Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values ...under the null hypothesis but informative of each test's power or prior probability of the null hypothesis (http://www.bioconductor.org/packages/IHW). IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.
Enhancers are genomic sequences that play a key role in regulating tissue-specific gene expression levels. An increasing number of diseases are linked to impaired enhancer function through ...chromosomal rearrangement, genetic variation within enhancers, or epigenetic modulation. Here, we review how these enhancer disruptions have recently been implicated in congenital disorders, cancers, and common complex diseases and address the implications for diagnosis and treatment. Although further fundamental research into enhancer function, target genes, and context is required, enhancer-targeting drugs and gene editing approaches show great therapeutic promise for a range of diseases.
Enhancer disruption is increasingly implicated as a disease-driving mechanism. Chromosomal rearrangements can cause an enhancer to drive aberrant gene expression, genetic variants in enhancers can impact a transcription factor binding site, and disease-associated epigenetic changes are enriched in enhancer regions.The three big challenges in enhancer research focus on systematically identifying functional enhancers, their target genes, and the context in which they are active.Bromo- and extra-terminal (BET) inhibitors are a new class of drugs that target enhancers and inhibit gene expression. They are under investigation as treatment for cancer and other diseases.Gene editing techniques elucidate enhancer function and are being used to selectively regulate or mutate disturbed enhancers.
Regulation of hematopoiesis during human development remains poorly defined. Here we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin ...sequencing (scATAC-seq) to over 8,000 human immunophenotypic blood cells from fetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream of hematopoietic stem cells (HSCs)/multipotent progenitors (MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSCs/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSCs/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental hematopoiesis in the context of blood pathologies and regenerative medicine.
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•The epigenetic and transcriptional landscape of human fetal hematopoiesis•Blood stem cells differentiate into three distinct oligopotent progenitor populations•Changes in motif accessibility in blood stem cells precede transcriptional priming•Refined sorting strategy to isolate and enrich for human fetal blood stem cells
Ranzoni et al. provide a detailed transcriptional and chromatin accessibility map of fetal liver and bone marrow hematopoietic stem cells (HSCs). Within HSCs, they revealed extensive epigenetic but not transcriptional priming. They identified transcriptional and functional differences between HSCs from liver and bone marrow.
Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling ...pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional program. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post‐translational modifications (PTMs) of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multi‐omics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.
Deciphering the impact of genetic variants on gene regulation is fundamental to understanding human disease. Although gene regulation often involves long-range interactions, it is unknown to what ...extent non-coding genetic variants influence distal molecular phenotypes. Here, we integrate chromatin profiling for three histone marks in lymphoblastoid cell lines (LCLs) from 75 sequenced individuals with LCL-specific Hi-C and ChIA-PET-based chromatin contact maps to uncover one of the largest collections of local and distal histone quantitative trait loci (hQTLs). Distal QTLs are enriched within topologically associated domains and exhibit largely concordant variation of chromatin state coordinated by proximal and distal non-coding genetic variants. Histone QTLs are enriched for common variants associated with autoimmune diseases and enable identification of putative target genes of disease-associated variants from genome-wide association studies. These analyses provide insights into how genetic variation can affect human disease phenotypes by coordinated changes in chromatin at interacting regulatory elements.
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•Analyses of variations in histone marks reveal histone QTLs in regulatory elements•Physically interacting loci show genetically coordinated chromatin levels•Regulatory elements sharing hQTLs are enriched in topologically associated domains•hQTLs are enriched for GWAS SNPs and enable identification of putative target genes
Genetic variation in regulatory elements can effect coordinate changes in chromatin state and gene expression at both local and distal sites, reflecting associations in a three-dimensional context. Integrating information from expression, chromatin modification, and chromosome contact analyses provides a framework for assessing disease-associated mutations.
Physical interactions between distal regulatory elements have a key role in regulating gene expression, but the extent to which these interactions vary between cell types and contribute to ...cell-type-specific gene expression remains unclear. Here, to address these questions as part of phase III of the Encyclopedia of DNA Elements (ENCODE), we mapped cohesin-mediated chromatin loops, using chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), and analysed gene expression in 24 diverse human cell types, including core ENCODE cell lines. Twenty-eight per cent of all chromatin loops vary across cell types; these variations modestly correlate with changes in gene expression and are effective at grouping cell types according to their tissue of origin. The connectivity of genes corresponds to different functional classes, with housekeeping genes having few contacts, and dosage-sensitive genes being more connected to enhancer elements. This atlas of chromatin loops complements the diverse maps of regulatory architecture that comprise the ENCODE Encyclopedia, and will help to support emerging analyses of genome structure and function.
The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge ...base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular‐level detail the system‐wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux‐signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed‐loop self‐regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.
Synopsis
Adaptations to fluctuating carbon source availability are of particular importance for bacteria. To understand these adaptations, it needs to be understood how a system's behavior emerges from the interactions between the characterized molecules (Kitano, 2002b). To attain such a system understanding of bacterial metabolic adaptations to carbon source availability, the coupling between the recognition and adjustment aspects and between the enzymatic and genetic regulatory layers must be understood. For many carbon sources, neither transmembrane sensors nor regulatory proteins with sensing function have been identified. Also, it remains unclear how multiple local regulations work together to accomplish a coherent adjustment on the systems level. In this paper, we show that (1) the interplay of the known interactions in E. coli's central metabolism is capable of recognizing carbon sources indirectly, and that (2) these molecular interactions can adjust E. coli's metabolic operation between growth on glycolytic and gluconeogenic carbon sources, and that (3) this adaptation is governed by general principles.
We hypothesized that the system‐level adaptations between growth on glycolytic and gluconeogenic carbon sources are accomplished by a system‐wide regulation architecture that emerges when the known enzymatic and transcriptional regulations become coupled through five transcription factor (TF)–metabolite interactions. To (1) assess whether such coupled molecular interactions can indeed work together to adapt metabolic operation, and if yes, (2) to understand this system‐level adaptation in molecular‐level detail, we constructed a large‐scale differential equation model. The model topology comprises the Embden–Meyerhoff pathway, the tricarboxylic acid (TCA) cycle, the glyoxylate (GLX) shunt, the anaplerotic reactions, the diversion of carbon flux to the GLX shunt, the uptake of glucose, the uptake and excretion of acetate, enzymatic regulation, transcriptional regulation by four TFs, and the regulation of these TFs’ activities through TF–metabolite interactions. We translated the topology into differential equations by assigning the most appropriate rate law to each interaction. The kinetic model comprises 47 ordinary differential equations and 193 parameters. Parameter values were estimated through application of the ‘divide‐and‐conquer approach’ (Kotte and Heinemann, 2009) on published experimental steady state‐omics data sets.
Model simulations reproduce E. coli's known physiological behavior in an environment with fluctuating carbon source availability. But how does the in silico cell recognize acetate without a transmembrane sensor for extracellular acetate or a TF binding to intracellular acetate? Similarly, it is unclear whether the glucose sensing function of the phosphotransferase system is the exclusive mechanism to recognize glucose, or whether this sensing function is integrated into a larger sensing architecture. The model suggests that the recognition is performed indirectly through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two distinct motifs, which we termed pathway usage and flux direction, to establish defined correlations between metabolic fluxes and the levels of certain, here termed flux‐signaling metabolites. The binding of these metabolites to TFs propagates the flux information to the transcriptional regulatory layer. A molecular sensor for intracellular metabolic flux is thus defined as a system of regulations and enzyme kinetics, comprising (1) either of the two motifs pathway usage or flux direction and (2) the binding of the thus established flux‐signaling metabolites to TF(s).
As the in silico cell establishes and uses sensors for several intracellular metabolic fluxes, the overall sensing architecture infers the present carbon sources from a pattern of metabolic fluxes and is as such of a distributed nature. The core of this sensing architecture is formed not by transmembrane sensors but by four flux sensors, which establish flux‐signaling metabolites according to the two proposed general motifs. These flux sensors use intracellular metabolic flux as a means to correlate the presence of extracellular carbon sources with the levels of intracellular metabolites. The recognition of glucose through the PTS transmembrane complex is embedded as one flux sensor in this distributed sensing architecture; the other three flux sensors function without the help of transmembrane complexes.
The in silico cell achieves the coupling between recognition and adjustment through its TFs, whose activities respond to the available carbon sources and at the same time regulate the expression of target genes. This combined recognition and adjustment, centered on the four TFs, closes four global feedback loops that overarch the metabolic and genetic layers as illustrated in Figure 6. The adaptation of the in silico cell arises from the global feedback loop‐embedded, flux sensor‐adjusted transcriptional regulation of the four TFs, with each TF performing one part of the overall adaptation. This adaptation incorporates both the influence of the metabolic on the genetic layer, achieved through TF–metabolite interactions, and of the genetic on the metabolic layer, achieved through the impact of adjusted enzyme levels on metabolic fluxes.
The existence of the global feedback architectures challenges the conventional view that top‐level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation. It suggests that the capability for closed‐loop self‐regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and regulation) to changing carbon sources.
To conclude, the presented differential equation model of E. coli's central metabolism offers a consistent explanation of how a multitude of known molecular interactions fit into a coherent systems picture; the interactions work together like gear wheels that mesh with one another to adapt central metabolism between growth on the glycolytic substrate glucose and the gluconeogenic substrate acetate. The deduced general functional principles provide the missing link to understand system‐level adaptations to carbon sources in molecular‐level detail. The proposed principles fall under the umbrella of distributed flux sensing. The flux sensing mechanism entails the binding of TFs to flux‐signaling metabolites, which are established through the motifs signaling of pathway usage and signaling of flux direction, and are embedded in global feedback loop architectures. These principles allow an autonomous adaptation of metabolic operation to growth in fluctuating environments.
We present a large‐scale differential equation model of E. coli's central metabolism and its enzymatic, transcriptional, and posttranslational regulation. This model reproduces E. coli's known physiological behavior.
We found that the interplay of known interactions in E. coli's central metabolism can indirectly recognize the presence of extracellular carbon sources through measuring intracellular metabolic flux patterns.
We found that E. coli's system‐level adaptations between glycolytic and gluconeogenic carbon sources are realized on the molecular level by global feedback architectures that overarch the enzymatic and transcriptional regulatory layers.
We found that the capability for closed‐loop self‐regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously to changing carbon sources (not requiring upstream sensing and signaling).
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning ...framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
Extensive Variation in Chromatin States Across Humans Kasowski, Maya; Kyriazopoulou-Panagiotopoulou, Sofia; Grubert, Fabian ...
Science (American Association for the Advancement of Science),
11/2013, Letnik:
342, Številka:
6159
Journal Article
Recenzirano
Odprti dostop
The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in ...chromatin states using five histone modifications, cohesin, and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence, and is associated with disruptions of transcription factor binding motifs. Overall, our results provide insights into chromatin variation among humans.
The epigenetic regulator
is frequently mutated in hematological diseases. Mutations have been shown to arise in hematopoietic stem cells early in disease development and lead to altered DNA ...methylation landscapes and an increased risk of hematopoietic malignancy. Here, we show by genome-wide mapping of TET2 binding sites in different cell types that TET2 localizes to regions of open chromatin and cell-type-specific enhancers. We find that deletion of
in native hematopoiesis as well as fully transformed acute myeloid leukemia (AML) results in changes in transcription factor (TF) activity within these regions, and we provide evidence that loss of TET2 leads to attenuation of chromatin binding of members of the basic helix-loop-helix (bHLH) TF family. Together, these findings demonstrate that TET2 activity shapes the local chromatin environment at enhancers to facilitate TF binding and provides an example of how epigenetic dysregulation can affect gene expression patterns and drive disease development.
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