On the effects of the modularity of gene regulatory networks on phenotypic variability and its association with robustness

Origins and evolution of biological novelty

Understanding the origins and impacts of novel traits has been a perennial interest in many realms of ecology and evolutionary biology. Here, we build on previous evolutionary and philosophical treatments of this subject to encompass novelties across biological scales and eco-evolutionary perspectives. By defining novelties as new features at one biological scale that have emergent effects at other biological scales, we incorporate many forms of novelty that have previously been treated in isolation (such as novelty from genetic mutations, new developmental pathways, new morphological features, and new species). Our perspective is based on the fundamental idea that the emergence of a novelty, at any biological scale, depends on its environmental and genetic context. Through this lens, we outline a broad array of generative mechanisms underlying novelty and highlight how genomic tools are transforming our understanding of the origins of novelty. Lastly, we present several case studies to illustrate how novelties across biological scales and systems can be understood based on common mechanisms of change and their environmental and genetic contexts. Specifically, we highlight how gene duplication contributes to the evolution of new complex structures in visual systems; how genetic exchange in symbiosis alters functions of both host and symbiont, resulting in a novel organism; and how hybridisation between species can generate new species with new niches.

Modularity of biological systems: a link between structure and function

This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.

Modularity of biological systems: a link between structure and function

This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.

Early effects of gene duplication on the robustness and phenotypic variability of gene regulatory networks

Background

Research on gene duplication is abundant and comes from a wide range of approaches, from high-throughput analyses and experimental evolution to bioinformatics and theoretical models. Notwithstanding, a consensus is still lacking regarding evolutionary mechanisms involved in evolution through gene duplication as well as the conditions that affect them. We argue that a better understanding of evolution through gene duplication requires considering explicitly that genes do not act in isolation. It demands studying how the perturbation that gene duplication implies percolates through the web of gene interactions. Due to evolution’s contingent nature, the paths that lead to the final fate of duplicates must depend strongly on the early stages of gene duplication, before gene copies have accumulated distinctive changes.

Methods

Here we use a widely-known model of gene regulatory networks to study how gene duplication affects network behavior in early stages. Such networks comprise sets of genes that cross-regulate. They organize gene activity creating the gene expression patterns that give cells their phenotypic properties. We focus on how duplication affects two evolutionarily relevant properties of gene regulatory networks: mitigation of the effect of new mutations and access to new phenotypic variants through mutation.

Results

Among other observations, we find that those networks that are better at maintaining the original phenotype after duplication are usually also better at buffering the effect of single interaction mutations and that duplication tends to enhance further this ability. Moreover, the effect of mutations after duplication depends on both the kind of mutation and genes involved in it. We also found that those phenotypes that had easier access through mutation before duplication had higher chances of remaining accessible through new mutations after duplication.

Conclusion

Our results support that gene duplication often mitigates the impact of new mutations and that this effect is not merely due to changes in the number of genes. The work that we put forward helps to identify conditions under which gene duplication may enhance evolvability and robustness to mutations.

Multivariate selection and the making and breaking of mutational pleiotropy

The role of mutations have been subject to many controversies since the formation of the Modern Synthesis of evolution in the early 1940ties. Geneticists in the early half of the twentieth century tended to view mutations as a limiting factor in evolutionary change. In contrast, natural selection was largely viewed as a “sieve” whose main role was to sort out the unfit but which could not create anything novel alone. This view gradually changed with the development of mathematical population genetics theory, increased appreciation of standing genetic variation and the discovery of more complex forms of selection, including balancing selection. Short-term evolutionary responses to selection are mainly influenced by standing genetic variation, and are predictable to some degree using information about the genetic variance–covariance matrix (G) and the strength and form of selection (e. g. the vector of selection gradients, β). However, predicting long-term evolution is more challenging, and requires information about the nature and supply of novel mutations, summarized by the mutational variance–covariance matrix (M). Recently, there has been increased attention to the role of mutations in general and M in particular. Some evolutionary biologists argue that evolution is largely mutation-driven and claim that mutation bias frequently results in mutation-biased adaptation. Strong similarities between G and M have also raised questions about the non-randomness of mutations. Moreover, novel mutations are typically not isotropic in their phenotypic effects and mutational pleiotropy is common. Here I discuss the evolutionary origin and consequences of mutational pleiotropy and how multivariate selection directly shapes G and indirectly M through changed epistatic relationships. I illustrate these ideas by reviewing recent literature and models about correlational selection, evolution of G and M, sexual selection and the fitness consequences of sexual antagonism.

Co-occurrence network analysis reveals loss of microbial interactions in anaerobic digester subjected to repeated organic load shocks

Fluctuations in the anaerobic digestion (AD) organic loading rate (OLR) cause shocks to the AD microbiome, which lead to unstable methane productivity. Managing these fluctuations requires a larger digester, which is impractical for community-scale applications, limiting the potential of AD in advancing a circular economy. To allow operation of small-scale AD while managing OLR fluctuations, we need to tackle the issue through elucidation of the microbial community dynamics via 16S rRNA gene sequencing. This study elucidated the interrelation of the AD performance and the dynamics of the microbial interactions within its microbiome in response to repeated high OLR shocks at different frequencies. The OLR shocks were equivalent to 4 times the baseline OLR of 2 g VS/L/d. We found that less frequent organic load shocks result to deterioration of methane productivity. Co-occurrence network analysis shows that this coincides with the breakdown of the microbiome network structure. This suggests loss of microbial interactions necessary in maintaining stable AD. Identification of species influencing the network structure revealed that a species under the genus Anaerovorax has the greatest influence, while orders Spirochaetales and Synergistales represent the greatest number of the influential species. We inferred that the impact imposed by the OLR shocks shifted the microbiome activity towards biochemical pathways that are not contributing to methane production. Establishing a small-scale AD system that permits OLR fluctuations would require developing an AD microbiome resilient to infrequent organic loading shocks.