RNA Fitness Landscapes Research Articles

Fitness Landscapes and Evolution of Catalytic RNA.

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.

Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data.

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

Dynamic RNA Fitness Landscapes of a Group I Ribozyme during Changes to the Experimental Environment.

Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl2). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.

In vitro selections with RNAs of variable length converge on a robust catalytic core.

In vitro selection is a powerful tool that can be used to understand basic principles of molecular evolution. We used in vitro selection to understand how changes in length and the accumulation of point mutations enable the evolution of functional RNAs. Using RNA populations of various lengths, we performed a series of in vitro experiments to select for ribozymes with RNA ligase activity. We identified a core ribozyme structure that was robust to changes in RNA length, high levels of mutagenesis, and increased selection pressure. Elaboration on this core structure resulted in improved activity which we show is consistent with a larger trend among functional RNAs in which increasing motif size can lead to an exponential improvement in fitness. We conclude that elaboration on conserved core structures is a preferred mechanism in RNA evolution. This conclusion, drawn from selections of RNAs from random sequences, is consistent with proposed evolutionary histories of specific biological RNAs. More generally, our results indicate that modern RNA structures can be used to infer ancestral structures. Our observations also suggest a mechanism by which structural outcomes of early RNA evolution would be largely reproducible even though RNA fitness landscapes consist of disconnected clusters of functional sequences.

Negative Epistasis in Experimental RNA Fitness Landscapes.

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.

13 RNA fitness landscapes

The origin of life is believed to have progressed through an RNA World, in which RNA acted as both genetic material and functional molecules. Understanding early evolution requires systematic knowledge of the relationship between RNA sequence and functional activity. In particular, knowing the structure of the fitness landscape of RNA is critical in estimating the probability of the emergence of functional sequences and the role of historical accident during evolution. Much theoretical work has been devoted to fitness landscapes, but experimental maps have been relatively limited. We use in vitro selection on a pool of short RNA sequences that nearly saturates sequence space to reconstruct the form of a comprehensive fitness landscape. We also study mutations during non-enzymatic polymerization to understand how early RNA replicators would ‘move’ in sequence space.

Rapid Construction of Empirical RNA Fitness Landscapes

Evolution is an adaptive walk through a hypothetical fitness landscape, which depicts the relationship between genotypes and the fitness of each corresponding phenotype. We constructed an empirical fitness landscape for a catalytic RNA by combining next-generation sequencing, computational analysis, and "serial depletion," an in vitro selection protocol. By determining the reaction rate constant for every point mutant of a catalytic RNA, we demonstrated that abundance in serially depleted pools correlates with biochemical activity (correlation coefficient r = 0.67, standard score Z = 7.4). Therefore, enumeration of each genotype by deep sequencing yielded a fitness landscape containing ~10(7) unique sequences, without requiring measurement of the phenotypic fitness for each sequence. High-throughput mapping between genotype and phenotype may apply to artificial selections, host-pathogen interactions, and other biomedically relevant evolutionary phenomena.

Adaptive evolutionary walks require neutral intermediates in RNA fitness landscapes

In RNA fitness landscapes with interconnected networks of neutral mutations, neutral precursor mutations can play an important role in facilitating the accessibility of epistatic adaptive mutant combinations. I use an exhaustively surveyed fitness landscape model based on short sequence RNA genotypes (and their secondary structure phenotypes) to calculate the minimum rate at which mutants initially appearing as neutral are incorporated into an adaptive evolutionary walk. I show first, that incorporating neutral mutations significantly increases the number of point mutations in a given evolutionary walk when compared to estimates from previous adaptive walk models. Second, that incorporating neutral mutants into such a walk significantly increases the final fitness encountered on that walk — indeed evolutionary walks including neutral steps often reach the global optimum in this model. Third, and perhaps most importantly, evolutionary paths of this kind are often extremely winding in their nature and have the potential to undergo multiple mutations at a given sequence position within a single walk; the potential of these winding paths to mislead phylogenetic reconstruction is briefly considered.