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Background As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism—RNA editing due to post-transcriptional changes of individual nucleotides—remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise. Results Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities. Conclusions AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

Childhood cancer survivors face increased cardiometabolic risks from cancer treatment exposures, yet mechanisms remain unclear. Here, epigenome-wide analysis identifies 1893 DNA methylation (DNAm) sites in peripheral-blood-mononuclear-cells (PBMCs) associated with at least one cardiometabolic risk factor (CMRF), including obesity ( n  = 1720), abnormal glucose ( n  = 201), hypertriglyceridemia ( n  = 145), hypercholesterolemia ( n  = 38) and hypertension ( n  = 34) in 2938 survivors from the St. Jude Lifetime Cohort. A core set of five DNAm sites near CPT1A and LMNA is associated with all CMRFs. Mediation analyses identify 24 sites mediating associations between treatments and CMRFs, implicating inflammatory and metabolic pathways. Notably, cg20370568, a cis-expression quantitative trait methylation site for ANTXR2 , mediates 20% of the effect of body-trunk-radiotherapy on abnormal glucose. These findings suggest that prior genotoxic cancer treatments may become biologically embedded through DNAm variations that could contribute to cardiometabolic dysfunction and highlight candidate biomarkers for refining risk stratification and guiding intervention strategies in survivorship care. The mechanisms underlying increased cardiometabolic risk from cancer treatment in childhood cancer survivors remain to be explored. Here, epigenome-wide analysis in childhood cancer survivors identified DNA methylation sites that mediate treatment-related cardiometabolic risks and are associated with inflammatory and metabolic pathways.

All‐trans retinoic acid (AtRA) is an active metabolite of vitamin A that influences many biological processes in development, differentiation, and metabolism. AtRA functions through activation of retinoid acid receptors (RARs). AtRA is shown to ameliorate hepatic steatosis, but the underlying mechanism is not well understood. In this study, we investigated the role of hepatocyte RAR alpha (RARα) in mediating the effect of AtRA on hepatosteatosis in mice. Hepatocyte‐specific Rarα−/− (L‐Rarα−/−) mice and their control mice were fed a chow diet, high‐fat diet (HFD), or a high‐fat/cholesterol/fructose (HFCF) diet. Some of the mice were also treated with AtRA. Loss of hepatocyte RARα‐induced hepatosteatosis in chow‐fed aged mice and HFD‐fed mice. AtRA prevented and reversed HFCF diet–induced obesity and hepatosteatosis in the control mice but not in L‐Rarα−/− mice. Furthermore, AtRA reduced hepatocyte fatty acid uptake and lipid droplet formation, dependent on hepatocyte RARα. Our data suggest that hepatocyte RARα plays an important role in preventing hepatosteatosis and mediates AtRA's effects on diet‐induced hepatosteatosis. Loss of hepatocyte retinoic acid receptor alpha (RARa) induces liver steatosis in aged mice or high fat diet‐fed mice. All‐trans retinoic acid attenuates diet‐induced hepatosteaosis largely dependent on activation of hepatocyte RARa.

Carriers of cancer predisposing variants are at an increased risk of developing subsequent malignant neoplasms among those who have survived childhood cancer. We aimed to investigate whether cancer predisposing variants contribute to the risk of subsequent malignant neoplasm-related late mortality (5 years or more after diagnosis). In this analysis, data were included from two retrospective cohort studies, St Jude Lifetime Cohort (SJLIFE) and the Childhood Cancer Survivor Study (CCSS), with prospective follow-up of patients who were alive for at least 5 years after diagnosis with childhood cancer (ie, long-term childhood cancer survivors) with corresponding germline whole genome or whole exome sequencing data. Cancer predisposing variants affecting 60 genes associated with well-established autosomal-dominant cancer-predisposition syndromes were characterised. Subsequent malignant neoplasms were graded using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) version 4.03 with modifications. Cause-specific late mortality was based on linkage with the US National Death Index and systematic cohort follow up. Fine-Gray subdistribution hazard models were used to estimate subsequent malignant neoplasm-related late mortality starting from the first biospecimen collection, treating non-subsequent malignant neoplasm-related deaths as a competing risk, adjusting for genetic ancestry, sex, age at diagnosis, and cancer treatment exposures. SJLIFE (NCT00760656) and CCSS (NCT01120353) are registered with ClinicalTrials.gov. 12 469 (6172 male and 6297 female) participants were included, 4402 from the SJLIFE cohort (median follow-up time since collection of the first biospecimen 7·4 years [IQR 3·1–9·4]) and 8067 from the CCSS cohort (median follow-up time since collection of the first biospecimen 12·6 years [2·2–16·6]). 641 (5·1%) of 12 469 participants carried cancer predisposing variants (294 [6·7%] in the SJLIFE cohort and 347 [4·3%] in the CCSS cohort), which were significantly associated with an increased severity of subsequent malignant neoplasms (CTCAE grade ≥4 vs grade <4: odds ratio 2·15, 95% CI 1·18–4·19, p=0·0085). 263 (2·1%) subsequent malignant neoplasm-related deaths (44 [1·0%] in the SJLIFE cohort; and 219 [2·7%] in the CCSS cohort) and 426 (3·4%) other-cause deaths (103 [2·3%] in SJLIFE; and 323 [4·0%] in CCSS) occurred. Cumulative subsequent malignant neoplasm-related mortality at 10 years after the first biospecimen collection in carriers of cancer predisposing variants was 3·7% (95% CI 1·2–8·5) in SJLIFE and 6·9% (4·1–10·7) in CCSS versus 1·5% (1·0–2·1) in SJLIFE and 2·1% (1·7–2·5) in CCSS in non-carriers. Carrying a cancer predisposing variant was associated with an increased risk of subsequent malignant neoplasm-related mortality (SJLIFE: subdistribution hazard ratio 3·40 [95% CI 1·37–8·43]; p=0·0082; CCSS: 3·58 [2·27–5·63]; p<0·0001). Identifying participants at increased risk of subsequent malignant neoplasms via genetic counselling and clinical genetic testing for cancer predisposing variants and implementing early personalised cancer surveillance and prevention strategies might reduce the substantial subsequent malignant neoplasm-related mortality burden. American Lebanese Syrian Associated Charities and US National Institutes of Health.

Background DNA methylation (DNAm) plays an important role in lipid metabolism, however, no epigenome-wide association study (EWAS) of lipid levels has been conducted among childhood cancer survivors. Here, we performed EWAS analysis with longitudinally collected blood lipid data from survivors in the St. Jude lifetime cohort study. Methods Among 2052 childhood cancer survivors of European ancestry (EA) and 370 survivors of African ancestry (AA), four types of blood lipids, including high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), and triglycerides (TG), were measured during follow-up beyond 5-years from childhood cancer diagnosis. For the exposure EWAS (i.e., lipids measured before blood draw for DNAm), the DNAm level was an outcome variable and each of the blood lipid level was an exposure variable; vice versa for the outcome EWAS (i.e., lipids measured after blood draw for DNAm). Results Among EA survivors, we identified 43 lipid-associated CpGs in the HDL ( n  = 7), TC ( n  = 3), and TG ( n  = 33) exposure EWAS, and 106 lipid-associated CpGs in the HDL ( n  = 5), LDL ( n  = 3), TC ( n  = 4), and TG ( n  = 94) outcome EWAS. Among AA survivors, we identified 15 lipid-associated CpGs in TG exposure ( n  = 6), HDL ( n  = 1), LDL ( n  = 1), TG ( n  = 5) and TC ( n  = 2) outcome EWAS with epigenome-wide significance ( P  < 9 × 10 −8 ). There were no overlapping lipids-associated CpGs between exposure and outcome EWAS among EA and AA survivors, suggesting that the DNAm changes of different CpGs could be the cause or consequence of blood lipid levels. In the meta-EWAS, 12 additional CpGs reached epigenome-wide significance. Notably, 32 out of 74 lipid-associated CpGs showed substantial heterogeneity ( P het  < 0.1 or I 2  > 70%) between EA and AA survivors, highlighting differences in DNAm markers of blood lipids between populations with diverse genetic ancestry. Ten lipid-associated CpGs were cis-expression quantitative trait methylation with their DNAm levels associated with the expression of corresponding genes, out of which seven were negatively associated. Conclusions We identified distinct signatures of DNAm for blood lipids as exposures or outcomes and between EA and AA survivors, revealing additional genes involved in lipid metabolism and potential novel targets for controlling blood lipids in childhood cancer survivors.

Background: As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism - RNA editing due to post-transcriptional changes of individual nucleotides - remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise. Results: Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD's capabilities. Conclusions: AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes. Footnotes * https://github.com/RNAdetective/AIDD

Many Zika virus (ZIKV) pathogenesis-related studies have focused primarily on virus-driven pathology and neurotoxicity, instead of considering the possibility of pathogenesis as an (unintended) consequence of host innate immunity: specifically, as the side-effect of an otherwise well-functioning machine. The hypothesis presented here suggests a new way of thinking about the role of host immune mechanisms in disease pathogenesis, focusing on dysregulation of post-transcriptional RNA editing as a candidate driver of a broad range of observed neurodevelopmental defects and neurodegenerative clinical symptoms in both infants and adults linked with ZIKV infections. We collect and synthesize existing evidence of ZIKV-mediated changes in expression of adenosine deaminases that act on RNA (ADARs), known links between abnormal RNA editing and pathogenesis, as well as ideas for potential translational applications, including genomic profile-based molecular diagnostic tools and/or treatment strategies.

Neuropsychiatric disorders, including depression and suicide, are becoming an increasing public health concern. Rising rates of both depression and suicide, exacerbated by the current COVID19 pandemic, have only hastened our need for objective and reliable diagnostic biomarkers. These can aide clinicians treating depressive disorders in both diagnosing and developing treatment plans. While differential gene expression analysis has highlighted the serotonin signaling cascade among other critical neurotransmitter pathways to underly the pathology of depression and suicide, the biological mechanisms remain elusive. Here we propose a novel approach to better understand molecular underpinnings of neuropsychiatric disorders by examining patterns of differential RNA editing by adenosine deaminases acting on RNA (ADARs). We take advantage of publicly available RNA-seq datasets to map ADAR editing landscapes in a global gene-centric view. We use a unique combination of Guttman scaling and random forest classification modeling to create, describe and compare ADAR editing profiles focusing on both spatial and biological sex differences. We use a subset of experimentally confirmed ADAR editing sites located in known protein coding regions, the excitome, to map ADAR editing profiles in Major Depressive Disorder (MDD) and suicide. Using Guttman scaling, we were able to describe significant changes in editing profiles across brain regions in males and females with respect to cause of death (COD) and MDD diagnosis. The spatial distribution of editing sites may provide insight into biological mechanisms under-pinning clinical symptoms associated with MDD and suicidal behavior. Additionally, we use random forest modeling including these differential profiles among other markers of global editing patterns in order to highlight potential biomarkers that offer insights into molecular changes underlying synaptic plasticity. Together, these models identify potential prognostic, diagnostic and therapeutic biomarkers for MDD diagnosis and/or suicide.