A computational screen for site selective A-to-I editing detects novel sites in neuron specific Hu proteins

Abstract

Background: Several bioinformatic approaches have previously been used to find novel sites of ADAR mediated A-to-I RNA editing in human. These studies have discovered thousands of genes that are hyper-edited in their non-coding intronic regions, especially in alu retrotransposable elements, but very few substrates that are site-selectively edited in coding regions. Known RNA edited substrates suggest, however, that site selective A-to-I editing is particularly important for normal brain development in mammals.

Results: We have compiled a screen that enables the identification of new sites of site-selective editing, primarily in coding sequences. To avoid hyper-edited repeat regions, we applied our screen to the alu-free mouse genome. Focusing on the mouse also facilitated better experimental verification. To identify candidate sites of RNA editing, we first performed an explorative screen based on RNA structure and genomic sequence conservation. We further evaluated the results of the explorative screen by determining which transcripts were enriched for A-G mismatches between the genomic template and the expressed sequence since the editing product, inosine (I), is read as guanosine (G) by the translational machinery. For expressed sequences, we only considered coding regions to focus entirely on re-coding events. Lastly, we refined the results from the explorative screen using a novel scoring scheme based on characteristics for known A-to-I edited sites. The extent of editing in the final candidate genes was verified using total RNA from mouse brain and 454 sequencing.

Conclusions: Using this method, we identified and confirmed efficient editing at one site in the Gabra3 gene. Editing was also verified at several other novel sites within candidates predicted to be edited. Five of these sites are situated in genes coding for the neuron-specific RNA binding proteins HuB and HuD.

Figure 1

Flowchart of the process. The process of surveying and assigning potentially RNA A-to-I edited sites is here described.

Figure 2

Free energy as a function of duplex length. The minimum free energy as a function of duplex length (in nt) for ten examples of different duplexes: perfect duplexes, known ADAR duplexes, random duplexes, and a random set from our candidate duplexes. We conclude that the trend is clear in the assumption that we would benefit from not being too strict in assigning parameters to StemPrediction.

Figure 3

The distribution of stems from StemPrediction. Distribution of free energy for all the 2,919,511 stems retrieved from StemPrediction (blue bars), and the 1,307,598 stems having a predicted stem loop shorter than 10,000 nt (red bars).

Figure 4

Phylogenetic tree relating the 17 vertebrate species used to evaluate conservation. Numbers on edges represent edge lengths measured in average substitutions per site. Black numbers are estimations made by Adam Siepel using PAML. Red numbers are estimated with the use of TimeTree [48] assuming local molecular clocks.

Figure 5

Genomic alignment of species at the R/G site of gluR-B. A 17-species alignment, visualized with TeXshade [49], of the genomic region overlapping gluR-B at the R/G editing site. The column corresponding to the edited site is shown in red, while the complementary site is shown in orange. The loop is shown in grey. We note: (1) extreme conservation, (2) lost conservation in Tetraodon, (3) the A-G mutation occurring in Tetraodon in the edited column. The green rectangle surrounds a 21-column window used as an example in Methods.

Figure 6

The conservation score distribution for section 75-92 Mb of Mm8 chromosome 3. The conservation score for a site (shown in green) is the sum of the parsimony term (red curve) and the tree term (blue curve) for that site. Approximate conservation score for the genome positions of GluR-B R/G (conservation score for highest scoring stem arm = 96.5) and GluR-B Q/R (93.4) are specified.

Figure 7

Distribution of the number of genes that overlap a certain number of A-G mismatches. The number of A-G mismatches are plotted against the number of genes in mouse. Bars for genes Ubc (which overlap 188 A-G mismatches), Mll5 (231), and Spna2 (420), are not shown.