Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies.

While regulatory T (Treg) cells are traditionally viewed as professional suppressors of antigen presenting cells and effector T cells in both autoimmunity and cancer, recent findings of distinct Treg cell functions in tissue maintenance suggest that their regulatory purview extends to a wider range of cells and is broader than previously assumed. To elucidate tumoral Treg cell 'connectivity' to diverse tumor-supporting accessory cell types, we explored immediate early changes in their single-cell transcriptomes upon punctual Treg cell depletion in experimental lung cancer and injury-induced inflammation. Before any notable T cell activation and inflammation, fibroblasts, endothelial and myeloid cells exhibited pronounced changes in their gene expression in both cancer and injury settings. Factor analysis revealed shared Treg cell-dependent gene programs, foremost, prominent upregulation of VEGF and CCR2 signaling-related genes upon Treg cell deprivation in either setting, as well as in Treg cell-poor versus Treg cell-rich human lung adenocarcinomas. Accordingly, punctual Treg cell depletion combined with short-term VEGF blockade showed markedly improved control of PD-1 blockade-resistant lung adenocarcinoma progression in mice compared to the corresponding monotherapies, highlighting a promising factor-based querying approach to elucidating new rational combination treatments of solid organ cancers.

dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.

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.

Identification of regulatory elements from nascent transcription using dREG.

Our genomes encode a wealth of transcription initiation regions (TIRs) that can be identified by their distinctive patterns of actively elongating RNA polymerase. We previously introduced dREG to identify TIRs using PRO-seq data. Here, we introduce an efficient new implementation of dREG that uses PRO-seq data to identify both uni- and bidirectionally transcribed TIRs with 70% improvement in accuracy, three- to fourfold higher resolution, and >100-fold increases in computational efficiency. Using a novel strategy to identify TIRs based on their statistical confidence reveals extensive overlap with orthogonal assays, yet also reveals thousands of additional weakly transcribed TIRs that were not identified by H3K27ac ChIP-seq or DNase-seq. Novel TIRs discovered by dREG were often associated with RNA polymerase III initiation, bound by pioneer transcription factors, or located in broad domains marked by repressive chromatin modifications. Our results suggest that transcription initiation can be a powerful tool for expanding the catalog of functional elements.

The ribosomal maturation factor P from Mycobacterium smegmatis facilitates the ribosomal biogenesis by binding to the small ribosomal protein S12.

The ribosomal maturation factor P (RimP) is a highly conserved protein in bacteria and has been shown to be important in ribosomal assembly in Escherichia coli Because of its central importance in bacterial metabolism, RimP represents a good potential target for drug design to combat human pathogens such as Mycobacterium tuberculosis However, to date, the only RimP structure available is the NMR structure of the ortholog in another bacterial pathogen, Streptococcus pneumoniae Here, we report a 2.2 Å resolution crystal structure of MSMEG_2624, the RimP ortholog in the close M. tuberculosis relative Mycobacterium smegmatis, and using in vitro binding assays, we show that MSMEG_2624 interacts with the small ribosomal protein S12, also known as RpsL. Further analyses revealed that the conserved residues in the linker region between the N- and C-terminal domains of MSMEG_2624 are essential for binding to RpsL. However, neither of the two domains alone was sufficient to form strong interactions with RpsL. More importantly, the linker region was essential for in vivo ribosomal biogenesis. Our study provides critical mechanistic insights into the role of RimP in ribosome biogenesis. We anticipate that the MSMEG_2624 crystal structure has the potential to be used for drug design to manage M. tuberculosis infections.

Cell-free RNA Liquid Biopsy to Monitor Hematopoietic Stem Cell Transplantation.

Hematopoietic Stem Cell Transplantation (HSCT) is one of the oldest and most successful immunotherapies. Yet, despite long-standing success in the use of HSCT for the treatment of blood cancers and severe immune disorders, monitoring post-transplant complications remains a challenge due to a lack of informative diagnostic tests. Here, we investigate the utility of cell-free RNA (cfRNA) in plasma as a liquid biopsy to monitor allogeneic HSCT recipients during and after treatment. We assayed longitudinal samples from 92 HSCT recipients by cfRNA sequencing and show that cfRNA provides insight into treatment and recovery trajectories, immune dynamics in response to transplantation, infection, and solid-tissue injury associated with Graft-Versus-Host Disease. Collectively, our results provide support for the use of plasma cfRNA profiling to monitor complications of HSCT.

The neuroendocrine transition in prostate cancer is dynamic and dependent on ASCL1.

Lineage plasticity is a recognized hallmark of cancer progression that can shape therapy outcomes. The underlying cellular and molecular mechanisms mediating lineage plasticity remain poorly understood. Here, we describe a versatile in vivo platform to identify and interrogate the molecular determinants of neuroendocrine lineage transformation at different stages of prostate cancer progression. Adenocarcinomas reliably develop following orthotopic transplantation of primary mouse prostate organoids acutely engineered with human-relevant driver alterations (e.g., Rb1-/-; Trp53-/-; cMyc+ or Pten-/-; Trp53-/-; cMyc+), but only those with Rb1 deletion progress to ASCL1+ neuroendocrine prostate cancer (NEPC), a highly aggressive, androgen receptor signaling inhibitor (ARSI)-resistant tumor. Importantly, we show this lineage transition requires a native in vivo microenvironment not replicated by conventional organoid culture. By integrating multiplexed immunofluorescence, spatial transcriptomics and PrismSpot to identify cell type-specific spatial gene modules, we reveal that ASCL1+ cells arise from KRT8+ luminal epithelial cells that progressively acquire transcriptional heterogeneity, producing large ASCL1+;KRT8- NEPC clusters. Ascl1 loss in established NEPC results in transient tumor regression followed by recurrence; however, Ascl1 deletion prior to transplantation completely abrogates lineage plasticity, yielding adenocarcinomas with elevated AR expression and marked sensitivity to castration. The dynamic feature of this model reveals the importance of timing of therapies focused on lineage plasticity and offers a platform for identification of additional lineage plasticity drivers.

The neuroendocrine transition in prostate cancer is dynamic and dependent on ASCL1.

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.

Mapping Transcription Regulation with Run-on and Sequencing Data Using the Web-Based tfTarget Gateway.

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.

Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems.

Whereas the cellular and molecular features of human inflammatory skin diseases are well characterized, their tissue context and systemic impact remain poorly understood. We thus profiled human psoriasis (PsO) as a prototypic immune-mediated condition with a high predilection for extracutaneous involvement. Spatial transcriptomics (ST) analyses of 25 healthy, active lesion, and clinically uninvolved skin biopsies and integration with public single-cell transcriptomics data revealed marked differences in immune microniches between healthy and inflamed skin. Tissue-scale cartography further identified core disease features across all active lesions, including the emergence of an inflamed suprabasal epidermal state and the presence of B lymphocytes in lesional skin. Both lesional and distal nonlesional samples were stratified by skin disease severity and not by the presence of systemic disease. This segregation was driven by macrophage-, fibroblast-, and lymphatic-enriched spatial regions with gene signatures associated with metabolic dysfunction. Together, these findings suggest that mild and severe forms of PsO have distinct molecular features and that severe PsO may profoundly alter the cellular and metabolic composition of distal unaffected skin sites. In addition, our study provides a valuable resource for the research community to study spatial gene organization of healthy and inflamed human skin.

Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology.

Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.

Lymphatics act as a signaling hub to regulate intestinal stem cell activity.

Barrier epithelia depend upon resident stem cells for homeostasis, defense, and repair. Epithelial stem cells of small and large intestines (ISCs) respond to their local microenvironments (niches) to fulfill a continuous demand for tissue turnover. The complexity of these niches and underlying communication pathways are not fully known. Here, we report a lymphatic network at the intestinal crypt base that intimately associates with ISCs. Employing in vivo loss of function and lymphatic:organoid cocultures, we show that crypt lymphatics maintain ISCs and inhibit their precocious differentiation. Pairing single-cell and spatial transcriptomics, we apply BayesPrism to deconvolve expression within spatial features and develop SpaceFold to robustly map the niche at high resolution, exposing lymphatics as a central signaling hub for the crypt in general and ISCs in particular. We identify WNT-signaling factors (WNT2, R-SPONDIN-3) and a hitherto unappreciated extracellular matrix protein, REELIN, as crypt lymphatic signals that directly govern the regenerative potential of ISCs.

Prediction of histone post-translational modification patterns based on nascent transcription data.

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.

Funmap2: an R package for QTL mapping using longitudinal phenotypes.

Quantitative trait locus (QTL) mapping has been used as a powerful tool for inferring the complexity of the genetic architecture that underlies phenotypic traits. This approach has shown its unique power to map the developmental genetic architecture of complex traits by implementing longitudinal data analysis. Here, we introduce the R package Funmap2 based on the functional mapping framework, which integrates prior biological knowledge into the statistical model. Specifically, the functional mapping framework is engineered to include longitudinal curves that describe the genetic effects and the covariance matrix of the trait of interest. Funmap2 chooses the type of longitudinal curve and covariance matrix automatically using information criteria. Funmap2 is available for download at https://github.com/wzhy2000/Funmap2.

Discovering Transcriptional Regulatory Elements From Run-On and Sequencing Data Using the Web-Based dREG Gateway.

Transcription is a chromatin mark that can be used effectively to identify the location of active enhancers and promoters, collectively known as transcriptional regulatory elements (TREs). We recently introduced dREG, a tool for the identification of TREs using run-on and sequencing (RO-seq) assays, including global run-on and sequencing (GRO-seq), precision run-on and sequencing (PRO-seq), and chromatin run-on and sequencing (ChRO-seq). In this protocol, we present step-by-step instructions for running dREG on an arbitrary run-on and sequencing dataset. Users provide dREG with bigWig files (in which each read is represented by a single base) representing the location of RNA polymerase in a cell or tissue sample of interest, and dREG returns a list of genomic regions that are predicted to be active TREs. Finally, we demonstrate the use of dREG regions in discovering transcription factors controlling response to a stimulus and predicting their target genes. Together, this protocol provides detailed instructions for running dREG on arbitrary run-on and sequencing data. © 2018 by John Wiley & Sons, Inc.

Physical confinement induces malignant transformation in mammary epithelial cells.

The physical microenvironment of tumor cells plays an important role in cancer initiation and progression. Here, we present evidence that confinement - a new physical parameter that is apart from matrix stiffness - can also induce malignant transformation in mammary epithelial cells. We discovered that MCF10A cells, a benign mammary cell line that forms growth-arrested polarized acini in Matrigel, transforms into cancer-like cells within the same Matrigel material following confinement in alginate shell hydrogel microcapsules. The confined cells exhibited a range of tumor-like behaviors, including uncontrolled cellular proliferation and invasion. Additionally, 4-6 weeks after transplantation into the mammary fad pads of immunocompromised mice, the confined cells formed large palpable masses that exhibited histological features similar to that of carcinomas. Taken together, our findings suggest that physical confinement represents a previously unrecognized mechanism for malignancy induction in mammary epithelial cells and also provide a new, microcapsule-based, high throughput model system for testing new breast cancer therapeutics.

Building a Science Gateway For Processing and Modeling Sequencing Data Via Apache Airavata.

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/.

A bi-stable feedback loop between GDNF, EGR1, and ERα contribute to endocrine resistant breast cancer.

Discovering regulatory interactions between genes that specify the behavioral properties of cells remains an important challenge. We used the dynamics of transcriptional changes resolved by PRO-seq to identify a regulatory network responsible for endocrine resistance in breast cancer. We show that GDNF leads to endocrine resistance by switching the active state in a bi-stable feedback loop between GDNF, EGR1, and the master transcription factor ERα. GDNF stimulates MAP kinase, activating the transcription factors SRF and AP-1. SRF initiates an immediate transcriptional response, activating EGR1 and suppressing ERα. Newly translated EGR1 protein activates endogenous GDNF, leading to constitutive GDNF and EGR1 up-regulation, and the sustained down-regulation of ERα. Endocrine resistant MCF-7 cells are constitutively in the GDNF-high/ ERα-low state, suggesting that the state in the bi-stable feedback loop may provide a 'memory' of endocrine resistance. Thus, we identified a regulatory network switch that contributes to drug resistance in breast cancer.

Dynamic evolution of regulatory element ensembles in primate CD4+ T cells.

How evolutionary changes at enhancers affect the transcription of target genes remains an important open question. Previous comparative studies of gene expression have largely measured the abundance of messenger RNA, which is affected by post-transcriptional regulatory processes, hence limiting inferences about the mechanisms underlying expression differences. Here, we directly measured nascent transcription in primate species, allowing us to separate transcription from post-transcriptional regulation. We used precision run-on and sequencing to map RNA polymerases in resting and activated CD4+ T cells in multiple human, chimpanzee and rhesus macaque individuals, with rodents as outgroups. We observed general conservation in coding and non-coding transcription, punctuated by numerous differences between species, particularly at distal enhancers and non-coding RNAs. Genes regulated by larger numbers of enhancers are more frequently transcribed at evolutionarily stable levels, despite reduced conservation at individual enhancers. Adaptive nucleotide substitutions are associated with lineage-specific transcription and at one locus, SGPP2, we predict and experimentally validate that multiple substitutions contribute to human-specific transcription. Collectively, our findings suggest a pervasive role for evolutionary compensation across ensembles of enhancers that jointly regulate target genes.

Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme.

The human genome encodes a variety of poorly understood RNA species that remain challenging to identify using existing genomic tools. We developed chromatin run-on and sequencing (ChRO-seq) to map the location of RNA polymerase for almost any input sample, including samples with degraded RNA that are intractable to RNA sequencing. We used ChRO-seq to map nascent transcription in primary human glioblastoma (GBM) brain tumors. Enhancers identified in primary GBMs resemble open chromatin in the normal human brain. Rare enhancers that are activated in malignant tissue drive regulatory programs similar to the developing nervous system. We identified enhancers that regulate groups of genes that are characteristic of each known GBM subtype and transcription factors that drive them. Finally we discovered a core group of transcription factors that control the expression of genes associated with clinical outcomes. This study characterizes the transcriptional landscape of GBM and introduces ChRO-seq as a method to map regulatory programs that contribute to complex diseases.

Transcriptional response to stress is pre-wired by promoter and enhancer architecture.

Programs of gene expression are executed by a battery of transcription factors that coordinate divergent transcription from a pair of tightly linked core initiation regions of promoters and enhancers. Here, to investigate how divergent transcription is reprogrammed upon stress, we measured nascent RNA synthesis at nucleotide-resolution, and profiled histone H4 acetylation in human cells. Our results globally show that the release of promoter-proximal paused RNA polymerase into elongation functions as a critical switch at which a gene's response to stress is determined. Highly transcribed and highly inducible genes display strong transcriptional directionality and selective assembly of general transcription factors on the core sense promoter. Heat-induced transcription at enhancers, instead, correlates with prior binding of cell-type, sequence-specific transcription factors. Activated Heat Shock Factor 1 (HSF1) binds to transcription-primed promoters and enhancers, and CTCF-occupied, non-transcribed chromatin. These results reveal chromatin architectural features that orient transcription at divergent regulatory elements and prime transcriptional responses genome-wide.Heat Shock Factor 1 (HSF1) is a regulator of stress-induced transcription. Here, the authors investigate changes to transcription and chromatin organization upon stress and find that activated HSF1 binds to transcription-primed promoters and enhancers, and to CTCF occupied, untranscribed chromatin.