Transcription and splicing kinetics are coordinated within both genes and higher-order chromatin domains [dataset2]
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE146164 | GEO | 2021/05/23
REPOSITORIES: GEO
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Project description:This SuperSeries is composed of the SubSeries listed below.
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Project description:Here, we set out to understand if and how splicing kinetics are coordinated beyond individual intron removal and if this potential coordination could regulate alternative splicing outcomes. By measuring rates of transcription and splicing genome-wide, we found that the rate of splicing is coordinated across introns within individual genes. Furthermore, we found that elongation rates and splicing rates are coordinated within TADs, as are alternative splicing changes that result from cell type differences, differentiation programs, and alterations in splicing factor levels. Overall, our work establishes that the kinetics of transcription and splicing are coordinated within the spatial organization of the genome, which could be a control point for alternative splicing regulation that can modulate the function of a cell.
2021-05-23 | GSE146161 | GEO
Project description:Transcription and splicing kinetics are coordinated within both genes and higher-order chromatin domains [dataset2]
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Project description:Transcription and splicing kinetics are coordinated within both genes and higher-order chromatin domains
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Project description:Transcription and splicing kinetics are coordinated within both genes and higher-order chromatin domains [dataset1]
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Project description:We statistically characterize the population spiking activity obtained from simultaneous recordings of neurons across all layers of a cortical microcolumn. Three types of models are compared: an Ising model which captures pairwise correlations between units, a Restricted Boltzmann Machine (RBM) which allows for modeling of higher-order correlations, and a semi-Restricted Boltzmann Machine which is a combination of Ising and RBM models. Model parameters were estimated in a fast and efficient manner using minimum probability flow, and log likelihoods were compared using annealed importance sampling. The higher-order models reveal localized activity patterns which reflect the laminar organization of neurons within a cortical column. The higher-order models also outperformed the Ising model in log-likelihood: On populations of 20 cells, the RBM had 10% higher log-likelihood (relative to an independent model) than a pairwise model, increasing to 45% gain in a larger network with 100 spatiotemporal elements, consisting of 10 neurons over 10 time steps. We further removed the need to model stimulus-induced correlations by incorporating a peri-stimulus time histogram term, in which case the higher order models continued to perform best. These results demonstrate the importance of higher-order interactions to describe the structure of correlated activity in cortical networks. Boltzmann Machines with hidden units provide a succinct and effective way to capture these dependencies without increasing the difficulty of model estimation and evaluation.
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Project description:It has long been posited that human and other large genomes are organized into higher-order (i.e., greater than gene-sized) functional domains. We hypothesized that diverse experimental data types generated by The ENCODE Project Consortium could be combined to delineate active and quiescent or repressed functional domains and thereby illuminate the higher-order functional architecture of the genome. To address this, we coupled wavelet analysis with hidden Markov models for unbiased discovery of "domain-level" behavior in high-resolution functional genomic data, including activating and repressive histone modifications, RNA output, and DNA replication timing. We find that higher-order patterns in these data types are largely concordant and may be analyzed collectively in the context of HeLa cells to delineate 53 active and 62 repressed functional domains within the ENCODE regions. Active domains comprise approximately 44% of the ENCODE regions but contain approximately 75%-80% of annotated genes, transcripts, and CpG islands. Repressed domains are enriched in certain classes of repetitive elements and, surprisingly, in evolutionarily conserved nonexonic sequences. The functional domain structure of the ENCODE regions appears to be largely stable across different cell types. Taken together, our results suggest that higher-order functional domains represent a fundamental organizing principle of human genome architecture.
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Project description:Cell death-inducing DFF45-like effector (CIDE) domains, initially identified in apoptotic nucleases, form a family with diverse functions ranging from cell death to lipid homeostasis. Here we show that the CIDE domains of Drosophila and human apoptotic nucleases Drep2, Drep4, and DFF40 all form head-to-tail helical filaments. Opposing positively and negatively charged interfaces mediate the helical structures, and mutations on these surfaces abolish nuclease activation for apoptotic DNA fragmentation. Conserved filamentous structures are observed in CIDE family members involved in lipid homeostasis, and mutations on the charged interfaces compromise lipid droplet fusion, suggesting that CIDE domains represent a scaffold for higher-order assembly in DNA fragmentation and other biological processes such as lipid homeostasis.
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Project description:The combination of covalent labeling (CL) and mass spectrometry (MS) has emerged as a useful tool for studying protein structure due to its good structural coverage, the ability to study proteins in mixtures, and its high sensitivity. Diethylpyrocarbonate (DEPC) is an effective CL reagent that can label N-termini and the side chains of several nucleophilic residues, providing information for about 30% of the residues in the average protein. For DEPC to provide accurate structural information, the extent of labeling must be controlled to minimize label-induced structural perturbations. In this work, we establish a quantitative correlation between general protein structural factors and DEPC reaction rates by measuring the reaction rate coefficients for several model proteins. Using principal component and regression analyses, we find that the solvent accessible surface areas of histidine and lysine residues in proteins are the primary factors that determine a protein's reactivity toward DEPC, despite the fact that other more abundant residues, such as tyrosine, threonine, and serine, are also labeled by DEPC. From the statistical analysis, a model emerges that can be used to predict the reactivity of a protein based on its structure and sequence, allowing the optimal DEPC concentration to be chosen for a given protein. The resulting model is supported by cross-validation studies and by accurately predicting of the reactivity of five test proteins. Overall, our model reveals interesting insight into the reactivity of proteins with DEPC, and it will facilitate identification of optimal DEPC labeling conditions for proteins.
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Project description:BackgroundSingle-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis.ResultsIntriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression.ConclusionsBy identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.
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