Genetic and environmental factors affecting cryptic variations in gene regulatory networks

Abstract

Background: Cryptic genetic variation (CGV) is considered to facilitate phenotypic evolution by producing visible variations in response to changes in the internal and/or external environment. Several mechanisms enabling the accumulation and release of CGVs have been proposed. In this study, we focused on gene regulatory networks (GRNs) as an important mechanism for producing CGVs, and examined how interactions between GRNs and the environment influence the number of CGVs by using individual-based simulations.

Results: Populations of GRNs were allowed to evolve under various stabilizing selections, and we then measured the number of genetic and phenotypic variations that had arisen. Our results showed that CGVs were not depleted irrespective of the strength of the stabilizing selection for each phenotype, whereas the visible fraction of genetic variation in a population decreased with increasing strength of selection. On the other hand, increasing the number of different environments that individuals encountered within their lifetime (i.e., entailing plastic responses to multiple environments) suppressed the accumulation of CGVs, whereas the GRNs with more genes and interactions were favored in such heterogeneous environments.

Conclusions: Given the findings that the number of CGVs in a population was largely determined by the size (order) of GRNs, we propose that expansion of GRNs and adaptation to novel environments are mutually facilitating and sustainable sources of evolvability and hence the origins of biological diversity and complexity.

Figure 1

A schematic example of a gene regulatory network model. The squares and diamonds represent cis- and trans-elements, respectively. For simplicity of explanation, only a few of the numbers used for mutual recognition between these elements are shown; however, each element had a number. Black arrow-headed lines and gray bar-headed lines represent transcriptional activation and repression, respectively, and the line weight denotes the intensity of the interaction. Note that different genes would be core genes in different environments.

Figure 2

A schematic example of evaluating phenotypic diversity. For simplicity, a two-dimensional space is shown; however, the phenotypic space used in our analysis was four-dimensional. The two axes denote the expression levels of phenotypic genes 1 and 2, respectively. The phenotype of an individual gives the coordinate of a point in this space. The space was divided into grid cells. The phenotypic diversity of a population was defined as the number of cells in which the individuals in the population were found. The circles and triangles denote the individuals’ phenotypes in normal and novel environments, respectively. In this example, the phenotypic diversity of the population was four in the normal environment (dark gray cells) and 13 in the novel environment (light gray cells and some dark gray cells). In this case, the number of cryptic genetic variations was calculated to be nine (i.e., 13−4).

Figure 3

Comparison of phenotypic diversity between normal and novel environments.P_novel was significantly larger than P_normal (Wilcoxon signed rank test, V=5825, P=4.298×10⁻¹⁴).

Figure 4

Relationships between cryptic variations and network properties under the default parameter condition. The weighted size of the network, genetic variations, and degree assortativity were selected as the variables that explained the number of cryptic variations using GLM model selection. Regression lines were drawn on the basis of simple linear regression analysis.

Figure 5

Relationships between cryptic variations and altered GRN parameters. Simulations were repeated 20 times for each parameter value, and each dot represents a simulation run. Regression lines were drawn on the basis of the linear regression statistics shown in Table 2; solid and dashed lines denote significant and non-significant correlations, respectively.

Figure 6

Relationships between variations in a population and environmental parameters. Simulations were repeated 20 times for each parameter value, and each dot represents a simulation run. Regression lines were drawn on the basis of the linear regression statistics shown in Table 2; solid and dashed lines denote significant and non-significant correlations, respectively.

Figure 7

Relationships between GRN features and environmental parameters. Simulations were repeated 20 times for each parameter value, and each dot represents a population mean of a simulation run. Regression lines were drawn on the basis of the linear regression statistics shown in Table S2; solid and dashed lines denote significant and non-significant correlations, respectively.