You'd Prefer An Argonaute

Ars2 Links the Nuclear Cap-Binding Complex to RNA Interference and Cell Proliferation

Joshua J. Gruber, D. Steven Zatechka, Leah R. Sabin, Jeongsik Yong, Julian J. Lum, Mei Kong, Wei-Xing Zong, Zhenxi Zhang, Chi-Kong Lau, Jason Rawlings, Sara Cherry, James N. Ihle, Gideon Dreyfuss and Craig B. Thompson

Cell 138 (2): 328-339, July 24, 2009.
doi:10.1016/j.cell.2009.04.046

This week’s long summary and analysis by David Garcia:

Synopsis:
The authors demonstrate a clear role for the mammalian Ars2 protein in cell proliferation, as well as an interaction with the Cap-Binding Complex. Depletion of Ars2 reduces the miRNA or siRNA directed repression of two reporter constructs, and levels of mature miRNAs for two out of four miRNAs investigated. Ars2 also co-precipitates with Drosha, but not Dicer, and pre- or mature let-7 can rescue some or all of the loss of repression of the reporters associated with Ars2 depletion. The authors are less successful at directly connecting Ars2’s effect on RNAi that they observed to a potential role in primary-miRNA to pre-miRNA processing by Drosha. The processing assays should have tested more substrates, and their variability is not explained clearly enough.

Detail:
This paper aims to demonstrate a critical role for the mammalian protein Ars2 in cell proliferation, and associate this role with other evidence that it also affects the stability or processing efficiency of a couple of primary miRNA transcripts. While initially setting out to study how mammalian Ars2 imparts a resistance to arsenic oxide treatment, the authors discovered previous studies had focused on a partial clone of the protein. In this study, the full-length protein affects arsenic treatment in a manner opposite to what had been demonstrated before; its reduction has a profound influence on cell proliferation; and it contains a number of domains common to RNA binding proteins, and homology to the Arabidopsis protein SERRATE known to affect the processing of primary miRNA transcripts by Dicer-Like 1 (the plant Drosha).

The paper starts by showing that when Ars2 is knocked down using shRNAs, 3T3 cells die more slowly compared to controls when treated with arsenic oxide (Fig1B). Also observed when depleting Ars2 is a cell proliferation defect, using colony formation assays and counting population doublings (Fig1D,F). To investigate further, the authors generate a floxed allele of Ars2 in mESCs, and then generate mice from which they derive immortalized MEFs that they can infect with Cre expressing retrovirus. These Ars2 depleted cells exhibit the same proliferation defects as had been seen with the cell lines (Fig2B). Examination of various tissues from the Ars2 depleted adult mice show that in tissues that have relatively high cell proliferation and normally high expression levels of Ars2, like hematopoietic tissues, there is decreased cellularity or increased apoptosis (Fig2D). In contrast, there are no such differences observed between the transgenic and WT mice in other lower proliferating tissues.

To find out what other proteins Ars2 interacts with, a flagged-Ars2 is expressed, and co-precipitates with several components of the cap-binding complex (CBC), including CBP80, CBP20, and importin-alpha (Fig3A). Ars2 and the CBC interact with 7-Methyl-Guanosine capped RNAs (Fig4A), as would be expected for a component of the CBC. In addition, Ars2 shuttles between the nucleus and cytoplasm (Fig4B), like other CBC components.

Given SERRATE’s known role in miRNA biogenesis in Arabidopsis, the authors address whether the mammalian Ars2 in involved in RNAi. Two types of luciferase reporters are created: one with 3X let-7 (7mer) miRNA binding sites derived from the C. elegans lin-28 3 prime UTR; and another with a perfectly matched site for the let-7 miRNA (~22mer match). In HeLa cells, they first transfect siRNAs to knock down Ars2 or CBP80 or Ago2 (positive control), and then transfect the reporters to see how repression compares to cells treated with a control siRNA. The siRNAs against Ars2 and CBP80 inhibit let-7 directed repression at levels comparable to knockdown of Ago2 for both reporters, with a more pronounced effect on the miRNA sites reporter (Fig5A).

I am surprised knocking down Ago2 only affected repression ~2.5 fold for the perfect site reporter, and not much greater. Let-7 levels are very high in HeLa cells. However, there could be some complications of knocking down Ago2 with an siRNA. Unfortunately, while the authors state a reporter with a mutated let-7 seed site led to a loss of repression under all conditions, they do not show how this loss of repression compares to the loss they observed from Ars2, CBP80, and Ago2 siRNAs. This would have been a useful control to show.

Next the authors show that addition of excess let-7 duplex can rescue loss of Ars2, but not Ago2, for both reporters (Fig5B), suggesting Ars2 may affect some step in miRNA biogenesis before Dicer. Depletion of Ars2 or DGCR8 with two different siRNAs each led to a decrease in mature let-7 levels (~50% reduction)(Fig5C,D), and the same story for mature miR-21 (expressed very highly in HeLa). While they couldn’t detect pre-let-7 by Northern (I’m surprised by this), they did see a reduction in pre-miR-21 in Ars2 depleted HeLa cells. The reduction in pre or mature was not observed for all miRNAs they probed: there was no change for miR-30a or miR-16 (FigS3).

To investigate which step of miRNA processing is affected by Ars2 depletion, they immunoprecipitate a tagged Drosha and Dicer from 293T cells. Ars2 and CBP80 both co-precipitate with Drosha but not Dicer, and this interaction is not RNA dependent (Fig6A). Addition of excess pre-let-7 rescued some of the loss of repression from Ars2 or CBP80 depleted cells (but not Ago2 depleted)(Fig6C). Depletion of Ars2 also led to a decrease in pri-miR-21 levels by qRT-PCR, indicating that Ars2 may also influence the stability of Drosha substrates (Fig6D).

A pri-miRNA processing assay is employed to address the role of Ars2 in affecting the fidelity of Drosha mediated processing. In these assays, they compare the processing of an in vitro transcribed pri-miR-155 between different cell extracts. The authors state that there is an observed reduction in pri to pre processing in the MEF Ars2 KO cells compared to WT (Fig6F). However, it may be problematic to compare two different cell extracts that are almost guaranteed to differ in more than just Ars2 expression. In the Ars2 KO, the “correct” pre band mostly disappears, but a slightly smaller band also present in the WT intensifies, without explanation. They only show data for a single pri-miRNA, 155 (but say comparable results were obtained for miR-21). They quantitate the reduction in “correct” processing as 3-fold for miR-155 (Fig6G).

The paper ends by weakly connecting the observed effect of Ars2 on cell proliferation with its potential effect on miRNA processing. The authors first re-visit the proliferation point by showing that serum starved MEFs that exit the cell cycle lose Ars2 expression (Fig7A). They confirm Ars2’s proliferation dynamic expression in the hematopoietic cell line Bax-/- Bax-/- (Fig7C). A primary miRNA processing assay, comparing processing of pri-miR-155 (in vitro transcribed as before) between 10% serum grown or 0.1% serum starved MEF extracts, is claimed to show a decrease in pri to pre in the Ars2 depleted serum starved extracts (Fig 7D). While there seems to be an increase in heterogeneity in pre, the total reduction in pre in the serum starved cells is not robust. As before, the implication that we are to assume that variability between different cell extracts, from different cell lines, or grown under vastly different conditions, cannot contribute to variability in pri to pre processing is, I think, not well founded. Moreover, the authors only show results from the testing of one miRNA, miR-155. There is insufficient explanation for bands nearby those they deem as “correctly processed.” While the authors have shown clearly how Ars2 affects cell proliferation, interacts with the CBC, and interacts with Drosha, I don’t believe they have clearly explained how Ars2 affects RNAi with their proposed mechanism of affecting miRNA biogenesis. Hopefully there will be stronger data to support this hypothesis in future papers.

Cellular MicroRNA and P Bodies Modulate Host-HIV-1 Interactions

Robin Nathans, Chia-ying Chu, Anna Kristina Serquina, Chih-Chung Lu, Hong Cao, and Tariq M. Rana

Molecular Cell 34 (6): 696–709, June 2009.
doi:10.1016/j.molcel.2009.06.003

This week’s summary and scrupulous analysis by Anonymous:

The authors showed that a host microRNA (miRNA), mir-29a, targets the 3’ untranslated region (3’ UTR) of HIV-1 directly and negatively regulates viral expression. mir-29a has two other family members (with the same seed sequence) that are also able to repress viral expression. In addition, the paper suggested that this negative regulation contributes to the latency of HIV-1 via the following model: Upon integration into the host genome and transcription, viral mRNAs are transported out into the cytoplasm where translation of viral proteins and virus assembly usually takes place. If however, the HIV-1 3’ UTR is targeted by mir-29a, the viral mRNA is sequestered into P bodies, translation is suppressed and virus assembly is prevented. Activation could occur when viral mRNAs are released from P bodies after certain stimuli. If this model is true, then mir-29a-mediated regulation would be acting as a checkpoint from viral latency to activation.

While the authors convincingly demonstrated that mir-29a negatively regulates viral expression via a direct interaction with the HIV-1 3’ UTR, the evidence presented for P body sequestration is not as strong. The viral RNA is certainly sequestered into cellular foci but three of the four markers used to identify P bodies in the paper can also be found in stress granules. The authors made a point that the negative regulation is due to translational suppression and not mRNA destabilization but not much was done to probe that the latter was not taking place.

Still, the authors did a nice job tying the relevance of this miRNA-mediated regulation to viral latency. Aligning the 3’ UTRs of various HIV-1 subtypes, they found that the mir-29a target site in the 3’ UTR is highly conserved. This is true for most subtypes except the O group HIV-1 RNAs, in which the non-conserved nucleotides in the seed match region would abolish interaction with mir-29a. The authors highlight the fact that O group HIV-1 is endemic to certain parts of Africa and is typically 100-fold less infectious than the widely-circulating M group HIV-1, whose viral subtypes maintain the conserved nucleotides in the mir-29a seed match site. Hence it is plausible that mir-29a interaction and HIV-1 infection capability could be linked. If this is true, it is remarkable that the relatively recently-evolved HIV-1 is able to co-opt the more ancient mir-29a (conserved across humans, mice, rats, dogs and chickens) to modulate its own life cycle.

It is worth mentioning that while HIV-1 produces more than 30 mRNAs, almost all of them share the same 3’ UTR. Hence even a single miRNA-mediated downregulation would prevent other viral proteins, such as the viral transcription activator Tat, from being made. This, in turn, would reinforce latency. Having said that, mir-29a is not the first miRNA identified to target the 3’ UTR of HIV-1. A previous paper (ref.) found that several miRNAs (though mir-29a was not one of them) target the HIV-1 3’ UTR and contributes to latency in resting primary CD4+ T lymphocytes, which are the cells usually infected by HIV-1. In the subtype alignment described above, the 3’ UTR is well-conserved even beyond the mir-29a seed match site. It would be interesting to see if the seed matches of these miRNAs also coincide with these other highly-conserved segments.

Reference:
Cellular microRNAs contribute to HIV-1 latency in resting primary CD4+ T lymphocytes
Huang et al., (2007) Nature Medicine 13: 1241-1247.

Therapeutic microRNA Delivery Suppresses Tumorigenesis in a Murine Liver Cancer Model

Janaiah Kota, Raghu R. Chivukula, Kathryn A. O’Donnell, Erik A. Wentzel, Chrystal L. Montgomery, Hun-Way Hwang, Tsung-Cheng Chang, Perumal Vivekanandan, Michael Torbenson, K. Reed Clark, Jerry R. Mendell and Joshua T. Mendell

Cell 137 (6): 1005-1017, June 2009.
doi:10.1016/j.cell.2009.04.021

This week’s exemplary summary and analysis by Anonymous:

A recent report by Kota and colleagues describes the systemic delivery of a tumor suppressive miRNA, miR-26a, to a murine model of hepatocellular carcinoma (HCC). In this study, it was shown that miR-26a, a miRNA previously shown to be down-regulated by the c-Myc oncogene, functions as a tumor suppressor via induction of a G1 arrest in a human HCC cell line. This G1 arrest was ascribed to repression of miR-26 targets Cyclin D2 and Cyclin E2. Furthermore, they described an adeno-associated viral vector that would express miR-26a in combination with eGFP. This virus was then delivered to mice over-expressing c-Myc in the liver, which has previously been shown to induce liver tumors. Compared to tumor-bearing mice infected with a virus expressing only eGFP, tumor-bearing mice infected with the virus expressing both eGFP and miR-26a had a substantial reduction in tumor burden without gross toxicity to the liver and other organs. This provides the first example of systemic miRNA delivery suppressing an established tumor in vivo.

While this observation has important therapeutic implications, there are several limitations to the study. First, while the in vitro data suggests that miR-26a acts as a tumor suppressor via cell cycle arrest, the in vivo data for miR-26a causing cell cycle exit is unconvincing. In particular, the use of Ki67 intensity as a measure of cell cycle status is not legitimate, as Ki67 is either present in cycling cells or absent in non-cycling cells. Thus, it appears that the more likely mechanism of tumor suppression in vivo is apoptosis, due to the robust increase in TUNEL staining. However, it is not clear which targets of miR-26a contribute to increased apoptosis. Moreover, while this study proves that miR-26a can suppress early-stage c-Myc liver tumors, it is not clear whether miR-26a can suppress late-stage, invasive HCC. Since most patients with HCC show up with significantly advanced disease, examination of miR-26a delivery in more aggressive lesions would be an important next step.

Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs

Aly A Khan, Doron Betel, Martin L Miller, Chris Sander, Christina S Leslie & Debora S Marks

Nature Biotechnology 27 (6): 549-55, June 2009.
doi:10.1038/nbt.1543

This week’s cerebral analysis by Graeme Doran:

Investigating the application of small RNAs to destabilize specific mRNA targets, researchers have observed a variety of non-specific ‘off-target’ effects – alterations in the expression level of mRNAs that do not contain a perfectly complementary target site for the small interfering RNA transfected into cells. In a minority of cases, the target mRNA is actually stabilized!

Briefly, ‘off-target’ effects have been broadly understood to derive from 4 main sources:

a) Partial complementarity between the small interfering RNA and mRNAs in the cell.

b) Stress responses due to transfection of foreign RNA.

c) Downstream secondary gene expression changes due to silencing of a specific target.

d) Disruption of the endogenous miRNA regulatory network due to competition between the cellular miRNA machinery and exogenously supplied small interfering or short hairpin RNAs.

To date, conflicting experiments have suggested that some si/shRNAs may inhibit endogenous miRNA activity in some scenarios, particularly when the silencing RNAs are present at high levels. This may occur through saturation of either or both the RISC and DICER activities in the cell, depending on the type of small RNA used.

This study from Khan et al. attempts a broad survey of previously published small RNA transfection experiments.

The key evidence presented are:

1) Transfection of small RNAs into immortalised cell lines produces a consistent de-repression of genes that contain an endogenous miRNA target site within their 3’UTR.

2) The extent of de-repression is quantitative – that is it depends both upon the extent of endogenous miRNA repression on a specific UTR, and the concentration of siRNA used in the transfection.

3) Cellular ARGONAUTE/RISC activity is likely subject to loading competition between small RNAs within cells.

Compiling an extensive set of data leads to some brevity of description in the methods. The authors use 4-species conservation as a criterion for miRNA site prediction. One would expect that the observed de-repression effect would be independent of the seed site conservation, as it is commonly possible to see miRNA repression on non-conserved seed sites, and the authors do not make clear whether this signal was present for the non-conserved site data or not.  Further, the normalization of predicted target site sets is not well described. One might envisage that the ‘baseline’ gene sets with no endogenous or exogenous seed sites would be shorter on average than gene sets with 1 or more conserved sites. Shorter UTRs have less scope for regulation by miRNAs or other
RNA binding factors, and so maybe would have less regulation to perturb.

The core data (Figure 2) indicates a consistent and significant de-repression of UTRs that contain conserved (higher confidence) endogenous miRNA target sites, but the data is correlative rather than conclusive – and would have been greatly enhanced by a simple luciferase assay to precisely determine the contribution of individual sequences to repression/de-repression in the context described. As is, the model and the data fit each other, but other modes of de-repression are not discounted experimentally. Data suggesting that the de-repression is dose-dependent seem to rely on relatively small sets of genes selected specifically for responsiveness to siRNA treatment and thus this aspect of the story is less convincing. Furthermore, there is little proof that concentrations required by ‘good’ siRNAs to silence genes (1-10nM) have a significant de-repressive effect on miRNA targets, and so the importance of the observed effect in therapeutic situations where delivery concentrations are low is still an open question.

The core findings of this paper, and the methodology employed, open up some interesting questions about the basic biology of RISC competition in cells. How, for instance, do rapidly induced miRNAs (such as miR-21 upon SMAD pathway activation) interact with the endogenous pool of miRISC? Is there present a surplus of free ARGONAUTE for such instances, or is ARGONAUTE protein concentration limiting in the cell? Do endogenous siRNAs – generated on cell stress or viral infection – displace miRNAs from their silencing role, and what is the half-life of endo-siRNA loaded RISC in vivo? Tumors commonly downregulate miRNA activity, and DICER depletion enhances tumorigenesis.  Can this also be achieved by limiting the available miRNA RISC in tumor cells?

Broadly speaking this was a stimulating paper, and whilst I don’t think that it breaks new ground in considering ‘off-target’ effects of small RNAs within cells, it provides some of the most convincing evidence of miRNA target de-repression to date. And beyond this, it provides the thought provoking concept of RISC competition, new questions upon which to focus, and a validated method for analyzing miRNA target de-repression.

Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps

Sung Wook Chi, Julie B. Zang, Aldo Mele & Robert B. Darnell

Nature, Advance Online Publication, June 17 2009.
Nature 460 (7254): 479-486, July 23 2009.
doi:10.1038/nature08170

This week’s deep summary and analysis by Noah Spies:

Despite the best efforts of numerous labs over the last decade, studying microRNA—messenger RNA interactions is still a slow and error-prone process of computational predictions based around sequence conservation (and a host of other sequence elements), supported by luciferase reporter assays, microRNA-transfection followed by micro-array or mass spec analysis, knockouts of microRNAs and components of their pathway, and other methods. So, it is with great excitement and some frustration that we receive this HITS-CLIP paper from the Darnell lab.

In a late 2008 paper, Darnell and colleagues developed the “HITS-CLIP” method, short for high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation. This mouthful-of-an-acronym method involves using ultraviolet light to cross-link proteins to nucleic acids, allowing stringent immunoprecipitation of direct protein—RNA complexes. In Chi et al (2009), the Darnell lab has focused this method on identifying interactions between the workhorse of the microRNA-induced silencing complex, Argonaute (Ago), the microRNAs bound in Ago, and the mRNAs targeted by those microRNAs. The authors found that immunoprecipitation of Ago from cross-linked cells produced two populations of Ago-RNA complexes: (1) Ago—microRNA complexes, which run at about 110 kDa after partial RNase digestion, and (2) Ago-mRNA complexes, which run closer to 130 kDa. By isolating these RNA populations separately and sequencing using Illumina, the authors were able to globally identify both Ago—microRNA and Ago-mRNA interactions.

The difficulty in analyzing these data comes from the heterogeneous population of microRNAs: which Ago-mRNA sequence tag corresponds to an Ago with which microRNA loaded? The authors first use a clever approach they dub “in silico CLIP” to simulate distributing sequence tags across messenger RNAs based on mRNA expression. This simulation provides a background level for the number of tags that would be expected by chance to simultaneously overlap one another, forming clusters. The authors then identify significantly enriched mRNA-sequencing tag clusters, and show that tags in most of these clusters are tightly distributed around the center, giving a sharp peak. For each cluster, then, the authors can search for 6—8mer microRNA seed matches within the cluster, and suggest which microRNA bound which mRNA clusters.

There was a significant enrichment of clusters at both ends of 3′ untranslated regions (UTRs), as was expected given prior research that most functional microRNA targets are in these regions. This study also identified many Ago—mRNA clusters in coding sequence, although not above background, and in introns and intergenic sequence, though the authors did not explore the explanation that these may simply be the result of unannotated transcripts and retained introns.

This method has the advantage over computational predictions of identifying true Ago—mRNA interactions, but these interactions do not necessarily result in noticeable down-regulation of the messenger RNA. To begin to assess how often these Ago—mRNA interactions are productive, the authors transfected a brain-specific microRNA, miR-124, into the cervical cancer cell line HeLa, and then used HITS-CLIP to identify Ago—mRNA clusters. The authors found that those mRNAs apparently bound by miR-124, according to HITS-CLIP, were significantly downregulated following transfection when compared to those with miR-124 sites computationally predicted by TargetScan. This was true at both the protein and the mRNA level across all transcripts, as well as when only looking at the brain-expressed messenger RNAs at the mRNA level, although brain-expressed genes did not show a convincing down-regulation at the protein level.

The authors end with a faulty Gene-Ontology—based analysis, comparing HITS-CLIP to previously published microRNA-target predictions. For the most highly expressed microRNAs in their study, the authors analyzed enrichment of various GO categories in HITS-CLIP mRNA clusters with the associated seed site. They found significant enrichment for several of these microRNAs for several of these neuronal GO categories. In comparing these results to microRNA-target predictions, the authors compared mRNAs with and without predicted microRNA target sites. However, this ignores the fact that many genes have very little conservation in their 3′ UTRs, and hence could not be predicted as targets of any microRNA. A better comparison might take transcripts targeted by non-expressed microRNAs as the background set, and compare these to those predicted to be targeted by the highly expressed microRNAs.

In summary, this is an exciting and powerful new technique, which will quickly broaden our understanding of microRNA regulation. A few issues marred what could have been an exceptionally interesting paper. First, the authors seemingly randomly cherry-pick their data for each figure panel, sometimes choosing conserved microRNAs, sometimes non-conserved; sometimes those clusters present in all their replicates, sometimes only those in two or more replicates; sometimes the top 30 most-expressed microRNAs, sometimes only the top 20. These decisions may have been well founded, or the results were similar regardless of which data they chose, but without clear explanations of why they conducted their analyses the way they did, it is difficult to express confidence in the robustness of their results. Secondly, the HITS-CLIP method has a huge advantage over target prediction methods in being able to identify non-conserved target sites, and yet the authors restricted most of their analyses to only those conserved microRNA targets. Finally, the authors chose not to make the raw HITS-CLIP sequencing data readily available online (submission to the NCBI Short Read Archive is the standard for sequencing data, as GEO is the standard for micro-array data), although one can hope that this will be rectified in the near future.

Update 7/24/09:

As Dr. Darnell calls attention to in his comment below, all of the raw data and UCSC links are now available. For them, visit the Darnell Lab Ago HITS-CLIP website here.