Cell type–specific actions of Bcl11b in early T-lineage and group 2 innate lymphoid cells
TL;DR: A shared enhancer supports initial Bcl11b locus opening in both pro-T and ILC2 lineages.
Abstract: The zinc finger transcription factor, Bcl11b, is expressed in T cells and group 2 innate lymphoid cells (ILC2s) among hematopoietic cells. In early T-lineage cells, Bcl11b directly binds and represses the gene encoding the E protein antagonist, Id2, preventing pro-T cells from adopting innate-like fates. In contrast, ILC2s co-express both Bcl11b and Id2. To address this contradiction, we have directly compared Bcl11b action mechanisms in pro-T cells and ILC2s. We found that Bcl11b binding to regions across the genome shows distinct cell type-specific motif preferences. Bcl11b occupies functionally different sites in lineage-specific patterns and controls totally different sets of target genes in these cell types. In addition, Bcl11b bears cell type-specific post-translational modifications and organizes different cell type-specific protein complexes. However, both cell types use the same distal enhancer region to control timing of Bcl11b activation. Therefore, although pro-T cells and ILC2s both need Bcl11b for optimal development and function, Bcl11b works substantially differently in these two cell types.
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TL;DR: This Review describes how a core group of transcription factors work to establish regulatory target specificity, epigenomic impact and irreversibility for T cell identity.
Abstract: Recent evidence has elucidated how multipotent blood progenitors transform their identities in the thymus and undergo commitment to become T cells. Together with environmental signals, a core group of transcription factors have essential roles in this process by directly activating and repressing specific genes. Many of these transcription factors also function in later T cell development, but control different genes. Here, we review how these transcription factors work to change the activities of specific genomic loci during early intrathymic development to establish T cell lineage identity. We introduce the key regulators and highlight newly emergent insights into the rules that govern their actions. Whole-genome deep sequencing-based analysis has revealed unexpectedly rich relationships between inherited epigenetic states, transcription factor-DNA binding affinity thresholds and influences of given transcription factors on the activities of other factors in the same cells. Together, these mechanisms determine T cell identity and make the lineage choice irreversible.
117 citations
TL;DR: These aging-associated CP-resident ILC2 exhibit unique functional and molecular properties, and their activation revitalizes the aged brain and alleviates aging- associated cognitive decline.
Abstract: Increasing evidence has challenged the traditional view about the immune privilege of the brain, but the precise roles of immune cells in regulating brain physiology and function remain poorly understood. Here, we report that tissue-resident group 2 innate lymphoid cells (ILC2) accumulate in the choroid plexus of aged brains. ILC2 in the aged brain are long-lived, are relatively resistant to cellular senescence and exhaustion, and are capable of switching between cell cycle dormancy and proliferation. They are functionally quiescent at homeostasis but can be activated by IL-33 to produce large amounts of type 2 cytokines and other effector molecules in vitro and in vivo. Intracerebroventricular transfer of activated ILC2 revitalized the aged brain and enhanced the cognitive function of aged mice. Administration of IL-5, a major ILC2 product, was sufficient to repress aging-associated neuroinflammation and alleviate aging-associated cognitive decline. Targeting ILC2 in the aged brain may provide new avenues to combat aging-associated neurodegenerative disorders.
42 citations
TL;DR: In this paper, a revised transcriptional circuit was proposed to explain the co-development of T cells and ILC2 cells from common progenitors in the thymus.
Abstract: Type 2 innate lymphoid cells (ILC2) contribute to immune homeostasis, protective immunity and tissue repair. Here we demonstrate that functional ILC2 cells can arise in the embryonic thymus from shared T cell precursors, preceding the emergence of CD4+CD8+ (double-positive) T cells. Thymic ILC2 cells migrated to mucosal tissues, with colonization of the intestinal lamina propria. Expression of the transcription factor RORα repressed T cell development while promoting ILC2 development in the thymus. From RNA-seq, assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation followed by sequencing (ChIP-seq) data, we propose a revised transcriptional circuit to explain the co-development of T cells and ILC2 cells from common progenitors in the thymus. When Notch signaling is present, BCL11B dampens Nfil3 and Id2 expression, permitting E protein-directed T cell commitment. However, concomitant expression of RORα overrides the repression of Nfil3 and Id2 repression, allowing ID2 to repress E proteins and promote ILC2 differentiation. Thus, we demonstrate that RORα expression represents a critical checkpoint at the bifurcation of the T cell and ILC2 lineages in the embryonic thymus.
34 citations
TL;DR: In this paper, the authors examined the transcriptional and epigenetic profiles of seven human blood natural killer (NK) cell populations, including adaptive NK cells, and uncovered a critical role for Bcl11b in promoting NK cell differentiation and function.
Abstract: Epigenetic landscapes can provide insight into regulation of gene expression and cellular diversity. Here, we examined the transcriptional and epigenetic profiles of seven human blood natural killer (NK) cell populations, including adaptive NK cells. The BCL11B gene, encoding a transcription factor (TF) essential for T cell development and function, was the most extensively regulated, with expression increasing throughout NK cell differentiation. Several Bcl11b-regulated genes associated with T cell signaling were specifically expressed in adaptive NK cell subsets. Regulatory networks revealed reciprocal regulation at distinct stages of NK cell differentiation, with Bcl11b repressing RUNX2 and ZBTB16 in canonical and adaptive NK cells, respectively. A critical role for Bcl11b in driving NK cell differentiation was corroborated in BCL11B-mutated patients and by ectopic Bcl11b expression. Moreover, Bcl11b was required for adaptive NK cell responses in a murine cytomegalovirus model, supporting expansion of these cells. Together, we define the TF regulatory circuitry of human NK cells and uncover a critical role for Bcl11b in promoting NK cell differentiation and function.
33 citations
TL;DR: In this paper, single and double Runx knockouts via Cas9 showed that target genes responding to Runx activity are not solely controlled by the dominant factor, Runx1, and Runx3 are coexpressed in single cells and bind to highly overlapping genomic sites.
Abstract: Runt domain-related (Runx) transcription factors are essential for early T cell development in mice from uncommitted to committed stages. Single and double Runx knockouts via Cas9 show that target genes responding to Runx activity are not solely controlled by the dominant factor, Runx1. Instead, Runx1 and Runx3 are coexpressed in single cells; bind to highly overlapping genomic sites; and have redundant, collaborative functions regulating genes pivotal for T cell development. Despite stable combined expression levels across pro-T cell development, Runx1 and Runx3 preferentially activate and repress genes that change expression dynamically during lineage commitment, mostly activating T-lineage genes and repressing multipotent progenitor genes. Furthermore, most Runx target genes are sensitive to Runx perturbation only at one stage and often respond to Runx more for expression transitions than for maintenance. Contributing to this highly stage-dependent gene regulation function, Runx1 and Runx3 extensively shift their binding sites during commitment. Functionally distinct Runx occupancy sites associated with stage-specific activation or repression are also distinguished by different patterns of partner factor cobinding. Finally, Runx occupancies change coordinately at numerous clustered sites around positively or negatively regulated targets during commitment. This multisite binding behavior may contribute to a developmental “ratchet” mechanism making commitment irreversible.
26 citations
References
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TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Availability: http://samtools.sourceforge.net
Contact: [email protected]
45,957 citations
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
30,684 citations
"Cell type–specific actions of Bcl11..." refers background in this paper
...4.0; Dobin et al., 2013) and postprocessedwith RSEM (v1....
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TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
29,413 citations
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Abstract: Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing webbased methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools
18,858 citations
TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
Abstract: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
14,524 citations