Mutational Robustness Research Articles

Cheating in Mutualisms Promotes Diversity and Complexity.

AbstractMutualisms such as those between flowering plants and their pollinators are common in nature. Yet understanding their persistence in the face of cheaters and identifying the mechanisms behind their stunning diversity provide formidable challenges for evolutionary biologists. To shed light onto these questions, we introduce an individual-based model of two coevolving species in which individuals of one species use a Boolean circuit to discriminate between cooperators and cheaters in the other species. This conveys the idea that interactions are often mediated by complex biological processes rather than the matching of a single trait, as often assumed in models of coevolution. Our results show that cheating promotes diversification and complex discrimination mechanisms at the cost of a higher risk for mutualism to collapse. This result is mediated by an inverse relationship between mutational robustness and organismal complexity.

Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution.

The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To understand the particularities of this process theoretically, evolutionary simulation (ES) alone is insufficient because the outcomes of ES depend on evolutionary pathways. We need a reference system for comparison. An appropriate reference system for this purpose is an ensemble of the randomly sampled genotypes. However, generating high-fitness genotypes by simple random sampling is difficult because such genotypes are rare. In this study, we used the multicanonical Monte Carlo method developed in statistical physics to construct a reference ensemble of GRNs and compared it with the outcomes of ES. We obtained the following results. First, mutational robustness was significantly higher in ES than in the reference ensemble at the same fitness level. Second, the emergence of a new phenotype, bistability, was delayed in evolution. Third, the bistable group of GRNs contains many mutationally fragile GRNs compared with those in the non-bistable group. This suggests that the delayed emergence of bistability is a consequence of the mutation-selection mechanism.

Quantitative Analysis for Evolution of Mutational Robustness and Bistability: Use of a Rare-Event Sampling Method

Quantitative Analysis for Evolution of Mutational Robustness and Bistability: Use of a Rare-Event Sampling Method

Polerovirus genomic variation.

The polerovirus (family Solemoviridae, genus Polerovirus) genome consists of single-, positive-strand RNA organized in overlapping open reading frames (ORFs) that, in addition to others, code for protein 0 (P0, a gene silencing suppressor), a coat protein (CP, ORF3), and a read-through domain (ORF5) that is fused to the CP to form a CP-read-through (RT) protein. The genus Polerovirus contains twenty-six virus species that infect a wide variety of plants from cereals to cucurbits, to peppers. Poleroviruses are transmitted by a wide range of aphid species in the genera Rhopalosiphum, Stiobion, Aphis, and Myzus. Aphid transmission is mediated both by the CP and by the CP-RT. In viruses, mutational robustness and structural flexibility are necessary for maintaining functionality in genetically diverse sets of host plants and vectors. Under this scenario, within a virus genome, mutations preferentially accumulate in areas that are determinants of host adaptation or vector transmission. In this study, we profiled genomic variation in poleroviruses. Consistent with their multifunctional nature, single-nucleotide variation and selection analyses showed that ORFs coding for P0 and the read-through domain within the CP-RT are the most variable and contain the highest frequency of sites under positive selection. An order/disorder analysis showed that protein P0 is not disordered. In contrast, proteins CP-RT and virus protein genome-linked (VPg) contain areas of disorder. Disorder is a property of multifunctional proteins with multiple interaction partners. The results described here suggest that using contrasting mechanisms, P0, VPg, and CP-RT mediate adaptation to host plants and to vectors and are contributors to the broad host and vector range of poleroviruses. Profiling genetic variation across the polerovirus genome has practical applications in diagnostics, breeding for resistance, and identification of susceptibility genes and contributes to our understanding of virus interactions with their host, vectors, and environment.

The Role of Ancestral Duplicated Genes in Adaptation to Growth on Lactate, a Non-Fermentable Carbon Source for the Yeast Saccharomyces cerevisiae.

The cell central metabolism has been shaped throughout evolutionary times when facing challenges from the availability of resources. In the budding yeast, Saccharomyces cerevisiae, a set of duplicated genes originating from an ancestral whole-genome and several coetaneous small-scale duplication events drive energy transfer through glucose metabolism as the main carbon source either by fermentation or respiration. These duplicates (~a third of the genome) have been dated back to approximately 100 MY, allowing for enough evolutionary time to diverge in both sequence and function. Gene duplication has been proposed as a molecular mechanism of biological innovation, maintaining balance between mutational robustness and evolvability of the system. However, some questions concerning the molecular mechanisms behind duplicated genes transcriptional plasticity and functional divergence remain unresolved. In this work we challenged S. cerevisiae to the use of lactic acid/lactate as the sole carbon source and performed a small adaptive laboratory evolution to this non-fermentative carbon source, determining phenotypic and transcriptomic changes. We observed growth adaptation to acidic stress, by reduction of growth rate and increase in biomass production, while the transcriptomic response was mainly driven by repression of the whole-genome duplicates, those implied in glycolysis and overexpression of ROS response. The contribution of several duplicated pairs to this carbon source switch and acidic stress is also discussed.

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic.

Understanding the molecular evolution of the SARS‐CoV‐2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three‐dimensional structures of SARS‐CoV‐2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID‐19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein–protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi‐Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein–protein and protein–nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure‐based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

A Mathematical Analysis of HDV Genotypes: From Molecules to Cells.

Hepatitis D virus (HDV) is classified according to eight genotypes. The various genotypes are included in the HDVdb database, where each HDV sequence is specified by its genotype. In this contribution, a mathematical analysis is performed on RNA sequences in HDVdb. The RNA folding predicted structures of the Genbank HDV genome sequences in HDVdb are classified according to their coarse-grain tree-graph representation. The analysis allows discarding in a simple and efficient way the vast majority of the sequences that exhibit a rod-like structure, which is important for the virus replication, to attempt to discover other biological functions by structure consideration. After the filtering, there remain only a small number of sequences that can be checked for their additional stem-loops besides the main one that is known to be responsible for virus replication. It is found that a few sequences contain an additional stem-loop that is responsible for RNA editing or other possible functions. These few sequences are grouped into two main classes, one that is well-known experimentally belonging to genotype 3 for patients from South America associated with RNA editing, and the other that is not known at present belonging to genotype 7 for patients from Cameroon. The possibility that another function besides virus replication reminiscent of the editing mechanism in HDV genotype 3 exists in HDV genotype 7 has not been explored before and is predicted by eigenvalue analysis. Finally, when comparing native and shuffled sequences, it is shown that HDV sequences belonging to all genotypes are accentuated in their mutational robustness and thermodynamic stability as compared to other viruses that were subjected to such an analysis.

The Mutational Robustness of the Genetic Code and Codon Usage in Environmental Context: A Non-Extremophilic Preference?

The genetic code was evolved, to some extent, to minimize the effects of mutations. The effects of mutations depend on the amino acid repertoire, the structure of the genetic code and frequencies of amino acids in proteomes. The amino acid compositions of proteins and corresponding codon usages are still under selection, which allows us to ask what kind of environment the standard genetic code is adapted to. Using simple computational models and comprehensive datasets comprising genomic and environmental data from all three domains of Life, we estimate the expected severity of non-synonymous genomic mutations in proteins, measured by the change in amino acid physicochemical properties. We show that the fidelity in these physicochemical properties is expected to deteriorate with extremophilic codon usages, especially in thermophiles. These findings suggest that the genetic code performs better under non-extremophilic conditions, which not only explains the low substitution rates encountered in halophiles and thermophiles but the revealed relationship between the genetic code and habitat allows us to ponder on earlier phases in the history of Life.

Phylogenetic analysis of mutational robustness based on codon usage supports that the standard genetic code does not prefer extreme environments

The mutational robustness of the genetic code is rarely discussed in the context of biological diversity, such as codon usage and related factors, often considered as independent of the actual organism’s proteome. Here we put the living beings back to picture and use distortion as a metric of mutational robustness. Distortion estimates the expected severities of non-synonymous mutations measuring it by amino acid physicochemical properties and weighting for codon usage. Using the biological variance of codon frequencies, we interpret the mutational robustness of the standard genetic code with regards to their corresponding environments and genomic compositions (GC-content). Employing phylogenetic analyses, we show that coding fidelity in physicochemical properties can deteriorate with codon usages adapted to extreme environments and these putative effects are not the artefacts of phylogenetic bias. High temperature environments select for codon usages with decreased mutational robustness of hydrophobic, volumetric, and isoelectric properties. Selection at high saline concentrations also leads to reduced fidelity in polar and isoelectric patterns. These show that the genetic code performs best with mesophilic codon usages, strengthening the view that LUCA or its ancestors preferred lower temperature environments. Taxonomic implications, such as rooting the tree of life, are also discussed.

µIVC-Useq: a microfluidic-assisted high-throughput functionnal screening in tandem with next generation sequencing and artificial neural network to rapidly characterize RNA molecules.

The function of an RNA is intimately linked to its structure. Many approaches encompassing X-ray crystallography, NMR, structural probing, or in silico predictions have been developed to establish structural models, sometimes with a precision down to atomic resolution. Yet these models still require experimental validation through the preparation and functional assay of mutants, which can rapidly become time consuming and laborious. Such limitations can be overcome using high-throughput functional screenings that may not only help in validating the model, but also inform on the mutational robustness of a structural element and the extent to which a sequence can be modified without altering RNA function, an important set of information to assist RNA engineering. We introduced the microfluidic-assisted in vitro compartmentalization (µIVC), an efficient and cost-effective screening strategy in which reactions are performed in picoliter droplets at rates of several thousand per second. We later improved µIVC efficiency by using it in tandem with high-throughput sequencing, though a laborious bioinformatic step was still required at the end of the process. In the present work, we further increased the automation level of the pipeline by implementing an artificial neural network enabling unsupervised bioinformatic analysis. We demonstrate the efficiency of this “µIVC-Useq” technology by rapidly identifying a set of sequences readily accepted by a key domain of the light-up RNA aptamer SRB-2. This work not only shed some new light on the way this aptamer can be engineered, but it also allowed us to easily identify new variants with an up to 10-fold improved performance.

Quantifying the Mutational Robustness of Protein-Coding Genes.

We use large-scale mutagenesis data and computer simulations to quantify the mutational robustness of protein-coding genes by taking into account constraints arising from protein function and the genetic code. Analyses of the distribution of amino acid substitutions from 18 mutagenesis studies revealed an average of 45% of neutral variants; while mutagenesis data of 12 proteins artificially designed under no other constraints but stability, reach an average of 60%. Simulations using a lattice protein model allow us to contrast these estimates to the expected mutational robustness of protein families by generating unbiased samples of foldable sequences, which we find to have 30% of neutral variants. In agreement with mutagenesis data of designed proteins, the model shows that maximally robust protein families might access up to twice the amount of neutral variants observed in the unbiased samples (i.e. 60%). A biophysical model of protein-ligand binding suggests that constraints associated to molecular function have only a moderate impact on robustness of approximately 5 to 10% of neutral variants; and that the direction of this effect depends on the relation between functional performance and thermodynamic stability. Although the genetic code constraints the access of a gene's nucleotide sequence to only 30% of the full distribution of amino acid mutations, it provides an extra 15 to 20% of neutral variants to the estimations above, such that the expected, observed, and maximal robustness of protein-coding genes are approximately 50, 65, and 75%, respectively. We discuss our results in the light of three main hypothesis put forward to explain the existence of mutationally robust genes.

Impact of Synonymous Genome Recoding on the HIV Life Cycle.

Synonymous mutations within protein coding regions introduce changes in DNA or messenger (m) RNA, without mutating the encoded proteins. Synonymous recoding of virus genomes has facilitated the identification of previously unknown virus biological features. Moreover, large-scale synonymous recoding of the genome of human immunodeficiency virus type 1 (HIV-1) has elucidated new antiviral mechanisms within the innate immune response, and has improved our knowledge of new functional virus genome structures, the relevance of codon usage for the temporal regulation of viral gene expression, and HIV-1 mutational robustness and adaptability. Continuous improvements in our understanding of the impacts of synonymous substitutions on virus phenotype – coupled with the decreased cost of chemically synthesizing DNA and improved methods for assembling DNA fragments – have enhanced our ability to identify potential HIV-1 and host factors and other aspects involved in the infection process. In this review, we address how silent mutagenesis impacts HIV-1 phenotype and replication capacity. We also discuss the general potential of synonymous recoding of the HIV-1 genome to elucidate unknown aspects of the virus life cycle, and to identify new therapeutic targets.

A Mathematical Analysis of RNA Structural Motifs in Viruses

RNA stem-loop structures play an important role in almost every step of the viral replication cycle. In this contribution, a mathematical analysis is performed on a large dataset of RNA secondary structure elements in the coding regions of viruses by using topological indices that capture the Laplacian eigenvalues of the associated RNA graph representations and thereby enable structural classification, supplemented by folding energy and mutational robustness. The application of such an analysis for viral RNA structural motifs is described, being able to extract structural categories such as stem-loop structures of different sizes according to the tree-graph representation of the RNA structure, in our attempt to find novel functional motifs. While the analysis is carried on a large dataset of viral RNA structures, it can be applied more generally to other data that involve RNA secondary structures in biological agents.

Evolution of Inverted Repeats in Membrane Transporters

The majority of membrane transport proteins of known structure exhibit internal repeat symmetry, with two or more structurally homologous domains. inverted repeat architecture, where the domains are antiparallel with respect to each other, is especially common. This architecture implies the existence of ancestral monomeric proteins that form dual-topology antiparallel dimers, before duplication and fusion led to the extant inverted repeat forms. The Fluc family of fluoride channels is the first family to be discovered that connects these two evolutionary extremes, with both dual topology and inverted repeat representatives. Here we use the Fluc family of fluoride channels to model genetic drift in a newly duplicated membrane protein. Using fluoride resistance assays, we pursue a deep mutational scan to describe how evolutionary forces like mutational robustness and paralogue interference play out after the duplication of single-domain membrane proteins. We connect these experiments to phylogenetic observations of the Fluc superfamily.

In vitro selected GUAA tetraloop-binding receptors with structural plasticity and evolvability towards natural RNA structural modules.

GNRA tetraloop-binding receptor interactions are key components in the macromolecular assembly of a variety of functional RNAs. In nature, there is an apparent bias for GAAA/11nt receptor and GYRA/helix interactions, with the former interaction being thermodynamically more stable than the latter. While past in vitro selections allowed isolation of novel GGAA and GUGA receptors, we report herein an in vitro selection that revealed several novel classes of specific GUAA receptors with binding affinities comparable to those from natural GAAA/11nt interactions. These GUAA receptors have structural homology with double-locked bulge RNA modules naturally occurring in ribosomal RNAs. They display mutational robustness that enables exploration of the sequence/phenotypic space associated to GNRA/receptor interactions through epistasis. Their thermodynamic self-assembly fitness landscape is characterized by a rugged neutral network with possible evolutionary trajectories toward natural GNRA/receptor interactions. High throughput sequencing analysis revealed synergetic mutations located away from the tertiary interactions that positively contribute to assembly fitness. Our study suggests that the repertoire of GNRA/receptor interactions is much larger than initially thought from the analysis of natural stable RNA molecules and also provides clues for their evolution towards natural GNRA/receptors.

Mutational and biophysical robustness in a prestabilized monobody

The fibronectin type III (FN3) monobody domain is a promising non-antibody scaffold, which features a less complex architecture than an antibody while maintaining analogous binding loops. We previously developed FN3Con, a hyperstable monobody derivative with diagnostic and therapeutic potential. Prestabilization of the scaffold mitigates the stability–function trade-off commonly associated with evolving a protein domain toward biological activity. Here, we aimed to examine if the FN3Con monobody could take on antibody-like binding to therapeutic targets, while retaining its extreme stability. We targeted the first of the Adnectin derivative of monobodies to reach clinical trials, which was engineered by directed evolution for binding to the therapeutic target VEGFR2; however, this function was gained at the expense of large losses in thermostability and increased oligomerization. In order to mitigate these losses, we grafted the binding loops from Adnectin-anti-VEGFR2 (CT-322) onto the prestabilized FN3Con scaffold to produce a domain that successfully bound with high affinity to the therapeutic target VEGFR2. This FN3Con-anti-VEGFR2 construct also maintains high thermostability, including remarkable long-term stability, retaining binding activity after 2 years of storage at 36 °C. Further investigations into buffer excipients doubled the presence of monomeric monobody in accelerated stability trials. These data suggest that loop grafting onto a prestabilized scaffold is a viable strategy for the development of monobody domains with desirable biophysical characteristics and that FN3Con is therefore well-suited to applications such as the evolution of multiple paratopes or shelf-stable diagnostics and therapeutics.

Selection enhances protein evolvability by increasing mutational robustness and foldability

Natural selection can promote or hinder a population's evolvability-the ability to evolve new and adaptive phenotypes-but the underlying mechanisms are poorly understood. To examine how the strength of selection affects evolvability, we subjected populations of yellow fluorescent protein to directed evolution under different selection regimes and then evolved them toward the new phenotype of green fluorescence. Populations under strong selection for the yellow phenotype evolved the green phenotype most rapidly. They did so by accumulating mutations that increase both robustness to mutations and foldability. Under weak selection, neofunctionalizing mutations rose to higher frequency at first, but more frequent deleterious mutations undermined their eventual success. Our experiments show how selection can enhance evolvability by enhancing robustness and create the conditions necessary for evolutionary success.

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside‐the‐box, predictions not found in other state‐of‐the‐art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.

The Signatures of Natural Selection and Molecular Evolution in Fusarium graminearum Virus 1

Fusarium graminearum virus 1 (FgV1) is a positive-sense ssRNA virus that confers hypovirulence in its fungal host, Fusarium graminearum. Like most mycoviruses, FgV1 exists in fungal cells, lacks an extracellular life cycle, and is therefore transmitted during sporulation or hyphal anastomosis. To understand FgV1 evolution and/or adaptation, we conducted mutation accumulation (MA) experiments by serial passage of FgV1 alone or with FgV2, 3, or 4 in F. graminearum. We expected that the effects of positive selection would be highly limited because of repeated bottleneck events. To determine whether selection on the virus was positive, negative, or neutral, we assessed both the phenotypic traits of the host fungus and the RNA sequences of FgV1. We inferred that there was positive selection on beneficial mutations in FgV1 based on the ratio of non-synonymous to synonymous substitutions (dN/dS), on the ratio of radical to conservation amino acid replacements (pNR/pNC), and by changes in the predicted protein structures. In support of this inference, we found evidence of positive selection only in the open reading frame 4 (ORF4) protein of DK21/FgV1 (MA line 1); mutations at amino acids 163A and 289H in the ORF4 of MA line 1 affected the entire structure of the protein predicted to be under positive selection. We also found, however, that deleterious mutations were a major driving force in viral evolution during serial passages. Linear relationships between changes in viral fitness and the number of mutations in each MA line demonstrated that some deleterious mutations resulted in fitness decline. Several mutations in MA line 1 were not shared with any of the other four MA lines (PH-1/FgV1, PH-1/FgV1 + 2, PH-1/FgV1 + 3, and PH-1/FgV1 + 4). This suggests that evolutionary pathways of the virus could differ with respect to hosts and also with respect to co-infecting viruses. The data also suggested that the differences among MA lines might also be explained by mutational robustness and other unidentified factors. Additional research is needed to clarify the effects of virus co-infection on the adaptation or evolution of FgV1 to its environments.

Uncovering the basis of protein-protein interaction specificity with a combinatorially complete library.

Protein-protein interaction specificity is often encoded at the primary sequence level. However, the contributions of individual residues to specificity are usually poorly understood and often obscured by mutational robustness, sequence degeneracy, and epistasis. Using bacterial toxin-antitoxin systems as a model, we screened a combinatorially complete library of antitoxin variants at three key positions against two toxins. This library enabled us to measure the effect of individual substitutions on specificity in hundreds of genetic backgrounds. These distributions allow inferences about the general nature of interface residues in promoting specificity. We find that positive and negative contributions to specificity are neither inherently coupled nor mutually exclusive. Further, a wild-type antitoxin appears optimized for specificity as no substitutions improve discrimination between cognate and non-cognate partners. By comparing crystal structures of paralogous complexes, we provide a rationale for our observations. Collectively, this work provides a generalizable approach to understanding the logic of molecular recognition.