High-sensitive nascent transcript sequencing reveals BRD4-specific control of widespread enhancer and target gene transcription.

Gene transcription by RNA polymerase II (Pol II) is under control of promoters and distal regulatory elements known as enhancers. Enhancers are themselves transcribed by Pol II correlating with their activity. How enhancer transcription is regulated and coordinated with transcription at target genes has remained unclear. Here, we developed a high-sensitive native elongating transcript sequencing approach, called HiS-NET-seq, to provide an extended high-resolution view on transcription, especially at lowly transcribed regions such as enhancers. HiS-NET-seq uncovers new transcribed enhancers in human cells. A multi-omics analysis shows that genome-wide enhancer transcription depends on the BET family protein BRD4. Specifically, BRD4 co-localizes to enhancer and promoter-proximal gene regions, and is required for elongation activation at enhancers and their genes. BRD4 keeps a set of enhancers and genes in proximity through long-range contacts. From these studies BRD4 emerges as a general regulator of enhancer transcription that may link transcription at enhancers and genes.

Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors.

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture-the type and strength of regulatory interconnections-and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.

Dynamic Modeling of Transcriptional Gene Regulatory Networks.

Diverse cellular phenotypes are determined by groups of transcription factors (TFs) and other regulators that influence each others' gene expression, forming transcriptional gene regulatory networks (GRNs). In many biological contexts, especially in development and associated diseases, the expression of the genes in GRNs is not static but evolves in time. Modeling the dynamics of GRN state is an important approach for understanding diverse cellular phenomena such as cell-fate specification, pluripotency and cell-fate reprogramming, oncogenesis, and tissue regeneration. In this protocol, we describe how to model GRNs using a data-driven dynamic modeling methodology, gene circuits. Gene circuits do not require knowledge of the GRN topology and connectivity but instead learn them from training data, making them very general and applicable to diverse biological contexts. We utilize the MATLAB-based gene circuit modeling software Fast Inference of Gene Regulation (FIGR) for training the model on quantitative gene expression data and simulating the GRN. We describe all the steps in the modeling life cycle, from formulating the model, training the model using FIGR, simulating the GRN, to analyzing and interpreting the model output. This protocol highlights these steps with the example of a dynamical model of the gap gene GRN involved in Drosophila segmentation and includes example MATLAB statements for each step.

Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data.

Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing (NGS) technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq (sRNA-Seq) data analysis proved to be challenging due to non-unique genomic origin, short length, and abundant post-transcriptional modifications of sRNA species. Here, we present Manatee, an algorithm for the quantification of sRNA classes and the detection of novel expressed non-coding loci. Manatee combines prior annotation of sRNAs with reliable alignment density information and extensive rescue of usually neglected multimapped reads to provide accurate transcriptome-wide sRNA expression quantification. Comparison of Manatee against state-of-the-art implementations using real and simulated data demonstrates its high accuracy across diverse sRNA classes. Manatee also goes beyond common pipelines by identifying and quantifying expression from unannotated loci and microRNA isoforms (isomiRs). It is user-friendly, can be easily incorporated in pipelines, and provides a simplified output suitable for direct usage in downstream analyses and functional studies.

Classification-Based Inference of Dynamical Models of Gene Regulatory Networks.

Cell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR's effectiveness on synthetic data generated from random gene circuits of up to 50 genes as well as experimental data from the gap gene system of Drosophila melanogaster, a benchmark for inferring dynamical GRN models. FIGR is faster than global non-linear optimization by a factor of 600 and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before.