Quantifying Antibiotic Resistance Evolution in Clinically-Relevant Microbes
Quantifying Antibiotic Resistance Evolution in Clinically-Relevant Microbes
批准号:
EP/N033671/1
负责人:
Robert Beardmore
金额:
$51.58万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2016
资助国家:
英国
项目状态:
已结题
起止时间:
2016 至 --
中文摘要
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英文摘要
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.
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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
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
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
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
DOI:
10.1093/molbev/msw292
发表时间:
2017-04-01
期刊:
Molecular biology and evolution
影响因子:
10.7
作者:
[Beardmore RE, Peña-Miller R, Gori F, Iredell J]
通讯作者:
Iredell J
共 8 条
Bacteriophage and Antibiotic Resistance: a Mathematical and Imaging Approach
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批准号:EP/I00503X/1
-
项目类别:Fellowship
-
资助金额:$160.73万
-
财政年份:2011
-
负责人:Robert Beardmore
-
依托单位:
Bacteriophage and Antibiotic Resistance: a Mathematical and Imaging Approach (C-DIP enhancement)
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批准号:EP/I018263/1
-
项目类别:Research Grant
-
资助金额:$10.67万
-
财政年份:2010
-
负责人:Robert Beardmore
-
依托单位:
The Optimal Deployment of Antibiotics: Whether, How and When to Switch
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批准号:G0802611/1
-
项目类别:Research Grant
-
资助金额:$11.46万
-
财政年份:2009
-
负责人:Robert Beardmore
-
依托单位:
国内基金
海外基金
水环境中新兴污染物类抗生素效应(Like-Antibiotic Effects,L-AE)作用机制研究
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批准号:21477024
-
项目类别:面上项目
-
资助金额:86.0万元
-
批准年份:2014
-
负责人:李丹
-
依托单位: