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. 2015 Sep 16:6:8194.
doi: 10.1038/ncomms9194.

Genetic mapping uncovers cis-regulatory landscape of RNA editing

Gokul Ramaswami  1 , Patricia Deng  1 , Rui Zhang  1 , Mary Anna Carbone  2 , Trudy F C Mackay  2 , Jin Billy Li  1
Affiliations

Genetic mapping uncovers cis-regulatory landscape of RNA editing

Gokul Ramaswami et al. Nat Commun. .

Abstract

Adenosine-to-inosine (A-to-I) RNA editing, catalysed by ADAR enzymes conserved in metazoans, plays an important role in neurological functions. Although the fine-tuning mechanism provided by A-to-I RNA editing is important, the underlying rules governing ADAR substrate recognition are not well understood. We apply a quantitative trait loci (QTL) mapping approach to identify genetic variants associated with variability in RNA editing. With very accurate measurement of RNA editing levels at 789 sites in 131 Drosophila melanogaster strains, here we identify 545 editing QTLs (edQTLs) associated with differences in RNA editing. We demonstrate that many edQTLs can act through changes in the local secondary structure for edited dsRNAs. Furthermore, we find that edQTLs located outside of the edited dsRNA duplex are enriched in secondary structure, suggesting that distal dsRNA structure beyond the editing site duplex affects RNA editing efficiency. Our work will facilitate the understanding of the cis-regulatory code of RNA editing.

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Figures

Figure 1
Figure 1. Quantifying RNA editing in the DGRP.
(a) Overview of experimental scheme. We used mmPCR-seq to quantify RNA editing levels in 789 sites within 605 loci in 131 D. melanogaster strains. (b) Percentage of editing sites that vary between strains. For each pairwise comparison between strains, the fraction of editing sites that have a 10% or greater editing level difference between the two strains is plotted. The red dashed line corresponds to the median value of 8%. (c,d) Editing variability in DGRP strains. Editing levels for (c) all 789-editing sites and (d) the 40 most variable editing sites are plotted. Editing sites are ranked by binomial variance, a metric of variability in the 131 strains.
Figure 2
Figure 2. Mapping RNA editing QTLs.
(a) Quantile–quantile (QQ) plot for association testing P values between 789 RNA editing sites and genetic variants in the same gene as each editing site. (b) Significance of association tests in relation to the distance between the editing site and variant. The solid red line is a cubic smoothing spline fit to the data. Transcripts were oriented such that negative and positive values represent variants transcriptionally up- and downstream of the editing site, respectively. (c) QQ plot for association tests between 545 edQTLs and additional editing sites that fell within 1 kb (red), between 1 kb and 10 kb (green), and between 10 kb and 50 kb (blue) from the original best-associated editing site. (d–g) Example of an RNA editing QTL in the CROL gene. Predicted local RNA secondary structure for the (d) G and (e) A alleles. Two editing sites influenced by the edQTL are shaded in red (numbered 1 and 2) and the edQTL is shaded in green. Relationship between editing levels and strain genotypes for the edQTL at the two associated editing sites (linear model), (f) chr2L:11796345 (site 1) and (g) chr2L:11796346 (site 2).
Figure 3
Figure 3. Prediction of ECSs.
(a) Overview of approaches to predicting proximal ECSs (Approach 1) and distal, intronic ECSs (Approach 2). (b) Numbers of editing sites identified in predicted ECSs (blue) compared with the control regions (red) of the same length indicated in (a). (c) The distribution of the distances between the editing site and the corresponding base in the ECS. (d,e) The distribution of (d) stem lengths and (e) max bulge sizes in the editing site side of the stem. The colour legend is the same as in (c). (f) The fraction of editing substrates that are base-paired at each position relative to the editing site (position 0, indicated in red), based on the predicted editing substrate structure. Negative positions are upstream (5′) of the editing site; positive positions are downstream (3′).
Figure 4
Figure 4. Effects of edQTLs on edited dsRNA structures.
We looked at structural features that distinguish 27 edQTLs with effect size ⩾0.025 and 100 control variants in edited dsRNAs. (a) Cartoon dsRNA containing an editing site (red), base-paired nucleotides (blue) and non-base-paired nucleotides (green). (b) Fraction of edQTLs and control variants positioned at base-paired nucleotides. edQTLs are significantly more likely than control variants to be base-paired (Fisher's exact test). (c) Cartoon depicting the comparison of dsRNA free energies between the two alleles for a hypothetical variant. (d) Difference in dsRNA free energies between the two alleles for edQTLs and control variants, calculated as the free energy of lower edited allele subtracted from the higher edited allele. The edQTLs have a significantly larger difference in free energy than the control variants, with the higher editing allele having lower (more stable) free energies (one-sided Mann–Whitney U-test). (e) Same cartoon dsRNA as in (a) showing base pair distances between stem nucleotides and the editing site. The editing site is centred at position zero and the portion of the editing site side of the stem transcriptionally downstream of the editing site as well as its paired portion of the ECS was classified as positive. (f) Base-pair distances from edQTLs and control variants to the editing site. The control variants tend to be significantly enriched at the 5′ end of the dsRNA (one-sided Mann–Whitney U-test).
Figure 5
Figure 5. Characterization of distal edQTLs.
(a) QTL effect sizes for 45 proximal edQTLs within the edited dsRNA and 213 distal edQTLs outside of the edited dsRNA. Effect sizes were calculated as half of the absolute value of the difference in average editing levels between the two homozygotes for each edQTL. Proximal edQTLs have significantly greater effect sizes (one-sided Mann–Whitney U-test). (b) Frequency of RNA editing sites nearby 213 distal edQTLs and 4247 matched control variants. For each variant, we calculated the number of RNA editing sites within sequence windows centred on the variant. (c) Distances from the editing sites to 28 dsRNA stems around distal edQTLs.
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