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.
Misevic et al., Int. Conf. Artif. Life, 2012<Best Paper Award>
Effects of public good properties on the evolution of cooperation
Cooperation is a still unsolved and ever-controversial topic in evolutionary biology. Why do organisms engage in activities with long-term communal benefits but short-term individual cost? A general answer remains elusive, suggesting many important factors must still be examined and better understood. Here we study cooperation based on the secretion of a public good molecule using Aevol, a digital platform inspired by microbial cooperation systems. Specifically, we focus on the environmental and physical properties of the public good itself, its mobility, durability, and cost. The intensity of cooperation that evolves in our digital populations, as measured by the amount of the public good molecule organisms secrete, strongly depends on the properties of such a molecule. Specifically, and somewhat counter intuitively, digital organisms evolve to secrete more when public good degrades or diffuses quickly. The evolution of secretion also depends on the interactions between the population structure and public good properties, not just their individual values. Environmental factors affecting population diversity have been extensively studied in the past, but here we show that physical aspects of the cooperation mechanism itself may be equally if not more important. Given the wide range of substrates and environments that support microbial cooperation in nature, our results highlight the need for careful consideration of public good properties when studying the evolution of cooperation in bacterial or computational models.
Robustness and evolvability of cooperation
Robustness and evolvability are indirectly selected properties of biological systems that still play a significant role in determining evolutionary trajectories. Understanding such second order evolution is even more challenging when considering traits related to cooperation, as the evolution of cooperation itself is governed by indirect selection. To examine the robustness and evolvability of cooperation, we used an agent-based model of digital evolution, Aevol. In Aevol individuals capable of cooperating via costly public good secretion evolve for thousands of generations in a classical tragedy of the commons scenario. We varied the cost of secreting the public good molecule between and within individual experiments and constructed and evaluated millions of mutants to quantify the organisms' position in the fitness landscape. Populations initially evolved at different regimes selecting against secretion, and then continued the evolution at a reasonably low cost of secretion. The populations that experienced a very strong selection against cooperation evolved less secretion than the ones that initially experienced a less drastic selection against cooperation via a high secretion cost. The mutational analysis revealed a correlation between the number of mutants with increased secretion and the secretion level across all costs of secretion. We also evolved several clones of each population to highlight a strong effect of history in general on cooperation. Our work shows that the history of cooperative interactions has an effect on evolutionary dynamics, a result likely to be relevant in any cooperative systems that are frequently experiencing changes in cost and benefit of cooperation.
Indirect Selection in Darwinian Evolution:
Mechanisms and Implications
Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness?
In the past few years, numerous research projects have focused on identifying and understanding scaling properties in the gene content of prokaryotes genomes and the intricacy of their regulation networks. Yet, and despite the increasing amount of data available, the origins of these scalings remain an open question. The RAevol model, a digital genetics model, provides us with an insight into the mechanisms involved in an evolutionary process. The results we present here show that (i ) our model reproduces qualitatively these scaling laws and that (ii ) these laws are not due to differences in lifestyles but to differences in the spontaneous rates of mutations and rearrangements. We argue that this is due to an indirect selective pressure for robustness that constrains the genome size.
Importance of the Rearrangement Rates on the Organization of Genome Transcription
The organization of genomes shows striking differences among the different life forms. These differences come along with important variations in the way genomes are transcribed, operon structures being frequent in short genomes and the exception in large ones, while ncRNAs are frequent in large genomes but rare in short ones. Here, we use the digital genetics model «aevol» to explore the influence of the mutation rates on these structures, showing that their diversity can be accurately reproduced when varying the rearrangement rate. This result points us to the mutational burden hypothesis as one of the main explanation. In this view, a specific level of mutational robustness indirectly leads to genome and transcriptome streamlining.
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.
Structuration des génomes par sélection indirecte de la variabilité mutationnelle,
une approche de modélisation et de simulation