HAL Id: hal-03052120
https://hal.archives-ouvertes.fr/hal-03052120
Submitted on 22 Dec 2020HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
The Impact of Neutral Mutations on Genome
Evolvability
Olivier Tenaillon, Ivan Matic
To cite this version:
Olivier Tenaillon, Ivan Matic. The Impact of Neutral Mutations on Genome Evolvability. Current Biology - CB, Elsevier, 2020, 30 (10), pp.R527-R534. �10.1016/j.cub.2020.03.056�. �hal-03052120�
Minireview
The Impact of Neutral Mutations on Genome Evolvability
Olivier Tenaillon1,2 andIvan Matic2,3,4,*
1IAME,, Inserm U1137, Paris, 75018, France 2Université de Paris, Paris, 75006, France
3Institut Cochin, Inserm U1016, Paris, 75014, France 4 CNRS, UMR8104, Paris, 75014, France
*Email: ivan.matic@inserm.fr
Abstract
Beneficial mutations are rare and deleterious mutations are purged by natural selection. As a result, the vast majority of mutations that accumulate in genomes belong to the class of neutral mutations. Over the last two decades, neutral mutations, despite their null effect on fitness, have been shown to affect evolvability by providing access to new phenotypes through subsequent mutations that would not have been available otherwise. Here we propose that in addition, many mutations — independent of their selective effects — can affect the mutability of neighboring DNA sequences and modulate the efficacy of homologous recombination. Such mutations do not change the spectrum of accessible phenotypes, but rather the rate at which new phenotypes will be produced. Therefore, neutral mutations that accumulate in genomes have an important long-term impact on the evolutionary fate of genomes.
Introduction
Mutations are permanent modifications of the genetic material and the major source of heritable phenotypic diversity, which is the substrate of natural selection. In a given environment, mutations can be classified according to their impact on fitness as lethal, deleterious, neutral or beneficial. A simple formalism is to assign mutations a coefficient of selection s, such that the fitness of a mutant relative to its parent is 1+s. The s values of beneficial, deleterious, lethal and neutral mutations are >0, <0, -1 and 0, respectively. These categories differ both in the frequencies at which they occur and also in the importance they have for researchers. Rare beneficial mutations are important to understand adaptation, whereas the relatively abundant deleterious and lethal mutations are valuable for understanding gene functions or the molecular determinants of diseases. Neutral mutations are typically used as markers to build genealogies or phylogenies. Indeed, a mutation without selective effect has an evolutionary fate that is completely governed by chance. This process results in a constant accumulation of neutral mutations that is only dependent on the mutation rate of the organism. Assuming that such rates are fairly constant across taxa, the accumulation of neutral mutations between two genomes can therefore be used to infer the time since their last common ancestor. As such, they should not have a specific impact on population evolution in the long term. However, as we discuss here, neutral mutations may affect genome evolution both in terms of the type of evolutionary path that will be followed as well as in the rate at which changes will occur. This impact of neutral mutations on evolvability is not only associated with their cryptic effects on phenotype accessibility, as previously suggested, but could also be linked to their direct impact on the rates of mutations and recombination.
Neutral Mutations, Cryptic Variation and Evolvability
The fields of population genetics and, later on, molecular evolution, in focusing on the fate of a given allele, have used neutral mutations mostly as markers of time. Yet, integration of neutral mutations into the adaptive landscape metaphor has connected neutral mutations to evolvability, that is, the capacity of organisms to generate adaptive diversity [1–3]. An adaptive landscape is an integrated representation of the connections between the possible genotypes and their associated reproductive success, referred to as fitness. In that perspective, the focus is not given to the fate of a given mutation, but rather to the set of mutations that will propagate in a population given all possible mutations and their interactions. The impact
of a mutation is therefore not restricted to its impact on fitness but also to the panel of genotypes and phenotypes it gives access to. Evolution is then seen as a walk — one that drives a population from one position in the adaptive landscape to another, with each step opening access to a slightly different set of mutations and, accordingly, selective effects. Selection that favors the accumulation of beneficial mutations is a major driving force of movement in the adaptive landscape. Yet, under the hypothesis that beneficial mutations are rare, evolving through the accumulation of neutral mutations appeared as an efficient way to move through the landscape, with deleterious mutations being purged by natural selection. Subsequently, for mathematical and conceptual convenience, it has often been proposed to limit adaptive landscapes to neutral networks, in which the adaptive landscape is only composed of mutations that are either neutral or very deleterious [4,5]. The adaptive landscape is then composed of networks of genotypes of equal fitness interconnected by mutations.
In these models, the connectivity of each genotype or the size of the neutral network accessible through neutral mutations has been connected to evolvability [1–3]. The simple underlying idea is that on a large neutral network, populations may accumulate high levels of genetic diversity and explore this large network. Consequently, they may have access to a larger number of genotypes through mutations, some of which may be beneficial in new conditions. Accordingly, using a modeling approach, it was shown that the time required to find a new specific phenotype was connected to the size of the network of neutral mutations. The idea is, in some ways, connected to the initial introduction of the adaptive landscape metaphor by Sewall Wright [6]. For him, fixation of slightly deleterious mutations through drift was necessary for populations to sample larger portions of the genotype space and escape from local fitness optima. However, the extent of neutral diversity observed in population sequences suggested that, rather than deleterious mutations fixed through drift, the exploration could proceed even more easily with the accumulation of neutral mutations.
What types of molecular mechanisms allow neutral mutations to change the range of accessible phenotypes? RNA-folding models played a pivotal role in our understanding of the biology of these processes. With the rise of computer programs predicting the folding of small RNA sequences (for example, see [7]), it became possible to study in silico the evolution of RNA sequences under a selective constraint acting on the fold the sequence will take. For a given RNA fold, one can assign to any sequence a fitness of 1 if the sequence is predicted to fold preferentially into that fold and 0 otherwise. Sequences that adopt the same fold may
have different neighboring sequences whose folds may differ. Hence, from these neutral networks, the accessibility of a new RNA fold from a preexisting one has been studied [4]. Similar approaches have been used with protein lattice models that simulate the folding in two- or three-dimensional lattices of short protein sequences, regulatory gene networks that generate a pattern of gene expression, and flux-balance analyses that predict the growth ability of a metabolic network on a different set of substrates [8]. With the development of high-throughput mutagenesis and sequencing, some experimental approaches have even validated the suggested principle that neutral genetic variation may promote adaptation. For instance, populations that had accumulated some neutral variation in a yellow fluorescent protein adapted faster to selection for green emission [9].
In these studies of adaptive landscapes, neutral mutations are essentially a source of cryptic phenotypic variation. In most cases, it is the degeneracy of the genotype-to-phenotype fitness map that allows some mutations to change the genotype but not fitness. Yet, as they change the genotype (and, eventually, some non-selected phenotypes) their mutational neighbors may have different phenotypes and fitness. A simple example of genotype-to-phenotype degeneracy is found in the genetic code, in which some mutations generate synonymous codons. Although they do not change the protein sequence, synonymous mutations may give access to new phenotypes by changing the type of amino acids that are accessible by subsequent mutations (Figure 1). This difference in sets of accessible amino acids is called codon volatility. For instance, a mutation of the arginine codon CGG to its synonym AGG changes the range of mutationally accessible amino acids at that site from glycine, tryptophan, glutamine, leucine, and proline, to glycine, tryptophan, lysine, threonine, methionine, and serine (Figure 1). Variability in codon volatility was used to improve the genetic diversity resulting from random mutagenesis in proteins [10]. Synonymous variants of an antibiotic resistance gene, when subjected to mutagenesis with error-prone PCR, resulted in different genetic improvements of the protein function. Gene pattern of codon volatility was even proposed to be a hallmark of genes under diversifying selection [11]. Many alternative mechanisms may lead to some degeneracy of phenotype-to-fitness mapping and serve as sources of cryptic variation. For instance, the flux in a metabolic pathway with highly expressed enzymes, is not affected by moderate changes in enzyme expression under non-limiting conditions [12]. Similarly, a highly stable protein is not affected by mutations that change protein stability. Consequently some apparently neutral mutations may modulate gene expression or protein stability and allow other larger-effect mutations to be tolerated,
including those that provide new functions [13]. For instance, in the beta-lactamase TEM1, the stabilizing mutation M182T has a marginal fitness effect but allows the subsequent accumulation of many destabilising mutations [14] including some that extend its activity to a larger spectrum of antibiotic molecules [15]. In all cases, in the adaptive landscape metaphor, neutral mutations are linked to evolvability through the fact that they change qualitatively the panel of accessible mutations and phenotypes due to some degeneracy in the genotype-to-phenotype fitness map.
This impact of neutral mutations on evolvability is now well documented [16]. In the remainder of this article, we focus on another impact of neutral mutations that is often neglected, the impact they have on evolutionarily important rates of mutation and recombination. Contrary to the previous cases, here mutations are not changing the spectrum of accessible phenotypes, but rather the rate at which these mutant phenotypes will be produced. Indeed, regardless of their direct or indirect impacts on the selected phenotypes, mutations that affect the machinery of DNA replication, repair and recombination can affect the long-term evolutionary fate of species.
Impact of Local DNA-Sequence Polymorphism on Mutability
Local nucleotide sequence has been shown to impact DNA polymerase fidelity, DNA repair efficacy and mutability by different mutagens [17–20]. Therefore, it is not surprising that the analysis of natural polymorphisms in microbial and eukaryotic genomes, cancer genomes, as well as experimental evidence, show that DNA-sequence context significantly impacts the probability of mutation generation. For example, biases of varying magnitude are found in the rates of substitution of the same base pair in different local sequence environments in primates [17]. Mutation accumulation and experimental evolution coupled to whole genome sequencing of Escherichia coli strains deficient in the mismatch repair system revealed that DNA polymerase introduces mutations preferentially at some specific genome sequences [20]. Mutations affecting a G in a CGT sequence were found to be about 10 times more likely than mutations affecting G in an AGT sequence [18]. Similarly, it was observed that up to two neighboring nucleotides significantly impact single base substitutions in the human germ-line [19].
DNA-sequence context was shown to also impact DNA-repair efficacy and therefore sequence mutability. For example, the efficacy of E. coli mismatch repair depends on the nature of a mismatch but also on the neighboring nucleotide sequences — that is, repair
efficacy increases with increasing G:C content of the mismatch-flanking DNA sequence [21]. Sequence context also impacts efficacy of base-excision repair and DNA polymerase insertion fidelity in eukaryotes [22,23]. For example, the human thymine glycosylase removes thymidine from G:T mismatches that result from the deamination of 5′ methylcytosine more efficiently when in a CpG, compared to ApG, CpC and TpC sequence contexts. The activity of the human DNA polymerase Pol β, which is the primary enzyme responsible for gap filling during base-excision repair, is affected by base composition of the primer terminus, with a terminal pyrimidine more efficiently extended than a purine. Impact of DNA-sequence context on DNA-repair efficacy and mutability can explain why local DNA sequence also impacts the probability of mutation generation by many endogenous and exogenous mutagens. For example, alkylating agents induce G:C to A:T transitions preferentially at sites preceded 5’ by a purine base in E. coli [24]. Hence, intrinsic thermodynamic and conformational properties of different DNA-sequence contexts dictate fidelity of DNA replication and the efficiency of DNA repair. This may explain why physical properties of DNA resulting from the local sequence are associated with the human mutational spectrum bias [25]. Mutations can also affect stability of local secondary DNA structures formed during transcription. This affects the amount of time spent by some bases in a single-stranded state, which leaves them chemically less stable than bases in double-stranded DNA, and therefore changes their mutability [26,27]. Importantly, these mechanistic effects contribute to the long-term patterns of diversity observed using comparative genomics. For instance, the impact of a neighboring base on polymerase fidelity, as revealed by mutation accumulation with mismatch-repair-deficient strains, is highly correlated with the biases observed in the accumulated mutations between two distant E. coli strains that are about 3% diverged [20]. Hence, these mechanistic biases in mutation rates have long-lasting signatures.
These variations in rates have not, so far, been included in adaptive landscape models. They do not change the panel of accessible genotypes per se, but rather change the rate at which these neighboring genotypes could be attained, therefore substantially contributing to the adaptive path taken. A change in rate could in some cases have large consequences. For instance, in the case of the evolution of a costly antibiotic resistance in the absence of antibiotics, differences in mutation rates favored the maintenance of resistance. The selection of a fitter, resistant clone due to the selection of a high-rate compensatory mutation was favored over the selection of the fitter sensitive genotype caused by the reversion of the antibiotic-resistance-causing mutation [28].
Impact of DNA-Sequence Polymorphism on Recombination Efficacy
Homologous recombination maintains genome stability by repairing DNA breaks and rescuing stalled or collapsed replication forks; but it also generates new combinations of DNA sequences through horizontal gene transfer in bacteria and meiosis in eukaryotes. There is ample evidence that DNA-sequence polymorphism, independently of its impact on fitness, has a major impact on the efficacy of homologous recombination. The homologous recombination process is initiated by combined action of a variety of nucleases and helicases that generate single-stranded DNA (ssDNA), the substrate for an evolutionarily conserved family of DNA recombinases, including RecA in bacteria and RAD51 in eukaryotes [29]. These proteins first bind to ssDNA to form nucleoprotein filaments and then engage in a search for target DNA sequences. Scanning DNA for target sites is very efficient and highly accurate even when they are extremely rare in the genome. The minimum threshold for DNA-sequence-identity recognition is eight consecutive, perfectly matching nucleotides and is conserved between bacterial RecA and yeast and human Rad51 [30]. When a given length of shared, uninterrupted sequence identity is found, DNA recombinases catalyse pairing of the ssDNA from the nucleoprotein filaments with the complementary strand from the double-stranded DNA, generating heteroduplex DNA (Figure 2A). This reaction, referred to as strand invasion, requires at least 26 and 250 nucleotides in bacteria and eukaryotes, respectively [31]. This minimal DNA-sequence-identity length required for homologous recombination is called the ‘minimal efficient-processing segment’.
Pairing of non-identical DNA sequences produces mismatched heteroduplex molecules (Figure 2B). RecA and Rad51 DNA recombinases can detect single mismatches at the initial stages of strand exchange, whose efficiency is strongly dependent on the location and distribution of mismatches. Mismatches near the 3' end of the incoming strand strongly impede strand exchange. Consequently, DNA-sequence divergences between recombining DNA molecules reduce the number of minimal efficient-processing segments and therefore the efficacy of homologous recombination [32,33].
Once established, heteroduplex DNA is extended by a unidirectional branch migration step. At this step of recombination, DNA recombinases tolerate nucleotide sequence divergence between recombining molecules. However, non-identity between the recombining DNA molecules downstream from the minimal efficient-processing segment tract results in generation of mismatched heteroduplexes (Figure 2B). Mismatched heteroduplex molecules
are recognized and efficiently destroyed by the mismatch repair system. The mismatch repair system acts as a fidelity element, which determines the length of the minimal efficient-processing segment by imposing an increased rigor during the homology search catalyzed by the nucleoprotein filaments [32,33]. Even a single mismatch is sufficient for mismatch repair to inhibit recombination [34], and additional mismatches have a cumulative negative effect on recombination efficacy [33].
Therefore, the degree and distribution of DNA-sequence divergence between recombining molecules are structural parameters that influence recombination efficiency because they affect the activity of recombination enzymes and determine whether the anti-recombinagenic activity of the mismatch repair system is triggered. Studies of the effects of DNA-sequence divergence on the efficiency of homologous recombination show that there is an exponential decrease in recombination efficacy with a linear increase in DNA divergence between recombining molecules. This relationship results from an exponential decrease in the number of available minimal efficient-processing segments due to the requirements of DNA-recombinase proteins for the sequence identity. The slope of this relationship is determined by mismatch repair system activity, which alters minimal efficient-processing segment size.
This effect may therefore have a broad range of impacts on genome dynamics at short and longer time scales. The activity of enzymes that control fidelity of homologous recombination to maintain genome stability is based on the degree of DNA-sequence divergence, which therefore plays a central role in the process (Figure 2) [35]. Ectopic, intra- and inter-chromosomal recombination between repeated DNA sequences can lead to alterations in genome structure in prokaryotes and eukaryotes, whereas recombination between sequences at identical, allelic, positions on homologous chromosomes can result in the loss of heterozygosity in eukaryotes. Both of these chromosomal rearrangements must be avoided as much as possible. For example, it was shown that the expression of a recessive heterozygous mutation in an inbred, highly homozygous mouse, occurs at elevated frequency due to the loss of heterozygosity via mitotic recombination [36] (Figure 2). In hybrids between distantly related mouse strains, the loss of heterozygosity frequency is much lower than in inbred strains due to the divergence in DNA sequence between maternal and paternal alleles that affects the efficacy of mitotic recombination due to the activity of the mismatch repair system. A loss of heterozygosity can result in strong selective effects, as it allows recessive alleles to become homozygous and therefore have an impact on phenotype. This can turn out to be beneficial as it facilitates the selection of recessive beneficial mutations [37].
However, the exact same process can turn out to be very deleterious, for example in the case of a loss of heterozygosity at tumor suppressor genes, as the fitness gain at the cellular level results in cancer development [38].
Short Term Effect of Neutral Mutations on the Rates of Mutation and Loss of Heterozygosity
The variations in rates of mutation or loss of heterozygosity due to the presence of these mutations are substantial: up to 3-fold changes in loss of heterozygosity between inbred versus outbred mice [36], or up to 10-fold changes in mutation rates at neighboring sites [18]. Yet, the question that remains open is — under which evolutionary conditions do these changes in rates impact evolvability? The accumulation of synonymous mutations is a slow process, so when can these mutations matter? It seems that the relevant conditions are the same as the ones needed for cryptic genetic variation to have an impact on evolvability: populations with high diversity due to large population size, high mutation rate, or both. In these conditions, the large within-population genetic diversity will lead to variability, among different polymorphic sites, in the local mutation rates, which will contribute further to the increase of diversity. Consider for instance a bacterial species with up to 3% genomic divergence between strains in the conserved part of the genome. Assuming a mutation is affecting the mutation rate at the focal base and the two neighboring ones, the rates differ at 9% of the genome between two randomly sampled strains. When these diverged strains face a new challenge, such as an antibiotic treatment, the panel of mutations quantitatively accessible will be larger. Moreover, such a selection applied independently to different diverged populations may lead to the recruitment of different beneficial mutations for adaptation and contribute subsequently to further species diversification (Figure 3). These effects could be even larger for viral populations that have both high mutation rate and high population sizes. For the impact of mutations on loss-of-heterozygosity rate variation, we can focus on the evolution in the soma of an organism. Let us consider an allele at a site that may facilitate the evolution to cancer when homozygous. If the most recent common ancestor of the chromosomal fragment carrying that allele is recent, the two copies will be similar over a long stretch (except for the mutation of interest) and the loss of heterozygosity will be high, leading to a high risk of cancer. Alternatively if the most recent common ancestor is ancient, due to chance or to the individual resulting from an admixture between diverged populations as frequently observed in humans [39], the accumulated mutations will reduce the loss of heterozygosity and the cancer risk will be low (Figure 4). In a genome wide association study,
this variability in the rate of loss of heterozygosity could be large enough to suggest an association, or the lack of an association, between the allele and cancer depending on the genetic variability present in the population used.
Long-Term Effect of Mutations on Horizontal Gene Transfer and Speciation
The impact of neutral mutations may also affect quantitatively other important processes due to recombination, involving this time the recombination of diverged sequences coming from another organism. In bacteria for instance, DNA-sequence divergence participates in the control of horizontal gene transfer. The log-linear relationship between recombination and DNA-sequence divergence was observed for conjugal gene transfer in Gram-negative bacteria [40] and for transformation of Gram-positive Bacillus and Streptococcus species [41,42]. These barriers may play an important role in blocking the propagation of adaptive alleles between different strains of a same species, and between different species. For instance, it was shown that in E. coli the acquisition of the ‘high pathogenicity island’ from Yersinia pestis in one strain was followed later on by within-species horizontal transfers through homologous recombination of regions flanking the island [43]. The 3% divergence observed genome-wide between various E. coli strains may contribute to a differential propagation of this pathogenic island within the species.
In addition to providing a barrier to the integration of a focal foreign-DNA fragment, these mechanisms may also contribute to the long-term process of speciation, which is defined as the loss of gene flow between lineages. It has, for instance, been proposed that the anti-recombination effect of neutral polymorphism could be sufficient to drive sympatric speciation in bacteria, provided that the recombination rate is low enough [44,45]. Speciation requires the accumulation of functional incompatibilities between lineages to ultimately stop gene flow. Interestingly, this model of speciation does not require the build-up of any phenotypic differences between populations. The accumulation of neutral diversity may be enough to create long-lasting barriers to genetic exchange and the further accumulation of genetic diversity. A concrete example of how DNA-sequence divergence could participate in the establishment and maintenance of genetic barriers is provided by the impact of sequence divergence during meiosis in yeast. It was shown that crosses between two closely related yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus produce only 1% of viable spores, most of which give rise to cells with high frequencies of aneuploidy that produce small, slow-growing colonies [46]. Such sterility, observed in many interspecies
crosses, is a basis of the biological species definition in yeast taxonomy. However, crossing of
S. cerevisiae and S. paradoxus lacking functional mismatch repair systems resulted in
increased meiotic recombination, decreased chromosome non-disjunction and improved spore viability. These observations suggest that DNA divergence together with an anti-recombination activity of the mismatch repair system during meiosis can contribute towards the establishment of post-zygotic species barriers between eukaryotic species.
Apart from the aforementioned models of bacterial diversification that specifically focused on these mechanisms, few adaptive landscape models of speciation include the accumulated mutations as a post-zygotic barrier. A related metaphor is that of the ‘holey landscape’ [47]. From a model with a large number of loci prone to generate genetic incompatibilities, emerged a landscape qualitatively composed of a genotypic space comprised of high-fitness peaks in the middle of which were some low-fitness holes. Qualitatively, two populations may evolve neutrally on the high-fitness plateau and find themselves on opposite sides of a hole, in which case recombinants are selected against. Although the model is based on different underlying mechanisms and the involved time scales may be different, some of the consequences may be similar. More recently, some quantitative genetic models derived from Fisher geometric models [48] have studied the build-up of post-zygotic counter-selection of recombinants [49,50] through long-term adaptation or evolution under stabilizing selection of populations evolving in isolation from each other. Including the impact of neutral mutations on hybrid fecundity could be an interesting perspective.
Concluding Remarks
In this article, we have reviewed the evidence suggesting that neutral mutations contribute to evolvability though two non-exclusive ways. First, some mutations without any detectable effect on fitness due to the degeneracy of the genotype to phenotype to fitness map may change the phenotypes that are accessible through future mutations. Second, some mutations — completely independently of their impact on the selected phenotypes — have an impact on the molecular machinery of DNA replication, repair and recombination. This may result in important changes in the local mutation and recombination rates. Whereas the former has been the subject of intense research from evolutionary biologists over the last two decades, the latter has been largely the private turf of molecular geneticists. How changes in rates impact evolvability is a question that now deserves to be better integrated into the adaptive
landscape perspective at both the theoretical and experimental levels, to better quantify its importance in shaping evolutionary trajectories.
Acknowledgments
IM was supported by FRM Grant DBF20160635736, by Labex “Who am I?” Idex ANR-11-IDEX-0005-01 / ANR-11-LABX-0071-Idex-Sorbonne Paris and by ANR-18-CE35-007-02 grant. OT was funded by the ANR Gewiep: ANR-18-CE35-0005-01 grant and by FRM Grant EQU201903007848.
References
1. Wagner, A. (2008). Robustness and evolvability: a paradox resolved. Proc. R. Soc. B Biol. Sci. 275, 91–100.
2. Lenski, R.E., Barrick, J.E., and Ofria, C. (2006). Balancing robustness and evolvability. PLoS Biol. 4, e428.
3. de Visser, J.A., Hermisson, J., Wagner, G.P., Ancel Meyers, L., Bagheri-Chaichian, H., Blanchard, J.L., Chao, L., Cheverud, J.M., Elena, S.F., Fontana, W., et al. (2003). Perspective: Evolution and detection of genetic robustness. Evolution 57, 1959–1972.
4. Huynen, M.A., Stadler, P.F., and Fontana, W. (1996). Smoothness within ruggedness: the role of neutrality in adaptation. Proc. Natl. Acad. Sci. USA 93, 397–401.
5. Wilke, C.O. (2001). Adaptive evolution on neutral networks. Bull. Math. Biol. 63, 715–730.
6. Wright, S. (1932). The roles of mutation, inbreeding, cross-breeding and selection in evolution. Proc. Sixth Annu. Congr. Genet. 1, 356–366.
7. Hofacker, I.L., Fontana, W., Stadler, P.F., Bonhoeffer, L.S., Tacker, M., and Schuster, P. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte Für Chem.
125, 167–188.
8. Wagner, A. (2011). The molecular origins of evolutionary innovations. Trends Genet.
27, 397–410.
9. Zheng, J., Payne, J.L., and Wagner, A. (2019). Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. Science 365, 347–353.
10. Cambray, G., and Mazel, D. (2008). Synonymous genes explore different evolutionary landscapes. PLOS Genet. 4, e1000256.
11. Plotkin, J.B., Dushoff, J., and Fraser, H.B. (2004). Detecting selection using a single genome sequence of M. tuberculosis and P. falciparum. Nature 428, 942–945.
12. Dykhuizen, D.E., Dean, A.M., and Hartl, D.L. (1987). Metabolic flux and fitness. Genetics 115, 25–31.
13. Tokuriki, N., Stricher, F., Serrano, L., and Tawfik, D.S. (2008). How protein stability and new functions trade off. PLOS Comput. Biol. 4, e1000002.
14. Jacquier, H., Birgy, A., Nagard, H.L., Mechulam, Y., Schmitt, E., Glodt, J., Bercot, B., Petit, E., Poulain, J., Barnaud, G., et al. (2013). Capturing the mutational landscape of the beta-lactamase TEM-1. Proc. Natl. Acad. Sci. USA 110, 13067–13072.
15. Weinreich, D.M., Delaney, N.F., Depristo, M.A., and Hartl, D.L. (2006). Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114. 16. Payne, J.L., and Wagner, A. (2019). The causes of evolvability and their evolution. Nat. Rev. Genet. 20, 24–38.
17. Blake, R.D., Hess, S.T., and Nicholson-Tuell, J. (1992). The influence of nearest neighbors on the rate and pattern of spontaneous point mutations. J. Mol. Evol. 34, 189–200. 18. Sung, W., Ackerman, M.S., Gout, J.-F., Miller, S.F., Williams, E., Foster, P.L., and Lynch, M. (2015). Asymmetric context-dependent mutation patterns revealed through mutation–accumulation experiments. Mol. Biol. Evol. 32, 1672–1683.
19. Krawczak, M., Ball, E.V., and Cooper, D.N. (1998). Neighboring-nucleotide effects on the rates of germ-line single-base-pair substitution in human genes. Am. J. Hum. Genet.
63, 474–488.
20. Couce, A., Caudwell, L.V., Feinauer, C., Hindré, T., Feugeas, J.-P., Weigt, M., Lenski, R.E., Schneider, D., and Tenaillon, O. (2017). Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria. Proc. Natl. Acad. Sci. USA
114, E9026–E9035.
21. Radman, M., and Wagner, R. (1986). Mismatch repair in Escherichia coli. Annu. Rev. Genet. 20, 523–538.
22. Donigan, K.A., and Sweasy, J.B. (2009). Sequence context-specific mutagenesis and base excision repair. Mol. Carcinog. 48, 362–368.
23. Mendelman, L.V., Boosalis, M.S., Petruska, J., and Goodman, M.F. (1989). Nearest neighbor influences on DNA polymerase insertion fidelity. J. Biol. Chem. 264, 14415–14423. 24. Cai, Z., and Fix, D. (2002). Neighboring base identity affects N-ethyl-N-nitrosourea-induced mutagenesis in Escherichia coli. Mutat. Res. 508, 71–81.
25. Nakken, S., Rødland, E.A., and Hovig, E. (2010). Impact of DNA physical properties on local sequence bias of human mutation. Hum. Mutat. 31, 1316–1325.
26. Hoede, C., Denamur, E., and Tenaillon, O. (2006). Selection acts on DNA secondary structures to decrease transcriptional mutagenesis. PLoS Genet. 2, e176.
27. Wright, B.E., Reschke, D.K., Schmidt, K.H., Reimers, J.M., and Knight, W. (2003). Predicting mutation frequencies in stem–loop structures of derepressed genes: implications for evolution. Mol. Microbiol. 48, 429–441.
antibiotic resistance in Escherichia coli. Proc. R. Soc. B Biol. Sci. 264, 1287–1291.
29. Kowalczykowski, S.C. (2015). An overview of the molecular mechanisms of recombinational DNA repair. Cold Spring Harb. Perspect. Biol. 7, a016410.
30. Lee, J.Y., Terakawa, T., Qi, Z., Steinfeld, J.B., Redding, S., Kwon, Y., Gaines, W.A., Zhao, W., Sung, P., and Greene, E.C. (2015). Base triplet stepping by the Rad51/RecA family of recombinases. Science 349, 977–981.
31. Rubnitz, J., and Subramani, S. (1984). The minimum amount of homology required for homologous recombination in mammalian cells. Mol. Cell. Biol. 4, 2253–2258.
32. Sagi, D., Tlusty, T., and Stavans, J. (2006). High fidelity of RecA-catalyzed recombination: a watchdog of genetic diversity. Nucleic Acids Res. 34, 5021.
33. Vulic, M., Dionisio, F., Taddei, F., and Radman, M. (1997). Molecular keys to speciation: DNA polymorphism and the control of genetic exchange in enterobacteria. Proc. Natl. Acad. Sci. USA 94, 9763–9767.
34. Shen, P., and Huang, H.V. (1989). Effect of base pair mismatches on recombination via the RecBCD pathway. Mol. Gen. Genet. 218, 358–360.
35. Waldman, A.S. (2008). Ensuring the fidelity of recombination in mammalian chromosomes. BioEssays 30, 1163–1171.
36. Shao, C., Stambrook, P.J., and Tischfield, J.A. (2001). Mitotic recombination is suppressed by chromosomal divergence in hybrids of distantly related mouse strains. Nat. Genet. 28, 169–172.
37. Mandegar, M.A., and Otto, S.P. (2007). Mitotic recombination counteracts the benefits of genetic segregation. Proc. R. Soc. B Biol. Sci. 274, 1301–1307.
38. Ryland, G.L., Doyle, M.A., Goode, D., Boyle, S.E., Choong, D.Y.H., Rowley, S.M., Li, J., Australian Ovarian Cancer Study Group, Bowtell, D.D.L., Tothill, R.W., et al. (2015). Loss of heterozygosity: what is it good for? BMC Med. Genomics 8, 45.
39. Reich, D. (2018). Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past. (Oxford: Oxford University Press).
40. Delmas, S., and Matic, I. (2005). Cellular response to horizontally transferred DNA in
Escherichia coli is tuned by DNA repair systems. DNA Repair 4, 221–229.
41. Zawadzki, P., Roberts, M.S., and Cohan, F.M. (1995). The log-linear relationship between sexual isolation and sequence divergence in Bacillus transformation is robust. Genetics 140, 917–932.
42. Majewski, J., Zawadzki, P., Pickerill, P., Cohan, F.M., and Dowson, C.G. (2000). Barriers to genetic exchange between bacterial species: Streptococcus pneumoniae transformation. J. Bacteriol. 182, 1016–1023.
43. Schubert, S., Picard, B., Gouriou, S., Heesemann, J., and Denamur, E. (2002). Yersinia high-pathogenicity island contributes to virulence in Escherichia coli causing extraintestinal infections. Infect. Immun. 70, 5335–5337.
evolution and stability of bacterial species. Genetics 207, 281–295.
45. Falush, D., Torpdahl, M., Didelot, X., Conrad, D.F., Wilson, D.J., and Achtman, M. (2006). Mismatch induced speciation in Salmonella: model and data. Philos. Trans. R. Soc. B Biol. Sci. 361, 2045–2053.
46. Hunter, N., Chambers, S.R., Louis, E.J., and Borts, R.H. (1996). The mismatch repair system contributes to meiotic sterility in an interspecific yeast hybrid. EMBO J. 15, 1726– 1733.
47. Gavrilets, S. (1997). Evolution and speciation on holey adaptive landscapes. Trends Ecol. Evol. 12, 307–312.
48. Tenaillon, O. (2014). The utility of Fisher’s geometric model in evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 45, 179–201.
49. Fraïsse, C., Gunnarsson, P.A., Roze, D., Bierne, N., and Welch, J.J. (2016). The genetics of speciation: Insights from Fisher’s geometric model. Evolution 70, 1450–1464. 50. Chevin, L.-M., Decorzent, G., and Lenormand, T. (2014). Niche dimensionality and the genetics of ecological speciation. Evolution, 1244–1256.
Figure legends
Figure 1: Impact of synonymous mutations on evolvability.
A coding DNA sequence is mutated with two synonymous mutations shown in bold. Although these mutations do not affect the protein sequence, the first one (CGG to AGG) affects the amino acids that are now accessible through subsequent mutation (amino acids specific to the different synonymous codons are in bold) and the second one (CTA to CTC) affects the mutation rate at the next base due to a change in the local sequence pattern from AGT to CGT, which is much more mutagenic.
Figure 2: Impact of synonymous mutations on efficacy of double-strand break repair by homologous recombination.
(A) Double-strand break repair by homologous recombination can lead to potentially deleterious genome rearrangements, like deletions and loss of heterozygosity. (B) A synonymous mutation present in the region of a double-strand breaks can diminish efficacy of the double-strand repair because it affects the activity of recombination enzymes and triggers the anti-recombinagenic activity of mismatch-repair enzymes.
Figure 3: The accumulation of single nucleotide polymorphisms in genomes can affect the local mutation rate in a non-negligible fraction of the genome and lead to the recruitment of different mutations in the face of a new selective pressure.
Here, we show the genealogy of three sequences with the occurrence of mutations shown with coloured diamonds. The resulting sequences differ in their local mutation rate, as show by the height of the histograms, which are coloured according to the colour of the mutation leading to the local mutation rate changes. When an environmental change is applied independently to these different clones, these local rate changes may lead to the selection of different beneficial mutations illustrated by red triangles and drive further genome differentiation.
Figure 4: Sequence diversity between the two chromosomal copies of an individual may affect the rate of loss of heterozygosity and consequently cancer rate.
Here, we show the genealogy of the two gene copies of two diploid individuals. The two copies of individual A have a recent common ancestor and differ only by a single mutation the red triangle, whereas the two copies of individual B differ by many mutations as their common ancestor is much older. The difference in these coalescence times can be due to chance or to individual B resulting from an admixture of two previously isolated populations. The difference in similarity between the two copies leads to an important (up to three-fold) difference in the rate at which loss of heterozygosity occurs. If the red triangle mutation promotes a cancer when present in two copies, individual A is much more likely than B to develop a cancer even though both are identically heterozygous at the causal mutation.
In Brief
Neutral mutations can have an important long-term impact on the evolutionary fate of genomes both in terms of the type of evolutionary path that will be followed as well as in the rate at which changes will occur.
CCG CGG CAC CTA GTG
Arg Arg Arg Gln Pro Leu Gly ArgPro Arg His Leu Val
Arg Ser Ser Lys Thr Met Gly Arg Trp
CCG AGG CAC CTC GTG
Val
Pro Arg His Leu
Met Leu Leu Met Leu Leu
DNA sequence
Protein sequence
Mutationally
accessible
amino acids
Change
in spectrum
Change in the rate
at next base
Synonymous mutations
Trp
Repeat 1 Repeat 2 Parent 1 chromosome Parent 2 chromosome DSB DSB Deletion Recombination intermediate: Heteroduplex DNA Parent 1 chromosome Parent 2 chromosome Gene conversion DSB
Mismatch repair
ce
of
ne
ut
ra
lmu
ta
tio
ns
m m m Heterozygocity Loss of heterozygocity Recombination intermediate: Mismatched heteroduplex DNAm
Repeated identical DNA sequences
RecA/Rad51 RecA/Rad51 Crossover High efficiency Low efficiency
