The implications of APOBEC3-mediated C-to-U RNA editing for human disease.

Intra-organism biodiversity is thought to arise from epigenetic modification of constituent genes and post-translational modifications of translated proteins. Here, we show that post-transcriptional modifications, like RNA editing, may also contribute. RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosine to uracil. RNAsee (RNA site editing evaluation) is a computational tool developed to predict the cytosines edited by these enzymes. We find that 4.5% of non-synonymous DNA single nucleotide polymorphisms that result in cytosine to uracil changes in RNA are probable sites for APOBEC3A/G RNA editing; the variant proteins created by such polymorphisms may also result from transient RNA editing. These polymorphisms are associated with over 20% of Medical Subject Headings across ten categories of disease, including nutritional and metabolic, neoplastic, cardiovascular, and nervous system diseases. Because RNA editing is transient and not organism-wide, future work is necessary to confirm the extent and effects of such editing in humans.

The Role of C-to-U RNA Editing in Human Biodiversity.

Intra-organism biodiversity is thought to arise from epigenetic modification of our constituent genes and post-translational modifications after mRNA is translated into proteins. We have found that post-transcriptional modification, also known as RNA editing, is also responsible for a significant amount of our biodiversity, substantively expanding this story. The APOBEC (apolipoprotein B mRNA editing catalytic polypeptide-like) family RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosines to uracils (C>U) in specific stem-loop structures.1,2 We used RNAsee (RNA site editing evaluation), a tool developed to predict the locations of APOBEC3A/G RNA editing sites, to determine whether known single nucleotide polymorphisms (SNPs) in DNA could be replicated in RNA via RNA editing. About 4.5% of non-synonymous SNPs which result in C>U changes in RNA, and about 5.4% of such SNPs labelled as pathogenic, were identified as probable sites for APOBEC3A/G editing. This suggests that the variant proteins created by these DNA mutations may also be created by transient RNA editing, with the potential to affect human health. Those SNPs identified as potential APOBEC3A/G-mediated RNA editing sites were disproportionately associated with cardiovascular diseases, digestive system diseases, and musculoskeletal diseases. Future work should focus on common sites of RNA editing, any variant proteins created by these RNA editing sites, and the effects of these variants on protein diversity and human health. Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential.

Computational Grafting of Epitopes.

Epitopes are the cornerstones for the development of rational vaccine design strategies. Conventionally, epitopes are used by chemical conjugation with the carrier protein. This chapter describes our computational epitope grafting methodology to identify the preferential grafting site in a carrier protein/scaffold. We have used the mota epitope as an example, as it was already experimentally validated by an independent group. In this chapter, we have provided sufficient details to enable the wet experimentalist to employ this computational methodology in their research objective. Scripts/programs are extensively described in this chapter and freely accessible through the provided link.

Effective holistic characterization of small molecule effects using heterogeneous biological networks.

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.

The impact of nucleosome structure on CRISPR/Cas9 fidelity.

The clustered regularly interspaced short palindromic repeats (CRISPR) Cas system is a powerful tool that has the potential to become a therapeutic gene editor in the near future. Cas9 is the best studied CRISPR system and has been shown to have problems that restrict its use in therapeutic applications. Chromatin structure is a known impactor of Cas9 targeting and there is a gap in knowledge on Cas9's efficacy when targeting such locations. To quantify at a single base pair resolution how chromatin inhibits on-target gene editing relative to off-target editing of exposed mismatching targets, we developed the gene editor mismatch nucleosome inhibition assay (GEMiNI-seq). GEMiNI-seq utilizes a library of nucleosome sequences to examine all target locations throughout nucleosomes in a single assay. The results from GEMiNI-seq revealed that the location of the protospacer-adjacent motif (PAM) sequence on the nucleosome edge drives the ability for Cas9 to access its target sequence. In addition, Cas9 had a higher affinity for exposed mismatched targets than on-target sequences within a nucleosome. Overall, our results show how chromatin structure impacts the fidelity of Cas9 to potential targets and highlight how targeting sequences with exposed PAMs could limit off-target gene editing, with such considerations improving Cas9 efficacy and resolving current limitations.

Antiviral Approaches against Influenza Virus.

Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact. Vaccines are the prophylaxis mainstay in the fight against influenza. However, vaccination fails to confer complete protection due to inadequate vaccination coverages, vaccine shortages, and mismatches with circulating strains. Antivirals represent an important prophylactic and therapeutic measure to reduce influenza-associated morbidity and mortality, particularly in high-risk populations. Here, we review current FDA-approved influenza antivirals with their mechanisms of action, and different viral- and host-directed influenza antiviral approaches, including immunomodulatory interventions in clinical development. Furthermore, we also illustrate the potential utility of machine learning in developing next-generation antivirals against influenza.

Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer.

Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform.

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Proteomic Network Analysis of Bronchoalveolar Lavage Fluid in Ex-Smokers to Discover Implicated Protein Targets and Novel Drug Treatments for Chronic Obstructive Pulmonary Disease.

Bronchoalveolar lavage of the epithelial lining fluid (BALF) can sample the profound changes in the airway lumen milieu prevalent in chronic obstructive pulmonary disease (COPD). We compared the BALF proteome of ex-smokers with moderate COPD who are not in exacerbation status to non-smoking healthy control subjects and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins for the treatment of COPD. Proteomic profiles of BALF were obtained from (1) never-smoker control subjects with normal lung function (n = 10) or (2) individuals with stable moderate (GOLD stage 2, FEV1 50−80% predicted, FEV1/FVC < 0.70) COPD who were ex-smokers for at least 1 year (n = 10). After identifying potential crucial hub proteins, drug−proteome interaction signatures were ranked by the computational analysis of novel drug opportunities (CANDO) platform for multiscale therapeutic discovery to identify potentially repurposable drugs. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that most likely ameliorate inflammatory processes. Proteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were significantly differentially expressed in COPD versus control. Functional annotation of the differentially expressed proteins was used to detail canonical pathways containing the differential expressed proteins. Topological network analysis demonstrated that four putative proteins act as central node proteins in COPD. The drugs with the most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The drugs identified using CANDO were subsequently analyzed using a knowledge-based technique to determine an optimal two-drug combination that had the most appropriate effect on the central node proteins. Network analysis of the BALF proteome identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.

Evaluating the performance of drug-repurposing technologies.

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

A Deep-Learning Proteomic-Scale Approach for Drug Design.

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

A fluorescence-based, gain-of-signal, live cell system to evaluate SARS-CoV-2 main protease inhibition.

The likelihood of continued circulation of COVID-19 and its variants, and novel coronaviruses due to future zoonotic transmissions, combined with the current paucity of coronavirus antivirals, emphasize the need for improved screening in developing effective antivirals for the treatment of infection by SARS-CoV-2 (CoV2) and other coronaviruses. Here we report the development of a live-cell based assay for evaluating the intracellular function of the critical, highly-conserved CoV2 target, the Main 3C-like protease (Mpro). This assay is based on expression of native wild-type mature CoV2 Mpro, the function of which is quantitatively evaluated in living cells through cleavage of a biosensor leading to loss of fluorescence. Evaluation does not require cell harvesting, allowing for multiple measurements from the same cells facilitating quantification of Mpro inhibition, as well as recovery of function upon removal of inhibitory drugs. The pan-coronavirus Mpro inhibitor, GC376, was utilized in this assay and effective inhibition of intracellular CoV2 Mpro was found to be consistent with levels required to inhibit CoV2 infection of human lung cells. We demonstrate that GC376 is an effective inhibitor of intracellular CoV2 Mpro at low micromolar levels, while other predicted Mpro inhibitors, bepridil and alverine, are not. Results indicate this system can provide a highly effective high-throughput coronavirus Mpro screening system.

Protein mimetic amyloid inhibitor potently abrogates cancer-associated mutant p53 aggregation and restores tumor suppressor function.

Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer's disease and type II diabetes, identified a tripyridylamide, ADH-6, that abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53's transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment effectively shrinks xenografts harboring mutant p53, while exhibiting no toxicity to healthy tissue, thereby substantially prolonging survival. This study demonstrates the successful application of a bona fide small-molecule amyloid inhibitor as a potent anticancer agent.

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform.

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

A synthetic small molecule stalls pre-mRNA splicing by promoting an early-stage U2AF2-RNA complex.

Dysregulated pre-mRNA splicing is an emerging Achilles heel of cancers and myelodysplasias. To expand the currently limited portfolio of small-molecule drug leads, we screened for chemical modulators of the U2AF complex, which nucleates spliceosome assembly and is mutated in myelodysplasias. A hit compound specifically enhances RNA binding by a U2AF2 subunit. Remarkably, the compound inhibits splicing of representative substrates and stalls spliceosome assembly at the stage of U2AF function. Computational docking, together with structure-guided mutagenesis, indicates that the compound bridges the tandem U2AF2 RNA recognition motifs via hydrophobic and electrostatic moieties. Cells expressing a cancer-associated U2AF1 mutant are preferentially killed by treatment with the compound. Altogether, our results highlight the potential of trapping early spliceosome assembly as an effective pharmacological means to manipulate pre-mRNA splicing. By extension, we suggest that stabilizing assembly intermediates may offer a useful approach for small-molecule inhibition of macromolecular machines.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK.

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug-Protein-Disease Data.

Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

Association of C>U RNA Editing with Human Disease Variants.

RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A>I) and "APOBEC"(C>U). Although APOBEC-mediated C>U editing has been associated with a number of human cancers, the extent of C>U editing in human disease remains unclear. Here, we performed an association study and found that at least 1293 human disease variants occur at sites predicted by sequence motif analysis (RNASee protocol) to undergo APOBEC3A/G C>U editing. These variants were associated with a wide array of human disease conditions ranging from cancer, metabolic disorders, retinopathies, cardiomyopathies, neurodegenerative disorders and immunodeficiencies. These results indicate that APOBEC mediated C>U RNA editing may have widespread and previously unreported contributions to human disease conditions.