Combinatorial microRNA activity is essential for the transition of pluripotent cells from proliferation into dormancy.

Dormancy is a key feature of stem cell function in adult tissues as well as in embryonic cells in the context of diapause. The establishment of dormancy is an active process that involves extensive transcriptional, epigenetic, and metabolic rewiring. How these processes are coordinated to successfully transition cells to the resting dormant state remains unclear. Here we show that microRNA activity, which is otherwise dispensable for preimplantation development, is essential for the adaptation of early mouse embryos to the dormant state of diapause. In particular, the pluripotent epiblast depends on miRNA activity, the absence of which results in the loss of pluripotent cells. Through the integration of high-sensitivity small RNA expression profiling of individual embryos and protein expression of miRNA targets with public data of protein-protein interactions, we constructed the miRNA-mediated regulatory network of mouse early embryos specific to diapause. We find that individual miRNAs contribute to the combinatorial regulation by the network, and the perturbation of the network compromises embryo survival in diapause. We further identified the nutrient-sensitive transcription factor TFE3 as an upstream regulator of diapause-specific miRNAs, linking cytoplasmic MTOR activity to nuclear miRNA biogenesis. Our results place miRNAs as a critical regulatory layer for the molecular rewiring of early embryos to establish dormancy.

Genome-wide measurement of RNA dissociation from chromatin classifies transcripts by their dynamics and reveals rapid dissociation of enhancer lncRNAs.

Long non-coding RNAs (lncRNAs) are involved in gene expression regulation in cis. Although enriched in the cell chromatin fraction, to what degree this defines their regulatory potential remains unclear. Furthermore, the factors underlying lncRNA chromatin tethering, as well as the molecular basis of efficient lncRNA chromatin dissociation and its impact on enhancer activity and target gene expression, remain to be resolved. Here, we developed chrTT-seq, which combines the pulse-chase metabolic labeling of nascent RNA with chromatin fractionation and transient transcriptome sequencing to follow nascent RNA transcripts from their transcription on chromatin to release and allows the quantification of dissociation dynamics. By incorporating genomic, transcriptomic, and epigenetic metrics, as well as RNA-binding protein propensities, in machine learning models, we identify features that define transcript groups of different chromatin dissociation dynamics. Notably, lncRNAs transcribed from enhancers display reduced chromatin retention, suggesting that, in addition to splicing, their chromatin dissociation may shape enhancer activity.

SND1 binds SARS-CoV-2 negative-sense RNA and promotes viral RNA synthesis through NSP9.

Regulation of viral RNA biogenesis is fundamental to productive SARS-CoV-2 infection. To characterize host RNA-binding proteins (RBPs) involved in this process, we biochemically identified proteins bound to genomic and subgenomic SARS-CoV-2 RNAs. We find that the host protein SND1 binds the 5' end of negative-sense viral RNA and is required for SARS-CoV-2 RNA synthesis. SND1-depleted cells form smaller replication organelles and display diminished virus growth kinetics. We discover that NSP9, a viral RBP and direct SND1 interaction partner, is covalently linked to the 5' ends of positive- and negative-sense RNAs produced during infection. These linkages occur at replication-transcription initiation sites, consistent with NSP9 priming viral RNA synthesis. Mechanistically, SND1 remodels NSP9 occupancy and alters the covalent linkage of NSP9 to initiating nucleotides in viral RNA. Our findings implicate NSP9 in the initiation of SARS-CoV-2 RNA synthesis and unravel an unsuspected role of a cellular protein in orchestrating viral RNA production.

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.

A systematic benchmark of machine learning methods for protein-RNA interaction prediction.

RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.

Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility.

Dysfunction of regulatory elements through genetic variants is a central mechanism in the pathogenesis of disease. To better understand disease etiology, there is consequently a need to understand how DNA encodes regulatory activity. Deep learning methods show great promise for modeling of biomolecular data from DNA sequence but are limited to large input data for training. Here, we develop ChromTransfer, a transfer learning method that uses a pre-trained, cell-type agnostic model of open chromatin regions as a basis for fine-tuning on regulatory sequences. We demonstrate superior performances with ChromTransfer for learning cell-type specific chromatin accessibility from sequence compared to models not informed by a pre-trained model. Importantly, ChromTransfer enables fine-tuning on small input data with minimal decrease in accuracy. We show that ChromTransfer uses sequence features matching binding site sequences of key transcription factors for prediction. Together, these results demonstrate ChromTransfer as a promising tool for learning the regulatory code.

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases.

The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB's role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region's propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of "high-confidence" hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution.

RNA-binding proteins (RBPs) are critical host factors for viral infection, however, large scale experimental investigation of the binding landscape of human RBPs to viral RNAs is costly and further complicated due to sequence variation between viral strains. To fill this gap, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP-viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments on more than 100 human RBPs. We evaluated conservation of RBP binding between six other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of mutations from 11 variants of concern on protein-RNA interaction, identifying a set of gain- and loss-of-binding events, as well as predicted the regulatory impact of putative future mutations. Lastly, we linked RBPs to functional, OMICs and COVID-19 patient data from other studies, and identified MBNL1, FTO and FXR2 RBPs as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and open new avenues for therapeutic intervention.

Screening for eukaryotic motifs in Legionella pneumophila reveals Smh1 as bacterial deacetylase of host histones.

Legionella pneumophila (L.p.) is a bacterial pathogen which is a common causative agent of pneumonia. In humans, it infects alveolar macrophages and transfers hundreds of virulence factors that interfere with cellular signalling pathways and the transcriptomic landscape to sustain its own replication. By this interaction, it has acquired eukaryote-like protein motifs by gene transfer events that partake in the pathogenicity of Legionella. In a computational screening approach for eukaryotic motifs in the transcriptome of Legionella, we identified the L.p. strain Corby protein ABQ55614 as putative histone-deacetylase and named it "suppressing modifier of histones 1" (Smh1). During infection, Smh1 is translocated from the Legionella vacuole into the host cytosol. When expressed in human macrophage THP-1 cells, Smh1 was localized predominantly in the nucleus, leading to broad histone H3 and H4 deacetylation, blunted expression of a large number of genes (e.g. IL-1ß and IL-8), and fostered intracellular bacterial replication. L.p. with a Smh1 knockdown grew normally in media but showed a slight growth defect inside the host cell. Furthermore, Smh1 showed a very potent histone deacetylation activity in vitro, e.g. at H3K14, that could be inhibited by targeted mutation of the putative catalytic center inferred by analogy with eukaryotic HDAC8, and with the deacetylase inhibitor trichostatin A. In summary, Smh1 displays functional homology with class I/II type HDACs. We identified Smh1 as a new Legionella virulence factor with a eukaryote-like histone-deacetylase activity that moderates host gene expression and might pave the way for further histone modifications.IMPORTANCELegionella pneumophila (L.p.) is a prominent bacterial pathogen, which is a common causative agent of pneumonia. In order to survive inside the host cell, the human macrophage, it profoundly interacts with host cell processes to advance its own replication. In this study, we identify a bacterial factor, Smh1, with yet unknown function as a host histone deacetylase. The activity of this factor in the host cell leads to attenuated gene expression and increased intracellular bacterial replication.

Single-cell RNA sequencing uncovers the nuclear decoy lincRNA PIRAT as a regulator of systemic monocyte immunity during COVID-19.

The systemic immune response to viral infection is shaped by master transcription factors, such as NF-κB, STAT1, or PU.1. Although long noncoding RNAs (lncRNAs) have been suggested as important regulators of transcription factor activity, their contributions to the systemic immunopathologies observed during SARS-CoV-2 infection have remained unknown. Here, we employed a targeted single-cell RNA sequencing approach to reveal lncRNAs differentially expressed in blood leukocytes during severe COVID-19. Our results uncover the lncRNA PIRAT (PU.1-induced regulator of alarmin transcription) as a major PU.1 feedback-regulator in monocytes, governing the production of the alarmins S100A8/A9, key drivers of COVID-19 pathogenesis. Knockout and transgene expression, combined with chromatin-occupancy profiling, characterized PIRAT as a nuclear decoy RNA, keeping PU.1 from binding to alarmin promoters and promoting its binding to pseudogenes in naïve monocytes. NF-κB-dependent PIRAT down-regulation during COVID-19 consequently releases a transcriptional brake, fueling alarmin production. Alarmin expression is additionally enhanced by the up-regulation of the lncRNA LUCAT1, which promotes NF-κB-dependent gene expression at the expense of targets of the JAK-STAT pathway. Our results suggest a major role of nuclear noncoding RNA networks in systemic antiviral responses to SARS-CoV-2 in humans.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

The function of Wtap in N6-adenosine methylation of mRNAs controls T cell receptor signaling and survival of T cells.

T cell antigen-receptor (TCR) signaling controls the development, activation and survival of T cells by involving several layers and numerous mechanisms of gene regulation. N6-methyladenosine (m6A) is the most prevalent messenger RNA modification affecting splicing, translation and stability of transcripts. In the present study, we describe the Wtap protein as essential for m6A methyltransferase complex function and reveal its crucial role in TCR signaling in mouse T cells. Wtap and m6A methyltransferase functions were required for the differentiation of thymocytes, control of activation-induced death of peripheral T cells and prevention of colitis by enabling gut RORγt+ regulatory T cell function. Transcriptome and epitranscriptomic analyses reveal that m6A modification destabilizes Orai1 and Ripk1 mRNAs. Lack of post-transcriptional repression of the encoded proteins correlated with increased store-operated calcium entry activity and diminished survival of T cells with conditional genetic inactivation of Wtap. These findings uncover how m6A modification impacts on TCR signal transduction and determines activation and survival of T cells.

Exposure to naphthalene and ß-pinene-derived secondary organic aerosol induced divergent changes in transcript levels of BEAS-2B cells.

The health effects of exposure to secondary organic aerosols (SOAs) are still limited. Here, we investigated and compared the toxicities of soot particles (SP) coated with ß-pinene SOA (SOAßPin-SP) and SP coated with naphthalene SOA (SOANap-SP) in a human bronchial epithelial cell line (BEAS-2B) residing at the air-liquid interface. SOAßPin-SP mostly contained oxygenated aliphatic compounds from ß-pinene photooxidation, whereas SOANap-SP contained a significant fraction of oxygenated aromatic products under similar conditions. Following exposure, genome-wide transcriptome responses showed an Nrf2 oxidative stress response, particularly for SOANap-SP. Other signaling pathways, such as redox signaling, inflammatory signaling, and the involvement of matrix metalloproteinase, were identified to have a stronger impact following exposure to SOANap-SP. SOANap-SP also induced a stronger genotoxicity response than that of SOAßPin-SP. This study elucidated the mechanisms that govern SOA toxicity and showed that, compared to SOAs derived from a typical biogenic precursor, SOAs from a typical anthropogenic precursor have higher toxicological potency, which was accompanied with the activation of varied cellular mechanisms, such as aryl hydrocarbon receptor. This can be attributed to the difference in chemical composition; specifically, the aromatic compounds in the naphthalene-derived SOA had higher cytotoxic potential than that of the ß-pinene-derived SOA.

Multi-ancestry genome-wide association study of asthma exacerbations.

BACKGROUND: Asthma exacerbations are a serious public health concern due to high healthcare resource utilization, work/school productivity loss, impact on quality of life, and risk of mortality. The genetic basis of asthma exacerbations has been studied in several populations, but no prior study has performed a multi-ancestry meta-analysis of genome-wide association studies (meta-GWAS) for this trait. We aimed to identify common genetic loci associated with asthma exacerbations across diverse populations and to assess their functional role in regulating DNA methylation and gene expression. METHODS: A meta-GWAS of asthma exacerbations in 4989 Europeans, 2181 Hispanics/Latinos, 1250 Singaporean Chinese, and 972 African Americans analyzed 9.6 million genetic variants. Suggestively associated variants (p ≤ 5 × 10-5 ) were assessed for replication in 36,477 European and 1078 non-European asthma patients. Functional effects on DNA methylation were assessed in 595 Hispanic/Latino and African American asthma patients and in publicly available databases. The effect on gene expression was evaluated in silico. RESULTS: One hundred and twenty-six independent variants were suggestively associated with asthma exacerbations in the discovery phase. Two variants independently replicated: rs12091010 located at vascular cell adhesion molecule-1/exostosin like glycosyltransferase-2 (VCAM1/EXTL2) (discovery: odds ratio (ORT allele ) = 0.82, p = 9.05 × 10-6 and replication: ORT allele  = 0.89, p = 5.35 × 10-3 ) and rs943126 from pantothenate kinase 1 (PANK1) (discovery: ORC allele  = 0.85, p = 3.10 × 10-5 and replication: ORC allele  = 0.89, p = 1.30 × 10-2 ). Both variants regulate gene expression of genes where they locate and DNA methylation levels of nearby genes in whole blood. CONCLUSIONS: This multi-ancestry study revealed novel suggestive regulatory loci for asthma exacerbations located in genomic regions participating in inflammation and host defense.

Effect of Atmospheric Aging on Soot Particle Toxicity in Lung Cell Models at the Air-Liquid Interface: Differential Toxicological Impacts of Biogenic and Anthropogenic Secondary Organic Aerosols (SOAs).

BACKGROUND: Secondary organic aerosols (SOAs) formed from anthropogenic or biogenic gaseous precursors in the atmosphere substantially contribute to the ambient fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] burden, which has been associated with adverse human health effects. However, there is only limited evidence on their differential toxicological impact. OBJECTIVES: We aimed to discriminate toxicological effects of aerosols generated by atmospheric aging on combustion soot particles (SPs) of gaseous biogenic (ß-pinene) or anthropogenic (naphthalene) precursors in two different lung cell models exposed at the air-liquid interface (ALI). METHODS: Mono- or cocultures of lung epithelial cells (A549) and endothelial cells (EA.hy926) were exposed at the ALI for 4 h to different aerosol concentrations of a photochemically aged mixture of primary combustion SP and ß-pinene (SOAßPIN-SP) or naphthalene (SOANAP-SP). The internally mixed soot/SOA particles were comprehensively characterized in terms of their physical and chemical properties. We conducted toxicity tests to determine cytotoxicity, intracellular oxidative stress, primary and secondary genotoxicity, as well as inflammatory and angiogenic effects. RESULTS: We observed considerable toxicity-related outcomes in cells treated with either SOA type. Greater adverse effects were measured for SOANAP-SP compared with SOAßPIN-SP in both cell models, whereas the nano-sized soot cores alone showed only minor effects. At the functional level, we found that SOANAP-SP augmented the secretion of malondialdehyde and interleukin-8 and may have induced the activation of endothelial cells in the coculture system. This activation was confirmed by comet assay, suggesting secondary genotoxicity and greater angiogenic potential. Chemical characterization of PM revealed distinct qualitative differences in the composition of the two secondary aerosol types. DISCUSSION: In this study using A549 and EA.hy926 cells exposed at ALI, SOA compounds had greater toxicity than primary SPs. Photochemical aging of naphthalene was associated with the formation of more oxidized, more aromatic SOAs with a higher oxidative potential and toxicity compared with ß-pinene. Thus, we conclude that the influence of atmospheric chemistry on the chemical PM composition plays a crucial role for the adverse health outcome of emissions. https://doi.org/10.1289/EHP9413.

Distal and proximal cis-regulatory elements sense X chromosome dosage and developmental state at the Xist locus.

Developmental genes such as Xist, which initiates X chromosome inactivation, are controlled by complex cis-regulatory landscapes, which decode multiple signals to establish specific spatiotemporal expression patterns. Xist integrates information on X chromosome dosage and developmental stage to trigger X inactivation in the epiblast specifically in female embryos. Through a pooled CRISPR screen in differentiating mouse embryonic stem cells, we identify functional enhancer elements of Xist at the onset of random X inactivation. Chromatin profiling reveals that X-dosage controls the promoter-proximal region, while differentiation cues activate several distal enhancers. The strongest distal element lies in an enhancer cluster associated with a previously unannotated Xist-enhancing regulatory transcript, which we named Xert. Developmental cues and X-dosage are thus decoded by distinct regulatory regions, which cooperate to ensure female-specific Xist upregulation at the correct developmental time. With this study, we start to disentangle how multiple, functionally distinct regulatory elements interact to generate complex expression patterns in mammals.

Integrated analysis of Xist upregulation and X-chromosome inactivation with single-cell and single-allele resolution.

To ensure dosage compensation between the sexes, one randomly chosen X chromosome is silenced in each female cell in the process of X-chromosome inactivation (XCI). XCI is initiated during early development through upregulation of the long non-coding RNA Xist, which mediates chromosome-wide gene silencing. Cell differentiation, Xist upregulation and gene silencing are thought to be coupled at multiple levels to ensure inactivation of exactly one out of two X chromosomes. Here we perform an integrated analysis of all three processes through allele-specific single-cell RNA-sequencing. Specifically, we assess the onset of random XCI in differentiating mouse embryonic stem cells, and develop dedicated analysis approaches. By exploiting the inter-cellular heterogeneity of XCI onset, we identify putative Xist regulators. Moreover, we show that transient Xist upregulation from both X chromosomes results in biallelic gene silencing right before transitioning to the monoallelic state, confirming a prediction of the stochastic model of XCI. Finally, we show that genetic variation modulates the XCI process at multiple levels, providing a potential explanation for the long-known X-controlling element (Xce) effect, which leads to preferential inactivation of a specific X chromosome in inter-strain crosses. We thus draw a detailed picture of the different levels of regulation that govern the initiation of XCI. The experimental and computational strategies we have developed here will allow us to profile random XCI in more physiological contexts, including primary human cells in vivo.

A MicroRNA Network Controls Legionella pneumophila Replication in Human Macrophages via LGALS8 and MX1.

Legionella pneumophila is an important cause of pneumonia. It invades alveolar macrophages and manipulates the immune response by interfering with signaling pathways and gene transcription to support its own replication. MicroRNAs (miRNAs) are critical posttranscriptional regulators of gene expression and are involved in defense against bacterial infections. Several pathogens have been shown to exploit the host miRNA machinery to their advantage. We therefore hypothesize that macrophage miRNAs exert positive or negative control over Legionella intracellular replication. We found significant regulation of 85 miRNAs in human macrophages upon L. pneumophila infection. Chromatin immunoprecipitation and sequencing revealed concordant changes of histone acetylation at the putative promoters. Interestingly, a trio of miRNAs (miR-125b, miR-221, and miR-579) was found to significantly affect intracellular L. pneumophila replication in a cooperative manner. Using proteome-analysis, we pinpointed this effect to a concerted downregulation of galectin-8 (LGALS8), DExD/H-box helicase 58 (DDX58), tumor protein P53 (TP53), and then MX dynamin-like GTPase 1 (MX1) by the three miRNAs. In summary, our results demonstrate a new miRNA-controlled immune network restricting Legionella replication in human macrophages.IMPORTANCE Cases of Legionella pneumophila pneumonia occur worldwide, with potentially fatal outcome. When causing human disease, Legionella injects a plethora of virulence factors to reprogram macrophages to circumvent immune defense and create a replication niche. By analyzing Legionella-induced changes in miRNA expression and genomewide chromatin modifications in primary human macrophages, we identified a cell-autonomous immune network restricting Legionella growth. This network comprises three miRNAs governing expression of the cytosolic RNA receptor DDX58/RIG-I, the tumor suppressor TP53, the antibacterial effector LGALS8, and MX1, which has been described as an antiviral factor. Our findings for the first time link TP53, LGALS8, DDX58, and MX1 in one miRNA-regulated network and integrate them into a functional node in the defense against L. pneumophila.

Kinetics of Xist-induced gene silencing can be predicted from combinations of epigenetic and genomic features.

To initiate X-Chromosome inactivation (XCI), the long noncoding RNA Xist mediates chromosome-wide gene silencing of one X Chromosome in female mammals to equalize gene dosage between the sexes. The efficiency of gene silencing is highly variable across genes, with some genes even escaping XCI in somatic cells. A gene's susceptibility to Xist-mediated silencing appears to be determined by a complex interplay of epigenetic and genomic features; however, the underlying rules remain poorly understood. We have quantified chromosome-wide gene silencing kinetics at the level of the nascent transcriptome using allele-specific Precision nuclear Run-On sequencing (PRO-seq). We have developed a Random Forest machine-learning model that can predict the measured silencing dynamics based on a large set of epigenetic and genomic features and tested its predictive power experimentally. The genomic distance to the Xist locus, followed by gene density and distance to LINE elements, are the prime determinants of the speed of gene silencing. Moreover, we find two distinct gene classes associated with different silencing pathways: a class that requires Xist-repeat A for silencing, which is known to activate the SPEN pathway, and a second class in which genes are premarked by Polycomb complexes and tend to rely on the B repeat in Xist for silencing, known to recruit Polycomb complexes during XCI. Moreover, a series of features associated with active transcriptional elongation and chromatin 3D structure are enriched at rapidly silenced genes. Our machine-learning approach can thus uncover the complex combinatorial rules underlying gene silencing during X inactivation.

Functional impacts of non-coding RNA processing on enhancer activity and target gene expression.

Tight regulation of gene expression is orchestrated by enhancers. Through recent research advancements, it is becoming clear that enhancers are not solely distal regulatory elements harboring transcription factor binding sites and decorated with specific histone marks, but they rather display signatures of active transcription, showing distinct degrees of transcription unit organization. Thereby, a substantial fraction of enhancers give rise to different species of non-coding RNA transcripts with an unprecedented range of potential functions. In this review, we bring together data from recent studies indicating that non-coding RNA transcription from active enhancers, as well as enhancer-produced long non-coding RNA transcripts, may modulate or define the functional regulatory potential of the cognate enhancer. In addition, we summarize supporting evidence that RNA processing of the enhancer-associated long non-coding RNA transcripts may constitute an additional layer of regulation of enhancer activity, which contributes to the control and final outcome of enhancer-targeted gene expression.