Dataset Information

Corrected and Republished from: BCL11A Is a Critical Component of a Transcriptional Network That Activates RAG Expression and V(D)J Recombination.

ABSTRACT: Recombination activating gene 1 (RAG1) and RAG2 are critical enzymes for initiating variable-diversity-joining [V(D)J] segment recombination, an essential process for antigen receptor expression and lymphocyte development. The BCL11A transcription factor is required for B cell and plasmacytoid dendritic cell (pDC) development, but its molecular function(s) in early B cell fate specification and commitment is unknown. We show here that the major B cell isoform, BCL11A-XL, binds directly to the RAG1 promoter as well as directly to regulatory regions of transcription factors previously implicated in both B cell and pDC development to activate RAG1 and RAG2 gene transcription in pro- and pre-B cells. We employed BCL11A overexpression with recombination substrates to demonstrate direct consequences of BCL11A/RAG modulation on V(D)J recombination. We conclude that BCL11A is a critical component of a transcriptional network that regulates B cell fate by controlling V(D)J recombination.

SUBMITTER: Lee BS

PROVIDER: S-EPMC5730723 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Corrected and Republished from: BCL11A Is a Critical Component of a Transcriptional Network That Activates RAG Expression and V(D)J Recombination.

Lee Baeck-Seung BS Lee Bum-Kyu BK Iyer Vishwanath R VR Sleckman Barry P BP Shaffer Arthur L AL Ippolito Gregory C GC Tucker Haley O HO Dekker Joseph D JD

Molecular and cellular biology 20171213 1

Recombination activating gene 1 (RAG1) and RAG2 are critical enzymes for initiating variable-diversity-joining [V(D)J] segment recombination, an essential process for antigen receptor expression and lymphocyte development. The BCL11A transcription factor is required for B cell and plasmacytoid dendritic cell (pDC) development, but its molecular function(s) in early B cell fate specification and commitment is unknown. We show here that the major B cell isoform, BCL11A-XL, binds directly to the <i ...[more]

PMID: 29038163

Similar Datasets

Project description:Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network.The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

Project description:BackgroundNetwork Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody) and/or temporal synchronicity (cytokinesis) to avoid full-scale computation from genome-wide databases.ResultsNCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5).ConclusionIn this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes. The regulated action for four selected cytokinesis-related genes (BUD4, CHS2, IQG1, and CDC5) belongs to the M-phase or M/G1 phase, consistent with the empirical observations that in S. cerevisiae, the Mcm1-Ndd1-Fkh2 transcription factor complex can regulate expression of the cytokinesis-related genes BUD4, CHS2, IQG1, and CDC5. Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

Dataset Information

Corrected and Republished from: BCL11A Is a Critical Component of a Transcriptional Network That Activates RAG Expression and V(D)J Recombination.

Publications

Corrected and Republished from: BCL11A Is a Critical Component of a Transcriptional Network That Activates RAG Expression and V(D)J Recombination.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets