Aligning functional network constraint to evolutionary outcomes

Summary It is likely that there are constraints on how evolution can progress, and well-known evolutionary phenomena such as convergent evolution, rapid adaptation, and genic evolution would be difficult to explain under the absence of any such evolutionary constraint. One dimension of constraint results from a finite number of environmental conditions, and thus natural selection scenarios, leading to convergent phenotypes. This limits which genetic variants are adaptive, and consequently, constrains how variation is inherited across generations. Another, less explored dimension of evolution is functional constraint at the molecular level. Some widely accepted examples for this dimension of evolutionary constraint include genetic linkage, codon position, and architecture of developmental genetic pathways, that together constrain how evolution can shape genomes through limiting which mutations can increase fitness. Genomic architecture, which describes how all gene products interact, has been discussed to be another dimension of functional genetic constraint. This notion had been largely discredited by the modern synthesis, especially because macroevolution was not always found to be perfectly deterministic. But debates on whether evolutionary constraint stems mostly from environmental (extrinsic) or genetic (intrinsic) factors have mostly been held at the intellectual level using sporadic evidence. Quantifying the relative contributions of these different dimensions of constraint is, however, fundamentally important to understand the mechanistic basis of seemingly deterministic evolutionary outcomes. In some model organisms, genetic constraint has already been quantitatively explored. Forays into testing the relationship between genomic architecture and evolution included studies on protein evolutionary rate variation in essential versus nonessential genes, and observations that the number of protein interactions within a cell (gene pleiotropy) determines the fitness effect of mutations. In this contribution, existing evidence for functional genetic constraint as shaping evolutionary outcomes is reviewed and testable hypotheses are defined for functional genetic constraint influencing (i) convergent evolution, (ii) rapid adaptation, and genic adaptation. An analysis of the yeast interactome incorporating recently published data on its evolution, reveals new support for the existence of genomic architecture as a functional genetic dimension of evolutionary constraint. As functional genetic networks are becoming increasingly available, evolutionary biologists should strive to evaluate functional genetic network constraint, against variables describing complex phenotypes and environments, for better understanding commonly observed deterministic patterns of evolution in non-model organisms. This may help to quantify the extrinsic versus intrinsic dimensions of evolutionary constraint, and result in a better understanding of how fast, effectively, or deterministically organisms adapt. Glossary Evolutionary constraint [1] the phenomenon of evolution producing a finite number of genomic and associated phenotypic outcomes from a near infinite number of possible genetic variants. Genetic constraint The portion of evolutionary constraint which is determined at the level of genes or their gene products, for example codon constraint or developmental genetic pathways. Functional network constraint The portion of network constraint attributed to the structure or architecture of gene interactions that can be expressed in form of a network. Networks consist of nodes (genes) and edges (functional interactions between these genes). Orthogenesis, Structuralism The idea that properties inherent in organisms can direct evolution. Structuralism bases these properties on functional relationships of components that organisms are made of. Orthogenesis usually also implies that evolution is directed towards a goal. This view is not accepted within the modern synthesis of evolution. Genic evolution The phenomenon of different evolutionary outcomes being the outcome of independent mutation and selection events in different genes. For example, the occurrence of convergent evolution in diverging populations, both of which are caused by evolution in distinct genes. Rapid adaptation The phenomenon of adaptive change in allele frequencies of a population to natural selection, taking place within just a few generations. Convergent evolution/convergence Similar phenotypes evolving from similar selective pressure. May be (but doesn’t have to be) caused at the genomic level through genomic re-use of the same genes or alleles, which is also called parallel genetic evolution or genomic re-use. Gene dispensability a variable to measure gene essentiality. The less dispensable a gene is for organismal growth and function, the more essential it is. Pleiotropy and cost of complexity Gene products with many functional interactions with other gene products are constrained to accumulate less nonsynonymous mutations, because this would negatively affect the phenotype in many ways. Consequently, more complex genome organisation leads to higher constraint. Gene expression level CAI The amount of mRNA produced by each gene in regular somatic cells. CAI (Codon Adaptation Index) is used as a substitute variable in this paper, and is derived from codon use bias in yeast that correlates with mRNA levels. Omega ω the ratio of nonsynonymous to synonymous substitutions dN/dS. It is assumed that dS remains constant, and dN is used as a measure for directional evolution. Gamma γ A score developed for quantifying or predicting events of rewiring functional connections between network nodes over the course of evolution. Developed on the example of five species of yeasts. Neighborhood connectivity A network statistic used to describe the structure of a functional genetic network. Describes the number of connections of all neighbors of each node. Highest values expected in intermediately located nodes Betweenness centrality A network statistic used to describe the structure of a functional genetic network, describing how a node lies within paths between other nodes. Nodes with many paths progressing through them may be important in transmitting information. Highest values expected in central nodes. Average shortest path length A network statistic used to describe the structure of a functional genetic network. Shortest distance between a node and other nodes. Highest values expected in peripheral nodes of a network.