Lonely microbes mutate faster

Lonely microbes mutate faster

A research team, led by the University of Manchester in collaboration with the Universities of Keele and Middlesex analysed 70 years of data and 500 different measurements of mutations in microbes. We interviewed Dr. Chris Knight who was senior author of the study.

Your research looked into the relation between microbe density and mutation rates. What were the main findings?

First: when we looked across all the estimates other people had made of mutation rates over the last 70 years, we could see a strong pattern in all sorts of different organisms: high population densities tend to have low mutation rates.

Second: when we tested the relationship ourselves in the laboratory, it turned out to be true: very different organisms – bacteria and yeast (which is more closely related to us than bacteria), each showed a similar pattern of mutation rate changing with population density.

Finally: we looked for the genes needed for this pattern, by testing organisms that had had particular genes removed. We found that the change in mutation rates specifically needs a gene involved in cleaning up damaged letters of the DNA code, before they’re put into the DNA and can cause mutations.

How is density defined? Is it a large number of the same type of microbes or a large number of microbes of different varieties?

At the moment we’ve only looked at large (or small) numbers of the same types of microbes in the same place at the same time; for instance, different densities of E. coli bacteria by themselves, or different densities of yeast by themselves. However it is true that in the ‘real’ world, microbes are often in mixed populations. How the pattern we’ve found plays out in mixed populations is an important question that we haven’t answered yet.

Can we say that evolution slows down in dense populations of microbes?

Mutation is a part of evolution, and we can say that for particular cells, mutation slows (i.e. becomes less likely for each cell division) in denser populations. But mutation is only the first step of evolution – what happens to those mutations afterwards, so how natural selection works on them, for instance, can be just as or more important for the overall speed of evolution. It will depend upon the circumstances. In some circumstances at least, we might expect mutation to be a limiting factor (e.g. a small population which needs a rare mutation to survive), so in those cases we might expect evolution as a whole to slow down or speed up, depending upon the density of the population.

Did your research findings suggest a reason for this effect?

This research took the first steps in finding a molecular reason for this effect – it needs a gene that produces a protein that removes particular oxidised nucleotides from the cell. This suggests that it has something to do with how those oxidised nucleotides, that can potentially cause particular mutations, are controlled.

How do the findings relate to the increasing antibiotic resistance of microbes?

The effect on mutation rate looks like it acts across the genome, but we especially looked at it at sites that give resistance to particular antibiotics. This means that the findings directly relate to the rate at which antibiotic resistance arises spontaneously in microbes. Wider issues of antibiotic resistance increasing in microbes are not all to do with the rate resistance arises spontaneously. Nonetheless, some antibiotics are not used more widely because spontaneous resistance is a major problem (e.g. we used rifampicin in some of our experiments, which is only used in combination and in TB, which is particularly slow-growing, because in other circumstances, spontaneous resistance stops it being effective).

In what ways can the findings be used to reduce the mutations that lead to antibiotic resistance?

First we need to discover more about how it works. However, you could imagine that, because this effect can change mutation rates ten-fold, we could potentially manipulate it to make it ten times less likely that bacteria you wanted to eliminate evolved resistance to the antibiotic you were using to eliminate them. If we can figure out how to do that, it would be a very useful thing.

What will be the next steps in your research?

The next steps go in two directions:

– firstly figuring out the mechanism of how it works – we already have a couple of genes we know to be involved, but we need to fit these clues together into a real understanding of what’s going on.

– secondly we need to work out how this plays out in circumstances nearer to the ‘real’ world, so how it affects the course of evolution over many generations, or in different sorts of culture.

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Photo’s: courtesy of Chris Knight

You can find the study on : https://doi.org/cb9s