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Quantifying Antibiotic Resistance Evolution in Clinically-Relevant Microbes

量化临床相关微生物的抗生素耐药性演变

基本信息

批准号：
EP/N033671/1
负责人：
Robert Beardmore
金额：
$ 51.58万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN033671%2F1
关键词：
Quantifying Antibiotic Resistance Evolution Clinically

项目摘要

Drug resistance is often observed when we treat infected patients with drugs that were discovered,or designed, usually at great cost, with the express purpose of curing people of their infectious disease.This happens, for example, to HIV patients, malaria sufferers or when a pathogenic microbe, like E. coli, finds its way into someone's bloodstream. Cancers can soon become resistant to the chemotherapeutic agents we throwat them too, & all because of evolution.The evolutionary march towards drug resistance can take time. It can be years, ordecades, after the introduction of a new drug before we see confirmation of clinical resistance to it anda ten-year timescale is thought typical of many antibiotics. Unfortunately, this stops pharmaceutical companies fromseeking new antibiotic molecules. After all, why should they spend 10 years, at great cost, seeking to curea disease with a pill that is profitable in the marketplace for only 10 more years?Intriguingly, drug resistance in tumours is seen in patients on a much shorter timescale,sometimes within months of the start of chemotherapy, depending on the drug used, the tumourtype, and on the individual patient. So why should we not observe a similar phenomenon for antibiotics?In fact, we do, & we are now seeing the emergence of datasets showing that bacterial pathogenscan evolve resistance within individual patients because of changes to the DNA of that bacteriumin a matter of mere weeks, even days; & it can be lethal.This proposal cites a 2015 study (Blair et al, PNAS) whereby resistance to antibiotic treatment in ablood-borne Salmonella infection was traced, week-by-week, over a 20-week period, whereupon the patient died.That whole-genome sequencing study, using a range of computer and physical modelling techniquesdesigned to track evolution in real time, showed very precisely how the resistance profile of the infection quicklychanged by altering expression levels and structures of a variety of proteins within the Salmonella population.Within a week the population had doubled the amount of efflux protein it was making, moreover, it was now making even better efflux proteins than the original, infecting Salmonella. The efflux proteins are used to pump the antibiotics from inside Salmonella cells to prevent the antibiotic from hitting its target, so they stop working, but this was just one of a variety of mechanisms identified that were shown to correlate with the changes in drug resistance that took place during treatment.It is important to mention 'plasmids', loops of DNA that are disseminated across the planet by differentmicrobial species that provide resistance to a range of antibiotics, given these, it seems our future ability to deal with microbial infection sits in a terribly parlous state if something is not done to mitigate such rapid evolution. But what can be done?Importantly, the 2015 study hints at possibilities. It shows that bacteria become susceptible to someantibiotics as they increase resistance to others; in other words there are cross- or collateral-sensitivities that emergeduring treatment. So, sometimes, one could use one, and then another antibiotic. This is not outlandish, it is anidea that has been trialled in the clinic for Helicobacter pylori infections, but little else, so we now need to findnovel cross sensitivities. We also need new ways of combining antibiotics into novel cocktails, & some of those areproposed here too.I claim that by bringing to bear modern tools of mathematical modelling and data analysis on microbes thatare subjected to antibiotics in the laboratory, by observing how they respond, we can find weak spotsin their defences that will help clinicians design new therapies & give pharma companies newmethodologies to use within their analysis pipelines. Indeed, this is happening now & I am seeking funding to continue the efforts of my group in this task.

当我们用那些被发现或设计出来的药物来治疗被感染的病人时，通常会观察到耐药性，这些药物通常是以很高的成本来发现或设计的，其明确的目的是治愈人们的传染病。大肠杆菌会进入某人的血液。癌症很快也会对我们的化疗药物产生抗药性，这都是进化的结果。在一种新药上市后，我们可能需要数年或数十年才能看到临床耐药性的确认，而许多抗生素通常需要10年的时间。不幸的是，这阻止了制药公司寻找新的抗生素分子。毕竟，他们为什么要花10年的时间，以巨大的代价，寻求治愈疾病的药丸，是有利可图的，在市场上只有10年？有趣的是，肿瘤耐药性在患者中出现的时间要短得多，有时在化疗开始后的几个月内，这取决于所用的药物、肿瘤类型和个体患者。那么，为什么我们不能在抗生素中观察到类似的现象呢？事实上，我们有，我们现在看到数据集的出现，显示细菌病原体可以在单个患者体内进化出耐药性，因为细菌的DNA在短短几周，甚至几天内发生了变化;这份提案引用了2015年的一项研究，（Blair等，PNAS），其中在20周的时间内逐周追踪了在斑点传播的沙门氏菌感染中对抗生素治疗的抗性，于是病人死了。全基因组测序研究，使用了一系列计算机和物理建模技术，旨在跟踪真实的时间进化，非常精确地显示了感染的耐药性是如何通过改变沙门氏菌种群中各种蛋白质的表达水平和结构而迅速改变的。在一周内，沙门氏菌种群的数量增加了一倍。此外，它现在制造的外排蛋白比原来的更好，感染沙门氏菌。外排蛋白用于将抗生素从沙门氏菌细胞内泵出，以防止抗生素击中其靶标，因此它们停止工作，但这只是已确定的与治疗期间发生的耐药性变化相关的多种机制之一。重要的是要提到“质粒”，DNA环通过不同的微生物物种散布在地球上，这些微生物物种提供了对一系列抗生素的抗性，如果不采取措施减缓这种快速进化，我们未来对付微生物感染的能力似乎处于一种非常危险的状态。但我们能做些什么呢？重要的是，2015年的研究暗示了可能性。它表明，细菌对某些抗生素敏感，因为它们对其他抗生素的耐药性增加;换句话说，在治疗过程中出现了交叉或间接敏感性。所以，有时候，人们可以使用一种，然后另一种抗生素。这并不奇怪，这是一个已经在幽门螺杆菌感染的临床试验中的想法，但其他很少，所以我们现在需要找到新的交叉敏感性。我们还需要将抗生素组合成新型鸡尾酒的新方法，其中一些也在这里提出。我认为，通过对实验室中受到抗生素影响的微生物进行数学建模和数据分析的现代工具，通过观察它们的反应，我们可以找到他们防御系统的弱点，这将帮助临床医生设计新的治疗方法，give pharmacy制药companies公司newmethodology方法to use within其analysis分析pipeline管道.事实上，这一点现在正在发生&我正在寻求资金，以继续我的小组在这项任务中的努力。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Alternative Evolutionary Paths to Bacterial Antibiotic Resistance Cause Distinct Collateral Effects.

DOI：
10.1093/molbev/msx158
发表时间：
2017-09-01
期刊：
Molecular biology and evolution
影响因子：
10.7
作者：
Barbosa C;Trebosc V;Kemmer C;Rosenstiel P;Beardmore R;Schulenburg H;Jansen G
通讯作者：
Jansen G

Author Correction: Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community.

作者更正：混合物种群落中抗生素耐药状态之间药物介导的代谢倾斜。

DOI：
10.1038/s41559-018-0678-0
发表时间：
2018
期刊：
Nature ecology & evolution
影响因子：
16.8
作者：
Beardmore RE
通讯作者：
Beardmore RE

Predicting microbial growth dynamics in response to nutrient availability.

DOI：
10.1371/journal.pcbi.1008817
发表时间：
2021-03
期刊：
PLoS computational biology
影响因子：
4.3
作者：
Nev OA;Lindsay RJ;Jepson A;Butt L;Beardmore RE;Gudelj I
通讯作者：
Gudelj I

Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community.

DOI：
10.1038/s41559-018-0582-7
发表时间：
2018-08
期刊：
Nature ecology & evolution
影响因子：
16.8
作者：
Beardmore RE;Cook E;Nilsson S;Smith AR;Tillmann A;Esquivel BD;Haynes K;Gow NAR;Brown AJP;White TC;Gudelj I
通讯作者：
Gudelj I

Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?