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Evolutionary innovations are qualitatively novel traits that emerge through evolution and increase biodiversity. The genetic mechanisms of innovation remain poorly understood. A systems view of innovation requires the analysis of genotype networks-the vast networks of genetic variants that produce the same phenotype. Innovations can occur at the intersection of two different genotype networks. However, the experimental characterization of genotype networks has been hindered by the vast number of genetic variants that need to be functionally analyzed. Here, we use high-throughput sequencing to study the fitness landscape at the intersection of the genotype networks of two catalytic RNA molecules (ribozymes). We determined the ability of numerous neighboring RNA sequences to catalyze two different chemical reactions, and we use these data as a proxy for a genotype to fitness map where two functions come in close proximity. We find extensive functional overlap, and numerous genotypes can catalyze both functions. We demonstrate through evolutionary simulations that these numerous points of intersection facilitate the discovery of a new function. However, the rate of adaptation of the new function depends upon the local ruggedness around the starting location in the genotype network. As a consequence, one direction of adaptation is more rapid than the other. We find that periods of neutral evolution increase rates of adaptation to the new function by allowing populations to spread out in their genotype network. Our study reveals the properties of a fitness landscape where genotype networks intersect and the consequences for evolutionary innovations. Our results suggest that historic innovations in natural systems may have been facilitated by overlapping genotype networks.

Hybrids between species exhibit plastic genomic architectures that could foster or slow down their adaptation. When challenged to evolve in an environment containing a UV mimetic drug, yeast hybrids have reduced adaptation rates compared to parents. We find that hybrids and their parents converge onto similar molecular mechanisms of adaptation by mutations in pleiotropic transcription factors, but at a different pace. After 100 generations, mutations in these genes tend to be homozygous in the parents but heterozygous in the hybrids. We hypothesize that a lower rate of loss of heterozygosity (LOH) in hybrids could limit fitness gain. Using genome editing, we first demonstrate that mutations display incomplete dominance, requiring homozygosity to show full impact and to entirely circumvent Haldane’s sieve, which favors the fixation of dominant mutations. Second, tracking mutations in earlier generations confirmed a different rate of LOH in hybrids. Together, these findings show that Haldane’s sieve slows down adaptation in hybrids, revealing an intrinsic constraint of hybrid genomic architecture that can limit the role of hybridization in adaptive evolution. Hybrids have complex genomes that influence their adaptive potential. This study reveals that yeast hybrids adapt slower than their parental species in a new environment, primarily due to a reduced rate of loss of heterozygosity in key genes.

Deciphering the structural variation across tumour genomes is crucial to determine the events driving tumour progression and better understand tumour adaptation and evolution. High grade serous ovarian cancer (HGSOC) is an exemplar tumour type showing extreme, but poorly characterised structural diversity. Here, we comprehensively describe the mutational landscape driving HGSOC, exploiting a large (N = 324), deeply whole genome sequenced dataset. We reveal two divergent evolutionary trajectories, affecting patient survival and involving differing genomic environments. One involves homologous recombination repair deficiency (HRD) while the other is dominated by whole genome duplication (WGD) with frequent chromothripsis, breakage-fusion-bridges and extra-chromosomal DNA. These trajectories contribute to structural variation hotspots, containing candidate driver genes with significantly altered expression. While structural variation predominantly drives tumorigenesis, we find high mtDNA mutation loads associated with shorter patient survival. We show that a combination of mutations in the mitochondrial and nuclear genomes impact prognosis, suggesting strategies for patient stratification. Tumour evolution and heterogeneity in high grade serous ovarian cancer (HGSOC) remains poorly characterised. Here, the authors investigate the genomic landscape of HGSOC and reveal two distinct evolutionary trajectories associated with clinical outcomes.

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

Phenotypic capacitance refers to the ability of a genome to accumulate mutations that are conditionally hidden and only reveal phenotype-altering effects after certain environmental or genetic changes. Capacitance has important implications for the evolution of novel forms and functions, but experimentally studied mechanisms behind capacitance are mostly limited to complex, multicomponent systems often involving several interacting protein molecules. Here we demonstrate phenotypic capacitance within a much simpler system, an individual RNA molecule with catalytic activity (ribozyme). This naturally occurring RNA molecule has a modular structure, where a scaffold module acts as an intramolecular chaperone that facilitates folding of a second catalytic module. Previous studies have shown that the scaffold module is not absolutely required for activity, but dramatically decreases the concentration of magnesium ions required for the formation of an active site. Here, we use an experimental perturbation of magnesium ion concentration that disrupts the folding of certain genetic variants of this ribozyme and use in vitro selection followed by deep sequencing to identify genotypes with altered phenotypes (catalytic activity). We identify multiple conditional mutations that alter the wild-type ribozyme phenotype under a stressful environmental condition of low magnesium ion concentration, but preserve the phenotype under more relaxed conditions. This conditional buffering is confined to the scaffold module, but controls the catalytic phenotype, demonstrating how modularity can enable phenotypic capacitance within a single macromolecule. RNA’s ancient role in life suggests that phenotypic capacitance may have influenced evolution since life’s origins.

Hybridization between species can either promote or impede adaptation. But we know very little about the genetic basis of hybrid fitness, especially in nondomesticated organisms, and when populations are facing environmental stress. We made genetically variable F2 hybrid populations from two divergent Saccharomyces yeast species. We exposed populations to ten toxins and sequenced the most resilient hybrids on low coverage using ddRADseq to investigate four aspects of their genomes: 1) hybridity, 2) interspecific heterozygosity, 3) epistasis (positive or negative associations between nonhomologous chromosomes), and 4) ploidy. We used linear mixed-effect models and simulations to measure to which extent hybrid genome composition was contingent on the environment. Genomes grown in different environments varied in every aspect of hybridness measured, revealing strong genotype–environment interactions. We also found selection against heterozygosity or directional selection for one of the parental alleles, with larger fitness of genomes carrying more homozygous allelic combinations in an otherwise hybrid genomic background. In addition, individual chromosomes and chromosomal interactions showed significant species biases and pervasive aneuploidies. Against our expectations, we observed multiple beneficial, opposite-species chromosome associations, confirmed by epistasis- and selection-free computer simulations, which is surprising given the large divergence of parental genomes (∼15%). Together, these results suggest that successful, stress-resilient hybrid genomes can be assembled from the best features of both parents without paying high costs of negative epistasis. This illustrates the importance of measuring genetic trait architecture in an environmental context when determining the evolutionary potential of genetically diverse hybrid populations.

Forest vulnerability to drought is expected to increase under anthropogenic climate change, and drought-induced mortality and community dynamics following drought have major ecological and societal impacts. Here,we showthat tree mortality concomitant with drought has led to short-term (mean 5 y, range 1 to 23 y after mortality) vegetation-type conversion in multiple biomes across the world (131 sites). Self-replacement of the dominant tree species was only prevalent in 21% of the examined cases and forests and woodlands shifted to nonwoody vegetation in 10% of them. The ultimate temporal persistence of such changes remains unknown but, given the key role of biological legacies in long-term ecological succession, this emerging picture of postdrought ecological trajectories highlights the potential for major ecosystem reorganization in the coming decades. Community changes were less pronounced under wetter postmortality conditions. Replacement was also influenced by management intensity, and postdrought shrub dominance was higher when pathogens acted as codrivers of tree mortality. Early change in community composition indicates that forests dominated by mesic species generally shifted toward more xeric communities, with replacing tree and shrub species exhibiting drier bioclimatic optima and distribution ranges. However, shifts toward more mesic communities also occurred and multiple pathways of forest replacement were observed for some species. Drought characteristics, species-specific environmental preferences, plant traits, and ecosystem legacies govern postdrought species turnover and subsequent ecological trajectories, with potential far-reaching implications for forest biodiversity and ecosystem services.

Abstract Self-cleaving ribozymes are genetic elements found in all domains of life, but their evolution remains poorly understood. A ribozyme located in the second intron of the cytoplasmic polyadenylation binding protein 3 gene (CPEB3) shows high sequence conservation in mammals, but little is known about the functional conservation of self-cleaving ribozyme activity across the mammalian tree of life or during the course of mammalian evolution. Here, we use a phylogenetic approach to design a mutational library and a deep sequencing assay to evaluate the in vitro self-cleavage activity of numerous extant and resurrected CPEB3 ribozymes that span over 100 My of mammalian evolution. We found that the predicted sequence at the divergence of placentals and marsupials is highly active, and this activity has been conserved in most lineages. A reduction in ribozyme activity appears to have occurred multiple different times throughout the mammalian tree of life. The in vitro activity data allow an evaluation of the predicted mutational pathways leading to extant ribozyme as well as the mutational landscape surrounding these ribozymes. The results demonstrate that in addition to sequence conservation, the self-cleavage activity of the CPEB3 ribozyme has persisted over millions of years of mammalian evolution.

Abstract Adaptation from standing genetic variation is an important process underlying evolution in natural populations, but we rarely get the opportunity to observe the dynamics of fitness and genomic changes in real time. Here, we used experimental evolution and Pool-Seq to track the phenotypic and genomic changes of genetically diverse asexual populations of the yeast Saccharomyces cerevisiae in four environments with different fitness costs. We found that populations rapidly and in parallel increased in fitness in stressful environments. In contrast, allele frequencies showed a range of trajectories, with some populations fixing all their ancestral variation in <30 generations and others maintaining diversity across hundreds of generations. We detected parallelism at the genomic level (involving genes, pathways, and aneuploidies) within and between environments, with idiosyncratic changes recurring in the environments with higher stress. In particular, we observed a tendency of becoming haploid-like in one environment, whereas the populations of another environment showed low overall parallelism driven by standing genetic variation despite high selective pressure. This work highlights the interplay between standing genetic variation and the influx of de novo mutations in populations adapting to a range of selective pressures with different underlying trait architectures, advancing our understanding of the constraints and drivers of adaptation.

Mutations and their effects on fitness are a fundamental component of evolution. The effects of some mutations change in the presence of other mutations, and this is referred to as epistasis. Epistasis can occur between mutations in different genes or within the same gene. A systematic study of epistasis requires the analysis of numerous mutations and their combinations, which has recently become feasible with advancements in DNA synthesis and sequencing. Here we review the mutational effects and epistatic interactions within RNA molecules revealed by several recent high-throughput mutational studies involving two ribozymes studied in vitro, as well as a tRNA and a snoRNA studied in yeast. The data allow an analysis of the distribution of fitness effects of individual mutations as well as combinations of two or more mutations. Two different approaches to measuring epistasis in the data both reveal a predominance of negative epistasis, such that higher combinations of two or more mutations are typically lower in fitness than expected from the effect of each individual mutation. These data are in contrast to past studies of epistasis that used computationally predicted secondary structures of RNA that revealed a predominance of positive epistasis. The RNA data reviewed here are more similar to that found from mutational experiments on individual protein enzymes, suggesting that a common thermodynamic framework may explain negative epistasis between mutations within macromolecules.