You'd Prefer An Argonaute

Vernalization-Mediated Epigenetic Silencing by a Long Intronic Noncoding RNA

Jae Bok Heo and Sibum Sung

Science Vol. 331 no. 6013 pp. 76-79, 7 January 2011.
DOI: 10.1126/science.1197349

This week’s straight summary/analysis by Carla Klattenhoff:

In this paper the authors present exciting findings about the role of a novel long ncRNA, termed COLDAIR, in the process of vernalization in Arabidopsis. Vernalization is a system that allows plants to sense prolonged exposure to cold and acquire the ability to flower rapidly in the spring. Previous work has established that prolonged cold results in epigenetic silencing of the floral repressor FLC, mediated by the conserved repressive complex PRC2. COLDAIR is expressed from a cryptic promoter in an intronic region of FLC during exposure to cold and binds to PRC2. Knockdown of COLDAIR results in delayed flowering after vernalization and consistent increased expression of FLC. This increase in FLC expression is correlated with decreased recruitment of PRC2 and H3K27 tri-methylation at the FLC locus. The authors conclude that COLDAIR is required to recruit PRC2 to the FLC locus during vernalization to stably repress FLC expression.

I think the data presented in this paper is solid and support the conclusions drawn by the authors. My only criticism is that the discussion of the mechanism and implications of this finding seemed a little simplistic and superficial.

Poly(A) Tail Recognition by a Viral RNA Element Through Assembly of a Triple Helix

Rachel M. Mitton-Fry, Suzanne J. DeGregorio, Jimin Wang, Thomas A. Steitz and Joan A. Steitz

Science Vol. 330, no. 6008, pp. 1244-1247, 26 November 2010.
DOI: 10.1126/science.1195858

This week’s to the point summary and analysis by Alex Robertson:

In this paper the authors report the first known endogenous example of a U rich loop capturing and protecting a poly(A) tail sequence. Through an intramolecular clamp mechanism, the viral polyadenylated nuclear RNA (PAN RNA) contains an expression and nuclear retention element (ENE) that protects the poly(A) tail by forming a triple helix. During the lytic phase of Kaposi’s sarcoma–associated herpesvirus lifecycle, PAN RNA is produced in extremely high levels, encompassing as much as 80% of the polyadenylated RNA in a cell. This PAN RNA is 1.1kb in length, has a 5’ cap and 3’ tail, is non-coding, and has unknown function. It expresses three ENEs which have been shown to protect it from degradation as well as protect other mRNAs in cis when inserted into their sequences.

The paper presents a crystal structure of the U rich internal loop of the ENE bound to A10. The structure is a triple helix which bears a stronger resemblance to riboswitches and pseudoknots (both intramolecular) than snoRNA-rRNA complex (intermolecular). This is in contrast to their predictions based on sequence similarity. The triple helix consists of U:A-U base triples which form planes and stack up analogously to double helices. When the bases are mutated to C:A-C in one position (the poly(A) is homogeneous and can move around) the structure is disrupted, destabilizing the PAN RNA in nuclear extract. Mutating the A to a G restores function/structure by allowing the C:G-C base triple to form. Based on this and other analyses in the paper, I am convinced that their structure and interpretation of the ENE’s function are correct. They speculate that since viruses borrow strategies from their hosts, there may be similar mechanisms in host organisms.

Unique functionality of 22-nt miRNAs in triggering RDR6-dependent siRNA biogenesis from target transcripts in Arabidopsis

Josh T Cuperus, Alberto Carbonell, Noah Fahlgren, Hernan Garcia-Ruiz, Russell T Burke, Atsushi Takeda, Christopher M Sullivan, Sunny D Gilbert, Taiowa A Montgomery & James C Carrington

Nature Structural & Molecular Biology Volume 17, Number 8, 997–1003, August 2010.
DOI: doi:10.1038/nsmb.1866

This week’s fluid summary and analysis by Vikram Agarwal:

Over the past half-decade, one of the questions that has persisted in field of plant small RNA biology is that of how 21-nt short interfering RNAs (siRNAs) emerge from transcripts that are targeted by microRNAs (miRNAs).  Only a few aspects of the pathway have been characterized: first, an Argonaute-miRNA complex recognizes its target and cleaves it; second, RDR6, and RNA-dependent RNA polymerase, is recruited to synthesize an antisense transcript using the cleaved transcript as a template; and finally, Dicer-like 1 (DCL1) recognizes the double stranded RNA and processively cleaves it into phased 21-nt RNAs, which are presumably loaded into new Argonautes to cleave new targets.  These siRNAs are assumed to predominantly act in cis, serving as a positive feedback mechanism to rapidly degrade the original miRNA target.  However, several that emerge from the noncoding TAS genes after miRNA-mediated cleavage are known to act in trans, guiding the downstream targeting of genes that coordinate the response to auxin, a phytohormone that is critical for proper plant growth and development.

Yet another observation that has been difficult to explain is why only a handful of miRNA-targeted transcripts produce these siRNAs, whereas the vast majority apparently do not recruit RDR6 and produce siRNAs.  In this article, Cuperus and colleagues seek to address these questions; they demonstrate that a common feature of most RDR6-dependent siRNA generating transcripts is their targeting by 22-nt miRNAs, and that this targeting is sufficient for the production of siRNAs.  They begin their study by exploring the distribution of small RNA size classes that arise from transcripts that are either targeted or not targeted by miRNAs.  As expected, they find targeted transcripts predominantly produce phased, 21-nt small RNAs, the signature of DCL1-mediated cleavage (Figure 1a,b).  Most importantly, they find that 21-nt-generating loci are overwhelmingly targeted by 22-nt small RNAs (Figure 1d).  This sharp asymmetry sets the stage for characterizing the biogenesis and role of 22-nt miRNAs.

Mining published small RNA sequencing libraries, they identify precursor loci that produce a mature miRNA primarily in the 21-nt or 22-nt species, both in Arabidopsis and rice (Figure 2b).  Probing for any structural biases in the precursors that give rise to 22-nt miRNAs, they find that most contain an asymmetric bulge in the miRNA-miRNA* pairing interface, though this is not an absolute requirement (Figure 2c,d).  Exploiting this knowledge, they construct artificial miRNAs (amiRNAs) of miR173 that contain a symmetric or asymmetric bulge, generating mature 21 and 22-nt mature miRNA species, respectively (Figure 3a,b).  Interestingly, only the 22-nt natural miRNA and 22-nt amiRNA, but not the 21-nt amiRNA, successfully guide phased siRNA production (Figure 3b,c), though all miRNAs are successfully loaded into Ago1 and cleave their targets (Figure 3d-f).  Moreover, these observations are not specific to miR173, but hold true for amiRNA constructs comparing 21 and 22-nt variants of miR473 and miR828 (Figure 4a-d).

Collectively, these results suggest a general mechanism –22-nt miRNAs are the key determinants that guide the subsequent synthesis of phased siRNAs.  Overall, this paper provides an explanation for a long-observed phenomenon.  However, the reader is left wondering about the underlying molecular mechanism: how can the seemingly innocuous addition of a single base on a mature miRNA recruit RDR6 and thereby orchestrate a completely novel molecular trajectory?  Why is it that there are still targets that produce siRNAs but are not targeted by a 22-nt miRNA, and conversely, why are there targets of 22-nt miRNAs that do not produce detectable secondary siRNAs?  Clearly, the proposed model does not cover all bases, and there is still much to be learned about the missing players in this pathway.

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.