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Discrete steps to antibiotic resistance

The views expressed are those of the author and are not necessarily those of Scientific American.

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I’ve been getting so exited about the awesome powers of bacteria on this blog lately that I’ve been neglecting to cover the nasty bacteria. More specifically the fascinating world of antibiotics, the antimicrobial elements that bacteria and fungi produce and that humans exploit, manufacture and synthesise in order to protect against bacterial infections.

Luckily a great paper (reference below) came out recently that explores three different types of antibiotic treatment and how bacteria have evolved to protect themselves from the antibiotic attack. Bacteria can evolve quickly, as they reproduce relatively fast, and can support a larger number of mutations within their cells than the average human or eukaryotic cell. This means that by the time most antibiotics officially hit the market somewhere in the bacterial kingdom a method of resistance already exists.

The antibiotic novamoxin. From wikimedia commons credit link below.

In order to study how bacteria evolve resistance, the researchers used a tool that they named the “morbidostat” (presumably “Death-counter” was already taken) to adjust the antibiotic concentration in the experimental growth media to ensure that the bacteria were always under a high evolutionary pressure to evolve resistance. This is important for studying bacteria that require multiple mutations in order to evolve the highest form of resistance. Using a fixed concentration of antibiotics would produce maybe one of two helpful mutations that give the mutated bacteria enough advantage to outcompete the non-mutated ones. Constantly adjusting the antibiotic concentration means that the bacteria are in a constant struggle for that extra edge of resistance to give them a selective advantage.

Using the morbidostat also means that the bacteria are kept at a low concentration (rather than the resistant bacteria out-competing everything else and then growing exponentially). This prevents nutrients from becoming a limiting factor as the culture only ever contains a managable number of bacteria.

Growth curves for bacterial population. Without the morbidostat resistant populations will grow exponentially. With the morbidostat the continuing increase in antibiotic concentration helps to control the population under fierce selection pressure. Image (c) me.

Using the morbidostat to control the cell cultures, they then set up five parallel experiments for three different antibiotics; chloramphenicol, doxycycline and trimethoprim. Every day, they took a sample of the culture to examine the levels of resistance within the culture. They found that while resistance to chloramphenicol and doxycycline increased smoothly over time, resistance to trimethoprim increased in a stepwise fashion, with no changes for a couple of days and then a sudden jump in resistance.

On the final day of the experiment they took one colony from each sample and sequenced the whole genome to see what genetic changes had occurred. Despite the fact that both chloramphenicol and doxycycline target the ribosome, no mutation to the ribosome was seen. This is not surprising as the ribosome is an important piece of cellular equipment and not to be tampered with! Instead, all the mutations in these bacteria were involved in transport and membrane proteins. The bacteria were acquiring mutations that allowed them to shuttle the antibiotic out of the cell. Not only that but the shuttling mechanism wasn’t hugely specific either; bacteria with chloramphenicol resistance were also resistant to doxycycline.

For trimethoprim on the other hand, all the mutations were found in the region of the protein that the antibiotic affects (the synthesis pathway of an important metabolic enzyme). Trimethoprim resistance therefore was not transferable, bacteria resistant to trimethoprim were unable to survive when challenged with chloramphenicol or doxycycline. The step-wise evolution was seen as the area for mutation was so small. Whereas there are many small mutations that can occur to improve the ability of the cell to shuttle antibiotics away, mutations that affect such a small specific area of DNA are quite rare. Hence the stepwise resistance rate; all the bacteria would be equally susceptible until one small mutation happened prompting the sudden fast growth of the resistant population and a sudden increase in resistance.

The full genome sequence of the bacteria doesn’t just allow researchers to find out which genes have mutated, it could also help to explore the exact order in which they did, putting together a developing resistance profile for the antibiotic response. That kind of detail would require many more cultures and experiments, but the techniques are all in place!

Credit for image 1

Ref: Toprak, E., Veres, A., Michel, J., Chait, R., Hartl, D., & Kishony, R. (2011). Evolutionary paths to antibiotic resistance under dynamically sustained drug selection Nature Genetics, 44 (1), 101-105 DOI: 10.1038/ng.1034

S.E. Gould About the Author: A biochemist with a love of microbiology, the Lab Rat enjoys exploring, reading about and writing about bacteria. Having finally managed to tear herself away from university, she now works for a small company in Cambridge where she turns data into manageable words and awesome graphs. Follow on Twitter @labratting.

The views expressed are those of the author and are not necessarily those of Scientific American.

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  1. 1. connorGGbamford 7:50 am 02/3/2012

    Just catching up on my bacterial knowledge thanks to your blog, great post. This was well timed with the recent ‘emergence’ of completely drug resistant TB. I wonder if the same mechanisms are shared across different taxonomic groups? And then to reverse engineer our drugs to at least inhibit this happening? If it is so predictable.

    Link to this
  2. 2. S.E. Gould in reply to S.E. Gould 6:26 am 02/7/2012

    I think that while the certainty of antibiotic resistance is predictable, and the methods of resistance always look incredibly obvious after they’ve happened, predicting which path resistance will take is a lot harder. As seen here, there are more than a few different forms resistance will take, and it isn’t always immediately obvious which.

    Different taxonomic groups do share the same subset of mechanisms (removing the antibiotic, disabling the antibiotic, removing the target or changing the target) but there are many different ways by which those can be achieved.

    Link to this

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