RNA editing in higher plant mitochondria: analysis of biochemistry and specificity

U-to-C RNA editing by synthetic PPR-DYW proteins in bacteria and human culture cells

Programmable RNA editing offers significant therapeutic potential for a wide range of genetic diseases. Currently, several deaminase enzymes, including ADAR and APOBEC, can perform programmable adenosine-to-inosine or cytidine-to-uridine RNA correction. However, enzymes to perform guanosine-to-adenosine and uridine-to-cytidine (U-to-C) editing are still lacking to complete the set of transition reactions. It is believed that the DYW:KP proteins, specific to seedless plants, catalyze the U-to-C reactions in mitochondria and chloroplasts. In this study, we designed seven DYW:KP domains based on consensus sequences and fused them to a designer RNA-binding pentatricopeptide repeat (PPR) domain. We show that three of these PPR-DYW:KP proteins edit targeted uridine to cytidine in bacteria and human cells. In addition, we show that these proteins have a 5′ but not apparent 3′ preference for neighboring nucleotides. Our results establish the DYW:KP aminase domain as a potential candidate for the development of a U-to-C editing tool in human cells.

DYW:KP domains, designed on proteins found in the mitochondria and chloroplasts of seedless plants, are fused to a designer RNA-binding pentatricopeptide repeat (PPR) domain to edit targeted uridine to cytidine in bacteria and human cells.

Characteristics and prediction of RNA editing sites in transcripts of the Moss Takakia lepidozioides chloroplast

RNA editing in land plant organelles is a process primarily involving the conversion of cytidine to uridine in pre-mRNAs. The process is required for gene expression in plant organelles, because this conversion alters the encoded amino acid residues and improves the sequence identity to homologous proteins. A recent study uncovered that proteins encoded in the nuclear genome are essential for editing site recognition in chloroplasts; the mechanisms by which this recognition occurs remain unclear. To understand these mechanisms, we determined the genomic and cDNA sequences of moss Takakia lepidozioides chloroplast genes, then computationally analyzed the sequences within −30 to +10 nucleotides of RNA editing sites (neighbor sequences) likely to be recognized by trans-factors. As the T. lepidozioides chloroplast has many RNA editing sites, the analysis of these sequences provides a unique opportunity to perform statistical analyses of chloroplast RNA editing sites. We divided the 302 obtained neighbor sequences into eight groups based on sequence similarity to identify group-specific patterns. The patterns were then applied to predict novel RNA editing sites in T. lepidozioides transcripts; ∼60% of these predicted sites are true editing sites. The success of this prediction algorithm suggests that the obtained patterns are indicative of key sites recognized by trans-factors around editing sites of T. lepidozioides chloroplast genes.

Genetic algorithm learning as a robust approach to RNA editing site prediction

BACKGROUND

RNA editing is one of several post-transcriptional modifications that may contribute to organismal complexity in the face of limited gene complement in a genome. One form, known as C --> U editing, appears to exist in a wide range of organisms, but most instances of this form of RNA editing have been discovered serendipitously. With the large amount of genomic and transcriptomic data now available, a computational analysis could provide a more rapid means of identifying novel sites of C --> U RNA editing. Previous efforts have had some success but also some limitations. We present a computational method for identifying C --> U RNA editing sites in genomic sequences that is both robust and generalizable. We evaluate its potential use on the best data set available for these purposes: C --> U editing sites in plant mitochondrial genomes.

RESULTS

Our method is derived from a machine learning approach known as a genetic algorithm. REGAL (RNA Editing site prediction by Genetic Algorithm Learning) is 87% accurate when tested on three mitochondrial genomes, with an overall sensitivity of 82% and an overall specificity of 91%. REGAL's performance significantly improves on other ab initio approaches to predicting RNA editing sites in this data set. REGAL has a comparable sensitivity and higher specificity than approaches which rely on sequence homology, and it has the advantage that strong sequence conservation is not required for reliable prediction of edit sites.

CONCLUSION

Our results suggest that ab initio methods can generate robust classifiers of putative edit sites, and we highlight the value of combinatorial approaches as embodied by genetic algorithms. We present REGAL as one approach with the potential to be generalized to other organisms exhibiting C --> U RNA editing.

Simple statistical models predict C-to-U edited sites in plant mitochondrial RNA

Background

RNA editing is the process whereby an RNA sequence is modified from the sequence of the corresponding DNA template. In the mitochondria of land plants, some cytidines are converted to uridines before translation. Despite substantial study, the molecular biological mechanism by which C-to-U RNA editing proceeds remains relatively obscure, although several experimental studies have implicated a role for cis-recognition. A highly non-random distribution of nucleotides is observed in the immediate vicinity of edited sites (within 20 nucleotides 5' and 3'), but no precise consensus motif has been identified.

Results

Data for analysis were derived from the the complete mitochondrial genomes of Arabidopsis thaliana, Brassica napus, and Oryza sativa; additionally, a combined data set of observations across all three genomes was generated. We selected datasets based on the 20 nucleotides 5' and the 20 nucleotides 3' of edited sites and an equivalently sized and appropriately constructed null-set of non-edited sites. We used tree-based statistical methods and random forests to generate models of C-to-U RNA editing based on the nucleotides surrounding the edited/non-edited sites and on the estimated folding energies of those regions. Tree-based statistical methods based on primary sequence data surrounding edited/non-edited sites and estimates of free energy of folding yield models with optimistic re-substitution-based estimates of ~0.71 accuracy, ~0.64 sensitivity, and ~0.88 specificity. Random forest analysis yielded better models and more exact performance estimates with ~0.74 accuracy, ~0.72 sensitivity, and ~0.81 specificity for the combined observations.

Conclusions

Simple models do moderately well in predicting which cytidines will be edited to uridines, and provide the first quantitative predictive models for RNA edited sites in plant mitochondria. Our analysis shows that the identity of the nucleotide -1 to the edited C and the estimated free energy of folding for a 41 nt region surrounding the edited C are the most important variables that distinguish most edited from non-edited sites. However, the results suggest that primary sequence data and simple free energy of folding calculations alone are insufficient to make highly accurate predictions.

Functions and mechanisms of RNA editing

RNA editing can be broadly defined as any site-specific alteration in an RNA sequence that could have been copied from the template, excluding changes due to processes such as RNA splicing and polyadenylation. Changes in gene expression attributed to editing have been described in organisms from unicellular protozoa to man, and can affect the mRNAs, tRNAs, and rRNAs present in all cellular compartments. These sequence revisions, which include both the insertion and deletion of nucleotides, and the conversion of one base to another, involve a wide range of largely unrelated mechanisms. Recent advances in the development of in vitro editing and transgenic systems for these varied modifications have provided a better understanding of similarities and differences between the biochemical strategies, regulatory sequences, and cellular factors responsible for such RNA processing events.

RNA editing: how a message is changed

Considerable progress has been made in unraveling the mechanistic features of RNA editing processes in a number of genetic systems. Recent highlights include the identification of the catalytic subunit of the mammalian apolipoprotein B mRNA editing enzyme as a zinc-dependent cytidine deaminase that binds to RNA, the demonstration that adenosines in brain glutamate receptor pre-mRNAs are converted into inosines and that double-stranded RNA A deaminase (dsRAD), the candidate enzyme, is another zinc-dependent RNA nucleotide deaminase, and a mounting body of evidence for a cleavage-ligation mechanism for U insertion/deletion editing in kinetoplastid protozoa.