Evidence of non-random mutation rates suggests a risk management strategy for bacterial evolution

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• Nicholas Luscombe (EBI)
• Monday 07 March 2011, 16:00-17:00
• Cancer Research UK Cambridge Research Institute, Room 215.

If you have a question about this talk, please contact Florian Markowetz.

Inigo Martincorena, Aswin SN Seshasayee & Nicholas M Luscombe

A fundamental principle in evolutionary theory is that mutations occur randomly with respect to their value to an organism; natural selection then governs whether these mutations are fixed in a population. This view has been challenged by long-standing theoretical models predicting that selection could modulate the rate of mutation itself. Indeed, at a global level, experimental studies have shown that the average mutation rate of a population can vary in response to environmental changes that demand greater genetic diversity. However, at a local level, our understanding of how mutation rates vary between different sites within a genome has been limited by technical difficulties in measuring them. As a result, single nucleotide substitutions – arguably the major form of mutation – are considered to occur randomly. Here, we present a general computational method for calculating mutation rates that overcomes previous limitations by combining comparative genomic and population genetic techniques. In applying the method to 34 E. coli genomes, we provide the most reliable estimates to date of local mutation rates at a genome-wide level. Remarkably, rates vary by more than 20-fold across 2,663 genes, and mutational hot and cold spots span ~10kb. Further, they are not randomly distributed with regard to their fitness effect: for the first time, we provide firm evidence that mutation rates correlate with selection strength. Contrary to previous theoretical models, point mutation rates have been evolutionarily optimised to minimise the risk of deleterious mutations among functionally important and highly expressed genes, rather than promoting mutations in those under strong positive selection. Current knowledge of factors influencing mutation rates – including transcription-coupled repair – do not explain these observations, indicating that additional mechanisms must be involved. The findings have important implications for our understanding of evolution and the control of mutations; moreover, they raise new questions that are immediately relevant to pathogen evolution and human diseases.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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