Evolutionary coupling between the deleteriousness of gene mutations and the amount of non-coding sequences

A Rigorous Framework to Classify the Postduplication Fate of Paralogous Genes

Gene duplication has a central role in evolution; still, little is known on the fates of the duplicated copies, their relative frequency, and on how environmental conditions affect them. Moreover, the lack of rigorous definitions concerning the fate of duplicated genes hinders the development of a global vision of this process. In this paper, we present a new framework aiming at characterizing and formally differentiating the fate of duplicated genes. Our framework has been tested via simulations, where the evolution of populations has been simulated using aevol, an in silico experimental evolution platform. Our results show several patterns that confirm some of the conclusions from previous studies, while also exhibiting new tendencies; this may open up new avenues to better understand the role of duplications as a driver of evolution.

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.

The genotype-phenotype map of an evolving digital organism

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10¹⁴¹ genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

The phenotype of an organism comprises the set of morphological and functional traits encoded by its genome. In natural evolving systems, phenotypes are organized into mutationally connected networks of genotypes, which increase the likelihood for an evolving population to encounter novel adaptive phenotypes (i.e., its evolvability). We do not know whether artificial systems, such as self-replicating and evolving computer programs—digital organisms—are more or less evolvable than natural systems. By studying how genotypes map onto phenotypes in digital organisms, we characterize many commonalities between natural and artificial evolving systems. In addition, we show that phenotypic complexity can both facilitate and constrain evolution, which harbors lessons not only for designing evolvable artificial systems, but also for synthetic biology.

Evolution of Genome Size in Asexual Digital Organisms

Genome sizes have evolved to vary widely, from 250 bases in viroids to 670 billion bases in some amoebas. This remarkable variation in genome size is the outcome of complex interactions between various evolutionary factors such as mutation rate and population size. While comparative genomics has uncovered how some of these evolutionary factors influence genome size, we still do not understand what drives genome size evolution. Specifically, it is not clear how the primordial mutational processes of base substitutions, insertions, and deletions influence genome size evolution in asexual organisms. Here, we use digital evolution to investigate genome size evolution by tracking genome edits and their fitness effects in real time. In agreement with empirical data, we find that mutation rate is inversely correlated with genome size in asexual populations. We show that at low point mutation rate, insertions are significantly more beneficial than deletions, driving genome expansion and the acquisition of phenotypic complexity. Conversely, the high mutational load experienced at high mutation rates inhibits genome growth, forcing the genomes to compress their genetic information. Our analyses suggest that the inverse relationship between mutation rate and genome size is a result of the tradeoff between evolving phenotypic innovation and limiting the mutational load.

Shape matters: lifecycle of cooperative patches promotes cooperation in bulky populations

Natural cooperative systems take many forms, ranging from one-dimensional cyanobacteria arrays to fractal-like biofilms. We use in silico experimental systems to study a previously overlooked factor in the evolution of cooperation, physical shape of the population. We compare the emergence and maintenance of cooperation in populations of digital organisms that inhabit bulky (100 × 100 cells) or slender (4 × 2500) toroidal grids. Although more isolated subpopulations of secretors in a slender population could be expected to favor cooperation, we find the opposite: secretion evolves to higher levels in bulky populations. We identify the mechanistic explanation for the shape effect by analyzing the lifecycle and dynamics of cooperator patches, from their emergence and growth, to invasion by noncooperators and extinction. Because they are constrained by the population shape, the cooperator patches expand less in slender than in bulky populations, leading to fewer cooperators, less public good secretion, and generally lower cooperation. The patch dynamics and mechanisms of shape effect are robust across several digital cooperation systems and independent of the underlying basis for cooperation (public good secretion or a cooperation game). Our results urge for a greater consideration of population shape in the study of the evolution of cooperation across experimental and modeling systems.

Evolution of communication protocols using an artificial regulatory network

I describe the Utrecht Machine (UM), a discrete artificial regulatory network designed for studying how evolution discovers biochemical computation mechanisms. The corresponding binary genome format is compatible with gene deletion, duplication, and recombination. In the simulation presented here, an agent consisting of two UMs, a sender and a receiver, must encode, transmit, and decode a binary word over time using the narrow communication channel between them. This communication problem has chicken-and-egg structure in that a sending mechanism is useless without a corresponding receiving mechanism. An in-depth case study reveals that a coincidence creates a minimal partial solution, from which a sequence of partial sending and receiving mechanisms evolve. Gene duplications contribute by enlarging the regulatory network. Analysis of 60,000 sample runs under a variety of parameter settings confirms that crossover accelerates evolution, that stronger selection tends to find clumsier solutions and finds them more slowly, and that there is implicit selection for robust mechanisms and genomes at the codon level. Typical solutions associate each input bit with an activation speed and combine them almost additively. The parents of breakthrough organisms sometimes have lower fitness scores than others in the population, indicating that populations can cross valleys in the fitness landscape via outlying members. The simulation exhibits back mutations and population-level memory effects not accounted for in traditional population genetics models. All together, these phenomena suggest that new evolutionary models are needed that incorporate regulatory network structure.

Genetic architecture promotes the evolution and maintenance of cooperation

When cooperation has a direct cost and an indirect benefit, a selfish behavior is more likely to be selected for than an altruistic one. Kin and group selection do provide evolutionary explanations for the stability of cooperation in nature, but we still lack the full understanding of the genomic mechanisms that can prevent cheater invasion. In our study we used Aevol, an agent-based, in silico genomic platform to evolve populations of digital organisms that compete, reproduce, and cooperate by secreting a public good for tens of thousands of generations. We found that cooperating individuals may share a phenotype, defined as the amount of public good produced, but have very different abilities to resist cheater invasion. To understand the underlying genetic differences between cooperator types, we performed bio-inspired genomics analyses of our digital organisms by recording and comparing the locations of metabolic and secretion genes, as well as the relevant promoters and terminators. Association between metabolic and secretion genes (promoter sharing, overlap via frame shift or sense-antisense encoding) was characteristic for populations with robust cooperation and was more likely to evolve when secretion was costly. In mutational analysis experiments, we demonstrated the potential evolutionary consequences of the genetic association by performing a large number of mutations and measuring their phenotypic and fitness effects. The non-cooperating mutants arising from the individuals with genetic association were more likely to have metabolic deleterious mutations that eventually lead to selection eliminating such mutants from the population due to the accompanying fitness decrease. Effectively, cooperation evolved to be protected and robust to mutations through entangled genetic architecture. Our results confirm the importance of second-order selection on evolutionary outcomes, uncover an important genetic mechanism for the evolution and maintenance of cooperation, and suggest promising methods for preventing gene loss in synthetically engineered organisms.

In silico experimental evolution: a tool to test evolutionary scenarios

Comparative genomics has revealed that some species have exceptional genomes, compared to their closest relatives. For instance, some species have undergone a strong reduction of their genome with a drastic reduction of their genic repertoire. Deciphering the causes of these atypical trajectories can be very difficult because of the many phenomena that are intertwined during their evolution (e.g. changes of population size, environment structure and dynamics, selection strength, mutation rates...). Here we propose a methodology based on synthetic experiments to test the individual effect of these phenomena on a population of simulated organisms. We developed an evolutionary model--aevol--in which evolutionary conditions can be changed one at a time to test their effects on genome size and organization (e.g. coding ratio). To illustrate the proposed approach, we used aevol to test the effects of a strong reduction in the selection strength on a population of (simulated) bacteria. Our results show that this reduction of selection strength leads to a genome reduction of ~35% with a slight loss of coding sequences (~15% of the genes are lost--mainly those for which the contribution to fitness is the lowest). More surprisingly, under a low selection strength, genomes undergo a strong reduction of the noncoding compartment (~55% of the noncoding sequences being lost). These results are consistent with what is observed in reduced Prochlorococcus strains (marine cyanobacteria) when compared to close relatives.

Virtual genomes in flux: an interplay of neutrality and adaptability explains genome expansion and streamlining

The picture that emerges from phylogenetic gene content reconstructions is that genomes evolve in a dynamic pattern of rapid expansion and gradual streamlining. Ancestral organisms have been estimated to possess remarkably rich gene complements, although gene loss is a driving force in subsequent lineage adaptation and diversification. Here, we study genome dynamics in a model of virtual cells evolving to maintain homeostasis. We observe a pattern of an initial rapid expansion of the genome and a prolonged phase of mutational load reduction. Generally, load reduction is achieved by the deletion of redundant genes, generating a streamlining pattern. Load reduction can also occur as a result of the generation of highly neutral genomic regions. These regions can expand and contract in a neutral fashion. Our study suggests that genome expansion and streamlining are generic patterns of evolving systems. We propose that the complex genotype to phenotype mapping in virtual cells as well as in their biological counterparts drives genome size dynamics, due to an emerging interplay between adaptation, neutrality, and evolvability.

In search of lost trajectories: Recovering the diversification of transposable elements

Transposable elements (TEs) are DNA sequences that have the capacity to move and duplicate within genomes, and occasionally between them. They are present in almost all species and are especially prevalent in eukaryotes where they can account for most of the genomic content. As a result of their dynamics and their mere presence, TEs can profoundly shape genomes and gene expression. With the current pace of sequencing technology improvement, a rapidly increasing number of genomes, particularly from non-model species, are being sequenced. However, the complete annotation of these genomes and especially of the TEs they contain, still poses fundamental difficulties. In a recent article, we presented a combined method that automatically annotates TEs with accuracy and sensitivity, and takes their diversification dynamics into account in the de novo annotation process. Here, we further discuss several additional aspects of our results, notably in the light of our knowledge of TE dynamics and the conceptual model behind the TE detection algorithms currently in use. In addition, we propose a new approach that uses simulations to improve algorithm performance.

Eco-evolutionary dynamics, coding structure and the information threshold

Background

The amount of information that can be maintained in an evolutionary system of replicators is limited by genome length, the number of errors during replication (mutation rate) and various external factors that influence the selection pressure. To date, this phenomenon, known as the information threshold, has been studied (both genotypically and phenotypically) in a constant environment and with respect to maintenance (as opposed to accumulation) of information. Here we take a broader perspective on this problem by studying the accumulation of information in an ecosystem, given an evolvable coding structure. Moreover, our setup allows for individual based as well as ecosystem based solutions. That is, all functions can be performed by individual replicators, or complementing functions can be performed by different replicators. In this setup, where both the ecosystem and the individual genomes can evolve their structure, we study how populations cope with high mutation rates and accordingly how the information threshold might be alleviated.

Results

We observe that the first response to increased mutation rates is a change in coding structure. At moderate mutation rates evolution leads to longer genomes with a higher diversity than at high mutation rates. Thus, counter-intuitively, at higher mutation rates diversity is reduced and the efficacy of the evolutionary process is decreased. Therefore, moderate mutation rates allow for more degrees of freedom in exploring genotype space during the evolutionary trajectory, facilitating the emergence of solutions. When an individual based solution cannot be attained due to high mutation rates, spatial structuring of the ecosystem can accommodate the evolution of ecosystem based solutions.

Conclusions

We conclude that the evolutionary freedom (eg. the number of genotypes that can be reached by evolution) is increasingly restricted by higher mutation rates. In the case of such severe mutation rates that an individual based solution cannot be evolved, the ecosystem can take over and still process the required information forming ecosystem based solutions. We provide a proof of principle for species fulfilling the different roles in an ecosystem when single replicators can no longer cope with all functions simultaneously. This could be a first step in crossing the information threshold.

RNA relics and origin of life

A number of small RNA sequences, located in different non-coding sequences and highly preserved across the tree of life, have been suggested to be molecular fossils, of ancient (and possibly primordial) origin. On the other hand, recent years have revealed the existence of ubiquitous roles for small RNA sequences in modern organisms, in functions ranging from cell regulation to antiviral activity. We propose that a single thread can be followed from the beginning of life in RNA structures selected only for stability reasons through the RNA relics and up to the current coevolution of RNA sequences; such an understanding would shed light both on the history and on the present development of the RNA machinery and interactions. After presenting the evidence (by comparing their sequences) that points toward a common thread, we discuss a scenario of genome coevolution (with emphasis on viral infectious processes) and finally propose a plan for the reevaluation of the stereochemical theory of the genetic code; we claim that it may still be relevant, and not only for understanding the origin of life, but also for a comprehensive picture of regulation in present-day cells.

Robustness: confronting lessons from physics and biology

The term robustness is encountered in very different scientific fields, from engineering and control theory to dynamical systems to biology. The main question addressed herein is whether the notion of robustness and its correlates (stability, resilience, self-organisation) developed in physics are relevant to biology, or whether specific extensions and novel frameworks are required to account for the robustness properties of living systems. To clarify this issue, the different meanings covered by this unique term are discussed; it is argued that they crucially depend on the kind of perturbations that a robust system should by definition withstand. Possible mechanisms underlying robust behaviours are examined, either encountered in all natural systems (symmetries, conservation laws, dynamic stability) or specific to biological systems (feedbacks and regulatory networks). Special attention is devoted to the (sometimes counterintuitive) interrelations between robustness and noise. A distinction between dynamic selection and natural selection in the establishment of a robust behaviour is underlined. It is finally argued that nested notions of robustness, relevant to different time scales and different levels of organisation, allow one to reconcile the seemingly contradictory requirements for robustness and adaptability in living systems.

The topology of the protein network influences the dynamics of gene order: from systems biology to a systemic understanding of evolution

Abstract Systems biology invites us to consider the dynamic interactions between the components of a living cell. Here, by evolving artificial organisms whose genomes encode protein networks, we show that a coupling emerges at the evolutionary time scale between the protein network and the structure of the genome. Gene order is more stable when the protein network is more densely connected, which most likely results from a long-term selection for mutational robustness. Understanding evolving organisms thus requires a systemic approach, taking into account the functional interactions between gene products, but also the global relationships between the genome and the proteome at the evolutionary time scale.

A Long-Term Evolutionary Pressure on the Amount of Noncoding DNA

A significant part of eukaryotic noncoding DNA is viewed as the passive result of mutational processes, such as the proliferation of mobile elements. However, sequences lacking an immediate utility can nonetheless play a major role in the long-term evolvability of a lineage, for instance by promoting genomic rearrangements. They could thus be subject to an indirect selection. Yet, such a long-term effect is difficult to isolate either in vivo or in vitro. Here, by performing in silico experimental evolution, we demonstrate that, under low mutation rates, the indirect selection of variability promotes the accumulation of noncoding sequences: Even in the absence of self-replicating elements and mutational bias, noncoding sequences constituted an important fraction of the evolved genome because the indirectly selected genomes were those that were variable enough to discover beneficial mutations. On the other hand, high mutation rates lead to compact genomes, much like the viral ones, although no selective cost of genome size was applied: The indirectly selected genomes were those that were small enough for the genetic information to be reliably transmitted. Thus, the spontaneous evolution of the amount of noncoding DNA strongly depends on the mutation rate. Our results suggest the existence of an additional pressure on the amount of noncoding DNA, namely the indirect selection of an appropriate trade-off between the fidelity of the transmission of the genetic information and the exploration of the mutational neighborhood. Interestingly, this trade-off resulted robustly in the accumulation of noncoding DNA so that the best individual leaves one offspring without mutation (or only neutral ones) per generation.