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

Abstract

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.

Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness?

“…Indeed, such a null experiment is necessary to test the classical idea that the environmental complexity is a major determinant of the complexity of the network. We have already shown that in Aevol the complexity of the genomes is strongly determined by the mutation rate (Knibbe et al, 2007a). We hence conceived this null experiment to test whether this pressure is strong enough to influence the complexity of the networks too, even in a simple environment.…”

“…These differences are due to robustness and evolvability constraints: large genomes cannot be maintained when organisms face high rearrangement rates. On the opposite, under low rates, large genomes are more evolvable (see Knibbe et al, 2007a and Section 4). On each figure the circle represents the whole genome (scale is different on each figure) while the gray arcs represent the coding regions.…”

“…This is what we observed with the Aevol model in which proteins had no regulatory activity. We showed that this scaling was the consequence of an indirect selection of lin- eages whose genomic structure allows for an appropriate trade-off between robustness and evolvability (Knibbe et al, 2007a(Knibbe et al, ,b, 2008. If the per-base mutation rate is high, large genomes with many genes cannot maintain their fitness due to the mutational load they undergo.…”

“…To study the evolution of the structure of genomes and gene networks, we have developed an integrated model, RAevol (Regulatory-Aevol).This model extends the Aevol model (Artificial evolution), previously developed in our team to study robustness and evolvability in artificial organisms (Knibbe et al, 2007a(Knibbe et al, ,b, 2008. We provide here an overview of the RAevol model.…”

Evolving Instrument Based on Symbiont-Host Metaphor: a Commensal Computation

“…Such an individual will be a symbiont in the commensal architecture, and the cluster centers that are encoded by its genome will be a set of canonical moves. Chameleoclust incorporates different bio-inspired features from the in silico experimental evolution formalisms of (Knibbe, Coulon, Mazet, Fayard, & Beslon, 2007) and (Crombach & Hogeweg, 2007), such as a variable genome length, both functional and nonfunctional genes, and mutation operators including large chromosomal rearrangements. These mutational operators may modify the genome elements but also the genome structure, and provides the algorithm with a large degree of freedom.…”