# Summary: “Mutation rates: How low can you go?” (Sniegowski and Raynes 2013)

This is a short summary of Sniegowski and Raynes (2013), a review about the evolution of the mutation rate, with an emphasis on the Drift Barrier Hypothesis (DBH). The summary is written in my own words, with a few footnotes and highlighting that express my thoughts.

1. Because most mutations are deleterious, selection favors decreased mutation rates1
2. Then what keeps the mutation rate from dropping to zero?
3. One force could be adaptive evolution which creates selection for the generation of beneficial mutations.2 This selection, however, is limited (Sniegowski et al. 2000).
4. Another force can be the fitness cost of increasing genomic replication fidelity3. This doesn’t seem to solve the problem, mainly because although the cost of fidelity in prokaryotes should be higher than in eukaryotes, the genomic mutation rate is lower4.
5. There is also a physio-chemical constraint on the fidelity of replication and repair mechanisms5. But this constraint should be similar to all species so it cannot explain the diversity.
6. DBH (Sung, Ackerman, et al. 2012): Reduction of the mutation rate has a diminishing effect on fitness6 and therefore at some point selection towards low mutation rates is weaker than drift. This effect is highly dependent on the population size.
7. “Drake’s Rule”: genomic mutation rates of DNA microbes7 are very similar (0.003 mutations per genome per generation) - the per base-pair mutation rate decreases with the genome size (Drake 1991). This suggest a universal selective force which acts to adjust the mutation rate of microbes. This rule was recently tested and confirmed in C. reinhardtii and M. florum (Sung, Tucker, et al. 2012), and it fits with the DBH, as microbes usually have very large populations.
8. In contrast, in eukaryotes, however, the per base-pair and genomic mutation rate increases with genome size. This fits with the DBH, because eukaryotes presumebly have small effective population sizes.
9. However, it is noted that both mutation rates and effective population sizes are very hard to measure and estimate.

# References

Dawson, Kevin J. 1998. “Evolutionarily stable mutation rates.” Journal of Theoretical Biology 194 (1): 143–57. doi:10.1006/jtbi.1998.0752.

Drake, John W. 1991. “A constant rate of spontaneous mutation in DNA-based microbes.” Proceedings of the National Academy of Sciences 88 (16): 7160–4. doi:10.1073/pnas.88.16.7160.

Furió, Victoria, Andrés Moya, and Rafael Sanjuán. 2005. “The cost of replication fidelity in an RNA virus.” Proceedings of the National Academy of Sciences 102 (29): 10233–7. doi:10.1073/pnas.0501062102.

Liberman, Uri, and Marcus W. Feldman. 1986. “Modifiers of mutation rate: a general reduction principle.” Theoretical Population Biology 30 (1): 125–42. http://www.ncbi.nlm.nih.gov/pubmed/3750215.

Loh, Ern, Jesse J Salk, and Lawrence A Loeb. 2010. “Optimization of DNA polymerase mutation rates during bacterial evolution.” Proceedings of the National Academy of Sciences 107 (3): 1154–9. doi:10.1073/pnas.0912451107.

Sniegowski, Paul D., and Yevgeniy Raynes. 2013. “Mutation rates: how low can you go?” Current Biology 23 (4). Elsevier: R147–9. doi:10.1016/j.cub.2013.01.018.

Sniegowski, Paul D., Philip J. Gerrish, and Richard E. Lenski. 1997. “Evolution of high mutation rates in experimental populations of E. coli.” Nature 387 (6634). Department of Biology, University of Pennsylvania, Philadelphia 19104, USA. paulsnie@sas.upenn.edu: 703–5. doi:10.1038/42701.

Sniegowski, Paul D., Philip J. Gerrish, Toby Johnson, and Aaron Shaver. 2000. “The evolution of mutation rates: separating causes from consequences.” BioEssays : News and Reviews in Molecular, Cellular and Developmental Biology 22 (12): 1057–66. doi:10.1002/1521-1878(200012)22:12<1057::AID-BIES3>3.0.CO;2-W.

Sung, Way, Matthew S. Ackerman, Samuel F. Miller, Thomas G. Doak, and Michael Lynch. 2012. “Drift-barrier hypothesis and mutation-rate evolution.” Proceedings of the National Academy of Sciences of the United States of America 109 (45): 18488–92. doi:10.1073/pnas.1216223109.

Sung, Way, Abraham E Tucker, Thomas G. Doak, Eunjin Choi, W Kelley Thomas, and Michael Lynch. 2012. “Extraordinary genome stability in the ciliate Paramecium tetraurelia.” Proceedings of the National Academy of Sciences 109 (47): 19339–44. doi:10.1073/pnas.1210663109.

Taddei, François, Miroslav Radman, John Maynard Smith, Bruno Toupance, Pierre-Henri Gouyon, and Bernard Godelle. 1997. “Role of mutator alleles in adaptive evolution.” Nature 387 (6634): 700–702. doi:10.1038/42696.

Torres-Barceló, Clara, Gabriel Cabot, Antonio Oliver, Angus Buckling, and R. Craig MacLean. 2013. “A trade-off between oxidative stress resistance and DNA repair plays a role in the evolution of elevated mutation rates in bacteria.” Proceedings of the Royal Society B: Biological Sciences 280 (1757): 20130007. doi:10.1098/rspb.2013.0007.

1. The reduction principle (Liberman and Feldman 1986)

2. Second-order selection (Taddei et al. 1997; Sniegowski, Gerrish, and Lenski 1997)

3. The cost of fidelity argument (Dawson 1998)

4. AFAIK, this cost was mainly shown in viruses so far (Furió, Moya, and Sanjuán 2005), but a recent study did show some direct advantage of mutators which do not depend on replication fidelity per se (Torres-Barceló et al. 2013)

5. However, anti-mutators do exist (Loh, Salk, and Loeb 2010), so it seems that the mutation rate is not at its lower physio-chemical level

6. Why? Because, as we have seen in previous posts, the population mean fitness decays exponentialy as the genomic per generation mutation rate increases: $\bar{\omega} = e^{-U}$.

7. That is, not including RNA viruses

Category: evolution