Rapid Construction of Empirical RNA Fitness Landscapes

Abstract

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 kobs for every point mutant of a catalytic RNA, we demonstrated that abundance in serially depleted pools correlates with biochemical activity (r = 0.67, Z = 7.4). Therefore, enumeration of each genotype by deep sequencing yielded a fitness landscape containing ~107 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.

Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments

“…If the map possesses sufficient (e.g., base-by-base) resolution in sequence space, potential evolutionary pathways can be discovered by identifying networks of sequences that are connected by small mutational steps. The importance of understanding the relationship between functional activity and sequence, together with the potential to study evolutionary issues, provides motivation for constructing and analyzing maps of molecular fitness landscapes [13][14][15]. However, until the advent of high-throughput sequencing techniques, maps of the fitness landscape were difficult, if not impossible, to obtain, owing to the small amount of sequencing information available via low-throughput techniques.…”

“…High-throughput sequencing (HTS) has been used to delineate fitness landscapes localized around a known ribozyme [13]. In such work, the fitness of a given sequence during the selection can be determined by a direct comparison of the frequency of that sequence before and after selection, owing to the relatively small number of variants.…”

“…Representation of the landscape therefore requires special consideration. Several groups have already addressed this important question [37,13,14] and have developed different computational tools, usually based on principle components analysis, to graphically represent appropriate aspects of the fitness landscape. However, when the landscape includes many different, essentially unrelated, peaks (e.g., [15]), it is not represented well by a small number of principal components.…”

The Effects of Stabilizing and Directional Selection on Phenotypic and Genotypic Variation in a Population of RNA Enzymes

“…An important feature of our study system is that the phenotypes are encoded in very small genotypes (<200 bp). For such genotypes, high throughput sequence technology can be used to analyze many genotypes in evolving RNA populations (Pitt and Ferre-D'Amare 2010;Hayden et al 2011). In other words, they allow us to study empirically how such populations spread through the vast genotype space of all possible RNA sequences.…”

Evolving Wonder-RNAs in a Test Tube

“…One way to expedite its discovery is to take advantage of the next-generation sequencing technologies that are capable of producing millions of sequence reads in a single sequencing reaction (Rothberg and Leamon 2008). Therefore, there is no need to wait for the ''signal'' to emerge from the evolving pool-one just needs to perform a limited number of selection cycles, send the partially enriched pool for sequencing, and pick the top-ranked sequence(s) to go to the next step (Pitt and Ferré-D'Amaré 2010). With all these wonderful tools or technologies at our disposal and surely better ones to come, it is just a matter of time until we can create large wonder-RNAs at ease, whether for pure scientific fun or for practical needs.…”