Atlas of nascent RNA transcripts reveals enhancer to gene linkages

LIET model: capturing the kinetics of RNA polymerase from loading to termination

Transcription by RNA polymerases is an exquisitely regulated step of the central dogma. Transcription is the primary determinant of cell-state, and most cellular perturbations impact transcription by altering polymerase activity. Thus, detecting changes in polymerase activity yields insight into most cellular processes. Nascent run-on sequencing provides a direct readout of polymerase activity, but no tools exist to model all aspects of this activity at genes. We focus on RNA polymerase II-responsible for transcribing protein-coding genes. We present the first model to capture the complete process of gene transcription. For individual genes, this model parameterizes each distinct stage of transcription-loading, initiation, elongation, and termination, hence LIET-in a biologically interpretable Bayesian mixture, which is applied to nascent run-on data. Our improved modeling of loading/initiation demonstrates these stages are characteristically different between sense and antisense strands. Applying LIET to 24 human cell-types, our analysis indicates the position of dissociation (the last step of termination) appears to be highly consistent, indicative of a tightly regulated process. Furthermore, by applying LIET to perturbation experiments, we demonstrate its ability to detect specific changes in pausing (5' end), strand-bias, and dissociation location (3' end)-opening the door to differential assessment of transcription at individual stages of individual genes.

TF Profiler: a transcription factor inference method that broadly measures transcription factor activity and identifies mechanistically distinct networks

TF Profiler is a method of inferring transcription factor (TF) regulatory activity, i.e., when a TF is present and actively participating in the regulation of transcription, directly from nascent sequencing assays such as PRO-seq and GRO-seq. While ChIP assays have measured DNA localization, they fall short of identifying when and where the effector domain of a transcription factor is active. Our method uses RNA polymerase activity to infer TF effector domain activity across hundreds of data sets and transcription factors. TF Profiler is broadly applicable, providing regulatory insights on any PRO-seq sample for any transcription factor with a known binding motif.

TFIIH kinase CDK7 drives cell proliferation through a common core transcription factor network

How cyclin-dependent kinase 7 (CDK7) coordinately regulates the cell cycle and RNA polymerase II transcription remains unclear. Here, high-resolution cryo-electron microscopy revealed how two clinically relevant inhibitors block CDK7 function. In cells, CDK7 inhibition rapidly suppressed transcription, but constitutively active genes were disproportionately affected versus stimulus-responsive. Distinct transcription factors (TFs) regulate constitutive versus stimulus-responsive genes. Accordingly, stimulus-responsive TFs were refractory to CDK7 inhibition whereas constitutively active "core" TFs were repressed. Core TFs (n = 78) are predominantly promoter associated and control cell cycle and proliferative gene expression programs across cell types. Mechanistically, rapid suppression of core TF function can occur through CDK7-dependent phosphorylation changes in core TFs and RB1. Moreover, CDK7 inhibition depleted core TF protein levels within hours, consistent with durable target gene suppression. Thus, a major but unappreciated biological function for CDK7 is regulation of a TF cohort that drives proliferation, revealing an apparent universal mechanism by which CDK7 coordinates RNAPII transcription with cell cycle CDK regulation.

Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity

Background

Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible.

Methods

We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools' resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls.

Results

We first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5' scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn's disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.

Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program

Estrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development. Dysregulation of ERα-mediated transcriptional program results in cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. A subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.

A transcription factor (TF) inference method that broadly measures TF activity and identifies mechanistically distinct TF networks

TF profiler is a method of inferring transcription factor regulatory activity, i.e. when a TF is present and actively regulating transcription, directly directly from nascent sequencing assays such as PRO-seq and GRO-seq. Transcription factors orchestrate transcription and play a critical role in cellular maintenance, identity and response to external stimuli. While ChIP assays have measured DNA localization, they fall short of identifying when and where transcription factors are actively regulating transcription. Our method, on the other hand, uses RNA polymerase activity to infer TF activity across hundreds of data sets and transcription factors. Based on these classifications we identify three distinct classes of transcription factors: ubiquitous factors that play roles in cellular homeostasis, driving basal gene programs across tissues and cell types, tissue specific factors that act almost exclusively at enhancers and are themselves regulated at transcription, and stimulus responsive TFs which are regulated post-transcriptionally but act predominantly at enhancers. TF profiler is broadly applicable, providing regulatory insights on any PRO-seq sample for any transcription factor with a known binding motif.