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RNA Journal Club 10/28/10

Posted in RNA Journal Club, RNAJC w/ review by YPAA on October 28, 2010

Rapid Construction of Empirical RNA Fitness Landscapes

Jason N. Pitt and Adrian R. Ferré-D’Amaré

Science Vol. 330, 376 – 379, 15 October 2010.
DOI: 10.1126/science.1192001

This week’s instructive summary and analysis by Xuebing Wu:

This paper is one of the newest examples of how next-generation sequencing technology is enabling us to do experiements previously not possible. Combining sequencing with in vitro selection, Pitt and Ferré-D’Amaré demonstrate that it’s now possible to measure fitness for millions of genotypes and build an empirical fitness landscape.

A fitness landscape is a map that connects genotypes and the fitness of their corresponding phenotypes, and it has been proposed that evolution is an adaptive walk through such a landscape, toward higher fitness. It would be cool to actually see such a landscape to decipher whether there are, for example, multiple optimal states in the path , and how evolution jumps from one state to the next. Constructing such a landscape, however, presents a huge challenge. For example, for a simple 20mer RNA molecule, there are roughly 1020 genotypes whose fitness you would need to measure to make a fitness landscape. More than 20 years ago we already had the technology to generate such vast genotypic space, by either chemically synthesizing DNA oligos, or large-scale mutagenesis of a template. So the real challenge is efficiently measuring the fitness for each genotype. “Fitness” itself is easily generalized–depending on the system and phenotype you are studying, fitness may be defined in different ways. It is, however, a widely accepted notion that in evolution, or population genetics, when there is selection pressure, the frequencies of genotypes with higher fitness increase. Therefore upon selection, an increase in genotype frequency can be used as a surrogate for fitness. This translates the problem of measuring the fitness of each genotype to measuring the frequency of each genotype, now feasible with next-generation sequencing technology.

The authors tested this idea using the Class II ligase, a ribozyme that catalyzes the ligation of its 5’ end to a substrate sequence. The authors had a couple reasons for choosing this molecule: it is short enough that its full length can be sequenced with high quality; and it is highly active, almost optimal in terms of catalytic activity such that its peak in a fitness landscape should be clear. Interestingly, this ligase (also known as “a4-11”) was isolated in David Bartel’s lab about 15 years ago, through multiple rounds of in vitro selection from a pool of totally random sequences. So the goal in the present study was to construct an empirical fitness landscape for this RNA ligase.

Recall that the change in genotype frequencies before and after in vitro selection serves as a measure of fitness. To select sequences capable of performing ligation, the authors incubated a pool of random RNA sequences with substrates covalently attached to beads, so that molecules with more ligase activity are more easily ligated to the substrates immobilized to beads. These selected RNAs are then reverse transcribed, PCR amplified, and sequenced.

The authors showed that selection enriches for sequences similar to the a4-11 wild-type sequence, and that sequences which come out earlier in their “serial depletion” are more biochemically active, which is not surprising. By creating ~160 single point mutants of the ligase and measuring their activity, they also showed that the frequency of genotypes in the selected pool was positively correlated with experimentally measured rate constants. This observation supports the use of genotype frequency as a surrogate for fitness. However, in my opinion, it would have been better if they had also shown that the change in genotype frequency upon selection positively correlates with measured rate constants, since it’s the change in frequency, not the frequency itself, which indicates fitness.

Overall, I think this is an interesting paper. The fitness landscape yields much more information than could be obtained from traditional in vitro selection experiments. However, outside of the ribozyme field, I don’t see clearly how such landscape can be used.

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