Biology:Evolvability

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Short description: Capacity of a system for adaptive evolution

Evolvability is defined as the capacity of a system for adaptive evolution. Evolvability is the ability of a population of organisms to not merely generate genetic diversity, but to generate adaptive genetic diversity, and thereby evolve through natural selection.[1][2][3]

In order for a biological organism to evolve by natural selection, there must be a certain minimum probability that new, heritable variants are beneficial. Random mutations, unless they occur in DNA sequences with no function, are expected to be mostly detrimental. Beneficial mutations are always rare, but if they are too rare, then adaptation cannot occur. Early failed efforts to evolve computer programs by random mutation and selection[4] showed that evolvability is not a given, but depends on the representation of the program as a data structure, because this determines how changes in the program map to changes in its behavior.[5] Analogously, the evolvability of organisms depends on their genotype–phenotype map.[6][7][8] This means that genomes are structured in ways that make beneficial changes more likely. This has been taken as evidence that evolution has created fitter populations of organisms that are better able to evolve.

Alternative definitions

Andreas Wagner[9] describes two definitions of evolvability. According to the first definition, a biological system is evolvable:

  • if its properties show heritable genetic variation, and
  • if natural selection can thus change these properties.

According to the second definition, a biological system is evolvable:

  • if it can acquire novel functions through genetic change, functions that help the organism survive and reproduce.

For example, consider an enzyme with multiple alleles in the population. Each allele catalyzes the same reaction, but with a different level of activity. However, even after millions of years of evolution, exploring many sequences with similar function, no mutation might exist that gives this enzyme the ability to catalyze a different reaction. Thus, although the enzyme's activity is evolvable in the first sense, that does not mean that the enzyme's function is evolvable in the second sense. However, every system evolvable in the second sense must also be evolvable in the first.

Pigliucci[10] recognizes three classes of definition, depending on timescale. The first corresponds to Wagner's first, and represents the very short timescales that are described by quantitative genetics.[11][12] He divides Wagner's second definition into two categories, one representing the intermediate timescales that can be studied using population genetics, and one representing exceedingly rare long-term innovations of form.

Pigliucci's second definition of evolvability includes Altenberg's[3] quantitative concept of evolvability, being not a single number, but the entire upper tail of the fitness distribution of the offspring produced by the population. This quantity was considered a "local" property of the instantaneous state of a population, and its integration over the population's evolutionary trajectory, and over many possible populations, would be necessary to give a more global measure of evolvability.

Generating more variation

More heritable phenotypic variation means more evolvability. While mutation is the ultimate source of heritable variation, its permutations and combinations also make a big difference. Sexual reproduction generates more variation (and thereby evolvability) relative to asexual reproduction (see evolution of sexual reproduction). Evolvability is further increased by generating more variation when an organism is stressed,[13] and thus likely to be less well adapted, but less variation when an organism is doing well. The amount of variation generated can be adjusted in many different ways, for example via the mutation rate, via the probability of sexual vs. asexual reproduction, via the probability of outcrossing vs. inbreeding, via dispersal, and via access to previously cryptic variants through the switching of an evolutionary capacitor. A large population size increases the influx of novel mutations in each generation.[14]

Enhancement of selection

Rather than creating more phenotypic variation, some mechanisms increase the intensity and effectiveness with which selection acts on existing phenotypic variation.[15] For example:

  • Mating rituals that allow sexual selection on "good genes", and so intensify natural selection.[15]
  • Large effective population size increasing the threshold value of the selection coefficient above which selection becomes an important player. This could happen through an increase in the census population size, decreasing genetic drift, through an increase in the recombination rate, decreasing genetic draft, or through changes in the probability distribution of the numbers of offspring.[15]
  • Recombination decreasing the importance of the Hill-Robertson effect, where different genotypes contain different adaptive mutations. Recombination brings the two alleles together, creating a super-genotype in place of two competing lineages.[15]
  • Shorter generation time.[15]

Robustness and evolvability

The relationship between robustness and evolvability depends on whether recombination can be ignored.[16] Recombination can generally be ignored in asexual populations and for traits affected by single genes.

Without recombination

Robustness in the face of mutation does not increase evolvability in the first sense. In organisms with a high level of robustness, mutations have smaller phenotypic effects than in organisms with a low level of robustness. Thus, robustness reduces the amount of heritable genetic variation on which selection can act. However, robustness may allow exploration of large regions of genotype space, increasing evolvability according to the second sense.[9][16] Even without genetic diversity, some genotypes have higher evolvability than others, and selection for robustness can increase the "neighborhood richness" of phenotypes that can be accessed from the same starting genotype by mutation. For example, one reason many proteins are less robust to mutation is that they have marginal thermodynamic stability, and most mutations reduce this stability further. Proteins that are more thermostable can tolerate a wider range of mutations and are more evolvable.[17] For polygenic traits, neighborhood richness contributes more to evolvability than does genetic diversity or "spread" across genotype space.[18]

With recombination

Temporary robustness, or canalisation, may lead to the accumulation of significant quantities of cryptic genetic variation. In a new environment or genetic background, this variation may be revealed and sometimes be adaptive.[16][19]

Factors affecting evolvability via robustness

Different genetic codes have the potential to change robustness and evolvability by changing the effect of single-base mutational changes.[20] [21]

Exploration ahead of time

When mutational robustness exists, many mutants will persist in a cryptic state. Mutations tend to fall into two categories, having either a very bad effect or very little effect: few mutations fall somewhere in between.[22][23] Sometimes, these mutations will not be completely invisible, but still have rare effects, with very low penetrance. When this happens, natural selection weeds out the very bad mutations, while leaving the others relatively unaffected.[24][25] While evolution has no "foresight" to know which environment will be encountered in the future, some mutations cause major disruption to a basic biological process, and will never be adaptive in any environment. Screening these out in advance leads to preadapted stocks of cryptic genetic variation.

Another way that phenotypes can be explored, prior to strong genetic commitment, is through learning. An organism that learns gets to "sample" several different phenotypes during its early development, and later sticks to whatever worked best. Later in evolution, the optimal phenotype can be genetically assimilated so it becomes the default behavior rather than a rare behavior. This is known as the Baldwin effect, and it can increase evolvability.[26][27]

Learning biases phenotypes in a beneficial direction. But an exploratory flattening of the fitness landscape can also increase evolvability even when it has no direction, for example when the flattening is a result of random errors in molecular and/or developmental processes. This increase in evolvability can happen when evolution is faced with crossing a "valley" in an adaptive landscape. This means that two mutations exist that are deleterious by themselves, but beneficial in combination. These combinations can evolve more easily when the landscape is first flattened, and the discovered phenotype is then fixed by genetic assimilation.[28][29][30]

Modularity

If every mutation affected every trait, then a mutation that was an improvement for one trait would be a disadvantage for other traits. This means that almost no mutations would be beneficial overall. But if pleiotropy is restricted to within functional modules, then mutations affect only one trait at a time, and adaptation is much less constrained. In a modular gene network, for example, a gene that induces a limited set of other genes that control a specific trait under selection may evolve more readily than one that also induces other gene pathways controlling traits not under selection.[15] Individual genes also exhibit modularity. A mutation in one cis-regulatory element of a gene's promoter region may allow the expression of the gene to be altered only in specific tissues, developmental stages, or environmental conditions rather than changing gene activity in the entire organism simultaneously.[15]

Evolution of evolvability

While variation yielding high evolvability could be useful in the long term, in the short term most of that variation is likely to be a disadvantage. For example, naively it would seem that increasing the mutation rate via a mutator allele would increase evolvability. But as an extreme example, if the mutation rate is too high then all individuals will be dead or at least carry a heavy mutation load. Short-term selection for low variation most of the time is usually thought[who?] likely to be more powerful than long-term selection for evolvability, making it difficult for natural selection to cause the evolution of evolvability. Other forces of selection also affect the generation of variation; for example, mutation and recombination may in part be byproducts of mechanisms to cope with DNA damage.[31]

When recombination is low, mutator alleles may still sometimes hitchhike on the success of adaptive mutations that they cause. In this case, selection can take place at the level of the lineage.[32] This may explain why mutators are often seen during experimental evolution of microbes. Mutator alleles can also evolve more easily when they only increase mutation rates in nearby DNA sequences, not across the whole genome: this is known as a contingency locus.

The evolution of evolvability is less controversial if it occurs via the evolution of sexual reproduction, or via the tendency of variation-generating mechanisms to become more active when an organism is stressed. The yeast prion [PSI+] may also be an example of the evolution of evolvability through evolutionary capacitance.[33][34] An evolutionary capacitor is a switch that turns genetic variation on and off. This is very much like bet-hedging the risk that a future environment will be similar or different.[35] Theoretical models also predict the evolution of evolvability via modularity.[36] When the costs of evolvability are sufficiently short-lived, more evolvable lineages may be the most successful in the long-term.[37] However, the hypothesis that evolvability is an adaptation is often rejected in favor of alternative hypotheses, e.g. minimization of costs.[10]

Applications

Evolvability phenomena have practical applications. For protein engineering we wish to increase evolvability, and in medicine and agriculture we wish to decrease it. Protein evolvability is defined as the ability of the protein to acquire sequence diversity and conformational flexibility which can enable it to evolve toward a new function.[38]

In protein engineering, both rational design and directed evolution approaches aim to create changes rapidly through mutations with large effects.[39][40] Such mutations, however, commonly destroy enzyme function or at least reduce tolerance to further mutations.[41][42] Identifying evolvable proteins and manipulating their evolvability is becoming increasingly necessary in order to achieve ever larger functional modification of enzymes.[43] Proteins are also often studied as part of the basic science of evolvability, because the biophysical properties and chemical functions can be easily changed by a few mutations.[44][45] More evolvable proteins can tolerate a broader range of amino acid changes and allow them to evolve toward new functions. The study of evolvability has fundamental importance for understanding very long term evolution of protein superfamilies.[46][47][48][49][50]

Many human diseases are capable of evolution. Viruses, bacteria, fungi and cancers evolve to be resistant to host immune defences, as well as pharmaceutical drugs.[51][52][53] These same problems occur in agriculture with pesticide[54] and herbicide[55] resistance. It is possible that we are facing the end of the effective life of most of available antibiotics.[56] Predicting the evolution and evolvability[57] of our pathogens, and devising strategies to slow or circumvent the development of resistance, demands deeper knowledge of the complex forces driving evolution at the molecular level.[58]

A better understanding of evolvability is proposed to be part of an Extended Evolutionary Synthesis.[59][60][61]

See also

  • Evolutionary trade-off

References

  1. ↑ "Experimental evolution: experimental evolution and evolvability". Heredity 100 (5): 464–70. May 2008. doi:10.1038/sj.hdy.6801095. PMID 18212804. 
  2. ↑ "Evolvability". Proceedings of the National Academy of Sciences of the United States of America 95 (15): 8420–7. July 1998. doi:10.1073/pnas.95.15.8420. PMID 9671692. Bibcode: 1998PNAS...95.8420K. 
  3. ↑ 3.0 3.1 Altenberg, Lee (1995). "Genome growth and the evolution of the genotype–phenotype map". Evolution and Biocomputation. Lecture Notes in Computer Science. 899. pp. 205–259. doi:10.1007/3-540-59046-3_11. ISBN 978-3-540-59046-0. 
  4. ↑ "A Learning Machine: Part I |". IBM Journal of Research and Development 2 (1): 2–13. 1958. doi:10.1147/rd.21.0002. 
  5. ↑ Altenberg, Lee (1994). Kinnear, Kenneth. ed. "The evolution of evolvability in genetic programming". Advances in Genetic Programming: 47–74. http://dynamics.org/Altenberg/PAPERS/EEGP/. 
  6. ↑ "Perspective: Complex adaptations and the evolution of evolvability". Evolution; International Journal of Organic Evolution 50 (3): 967–976. June 1996. doi:10.1111/j.1558-5646.1996.tb02339.x. PMID 28565291. 
  7. ↑ Bianco, Simone (2022-04-01). "Artificial Intelligence: Bioengineers' Ultimate Best Friend". GEN Biotechnology (Mary Ann Liebert) 1 (2): 140–141. doi:10.1089/genbio.2022.29027.sbi. ISSN 2768-1572. 
  8. ↑ "The evolution, evolvability and engineering of gene regulatory DNA". Nature 603 (7901): 455–463. March 2022. doi:10.1038/s41586-022-04506-6. PMID 35264797. Bibcode: 2022Natur.603..455V. 
  9. ↑ 9.0 9.1 Wagner A (2005). Robustness and evolvability in living systems. Princeton Studies in Complexity. Princeton University Press. ISBN 978-0-691-12240-3. 
  10. ↑ 10.0 10.1 "Is evolvability evolvable?". Nature Reviews Genetics 9 (1): 75–82. January 2008. doi:10.1038/nrg2278. PMID 18059367. https://philpapers.org/archive/PIGIEE.pdf. 
  11. ↑ "Comparing evolvability and variability of quantitative traits". Genetics 130 (1): 195–204. January 1992. doi:10.1093/genetics/130.1.195. PMID 1732160. 
  12. ↑ "Heritability is not evolvability.". Evolutionary Biology 38 (3): 258–277. September 2011. doi:10.1007/s11692-011-9127-6. 
  13. ↑ "The evolution of stress-induced hypermutation in asexual populations". Evolution; International Journal of Organic Evolution 66 (7): 2315–28. July 2012. doi:10.1111/j.1558-5646.2012.01576.x. PMID 22759304. 
  14. ↑ "Evidence that adaptation in Drosophila is not limited by mutation at single sites". PLOS Genetics 6 (6): e1000924. June 2010. doi:10.1371/journal.pgen.1000924. PMID 20585551. 
  15. ↑ 15.0 15.1 15.2 15.3 15.4 15.5 15.6 "Adaptive evolution: evaluating empirical support for theoretical predictions". Nature Reviews Genetics 13 (12): 867–77. December 2012. doi:10.1038/nrg3322. PMID 23154809. 
  16. ↑ 16.0 16.1 16.2 "Robustness and evolvability". Trends in Genetics 26 (9): 406–14. September 2010. doi:10.1016/j.tig.2010.06.002. PMID 20598394. 
  17. ↑ "Protein stability promotes evolvability". Proceedings of the National Academy of Sciences of the United States of America 103 (15): 5869–74. April 2006. doi:10.1073/pnas.0510098103. PMID 16581913. Bibcode: 2006PNAS..103.5869B. 
  18. ↑ "Compensatory evolution and the origins of innovations". Genetics 193 (4): 1209–20. April 2013. doi:10.1534/genetics.112.148627. PMID 23335336. 
  19. ↑ "Degeneracy: a design principle for achieving robustness and evolvability". Journal of Theoretical Biology 263 (1): 143–53. March 2010. doi:10.1016/j.jtbi.2009.11.008. PMID 19925810. Bibcode: 2010JThBi.263..143W. 
  20. ↑ "The genetic code constrains yet facilitates Darwinian evolution". Nucleic Acids Research 41 (15): 7420–8. August 2013. doi:10.1093/nar/gkt536. PMID 23754851. 
  21. ↑ "Refactoring the Genetic Code for Increased Evolvability". mBio 8 (6). November 2017. doi:10.1128/mBio.01654-17. PMID 29138304. 
  22. ↑ "The distribution of fitness effects of new mutations". Nature Reviews Genetics 8 (8): 610–8. August 2007. doi:10.1038/nrg2146. PMID 17637733. 
  23. ↑ "Low frequency of mutations with strongly deleterious but nonlethal fitness effects". Evolution; International Journal of Organic Evolution 63 (8): 2164–71. August 2009. doi:10.1111/j.1558-5646.2009.00713.x. PMID 19473394. 
  24. ↑ "Cryptic genetic variation is enriched for potential adaptations". Genetics 172 (3): 1985–91. March 2006. doi:10.1534/genetics.105.051649. PMID 16387877. 
  25. ↑ "Evolution of molecular error rates and the consequences for evolvability". Proceedings of the National Academy of Sciences of the United States of America 108 (3): 1082–7. January 2011. doi:10.1073/pnas.1012918108. PMID 21199946. Bibcode: 2011PNAS..108.1082R. 
  26. ↑ "How learning can guide evolution". Complex Systems 1: 495–502. 1987. 
  27. ↑ "The effect of phenotypic plasticity on evolution in multipeaked fitness landscapes". Journal of Evolutionary Biology 19 (5): 1555–70. September 2006. doi:10.1111/j.1420-9101.2006.01125.x. PMID 16910985. 
  28. ↑ "Rate of adaptive peak shifts with partial genetic robustness". Evolution; International Journal of Organic Evolution 61 (8): 1847–56. August 2007. doi:10.1111/j.1558-5646.2007.00166.x. PMID 17683428. 
  29. ↑ "The look-ahead effect of phenotypic mutations". Biology Direct 3 (1): 18. May 2008. doi:10.1186/1745-6150-3-18. PMID 18479505. 
  30. ↑ "Complex adaptations can drive the evolution of the capacitor [PSI, even with realistic rates of yeast sex"]. PLOS Genetics 5 (6): e1000517. June 2009. doi:10.1371/journal.pgen.1000517. PMID 19521499. 
  31. ↑ "On fitness and adaptedness and their role in evolutionary explanation". Journal of the History of Biology 19 (2): 289–302. 1986. doi:10.1007/bf00138880. PMID 11611993. 
  32. ↑ Eshel I (1973). "Clone-selection and optimal rates of mutation". Journal of Applied Probability 10 (4): 728–738. doi:10.2307/3212376. 
  33. ↑ "The evolution of the evolvability properties of the yeast prion [PSI+]". Evolution; International Journal of Organic Evolution 57 (7): 1498–512. July 2003. doi:10.1111/j.0014-3820.2003.tb00358.x. PMID 12940355. 
  34. ↑ "The spontaneous appearance rate of the yeast prion [PSI+ and its implications for the evolution of the evolvability properties of the [PSI+] system"]. Genetics 184 (2): 393–400. February 2010. doi:10.1534/genetics.109.110213. PMID 19917766. 
  35. ↑ "The evolution of bet-hedging adaptations to rare scenarios". Theoretical Population Biology 72 (4): 560–75. December 2007. doi:10.1016/j.tpb.2007.08.006. PMID 17915273. 
  36. ↑ "Evolution of evolvability in a developmental model". Evolution; International Journal of Organic Evolution 62 (2): 301–15. February 2008. doi:10.1111/j.1558-5646.2007.00303.x. PMID 18031304. 
  37. ↑ "Second-order selection for evolvability in a large Escherichia coli population". Science 331 (6023): 1433–6. March 2011. doi:10.1126/science.1198914. PMID 21415350. Bibcode: 2011Sci...331.1433W. 
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  39. ↑ "Introduction to current and future protein therapeutics: a protein engineering perspective". Experimental Cell Research 317 (9): 1261–9. May 2011. doi:10.1016/j.yexcr.2011.02.013. PMID 21371474. 
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  41. ↑ "Stability effects of mutations and protein evolvability". Current Opinion in Structural Biology 19 (5): 596–604. October 2009. doi:10.1016/j.sbi.2009.08.003. PMID 19765975. 
  42. ↑ "Evolution of an antibiotic resistance enzyme constrained by stability and activity trade-offs". Journal of Molecular Biology 320 (1): 85–95. June 2002. doi:10.1016/s0022-2836(02)00400-x. PMID 12079336. 
  43. ↑ "Natural history as a predictor of protein evolvability". Protein Engineering, Design & Selection 19 (10): 439–42. October 2006. doi:10.1093/protein/gzl029. PMID 16868005. 
  44. ↑ "Initial mutations direct alternative pathways of protein evolution". PLOS Genetics 7 (3): e1001321. March 2011. doi:10.1371/journal.pgen.1001321. PMID 21408208. 
  45. ↑ "Protein stability promotes evolvability". Proceedings of the National Academy of Sciences of the United States of America 103 (15): 5869–74. April 2006. doi:10.1073/pnas.0510098103. PMID 16581913. Bibcode: 2006PNAS..103.5869B. 
  46. ↑ "Protein superfamily evolution and the last universal common ancestor (LUCA)". Journal of Molecular Evolution 63 (4): 513–25. October 2006. doi:10.1007/s00239-005-0289-7. PMID 17021929. Bibcode: 2006JMolE..63..513R. 
  47. ↑ "What makes a protein fold amenable to functional innovation? Fold polarity and stability trade-offs". Journal of Molecular Biology 425 (14): 2609–21. July 2013. doi:10.1016/j.jmb.2013.03.033. PMID 23542341. 
  48. ↑ Wagner, Andreas (2011-07-14). The origins of evolutionary innovations : a theory of transformative change in living systems. Oxford University Press. ISBN 978-0-19-969259-0. 
  49. ↑ Minelli, Alessandro; Boxshall, Geoffrey; Fusco, Giuseppe (2013-04-23). Arthropod biology and evolution : molecules, development, morphology. Springer. ISBN 978-3-642-36159-3. 
  50. ↑ "Is evolvability evolvable?". Nature Reviews Genetics 9 (1): 75–82. January 2008. doi:10.1038/nrg2278. PMID 18059367. https://philpapers.org/archive/PIGIEE.pdf. 
  51. ↑ "Cancer as an evolutionary and ecological process". Nature Reviews. Cancer 6 (12): 924–35. December 2006. doi:10.1038/nrc2013. PMID 17109012. 
  52. ↑ "Understanding the drug resistance mechanism of hepatitis C virus NS3/4A to ITMN-191 due to R155K, A156V, D168A/E mutations: a computational study". Biochimica et Biophysica Acta (BBA) - General Subjects 1820 (10): 1526–34. October 2012. doi:10.1016/j.bbagen.2012.06.001. PMID 22698669. 
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  54. ↑ "Forty years of erratic insecticide resistance evolution in the mosquito Culex pipiens". PLOS Genetics 3 (11): e205. November 2007. doi:10.1371/journal.pgen.0030205. PMID 18020711. 
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  59. ↑ "Do we need an extended evolutionary synthesis?". Evolution; International Journal of Organic Evolution 61 (12): 2743–9. December 2007. doi:10.1111/j.1558-5646.2007.00246.x. PMID 17924956. https://philpapers.org/archive/PIGDWN.pdf. 
  60. ↑ "An extended synthesis for evolutionary biology". Annals of the New York Academy of Sciences 1168 (1): 218–28. June 2009. doi:10.1111/j.1749-6632.2009.04578.x. PMID 19566710. Bibcode: 2009NYASA1168..218P. https://philpapers.org/archive/PIGAES.pdf. 
  61. ↑ "Beyond DNA: integrating inclusive inheritance into an extended theory of evolution". Nature Reviews Genetics 12 (7): 475–86. June 2011. doi:10.1038/nrg3028. PMID 21681209. 



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