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Abstract Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain.

Abstract Background Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). Methods The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. Results Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). Conclusions Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

We recently showed that streptozotocin (STZ) injections in rats lead to the development of painful peripheral diabetic neuropathy (PDN) accompanied by enhancement of CaV3.2 T-type calcium currents (T-currents) and hyperexcitability in dorsal root ganglion (DRG) neurons. Here we used the classical peripherally acting T-channel blocker mibefradil to examine the role of CaV3.2 T-channels as pharmacological targets for treatment of painful PDN. When administered intraperitoneally (i.p.), at clinically relevant doses, mibefradil effectively alleviated heat, cold and mechanical hypersensitivities in STZ-treated diabetic rats in a dose-dependent manner. We also found that CaV3.2 antisense (AS)-treated diabetic rats exhibit a significant decrease in painful PDN compared with mismatch antisense (MIS)-treated diabetic rats. Co-treatment with mibefradil (9 mg/kg i.p.) resulted in reversal of heat, cold and mechanical hypersensitivity in MIS-treated but not in AS-treated diabetic rats, suggesting that mibefradil and CaV3.2 AS share the same cellular target. Using patch-clamp recordings from acutely dissociated DRG neurons, we demonstrated that mibefradil similarly blocked T-currents in diabetic and healthy rats in a voltage-dependent manner by stabilizing inactive states of T-channels. We conclude that antihyperalgesic and antiallodynic effects of mibefradil in PDN are at least partly mediated by inhibition of CaV3.2 channels in peripheral nociceptors. Hence, peripherally acting voltage-dependent T-channel blockers could be very useful in the treatment of painful symptoms of PDN.