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Abstract The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

IntroductionPancreatic ductal adenocarcinoma (PDAC) remains a leading cause of cancer-related deaths worldwide with limited treatment options due to extensive radiation and chemotherapy resistance. Monotherapy with immune checkpoint blockade showed no survival benefit. A combination of immunomodulation and radiotherapy may offer new treatment strategies, as demonstrated for non-small cell lung cancer. Radiation-induced anti-tumour immunity is mediated through cytosolic nucleic acid sensing pathways that drive the expression of interferon beta-1 (IFNB1) and proinflammatory cytokines.MethodsHuman PDAC cell lines (PANC-1, MIA PaCa-2, BxPC-3) were treated with X-rays and protons. Immunogenic cell death was measured based on HMGB1 release. Cytosolic dsDNA and dsRNA were analysed by immunofluorescence microscopy. Cell cycle progression, MHC-I and PD-L1 expression were determined by flow cytometry. Galectin-1 and IFNB1 were measured by ELISA. The expression levels and the phosphorylation status of the cGAS/STING and RIG-I/MAVS signalling pathways were analysed by western blotting, the expression of IFNB1 and proinflammatory cytokines was determined by RT-qPCR and genome-wide by RNA-seq. CRISPR-Cas9 knock-outs and inhibitors were used to elucidate the relevance of STING, MAVS and NF-κB for radiation-induced IFNB1 activation.ResultsWe demonstrate that a clinically relevant X-ray hypofractionation regimen (3x8 Gy) induces immunogenic cell death and activates IFNB1 and proinflammatory cytokines. Fractionated radiation induces G2/M arrest and accumulation of cytosolic DNA in PDAC cells, which partly originates from mitochondria. RNA-seq analysis shows a global upregulation of type I interferon response and NF-κB signalling in PDAC cells following 3x8 Gy. Radiation-induced immunogenic response is regulated by STING, MAVS and NF-κB. In addition to immunostimulation, radiation also induces immunosuppressive galectin-1. No significant changes in MHC-I or PD-L1 expression were observed. Moreover, PDAC cell lines show similar radiation-induced immune effects when exposed to single-dose protons or photons.ConclusionOur findings provide a rationale for combinatorial radiation-immunomodulatory treatment approaches in PDAC using conventional photon-based or proton beam radiotherapy.

Abstract Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Abstract Disease-causing mutations in genes encoding transcription factors (TFs) can affect TF interactions with their cognate DNA-binding motifs. Whether and how TF mutations impact upon the binding to TF composite elements (CE) and the interaction with other TFs is unclear. Here, we report a distinct mechanism of TF alteration in human lymphomas with perturbed B cell identity, in particular classic Hodgkin lymphoma. It is caused by a recurrent somatic missense mutation c.295 T > C (p.Cys99Arg; p.C99R) targeting the center of the DNA-binding domain of Interferon Regulatory Factor 4 (IRF4), a key TF in immune cells. IRF4-C99R fundamentally alters IRF4 DNA-binding, with loss-of-binding to canonical IRF motifs and neomorphic gain-of-binding to canonical and non-canonical IRF CEs. IRF4-C99R thoroughly modifies IRF4 function by blocking IRF4-dependent plasma cell induction, and up-regulates disease-specific genes in a non-canonical Activator Protein-1 (AP-1)-IRF-CE (AICE)-dependent manner. Our data explain how a single mutation causes a complex switch of TF specificity and gene regulation and open the perspective to specifically block the neomorphic DNA-binding activities of a mutant TF.

Abstract Transcription is regulated by a multitude of activators and repressors, which bind to the RNA polymerase II (Pol II) machinery and modulate its progression. Death-inducer obliterator 3 (DIDO3) and PHD finger protein 3 (PHF3) are paralogue proteins that regulate transcription elongation by docking onto phosphorylated serine-2 in the C-terminal domain (CTD) of Pol II through their SPOC domains. Here, we show that DIDO3 and PHF3 form a complex that bridges the Pol II elongation machinery with chromatin and RNA processing factors and tethers Pol II in a phase-separated microenvironment. Their SPOC domains and C-terminal intrinsically disordered regions are critical for transcription regulation. PHF3 and DIDO exert cooperative and antagonistic effects on the expression of neuronal genes and are both essential for neuronal differentiation. In the absence of PHF3, DIDO3 is upregulated as a compensatory mechanism. In addition to shared gene targets, DIDO specifically regulates genes required for lipid metabolism. Collectively, our work reveals multiple layers of gene expression regulation by the DIDO3 and PHF3 paralogues, which have specific, co-regulatory and redundant functions in transcription.

Here the authors establish the SPOC domain as a universal reader of the RNA Pol II CTD code and a versatile reader of phosphoserine marks found in co- and post-transcriptional regulators such as m6A writer and reader proteins.

Abstract Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.

Abstract Background Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA’s Epigenomics Quality Control Group. Results Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. Conclusions The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Here the authors identify PHF3 SPOC domain as a reader of the phosphorylated RNA polymerase II (Pol II) C-terminal domain. They show that PHF3 clusters with Pol II complexes in cells, drives phase separation of Pol II in vitro, and regulates neuronal gene expression and neuronal differentiation.

Abstract In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61–0.65]), SAPS2 (0.72 [95% CI 0.71–0.74]) and SOFA (0.76 [95% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95% CI 0.59–0.65]; clinical: 0.56 [95% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

The transcriptional programs of nuclei in the muscle syncytium were assumed to be homogenous except at the neuromuscular and myotendinous junctions. Here, using single-nucleus transcriptomics, the authors reveal a previously unrecognized diversity and dynamics of myonuclear transcriptional programs.

Inflammasomes are crucial gatekeepers of the immune response, but their maladaptive activation associates with inflammatory pathologies. Besides canonical activation, monocytes can trigger non-transcriptional or rapid inflammasome activation that has not been well defined in the context of acute myocardial infarction (AMI). Rapid transcription-independent inflammasome activation induced by simultaneous TLR priming and triggering stimulus was measured by caspase-1 (CASP1) activity and interleukin release. Both classical and intermediate monocytes from healthy donors exhibited robust CASP1 activation, but only classical monocytes produced high mature interleukin-18 (IL18) release. We also recruited a limited number of coronary artery disease (CAD, n=31) and AMI (n=29) patients to evaluate their inflammasome function and expression profiles. Surprisingly, monocyte subpopulations isolated from blood collected during percutaneous coronary intervention (PCI) from AMI patients presented diminished CASP1 activity and abrogated IL18 release despite increased NLRP3 gene expression. This unexpected attenuated rapid inflammasome activation was accompanied by a significant increase of TNFAIP3 and IRAKM expression. Moreover, TNFAIP3 protein levels of circulating monocytes showed positive correlation with high sensitive troponin T (hsTnT), implying an association between TNFAIP3 upregulation and the severity of tissue injury. We suggest this monocyte attenuation to be a protective phenotype aftermath following a very early inflammatory wave in the ischemic area. Damage-associated molecular patterns (DAMPs) or other signals trigger a transitory negative feedback loop within newly recruited circulating monocytes as a mechanism to reduce post-injury tissue damage.

Deep learning is becoming a popular approach for understanding biological processes but can be hard to adapt to new questions. Here, the authors develop Janggu, a python library that aims to ease data acquisition and model evaluation and facilitate deep learning applications in genomics.

Summary: DNA accessibility of cis-regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many genes regulating embryonic development have been identified, the underlying CRE dynamics controlling their expression remain largely uncharacterized. To address this, we produced a multimodal resource and genomic regulatory map for the zebrafish community, which integrates single-cell combinatorial indexing assay for transposase-accessible chromatin with high-throughput sequencing (sci-ATAC-seq) with bulk histone PTMs and Hi-C data to achieve a genome-wide classification of the regulatory architecture determining transcriptional activity in the 24-h post-fertilization (hpf) embryo. We characterized the genome-wide chromatin architecture at bulk and single-cell resolution, applying sci-ATAC-seq on whole 24-hpf stage zebrafish embryos, generating accessibility profiles for ∼23,000 single nuclei. We developed a genome segmentation method, ScregSeg (single-cell regulatory landscape segmentation), for defining regulatory programs, and candidate CREs, specific to one or more cell types. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization and identified new regulatory principles between chromatin modalities prevalent during zebrafish development. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos identified novel cellular roles for this hematovascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work defines regulatory architecture and principles in the zebrafish embryo and establishes a resource of cell-type-specific genome-wide regulatory annotations and candidate CREs, providing a valuable open resource for genomics, developmental, molecular, and computational biology.

Herpes simplex virus 1 (HSV-1) induces major reprogramming of the host cell environment. Here, the authors profile the transcriptomes of individual HSV-1-infected human fibroblasts and provide evidence of an antiviral program mediated by the activation of the host transcription factor NRF2.

MicroRNAs are mediators of post-transcriptional gene expression silencing. Here authors provide a transcriptome-wide map of miRNA target sites in Drosophila.

Abstract Background Long non-coding RNAs (lncRNAs) have emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. However, the role and expression pattern of lncRNAs in molecular subtypes of B cell acute lymphoblastic leukemia (BCP-ALL) have not yet been investigated. Here, we assess to what extent lncRNA expression and DNA methylation is driving the progression of relapsed BCP-ALL subtypes and we determine if the expression and DNA methylation profile of lncRNAs correlates with established BCP-ALL subtypes. Methods We performed RNA sequencing and DNA methylation (Illumina Infinium microarray) of 40 diagnosis and 42 relapse samples from 45 BCP-ALL patients in a German cohort and quantified lncRNA expression. Unsupervised clustering was applied to ascertain and confirm that the lncRNA-based classification of the BCP-ALL molecular subtypes is present in both our cohort and an independent validation cohort of 47 patients. A differential expression and differential methylation analysis was applied to determine the subtype-specific, relapse-specific, and differentially methylated lncRNAs. Potential functions of subtype-specific lncRNAs were determined by using co-expression-based analysis on nearby (cis) and distally (trans) located protein-coding genes. Results Using an integrative Bioinformatics analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific and 942 relapse-specific lncRNAs and the methylation profile of three subtypes of BCP-ALL. The 1235 subtype-specific lncRNA signature represented a similar classification of the molecular subtypes of BCP-ALL in the independent validation cohort. We identified a strong correlation between the DUX4-specific lncRNAs and genes involved in the activation of TGF-β and Hippo signaling pathways. Similarly, Ph-like-specific lncRNAs were correlated with genes involved in the activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we found 23 promoter methylated lncRNAs epigenetically facilitating their expression levels. Conclusion Here, we describe a set of subtype-specific and relapse-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. The subtype-specific lncRNAs are reproducible and can effectively stratify BCP-ALL subtypes. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining which lncRNAs are involved in the pathogenesis of disease and are relevant for the stratification of BCP-ALL subtypes.

Summary: How regulatory sequences control gene expression is fundamental for explaining phenotypes in health and disease. Regulatory elements must ultimately be understood within their genomic environment and development- or tissue-specific contexts. Because this is technically challenging, few regulatory elements have been characterized in vivo. Here, we use inducible Cas9 and multiplexed guide RNAs to create hundreds of mutations in enhancers/promoters and 3′ UTRs of 16 genes in C. elegans. Our software crispr-DART analyzes indel mutations in targeted DNA sequencing. We quantify the impact of mutations on expression and fitness by targeted RNA sequencing and DNA sampling. When applying our approach to the lin-41 3′ UTR, generating hundreds of mutants, we find that the two adjacent binding sites for the miRNA let-7 can regulate lin-41 expression independently of each other. Finally, we map regulatory genotypes to phenotypic traits for several genes. Our approach enables parallel analysis of regulatory sequences directly in animals.

Summary: Detailed knowledge of the molecular biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is crucial for understanding of viral replication, host responses, and disease progression. Here, we report gene expression profiles of three SARS-CoV- and SARS-CoV-2-infected human cell lines. SARS-CoV-2 elicited an approximately two-fold higher stimulation of the innate immune response compared to SARS-CoV in the human epithelial cell line Calu-3, including induction of miRNA-155. Single-cell RNA sequencing of infected cells showed that genes induced by virus infections were broadly upregulated, whereas interferon beta/lambda genes, a pro-inflammatory cytokines such as IL-6, were expressed only in small subsets of infected cells. Temporal analysis suggested that transcriptional activities of interferon regulatory factors precede those of nuclear factor κB. Lastly, we identified heat shock protein 90 (HSP90) as a protein relevant for the infection. Inhibition of the HSP90 activity resulted in a reduction of viral replication and pro-inflammatory cytokine expression in primary human airway epithelial cells.

Summary: Motor neurons in the spinal cord are found grouped in nuclear structures termed pools, whose position is precisely orchestrated during development. Despite the emerging role of pool organization in the assembly of spinal circuits, little is known about the morphogenetic programs underlying the patterning of motor neuron subtypes. We applied three-dimensional analysis of motor neuron position to reveal the roles and contributions of cell adhesive function by inactivating N-cadherin, catenin, and afadin signaling. Our findings reveal that nuclear organization of motor neurons is dependent on inside-out positioning, orchestrated by N-cadherin, catenin, and afadin activities, controlling cell body layering on the medio-lateral axis. In addition to this lamination-like program, motor neurons undergo a secondary, independent phase of organization. This process results in segregation of motor neurons along the dorso-ventral axis of the spinal cord, does not require N-cadherin or afadin activity, and can proceed even when medio-lateral positioning is perturbed.

Abstract Polycomb repression in mouse embryonic stem cells (ESCs) is tightly associated with promoter co‐occupancy of RNA polymerase II (RNAPII) which is thought to prime genes for activation during early development. However, it is unknown whether RNAPII poising is a general feature of Polycomb repression, or is lost during differentiation. Here, we map the genome‐wide occupancy of RNAPII and Polycomb from pluripotent ESCs to non‐dividing functional dopaminergic neurons. We find that poised RNAPII complexes are ubiquitously present at Polycomb‐repressed genes at all stages of neuronal differentiation. We observe both loss and acquisition of RNAPII and Polycomb at specific groups of genes reflecting their silencing or activation. Strikingly, RNAPII remains poised at transcription factor genes which are silenced in neurons through Polycomb repression, and have major roles in specifying other, non‐neuronal lineages. We conclude that RNAPII poising is intrinsically associated with Polycomb repression throughout differentiation. Our work suggests that the tight interplay between RNAPII poising and Polycomb repression not only instructs promoter state transitions, but also may enable promoter plasticity in differentiated cells.

Abstract Background Herpesviruses can infect a wide range of animal species. Herpes simplex virus 1 (HSV-1) is one of the eight herpesviruses that can infect humans and is prevalent worldwide. Herpesviruses have evolved multiple ways to adapt the infected cells to their needs, but knowledge about these transcriptional and post-transcriptional modifications is sparse. Results Here, we show that HSV-1 induces the expression of about 1000 antisense transcripts from the human host cell genome. A subset of these is also activated by the closely related varicella zoster virus. Antisense transcripts originate either at gene promoters or within the gene body, and they show different susceptibility to the inhibition of early and immediate early viral gene expression. Overexpression of the major viral transcription factor ICP4 is sufficient to turn on a subset of antisense transcripts. Histone marks around transcription start sites of HSV-1-induced and constitutively transcribed antisense transcripts are highly similar, indicating that the genetic loci are already poised to transcribe these novel RNAs. Furthermore, an antisense transcript overlapping with the BBC3 gene (also known as PUMA) transcriptionally silences this potent inducer of apoptosis in cis. Conclusions We show for the first time that a virus induces widespread antisense transcription of the host cell genome. We provide evidence that HSV-1 uses this to downregulate a strong inducer of apoptosis. Our findings open new perspectives on global and specific alterations of host cell transcription by viruses.

Subcellular localization of RNAs and proteins is important for polarized cells such as neurons. Here the authors differentiate mouse embryonic stem cells into neurons, and analyze the local transcriptome, proteome, and translated transcriptome in their cell bodies and neurites, providing a unique resource for future studies on neuronal polarity.

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

We have developed an enhanced form of reduced representation bisulfite sequencing with extended genomic coverage, which resulted in greater capture of DNA methylation information of regions lying outside of traditional CpG islands. Applying this method to primary human bone marrow specimens from patients with Acute Myelogeneous Leukemia (AML), we demonstrated that genetically distinct AML subtypes display diametrically opposed DNA methylation patterns. As compared to normal controls, we observed widespread hypermethylation in IDH mutant AMLs, preferentially targeting promoter regions and CpG islands neighboring the transcription start sites of genes. In contrast, AMLs harboring translocations affecting the MLL gene displayed extensive loss of methylation of an almost mutually exclusive set of CpGs, which instead affected introns and distal intergenic CpG islands and shores. When analyzed in conjunction with gene expression profiles, it became apparent that these specific patterns of DNA methylation result in differing roles in gene expression regulation. However, despite this subtype-specific DNA methylation patterning, a much smaller set of CpG sites are consistently affected in both AML subtypes. Most CpG sites in this common core of aberrantly methylated CpGs were hypermethylated in both AML subtypes. Therefore, aberrant DNA methylation patterns in AML do not occur in a stereotypical manner but rather are highly specific and associated with specific driving genetic lesions.

Non-coding DNA conservation across species has been often used as a predictor for transcriptional enhancer activity. However, only a few systematic analyses of the function of these highly conserved non-coding regions (HCNRs) have been performed. Here we use zebrafish transgenic assays to perform a systematic study of 113 HCNRs from human chromosome 16. By comparing transient and stable transgenesis, we show that the first method is highly inefficient, leading to 40% of false positives and 20% of false negatives. When analyzed in stable transgenic lines, a great majority of HCNRs were active in the central nervous system, although some of them drove expression in other organs such as the eye and the excretory system. Finally, by testing a fraction of the HCNRs lacking enhancer activity for in vivo insulator activity, we find that 20% of them may contain enhancer-blocking function. Altogether our data indicate that HCNRs may contain different types of cis-regulatory activity, including enhancer, insulators as well as other not yet discovered functions.

Mammalian genomes harbor a larger than expected number of complex loci, in which multiple genes are coupled by shared transcribed regions in antisense orientation and/or by bidirectional core promoters. To determine the incidence, functional significance, and evolutionary context of mammalian complex loci, we identified and characterized 5,248 cis-antisense pairs, 1,638 bidirectional promoters, and 1,153 chains of multiple cis-antisense and/or bidirectionally promoted pairs from 36,606 mouse transcriptional units (TUs), along with 6,141 cis-antisense pairs, 2,113 bidirectional promoters, and 1,480 chains from 42,887 human TUs. In both human and mouse, 25% of TUs resided in cis-antisense pairs, only 17% of which were conserved between the two organisms, indicating frequent species specificity of antisense gene arrangements. A sampling approach indicated that over 40% of all TUs might actually be in cis-antisense pairs, and that only a minority of these arrangements are likely to be conserved between human and mouse. Bidirectional promoters were characterized by variable transcriptional start sites and an identifiable midpoint at which overall sequence composition changed strand and the direction of transcriptional initiation switched. In microarray data covering a wide range of mouse tissues, genes in cis-antisense and bidirectionally promoted arrangement showed a higher probability of being coordinately expressed than random pairs of genes. In a case study on homeotic loci, we observed extensive transcription of nonconserved sequences on the noncoding strand, implying that the presence rather than the sequence of these transcripts is of functional importance. Complex loci are ubiquitous, host numerous nonconserved gene structures and lineage-specific exonification events, and may have a cis-regulatory impact on the member genes.

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.