Evolution of drift robustness in small populations

Self-replicating artificial neural networks give rise to universal evolutionary dynamics

In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can naturally emerge in a self-replicator model when both selection and mutation are implicit and endogenous. We therefore suggest that SeRANN can be applied to explore and test various evolutionary dynamics and hypotheses.

Modelling speciation: Problems and implications

Darwin's and Wallace's 1859 explanation that novel speciation resulted from natural variants that had been subjected to selection was refined over the next 150 years as genetic inheritance and the importance of mutation-induced change were discovered, the quantitative theory of evolutionary population genetics was produced, the speed of genetic change in small populations became apparent and the ramifications of the DNA revolution became clear. This paper first discusses the modern view of speciation in its historical context. It then uses systems-biology approaches to consider the many complex processes that underpin the production of a new species; these extend in scale from genes to populations with the processes of variation, selection and speciation being affected by factors that range from mutation to climate change. Here, events at a particular scale level (e.g. protein network activity) are activated by the output of the level immediately below (i.e. gene expression) and generate a new output that activates the layer above (e.g. embryological development), with this change often being modulated by feedback from higher and lower levels. The analysis shows that activity at each level in the evolution of a new species is marked by stochastic activity, with mutation of course being the key step for variation. The paper examines events at each of these scale levels and particularly considers how the pathway by which mutation leads to phenotypic variants and the wide range of factors that drive selection can be investigated computationally. It concludes that, such is the complexity of speciation, most steps in the process are currently difficult to model and that predictions about future speciation will, apart from a few special cases, be hard to make. The corollary is that opportunities for novel variants to form are maximised.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

Low protein expression enhances phenotypic evolvability by intensifying selection on folding stability

Protein abundance affects the evolution of protein genotypes, but we do not know how it affects the evolution of protein phenotypes. Here we investigate the role of protein abundance in the evolvability of green fluorescent protein (GFP) towards the novel phenotype of cyan fluorescence. We evolve GFP in E. coli through multiple cycles of mutation and selection and show that low GFP expression facilitates the evolution of cyan fluorescence. A computational model whose predictions we test experimentally helps explain why: lowly expressed proteins are under stronger selection for proper folding, which facilitates their evolvability on short evolutionary time scales. The reason is that high fluorescence can be achieved by either few proteins that fold well or by many proteins that fold less well. In other words, we observe a synergy between a protein's scarcity and its stability. Because many proteins meet the essential requirements for this scarcity-stability synergy, it may be a widespread mechanism by which low expression helps proteins evolve new phenotypes and functions.

Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System

Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such transitions have profoundly shaped natural evolutionary history and occur in two forms: fraternal transitions involve lower-level entities that are kin (e.g., transitions to multicellularity or to eusocial colonies), while egalitarian transitions involve unrelated individuals (e.g., the origins of mitochondria). The necessary conditions and evolutionary mechanisms for these transitions to arise continue to be fruitful targets of scientific interest. Here, we examine a range of fraternal transitions in populations of open-ended self-replicating computer programs. These digital cells were allowed to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enabled preferential communication and cooperation between cells. We repeatedly observed group-level traits that are characteristic of a fraternal transition. These included reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. We report eight case studies from replicates where transitions occurred and explore the diverse range of adaptive evolved multicellular strategies.

Why are viral genomes so fragile? The bottleneck hypothesis

If they undergo new mutations at each replication cycle, why are RNA viral genomes so fragile, with most mutations being either strongly deleterious or lethal? Here we provide theoretical and numerical evidence for the hypothesis that genetic fragility is partly an evolutionary response to the multiple population bottlenecks experienced by viral populations at various stages of their life cycles. Modelling within-host viral populations as multi-type branching processes, we show that mutational fragility lowers the rate at which Muller’s ratchet clicks and increases the survival probability through multiple bottlenecks. In the context of a susceptible-exposed-infectious-recovered epidemiological model, we find that the attack rate of fragile viral strains can exceed that of more robust strains, particularly at low infectivities and high mutation rates. Our findings highlight the importance of demographic events such as transmission bottlenecks in shaping the genetic architecture of viral pathogens.

Given that most mutations are deleterious, high mutation rates carry a significant evolutionary cost. To reduce this burden, an obvious evolutionary solution would be to reduce the fitness cost of mutations by becoming more robust; this solution is indeed selected in populations of constantly large size. Here, we show that when populations regularly experience bottlenecks, as viruses do upon transmission to a new host, a less obvious solution becomes more viable: namely, to increase the fitness cost of mutations so that unfit mutants are less likely to fix at each passage. This could explain why viruses—especially RNA viruses—do in fact have very fragile genomes.

Universal Constraints on Protein Evolution in the Long-Term Evolution Experiment with Escherichia coli

Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski's long-term evolution experiment with Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anticorrelates with mRNA abundance, protein abundance, and degree of protein-protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly.

Changes in Cell Size and Shape during 50,000 Generations of Experimental Evolution with Escherichia coli

Bacteria adopt a wide variety of sizes and shapes, with many species exhibiting stereotypical morphologies. How morphology changes, and over what timescales, is less clear. Previous work examining cell morphology in an experiment with Escherichia coli showed that populations evolved larger cells and, in some cases, cells that were less rod-like. That experiment has now run for over two more decades. Meanwhile, genome sequence data are available for these populations, and new computational methods enable high-throughput microscopic analyses. In this study, we measured stationary-phase cell volumes for the ancestor and 12 populations at 2,000, 10,000, and 50,000 generations, including measurements during exponential growth at the last time point. We measured the distribution of cell volumes for each sample using a Coulter counter and microscopy, the latter of which also provided data on cell shape. Our data confirm the trend toward larger cells while also revealing substantial variation in size and shape across replicate populations. Most populations first evolved wider cells but later reverted to the ancestral length-to-width ratio. All but one population evolved mutations in rod shape maintenance genes. We also observed many ghost-like cells in the only population that evolved the novel ability to grow on citrate, supporting the hypothesis that this lineage struggles with maintaining balanced growth. Lastly, we show that cell size and fitness remain correlated across 50,000 generations. Our results suggest that larger cells are beneficial in the experimental environment, while the reversion toward ancestral length-to-width ratios suggests partial compensation for the less favorable surface area-to-volume ratios of the evolved cells.IMPORTANCE Bacteria exhibit great morphological diversity, yet we have only a limited understanding of how their cell sizes and shapes evolve and of how these features affect organismal fitness. This knowledge gap reflects, in part, the paucity of the fossil record for bacteria. In this study, we revived and analyzed samples extending over 50,000 generations from 12 populations of experimentally evolving Escherichia coli to investigate the relation between cell size, shape, and fitness. Using this "frozen fossil record," we show that all 12 populations evolved larger cells concomitant with increased fitness, with substantial heterogeneity in cell size and shape across the replicate lines. Our work demonstrates that cell morphology can readily evolve and diversify, even among populations living in identical environments.

An interplay of resource availability, population size and mutation rate potentiates the evolution of metabolic signaling

Background

Asexually reproducing populations of single cells evolve through mutation, natural selection, and genetic drift. Environmental conditions in which the evolution takes place define the emergent fitness landscapes. In this work, we used Avida—a digital evolution framework—to uncover a hitherto unexplored interaction between mutation rates, population size, and the relative abundance of metabolizable resources, and its effect on evolutionary outcomes in small populations of digital organisms.

Results

Over each simulation, the population evolved to one of several states, each associated with a single dominant phenotype with its associated fitness and genotype. For a low mutation rate, acquisition of fitness by organisms was accompanied with, and dependent on, an increase in rate of genomic replication. At an increased mutation rate, phenotypes with high fitness values were similarly achieved through enhanced genome replication rates. In addition, we also observed the frequent emergence of suboptimal fitness phenotype, wherein neighboring organisms signaled to each other information relevant to performing metabolic tasks. This metabolic signaling was vital to fitness acquisition and was correlated with greater genotypic and phenotypic heterogeneity in the population. The frequency of appearance of signaling populations increased with population size and with resource abundance.

Conclusions

Our results reveal a minimal set of environment–genotype interactions that lead to the emergence of metabolic signaling within evolving populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01782-0.

A comparative analysis of SLA-DRB1 genetic diversity in Colombian (creoles and commercial line) and worldwide swine populations

Analysing pig class II mayor histocompatibility complex (MHC) molecules is mainly related to antigen presentation. Identifying frequently-occurring alleles in pig populations is an important aspect to be considered when developing peptide-based vaccines. Colombian creole pig populations have had to adapt to local conditions since entering Colombia; a recent census has shown low amounts of pigs which is why they are considered protected by the Colombian government. Commercial hybrids are more attractive regarding production. This research has been aimed at describing the allele distribution of Colombian pigs from diverse genetic backgrounds and comparing Colombian SLA-DRB1 locus diversity to that of internationally reported populations. Twenty SLA-DRB1 alleles were identified in the six populations analysed here using sequence-based typing. The amount of alleles ranged from six (Manta and Casco Mula) to nine (San Pedreño). Only one allele (01:02) having > 5% frequency was shared by all three commercial line populations. Allele 02:01:01 was shared by five populations (around > 5% frequency). Global F_ST indicated that pig populations were clearly structured, as 20.6% of total allele frequency variation was explained by differences between populations (F_ST = 0.206). This study’s results confirmed that the greatest diversity occurred in wild boars, thereby contrasting with low diversity in domestic pig populations.

Canalization and Robustness in Human Genetics and Disease

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.

Moderate Amounts of Epistasis are Not Evolutionarily Stable in Small Populations

High mutation rates select for the evolution of mutational robustness where populations inhabit flat fitness peaks with little epistasis, protecting them from lethal mutagenesis. Recent evidence suggests that a different effect protects small populations from extinction via the accumulation of deleterious mutations. In drift robustness, populations tend to occupy peaks with steep flanks and positive epistasis between mutations. However, it is not known what happens when mutation rates are high and population sizes are small at the same time. Using a simple fitness model with variable epistasis, we show that the equilibrium fitness has a minimum as a function of the parameter that tunes epistasis, implying that this critical point is an unstable fixed point for evolutionary trajectories. In agent-based simulations of evolution at finite mutation rate, we demonstrate that when mutations can change epistasis, trajectories with a subcritical value of epistasis evolve to decrease epistasis, while those with supercritical initial points evolve towards higher epistasis. These two fixed points can be identified with mutational and drift robustness, respectively.

Cell-line specific RNA editing patterns in Trypanosoma brucei suggest a unique mechanism to generate protein variation in a system intolerant to genetic mutations

Trypanosoma brucei possesses a highly complex RNA editing system that uses guide RNAs to direct the insertion and deletion of uridines in mitochondrial mRNAs. These changes extensively alter the target mRNAs and can more than double them in length. Recently, analyses showed that several of the edited genes possess the capacity to encode two different protein products. The overlapped reading frames can be accessed through alternative RNA editing that shifts the translated reading frame. In this study, we analyzed the editing patterns of three putative dual-coding genes, ribosomal protein S12 (RPS12), the 5′ editing domain of NADH dehydrogenase subunit 7 (ND7 5′), and C-rich region 3 (CR3). We found evidence that alternatively 5′-edited ND7 5′ and CR3 transcripts are present in the transcriptome, providing evidence for the use of dual ORFs in these transcripts. Moreover, we found that CR3 has a complex set of editing pathways that vary substantially between cell lines. These findings suggest that alternative editing can work to introduce genetic variation in a system that selects against nucleotide mutations.

Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure

Background

Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don’t take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions.

Results

Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes.

Conclusion

Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature.

Why and how to study genetic changes with context-dependent effects

The phenotypic impacts of a genetic change can depend on genetic background (e.g. epistasis), as well as other contexts including environment, developmental stage, cell type, disease state, and higher-order combinations thereof. Recent advances in high-throughput phenotyping are uncovering examples of context dependence faster than genotype-phenotype maps and other core concepts are changing to reflect the dynamic nature of biological systems. Here, we review several approaches to study context dependence and their findings. In our opinion, these findings encourage more studies that examine the spectrum of effects a genetic change may have, as opposed to studies that exclusively measure the impact of a genetic change in a particular context. Studies that elucidate the mechanisms that cause the effects of genetic change to vary with context are of special interest. Previous studies of the mechanisms underlying context dependence have improved predictions of phenotype from genotype and have provided insight about how biological systems function and evolve.

Examining Community Stability in the Face of Mass Extinction in Communities of Digital Organisms

Digital evolution is a computer-based instantiation of Darwinian evolution in which short self-replicating computer programs compete, mutate, and evolve. It is an excellent platform for addressing topics in long-term evolution and paleobiology, such as mass extinction and recovery, with experimental evolutionary approaches. We evolved model communities with ecological interdependence among community members, which were subjected to two principal types of mass extinction: a pulse extinction that killed randomly, and a selective press extinction involving an alteration of the abiotic environment to which the communities had to adapt. These treatments were applied at two different strengths, along with unperturbed control experiments. We examined how stability in the digital communities was affected from the perspectives of division of labor, relative shift in rank abundance, and genealogical connectedness of the community's component ecotypes. Mass extinction that was due to a Strong Press treatment was most effective in producing reshaped communities that differed from the pre-treatment ones in all of the measured perspectives; weaker versions of the treatments did not generally produce significant departures from a Control treatment; and results for the Strong Pulse treatment generally fell between those extremes. The Strong Pulse treatment differed from others in that it produced a slight but detectable shift towards more generalized communities. Compared to Press treatments, Pulse treatments also showed a greater contribution from re-evolved ecological doppelgangers rather than new ecotypes. However, relatively few Control communities showed stability in any of these metrics over the whole course of the experiment, and most did not represent stable states (by some measure of stability) that were disrupted by the extinction treatments. Our results have interesting, broad qualitative parallels with findings from the paleontological record, and show the potential of digital evolution studies to illuminate many aspects of mass extinction and recovery by addressing them in a truly experimental manner.

Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest

Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore fitness through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to flat genotypes (with low fitness but high robustness) outcompeting fit genotypes (with high fitness but low robustness)-a phenomenon known as survival of the flattest. While this effect was previously explored using the digital evolution system Avida, a complete analysis of the local fitness landscapes of fit and flat genotypes has been lacking, leading to uncertainty about the genetic basis of the survival-of-the-flattest effect. Here, we repeated the survival-of-the-flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that the flat genotypes, compared to the fit genotypes, had a reduced likelihood of deleterious mutations as well as an increased likelihood of neutral and, surprisingly, of lethal mutations. This trend holds for mutants one to four substitutions away from the wild-type sequence. We also found that flat genotypes have, on average, no epistasis between mutations, while fit genotypes have, on average, positive epistasis. Our results demonstrate that the genetic causes of mutational robustness on complex fitness landscapes are multifaceted. While the traditional idea of the survival of the flattest emphasized the evolution of increased neutrality, others have argued for increased mutational sensitivity in response to strong mutational loads. Our results show that both increased neutrality and increased lethality can lead to the evolution of mutational robustness. Furthermore, strong negative epistasis is not required for mutational sensitivity to lead to mutational robustness. Overall, these results suggest that mutational robustness is achieved by minimizing heritable deleterious variation.

Admixture on the northern front: population genomics of range expansion in the white-footed mouse (Peromyscus leucopus) and secondary contact with the deer mouse (Peromyscus maniculatus)

Range expansion has genetic consequences expected to result in differentiated wave-front populations with low genetic variation and potentially introgression from a local species. The northern expansion of Peromyscus leucopus in southern Quebec provides an opportunity to test these predictions using population genomic tools. Our results show evidence of recent and post-glacial expansion. Genome-wide variation in P. leucopus indicates two post-glacial lineages are separated by the St. Lawrence River, with a more recent divergence of populations isolated by the Richelieu River. In two of three transects we documented northern populations with low diversity in at least one genetic measure, although most relationships were not significant. Consistent with bottlenecks and allele surfing during northward expansion, we document a northern-most population with low nucleotide diversity, divergent allele frequencies and the most private alleles, and observed heterozygosity indicates outcrossing. Ancestry proportions revealed putative hybrids of P. leucopus and P. maniculatus. A formal test for gene flow confirmed secondary contact, showing that a reticulate population phylogeny between P. maniculatus and P. leucopus was a better fit to the data than a bifurcating model without gene flow. Thus, we provide the first genomic evidence of gene flow between this pair of species in natural populations. Understanding the evolutionary consequences of secondary contact is an important conservation concern as climate-induced range expansions are expected to result in new hybrid zones between closely related species.