RNA Journal Club 6/25/09
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.
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.
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.