The duplication of genomes and genetic networks and its potential for evolutionary adaptation and survival during environmental turmoil

Abstract

The importance of whole-genome duplication (WGD) for evolution is controversial. Whereas some view WGD mainly as detrimental and an evolutionary dead end, there is growing evidence that polyploidization can help overcome environmental change, stressful conditions, or periods of extinction. However, despite much research, the mechanistic underpinnings of why and how polyploids might be able to outcompete or outlive nonpolyploids at times of environmental upheaval remain elusive, especially for autopolyploids, in which heterosis effects are limited. On the longer term, WGD might increase both mutational and environmental robustness due to redundancy and increased genetic variation, but on the short-or even immediate-term, selective advantages of WGDs are harder to explain. Here, by duplicating artificially generated Gene Regulatory Networks (GRNs), we show that duplicated GRNs-and thus duplicated genomes-show higher signal output variation than nonduplicated GRNs. This increased variation leads to niche expansion and can provide polyploid populations with substantial advantages to survive environmental turmoil. In contrast, under stable environments, GRNs might be maladaptive to changes, a phenomenon that is exacerbated in duplicated GRNs. We believe that these results provide insights into how genome duplication and (auto)polyploidy might help organisms to adapt quickly to novel conditions and to survive ecological uproar or even cataclysmic events.

Keywords: cataclysmic events; environmental turmoil; gene regulatory networks; polyploidy; whole-genome duplication.

Fig. 1.

Two examples of an aGRN of 10 nodes generated by the preferential attachment algorithm. All nodes represent regulatory genes or proteins, except nodes 8 and 9 in both networks, which are output nodes. Nodes 5 and 6 can act as input nodes since all edges are outgoing. Weight values are also indicated. Positive weight values represent induction, while negative weight values indicate repression (as for example in gene expression). The topology of a specific aGRN is unique and can be considered the genotype, while the output nodes or node values define the phenotype. See text for details.

Fig. 2.

Example of a simple or ancestral (Left) and duplicated (Right) aGRN.

Fig. 3.

PTA (Methods) comparing a population of single versus its duplicated networks. (A) The value of one output node is plotted against the value of the second output node for simple (blue dots) and duplicated (orange) networks for a simple GRN of 10 nodes. Thinning has been applied and from the 1,000,000 values only a fraction is shown, to facilitate interpretation. (B) The variance for simple and duplicated networks for networks of 10, 20, 40, and 80 nodes (400 networks consisting of 10K single/double networks per size category). Variance of the output is increasing with node additions, but duplicated genomes always have higher variance compared to their unduplicated counterparts. Red arrows denote the difference in variance between duplicated networks and random networks with an equal number of nodes but not having the typical duplicated topology (structure doubling versus node doubling) (C) Cumulative density function of the phenotypic variance σ as measured by multiplying variance of both (mean) output node values in the 10K simulated simple GRNs of 10 nodes and their duplicated counterparts. (D) Angular dispersion of the relative angles between the single and doubled networks for 10K simulations of simple GRNs of 10 nodes and their duplicated counterparts.

Fig. 4.

Mean fitness $\overline{\mathit{w}}$ of simple and duplicated networks relative to the fitness of the population of simple networks in the reference environment, as a function of different input values, assuming a Gaussian fitness function (Eq. 3) (A) and a linear fitness function (Eq. 2) (B) Differential fitness as a function of different input variables that represent environmental change with fitness modelled by a Gaussian (C) and linear function (D) for networks of different size. Note that we only report relative $\overline{\mathit{w}}$ for input values leading to non-zero values for the single network.

Fig. 5.

Three-dimensional representation of fitness landscapes in which hills, corresponding to local adaptive peaks, are surrounded by valleys or depressions, corresponding to regions of the phenotype space where no survival is possible. Polyploidy may allow a wider and faster exploration of phenotypic space, ultimately conferring a potential adaptive advantage under challenging environmental conditions. Blue–green dots are individuals that can survive; red dots denote organisms that cannot survive. In a stable environment (Top Left), nonpolyploid organisms are expected to have reached their local adaptive peaks. WGD results in an expansion of the phenotypic space covered by the population, although some polyploid genotypes might survive, most polyploids cannot survive in this environment (Bottom-Left). Adaptive landscapes are readily distorted by environmental challenges, such as cataclysmic or extinction events (Right), resulting in shifts in the relative locations of their adaptive peaks. Under these conditions, although most diploids are expected to perish (Top Right), some polyploid organisms (which could be referred to as “hopeful monsters”), featured by wider accessible phenotype space (see text for details), have better chances to fall near the peak of a newly formed adaptive hill and thus to acquire the necessary evolutionary innovations to colonize novel niches (Bottom Right).