Characterization and comparison of human nuclear and cytosolic editomes

Abstract

We developed a robust computational statistical framework to identify RNA editing events from RNA-Seq data with high specificity. Our approach handles several outstanding challenges of genome-wide editing analyses, including the effect of editing on read alignment and the utilization of redundant reads. By applying this framework, we characterized the nuclear and cytosolic editomes of seven human cell lines. We found that 93.8-99.2% of the editing events are A-to-G (or A-to-I). Nuclear transcriptomes contain many more editing events than cytosolic transcriptomes. Most of the sites exhibiting nucleus-specific editing are in introns or novel intergenic transcripts that are preferentially localized in the nucleus regardless of their editing status, arguing against the role of editing in nuclear retention. In contrast, many sites that exhibit cytosol-specific editing show comparable nuclear and cytosolic expression, suggesting the differential subcellular compartmentalization of the edited and the unedited alleles. We found that RNA editing is globally associated with the modification of microRNA regulation in 3' untranslated regions, whereas editing events in coding regions are rare and tend to be synonymous. Interestingly, A-to-G editing at derived alleles in the human lineage tends to result in reversion back to the ancestral forms at the RNA level. This suggests that editing can mediate RNA memory on evolutionary time-scales to maintain ancestral genetic information.

Keywords: high-throughput sequencing; nucleo-cytoplasmic localization.

Fig. 1.

Nuclear and cytosolic editomes. (A) Number of A-to-G and non–A-to-G editing events in the nucleus and cytosol of seven human cell lines. (B) Distribution of the editing events in the human genome. “Noncoding exon” here means exons of noncoding transcripts. The red line in each bar marks the number of A-to-G events in the corresponding category. (C) Number of editing events per 100 expressed positions in different annotation categories. Expressed positions are defined as those covered by at least 99 reads. For each annotation category, the measures for the seven cell lines are shown.

Fig. 2.

Nuclear and cytosolic coverage of sites whose editing is detected in only one subcellular location. The read coverage at the site includes both the edited and unedited alleles. (A) Sites with cytosol-specific editing. For the sites in introns, the nuclear coverage was even higher than the cytosolic coverage (P < 10⁻⁶⁰; paired Wilcox tests). For the sites in exons of noncoding transcripts (“noncoding exon”), CDSs, and 5′ UTRs, the differences between the nuclear and the cytosolic coverage are not significant (P > 0.1; paired Wilcox tests). (B) Sites with nucleus-specific editing. The sites excluding those in 3′ UTRs and CDSs have low cytosolic coverage (median coverage 3–9). Outliers are not plotted on the boxplots.

Fig. 3.

Functional consequences of RNA editing. (A) Effect of RNA editing on miRNA targets in 3′ UTRs. A random set of 12,718 positions of nucleotide A in 3′ UTRs is changed to G. Compared with the random set, the edited alleles tend to disrupt the original miRNA targets, or create new miRNA targets, or switch miRNA targets (***P < 10⁻⁶⁰; proportion tests). (B) Effect of RNA editing on coding. Both non–A-to-G and A-to-G events tend to be synonymous, compared with the random situation in which all CDS positions are changed to the other three nucleotides (**P < 10⁻²⁰ for non–A-to-G and *P < 10⁻¹⁰ for A-to-G; proportion tests). The errors bars show the 95% confidence intervals.

Fig. 4.

Editing-mediated RNA memory of evolution. (A) RNA memory through editing. (B) RNA memory in humans. For positions mutated during evolution and edited at the RNA level in humans, their edited alleles have a significantly higher chance to match the ancestral alleles compared with the control situation (except for non–A-to-G editing in relation to the distant ancestor). Corresponding control sets are the randomly selected human positions of the same nucleotides as the editable sites and were mutated from the ancestors. (C) RNA memory in mice. Only A-to-G editing shows a significantly higher chance to reverse DNA alternations to the recent mouse ancestor sequence. The errors bars show the 95% confidence intervals (****P < 10⁻¹⁵⁰, ***P < 10⁻³⁰, **P < 10⁻⁶, and *P < 10⁻⁴; otherwise, P > 0.01; proportion tests).