Langbahn Team – Weltmeisterschaft

Mutation rate

Recently reported estimates of the human genome-wide mutation rate. The human germline mutation rate is approximately 0.5×10−9 per basepair per year.[1]

In genetics, the mutation rate is the frequency of new mutations in a single gene, nucleotide sequence, or organism over time.[2] Mutation rates are not constant and are not limited to a single type of mutation; there are many different types of mutations. Mutation rates are given for specific classes of mutations. Point mutations are a class of mutations which are changes to a single base. Missense, nonsense, and synonymous mutations are three subtypes of point mutations. The rate of these types of substitutions can be further subdivided into a mutation spectrum which describes the influence of the genetic context on the mutation rate.[3]

There are several natural units of time for each of these rates, with rates being characterized either as mutations per base pair per cell division, per gene per generation, or per genome per generation. The mutation rate of an organism is an evolved characteristic and is strongly influenced by the genetics of each organism, in addition to strong influence from the environment. The upper and lower limits to which mutation rates can evolve is the subject of ongoing investigation. However, the mutation rate does vary over the genome.[4]

When the mutation rate in humans increases certain health risks can occur, for example, cancer and other hereditary diseases. Having knowledge of mutation rates is vital to understanding the future of cancers and many hereditary diseases.[5]

Background

Different genetic variants within a species are referred to as alleles, therefore a new mutation can create a new allele. In population genetics, each allele is characterized by a selection coefficient, which measures the expected change in an allele's frequency over time. The selection coefficient can either be negative, corresponding to an expected decrease, positive, corresponding to an expected increase, or zero, corresponding to no expected change. The distribution of fitness effects of new mutations is an important parameter in population genetics and has been the subject of extensive investigation.[6] Although measurements of this distribution have been inconsistent in the past, it is now generally thought that the majority of mutations are mildly deleterious, that many have little effect on an organism's fitness, and that a few can be favorable.

Because of natural selection, unfavorable mutations will typically be eliminated from a population while favorable changes are generally kept for the next generation, and neutral changes accumulate at the rate they are created by mutations. This process happens by reproduction. In a particular generation the 'best fit' survive with higher probability, passing their genes to their offspring. The sign of the change in this probability defines mutations to be beneficial, neutral or harmful to organisms.[7]

Measurement

An organism's mutation rates can be measured by a number of techniques.

One way to measure the mutation rate is by the fluctuation test, also known as the Luria–Delbrück experiment. This experiment demonstrated that bacteria mutations occur in the absence of selection instead of the presence of selection.[8]

This is very important to mutation rates because it proves experimentally mutations can occur without selection being a component—in fact, mutation and selection are completely distinct evolutionary forces. Different DNA sequences can have different propensities to mutation (see below) and may not occur randomly.[9]

The most commonly measured class of mutations are substitutions, because they are relatively easy to measure with standard analyses of DNA sequence data. However substitutions have a substantially different rate of mutation (10−8 to 10−9 per generation for most cellular organisms) than other classes of mutation, which are frequently much higher (~10−3 per generation for satellite DNA expansion/contraction[10]).

Substitution rates

Many sites in an organism's genome may admit mutations with small fitness effects. These sites are typically called neutral sites. Theoretically mutations under no selection become fixed between organisms at precisely the mutation rate. Fixed synonymous mutations, i.e. synonymous substitutions, are changes to the sequence of a gene that do not change the protein produced by that gene. They are often used as estimates of that mutation rate, despite the fact that some synonymous mutations have fitness effects. As an example, mutation rates have been directly inferred from the whole genome sequences of experimentally evolved replicate lines of Escherichia coli B.[11]

Mutation accumulation lines

A particularly labor-intensive way of characterizing the mutation rate is the mutation accumulation line.

Mutation accumulation lines have been used to characterize mutation rates with the Bateman-Mukai Method and direct sequencing of well-studied experimental organisms ranging from intestinal bacteria (E. coli), roundworms (C. elegans), yeast (S. cerevisiae), fruit flies (D. melanogaster), and small ephemeral plants (A. thaliana).[12]

Variation in mutation rates

Phylogram showing three groups, one of which has strikingly longer branches than the two others
Generation time affects mutation rates: The long-lived woody bamboos (tribes Arundinarieae and Bambuseae) have lower mutation rates (short branches in the phylogenetic tree) than the fast-evolving herbaceous bamboos (Olyreae).

Mutation rates differ between species and even between different regions of the genome of a single species. Mutation rates can also differ even between genotypes of the same species; for example, bacteria have been observed to evolve hypermutability as they adapt to new selective conditions.[13] These different rates of nucleotide substitution are measured in substitutions (fixed mutations) per base pair per generation. For example, mutations in intergenic, or non-coding, DNA tend to accumulate at a faster rate than mutations in DNA that is actively in use in the organism (gene expression). That is not necessarily due to a higher mutation rate, but to lower levels of purifying selection. A region which mutates at predictable rate is a candidate for use as a molecular clock.

If the rate of neutral mutations in a sequence is assumed to be constant (clock-like), and if most differences between species are neutral rather than adaptive, then the number of differences between two different species can be used to estimate how long ago two species diverged (see molecular clock). In fact, the mutation rate of an organism may change in response to environmental stress. For example, UV light damages DNA, which may result in error prone attempts by the cell to perform DNA repair.

The human mutation rate is higher in the male germ line (sperm) than the female (egg cells), but estimates of the exact rate have varied by an order of magnitude or more. This means that a human genome accumulates around 64 new mutations per generation because each full generation involves a number of cell divisions to generate gametes.[14] Human mitochondrial DNA has been estimated to have mutation rates of ~3× or ~2.7×10−5 per base per 20 year generation (depending on the method of estimation);[15] these rates are considered to be significantly higher than rates of human genomic mutation at ~2.5×10−8 per base per generation.[16] Using data available from whole genome sequencing, the human genome mutation rate is similarly estimated to be ~1.1×10−8 per site per generation.[17]

The rate for other forms of mutation also differs greatly from point mutations. An individual microsatellite locus often has a mutation rate on the order of 10−4, though this can differ greatly with length.[18]

Some sequences of DNA may be more susceptible to mutation. For example, stretches of DNA in human sperm which lack methylation are more prone to mutation.[19]

In general, the mutation rate in unicellular eukaryotes (and bacteria) is roughly 0.003 mutations per genome per cell generation.[14] However, some species, especially the ciliate of the genus Paramecium have an unusually low mutation rate. For instance, Paramecium tetraurelia has a base-substitution mutation rate of ~2 × 10−11 per site per cell division. This is the lowest mutation rate observed in nature so far, being about 75× lower than in other eukaryotes with a similar genome size, and even 10× lower than in most prokaryotes. The low mutation rate in Paramecium has been explained by its transcriptionally silent germ-line nucleus, consistent with the hypothesis that replication fidelity is higher at lower gene expression levels.[20]

The highest per base pair per generation mutation rates are found in viruses, which can have either RNA or DNA genomes. DNA viruses have mutation rates between 10−6 to 10−8 mutations per base per generation, and RNA viruses have mutation rates between 10−3 to 10−5 per base per generation.[14]

Mutation spectrum

A mutation spectrum is a distribution of rates or frequencies for the mutations relevant in some context, based on the recognition that rates of occurrence are not all the same. In any context, the mutation spectrum reflects the details of mutagenesis and is affected by conditions such as the presence of chemical mutagens or genetic backgrounds with mutator alleles or damaged DNA repair systems. The most fundamental and expansive concept of a mutation spectrum is the distribution of rates for all individual mutations that might happen in a genome (e.g., [21]). From this full de novo spectrum, for instance, one may calculate the relative rate of mutation in coding vs non-coding regions. Typically the concept of a spectrum of mutation rates is simplified to cover broad classes such as transitions and transversions (figure), i.e., different mutational conversions across the genome are aggregated into classes, and there is an aggregate rate for each class.

In many contexts, a mutation spectrum is defined as the observed frequencies of mutations identified by some selection criterion, e.g., the distribution of mutations associated clinically with a particular type of cancer,[22] or the distribution of adaptive changes in a particular context such as antibiotic resistance (e.g., [23] ). Whereas the spectrum of de novo mutation rates reflects mutagenesis alone, this kind of spectrum may also reflect effects of selection and ascertainment biases (e.g., both kinds of spectrum are used in [24]).

Transitions (Alpha) and transversions (Beta).

Evolution

The theory on the evolution of mutation rates identifies three principal forces involved: the generation of more deleterious mutations with higher mutation, the generation of more advantageous mutations with higher mutation, and the metabolic costs and reduced replication rates that are required to prevent mutations. Different conclusions are reached based on the relative importance attributed to each force. The optimal mutation rate of organisms may be determined by a trade-off between costs of a high mutation rate,[25] such as deleterious mutations, and the metabolic costs of maintaining systems to reduce the mutation rate (such as increasing the expression of DNA repair enzymes.[26] or, as reviewed by Bernstein et al.[27] having increased energy use for repair, coding for additional gene products and/or having slower replication). Secondly, higher mutation rates increase the rate of beneficial mutations, and evolution may prevent a lowering of the mutation rate in order to maintain optimal rates of adaptation.[28][29] As such, hypermutation enables some cells to rapidly adapt to changing conditions in order to avoid the entire population from becoming extinct.[30] Finally, natural selection may fail to optimize the mutation rate because of the relatively minor benefits of lowering the mutation rate, and thus the observed mutation rate is the product of neutral processes.[31][32]

Studies have shown that treating RNA viruses such as poliovirus with ribavirin produce results consistent with the idea that the viruses mutated too frequently to maintain the integrity of the information in their genomes.[33] This is termed error catastrophe.

The characteristically high mutation rate of HIV (Human Immunodeficiency Virus) of 3 x 10−5 per base and generation, coupled with its short replication cycle leads to a high antigen variability, allowing it to evade the immune system.[34]

See also

References

  1. ^ Scally A (December 2016). "The mutation rate in human evolution and demographic inference". Current Opinion in Genetics & Development. 41: 36–43. doi:10.1016/j.gde.2016.07.008. PMID 27589081. Archived from the original on 2021-01-02. Retrieved 2020-09-08.
  2. ^ Crow JF (August 1997). "The high spontaneous mutation rate: is it a health risk?". Proceedings of the National Academy of Sciences of the United States of America. 94 (16): 8380–8386. Bibcode:1997PNAS...94.8380C. doi:10.1073/pnas.94.16.8380. PMC 33757. PMID 9237985.
  3. ^ Pope CF, O'Sullivan DM, McHugh TD, Gillespie SH (April 2008). "A practical guide to measuring mutation rates in antibiotic resistance". Antimicrobial Agents and Chemotherapy. 52 (4): 1209–1214. doi:10.1128/AAC.01152-07. PMC 2292516. PMID 18250188.
  4. ^ Moxon ER, Rainey PB, Nowak MA, Lenski RE (January 1994). "Adaptive evolution of highly mutable loci in pathogenic bacteria". Current Biology. 4 (1): 24–33. Bibcode:1994CBio....4...24M. doi:10.1016/s0960-9822(00)00005-1. PMID 7922307.
  5. ^ Tomlinson IP, Novelli MR, Bodmer WF (December 1996). "The mutation rate and cancer". Proceedings of the National Academy of Sciences of the United States of America. 93 (25): 14800–14803. Bibcode:1996PNAS...9314800T. doi:10.1073/pnas.93.25.14800. PMC 26216. PMID 8962135.
  6. ^ Eyre-Walker A, Keightley PD (August 2007). "The distribution of fitness effects of new mutations". Nature Reviews. Genetics. 8 (8): 610–618. doi:10.1038/nrg2146. PMID 17637733. S2CID 10868777.
  7. ^ Scally A, Durbin R (October 2012). "Revising the human mutation rate: implications for understanding human evolution". Nature Reviews. Genetics. 13 (10): 745–753. doi:10.1038/nrg3295. PMID 22965354. S2CID 18944814.
  8. ^ Fry M (2016). "Chapter 4.5: Luria–Delbrück experiment". Landmark Experiments in Molecular Biology. Netherlands: Elsevier Science. p. 120. ISBN 978-0-12-802108-8.
  9. ^ Monroe JG, Srikant T, Carbonell-Bejerano P, Becker C, Lensink M, Exposito-Alonso M, et al. (February 2022). "Mutation bias reflects natural selection in Arabidopsis thaliana". Nature. 602 (7895): 101–105. Bibcode:2022Natur.602..101M. doi:10.1038/s41586-021-04269-6. PMC 8810380. PMID 35022609.
  10. ^ Flynn JM, Caldas I, Cristescu ME, Clark AG (October 2017). "Selection Constrains High Rates of Tandem Repetitive DNA Mutation in Daphnia pulex". Genetics. 207 (2): 697–710. doi:10.1534/genetics.117.300146. PMC 5629333. PMID 28811387. Archived from the original on 2020-03-21. Retrieved 2020-03-21.
  11. ^ Wielgoss S, Barrick JE, Tenaillon O, Cruveiller S, Chane-Woon-Ming B, Médigue C, et al. (August 2011). "Mutation Rate Inferred From Synonymous Substitutions in a Long-Term Evolution Experiment With Escherichia coli". G3. 1 (3): 183–186. doi:10.1534/g3.111.000406. PMC 3246271. PMID 22207905.
  12. ^ Ossowski S, Schneeberger K, Lucas-Lledó JI, Warthmann N, Clark RM, Shaw RG, et al. (January 2010). "The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana". Science. 327 (5961): 92–94. Bibcode:2010Sci...327...92O. doi:10.1126/science.1180677. PMC 3878865. PMID 20044577.
  13. ^ Wielgoss S, Barrick JE, Tenaillon O, Wiser MJ, Dittmar WJ, Cruveiller S, et al. (January 2013). "Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load". Proceedings of the National Academy of Sciences of the United States of America. 110 (1): 222–227. Bibcode:2013PNAS..110..222W. doi:10.1073/pnas.1219574110. PMC 3538217. PMID 23248287.
  14. ^ a b c Drake JW, Charlesworth B, Charlesworth D, Crow JF (April 1998). "Rates of spontaneous mutation". Genetics. 148 (4): 1667–1686. doi:10.1093/genetics/148.4.1667. PMC 1460098. PMID 9560386. Archived from the original on 2010-08-21. Retrieved 2007-09-27.
  15. ^ Schneider S, Excoffier L (July 1999). "Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA". Genetics. 152 (3): 1079–1089. doi:10.1093/genetics/152.3.1079. PMC 1460660. PMID 10388826. Archived from the original on 2008-09-08. Retrieved 2008-02-25.
  16. ^ Nachman MW, Crowell SL (September 2000). "Estimate of the mutation rate per nucleotide in humans". Genetics. 156 (1): 297–304. doi:10.1093/genetics/156.1.297. PMC 1461236. PMID 10978293. Archived from the original on 2011-04-08. Retrieved 2007-10-19.
  17. ^ Roach JC, Glusman G, Smit AF, Huff CD, Hubley R, Shannon PT, et al. (April 2010). "Analysis of genetic inheritance in a family quartet by whole-genome sequencing". Science. 328 (5978): 636–639. Bibcode:2010Sci...328..636R. doi:10.1126/science.1186802. PMC 3037280. PMID 20220176.
  18. ^ Whittaker JC, Harbord RM, Boxall N, Mackay I, Dawson G, Sibly RM (June 2003). "Likelihood-based estimation of microsatellite mutation rates". Genetics. 164 (2): 781–787. doi:10.1093/genetics/164.2.781. PMC 1462577. PMID 12807796. Archived from the original on 2011-11-28. Retrieved 2011-05-03.
  19. ^ Gravtiz L (28 June 2012). "Lack of DNA modification creates hotspots for mutations". Simons Foundation Autism Research Initiative. Archived from the original on 5 March 2014. Retrieved 20 February 2014.
  20. ^ Sung W, Tucker AE, Doak TG, Choi E, Thomas WK, Lynch M (November 2012). "Extraordinary genome stability in the ciliate Paramecium tetraurelia". Proceedings of the National Academy of Sciences of the United States of America. 109 (47): 19339–19344. Bibcode:2012PNAS..10919339S. doi:10.1073/pnas.1210663109. PMC 3511141. PMID 23129619.
  21. ^ López-Cortegano E, Craig RJ, Chebib J, Balogun EJ, Keightley PD (January 2023). "Rates and spectra of de novo structural mutations in Chlamydomonas reinhardtii". Genome Research. 33 (1): 45–60. doi:10.1101/gr.276957.122. PMC 9977147. PMID 36617667.
  22. ^ Cao W, Wang X, Li JC (2013). "Hereditary breast cancer in the Han Chinese population". Journal of Epidemiology. 23 (2): 75–84. doi:10.2188/jea.je20120043. PMC 3700245. PMID 23318652.
  23. ^ Levy DD, Sharma B, Cebula TA (July 2004). "Single-nucleotide polymorphism mutation spectra and resistance to quinolones in Salmonella enterica serovar Enteritidis with a mutator phenotype". Antimicrobial Agents and Chemotherapy. 48 (7): 2355–2363. doi:10.1128/AAC.48.7.2355-2363.2004. PMC 434170. PMID 15215081.
  24. ^ Cano AV, Rozhoňová H, Stoltzfus A, McCandlish DM, Payne JL (February 2022). "Mutation bias shapes the spectrum of adaptive substitutions". Proceedings of the National Academy of Sciences of the United States of America. 119 (7). Bibcode:2022PNAS..11919720C. doi:10.1073/pnas.2119720119. PMC 8851560. PMID 35145034.
  25. ^ Altenberg L (June 2011). "An evolutionary reduction principle for mutation rates at multiple Loci". Bulletin of Mathematical Biology. 73 (6): 1227–1270. arXiv:0909.2454. doi:10.1007/s11538-010-9557-9. PMID 20737227. S2CID 15027684.
  26. ^ Sniegowski PD, Gerrish PJ, Johnson T, Shaver A (December 2000). "The evolution of mutation rates: separating causes from consequences". BioEssays. 22 (12): 1057–1066. doi:10.1002/1521-1878(200012)22:12<1057::AID-BIES3>3.0.CO;2-W. PMID 11084621. S2CID 36771934.
  27. ^ Bernstein H, Hopf FA, Michod RE (1987). "The molecular basis of the evolution of sex". Molecular Genetics of Development. Advances in Genetics. Vol. 24. pp. 323–70. doi:10.1016/s0065-2660(08)60012-7. ISBN 9780120176243. PMID 3324702.
  28. ^ Orr HA (June 2000). "The rate of adaptation in asexuals". Genetics. 155 (2): 961–968. doi:10.1093/genetics/155.2.961. PMC 1461099. PMID 10835413. Archived from the original on 2022-06-25. Retrieved 2014-11-01.
  29. ^ Wielgoss S, Barrick JE, Tenaillon O, Wiser MJ, Dittmar WJ, Cruvellier S, et al. (November 13, 2012). "Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load". Proceedings of the National Academy of Sciences, USA. 110: 222–227. doi:10.1073/pnas.12195741 (inactive 1 November 2024).{{cite journal}}: CS1 maint: DOI inactive as of November 2024 (link)
  30. ^ Swings T, Van den Bergh B, Wuyts S, Oeyen E, Voordeckers K, Verstrepen KJ, et al. (May 2017). "Adaptive tuning of mutation rates allows fast response to lethal stress in Escherichia coli". eLife. 6. doi:10.7554/eLife.22939. PMC 5429094. PMID 28460660.
  31. ^ Lynch M (August 2010). "Evolution of the mutation rate". Trends in Genetics. 26 (8): 345–352. doi:10.1016/j.tig.2010.05.003. PMC 2910838. PMID 20594608.
  32. ^ Sung W, Ackerman MS, Miller SF, Doak TG, Lynch M (November 2012). "Drift-barrier hypothesis and mutation-rate evolution". Proceedings of the National Academy of Sciences of the United States of America. 109 (45): 18488–18492. Bibcode:2012PNAS..10918488S. doi:10.1073/pnas.1216223109. PMC 3494944. PMID 23077252.
  33. ^ Crotty S, Cameron CE, Andino R (June 2001). "RNA virus error catastrophe: direct molecular test by using ribavirin". Proceedings of the National Academy of Sciences of the United States of America. 98 (12): 6895–6900. Bibcode:2001PNAS...98.6895C. doi:10.1073/pnas.111085598. PMC 34449. PMID 11371613.
  34. ^ Rambaut A, Posada D, Crandall KA, Holmes EC (January 2004). "The causes and consequences of HIV evolution". Nature Reviews. Genetics. 5 (1): 52–61. doi:10.1038/nrg1246. PMID 14708016. S2CID 5790569. Archived from the original on 2019-11-09. Retrieved 2019-05-28.
Quelle: https://en.wikipedia.org/wiki/Mutation_rates