Photo Credit: Janice Haney, Centers for Disease Control and Prevention
Bacterial Evolution in the Lab
Amanda Coutts, 11th February 2012
Amanda Coutts, 11th February 2012
Antibiotic resistance is a growing global health concern. The spread of which, in part, is due to the widespread use of antibiotics. From MRSA (methicillin-resistant Staphylococcus aureus) to the reports of totally antibiotic resistant tuberculosis (1), the challenge for scientists is to understand how antibiotic resistance develops and to come up with new antibiotics and treatments to combat these bacteria.
How do antibiotics work? Well, not all antibiotics work in the same way; antibiotics target different bacterial proteins and can be specific to different types of bacteria, but ultimately they either kill the bacteria or stop them from growing.
One reason bacteria develop resistance is because they grow so quickly and can acquire spontaneous genetic mutations that result in the bacteria no longer responding to the antibiotic (fig 1). This can occur, for example, due to a mutation in the bacterial DNA that results in changes to the protein that the antibiotic targets.
How do antibiotics work? Well, not all antibiotics work in the same way; antibiotics target different bacterial proteins and can be specific to different types of bacteria, but ultimately they either kill the bacteria or stop them from growing.
One reason bacteria develop resistance is because they grow so quickly and can acquire spontaneous genetic mutations that result in the bacteria no longer responding to the antibiotic (fig 1). This can occur, for example, due to a mutation in the bacterial DNA that results in changes to the protein that the antibiotic targets.
For scientists trying to understand the development of antibiotic resistance, a significant technical challenge is to model the evolution of bacterial resistance in the laboratory.
A recent paper by Toprak and colleagues from Harvard University, published in Nature Genetics (2), describes a novel microbial selection device called the 'morbidostat' which they used to model the evolution of antibiotic resistance. In their technique, a computer program continuously adjusts the concentration of antibiotic in the morbidostat, while simultaneously keeping the bacterial population at low densities (fig 2). In this way the antibiotic concentration can be adjusted to reflect the rate at which resistance evolves: more bacteria means increased resistance, so the antibiotic concentration gets increased.
Dr Jim Caryl, an expert in antibiotic resistance from the University of Leeds says that ‘traditional techniques for isolating antibiotic resistant strains employ fixed-concentration of antibiotics. In the body, bacteria causing an infection are under constant pressure from both host defenses and continued dosing of antibiotic therapy. So whilst traditional approaches may result in resistant bacteria, the way these cells resist the antibiotic may be just one of a number of final possibilities the population could have taken’.
To understand the underlying genetic changes that could account for the evolution of antibiotic resistance in this study, the researchers performed whole-genome sequencing on the bacteria to identify mutations that occurred as resistance evolved. This allowed them to identify specific genetic changes that result in antibiotic resistance.
‘The morbidostat creates an environment of continued selective pressure, forcing bacteria to reveal all the various adaptive tricks they have up their sleeves’ says Dr Caryl. ‘It may also help identify the mutations that mitigate the ‘costs’ of antibiotic resistance even when the selection has stopped. Crucially, this approach is greatly enhanced by the now comparatively cheap cost of whole-genome sequencing, permitting whole genetic analysis of daily samples from a multi-day selection experiment, to spot the changes to DNA in the population over time’ adds Dr Caryl.
An important question is how this type of system may relate to what happens in real-life? ‘The morbidostat can be used to study whether evolution of resistance follows set pathways, with the same genetic mutations occurring in a sequential manner. This might permit clinicians to pre-empt the appearance of highly resistant strains by screening for ‘early indicator-mutations’, and to change the treatment regime accordingly’ explains Dr Caryl. He also adds that ‘the morbidostat can be used to study how combinations of different antibiotics effect the evolution of antibiotic resistance, as sometimes two drugs combined can have a greater effect, and conversely, some combinations of antibiotic reduce the effect of each other’.
Studies of this type can help to understand and perhaps even predict how and when bacteria will evolve antibiotic resistance. This is key in developing new therapies and treatment regimes for an important and urgent human health issue.
Interested in more about Antibiotic resistance? Check out the podcast from Dr. Jim Caryl's SciBar.
1. Nature doi:10.1038/nature.2012.9797.
2. Nature Genetics 44: 11-12, 2012.
A recent paper by Toprak and colleagues from Harvard University, published in Nature Genetics (2), describes a novel microbial selection device called the 'morbidostat' which they used to model the evolution of antibiotic resistance. In their technique, a computer program continuously adjusts the concentration of antibiotic in the morbidostat, while simultaneously keeping the bacterial population at low densities (fig 2). In this way the antibiotic concentration can be adjusted to reflect the rate at which resistance evolves: more bacteria means increased resistance, so the antibiotic concentration gets increased.
Dr Jim Caryl, an expert in antibiotic resistance from the University of Leeds says that ‘traditional techniques for isolating antibiotic resistant strains employ fixed-concentration of antibiotics. In the body, bacteria causing an infection are under constant pressure from both host defenses and continued dosing of antibiotic therapy. So whilst traditional approaches may result in resistant bacteria, the way these cells resist the antibiotic may be just one of a number of final possibilities the population could have taken’.
To understand the underlying genetic changes that could account for the evolution of antibiotic resistance in this study, the researchers performed whole-genome sequencing on the bacteria to identify mutations that occurred as resistance evolved. This allowed them to identify specific genetic changes that result in antibiotic resistance.
‘The morbidostat creates an environment of continued selective pressure, forcing bacteria to reveal all the various adaptive tricks they have up their sleeves’ says Dr Caryl. ‘It may also help identify the mutations that mitigate the ‘costs’ of antibiotic resistance even when the selection has stopped. Crucially, this approach is greatly enhanced by the now comparatively cheap cost of whole-genome sequencing, permitting whole genetic analysis of daily samples from a multi-day selection experiment, to spot the changes to DNA in the population over time’ adds Dr Caryl.
An important question is how this type of system may relate to what happens in real-life? ‘The morbidostat can be used to study whether evolution of resistance follows set pathways, with the same genetic mutations occurring in a sequential manner. This might permit clinicians to pre-empt the appearance of highly resistant strains by screening for ‘early indicator-mutations’, and to change the treatment regime accordingly’ explains Dr Caryl. He also adds that ‘the morbidostat can be used to study how combinations of different antibiotics effect the evolution of antibiotic resistance, as sometimes two drugs combined can have a greater effect, and conversely, some combinations of antibiotic reduce the effect of each other’.
Studies of this type can help to understand and perhaps even predict how and when bacteria will evolve antibiotic resistance. This is key in developing new therapies and treatment regimes for an important and urgent human health issue.
Interested in more about Antibiotic resistance? Check out the podcast from Dr. Jim Caryl's SciBar.
1. Nature doi:10.1038/nature.2012.9797.
2. Nature Genetics 44: 11-12, 2012.
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