The dynamics of drug resistance within hospital populations of Gram-negative bacteria

医院革兰氏阴性菌群体的耐药动态

• 批准号: MR/P014658/1
• 负责人: Gwenan Knight
• 金额: $ 41.71万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FP014658%2F1
• 关键词: dynamics; drug; resistance; within; hospital

When bacteria become resistant to antibiotics the infections they cause are harder to treat. In the UK, and globally, we are seeing an increase in the number of infections being caused by antibiotic resistant bacteria. This could lead us back into the pre-antibiotic era before the early 1900s when infections of simple cuts may become life threatening and cancer treatments which suppress the immune system, and rely on antibiotics to prevent infections, will be unusable. Antibiotic resistance arises through the use and misuse of antibiotics and so, even if we develop new antibiotics, it is likely that we will always be faced with the problem of resistant strains. We need to consider how best to preserve our existing antibiotics without compromising patient care. One of the ways to do this is to look at why some places have less resistance than others and to try to work out what they are doing right. For example, some hospitals have fewer infections with resistant bacteria than others. In this project I will explore what the reasons for these differences might be and translate these findings into ways to better prevent resistance from spreading. To do this I will build mathematical models of antibiotic resistance spread. Mathematical models are frameworks in which different subpopulations are separated out and the rates at which these sub-groups increase or decrease are calculated. For example, a model would split a hospital population into those with and without infections with resistant or susceptible bacteria. It would then consider at what rate and by what mechanism people become infected, and then explore what the difference is between those with and without resistance. By writing this framework down mathematically, we get a better understanding of the processes underlying the spread of resistance and can identify the key targets - for example overuse of a certain type of antibiotic - that need to be tackled. From this understanding of the resistance spread, the model can be used to predict what will happen in the future without interventions and then compare this to what would happen if certain interventions were introduced. I will build mathematical models that capture what is happening in different hospitals and determine why some have lower rates of resistance than others. In particular, I will look at the development of resistance within a group of bacteria called the Gram-negatives. These bacteria are often found living in the gut, but they can travel to other parts of the body and, in the UK, are the most common cause of serious hospital-associated infections such as bacteraemia (infection of the blood). Increasingly we are seeing strains of these bacteria becoming resistant to common, powerful antibiotics and so they are a key contributor to antibiotic resistance. Groups of bacteria can have very different characteristics and can grow extremely rapidly. Their genetic make-up is very flexible which means that new genetic changes can occur or new pieces of genetic material can jump between bacteria creating and spreading resistance. In a bacterial population there will then be many different strains that may have many different resistances. This diversity has rarely been considered in mathematical models before, and so we may be missing a key part of resistance evolution. In this project I will develop mathematical models to incorporate this diversity and to determine how much of an impact it is having on resistance spread.Mathematical models must be grounded in data in order to be relevant to clinicians and public health. In this project I will use the newly collected hospital level data on antibiotic usage and resistance to both gain parameters for my models and to determine what patterns the models should capture. My results will then be immediately useful for clinicians and the NHS, and will directly influence the interventions used to control the appearance and spread of antibiotic resistance.

当细菌对抗生素产生耐药性时，它们引起的感染更难治疗。在英国和全球范围内，我们看到由抗生素耐药性细菌引起的感染数量增加。这可能会导致我们回到20世纪初之前的前抗生素时代，当时简单的伤口感染可能会危及生命，而抑制免疫系统并依赖抗生素预防感染的癌症治疗将无法使用。抗生素耐药性是通过使用和滥用抗生素而产生的，因此，即使我们开发了新的抗生素，我们也可能永远面临耐药菌株的问题。我们需要考虑如何最好地保护我们现有的抗生素，而不影响病人的护理。其中一个方法是看看为什么有些地方的阻力比其他地方小，并试图找出他们做得对的地方。例如，一些医院的耐药细菌感染比其他医院少。在这个项目中，我将探讨这些差异的原因可能是什么，并将这些发现转化为更好地防止耐药性传播的方法。为此，我将建立抗生素耐药性传播的数学模型。数学模型是一个框架，在这个框架中，不同的亚群被分离出来，并计算出这些亚群增加或减少的速度。例如，一个模型将医院人群分为有和没有感染耐药或敏感细菌的人群。然后，它将考虑人们感染的速度和机制，然后探索有抵抗力和没有抵抗力的人之间的区别。通过用数学的方法写下这个框架，我们可以更好地理解耐药性传播的过程，并可以确定需要解决的关键目标-例如某种抗生素的过度使用。从对耐药性传播的这种理解出发，该模型可用于预测未来在没有干预的情况下会发生什么，然后将其与引入某些干预措施后会发生的情况进行比较。我将建立数学模型，捕捉不同医院发生的情况，并确定为什么有些医院的耐药率低于其他医院。特别是，我将研究一组称为革兰氏阴性菌的细菌内的耐药性的发展。这些细菌通常生活在肠道中，但它们可以传播到身体的其他部位，在英国，它们是严重医院相关感染的最常见原因，如菌血症（血液感染）。我们越来越多地看到这些细菌的菌株对常见的强效抗生素产生耐药性，因此它们是抗生素耐药性的关键因素。不同的细菌群可能具有非常不同的特性，并且可以非常快速地生长。它们的基因组成非常灵活，这意味着新的基因变化可以发生，或者新的基因物质可以在细菌之间跳跃，产生和传播抗性。在一个细菌群体中，会有许多不同的菌株，它们可能具有许多不同的耐药性。这种多样性以前很少在数学模型中考虑，因此我们可能错过了抗性进化的关键部分。在这个项目中，我将开发数学模型，以纳入这种多样性，并确定它对耐药传播的影响有多大。数学模型必须以数据为基础，以便与临床医生和公共卫生相关。在这个项目中，我将使用新收集的关于抗生素使用和耐药性的医院级数据，为我的模型获得参数，并确定模型应该捕获什么样的模式。我的研究结果将立即对临床医生和NHS有用，并将直接影响用于控制抗生素耐药性的出现和传播的干预措施。

项目成果

Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks.

在Covid-19爆发中存在过度传播的情况下，向后接触的含义。

• DOI: 10.12688/wellcomeopenres.16344.3
• 发表时间: 2020
• 期刊: Wellcome open research
• 作者: Endo A;Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group;Leclerc QJ;Knight GM;Medley GF;Atkins KE;Funk S;Kucharski AJ
• 通讯作者: Kucharski AJ

The contribution of asymptomatic SARS-CoV-2 infections to transmission on the Diamond Princess cruise ship.

• DOI: 10.7554/elife.58699
• 发表时间: 2020-08-24
• 期刊: eLife
• 影响因子: 7.7
• 作者: Emery JC;Russell TW;Liu Y;Hellewell J;Pearson CA;CMMID COVID-19 Working Group;Knight GM;Eggo RM;Kucharski AJ;Funk S;Flasche S;Houben RM
• 通讯作者: Houben RM

Dehydration Tolerance in Epidemic versus Nonepidemic MRSA Demonstrated by Isothermal Microcalorimetry.

• DOI: 10.1128/spectrum.00615-22
• 发表时间: 2022-10-26
• 期刊: MICROBIOLOGY SPECTRUM
• 影响因子: 3.7
• 作者: Baede, Valerie O.;Tavakol, Mehri;Vos, Margreet C.;Knight, Gwenan M.;van Wamel, Willem J. B.
• 通讯作者: van Wamel, Willem J. B.