌体在细菌内部复制过程中无意中包装了抗性基因，因此充当了抗性传播的载体。人们正在寻求耐药性传播的跨学科解决方案，就像传染病传播的解决方案一样，但很少有人提出数学模型来研究这些机制，并量化如何使用联合疗法来最大限度地减少耐药性传播。本项目的目的是探索和更好地了解MRSA中ABR传播动态的机制。为此，将开发新的数学模型，并根据从实验室实验中获得的实验数据提供信息。这些模型将研究细菌、噬菌体和抗生素浓度之间的相互作用，基于转导等关键机制。相关参数将通过使用高性能集群计算和统计方法（如马尔可夫链蒙特卡罗方法）来量化，以将模型拟合到从我自己的实验室实验中获得的大数据集。如果有临床资料，也可以使用。在用实验结果验证这些模型之后，我将确定耐药性传播的主要过程和因素，并用模型编码不同的场景，以评估抗生素和噬菌体消除ABR的可能干预策略。还将调查使耐药性传播最大化的最坏情况，以确定最危险的做法。最后，我将在实验室中测试这些最优策略和最差策略，以验证模型结果。这项研究将促进对ABR的了解，为进一步的体内和其他类型细菌的调查提供信息，并有助于可能为现实世界的公共卫生战略提供信息并保护和挽救生命的研究。这个项目将使我能够探索并为ABR的前沿研究做出贡献，ABR是全球卫生领域最大的挑战之一。我将与不同学科的科学家合作进行跨学科的研究，发展跨学科的技能，同时扩展我的数学知识和科学交流。

Antibiotic resistance (ABR) is a major global health problem currently responsible for around 700,000 deaths per year worldwide and is predicted to cause 10 million deaths per year by 2050 if no critical action is taken. ABR arises when strains of bacteria survive exposure to levels of antibiotics that would normally kill them and they are allowed to grow and spread, dominating other bacteria which die from the selective pressure. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic bacterial pathogen responsible for 'superbugs' endemic to hospital settings around the world and has the second-highest incidence of infections caused by antibiotic-resistant bacteria in the EU and EEA. While no individual strains have acquired resistance to all antimicrobials - the threat of ABR presents the serious possibility of further resistance development, meaning infections can become more difficult, even impossible, to treat thereby causing more fatalities. Furthermore, resistance development threatens the safety of many other medical procedures such as organ transplants and chemotherapy, where immunosuppressed patients rely on antibiotics to fight infections the body would normally clear itself.To combat this issue, countries are now trying to limit the unnecessary use of antibiotics to treat infections and investigate alternative courses of treatment such as phage therapy - where viruses called bacteriophages are used to infect and kill bacteria. However, little research has been conducted into the mechanisms underlying resistance gene transfer, including generalised transduction, the prominent mechanism of transfer in MRSA. Here, bacteriophages inadvertently package a resistance gene during replication inside a bacterium and consequently act as vectors for the transmission of resistance. Cross-disciplinary solutions to resistance spread, like those of infectious disease spread, are being sought, but few mathematical models have been proposed to investigate these mechanisms and quantify how combination therapies might be used to minimise resistance spread.The aim of this project is to explore and better understand the mechanisms underpinning ABR transmission dynamics in MRSA. To do this, new mathematical models will be developed and informed by experimental data obtained from laboratory experiments. These models will study the interaction between bacteria, bacteriophage, and antibiotic concentrations, based on key mechanisms such as transduction. The associated parameters will be quantified by using high-performance cluster computing and statistical methods such as Markov chain Monte-Carlo methods to fit models to large sets of data obtained from my own laboratory experiments. Clinical data, if available, may also be used. Following the verification of these models with experimental outcomes, I will identify the leading processes and factors for resistance spread and encode different scenarios with the model to evaluate possible intervention strategies for eliminating ABR with antibiotics and phage. Worst-case scenarios that maximise resistance spread will also be investigated to identify the most dangerous practices. Finally, I will test these optimal and worst strategies in the laboratory to authenticate the model results. The study will advance understanding of ABR, informing further investigations in vivo and for other types of bacteria, as well as contributing to research that potentially informs real-world public health strategies and protects and saves lives.The project will allow me to explore and contribute to cutting-edge research in ABR, one of the biggest challenges in global health. I will conduct cross-disciplinary research in collaboration with scientists across a wide range of disciplines, developing interdisciplinary skills while extending my mathematical knowledge and scientific communication.