Dataset Information

Regulated Capture of V? Gene Topologically Associating Domains by Transcription Factories.

ABSTRACT: Expression of vast repertoires of antigen receptors by lymphocytes, with each cell expressing a single receptor, requires stochastic activation of individual variable (V) genes for transcription and recombination. How this occurs remains unknown. Using single-cell RNA sequencing (scRNA-seq) and allelic variation, we show that individual pre-B cells monoallelically transcribe divergent arrays of V? genes, thereby opening stochastic repertoires for subsequent V?-J? recombination. Transcription occurs upon translocation of V? genes to RNA polymerase II arrayed on the nuclear matrix in transcription factories. Transcription is anchored by CTCF-bound sites or E2A-loaded V? promotors and continues over large genomic distances delimited only by topological associating domains (TADs). Prior to their monoallelic activation, V? loci are transcriptionally repressed by cyclin D3, which prevents capture of V? gene containing TADs by transcription factories. Cyclin D3 also represses protocadherin, olfactory, and other monoallelically expressed genes, suggesting a widely deployed mechanism for coupling monoallelic gene activation with cell cycle exit.

SUBMITTER: Karki S

PROVIDER: S-EPMC6310487 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Publications

Regulated Capture of Vκ Gene Topologically Associating Domains by Transcription Factories.

Karki Sophiya S Kennedy Domenick E DE Mclean Kaitlin K Grzybowski Adrian T AT Maienschein-Cline Mark M Banerjee Shiladitya S Xu Heping H Davis Elizabeth E Mandal Malay M Labno Christine C Powers Sarah E SE Le Beau Michelle M MM Dinner Aaron R AR Singh Harinder H Ruthenburg Alexander J AJ Clark Marcus R MR

Cell reports 20180801 9

Expression of vast repertoires of antigen receptors by lymphocytes, with each cell expressing a single receptor, requires stochastic activation of individual variable (V) genes for transcription and recombination. How this occurs remains unknown. Using single-cell RNA sequencing (scRNA-seq) and allelic variation, we show that individual pre-B cells monoallelically transcribe divergent arrays of Vκ genes, thereby opening stochastic repertoires for subsequent Vκ-Jκ recombination. Transcription occ ...[more]

PMID: 30157436

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Project description:Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix [Formula: see text], where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in [Formula: see text] within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.

Dataset Information

Regulated Capture of V? Gene Topologically Associating Domains by Transcription Factories.

Publications

Regulated Capture of Vκ Gene Topologically Associating Domains by Transcription Factories.

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