Our understanding of the dynamic and evolution of RNA editing in angiosperms is in part limited by the few editing sites identified to date. This study identified 10,217 editing sites from 17 diverse angiosperms. Our analyses confirmed the universality of certain features of RNA editing, and offer new evidence behind the loss of editing sites in angiosperms. RNA editing is a post-transcriptional process that substitutes cytidines (C) for uridines (U) in organellar transcripts of angiosperms. These substitutions mostly take place in mitochondrial messenger RNAs at specific positions called editing sites. By means of publicly available RNA-seq data, this study identified 10,217 editing sites in mitochondrial protein-coding genes of 17 diverse angiosperms. Even though other types of mismatches were also identified, we did not find evidence of non-canonical editing processes. The results showed an uneven distribution of editing sites among species, genes, and codon positions. The analyses revealed that editing sites were conserved across angiosperms but there were some species-specific sites. Non-synonymous editing sites were particularly highly conserved (~ 80%) across the plant species and were efficiently edited (80% editing extent). In contrast, editing sites at third codon positions were poorly conserved (~ 30%) and only partially edited (~ 40% editing extent). We found that the loss of editing sites along angiosperm evolution is mainly occurring by replacing editing sites with thymidines, instead of a degradation of the editing recognition motif around editing sites. Consecutive and highly conserved editing sites had been replaced by thymidines as result of retroprocessing, by which edited transcripts are reverse transcribed to cDNA and then integrated into the genome by homologous recombination. This phenomenon was more pronounced in eudicots, and in the gene cox1. These results suggest that retroprocessing is a widespread driving force underlying the loss of editing sites in angiosperm mitochondria.

Angiosperm mitochondrial horizontal gene transfer (HGT) has been widely reported during the past decades. With a few exceptions, foreign sequences are mitochondrial genes or intronic regions from other plants, indicating that HGT has played a major role in shaping mitochondrial genome evolution. Host-parasite relationships are a valuable system to study this phenomenon due to the high frequency of HGT. In particular, the interaction between mimosoid legumes and holoparasites of the genus Lophophytum represents an outstanding opportunity to discern HGT events. The mitochondrial genome of the holoparasite L. mirabile has remarkable properties, the most extraordinary of which is the presence of 34 out of 43 mitochondrial protein genes acquired from its legume host, with the stunning replacement of up to 26 native homologs. However, the origin of the intergenic sequences that represent the majority (>90%) of the L. mirabile mtDNA remains largely unknown. The lack of mitochondrial sequences available from the donor angiosperm lineage (mimosoid legumes) precluded a large-scale evolutionary study. We sequenced and assembled the mitochondrial genome of the mimosoid Acacia ligulata and performed genome wide comparisons with L. mirabile. The A. ligulata mitochondrial genome is almost 700 kb in size, encoding 60 genes. About 60% of the L. mirabile mtDNA had greatest affinity to members of the family Fabaceae (~49% to mimosoids in particular) with an average sequence identity of ~96%, including genes but mostly intergenic regions. These findings strengthen the mitochondrial fusion compatibility model for angiosperm mitochondrion-to-mitochondrion HGT.

The gene ontology (GO) provides a hierarchical structure with a controlled vocabulary composed of terms describing functions and localization of gene products. Recent works propose vector representations, also known as embeddings, of GO terms that capture meaningful information about them. Significant performance improvements have been observed when these representations are used on diverse downstream tasks, such as the measurement of semantic similarity between GO terms and functional similarity between proteins. Despite the success shown by these approaches, existing embeddings of GO terms still fail to capture crucial structural features of the GO. Here, we present anc2vec, a novel protocol based on neural networks for constructing vector representations of GO terms by preserving three important ontological features: its ontological uniqueness, ancestors hierarchy and sub-ontology membership. The advantages of using anc2vec are demonstrated by systematic experiments on diverse tasks: visualization, sub-ontology prediction, inference of structurally related terms, retrieval of terms from aggregated embeddings, and prediction of protein–protein interactions. In these tasks, experimental results show that the performance of anc2vec representations is better than those of recent approaches. This demonstrates that higher performances on diverse tasks can be achieved by embeddings when the structure of the GO is better represented. Full source code and data are available at https://github.com/sinc-lab/anc2vec.