Svetlana Lebedeva, Marvin Jens, Kathrin Theil, Björn Schwanhäusser, Matthias Selbach, Markus Landthaler, and Nikolaus Rajewsky
Molecular Cell 43, 1–13, 5 August 2011.
This week’s crystal clear summary and analysis by David Koppstein:
Two papers were recently and concomitantly published in Molecular Cell (Lebedeva et al, 2011; Mukherjee et al, 2011) describing the transcriptome-wide binding sites of the RBP HuR by employing the second-generation high-throughput technique PAR-CLIP (photoactivable ribonucleoside crosslinking and immunoprecipitation). HuR is a member of the Hu/ELAV family of proteins, which each have three RRM domains and whose knockouts are embryonic lethal. Unlike the rest of its family members, which are restricted to the nervous system and have been reported to affect alternative splicing, HuR is ubiquitously expressed and has been previously implicated in stabilizing messages with AU-Rich Elements (AREs) in their 3’-UTRs (Fan and Steitz, 1998). The mechanism of stabilization is unclear, although it is thought to sterically interfere with binding of other ARE destabilization factors such as hnRNP D and TTP. There have also been conflicting reports about crosstalk between HuR and miRISC. One group found that binding of HuR by CAT-1 relieves miRNA repression (Bhattacharya et al, 2006), while another found that HuR recruits let-7 to repress c-myc (Kim et al, 2009).
Consistent with previous results, the majority of HuR binding sites were in the 3’-UTR. Unexpectedly, both groups found a large fraction of reads mapping to intronic regions. Upon closer inspection, it was found that there is a distinct peak of HuR clusters ~20bp upstream of the 3’ splice site of introns. This is probably not an experimental artifact of the 4SU that was used, since the Rajewsky group also used 6SG to crosslink and recovered many of the same intronic polypyrimidine motifs. The role of binding to the 3’ splice site has yet to be elucidated, however, since both groups were unable to find significant correlations with exon inclusion or exclusion upon HuR knockdown.
Both groups showed that, upon knocking down HuR with siRNAs, messages with HuR clusters mapping to them are destabilized, confirming the role of HuR in stabilizing messages. Interestingly, the Keene group provided evidence that more HuR binding sites correlated with more destabilization upon HuR knockdown. Furthermore, messages with purely intronic binding sites were just as destabilized, if not more so, than messages with purely 3’-UTR sites. The Rajewsky group also performed pulsed SILAC, and found that protein synthesis levels essentially mirrored mRNA levels, in contrast to previous suggestions that HuR modulates translation (Mazan-Mamczarz et al, 2003).
The Rajewsky group also compared small RNA profiles of mock- and anti-HuR siRNA-transfected cells, perhaps looking for HuR regulation of miRNA levels. Although most miRNAs remained unperturbed, the authors found that miR-7 was strongly upregulated upon HuR knockdown. Although the mechanism of this regulation is unclear, the presence of clusters adjacent to the mirtron precursor of miR-7 in hnRNP K is suggestive of direct regulation, as opposed to some secondary effect.
In contrast, the Keene group utilized Tuschl’s cocktail of anti-miR 2’-OMe oligonucleotides to block the efficacy of several microRNAs, and then made mRNA-seq libraries. They found that messages with miRNA sites overlapping the HuR binding site were less affected by blocking the miRNA than those without, consistent with the idea that HuR can sterically inhibit binding of miRISC. The Rajewsky group also observed that miRNA seed sites tend not to overlap with HuR binding sites.
These two papers made several important and novel findings, including prevalent intronic binding of HuR, the increased efficacy of multiple binding sites, and the regulation of miR-7. Further studies will be needed to elucidate the role of binding upstream of the 3’ splice site, and the mechanism and biological relevance of miR-7 regulation. Neither paper examined the role of HuR in stress conditions, which has been established as a role of HuR previously (Meisner et al, 2010). Altogether, these papers are an interesting read about an important RBP and an exercise in interpreting high-throughput experimental data.
Xuecheng Ye, Nian Huang, Ying Liu, Zain Paroo, Carlos Huerta, Peng Li, She Chen, Qinghua Liu & Hong Zhang
Nature Structural & Molecular Biology AOP, 8 May 2011.
This week’s terrific summary and analysis by Alex Subtelny:
An important and lingering question in the biochemistry of mammalian RNAi is how a small RNA duplex is loaded into Argonaute (Ago), the core component of the RNA-induced silencing complex (RISC). Previously, several groups reported that this process is mediated by Dicer and TRBP, including one which showed that RISC loading and activation could be reconstituted in vitro with purified human Ago2, Dicer, TRBP and a pre-miRNA hairpin (MacRae et al., 2008). However, this model is challenged by the findings that (1) miRNAs injected into zebrafish embryos lacking both maternal and zygotic Dicer are capable of repressing target mRNAs (Giraldez et al., 2005; admittedly, this finding is from a non-mammalian system, although one that could be relatively similar to mammals in terms of RISC loading), and (2) ATP greatly enhances loading of human RISC (Yoda et al., 2010), consistent with the involvement of Hsc70/Hsp90, which was later shown to promote RISC loading in Drosophila and human (Iwasaki et al., 2010). Two years ago, Qinghua Liu and colleagues reported the discovery in Drosophila of C3PO, a complex of Trax and translin proteins that possesses endoribonuclease activity and promotes RISC activation, apparently by removing the cleaved passenger strand of a loaded siRNA duplex. Trax and translin are conserved to humans, raising the question of whether C3PO functions analogously in mammals. Moreover, since the authors of the present study failed to reconstitute RISC activity in vitro with purified human Ago2, Dicer, TRBP and double-stranded siRNA duplex (ds-siRNA), another question is which other factors are required for mammalian RISC loading and activation (and whether C3PO is one of these).
In this paper, Ye et al. reconstitute RISC activity with human Ago2, ds-siRNA and C3PO, and show that the latter is required for the efficient removal of the Ago2-cleaved passenger strand. Moreover, they report the crystal structure of human C3PO, which resembles a partial structure of the Drosophila C3PO complex reported in a simultaneous advance online publication in NSMB (Tian et al., 2011). To identify the factors that activate human RISC, the authors supplied purified hAgo2 with ds-siRNA and chromatographic fractions of HeLa extract, and measured cleavage of a target mRNA with a fully complementary sequence to the siRNA guide strand. The fractions containing TRAX and translin reconstituted ds-siRNA programmed RISC activity when combined with hAgo2, as did purified recombinant C3PO. How, then, to reconcile these results with previous reports of Ago2, Dicer and TRBP being necessary and sufficient for RISC activation? Ye et al. found that Dicer and TRBP did not enhance ds-siRNA programmed RISC activity in the presence of C3PO or that these two factors were even necessary for ds-siRNA binding by hAgo2, but that they were required together with C3PO for shRNA-triggered target cleavage. Thus, Dicer and TRBP appear to function exclusively in converting a precursor hairpin into a mature siRNA duplex. The ability of MacRae et al. to reconstitute RISC activity with Dicer and TRBP can be explained if the pre-miRNA they used somehow acted as an ss-siRNA, which for mammalian Agos can initiate RISC activity as such in the absence of C3PO. In support of this explanation, Yoda et al. found that cleavage of a target RNA could be achieved with only purified recombinant hAgo2 programmed with a pre-miRNA whose 5’ arm was complementary to the target. After demonstrating that C3PO is required for RISC activation in vitro, Ye et al. showed that this result is relevant in vivo by rescuing ds-siRNA programmed target cleavage in translin-null MEF cell lysates and cells with purified C3PO and a translin transgene, respectively.
The authors then performed what was almost certainly the non-trivial task of obtaining a crystal structure of the human C3PO complex, using full length Trax and translin. The complex is a barrel-shaped hetero-octamer containing six translin and two TRAX subunits that enclose a central cavity. The top and bottom “halves” of the barrel, each of which consists of one TRAX and three translin molecules, possess a right-handed superhelical shift that is formed by interactions between TRAX and translin #1 and between adjacent translins and abolished at the interface between translin #3 and TRAX. The authors believe that this shift accounts for why C3PO exhibits only a single stoichiometry and arrangement of subunits. Indeed, ablating two salt-bridge interactions at the translin #3-TRAX interface yielded complexes of abnormal stoichiometry and greatly reduced C3PO ssRNA binding and cleavage activity. Intriguingly, the four putative catalytic Glu and Asp residues of C3PO (identified by conservation and by their proximity to the sole Mn2+ ion in a second, co-crystal structure of C3PO and Mn2+) are located inside the barrel. Mutating these amino acids abolished ssRNA cleavage, but not binding (whereas mutating several nearby basic residues thought to interact with the ssRNA backbone resulted in a loss of both activities. These mutations did not perturb the structure of the complex, based on size-exclusion chromatography analysis). To support their hypothesis that ssRNA cleavage occurs in the interior of the complex, Ye et al. showed that incubating C3PO with an ssRNA shifted its gel filtration profile by the molecular weight of the RNA. Moreover, the catalytic (and RNA-binding) mutants of C3PO were incapable of activating ds-siRNA programmed RISC activity when combined with hAgo2. Finally, the authors showed that removal of the passenger strand fragments generated by Ago2 cleavage is not a passive process, but is promoted by the endonuclease activity of C3PO, as also appears to be the case for fly RISC. Importantly, C3PO prefers to act on a nicked duplex; little passenger strand degradation was seen when purified C3PO was incubated with intact siRNA duplex.
All in all, this paper is a very impressive combination of biochemistry and structural biology, providing insight into how C3PO activates RISC and how it works as a macromolecular machine. Nevertheless, one is left wondering how an ssRNA substrate can access the catalytic residues in the interior of the octamer. The authors posit several models for this, including one where the C3PO octamer transiently opens to let an ssRNA into the barrel and another where C3PO binds a substrate as a tetramer prior to formation of the full octamer (somewhat analogously to the bacterial GroES/GroEL chaperonin complex). A direct demonstration that an ssRNA substrate can enter the interior of the C3PO barrel might be a co-crystal structure of C3PO and cleavage-resistant ssRNA, though this approach is not without problems (including poor resolution for the electron density map of the ssRNA if the RNA is conformationally flexible inside the barrel, and potentially limited physiological relevance of the complex in the crystal). Another question is whether additional factors may work together with C3PO to promote passenger strand removal from an Ago-loaded small RNA duplex in mammals. Regardless of these unresolved questions, this paper represents an important piece of the puzzle of how an active RISC complex is formed, and is also an enjoyable read.
Elitza Deltcheva, Krzysztof Chylinski, Cynthia M. Sharma, Karine Gonzales, Yanjie Chao, Zaid A. Pirzada, Maria R. Eckert, Jörg Vogel & Emmanuelle Charpentier
Nature 471, 602–607, 31 March 2011.
This week’s superb summary and analysis by Josien van Wolfswinkel:
Over the last couple of decades CRISPR has become known as the prokaryote version of an adaptive immune response against viruses. In contrast to the vertebrate version of immunity, this system functions not on a protein level, but via recognition of the DNA sequence of the invader by crRNAs.
The CRISPR system consists of two units that together form the CRISPR locus: the “clustered, regularly interspaced short palindromic repeats” (CRISPRs), and the CRISPR associated (cas) proteins which are encoded as a group directly adjacent to the CRISPRs. A CRISPR sequence consists of ~30nt long repeats which are interrupted by ~40nt sequences that can be derived from phages. The whole sequence is initially transcribed as a long single-stranded pre-crRNA, from which mature crRNAs consisting of a single spacer sequence with some surrounding repeat sequence are processed by the cas proteins. Based on the combination of proteins encoded in the cluster, 8 types of CRISPR clusters can be distinguished. Most of them contain an enzyme that is known to perform the RNA processing reaction, however in two types of clusters (Nmeni and Dvulg) no processing enzyme had been found so far.
In this paper Delcheva et al. describe a novel pathway for the processing of mature crRNA from the pre-crRNA primary transcript. First, the authors established that many clinical isolates of Streptococcus pyogenes contain two types of CRISPR clusters (Nmeni and Dvulg), of which only the first type is expressed. The S. pyogenes Nmeni cluster produces mature crRNAs with a 5′ monoP, suggesting that these are not primary transcripts, yet none of the known CRISPR processing enzymes is encoded among the cas genes of this cluster. The authors identified a locus adjacent to the CRISPR cluster, which produces two primary transcripts and one processed RNA species at high levels, and named this the trans-activating crRNA (tracrRNA). Closer inspection of the locus sequence revealed a 25 nucleotide stretch present in both primary tracrRNA transcripts that has almost perfect complementarity to part of the repeat in the pre-crRNA primary transcript. Notably, the 5′ end of the processed tracrRNA, as well as the 3′ end of mature crRNAs are located within this basepairing region.
The authors used deletions of both tracrRNA and pre-crRNA loci to show that the production of mature crRNA depends on co-processing with the tracrRNA. The positioning of the cleavage sites on the pre-crRNA/tracrRNA duplex shows a 2nt 3′ overhang, suggestive of processing by an RNase III type enzyme. Indeed deletion of the S. pyogenes RNase III gene rnc abolished the co-processing, and recombinant Rnc was sufficient to drive co-processing of pre-crRNA and tracrRNA in vitro. In vivo however, the cas gene csn1 (but none of the other cas genes) was also required.
The authors then asked whether this CRISPR cluster can effectively confer resistance to invading phages or plasmids. They created a plasmid containing a sequence identical to one of the spacers in the CRISPR locus and found that this plasmid cannot be transfected into wildtype S. pyogenes, but is accepted by mutants in pre-crRNA, tracrRNA, rnc, or csn1.
Finally the authors identified tracrRNA loci in other species carrying the Nmeni type CRISPR cluster, and show that RNA from these loci is expressed and processed with similar dynamics as in S. pyogenes. Therefore, the mechanism described in this paper may well be a general mechanism for processing crRNA from Nmeni type clusters.
Technically, the paper is solid, but it is the conceptual aspects of it that make it remarkable. First, there have been many indications that CRISPR clusters have been transmitted between bacteria by horizontal gene transfer, and so far the clusters seemed to function as autonomous entities, which are independent of the rest of the bacterial genome. The mechanism for crRNA processing described in this paper is the first report of CRISPR dependency on unlinked loci (i.e. the Rnc that is required for the processing is not present in the CRISPR cluster). It is unclear whether this is due to a loss of independence of the Nmeni cluster, or whether the Nmeni cluster actually represents an ancestral minimal version of the CRISPR system. Second, these Nmeni crRNAs lack the 8nt 5′ repeat-derived tag–defined by positioning of the processing enzyme– characteristic of previously studied crRNAs in other species. The presence of these repeat-derived tags has also been shown to be important for the discrimination between self and non-self. In contrast, in the Nmeni-specific mechanism described in this paper, the repeat-derived tag is on the 3′ end, and therefore it is this end of the mature crRNA that is precisely defined (in this case by the Rnc and the base-pairing with the tracrRNA). This suggests that the mechanism of self versus non-self discrimination could be similar even between classes of crRNAs that differ in the positioning of their repeat-derived tags at opposite ends.
Finally, there is the very tempting parallel between this type of crRNA processing in prokaryotes and the diverse regulatory small RNA pathways in eukaryotes. Both systems use the functionality of an RNase III type enzyme to create small RNAs that are used in a silencing response. The remaining parts of the biogenesis pathways and the modes of silencing differ substantially, but nevertheless, it is interesting that the use of RNase III for regulatory small RNA processing is so widespread.
Kota Yanagitani, Yukio Kimata, Hiroshi Kadokura, Kenji Kohno
Science Vol. 331 no. 6017 pp. 586-589, 4 February 2011.
This week’s articulate summary and analysis by Anna Drinnenberg:
This paper from Yanagitani et al. further characterizes a mechanism involving an unconventional splicing event of the XBP1 mRNA that controls a cellular response to the accumulation of unfolded proteins in the endoplasmic reticulum (ER). For this splicing event to occur, it is thought that the nascent XBP1u (u – unspliced) protein, while still part of the mRNA-ribosome-nascent chain (R-RNC) complex, recruits the whole complex to the ER membrane, where a protein localized within the membrane processes the XBP1u mRNA into its spliced XBP1s (s – spliced) form. The HR2 region of the XBP1u protein that was suggested to be important for this recruitment, however, is located at the very C-terminus of the protein. Therefore HR2 is exposed from the ribosomal tunnel for only a brief period before translation is finished, which leads to the question of how the R-RNC complex can still persist while being recruited to the ER by HR2. Therefore, the authors hypothesize that a translational pause must occur to ensure sufficient time for the ER-recruitment of the R-RNC complex and splicing of the XBP1u mRNA.
Using in vitro studies, they convincingly showed a pause during translation of the XBP1u protein by detection of translational intermediates composed of a tRNA covalently attached to the nascent polypeptides, whereas translation of XBP1s protein that had a different C-terminal region lacking HR2 showed no delay. Furthermore, the authors narrowed down the region responsible for the translational pause to the evolutionary conserved C-terminal part of the XBP1u protein, namely the last 26-amino acids. Exchanging many of these amino acids for alanine decreased or abolished translational pausing, whereas mutating a serine residue at position 255 (S255A mutant) increased pausing, interestingly. The authors try to explain the effect of the S255A mutant by hypothesizing that this residue might ensure an appropriate efficiency of translational pausing to recruit the R-RNC complex, while preventing undesired translational arrest (which would not relieve the spliced mRNA). For all subsequent analysis they included two mutant constructs (in addition to the S255A construct) that nearly completely abolish translational pausing.
After showing that translational pausing also happens in vivo, they demonstrated that in vitro it also appeared to be required for efficient membrane recruitment through the HR2 region. An in vivo demonstration of the R-RNC recruitment would still be worthwhile since the complexity of the intracelluar environment through which such an R-RNC complex would have to traverse is certainly much greater than their in vitro system.
Returning to the molecular effects of membrane recruitment of the R-RNC complex, the authors showed that translation pausing is important, but not absolutely necessary, to ensure efficient splicing of the XBP1u mRNA. While there was certainly a decrease in splicing efficiency without pausing, the effect seemed to be relatively small. However, it is still possible that this subtle decrease in splicing efficiency has greater physiological consequences during a response to ER stress. Mutating a combination of the amino acids that contributed to translational pausing, instead of one at a time, might have also yielded bigger effects on splicing.
Overall, the authors performed a very thorough study showing translation pausing of the XBP1u mRNA and demonstrating its importance for splicing of the XBP1u mRNA. The authors speculate that a physical interaction between the nascent peptide and the ribosomal tunnel might explain the translational pause as it has been observed for the bacterial SecM and TnaC proteins. An important follow-up question is: Is translational pausing a more widespread phenomenon than can be predicted based on the amino acid composition of a protein? As was suggested in an accompanying perspective by David Ron and Koreaki Ito in Science, recent data mapping the progression of ribosomes across mRNAs at single nucleotide resolution (Ingolia et al., Science 2009) will be crucial in answering this question.
Joanna Y. Ip, Dominic Schmidt, Qun Pan, Arun K. Ramani, Andrew G. Fraser, Duncan T. Odom and Benjamin J. Blencowe
Genome Research, Advance Online 16 December 2010.
This week’s enlightening summary and analysis by Charles Lin:
There has been a growing appreciation in the last decade that RNA processing and transcription do not occur in isolation. Events thought to be exclusively transcriptional, such as chromatin modifications, elongation rate, and elongation complex factors have been found to interact with almost every aspect of RNA processing—from 5’ cap formation to 3’ end processing and export. Moore and Proudfoot have an excellent 2009 review that describes these interactions in detail.
Of focus this week in RNA journal club is the relationship between alternative splicing (AS) and elongation capacity of the RNA Pol II complex. Ip et al., use multiple techniques to reduce the elongation capacity of RNA Pol II and assay the effect on alternative splicing. They use these perturbations of RNA Pol II to examine two longstanding models coupling elongation/splicing interactions.
The first, the kinetic model, reviewed in Kornblihtt 2006, states that the speed of the RNA Pol II can influence AS through competitive kinetics of 3’ Splice Site choice. The second, the recruitment model, most recently reviewed by Luco et al., 2011 emphasizes the role of chromatin adaptor complexes to couple the splicing apparatus to chromatin modifications and the RNA Pol II.
Ip et al., inhibited RNA Pol II elongation through multiple mechanisms. Inhibition caused a majority of genes to decrease mRNA expression. AS was assayed with a custom array platform that interrogated exon inclusion/exclusion. The authors did not find a compelling trend towards inclusion or exclusion. Instead they focused on the set of genes that experienced exon inclusion. These genes were enriched for splicing factors and in some cases exon inclusion resulted in the addition of a premature termination codon leading to NMD mediated down regulation. This presented an enticing mechanism for coupled coordinated regulation of elongation and splicing machinery. When elongation is down regulated, it causes splicing machinery to be consequently down regulated through NMD.
The authors also find that inclusion of exons is often associated with increased RNA Pol II density flanking the exon. Increased RNA Pol II may be a function of polymerase stalling at the exon or simply a result of a slower elongating complex. It’s unclear whether the RNA Pol II accumulation represents an opportunity for a weaker 3’ Splice Site to be recognized (kinetic model) or additional recruitment of adaptor factors.
Ip et al.’s findings do not discredit one model or the other, and indeed it’s possible for these two models to co-exist. One potential reason for this is that the elongation kinetics of RNA Pol II are intrinsically linked to the ability of the elongating complex to recruit elongation factors/chromatin adaptors. In particular, several of the methods employed by Ip et al. to inhibit elongation kinetics do so by reducing or eliminating serine phosphorylation on the RNA Pol II C-terminal domain repeats. Phosphorylation of these repeats is responsible for both enhanced processivity of the enzyme and also serves as a scaffold for many elongation specific factors.
At the end of the day, the authors propose a set of enticing models built upon their observations. That many of these splicing changes were reproduced through orthologous methods lends weight to the idea that globally, splicing and elongation are coupled processes, and regulation of one may lead to coordinated regulation of the other.
Jae Bok Heo and Sibum Sung
Science Vol. 331 no. 6013 pp. 76-79, 7 January 2011.
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.
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.
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.
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.
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.
Jason N. Pitt and Adrian R. Ferré-D’Amaré
Science Vol. 330, 376 – 379, 15 October 2010.
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.
Mario H. Bengtson & Claudio A. P. Joazeiro
Nature 467, 470–473, 23 September 2010.
This week’s insightful summary and analysis by David Weinberg:
In their 2010 Nature paper, Bengtson & Joazeiro demonstrate that proteins being translated from non-stop mRNAs are targeted to the proteasome by the E3 ubiquitin ligase Ltn1. A non-stop mRNA is defined as any mRNA that lacks a stop codon that is recognized by the ribosome. Natural causes of non-stop mRNAs may include mutations in termination codons (at the DNA level or due to transcription errors), readthrough of bona fide termination codons, premature or alternative cleavage and polyadenylation within coding regions, or the initiation of 3′-5′ mRNA decay on messages being translated. Because a non-stop mRNA can only be recognized as such after at least one round of translation, non-stop protein products are necessarily generated from non-stop mRNAs. The translation of non-stop mRNAs is problematic for the cell because it results in the production of aberrant proteins and, perhaps more importantly, sequesters ribosomes as a result of the failure to recruit release factors. While the quality control pathway that recognizes non-stop mRNAs in eukaryotes has come into focus over the past decade, the details of how non-stop protein products are recognized and degraded has been relatively under-studied. Here, the authors synthesize previously-published results with their own observations to provide the first comprehensive picture of the non-stop protein decay pathway in eukaryotes.
The story began in 2009 when Joazeiro’s lab identified the E3 ubiquitin ligase Listerin in a forward genetics screen for neurodegeneration in mice. Seeking to gain insight into the cellular function of Listerin, the authors turned to its homolog Ltn1 in the budding yeast S. cerevisiae. Since Ltn1 had been previously pulled out in a yeast genetic screen for non-stop decay genes, Bengtson & Joazeiro go after the precise role of Ltn1 in this relatively-uncharacterized pathway. After verifying previously-published results that convincingly demonstrate a role for Ltn1 in the quality control of non-stop proteins, the authors go one step further and implicate its ability to specifically bind to and ubiquitinate non-stop proteins as an essential part of this pathway.
Although the E2-binding RING domain in Ltn1 is shown to be required for its function in non-stop protein decay, the identity of the E2 binding partner is not addressed here. A traditional pulse-chase experiment is used to show that Ltn1 promotes the turnover of newly-synthesized non-stop proteins, which raises the question of how non-stop proteins are recognized as aberrant and thereby targeted for degradation. A hint (or perhaps even the answer) came from previously-published observations that hard-coding 12 Lys residues in an otherwise-normal protein causes instability and that long tracts of Lys or Arg cause translational arrest (presumably due to electrostatic interactions between the nascent peptide and the ribosome exit tunnel). If a ribosome were to translate through the 3′-UTR of a non-stop mRNA and reach the poly-A tail, translation through the poly-A tail would naturally generate a C-terminal poly-Lys tract in the protein product that might similarly stall translation. Indeed, the authors show that hard-coding Lys residues recruits Ltn1 and leads to ubiquitination. Intriguingly, products associated with apparently-stalled ribosomes are specifically targeted to Ltn1, while the protein products from ribosomes that efficiently translate through the poly-K tract are not. This suggests that the translational stall, rather than the poly-Lys tract, is the signal for Ltn1 recruitment. Unfortunately the authors don’t address perhaps the most interesting follow-up question here: Does any translational stall (e.g., one caused by stretches of rare codons) trigger Ltn1-dependent ubiquitination, or is it somehow specific for non-stop proteins? Aside from identifying the poly-K tract as sufficient for Ltn1 recruitment, no additional insight is provided into how this is accomplished at the molecular level. Additional experiments show that the nascent non-stop protein is associated with ribosomes and, moreover, that Ltn1 itself is predominantly associated with ribosomes.
The paper concludes with an attempt to demonstrate the biological relevance of Ltn1 by identifying a phenotype in the ltn1 knock-out strain. While the strain shows no growth defect in standard media, the addition of either an antibiotic or nonsense suppressor mutation – both which would facilitate stop codon readthrough – reveals a slow-growth phenotype for the knock-out strain. Thus, the authors conclude that Ltn1 confers resistance to stress caused by the production of non-stop proteins, but it is unclear if the slow-growth phenotypes are due to the accumulation of the non-stop proteins themselves or the depletion of translation-competent ribosomes.
In my opinion, the most interesting aspect of the pathway characterized in this paper is how it compares to the analogous pathway used by prokaryotes. The ssrA/tmRNA pathway in prokaryotes similarly depends on the tagging of stalled nascent polypeptides and their subsequent degradation by energy-dependent proteases. However, in the case of prokaryotes – whose mRNA lack poly-A tails – the tagging sequence is provided in trans by a tmRNA molecule that recognizes ribosomes that have reached the end of an mRNA. In contrast, eukaryotes appear to take advantage of the existing poly-A tail to accomplish a similar feat without the need for a trans-acting factor. Interestingly, the tmRNA includes a stop codon that triggers translation termination of non-stop messages, while the eukaryotic pathway appears to never ‘officially’ terminate translation. This key difference perhaps warrants further investigation, as it seems unlikely that eukaryotes would altogether bypass a requirement for translation termination to recycle ribosomes from non-stop mRNAs.
While many questions remain – including how this function for Ltn1 is related to the neurodegeneration phenotype observed in Listerin mutant mice – this paper provides a satisfying initial characterization of the eukaryotic non-stop protein decay pathway, albeit with the help of many previously-published results and limited novel insight.
Hani S. Zaher & Rachel Green
Nature Vol 457, 8 January 2009.
This week’s cogent summary and analysis by Josh Arribere:
The authors initiate the paper with a discussion of known quality control mechanisms in protein synthesis. They present the overall rate in vivo as being in the range of 6e-4 and 5e-3, and state that their own in vitro measurements of fidelity are in the range of 1e-4 and 2e-3. From the overlap of these two ranges, it is not readily apparent that a new quality control mechanism need exist, but the true motivation for the study becomes apparent shortly. In the process of making an oligopeptide in vitro, the authors failed, and instead observed a miscoding event that led to premature termination.
It is from this observation the authors begin the paper. They demonstrate that although the rate of RF2-stimulated hydrolysis (release of the nascent peptide) is comparable for correct vs. miscoded events (fig 1c), the Km is ~10 fold less (fig 1d). Such a difference is surprising given the one base pair change between the two constructs. Furthermore, they demonstrate that the miscoded construct is subject to RF2-mediated release (increase in the rate of hydrolysis, fig 1f), albeit inefficiently, even when the A site lacks a stop codon. The Km is also decreased in the miscoded event, leading to an overall ~300 fold increase in the second order rate constant (fig S4). Different mismatch events and A-site codons argue that the observed phenomenon is not a peculiarity of their original construct (fig 1f). Moreover, of all the mismatches in the P-site, one is tolerated, namely, the G:U wobble base pair in the third position, as to be expected given the degenerate nature of the genetic code (fig S8). Thus the proposed mechanisms are compatible with known biology.
Primer extension assays demonstrate the ribosome has not shifted frame (fig S5), and although P-site tRNA dropoff is increased in the miscoded case, the rate of dropoff is ~2.5 fold slower than RF2-mediated release. 2.5 fold is a rather small gap, and the rate of RF2-mediated release is still two orders of magnitude slower than the rate of elongation. However, upon addition of RF3 (a class II release factor) and RF2, the rate of release increases another 10 fold (fig 2). This puts release following a miscoding event on par with the rate of chain termination, but still slower than elongation (~2/sec). So what happens if the tRNA beats the RFs to the ribosome? The rate of peptidyl transfer is not inhibited (fig 3a black bars), though the ribosome has ~10x diminished capacity to correctly incorporate the next amino acid following a miscoding event (fig 3a white bars). Examining the nature of the peptides formed reveals predominantly the correct tripeptide product in the correctly coded case. However, multiple seemingly random tripeptide products are formed following a P-site mismatch (fig 3b). Thus a single mismatch in the P site leads to a general loss of ribosome fidelity.
One of the consequences of these multiple miscoding events is a further stimulation of release by RF2 and RF3 (fig 4b,c,d). Peculiarly, an E-site mismatch alone does not stimulate release (except in the “buffer-dependent” instance of fig4b), but does stimulate release together with a P-site mismatch. This begs the question: how is an E-site mismatch only sensed together with a P-site mismatch and not by itself? Frame maintenance is somewhat compromised only in the doubly miscoded case (fig S13). Of interest, the only case where an E-site mismatch alone led to stimulation of hydrolysis (MNKF, fig 4b), also exhibits an abnormal primer-extension banding pattern similar to the doubly miscoded event (fig S13b, compare last two lanes). At any rate with this further RF2/3-stimulated increase in release for the doubly miscoded event, the rate of peptide hydrolysis (~1/sec) is now on par with elongation (~2/sec), making it a kinetically viable pathway in protein synthesis.
All of the above observations, together with the rate constants and concentrations of protein synthesis factors, are incorporated into a model (fig 5a). Testing the model with a S100 extract (supernatant of a 100,000g cell lysate) confirmed some of the predictions of the model. Following an initial miscoding event, the next correct amino acid is added ~30% of the time, and subsequently a relatively low loss of yield for this product is observed (fig 5c 3rd columns). Since the doubly miscoded event contains multiple species (many AAs possible), it is not possible to measure the “Incorrect PT” arrow in fig 5a with the TLC assay (and the authors note this). One confusing point is the apparent increase in MN-matched formation between the di- and tripeptide (fig 5c 2nd column of di-, tripeptide), though the authors do not comment on this.
The authors come an incredibly long way from a failed experiment (oligopeptide production) to discover proofreading by the ribosome. They keep an eye on rate constants to demonstrate the phenomena they are studying are kinetically relevant. Different in vitro translation labs each favor particular buffer systems (buffers A, B, C, D in this paper), and the authors quell arguments by repeating some of their observations in multiple buffer systems (for instance, fig S15). This is important since each buffer seems to have its own peculiarities (see fig 4b, S1, S3, S11), and it is not readily apparent what this means, nor which buffer, if any, is the “correct” one.
There are many future directions for further study, and some are mentioned in the paper. One that was not mentioned is the fate of the released peptide. A miscoded, truncated peptide is a potential dominant negative nightmare for the cell. Clearly there must be a tight coupling in the cell between peptide release and degradation. Subsequent unpublished experiments have shown that the mRNA is destabilized following miscoding, though I do not know about the fate of the nascent peptide. It would be very interesting to know what discerns miscoding and RF2/3 stimulated release from normal stop-codon mediated release.
Michael Kertesz, Yue Wan, Elad Mazor, John L. Rinn, Robert C. Nutter, Howard Y. Chang & Eran Segal
Nature Vol 467, 2 September 2010.
This week’s summary and analysis by David Garcia:
In contrast to experimental methods for probing RNA secondary structure such as footprinting or SHAPE, the novel method described in this paper, called PARS (Parallel Analysis of RNA Structure), offers a significant advancement: the ability to work on a grand scale. The authors applied PARS to thousands of mRNAs simultaneously from S. cerevisiae, but the technique could in theory be applied to any population of RNAs for which sequence is known, and which can be selected and folded in vitro.
That the technique analyzes RNAs folded in vitro is a valid concern, as we might not want to get too excited about the fidelity of mRNA structure formed in a test tube versus how it actually happens, probably co-transcriptionally, in the cell, especially on this scale. But to my knowledge, all other currently available methods for analyzing RNA secondary structure are in vitro too. And it doesn’t exclude the authors from noting some interesting similarities to a published in vivo ribosome profiling dataset. When a full-blown genome-wide in vivo structure approach arrives, PARS data will be a useful comparison as well.
At the core of the method is detection of which nucleotides in RNAs are either paired or unpaired, to reveal a picture (relatively low resolution in this iteration) of secondary structure on a genome-wide level. It relies on the different specificities of two nucleases, RNase V1 which preferentially cleaves phosphodiester bonds 3’ of double-stranded RNA, and S1 nuclease which preferentially cleaves phosphodiester bonds 3’ of single-stranded RNA. The authors subjected a pool of poly-A selected yeast mRNA to either enzyme, followed by base hydrolysis mediated random fragmentation to generate smaller molecules amenable to cloning and sequencing by SOLiD. After aligning the reads, they produced profiles for each RNase that, based on where and how frequently reads clustered along an mRNA in either the V1 or S1 libraries, represent which portions of the RNA were double or single stranded. A ratio of signals from each library is expressed in the PARS score (log2 ratio of V1 over S1), such that a larger/positive score represents a more double-stranded region, a smaller/negative score more single-stranded.
Now the first issue to be raised is that they did not perform a minus nuclease negative control, as is standard in footprinting experiments. This would help reveal how much of their library results from endogenous degradation products (or during cell lysis) which have 5’-phosphoryl ends and make it through selection. While this “contamination” is probably small, the control seems basic to me. On the plus side they did check for several other biases in their method, but I won’t go into detail here.
Next they compared PARS and traditional footprinting profiles for several endogenous mRNAs, as well as other RNAs they spiked into their library (domains from HOTAIR and the Tetrahymena group I ribozyme). They see strong overlap between the profiles. They also show strong agreement between PARS scores and known secondary structures for a few well-characterized domains of endogenous mRNAs. This data represents a convincing proof of principle, and now the task is, of course, to see if there’s a tangible way to assess PARS’s accuracy throughout a large dataset.
While they saw an overall strong correlation between PARS and Vienna scores (predicted double-stranded probability), even when they looked at only nucleotides with very strong PARS scores (high or low), a little less than half in each set could still fill out the entire distribution of Vienna scores, meaning a decent fraction were contradictory. It’s hard to conclude too much from these apples and oranges comparisons, but hopefully the two methods will be complementary in many cases, as the authors stress.
Using their PARS dataset, they highlight five global properties of yeast mRNAs. Number one: based on PARS scores, the CDS was more structured than UTRs. I found this to be quite intriguing, and perhaps I haven’t appreciated how intrinsic structure is to the sequence of raw nucleotides, absent of proteins. Unfortunately, what they did not address with this result is how much structural differences relate to sequence composition differences between the UTRs and the CDS. Since UTRs are more AU rich, could this explain the result? Or what fraction does it not explain? I realize this gets into a kind of a chicken and egg debate, because it has been shown by many that the CDS and UTRs differ in numerous ways, which are likely highly intercorrelated, and so one cannot really say what is controlling what. Still, I think this should have been checked.
Finding number two: when they looked at average PARS scores along the CDS (not in the UTRs), they saw the strongest periodic signal in 3-nt cycles, with the first position of each codon scoring the lowest average PARS score. They also saw a strong correlation between the amplitude of this 3-nt cycle and translational efficiency, as measured by average ribosome occupancy from Ingolia et al. Thus this cycle could in some way facilitate ribosome translocation, and messages that utilize it most effectively are rewarded with increased translation. It’s an interesting observation made by linking an in vivo and in vitro dataset. The system seems all so intelligently designed.
Finding number three: a small anti-correlation between mRNA structure around the translation start site and translation efficiency (again, via ribosome density from Ingolia et al.). It was clearest when the authors clustered subsets of messages into groups where the average PARS scores where distinct. Finding number four, which the authors describe as a “rich picture of biological coordination,” didn’t make much sense to me, it involved GO analysis. Maybe it was too rich for me.
Their last finding was that transcripts that encode signal peptides had less structure in portions of the 5’ UTR and the first ~30 nucleotides in the CDS compared to non-signal peptide encoding transcripts. They might have checked to see whether this effect was due in part to the sequence/codon constraints in these regions required to code for the signal peptide itself.
PARS should be a highly useful method for probing RNA structure on a genome-wide scale. While this study has nucleotide resolution, it’s low, and so better suited for systematic analysis rather than molecule-by-molecule structure determination. More controls, testing conditions, and deeper sequencing will reveal more. In the absence of any directly comparable dataset, the authors present some intriguing similarities to the Ingolia et al. dataset, implying that a measureable fraction of RNA function in vivo is inherent to sequence itself, perhaps no big surprise, but cool to ponder nonetheless. The findings could have benefited from more computational rigor, with respect to sequence constraints that may partly explain structural differences.
The in vivo main course could take a while–snack judiciously on PARS in the meantime.
Elaine R. Lee, Jenny L. Baker, Zasha Weinberg, Narasimhan Sudarsan, Ronald R. Breaker
Science Vol. 329. no. 5993, pp. 845 – 848, 13 August 2010.
This week’s methodical summary and analysis by Alex Subtelny:
From the lab that discovered riboswitches comes this paper, which describes a bacterial riboswitch that allosterically controls the self-splicing of a ribozyme located immediately downstream. This unusual tandem arrangement was discovered upstream of a putative C. difficile virulence gene (CD3246) during a computational search for new riboswitches, including those for cyclic di-guanosyl 5’-monophosphate (c-di-GMP), an important bacterial second messenger that regulates the transition between motile and biofilm states. Interestingly, the riboswitch in question was located far (~600 nucleotides) upstream of its associated ORF and appeared to lack the typical expression structures associated with riboswitches. Instead, the intervening sequence between the riboswitch and the ORF contained what looked like a group I ribozyme. This raised two intriguing possibilities: i) that the c-di-GMP aptamer allosterically regulates self-splicing of the ribozyme, and ii) that unlike most group I ribozymes, which are part of selfish genetic elements, this one might perform a beneficial function for its host.
The authors first demonstrate that the putative riboswitch aptamer indeed binds c-di-GMP with high affinity and specificity. Then, they dissect the mechanism of the tandem riboswitch-ribozyme through a beautiful series of in vitro experiments with mutants that disrupt or restore key secondary structure elements. Binding of c-di-GMP to the aptamer stabilizes a base-pairing architecture that favors splicing of the region upstream of the ribozyme (the 5’ exon) to the region downstream (the 3’ exon), which contains the ORF for the virulence gene. In the absence of the ligand, a different base-pairing structure is favored, leading to the formation of an alternative excision product consisting of a fragment of the 3’ exon. The authors support their splicing assays with kinetic experiments showing that c-di-GMP causes a ~12-fold increase in the rate of 5’-3’ spliced product formation and a modest decrease in the rate of formation of the alternative 3’ excision product. Finally, the authors present an elegantly convincing model to explain how alternative processing of the mRNA might affect the expression of the virulence gene. 5’-3’ splicing, which is favored in the presence of c-di-GMP, generates a ribosome binding site situated an optimal distance from the start codon, which in the precursor mRNA is concealed by being part of a stem-loop. In contrast, the alternative 3’ excision product lacks a ribosome binding site (since only five nucleotides are left upstream of the start codon), preventing translation of the downstream ORF. Thus, according to this model, the mRNA for the virulence gene is competent for translation only in the presence of c-di-GMP.
While the authors do an excellent job of showing that c-di-GMP regulates alternative ribozyme self-splicing in vitro and present a highly plausible model for how this might regulate virulence gene expression, they stop there. They provide little evidence to support the in vivo relevance of the riboswitch-regulated ribozyme, and, in particular, to show that it performs a beneficial function for the host. In one of their supplemental figures, the authors show that the major RT-PCR product for CD3246 (using primers corresponding to the aptamer and the interior of the ORF) is 5’-3’ spliced, and that the extent of splicing increases with culture age, which is associated with an increased concentration of c-di-GMP. However, they do not show that 5’-3’ splicing results in increased protein output. This could conceivably be accomplished by placing the riboswitch-ribozyme (or mutants thereof) upstream of a reporter gene, introducing this fusion into their C. difficile strain or another bacterial species, and measuring levels of the reporter normalized to another, control reporter. Moreover, the authors do not address in the paper the (rather unlikely but) possible existence of alternative transcriptional start sites within the body of the riboswitch-ribozyme that, if highly used, might call into question the relevance of their model for the translational regulation of CD3246 expression. In addition, we are left with several other key questions: what is the function of CD3246? And why is it important for its expression to be regulated by c-di-GMP? Insight into these questions, as well as those discussed earlier, would strengthen the authors’ hypothesis that group I ribozymes can be co-opted into performing beneficial functions for their hosts.