Adenosine-to-inosine RNA editing is a system of post-transcriptional modification widely distributed in metazoans which is catalyzed by ADAR enzymes and occurs mostly in double-stranded RNA (dsRNA) before splicing. This type of RNA editing changes the genetic code, as inosine generally pairs with cytosine in contrast to adenosine, and this expectably modulates RNA splicing. We review the interconnections between RNA editing and splicing in the context of human cancer. The editing of transcripts may have various effects on splicing, and resultant alternatively spliced isoforms may be either tumor-suppressive or oncogenic. Dysregulated RNA splicing in cancer often causes the release of excess amounts of dsRNA into cytosol, where specific dsRNA sensors provoke antiviral-like responses, including type I interferon signaling. These responses may arrest cell division, causing apoptosis and, externally, stimulate antitumor immunity. Thus, small-molecule spliceosome inhibitors have been shown to facilitate the antiviral-like signaling and are considered to be potential cancer therapies. In turn, a cytoplasmic isoform of ADAR can deaminate dsRNA in cytosol, thereby decreasing its levels and diminishing antitumor innate immunity. We propose that complete or partial inhibition of ADAR may enhance the proapoptotic and cytotoxic effects of splicing inhibitors and that it may be considered a promising addition to cancer therapies targeting RNA splicing.

RNA editing by adenosine deaminases of the ADAR family attracts a growing interest of researchers, both zoologists studying ecological and evolutionary plasticity of invertebrates and medical biochemists focusing on the mechanisms of cancer and other human diseases. These enzymes deaminate adenosine residues in the double-stranded (ds) regions of RNA with the formation of inosine. As a result, some RNAs change their three-dimensional structure and functions. Adenosine-to-inosine editing in the mRNA coding sequences may cause amino acid substitutions in the encoded proteins. Here, we reviewed current concepts on the functions of two active ADAR isoforms identified in mammals (including humans). The ADAR1 protein, which acts non-specifically on extended dsRNA regions, is capable of immunosuppression via inactivation of the dsRNA interactions with specific sensors inducing the cell immunity. Expression of a specific ADAR1 splicing variant is regulated by the type I interferons by the negative feedback mechanism. It was shown that immunosuppressing effects of ADAR1 facilitate progression of some types of cancer. On the other hand, changes in the amino acid sequences resulting from the mRNA editing by the ADAR enzymes can result in the formation of neoantigens that can activate the antitumor immunity. The ADAR2 isoform acts on RNA more selectively; its function is associated with the editing of mRNA coding regions and can lead to the amino acid substitutions, in particular, those essential for the proper functioning of some neurotransmitter receptors in the central nervous system.

Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.

Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR.

Mass spectrometry-based proteome analysis usually implies matching mass spectra of proteolytic peptides to amino acid sequences predicted from nucleic acid sequences. At the same time, due to the stochastic nature of the method when it comes to proteome-wide analysis, in which only a fraction of peptides are selected for sequencing, the completeness of protein sequence identification is undermined. Likewise, the reliability of peptide variant identification in proteogenomic studies is suffering. We propose a way to interpret shotgun proteomics results, specifically in data-dependent acquisition mode, as protein sequence coverage by multiple reads, just as it is done in the field of nucleic acid sequencing for the calling of single nucleotide variants. Multiple reads for each position in a sequence could be provided by overlapping distinct peptides, thus, confirming the presence of certain amino acid residues in the overlapping stretch with much lower false discovery rate than conventional 1%. The source of overlapping distinct peptides are, first, miscleaved tryptic peptides in combination with their properly cleaved counterparts, and, second, peptides generated by several proteases with different specificities after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease proteomic datasets and our own data generated for HEK-293 cell line digests obtained using trypsin, LysC and GluC proteases. From 5000 to 8000 protein groups are identified for each digest corresponding to up to 30% of the whole proteome coverage. Most of this coverage was provided by a single read, while up to 7% of the observed protein sequences were covered two-fold and more. The proteogenomic analysis of HEK-293 cell line revealed 36 peptide variants associated with SNP, seven of which were supported by multiple reads. The efficiency of the multiple reads approach depends strongly on the depth of proteome analysis, the digesting features such as the level of miscleavages, and will increase with the number of different proteases used in parallel proteome digestion. Graphical