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CircRNAs are an important class of RNAs with diverse cellular functions in human physiology and disease. A thorough knowledge of circRNAs including their biogenesis and subcellular distribution is important to understand their roles in a wide variety of processes. However, the analysis of circRNAs from total RNA sequencing data remains challenging. Therefore, we developed Calcifer, a versatile workflow for circRNA annotation. Using Calcifer, we analysed APEX-Seq data to compare circRNA occurrence between whole cells, nucleus and subnuclear compartments. We generally find that circRNAs show higher abundance in whole cells compared to nuclear samples, consistent with their accumulation in the cytoplasm. The notable exception is the single-exon circRNA circCANX(9), which is unexpectedly enriched in the nucleus. In addition, we observe that circFIRRE prevails over the linear lncRNA FIRRE in both the cytoplasm and the nucleus. Zooming in on the subnuclear compartments, we show that circRNAs are strongly depleted from nuclear speckles, indicating that excess splicing factors in this compartment counteract back-splicing. Our results thereby provide valuable insights into the subnuclear distribution of circRNAs. Regarding circRNA function, we surprisingly find that the majority of all detected circRNAs possess complete open reading frames with potential for cap-independent translation. Overall, we show that Calcifer is an easy-to-use, versatile and sustainable workflow for the annotation of circRNAs which expands the repertoire of circRNA tools and allows to gain new insights into circRNA distribution and function.

Abstract Background Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) appearing in non-coding genomic regions in CVDs. The SNPs may alter gene expression by modifying transcription factor (TF) binding sites and lead to functional consequences in cardiovascular traits or diseases. To understand the underlying molecular mechanisms, it is crucial to identify which variations are involved and how they affect TF binding. Methods The SNEEP (SNP exploration and analysis using epigenomics data) pipeline was used to identify regulatory SNPs, which alter the binding behavior of TFs and link GWAS SNPs to their potential target genes for six CVDs. The human-induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs), monoculture cardiac organoids (MCOs) and self-organized cardiac organoids (SCOs) were used in the study. Gene expression, cardiomyocyte size and cardiac contractility were assessed. Results By using our integrative computational pipeline, we identified 1905 regulatory SNPs in CVD GWAS data. These were associated with hundreds of genes, half of them non-coding RNAs (ncRNAs), suggesting novel CVD genes. We experimentally tested 40 CVD-associated non-coding RNAs, among them RP11-98F14.11, RPL23AP92, IGBP1P1, and CTD-2383I20.1, which were upregulated in hiPSC-CMs, MCOs and SCOs under hypoxic conditions. Further experiments showed that IGBP1P1 depletion rescued expression of hypertrophic marker genes, reduced hypoxia-induced cardiomyocyte size and improved hypoxia-reduced cardiac contractility in hiPSC-CMs and MCOs. Conclusions IGBP1P1 is a novel ncRNA with key regulatory functions in modulating cardiomyocyte size and cardiac function in our disease models. Our data suggest ncRNA IGBP1P1 as a potential therapeutic target to improve cardiac function in CVDs.

Abstract Fatty liver disease or the accumulation of fat in the liver, has been reported to affect the global population. This comes with an increased risk for the development of fibrosis, cirrhosis, and hepatocellular carcinoma. Yet, little is known about the effects of a diet containing high fat and alcohol towards epigenetic aging, with respect to changes in transcriptional and epigenomic profiles. In this study, we took up a multi-omics approach and integrated gene expression, methylation signals, and chromatin signals to study the epigenomic effects of a high-fat and alcohol-containing diet on mouse hepatocytes. We identified four relevant gene network clusters that were associated with relevant pathways that promote steatosis. Using a machine learning approach, we predict specific transcription factors that might be responsible to modulate the functionally relevant clusters. Finally, we discover four additional CpG loci and validate aging-related differential CpG methylation. Differential CpG methylation linked to aging showed minimal overlap with altered methylation in steatosis.

Abstract Aim Chemoresistance is a major cause of treatment failure in colorectal cancer (CRC) therapy. In this study, the impact of the IGF2BP family of RNA-binding proteins on CRC chemoresistance was investigated using in silico, in vitro, and in vivo approaches. Methods Gene expression data from a well-characterized cohort and publicly available cross-linking immunoprecipitation sequencing (CLIP-Seq) data were collected. Resistance to chemotherapeutics was assessed in patient-derived xenografts (PDXs) and patient-derived organoids (PDOs). Functional studies were performed in 2D and 3D cell culture models, including proliferation, spheroid growth, and mitochondrial respiration analyses. Results We identified IGF2BP2 as the most abundant IGF2BP in primary and metastastatic CRC, correlating with tumor stage in patient samples and tumor growth in PDXs. IGF2BP2 expression in primary tumor tissue was significantly associated with resistance to selumetinib, gefitinib, and regorafenib in PDOs and to 5-fluorouracil and oxaliplatin in PDX in vivo. IGF2BP2 knockout (KO) HCT116 cells were more susceptible to regorafenib in 2D and to oxaliplatin, selumitinib, and nintedanib in 3D cell culture. Further, a bioinformatic analysis using CLIP data suggested stabilization of target transcripts in primary and metastatic tumors. Measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) revealed a decreased basal OCR and an increase in glycolytic ATP production rate in IGF2BP2 KO. In addition, real-time reverse transcriptase polymerase chain reaction (qPCR) analysis confirmed decreased expression of genes of the respiratory chain complex I, complex IV, and the outer mitochondrial membrane in IGF2BP2 KO cells. Conclusions IGF2BP2 correlates with CRC tumor growth in vivo and promotes chemoresistance by altering mitochondrial respiratory chain metabolism. As a druggable target, IGF2BP2 could be used in future CRC therapy to overcome CRC chemoresistance.

For medicine to fulfill its promise of personalized treatments based on a better understanding of disease biology, computational and statistical tools must exist to analyze the increasing amount of patient data that becomes available. A particular challenge is that several types of data are being measured to cope with the complexity of the underlying systems, enhance predictive modeling and enrich molecular understanding.Here we review a number of recent approaches that specialize in the analysis of multimodal data in the context of predictive biomedicine. We focus on methods that combine different OMIC measurements with image or genome variation data. Our overview shows the diversity of methods that address analysis challenges and reveals new avenues for novel developments.

How and whether histone modifications regulate distinct gene networks remains insufficiently understood. Here Cattaneo et al show that DOT1L catalyzed H3K79me2 regulates fetal chamber-specific gene expression and neonatal cardiomyocyte cell cycle withdrawal to coordinate heart development.

Using a composite bioinformatics approach, the DNA:DNA:RNA triplex-forming lncRNAs HIF1α-AS1 was identified in human endothelial cells which recruits an epigenetic silencing complex to limit expression of triplex target genes.

Summary: Circular RNAs are generated by backsplicing and control cellular signaling and phenotypes. Pericytes stabilize capillary structures and play important roles in the formation and maintenance of blood vessels. Here, we characterize hypoxia-regulated circular RNAs (circRNAs) in human pericytes and show that the circular RNA of procollagen-lysine,2-oxoglutarate 5-dioxygenase-2 (circPLOD2) is induced by hypoxia and regulates pericyte functions. Silencing of circPLOD2 affects pericytes and increases proliferation, migration, and secretion of soluble angiogenic proteins, thereby enhancing endothelial migration and network capability. Transcriptional and epigenomic profiling of circPLOD2-depleted cells reveals widespread changes in gene expression and identifies the transcription factor krüppel-like factor 4 (KLF4) as a key effector of the circPLOD2-mediated changes. KLF4 depletion mimics circPLOD2 silencing, whereas KLF4 overexpression reverses the effects of circPLOD2 depletion on proliferation and endothelial-pericyte interactions. Together, these data reveal an important function of circPLOD2 in controlling pericyte proliferation and capillary formation and show that the circPLOD2-mediated regulation of KLF4 significantly contributes to the transcriptional response to hypoxia.

Abstract The transcription factor vitamin D receptor (VDR) is the high affinity nuclear target of the biologically active form of vitamin D3 (1,25(OH)2D3). In order to identify pure genomic transcriptional effects of 1,25(OH)2D3, we used VDR cistrome, transcriptome and open chromatin data, obtained from the human monocytic cell line THP-1, for a novel hierarchical analysis applying three bioinformatics approaches. We predicted 75.6% of all early 1,25(OH)2D3-responding (2.5 or 4 h) and 57.4% of the late differentially expressed genes (24 h) to be primary VDR target genes. VDR knockout led to a complete loss of 1,25(OH)2D3–induced genome-wide gene regulation. Thus, there was no indication of any VDR-independent non-genomic actions of 1,25(OH)2D3 modulating its transcriptional response. Among the predicted primary VDR target genes, 47 were coding for transcription factors and thus may mediate secondary 1,25(OH)2D3 responses. CEBPA and ETS1 ChIP-seq data and RNA-seq following CEBPA knockdown were used to validate the predicted regulation of secondary vitamin D target genes by both transcription factors. In conclusion, a directional network containing 47 partly novel primary VDR target transcription factors describes secondary responses in a highly complex vitamin D signaling cascade. The central transcription factor VDR is indispensable for all transcriptome-wide effects of the nuclear hormone.

Endothelial cells play a critical role in the adaptation of tissues to injury and show a remarkable plasticity. Here the authors show, using single cell sequencing, that endothelial cells acquire a transient mesenchymal state associated with metabolic adaptation after myocardial infarction.

Abstract Background Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter–enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability. Results We have extended our $$\textsc{TEPIC}$$ TEPIC framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer–promoter loops involving YY1 in different cell lines. Conclusion We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimization steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.

Background: Based on reports on elevated cholesterol levels in cancer cells, strategies to lower cholesterol synthesis have been suggested as an antitumour strategy. However, cholesterol depletion has also been shown to induce tumour-promoting actions in tumour-associated macrophages (TAMs). Methods: We performed lipidomic and transcriptomic analyses of human lung cancer material. To assess whether the TAM phenotype is shaped by secreted factors produced by tumour cells, primary human monocyte-derived macrophages were polarized towards a TAM-like phenotype using tumour cell-conditioned medium. Findings: Lipidomic analysis of lung adenocarcinoma (n=29) and adjacent non-tumour tissues (n=22) revealed a significant accumulation of free cholesterol and cholesteryl esters within the tumour tissue. In contrast, cholesterol levels were reduced in TAMs isolated from lung adenocarcinoma tissues when compared with alveolar macrophages (AMs) obtained from adjacent non-tumour tissues. Bulk-RNA-Seq revealed that genes involved in cholesterol biosynthesis and metabolism were downregulated in TAMs, while cholesterol efflux transporters were upregulated. In vitro polarized TAM-like macrophages showed an attenuated lipogenic gene expression signature and exhibited lower cholesterol levels compared with non-polarized macrophages. A genome-wide comparison by bulk RNA-Seq confirmed a high similarity of ex vivo TAMs and in vitro TAM-like macrophages. Modulation of intracellular cholesterol levels by either starving, cholesterol depletion, or efflux transporter inhibition indicated that cholesterol distinctly shapes macrophage gene expression. Interpretation: Our data show an opposite dysregulation of cholesterol homeostasis in tumour tissue vs. TAMs. Polarization of in vitro differentiated macrophages by tumour cell-conditioned medium recapitulates key features of ex vivo TAMs. Funding: Deutsche Forschungsgemeinschaft (DFG), Landesforschungsf €orderungsprogramm Saarland (LFPP).

Abstract Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.

Objective: Oxidative stress is a risk factor for atherosclerosis. NADPH oxidases of the Nox family produce ROS but their contribution to atherosclerosis development is less clear. Nox2 promotes and Nox4 rather limits atherosclerosis. Although Nox1 with its cytosolic co-factors are largely expressed in epithelial cells, a role for Nox1 for atherosclerosis development was suggested. To further define the role of this homologue, the role of its essential cytosolic cofactor, NoxO1, was determined for atherosclerosis development with the aid of knockout mice. Methods and results: Wildtype (WT) and NoxO1 knockout mice were treated with high fat diet and adeno-associated virus (AAV) overexpressing pro-protein convertase subtilisin/kexin type 9 (PCSK9) to induce hepatic low-density lipoprotein (LDL) receptor loss. As a result, massive hypercholesterolemia was induced and spontaneous atherosclerosis developed within three month. Deletion of NoxO1 reduced atherosclerosis formation in brachiocephalic artery and aortic arch in female but not male NoxO1−/− mice as compared to WT littermates. This was associated with a reduced pro-inflammatory cytokine signature in the plasma of female but not male NoxO1−/− mice. MACE-RNAseq of the vessel did not reveal this signature and the expression of the Nox1/NoxO1 system was low to not detectable. Conclusions: The scaffolding protein NoxO1 plays some role in atherosclerosis development in female mice probably by attenuating the global inflammatory burden.

Abstract Background Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. Results We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. Conclusions Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).

The K1 A/B toxin secreted by virus-infected Saccharomyces cerevisiae strains kills sensitive cells via disturbance of cytoplasmic membrane functions. Despite decades of research, the mechanisms underlying K1 toxicity and immunity have not been elucidated yet. In a novel approach, this study aimed to characterize transcriptome changes in K1-treated sensitive yeast cells in a time-dependent manner. Global transcriptional profiling revealed substantial cellular adaptations in target cells resulting in 1,189 differentially expressed genes in total. Killer toxin K1 induced oxidative, cell wall and hyperosmotic stress responses as well as rapid down-regulation of transcription and translation. Essential pathways regulating energy metabolism were also significantly affected by the toxin. Remarkably, a futile cycle of the osmolytes trehalose and glycogen was identified probably representing a critical feature of K1 intoxication. In silico analysis suggested several transcription factors involved in toxin-triggered signal transduction. The identified transcriptome changes provide valuable hints to illuminate the still unknown molecular events leading to K1 toxicity and immunity implicating an evolutionarily conserved response at least initially counteracting ionophoric toxin action.

Understanding the role of short-interfering RNA (siRNA) in diverse biological processes is of current interest and often approached through small RNA sequencing. However, analysis of these datasets is difficult due to the complexity of biological RNA processing pathways, which differ between species. Several properties like strand specificity, length distribution, and distribution of soft-clipped bases are few parameters known to guide researchers in understanding the role of siRNAs. We present RAPID, a generic eukaryotic siRNA analysis pipeline, which captures information inherent in the datasets and automatically produces numerous visualizations as user-friendly HTML reports, covering multiple categories required for siRNA analysis. RAPID also facilitates an automated comparison of multiple datasets, with one of the normalization techniques dedicated for siRNA knockdown analysis, and integrates differential expression analysis using DESeq2. Availability and Implementation RAPID is available under MIT license at https://github.com/SchulzLab/RAPID. We recommend using it as a conda environment available from https://anaconda.org/bioconda/rapid

Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).