Molecular evolution of catalysis

Viral Fitness Landscapes in Diverse Host Species Reveal Multiple Evolutionary Lines for the NS1 Gene of Influenza A Viruses

Influenza A viruses (IAVs) have a remarkable tropism in their ability to circulate in both mammalian and avian species. The IAV NS1 protein is a multifunctional virulence factor that inhibits the type I interferon host response through a myriad of mechanisms. How NS1 has evolved to enable this remarkable property across species and its specific impact in the overall replication, pathogenicity, and host preference remain unknown. Here we analyze the NS1 evolutionary landscape and host tropism using a barcoded library of recombinant IAVs. Results show a surprisingly great variety of NS1 phenotypes according to their ability to replicate in different hosts. The IAV NS1 genes appear to have taken diverse and random evolutionary pathways within their multiple phylogenetic lineages. In summary, the high evolutionary plasticity of this viral protein underscores the ability of IAVs to adapt to multiple hosts and aids in our understanding of its global prevalence.

Muñoz-Moreno et al. report that influenza A virus NS1 undergoes diverse and unpredictable evolutionary pathways based on its different phylogenetic lineages. A high-throughput approach using a barcoded library is used to test the interactions between NS1-recombinant viruses and to study their preference for specific or multiple hosts.

Evolution of RNA-Based Networks

RNA molecules have served for decades as a paradigmatic example of molecular evolution that is tractable both in in vitro experiments and in detailed computer simulation. The adaptation of RNA sequences to external selection pressures is well studied and well understood. The de novo innovation or optimization of RNA aptamers and riboswitches in SELEX experiments serves as a case in point. Likewise, fitness landscapes building upon the efficiently computable RNA secondary structures have been a key toward understanding realistic fitness landscapes. Much less is known, however, on models in which multiple RNAs interact with each other, thus actively influencing the selection pressures acting on them. From a computational perspective, RNA-RNA interactions can be dealt with by same basic methods as the folding of a single RNA molecule, although many details become more complicated. RNA-RNA interactions are frequently employed in cellular regulation networks, e.g., as miRNA bases mRNA silencing or in the modulation of bacterial mRNAs by small, often highly structured sRNAs. In this chapter, we summarize the key features of networks of replicators. We highlight the differences between quasispecies-like models describing templates copied by an external replicase and hypercycle similar to autocatalytic replicators. Two aspects are of importance: the dynamics of selection within a population, usually described by conventional dynamical systems, and the evolution of replicating species in the space of chemical types. Product inhibition plays a key role in modulating selection dynamics from survival of the fittest to extinction of unfittest. The sequence evolution of replicators is rather well understood as approximate optimization in a fitness landscape for templates that is shaped by the sequence-structure map of RNA. Some of the properties of this map, in particular shape space covering and extensive neutral networks, give rise to evolutionary patterns such as drift-like motion in sequence space, akin to the behavior of RNA quasispecies. In contrast, very little is known about the influence of sequence-structure maps on autocatalytic replication systems.

Comprehensive experimental fitness landscape and evolutionary network for small RNA

The origin of life is believed to have progressed through an RNA world, in which RNA acted as both genetic material and functional molecules. The structure of the evolutionary fitness landscape of RNA would determine natural selection for the first functional sequences. Fitness landscapes are the subject of much speculation, but their structure is essentially unknown. Here we describe a comprehensive map of a fitness landscape, exploring nearly all of sequence space, for short RNAs surviving selection in vitro. With the exception of a small evolutionary network, we find that fitness peaks are largely isolated from one another, highlighting the importance of historical contingency and indicating that natural selection would be constrained to local exploration in the RNA world.

Neutral network sizes of biological RNA molecules can be computed and are not atypically small

Background

Neutral networks or sets consist of all genotypes with a given phenotype. The size and structure of these sets has a strong influence on a biological system's robustness to mutations, and on its evolvability, the ability to produce phenotypic variation; in the few studied cases of molecular phenotypes, the larger this set, the greater both robustness and evolvability of phenotypes. Unfortunately, any one neutral set contains generally only a tiny fraction of genotype space. Thus, current methods cannot measure neutral set sizes accurately, except in the smallest genotype spaces.

Results

Here we introduce a generalized Monte Carlo approach that can measure neutral set sizes in larger spaces. We apply our method to the genotype-to-phenotype mapping of RNA molecules, and show that it can reliably measure neutral set sizes for molecules up to 100 bases. We also study neutral set sizes of RNA structures in a publicly available database of functional, noncoding RNAs up to a length of 50 bases. We find that these neutral sets are larger than the neutral sets in 99.99% of random phenotypes. Software to estimate neutral network sizes is available at .

Conclusion

The biological RNA structures we examined are more abundant than random structures. This indicates that their robustness and their ability to produce new phenotypic variants may also be high.

Evolution of complexity in RNA-like replicator systems

BACKGROUND

The evolution of complexity is among the most important questions in biology. The evolution of complexity is often observed as the increase of genetic information or that of the organizational complexity of a system. It is well recognized that the formation of biological organization--be it of molecules or ecosystems--is ultimately instructed by the genetic information, whereas it is also true that the genetic information is functional only in the context of the organization. Therefore, to obtain a more complete picture of the evolution of complexity, we must study the evolution of both information and organization.

RESULTS

Here we investigate the evolution of complexity in a simulated RNA-like replicator system. The simplicity of the system allows us to explicitly model the genotype-phenotype-interaction mapping of individual replicators, whereby we avoid preconceiving the functionality of genotypes (information) or the ecological organization of replicators in the model. In particular, the model assumes that interactions among replicators--to replicate or to be replicated--depend on their secondary structures and base-pair matching. The results showed that a population of replicators, originally consisting of one genotype, evolves to form a complex ecosystem of up to four species. During this diversification, the species evolve through acquiring unique genotypes with distinct ecological functionality. The analysis of this diversification reveals that parasitic replicators, which have been thought to destabilize the replicator's diversity, actually promote the evolution of diversity through generating a novel "niche" for catalytic replicators. This also makes the current replicator system extremely stable upon the evolution of parasites. The results also show that the stability of the system crucially depends on the spatial pattern formation of replicators. Finally, the evolutionary dynamics is shown to significantly depend on the mutation rate.

CONCLUSION

The interdependence of information and organization can play an important role for the evolution of complexity. Namely, the emergent ecosystem supplies a context in which a novel phenotype gains functionality. Realizing such a phenotype, novel genotypes can evolve, which, in turn, results in the evolution of more complex ecological organization. Hence, the evolutionary feedback between information and organization, and thereby the evolution of complexity.

Why are proteins so robust to site mutations?

There have been repeated observations that proteins are surprisingly robust to site mutations, enduring significant numbers of substitutions with little change in structure, stability, or function. These results are almost paradoxical in light of what is known about random heteropolymers and the sensitivity of their properties to seemingly trivial mutations. To address this discrepancy, the preservation of biological protein properties in the presence of mutation has been interpreted as indicating the independence of selective pressure on such properties. Such results also lead to the prediction that de novo protein design should be relatively easy, in contrast to what is observed. Here, we use a computational model with lattice proteins to demonstrate how this robustness can result from population dynamics during the evolutionary process. As a result, sequence plasticity may be a characteristic of evolutionarily derived proteins and not necessarily a property of designed proteins. This suggests that this robustness must be re-interpreted in evolutionary terms, and has consequences for our understanding of both in vivo and in vitro protein evolution.

Why are proteins marginally stable?

Most globular proteins are marginally stable regardless of size or activity. The most common interpretation is that proteins must be marginally stable in order to function, and so marginal stability represents the results of positive selection. We consider the issue of marginal stability directly using model proteins and the dynamical aspects of protein evolution in populations. We find that the marginal stability of proteins is an inherent property of proteins due to the high dimensionality of the sequence space, without regard to protein function. In this way, marginal stability can result from neutral, non-adaptive evolution. By allowing evolving protein sub-populations with different stability requirements for functionality to complete, we find that marginally stable populations of proteins tend to dominate. Our results show that functionalities consistent with marginal stability have a strong evolutionary advantage, and might arise because of the natural tendency of proteins towards marginal stability.

The topology of the possible: formal spaces underlying patterns of evolutionary change

The current implementation of the Neo-Darwinian model of evolution typically assumes that the set of possible phenotypes is organized into a highly symmetric and regular space equipped with a notion of distance, for example, a Euclidean vector space. Recent computational work on a biophysical genotype-phenotype model based on the folding of RNA sequences into secondary structures suggests a rather different picture. If phenotypes are organized according to genetic accessibility, the resulting space lacks a metric and is formalized by an unfamiliar structure, known as a pre-topology. Patterns of phenotypic evolution-such as punctuation, irreversibility, modularity--result naturally from the properties of this space. The classical framework, however, addresses these patterns by exclusively invoking natural selection on suitably imposed fitness landscapes. We propose to extend the explanatory level for phenotypic evolution from fitness considerations alone to include the topological structure of phenotype space as induced by the genotype-phenotype map. We introduce the mathematical concepts and tools necessary to formalize the notion of accessibility pre-topology relative to which we can speak of continuity in the genotype-phenotype map and in evolutionary trajectories. We connect the factorization of a pre-topology into a product space with the notion of phenotypic character and derive a condition for factorization. Based on anecdotal evidence from the RNA model, we conjecture that this condition is not globally fulfilled, but rather confined to regions where the genotype-phenotype map is continuous. Equivalently, local regions of genotype space on which the map is discontinuous are associated with the loss of character autonomy. This is consistent with the importance of these regions for phenotypic innovation. The intention of the present paper is to offer a perspective, a framework to implement this perspective, and a few results illustrating how this framework can be put to work. The RNA case is used as an example throughout the text.