Genomic mutation rates that neutralize adaptive evolution and natural selection

Continuous evolution of user-defined genes at 1-million-times the genomic mutation rate

When nature maintains or evolves a gene's function over millions of years at scale, it produces a diversity of homologous sequences whose patterns of conservation and change contain rich structural, functional, and historical information about the gene. However, natural gene diversity likely excludes vast regions of functional sequence space and includes phylogenetic and evolutionary eccentricities, limiting what information we can extract. We introduce an accessible experimental approach for compressing long-term gene evolution to laboratory timescales, allowing for the direct observation of extensive adaptation and divergence followed by inference of structural, functional, and environmental constraints for any selectable gene. To enable this approach, we developed a new orthogonal DNA replication (OrthoRep) system that durably hypermutates chosen genes at a rate of >10 -4 substitutions per base in vivo . When OrthoRep was used to evolve a conditionally essential maladapted enzyme, we obtained thousands of unique multi-mutation sequences with many pairs >60 amino acids apart (>15% divergence), revealing known and new factors influencing enzyme adaptation. The fitness of evolved sequences was not predictable by advanced machine learning models trained on natural variation. We suggest that OrthoRep supports the prospective and systematic discovery of constraints shaping gene evolution, uncovering of new regions in fitness landscapes, and general applications in biomolecular engineering.

Molecular evidence for the adaptive evolution in euryhaline bivalves

Marine bivalves inhabiting intertidal and estuarine areas are frequently exposed to salinity stress due to persistent rainfall and drought. Through prolonged adaptive evolution, numerous bivalves have developed eurysalinity, which are capable of tolerating a wide range of salinity fluctuations through the sophisticated regulation of physiological metabolism. Current research has predominantly focused on investigating the physiological responses of bivalves to salinity stress, leaving a significant gap in our understanding of the adaptive evolutionary characteristics in euryhaline bivalves. Here, comparative genomics analyses were performed in two groups of bivalve species, including 7 euryhaline species and 5 stenohaline species. We identified 24 significantly expanded gene families and 659 positively selected genes in euryhaline bivalves. A significant co-expansion of solute carrier family 23 (SLC23) facilitates the transmembrane transport of ascorbic acids in euryhaline bivalves. Positive selection of antioxidant genes, such as GST and TXNRD, augments the capacity of active oxygen species (ROS) scavenging under salinity stress. Additionally, we found that the positively selected genes were significantly enriched in KEGG pathways associated with carbohydrates, lipids and amino acids metabolism (ALDH, ADH, and GLS), as well as GO terms related to transmembrane transport and inorganic anion transport (SLC22, CLCND, and VDCC). Positive selection of MCT might contribute to prevent excessive accumulation of intracellular lactic acids during anaerobic metabolism. Positive selection of PLA2 potentially promote the removal of damaged membranes lipids under salinity stress. Our findings suggest that adaptive evolution has occurred in osmoregulation, ROS scavenging, energy metabolism, and membrane lipids adjustments in euryhaline bivalves. This study enhances our understanding of the molecular mechanisms underlying the remarkable salinity adaption of euryhaline bivalves.

Kimura's Theory of Non-Adaptive Radiation and Peto's Paradox: A Missing Link?

Karyotype diversity reflects genome integrity and stability. A strong correlation between karyotype diversity and species richness, meaning the number of species in a phylogenetic clade, was first reported in mammals over forty years ago: in mammalian phylogenetic clades, the standard deviation of karyotype diversity (KD) closely corresponded to species richness (SR) at the order level. These initial studies, however, did not control for phylogenetic signal, raising the possibility that the correlation was due to phylogenetic relatedness among species in a clade. Accordingly, karyotype diversity trivially reflects species richness simply as a passive consequence of adaptive radiation. A more recent study in mammals controlled for phylogenetic signals and established the correlation as phylogenetically independent, suggesting that species richness cannot, in itself, explain the observed corresponding karyotype diversity. The correlation is, therefore, remarkable because the molecular mechanisms contributing to karyotype diversity are evolutionarily independent of the ecological mechanisms contributing to species richness. Recently, it was shown in salamanders that the two processes generating genome size diversity and species richness were indeed independent and operate in parallel, suggesting a potential non-adaptive, non-causal but biologically meaningful relationship. KD depends on mutational input generating genetic diversity and reflects genome stability, whereas species richness depends on ecological factors and reflects natural selection acting on phenotypic diversity. As mutation and selection operate independently and involve separate and unrelated evolutionary mechanisms-there is no reason a priori to expect such a strong, let alone any, correlation between KD and SR. That such a correlation exists is more consistent with Kimura's theory of non-adaptive radiation than with ecologically based adaptive theories of macro-evolution, which are not excluded in Kimura's non-adaptive theory. The following reviews recent evidence in support of Kimura's proposal, and other findings that contribute to a wider understanding of the molecular mechanisms underlying the process of non-adaptive radiation.

Toward a theory of evolution as multilevel learning

We apply the theory of learning to physically renormalizable systems in an attempt to outline a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. It is shown that these cornerstone phenomena of biology emerge from the fundamental features of learning dynamics such as the existence of a loss function, which is minimized during learning. We then sketch the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back propagation) and predicting (forward propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution (in particular, the origin of life), have to be analyzed in the thermodynamic limit, which is described in detail in the paper by Vanchurin et al. [V. Vanchurin, Y. I. Wolf, E. V. Koonin, M. I. Katsnelson, Proc. Natl. Acad. Sci. U.S.A. 119, 10.1073/pnas.2120042119 (2022)].

Evolutionary innovation using EDGE, a system for localized elevated mutagenesis

Mutations arising across the whole genome can hinder the emergence of evolutionary innovation required for adaptation because many mutations are deleterious. This trade-off is overcome by elevated mutagenesis to localized loci. Examples include phase variation and diversity-generating retroelements. However, these mechanisms are rare in nature; and all have narrow mutational spectra limiting evolutionary innovation. Here, we engineer a platform of Experimental Designed Genic Evolution (EDGE) to study the potential for evolutionary novelty at a single locus. Experimental evolution with EDGE shows that bacterial resistance to a novel antibiotic readily evolves, provided that elevated mutagenesis is focused on a relevant gene. A model is proposed to account for the cost and benefit of such single loci to adaptation in a changing environment and explains their high mutation rates, limited innovation, and the rarity of localized mutagenesis in nature. Overall, our results suggest that localized mutation systems can facilitate continuing adaptive evolution without necessarily restricting the spectrum of mutations. EDGE has utility in dissecting the complex process of adaptation with its localized, efficient evolution.

The adaptive potential of circular DNA accumulation in ageing cells

Carefully maintained and precisely inherited chromosomal DNA provides long-term genetic stability, but eukaryotic cells facing environmental challenges can benefit from the accumulation of less stable DNA species. Circular DNA molecules lacking centromeres segregate randomly or asymmetrically during cell division, following non-Mendelian inheritance patterns that result in high copy number instability and massive heterogeneity across populations. Such circular DNA species, variously known as extrachromosomal circular DNA (eccDNA), microDNA, double minutes or extrachromosomal DNA (ecDNA), are becoming recognised as a major source of the genetic variation exploited by cancer cells and pathogenic eukaryotes to acquire drug resistance. In budding yeast, circular DNA molecules derived from the ribosomal DNA (ERCs) have been long known to accumulate with age, but it is now clear that aged yeast also accumulate other high-copy protein-coding circular DNAs acquired through both random and environmentally-stimulated recombination processes. Here, we argue that accumulation of circular DNA provides a reservoir of heterogeneous genetic material that can allow rapid adaptation of aged cells to environmental insults, but avoids the negative fitness impacts on normal growth of unsolicited gene amplification in the young population.

Mutation and Selection in Bacteria: Modelling and Calibration

Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection–mutation model has unobservable fitness parameters, and, to estimate them, we use an Approximate Bayesian Computation algorithm. The algorithms are illustrated using in vitro data for phase variable genes of Campylobacter jejuni.

High mutation rates limit evolutionary adaptation in Escherichia coli

Mutation is fundamental to evolution, because it generates the genetic variation on which selection can act. In nature, genetic changes often increase the mutation rate in systems that range from viruses and bacteria to human tumors. Such an increase promotes the accumulation of frequent deleterious or neutral alleles, but it can also increase the chances that a population acquires rare beneficial alleles. Here, we study how up to 100-fold increases in Escherichia coli's genomic mutation rate affect adaptive evolution. To do so, we evolved multiple replicate populations of asexual E. coli strains engineered to have four different mutation rates for 3000 generations in the laboratory. We measured the ability of evolved populations to grow in their original environment and in more than 90 novel chemical environments. In addition, we subjected the populations to whole genome population sequencing. Although populations with higher mutation rates accumulated greater genetic diversity, this diversity conveyed benefits only for modestly increased mutation rates, where populations adapted faster and also thrived better than their ancestors in some novel environments. In contrast, some populations at the highest mutation rates showed reduced adaptation during evolution, and failed to thrive in all of the 90 alternative environments. In addition, they experienced a dramatic decrease in mutation rate. Our work demonstrates that the mutation rate changes the global balance between deleterious and beneficial mutational effects on fitness. In contrast to most theoretical models, our experiments suggest that this tipping point already occurs at the modest mutation rates that are found in the wild.

Evolution with a seed bank: The population genetic consequences of microbial dormancy

Dormancy is a bet‐hedging strategy that allows organisms to persist through conditions that are suboptimal for growth and reproduction by entering a reversible state of reduced metabolic activity. Dormancy allows a population to maintain a reservoir of genetic and phenotypic diversity (i.e., a seed bank) that can contribute to the long‐term survival of a population. This strategy can be potentially adaptive and has long been of interest to ecologists and evolutionary biologists. However, comparatively little is known about how dormancy influences the fundamental evolutionary forces of genetic drift, mutation, selection, recombination, and gene flow. Here, we investigate how seed banks affect the processes underpinning evolution by reviewing existing theory, implementing novel simulations, and determining how and when dormancy can influence evolution as a population genetic process. We extend our analysis to examine how seed banks can alter macroevolutionary processes, including rates of speciation and extinction. Through the lens of population genetic theory, we can understand the extent that seed banks influence the evolutionary dynamics of microorganisms as well as other taxa.

Arresting Evolution

Evolution in the form of selective breeding has long been harnessed as a useful tool by humans. However, rapid evolution can also be a danger to our health and a stumbling block for biotechnology. Unwanted evolution can underlie the emergence of drug and pesticide resistance, cancer, and weeds. It makes live vaccines and engineered cells inherently unreliable and unpredictable, and therefore potentially unsafe. Yet, there are strategies that have been and can possibly be used to stop or slow many types of evolution. We review and classify existing population genetics-inspired methods for arresting evolution. Then, we discuss how genome editing techniques enable a radically new set of approaches to limit evolution.

Genetic drift, selection and the evolution of the mutation rate

As one of the few cellular traits that can be quantified across the tree of life, DNA-replication fidelity provides an excellent platform for understanding fundamental evolutionary processes. Furthermore, because mutation is the ultimate source of all genetic variation, clarifying why mutation rates vary is crucial for understanding all areas of biology. A potentially revealing hypothesis for mutation-rate evolution is that natural selection primarily operates to improve replication fidelity, with the ultimate limits to what can be achieved set by the power of random genetic drift. This drift-barrier hypothesis is consistent with comparative measures of mutation rates, provides a simple explanation for the existence of error-prone polymerases and yields a formal counter-argument to the view that selection fine-tunes gene-specific mutation rates.

Dynamics and Fate of Beneficial Mutations Under Lineage Contamination by Linked Deleterious Mutations

Beneficial mutations drive adaptive evolution, yet their selective advantage does not ensure their fixation. Haldane's application of single-type branching process theory showed that genetic drift alone could cause the extinction of newly arising beneficial mutations with high probability. With linkage, deleterious mutations will affect the dynamics of beneficial mutations and might further increase their extinction probability. Here, we model the lineage dynamics of a newly arising beneficial mutation as a multitype branching process. Our approach accounts for the combined effects of drift and the stochastic accumulation of linked deleterious mutations, which we call lineage contamination We first study the lineage-contamination phenomenon in isolation, deriving dynamics and survival probabilities (the complement of extinction probabilities) of beneficial lineages. We find that survival probability is zero when [Formula: see text] where U is deleterious mutation rate and [Formula: see text] is the selective advantage of the beneficial mutation in question, and is otherwise depressed below classical predictions by a factor bounded from below by [Formula: see text] We then put the lineage contamination phenomenon into the context of an evolving population by incorporating the effects of background selection. We find that, under the combined effects of lineage contamination and background selection, ensemble survival probability is never zero but is depressed below classical predictions by a factor bounded from below by [Formula: see text] where [Formula: see text] is mean selective advantage of beneficial mutations, and [Formula: see text] This factor, and other bounds derived from it, are independent of the fitness effects of deleterious mutations. At high enough mutation rates, lineage contamination can depress fixation probabilities to values that approach zero. This fact suggests that high mutation rates can, perhaps paradoxically, (1) alleviate competition among beneficial mutations, or (2) potentially even shut down the adaptive process. We derive critical mutation rates above which these two events become likely.

Two sides of the same coin: A population genetics perspective on lethal mutagenesis and mutational meltdown

The extinction of RNA virus populations upon application of a mutagenic drug is frequently referred to as evidence for the existence of an error threshold, above which the population cannot sustain the mutational load. To explain the extinction process after reaching this threshold, models of lethal mutagenesis have been proposed, in which extinction is described as a deterministic (and thus population size-independent) process. As a separate body of literature, the population genetics community has developed models of mutational meltdown, which focus on the stochastic (and thus population-size dependent) processes governing extinction. However, recent extensions of both models have blurred these boundaries. Here, we first clarify definitions in terms of assumptions, expectations, and relevant parameter spaces, and then assess similarities and differences. As concepts from both fields converge, we argue for a unified theoretical framework that is focused on the evolutionary processes at play, rather than dispute over terminology.

RNA Recombination Enhances Adaptability and Is Required for Virus Spread and Virulence

Mutation and recombination are central processes driving microbial evolution. A high mutation rate fuels adaptation but also generates deleterious mutations. Recombination between two different genomes may resolve this paradox, alleviating effects of clonal interference and purging deleterious mutations. Here we demonstrate that recombination significantly accelerates adaptation and evolution during acute virus infection. We identified a poliovirus recombination determinant within the virus polymerase, mutation of which reduces recombination rates without altering replication fidelity. By generating a panel of variants with distinct mutation rates and recombination ability, we demonstrate that recombination is essential to enrich the population in beneficial mutations and purge it from deleterious mutations. The concerted activities of mutation and recombination are key to virus spread and virulence in infected animals. These findings inform a mathematical model to demonstrate that poliovirus adapts most rapidly at an optimal mutation rate determined by the trade-off between selection and accumulation of detrimental mutations.

A comparative study on karyotypic diversification rate in mammals

Chromosomal rearrangements have a relevant role in organismic evolution. However, little is known about the mechanisms that lead different phylogenetic clades to have different chromosomal rearrangement rates. Here, we investigate the causes behind the wide karyotypic diversity exhibited by mammals. In particular, we analyzed the role of metabolic, reproductive, biogeographic and genomic characteristics on the rates of macro- and microstructural karyotypic diversification (rKD) using comparative phylogenetic methods. We found evidence that reproductive characteristics such as larger litter size per year and longevity, by allowing a higher number of meioses in absolute time, favor a higher probability of chromosomal change. Furthermore, families with large geographic distributions but containing species with restricted geographic ranges showed a greater probability of fixation of macrostructural chromosomal changes in different geographic areas. Finally, rKD does not evolve by Brownian motion because the mutation rate depends on the concerted evolution of repetitive sequences. The decisive factors of rKD evolution will be natural selection, genetic drift and meiotic drive that will eventually allow or not the fixation of the rearrangements. Our results indicate that mammalian karyotypic diversity is influenced by historical and adaptive mechanisms where reproductive and genomic factors modulate the rate of chromosomal change.

Empirical complexities in the genetic foundations of lethal mutagenesis

From population genetics theory, elevating the mutation rate of a large population should progressively reduce average fitness. If the fitness decline is large enough, the population will go extinct in a process known as lethal mutagenesis. Lethal mutagenesis has been endorsed in the virology literature as a promising approach to viral treatment, and several in vitro studies have forced viral extinction with high doses of mutagenic drugs. Yet only one empirical study has tested the genetic models underlying lethal mutagenesis, and the theory failed on even a qualitative level. Here we provide a new level of analysis of lethal mutagenesis by developing and evaluating models specifically tailored to empirical systems that may be used to test the theory. We first quantify a bias in the estimation of a critical parameter and consider whether that bias underlies the previously observed lack of concordance between theory and experiment. We then consider a seemingly ideal protocol that avoids this bias-mutagenesis of virions-but find that it is hampered by other problems. Finally, results that reveal difficulties in the mere interpretation of mutations assayed from double-strand genomes are derived. Our analyses expose unanticipated complexities in testing the theory. Nevertheless, the previous failure of the theory to predict experimental outcomes appears to reside in evolutionary mechanisms neglected by the theory (e.g., beneficial mutations) rather than from a mismatch between the empirical setup and model assumptions. This interpretation raises the specter that naive attempts at lethal mutagenesis may augment adaptation rather than retard it.