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The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription.

Lineage plasticity is a hallmark of cancer progression that impacts therapy outcomes, yet the mechanisms mediating this process remain unclear. Here, we introduce a versatile in vivo platform to interrogate neuroendocrine lineage transformation throughout prostate cancer progression. Transplanted mouse prostate organoids with human-relevant driver mutations (Rb1 -/- ; Trp53 -/- ; cMyc + or Pten -/- ; Trp53 -/- ; cMyc + ) develop adenocarcinomas, but only those with Rb1 deletion advance to aggressive, ASCL1 + neuroendocrine prostate cancer (NEPC) resistant to androgen receptor signaling inhibitors. Notably, this transition requires an in vivo microenvironment not replicated by conventional organoid culture. Using multiplexed immunofluorescence and spatial transcriptomics, we reveal that ASCL1 + cells arise from KRT8 + luminal cells, progressing into transcriptionally heterogeneous ASCL1 + ;KRT8 - NEPC. Ascl1 loss in established NEPC causes transient regression followed by recurrence, but its deletion before transplantation abrogates lineage plasticity, resulting in castration-sensitive adenocarcinomas. This dynamic model highlights the importance of therapy timing and offers a platform to identify additional lineage plasticity drivers., (© 2024. The Author(s).)

Histone modifications (HMs) are pivotal in various biological processes, including transcription, replication, and DNA repair, significantly impacting chromatin structure. These modifications underpin the molecular mechanisms of cell-type-specific gene expression and complex diseases. However, annotating HMs across different cell types solely using experimental approaches is impractical due to cost and time constraints. Herein, we present dHICA (deep histone imputation using chromatin accessibility), a novel deep learning framework that integrates DNA sequences and chromatin accessibility data to predict multiple HM tracks. Employing the transformer architecture alongside dilated convolutions, dHICA boasts an extensive receptive field and captures more cell-type-specific information. dHICA outperforms state-of-the-art baselines and achieves superior performance in cell-type-specific loci and gene elements, aligning with biological expectations. Furthermore, dHICA's imputations hold significant potential for downstream applications, including chromatin state segmentation and elucidating the functional implications of SNPs (Single Nucleotide Polymorphisms). In conclusion, dHICA serves as a valuable tool for advancing the understanding of chromatin dynamics, offering enhanced predictive capabilities and interpretability., (© The Author(s) 2024. Published by Oxford University Press.)

Run-on and sequencing assays like GRO-seq, PRO-seq, and ChRO-seq allow for joint profiling of transcription activity of transcriptional regulatory elements (TREs), i.e., promoters and active enhancers, and target genes. Variation in biological conditions, such as treated vs. control, results in changes in the activity of transcription factors (TFs), which induces concerted changes in TREs and target genes. By modeling the differences between two biological conditions, we developed the computational pipeline known as tfTarget that predicts a set of putative TREs and target genes responding to each TF under the biological condition of interest. In this chapter, we demonstrate the use of the new web-based tfTarget in mapping transcription regulation using run-on sequencing data., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The amount of DNA sequencing data has been exponentially growing during the past decade due to advances in sequencing technology. Processing and modeling large amounts of sequencing data can be computationally intractable for desktop computing platforms. High performance computing (HPC) resources offer advantages in terms of computing power, and can be a general solution to these problems. Using HPCs directly for computational needs requires skilled users who know their way around HPCs and acquiring such skills take time. Science gateways acts as the middle layer between users and HPCs, providing users with the resources to accomplish compute-intensive tasks without requiring specialized expertise. We developed a web-based computing platform for genome biologists by customizing the PHP Gateway for Airavata (PGA) framework that accesses publicly accessible HPC resources via Apache Airavata. This web computing platform takes advantage of the Extreme Science and Engineering Discovery Environment (XSEDE) which provides the resources for gateway development, including access to CPU, GPU, and storage resources. We used this platform to develop a gateway for the dREG algorithm, an online computing tool for finding functional regions in mammalian genomes using nascent RNA sequencing data. The dREG gateway provides its users a free, powerful and user-friendly GPU computing resource based on XSEDE, circumventing the need of specialized knowledge about installation, configuration, and execution on an HPC for biologists. The dREG gateway is available at: https://dREG.dnasequence.org/.