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Biomedical research frequently uses murine models to study disease mechanisms. However, the translation of these findings to human disease remains a significant challenge. In order to improve the comparability of mouse and human data, we present a cross-species integration pipeline for single-cell transcriptomic assays. The pipeline merges expression matrices and assigns clear orthologous relationships. Starting from Ensembl ortholog assignments, we allocated 82% of mouse genes to unique orthologs by using additional publicly available resources such as Uniprot, and NCBI databases. For genes with multiple matches, we employed the Needleman-Wunsch global alignment based on either amino acid or nucleotide sequence to identify the ortholog with the highest degree of similarity. The workflow was tested for its functionality and efficiency by integrating scRNA-seq datasets from heart failure patients with the corresponding mouse model. We were able to assign unique human orthologs to up to 80% of the mouse genes, utilizing the known 17,492 orthologous pairs. Curiously, the integration process enabled the identification of both common and unique regulatory pathways between species in heart failure. In conclusion, our pipeline streamlines the integration process, enhances gene nomenclature alignment and simplifies the translation of mouse models to human disease. We have made the OrthoIntegrate R-package accessible on GitHub (https://github.com/MarianoRuzJurado/OrthoIntegrate), which includes the assignment of ortholog definitions for human and mouse, as well as the pipeline for integrating single cells.

Midbrain dopaminergic neurons (mDANs) control voluntary movement, cognition, and reward behavior under physiological conditions and are implicated in human diseases such as Parkinson’s disease (PD). Many transcription factors (TFs) controlling human mDAN differentiation during development have been described, but much of the regulatory landscape remains undefined. Using a tyrosine hydroxylase (TH) iPSC reporter line, we have generated time series transcriptomic and epigenomic profiles of purified mDANs during differentiation. Integrative analysis predicted novel central regulators of mDAN differentiation and super-enhancers were used to prioritize key TFs. We find LBX1, NHLH1 and NR2F1/2 to be necessary for mDAN differentiation and show that overexpression of either LBX1 or NHLH1 can also improve mDAN specification. NHLH1 is necessary for the induction of neuronal miR-124, while LBX1 regulates cholesterol biosynthesis, possibly through mTOR signaling. Consistently, rapamycin treatment led to an inhibition of mDAN differentiation. Thus, our work reveals novel regulators of human mDAN differentiation.

MotivationDNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding.ResultsWe present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy.AvailabilityAn implementation of FAME is open source and licensed under GPL-3.0 athttps://github.com/FischerJo/FAME.

Circular RNAs (circRNAs) are generated by back-splicing and control cellular signaling and phenotypes. Pericytes stabilize the capillary structure and play an important role in the formation and maintenance of new blood vessels. Here, we characterized hypoxia-regulated circRNAs in human pericytes and showed that circPLOD2 is induced by hypoxia and regulates pericyte function. Silencing of circPLOD2 increased pericyte proliferation, endothelial-pericyte interaction and tube formation. Transcriptional profiling of circPLOD2-depleted cells and epigenomic analyses revealed widespread changes in gene expression and identified the circPLOD2-dependent regulation of the transcription factor KLF4 as a key effector of these changes. Importantly, overexpression ofKLF4was sufficient to reverse the effects on pericyte proliferation and endothelial-pericyte interactions observed after circPLOD2 depletion. Together, these data revealed a novel function of circPLOD2 in the control of pericyte proliferation and capillary formation and showed that circPLOD2-mediated regulation of KLF4 significantly contributes to the transcriptional response to hypoxia.HighlightscircPLOD2 is upregulated in hypoxia in human vascular pericytesSelective depletion of circPLOD2, but not linearPLOD2mRNA, changes pericyte migration and endothelial-pericyte interactioncircPLOD2 depletion triggers widespread changes in gene expression that are mirrored in the transcriptional hypoxia responseEpigenomic analyses pinpoint the transcription factor KLF4 as a central player in circPLOD2-mediated expression changesKLF4overexpression is sufficient to rescue the changes in pericyte function caused by circPLOD2 depletion

Mechanisms by which specific histone modifications regulate distinct gene regulatory networks remain little understood. We investigated how H3K79me2, a modification catalyzed by DOT1L and previously considered a general transcriptional activation mark, regulates gene expression in mammalian cardiogenesis. Early embryonic cardiomyocyte ablation ofDot1lrevealed that H3K79me2 does not act as a general transcriptional activator, but rather regulates highly specific gene regulatory networks at two critical cardiogenic junctures: left ventricle patterning and postnatal cardiomyocyte cell cycle withdrawal. Mechanistic analyses revealed that H3K79me2 in two distinct domains, gene bodies and regulatory elements, synergized to promote expression of genes activated by DOT1L. Surprisingly, these analyses also revealed that H3K79me2 in specific regulatory elements contributed to silencing genes usually not expressed in cardiomyocytes. As DOT1L mutants had increased numbers of postnatal mononuclear cardiomyocytes and prolonged cardiomyocyte cell cycle activity, controlled inhibition of DOT1L might be a strategy to promote cardiac regeneration post-injury.

Long non-coding RNAs (lncRNAs) can act as regulatory RNAs which, by altering the expression of target genes, impact on the cellular phenotype and cardiovascular disease development. Endothelial lncRNAs and their vascular functions are largely undefined. Deep RNA-Seq and FANTOM5 CAGE analysis revealed the lncRNA LINC00607 to be highly enriched in human endothelial cells. LINC00607 was induced in response to hypoxia, arteriosclerosis regression in non-human primates and also in response to propranolol used to induce regression of human arteriovenous malformations. siRNA knockdown or CRISPR/Cas9 knockout of LINC00607 attenuated VEGF-A-induced angiogenic sprouting. LINC00607 knockout in endothelial cells also integrated less into newly formed vascular networks in an in vivo assay in SCID mice. Overexpression of LINC00607 in CRISPR knockout cells restored normal endothelial function. RNA- and ATAC-Seq after LINC00607 knockout revealed changes in the transcription of endothelial gene sets linked to the endothelial phenotype and in chromatin accessibility around ERG-binding sites. Mechanistically, LINC00607 interacted with the SWI/SNF chromatin remodeling protein BRG1. CRISPR/Cas9-mediated knockout of BRG1 in HUVEC followed by CUT&RUN revealed that BRG1 is required to secure a stable chromatin state, mainly on ERG-binding sites. In conclusion, LINC00607 is an endothelial-enriched lncRNA that maintains ERG target gene transcription by interacting with the chromatin remodeler BRG1.

The unicellular ciliate Paramecium contains a large vegetative macronucleus with several unusual characteristics including an extremely high coding density and high polyploidy. As macronculear chromatin is devoid of heterochromatin our study characterizes the functional epigenomic organisation necessary for gene regulation and proper PolII activity. Histone marks (H3K4me3, H3K9ac, H3K27me3) revealed no narrow peaks but broad domains along gene bodies, whereas intergenic regions were devoid of nucleosomes. Our data implicates H3K4me3 levels inside ORFs to be the main factor to associate with gene expression and H3K27me3 appears to occur as a bistable domain with H3K4me3 in plastic genes. Surprisingly, silent and lowly expressed genes show low nucleosome occupancy suggesting that gene inactivation does not involve increased nucleosome occupancy and chromatin condensation. Due to a high occupancy of Pol II along highly expressed ORFs, transcriptional elongation appears to be quite different to other species. This is supported by missing heptameric repeats in the C-terminal domain of Pol II and a divergent elongation system. Our data implies that unoccupied DNA is the default state, whereas gene activation requires nucleosome recruitment together with broad domains of H3K4me3. This could represent a buffer for paused Pol II along ORFs in absence of elongation factors of higher eukaryotes.

SUMMARYTumor microenvironment-targeted therapies are emerging as promising treatment options for different cancer types. Tumor-associated macrophages and microglia (TAMs) represent an abundant non-malignant cell type in brain metastases and have been proposed to modulate metastatic colonization and outgrowth. We used an inhibitor of colony stimulating factor 1 receptor (CSF1R) to target TAMs at distinct stages of the metastatic cascade in preclinical breast-to-brain metastasis models and found that CSF1R inhibition leads to anti-tumor responses in prevention and intervention trials. However, in established brain metastases, compensatory CSF2Rb-STAT5-mediated pro-inflammatory TAM activation blunted the ultimate efficacy of CSF1R inhibition by inducing neuro-inflammation gene signatures in association with wound repair responses that fostered tumor recurrence. Consequently, combined blockade of CSF1R and STAT5 signaling led to sustained tumor control, a normalization of microglial activation states and amelioration of neuronal damage.

With the rise of single cell RNA sequencing new bioinformatic tools became available to handle specific demands, such as quantifying unique molecular identifiers and correcting cell barcodes. Here, we analysed several datasets with the most common alignment tools for scRNA-seq data. We evaluated differences in the whitelisting, gene quantification, overall performance and potential variations in clustering or detection of differentially expressed genes.We compared the tools Cell Ranger 5, STARsolo, Kallisto and Alevin on three published datasets for human and mouse, sequenced with different versions of the 10X sequencing protocol.Striking differences have been observed in the overall runtime of the mappers. Besides that Kallisto and Alevin showed variances in the number of valid cells and detected genes per cell. Kallisto reported the highest number of cells, however, we observed an overrepresentation of cells with low gene content and unknown celtype. Conversely, Alevin rarely reported such low content cells.Further variations were detected in the set of expressed genes. While STARsolo, Cell Ranger 5 and Alevin released similar gene sets, Kallisto detected additional genes from the Vmn and Olfr gene family, which are likely mapping artifacts. We also observed differences in the mitochondrial content of the resulting cells when comparing a prefiltered annotation set to the full annotation set that includes pseudogenes and other biotypes.Overall, this study provides a detailed comparison of common scRNA-seq mappers and shows their specific properties on 10X Genomics data.Key messagesMapping and gene quantifications are the most resource and time intensive steps during the analysis of scRNA-Seq data.The usage of alternative alignment tools reduces the time for analysing scRNA-Seq data.Different mapping strategies influence key properties of scRNA-SEQ e.g. total cell counts or genes per cellA better understanding of advantages and disadvantages for each mapping algorithm might improve analysis results.

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that are affected by DNA methylation. Overall, we find that CpG methylation decreases the likelihood of binding for the majority of TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding.

Most sRNA biogenesis mechanisms involve either RNAseIII cleavage or ping-pong amplification by different Piwi proteins harboring slicer activity. Here, we follow the question why the mechanism of transgene-induced silencing in the ciliate Paramecium needs both Dicer activity and two Ptiwi proteins. This pathway involves primary siRNAs produced from non-translatable transgenes and secondary siRNAs from endogenous remote loci. Our data does not indicate any signatures from ping-pong amplification but Dicer cleavage of long dsRNA. We show that Ptiwi13 and 14 have different preferences for primary and secondary siRNAs but do not load them mutually exclusive. Both Piwis enrich for antisense RNAs and Ptiwi14 loaded siRNAs show a 5′-U signature. Both Ptiwis show in addition a general preference for Uridine-rich sRNAs along the entire sRNA length. Our data indicates both Ptiwis and 2’-O-methylation to contribute to strand selection of Dicer cleaved siRNAs. This unexpected function of two distinct vegetative Piwis extends the increasing knowledge of the diversity of Piwi functions in diverse silencing pathways. As both Ptiwis show differential subcellular localisation, Ptiwi13 in the cytoplasm and Ptiwi14 in the vegetative macronucleus, we conclude that cytosolic and nuclear silencing factors are necessary for efficient chromatin silencing.

Single-molecule sequencing technologies have the potential to improve measurement and analysis of long RNA molecules expressed in cells. However, analysis of error-prone long RNA reads is a current challenge. We present AERON for the estimation of transcript expression and prediction of gene-fusion events. AERON uses an efficient read-to-graph alignment algorithm to obtain accurate estimates for noisy reads. We demonstrate AERON to yield accurate expression estimates on simulated and real datasets. It is the first method to reliably call gene-fusion events from long RNA reads. Sequencing the K562 transcriptome, we used AERON and found known as well as novel gene-fusion events.

Understanding the complexity of transcriptional regulation is a major goal of computational biology. Because experimental linkage of regulatory sites to genes is challenging, computational methods considering epigenomics data have been proposed to create tissue-specific regulatory maps. However, we showed that these approaches are not well suited to account for the variations of the regulatory landscape between cell-types. To overcome these drawbacks, we developed a new method called STITCHIT, that identifies and links putative regulatory sites to genes. Within STITCHIT, we consider the chromatin accessibility signal of all samples jointly to identify regions exhibiting a signal variation related to the expression of a distinct gene. STITCHIToutperforms previous approaches in various validation experiments and was used with a genome-wide CRISPR-Cas9 screen to prioritize novel doxorubicin-resistance genes and their associated non-coding regulatory regions. We believe that our work paves the way for a more refined understanding of transcriptional regulation at the gene-level.

1AbstractTransposase-Accessible Chromatin (ATAC) followed by sequencing (ATAC-seq) is a simple and fast protocol for detection of open chromatin. However, computational footprinting in ATAC-seq, i.e. search for regions with depletion of cleavage events due to transcription factor binding sites, has been poorly explored so far. We propose HINT-ATAC, a footprinting method that addresses ATAC-seq specific protocol artifacts. HINT-ATAC uses a probabilistic framework based on Variable-order Markov models to learn the complex sequence cleavage preferences of the transposase enzyme. Moreover, we observed specific strand specific cleavage patterns around the binding sites of transcription factors, which are determined by local nucleosome architecture. HINT-ATAC explores local nucleosome architecture to significantly outperform competing footprinting methods in predicting transcription factor binding sites by ChIP-seq. HINT-ATAC is an open source software and available online at www.regulatory-genomics.org/hint

BackgroundBidirectional 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.ResultsWe 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.ConclusionsOur 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.

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cells (MSCs) towards adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages. To prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. We identified aryl hydrocarbon receptor (AHR) and Glis family zinc finger 1 (GLIS1) as mesenchymal key TFs controlled by dynamic MSC-specific super-enhancers that become repressed in both lineages. AHR and GLIS1 control differentiation-induced genes and we propose they function as guardians of mesenchymal multipotency.

De Bruijn graphs are a common assembly data structure for large sequencing datasets. But with the advances in sequencing technologies, assembling high coverage datasets has become a computational challenge. Read normalization, which removes redundancy in large datasets, is widely applied to reduce resource requirements. Current normalization algorithms, though efficient, provide no guarantee to preserve important k-mers that form connections between regions in the graph. Here, normalization is phrased as a set multi-cover problem on reads and a heuristic algorithm, ORNA, is proposed. ORNA normalizes to the minimum number of reads required to retain all k-mers and their relative kmer abundances from the original dataset. Hence, all connections and coverage information from the original graph are preserved. ORNA was tested on various RNA-seq datasets with different coverage values. It was compared to the current normalization algorithms and was found to be performing better. It is shown that combining read error correction and normalization allows more accurate and resource efficient RNA assemblies compared to the original dataset. Further, an application was proposed in which multiple datasets were combined and normalized to predict novel transcripts that would have been missed otherwise. Finally, ORNA is a general purpose normalization algorithm that is fast and significantly reduces datasets with little loss of assembly quality.ORNA can be found under https://github.com/SchulzLab/ORNA