Systematic analysis and characterization of long non-coding RNA genes in inflammatory bowel disease.

The cases of inflammatory bowel disease (IBD) are increasing rapidly around the world. Due to the multifactorial causes of IBD, there is an urgent need to understand the pathogenesis of IBD. As such, the usage of high-throughput techniques to profile genetic mutations, microbiome environments, transcriptome and proteome (e.g. lipidome) is increasing to understand the molecular changes associated with IBD, including two major etiologies of IBD: Crohn disease (CD) and ulcerative colitis (UC). In the case of transcriptome data, RNA sequencing (RNA-seq) technique is used frequently. However, only protein-coding genes are analyzed, leaving behind all other RNAs, including non-coding RNAs (ncRNAs) to be unexplored. Among these ncRNAs, long non-coding RNAs (lncRNAs) may hold keys to understand the pathogenesis of IBD as lncRNAs are expressed in a cell/tissue-specific manner and dysregulated in a disease, such as IBD. However, it is rare that RNA-seq data are analyzed for lncRNAs. To fill this gap in knowledge, we re-analyzed RNA-seq data of CD and UC patients compared with the healthy donors to dissect the expression profiles of lncRNA genes. As inflammation plays key roles in the pathogenesis of IBD, we conducted loss-of-function experiments to provide functional data of IBD-specific lncRNA, lung cancer associated transcript 1 (LUCAT1), in an in vitro model of macrophage polarization. To further facilitate the lncRNA research in IBD, we built a web database, IBDB (https://ibd-db.shinyapps.io/IBDB/), to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in IBD patients compared with healthy donors.

Exploration and analysis of R-loop mapping data with RLBase.

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. In 2012, Ginno et al. introduced the first R-loop mapping method. Since that time, dozens of R-loop mapping studies have been conducted, yielding hundreds of publicly available datasets. Current R-loop databases provide only limited access to these data. Moreover, no web tools for analyzing user-supplied R-loop datasets have yet been described. In our recent work, we reprocessed 810 R-loop mapping samples, building the largest R-loop data resource to date. We also defined R-loop consensus regions and developed a framework for R-loop data analysis. Now, we introduce RLBase, a user-friendly database that provides the capability to (i) explore hundreds of public R-loop mapping datasets, (ii) explore R-loop consensus regions, (iii) analyze user-supplied data and (iv) download standardized and reprocessed datasets. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/shiny/rlbase/.

Systematic Analysis of Long Non-Coding RNA Genes in Nonalcoholic Fatty Liver Disease.

The largest solid organ in humans, the liver, performs a variety of functions to sustain life. When damaged, cells in the liver can regenerate themselves to maintain normal liver physiology. However, some damage is beyond repair, which necessitates liver transplantation. Increasing rates of obesity, Western diets (i.e., rich in processed carbohydrates and saturated fats), and cardiometabolic diseases are interlinked to liver diseases, including non-alcoholic fatty liver disease (NAFLD), which is a collective term to describe the excess accumulation of fat in the liver of people who drink little to no alcohol. Alarmingly, the prevalence of NAFLD extends to 25% of the world population, which calls for the urgent need to understand the disease mechanism of NAFLD. Here, we performed secondary analyses of published RNA sequencing (RNA-seq) data of NAFLD patients compared to healthy and obese individuals to identify long non-coding RNAs (lncRNAs) that may underly the disease mechanism of NAFLD. Similar to protein-coding genes, many lncRNAs are dysregulated in NAFLD patients compared to healthy and obese individuals, suggesting that understanding the functions of dysregulated lncRNAs may shed light on the pathology of NAFLD. To demonstrate the functional importance of lncRNAs in the liver, loss-of-function experiments were performed for one NAFLD-related lncRNA, LINC01639, which showed that it is involved in the regulation of genes related to apoptosis, TNF/TGF, cytokine signaling, and growth factors as well as genes upregulated in NAFLD. Since there is no lncRNA database focused on the liver, especially NAFLD, we built a web database, LiverDB, to further facilitate functional and mechanistic studies of hepatic lncRNAs.

Development and characterization of a mass cytometry panel for detecting the effect of acute doxorubicin exposure on murine cardiac nonmyocytes.

Childhood cancer survivors (CCSs) face lifelong side effects related to their treatment with chemotherapy. Anthracycline agents, such as doxorubicin (DOX), are important in the treatment of childhood cancers but are associated with cardiotoxicity. Cardiac toxicities represent a significant source of chronic disability that cancer survivors face; despite this, the chronic cardiotoxicity phenotype and how it relates to acute toxicity remains poorly defined. To address this critical knowledge gap, we studied the acute effect of DOX on murine cardiac nonmyocytes in vivo. Determination of the acute cellular effects of DOX on nonmyocytes, a cell pool with finite replicative capacity, provides a basis for understanding the pathogenesis of the chronic heart disease that CCSs face. To investigate the acute cellular effects of DOX, we present single-cell RNA sequencing (scRNAseq) data from homeostatic cardiac nonmyocytes and compare it with preexisting datasets, as well as a novel CyTOF datasets. SCANPY, a python-based single-cell analysis, was used to assess the heterogeneity of cells detected in scRNAseq and CyTOF. To further assist in CyTOF data annotation, joint analyses of scRNAseq and CyTOF data using an artificial neural network known as sparse autoencoder for clustering, imputation, and embedding (SAUCIE) are performed. Lastly, the panel is tested on a mouse model of acute DOX exposure at two time points (24 and 72 h) after the last dose of doxorubicin and examined with joint clustering. In sum, we report the first ever CyTOF study of cardiac nonmyocytes and characterize the effect of acute DOX exposure with scRNAseq and CyTOF.NEW & NOTEWORTHY We describe the first mass cytometry studies of murine cardiac nonmyocytes. The mass cytometry panel is compared with single-cell RNA sequencing data. Homeostatic cardiac nonmyocytes are characterized by mass cytometry to identify and quantify four major cell populations: endothelial cells, fibroblasts, leukocytes, and pericytes. The single-cell acute nonmyocyte response to doxorubicin is studied at 24 and 72 h after doxorubicin exposure given daily for 5 days at a dose of 4 mg/kg/day.

Quality-controlled R-loop meta-analysis reveals the characteristics of R-loop consensus regions.

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 810 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate R-loop data quality control method, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called 'R-loop regions' (RL regions). In the process, we identified a stark divergence between RL regions detected by S9.6 and dRNH-based mapping methods, particularly with respect to R-loop size, location, and colocalization with RNA binding factors. Taken together, this work provides a much-needed method to assess R-loop data quality and offers novel context regarding the differences between dRNH- and S9.6-based R-loop mapping approaches.

Current Status of Epitranscriptomic Marks Affecting lncRNA Structures and Functions.

Long non-coding RNAs (lncRNAs) belong to a class of non-protein-coding RNAs with their lengths longer than 200 nucleotides. Most of the mammalian genome is transcribed as RNA, yet only a small percent of the transcribed RNA corresponds to exons of protein-coding genes. Thus, the number of lncRNAs is predicted to be several times higher than that of protein-coding genes. Because of sheer number of lncRNAs, it is often difficult to elucidate the functions of all lncRNAs, especially those arising from their relationship to their binding partners, such as DNA, RNA, and proteins. Due to their binding to other macromolecules, it has become evident that the structures of lncRNAs influence their functions. In this regard, the recent development of epitranscriptomics (the field of study to investigate RNA modifications) has become important to further elucidate the structures and functions of lncRNAs. In this review, the current status of lncRNA structures and functions influenced by epitranscriptomic marks is discussed.

FibroDB: Expression Analysis of Protein-Coding and Long Non-Coding RNA Genes in Fibrosis.

Most long non-coding RNAs (lncRNAs) are expressed at lower levels than protein-coding genes and their expression is often restricted to specific cell types, certain time points during development, and various stress and disease conditions, respectively. To revisit this long-held concept, we focused on fibroblasts, a common cell type in various organs and tissues. Using fibroblasts and changes in their expression profiles during fibrosis as a model system, we show that the overall expression level of lncRNA genes is significantly lower than that of protein-coding genes. Furthermore, we identified lncRNA genes whose expression is upregulated during fibrosis. Using dermal fibroblasts as a model, we performed loss-of-function experiments and show that the knockdown of the lncRNAs LINC00622 and LINC01711 result in gene expression changes associated with cellular and inflammatory responses, respectively. Since there are no lncRNA databases focused on fibroblasts and fibrosis, we built a web application, FibroDB, to further promote functional and mechanistic studies of fibrotic lncRNAs.

Correlation AnalyzeR: functional predictions from gene co-expression correlations.

BACKGROUND: Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills. RESULTS: We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses. CONCLUSIONS: Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .

Pathogenic Tau Causes a Toxic Depletion of Nuclear Calcium.

Synaptic activity-induced calcium (Ca2+) influx and subsequent propagation into the nucleus is a major way in which synapses communicate with the nucleus to regulate transcriptional programs important for activity-dependent survival and memory formation. Nuclear Ca2+ shapes the transcriptome by regulating cyclic AMP (cAMP) response element-binding protein (CREB). Here, we utilize a Drosophila model of tauopathy and induced pluripotent stem cell (iPSC)-derived neurons from humans with Alzheimer's disease to study the effects of pathogenic tau, a pathological hallmark of Alzheimer's disease and related tauopathies, on nuclear Ca2+. We find that pathogenic tau depletes nuclear Ca2+ and CREB to drive neuronal death, that CREB-regulated genes are over-represented among differentially expressed genes in tau transgenic Drosophila, and that activation of big potassium (BK) channels elevates nuclear Ca2+ and suppresses tau-induced neurotoxicity. Our studies identify nuclear Ca2+ depletion as a mechanism contributing to tau-induced neurotoxicity, adding an important dimension to the calcium hypothesis of Alzheimer's disease.

Cohesin SA1 and SA2 are RNA binding proteins that localize to RNA containing regions on DNA.

Cohesin SA1 (STAG1) and SA2 (STAG2) are key components of the cohesin complex. Previous studies have highlighted the unique contributions by SA1 and SA2 to 3D chromatin organization, DNA replication fork progression, and DNA double-strand break (DSB) repair. Recently, we discovered that cohesin SA1 and SA2 are DNA binding proteins. Given the recently discovered link between SA2 and RNA-mediated biological pathways, we investigated whether or not SA1 and SA2 directly bind to RNA using a combination of bulk biochemical assays and single-molecule techniques, including atomic force microscopy (AFM) and the DNA tightrope assay. We discovered that both SA1 and SA2 bind to various RNA containing substrates, including ssRNA, dsRNA, RNA:DNA hybrids, and R-loops. Importantly, both SA1 and SA2 localize to regions on dsDNA that contain RNA. We directly compared the SA1/SA2 binding and R-loops sites extracted from Chromatin Immunoprecipitation sequencing (ChIP-seq) and DNA-RNA Immunoprecipitation sequencing (DRIP-Seq) data sets, respectively. This analysis revealed that SA1 and SA2 binding sites overlap significantly with R-loops. The majority of R-loop-localized SA1 and SA2 are also sites where other subunits of the cohesin complex bind. These results provide a new direction for future investigation of the diverse biological functions of SA1 and SA2.

Reconstruction of Ewing Sarcoma Developmental Context from Mass-Scale Transcriptomics Reveals Characteristics of EWSR1-FLI1 Permissibility.

Ewing sarcoma is an aggressive pediatric cancer of enigmatic cellular origins typically resulting from a single translocation event t (11; 22) (q24; q12). The resulting fusion gene, EWSR1-FLI1, is toxic or unstable in most primary tissues. Consequently, attempts to model Ewing sarcomagenesis have proven unsuccessful thus far, highlighting the need to identify the cellular features which permit stable EWSR1-FLI1 expression. By re-analyzing publicly available RNA-Sequencing data with manifold learning techniques, we uncovered a group of Ewing-like tissues belonging to a developmental trajectory between pluripotent, neuroectodermal, and mesodermal cell states. Furthermore, we demonstrated that EWSR1-FLI1 expression levels control the activation of these developmental trajectories within Ewing sarcoma cells. Subsequent analysis and experimental validation demonstrated that the capability to resolve R-loops and mitigate replication stress are probable prerequisites for stable EWSR1-FLI1 expression in primary tissues. Taken together, our results demonstrate how EWSR1-FLI1 hijacks developmental gene programs and advances our understanding of Ewing sarcomagenesis.