RESUMO
Type 1 regulatory T cells (Tr1 cells) are induced by interleukin-27 (IL-27) and have critical roles in the control of autoimmunity and resolution of inflammation. We found that the transcription factors IRF1 and BATF were induced early on after treatment with IL-27 and were required for the differentiation and function of Tr1 cells in vitro and in vivo. Epigenetic and transcriptional analyses revealed that both transcription factors influenced chromatin accessibility and expression of the genes required for Tr1 cell function. IRF1 and BATF deficiencies uniquely altered the chromatin landscape, suggesting that these factors serve a pioneering function during Tr1 cell differentiation.
Assuntos
Fatores de Transcrição de Zíper de Leucina Básica/metabolismo , Diferenciação Celular/imunologia , Cromatina/metabolismo , Fator Regulador 1 de Interferon/metabolismo , Linfócitos T Reguladores/imunologia , Linfócitos T Reguladores/metabolismo , Animais , Doenças Autoimunes/genética , Doenças Autoimunes/imunologia , Doenças Autoimunes/metabolismo , Autoimunidade , Fatores de Transcrição de Zíper de Leucina Básica/genética , Diferenciação Celular/genética , Cromatina/genética , Análise por Conglomerados , Citocinas/metabolismo , Citocinas/farmacologia , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Fator Regulador 1 de Interferon/genética , Camundongos , Camundongos Knockout , Regiões Promotoras Genéticas , Subpopulações de Linfócitos T/efeitos dos fármacos , Subpopulações de Linfócitos T/imunologia , Subpopulações de Linfócitos T/metabolismo , Linfócitos T Reguladores/citologia , Linfócitos T Reguladores/efeitos dos fármacos , Fatores de Transcrição/metabolismo , TranscriptomaRESUMO
Innate lymphoid cells (ILCs) promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. ILCs exhibit phenotypic and functional plasticity in response to environmental stimuli, yet the transcriptional regulatory networks (TRNs) that control ILC function are largely unknown. Here, we integrate gene expression and chromatin accessibility data to infer regulatory interactions between transcription factors (TFs) and genes within intestinal type 1, 2, and 3 ILC subsets. We predicted the "core" TFs driving ILC identities, organized TFs into cooperative modules controlling distinct gene programs, and validated roles for c-MAF and BCL6 as regulators affecting type 1 and type 3 ILC lineages. The ILC network revealed alternative-lineage-gene repression, a mechanism that may contribute to reported plasticity between ILC subsets. By connecting TFs to genes, the TRNs suggest means to selectively regulate ILC effector functions, while our network approach is broadly applicable to identifying regulators in other in vivo cell populations.
Assuntos
Intestinos/fisiologia , Subpopulações de Linfócitos/fisiologia , Linfócitos/fisiologia , Animais , Diferenciação Celular , Linhagem da Célula , Plasticidade Celular , Montagem e Desmontagem da Cromatina , Repressão Epigenética , Redes Reguladoras de Genes , Imunidade Inata , Imunomodulação , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Proteínas Proto-Oncogênicas c-bcl-6/genética , Proteínas Proto-Oncogênicas c-maf/genética , TranscriptomaRESUMO
MOTIVATION: Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. RESULTS: In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. AVAILABILITY AND IMPLEMENTATION: The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Redes Reguladoras de Genes , Software , Animais , Camundongos , Genômica , Genoma , CromatinaRESUMO
ABSTRACT: This case report highlights a rare type of primary spinal cord tumor, myxopapillary ependymoma, in a pediatric patient who presented to clinic with worsening chronic unilateral thigh pain and neurologic deficits. He was appropriately treated with total gross resection of the tumor and adjuvant radiotherapy and was cleared for competitive sports without any restriction within 1 year of his diagnosis and treatment. Although most musculoskeletal complaints among pediatric patients are of benign etiology, as evidenced by our case, clinicians should have a low threshold to further investigate with advanced imaging modalities should the clinical history and examination be consistent with a more concerning pathologic process.
RESUMO
Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)-seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs ("TF-TF modules") in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.
Assuntos
Cromatina/genética , Redes Reguladoras de Genes , Células Th17/metabolismo , Fatores de Transcrição/metabolismo , Diferenciação Celular , Cromatina/química , Montagem e Desmontagem da Cromatina , Humanos , Ligação Proteica , Software , Células Th17/citologiaRESUMO
Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.
Assuntos
Bactérias/metabolismo , Microbiota , Animais , Benchmarking , Cianobactérias/metabolismo , Fibrose Cística/microbiologia , Doenças Inflamatórias Intestinais/microbiologia , Camundongos , Redes Neurais de Computação , Pseudomonas aeruginosa/metabolismoRESUMO
For investigations into fate specification and cell rearrangements in live images of preimplantation embryos, automated and accurate 3D instance segmentation of nuclei is invaluable; however, the performance of segmentation methods is limited by the images' low signal-to-noise ratio and high voxel anisotropy and the nuclei's dense packing and variable shapes. Supervised machine learning approaches have the potential to radically improve segmentation accuracy but are hampered by a lack of fully annotated 3D data. In this work, we first establish a novel mouse line expressing near-infrared nuclear reporter H2B-miRFP720. H2B-miRFP720 is the longest wavelength nuclear reporter in mice and can be imaged simultaneously with other reporters with minimal overlap. We then generate a dataset, which we call BlastoSPIM, of 3D microscopy images of H2B-miRFP720-expressing embryos with ground truth for nuclear instance segmentation. Using BlastoSPIM, we benchmark the performance of five convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method across preimplantation development. Stardist-3D, trained on BlastoSPIM, performs robustly up to the end of preimplantation development (> 100 nuclei) and enables studies of fate patterning in the late blastocyst. We, then, demonstrate BlastoSPIM's usefulness as pre-train data for related problems. BlastoSPIM and its corresponding Stardist-3D models are available at: blastospim.flatironinstitute.org.
RESUMO
Congenital abnormalities of the kidney and urinary tract are among the most common birth defects, affecting 3% of newborns. The human kidney forms around a million nephrons from a pool of nephron progenitors over a 30-week period of development. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlage, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single-cell RNA sequencing identified progenitor states, which were spatially mapped to the nephron anatomy, enabling the generation of functional gene networks predicting interactions within and between nephron cell types. Network mining identified known developmental disease genes and predicted targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease and enhance efforts to generate new kidney structures.
Assuntos
Regulação da Expressão Gênica no Desenvolvimento , Néfrons/metabolismo , Transcriptoma , Animais , Humanos , Camundongos , Néfrons/citologia , Néfrons/embriologia , Proteoma/genética , Proteoma/metabolismo , RNA-Seq , Análise de Célula ÚnicaRESUMO
Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During HIV-1 infection, human dendritic cells (DCs) can detect the virus through an innate sensing pathway, leading to antiviral interferon and DC maturation. Here, we develop an iterative experimental and computational approach to map the HIV-1 innate response circuitry in monocyte-derived DCs (MDDCs). By integrating genome-wide chromatin accessibility with expression kinetics, we infer a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observe that an interferon response is required, yet insufficient, to drive MDDC maturation and identify PRDM1 and RARA as essential regulators of the interferon response and MDDC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the response to infection.
Assuntos
Células Dendríticas/metabolismo , Redes Reguladoras de Genes , HIV-1/imunologia , Imunidade Inata/genética , Monócitos/metabolismo , Diferenciação Celular , Cromatina/metabolismo , Células Dendríticas/virologia , Feminino , Regulação da Expressão Gênica , Células HEK293 , Infecções por HIV/imunologia , Infecções por HIV/virologia , Humanos , Interferon Tipo I/metabolismo , Masculino , Monócitos/virologia , Regiões Promotoras Genéticas/genética , Receptor alfa de Ácido Retinoico/metabolismo , Fatores de Transcrição/metabolismo , Transcriptoma/genéticaRESUMO
Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify pathway dynamics, distinguish regional differences between microglia and astrocyte populations at early time points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.
Assuntos
Esclerose Lateral Amiotrófica/genética , Expressão Gênica , Neurônios Motores/metabolismo , Medula Espinal/metabolismo , Esclerose Lateral Amiotrófica/patologia , Animais , Astrócitos/metabolismo , Astrócitos/patologia , Modelos Animais de Doenças , Perfilação da Expressão Gênica , Humanos , Camundongos , Microglia/metabolismo , Microglia/patologia , Neurônios Motores/patologia , Músculo Esquelético/patologia , Músculo Esquelético/fisiopatologia , Degeneração Neural/genética , Degeneração Neural/fisiopatologia , Neuroglia/metabolismo , Neuroglia/patologia , Mudanças Depois da Morte , Análise Espaço-Temporal , Medula Espinal/patologia , TranscriptomaRESUMO
Gut-dwelling Prevotella copri (P. copri), the most prevalent Prevotella species in the human gut, have been associated with diet and disease. However, our understanding of their diversity and function remains rudimentary because studies have been limited to 16S and metagenomic surveys and experiments using a single type strain. Here, we describe the genomic diversity of 83 P. copri isolates from 11 human donors. We demonstrate that genomically distinct isolates, which can be categorized into different P. copri complex clades, utilize defined sets of polysaccharides. These differences are exemplified by variations in susC genes involved in polysaccharide transport as well as polysaccharide utilization loci (PULs) that were predicted in part from genomic and metagenomic data. Functional validation of these PULs showed that P. copri isolates utilize distinct sets of polysaccharides from dietary plant, but not animal, sources. These findings reveal both genomic and functional differences in polysaccharide utilization across human intestinal P. copri strains.