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

MRSA 控制建模：我们可以利用噬菌体和抗生素来阻止耐药性传播吗？

• 批准号: 2578703
• 金额: --
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2578703
• 关键词: Modelling; MRSA; control; harness; phage

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.

抗生素耐药性（ABR）是一个主要的全球健康问题，目前每年造成全球约70万人死亡，如果不采取关键行动，预计到2050年每年将造成1000万人死亡。当细菌菌株在暴露于通常会杀死它们的抗生素水平下存活下来时，ABR就会出现，并且它们被允许生长和传播，支配其他死于选择压力的细菌。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甲氧西林细菌引起的感染发病率第二高。虽然没有个别菌株对所有抗生素都具有耐药性，但ABR的威胁提出了进一步耐药性发展的严重可能性，这意味着感染可能变得更加困难，甚至不可能治疗，从而导致更多的死亡。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，其中免疫抑制的患者依赖抗生素来对抗身体通常会自行清除的感染。各国现在正试图限制不必要地使用抗生素来治疗感染，并研究诸如噬菌体疗法等替代疗法-其中被称为噬菌体的病毒被用来感染和杀死细菌。然而，很少有研究已经进行耐药基因转移的机制，包括广义转导，在MRSA的转移的突出机制。在这里，噬菌体在细菌内复制过程中无意中包装了一个抗性基因，因此充当了传播抗性的载体。跨学科的解决方案，耐药传播，如那些传染病的传播，正在寻求，但很少有数学模型已被提出来调查这些机制，并量化如何组合疗法可能被用来最大限度地减少耐药spread.The项目的目的是探索和更好地了解ABR的机制，在MRSA的传播动力学的基础。为此，将开发新的数学模型，并通过实验室实验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）进行量化，以将模型拟合到从我自己的实验室实验中获得的大量数据集。如果可用，也可以使用临床数据。在用实验结果验证这些模型之后，我将确定耐药传播的主要过程和因素，并用模型编码不同的场景，以评估用抗生素和噬菌体消除ABR的可能干预策略。还将调查最大化耐药性传播的最坏情况，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，以验证模型的结果。这项研究将促进对ABR的理解，为进一步的体内研究和其他类型的细菌提供信息，并为可能为现实世界的公共卫生战略提供信息并保护和拯救生命的研究做出贡献。该项目将使我能够探索ABR的前沿研究并为之做出贡献，这是全球健康的最大挑战之一。我将进行跨学科的研究与科学家在广泛的学科合作，发展跨学科的技能，同时扩大我的数学知识和科学交流。