Dataset Information

Sequential entry of components of the gene expression machinery into daughter nuclei.

ABSTRACT: In eukaryotic cells, RNA polymerase II (RNA pol II) transcription and pre-mRNA processing are coordinated events. We have addressed how individual components of the transcription and pre-mRNA processing machinery are organized during mitosis and subsequently recruited into the newly formed daughter nuclei. Interestingly, localization studies of numerous RNA pol II transcription and pre-mRNA processing factors revealed a nonrandom and sequential entry of these factors into daughter nuclei after nuclear envelope/lamina formation. The initiation competent form of RNA pol II and general transcription factors appeared in the daughter nuclei simultaneously, but prior to pre-mRNA processing factors, whereas the elongation competent form of RNA pol II was detected even later. The differential entry of these factors rules out the possibility that they are transported as a unitary complex. Telophase nuclei were competent for transcription and pre-mRNA splicing concomitant with the initial entry of the respective factors. In addition, our results revealed a low turnover rate of transcription and pre-mRNA splicing factors during mitosis. We provide evidence to support a model in which the entry of the RNA pol II gene expression machinery into newly forming daughter nuclei is a staged and ordered process.

SUBMITTER: Prasanth KV

PROVIDER: S-EPMC151578 | biostudies-literature | 2003 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sequential entry of components of the gene expression machinery into daughter nuclei.

Prasanth Kannanganattu V KV Sacco-Bubulya Paula A PA Prasanth Supriya G SG Spector David L DL

Molecular biology of the cell 20030301 3

In eukaryotic cells, RNA polymerase II (RNA pol II) transcription and pre-mRNA processing are coordinated events. We have addressed how individual components of the transcription and pre-mRNA processing machinery are organized during mitosis and subsequently recruited into the newly formed daughter nuclei. Interestingly, localization studies of numerous RNA pol II transcription and pre-mRNA processing factors revealed a nonrandom and sequential entry of these factors into daughter nuclei after n ...[more]

PMID: 12631722

Similar Datasets

Project description:BackgroundThree-dimensional chromatin loop structures connect regulatory elements to their target genes in regions known as anchors. In complex plant genomes, such as maize, it has been proposed that loops span heterochromatic regions marked by higher repeat content, but little is known on their spatial organization and genome-wide occurrence in relation to transcriptional activity.ResultsHere, ultra-deep Hi-C sequencing of maize B73 leaf tissue was combined with gene expression and open chromatin sequencing for chromatin loop discovery and correlation with hierarchical topologically-associating domains (TADs) and transcriptional activity. A majority of all anchors are shared between multiple loops from previous public maize high-resolution interactome datasets, suggesting a highly dynamic environment, with a conserved set of anchors involved in multiple interaction networks. Chromatin loop interiors are marked by higher repeat contents than the anchors flanking them. A small fraction of high-resolution interaction anchors, fully embedded in larger chromatin loops, co-locate with active genes and putative protein-binding sites. Combinatorial analyses indicate that all anchors studied here co-locate with at least 81.5% of expressed genes and 74% of open chromatin regions. Approximately 38% of all Hi-C chromatin loops are fully embedded within hierarchical TAD-like domains, while the remaining ones share anchors with domain boundaries or with distinct domains. Those various loop types exhibit specific patterns of overlap for open chromatin regions and expressed genes, but no apparent pattern of gene expression. In addition, up to 63% of all unique variants derived from a prior public maize eQTL dataset overlap with Hi-C loop anchors. Anchor annotation suggests that < 7% of all loops detected here are potentially devoid of any genes or regulatory elements. The overall organization of chromatin loop anchors in the maize genome suggest a loop modeling system hypothesized to resemble phase separation of repeat-rich regions.ConclusionsSets of conserved chromatin loop anchors mapping to hierarchical domains contains core structural components of the gene expression machinery in maize. The data presented here will be a useful reference to further investigate their function in regard to the formation of transcriptional complexes and the regulation of transcriptional activity in the maize genome.

Project description:High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Dataset Information

Sequential entry of components of the gene expression machinery into daughter nuclei.

Publications

Sequential entry of components of the gene expression machinery into daughter nuclei.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets