Eli Eisenberg

Distinguishing self from non-self is a major challenge for the immune system. Endogenous cytoplasmic double-stranded RNA (dsRNA) can mimic viral RNA and ac

A-to-I RNA editing, catalyzed by the adenosine deaminase acting on RNA (ADAR) enzymes, is a posttranscriptional process that modifies RNA sequences and diversifies the transcriptome. ADARs bind to double-stranded RNA (dsRNA), and their specificity and efficiency are affected by the structural properties of these substructures. In most cases, the dsRNA structure arises from homology between two segments of the same RNA molecule that fold into RNA stem structures. Another possible source of dsRNA is cotranscription of sense and antisense strands of the same genomic region. Binding of these complementary, naturally occurring, antisense transcripts (NATs) results in a perfect RNA duplex, which may be targeted by ADARs. To explore the scope of ADAR editing of NAT-derived dsRNA, we examine editing levels at genome locations where both strands are transcribed. Our findings indicate that editing is rare in regions for which both strands cotranscribe. Moreover, even when RNA editing does occur in NAT regions, it is typically associated with secondary structures on a single strand, suggesting that editing depends on intramolecular structures rather than binding of NATs.

Adenosine-to-inosine (A-I) RNA editing is one of the most abundant post-transcriptional RNA modification processes. However, the roles of A-I RNA editing in the evolution and functions of primate brains are underexplored. Here, we perform whole-genome and whole-transcriptome sequencing of 39 anatomically defined brain regions of adult Macaca fascicularis and identify 2 782 079 A-I editing sites, including 2009 recoding sites enriched in genes related to neurotransmission functions. Most of macaque brain A-I editing sites are detected in the cerebral cortex, cerebellum, and amygdala. The brain A-I editing activity is associated with the estimated proportion of neurons to some extent. Comparison of A-I editing in mouse, pig, macaque, and human brains reveals that primates exhibit higher editing levels, specifically enriched in genes encoding neurotransmitter receptors. We identify 478 598 conserved brain editing sites between human and macaque brains, mostly found in the cerebral cortex and enriched in genes related to the cytoskeletal system and ubiquitin-dependent protein degradation pathway. Our study sheds light on the importance of post-transcriptional A-I RNA editing in the evolution and function of nervous systems.

Adenosine deaminases acting on RNA (ADAR) enzymes constitute a natural cellular mechanism that induces A-to-I(G) editing, introducing genetic changes at the RNA level. Recently, interest in the endogenous-ADAR editor has emerged for correcting genetic mutations, consisting of a programmed oligonucleotide that attracts the native ADAR, thereby offering opportunities for medical therapy. Here, we systematically chart the scope of cancer mutations that endogenous-ADAR can correct. First, analyzing germline single nucleotide variants in cancer predisposition genes, we find that endogenous-ADAR can revert a fifth of them, reducing the risk of cancer development later in life. Second, examining somatic mutations across various cancer types, we find that it has the potential to correct at least one driver mutation in over a third of the samples, suggesting a promising future treatment strategy. We also highlight key driver mutations that are amenable to endogenous-ADAR, and are thus of special clinical interest. As using endogenous-ADAR entails delivering relatively small payloads, the prospects of delivering endogenous-ADAR to various cancers seem promising. We expect that the large scope of correctable mutations that are systematically charted here for the first time will pave the way for a new era of cancer treatment options.

A-to-I RNA editing is the most common non-transient epitranscriptome modification. It plays several roles in human physiology and has been linked to several disorders. Large-scale deep transcriptome sequencing has fostered the characterization of A-to-I editing at the single nucleotide level and the development of dedicated computational resources. REDIportal is a unique and specialized database collecting ∼16 million of putative A-to-I editing sites designed to face the current challenges of epitranscriptomics. Its running version has been enriched with sites from the TCGA project (using data from 31 studies). REDIportal provides an accurate, sustainable and accessible tool enriched with interconnections with widespread ELIXIR core resources such as Ensembl, RNAcentral, UniProt and PRIDE. Additionally, REDIportal now includes information regarding RNA editing in putative double-stranded RNAs, relevant for the immune-related roles of editing, as well as an extended catalog of recoding events. Finally, we report a reliability score per site calculated using a deep learning model trained using a huge collection of positive and negative instances. REDIportal is available at http://srv00.recas.ba.infn.it/atlas/.

High-throughput sequencing (HTS) is the most established technique to measure transcript abundance. HTS reads often contain uncertain or low-quality base calls that introduce ambiguity in determining the underlying sequence. In many applications, these unresolved nucleotides are handled by looking at the consensus sequence of all HTS reads. However, this approach is not applicable where sequence heterogeneity is of biological relevance. To gauge the biological complexity of a set of HTS reads in face of unresolved base calls, one may apply the parsimony principle, i.e., find a smallest set of sequences that cover all ambiguous reads. But, no method to date solves this problem optimally. Here, we present FiSSC, a new method to find a smallest sequence cover of a set of ambiguous reads. We first prove that the problem is NP-hard. We then present filtering steps that preserve optimal solution size, and an integer-linear-programming formulation, which together form FiSSC. We tested FiSSC on A-to-I RNA editing datasets with binary ambiguities. FiSSC outperformed all baseline methods and achieved optimal results in all but one dataset. We expect FiSSC to advance the study of sequence variation and biological complexity of ambiguous reads in various biological domains.