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Details of Grant 

EPSRC Reference: EP/N033671/1
Title: Quantifying Antibiotic Resistance Evolution in Clinically-Relevant Microbes
Principal Investigator: Beardmore, Professor RE
Other Investigators:
Researcher Co-Investigators:
Project Partners:
AstraZeneca Royal Devon and Exeter NHS Fdn Trust Stoke on Trent Sixth Form College
Westmead Hospital
Department: Biosciences
Organisation: University of Exeter
Scheme: EPSRC Fellowship
Starts: 01 October 2016 Ends: 30 September 2018 Value (£): 404,819
EPSRC Research Topic Classifications:
Medical science & disease Microbiology
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
16 May 2016 HT Impact Fellowships Interviews Announced
Summary on Grant Application Form
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 throw

at them too, & all because of evolution.

The evolutionary march towards drug resistance can take time. It can be years, or

decades, after the introduction of a new drug before we see confirmation of clinical resistance to it and

a ten-year timescale is thought typical of many antibiotics. Unfortunately, this stops pharmaceutical companies from

seeking new antibiotic molecules. After all, why should they spend 10 years, at great cost, seeking to cure

a 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 tumour

type, 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 pathogens

can evolve resistance within individual patients because of changes to the DNA of that bacterium

in 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 a

blood-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 techniques

designed to track evolution in real time, showed very precisely how the resistance profile of the infection quickly

changed 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 different

microbial 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 some

antibiotics as they increase resistance to others; in other words there are cross- or collateral-sensitivities that emerge

during treatment. So, sometimes, one could use one, and then another antibiotic. This is not outlandish, it is an

idea that has been trialled in the clinic for Helicobacter pylori infections, but little else, so we now need to find

novel cross sensitivities. We also need new ways of combining antibiotics into novel cocktails, & some of those are

proposed here too.

I claim that by bringing to bear modern tools of mathematical modelling and data analysis on microbes that

are subjected to antibiotics in the laboratory, by observing how they respond, we can find weak spots

in their defences that will help clinicians design new therapies & give pharma companies new

methodologies 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.
Key Findings
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Organisation Website: http://www.ex.ac.uk