Dataset Information

Priming of transcriptional memory responses via the chromatin accessibility landscape in T cells.

ABSTRACT: Memory T cells exhibit transcriptional memory and "remember" their previous pathogenic encounter to increase transcription on re-infection. However, how this transcriptional priming response is regulated is unknown. Here we performed global FAIRE-seq profiling of chromatin accessibility in a human T cell transcriptional memory model. Primary activation induced persistent accessibility changes, and secondary activation induced secondary-specific opening of previously less accessible regions associated with enhanced expression of memory-responsive genes. Increased accessibility occurred largely in distal regulatory regions and was associated with increased histone acetylation and relative H3.3 deposition. The enhanced re-stimulation response was linked to the strength of initial PKC-induced signalling, and PKC-sensitive increases in accessibility upon initial stimulation showed higher accessibility on re-stimulation. While accessibility maintenance was associated with ETS-1, accessibility at re-stimulation-specific regions was linked to NFAT, especially in combination with ETS-1, EGR, GATA, NF?B, and NR4A. Furthermore, NFATC1 was directly regulated by ETS-1 at an enhancer region. In contrast to the factors that increased accessibility, signalling from bHLH and ZEB family members enhanced decreased accessibility upon re-stimulation. Interplay between distal regulatory elements, accessibility, and the combined action of sequence-specific transcription factors allows transcriptional memory-responsive genes to "remember" their initial environmental encounter.

SUBMITTER: Tu WJ

PROVIDER: S-EPMC5357947 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Priming of transcriptional memory responses via the chromatin accessibility landscape in T cells.

Tu Wen Juan WJ Hardy Kristine K Sutton Christopher R CR McCuaig Robert R Li Jasmine J Dunn Jenny J Tan Abel A Brezar Vedran V Morris Melanie M Denyer Gareth G Lee Sau Kuen SK Turner Stephen J SJ Seddiki Nabila N Smith Corey C Khanna Rajiv R Rao Sudha S

Scientific reports 20170320

Memory T cells exhibit transcriptional memory and "remember" their previous pathogenic encounter to increase transcription on re-infection. However, how this transcriptional priming response is regulated is unknown. Here we performed global FAIRE-seq profiling of chromatin accessibility in a human T cell transcriptional memory model. Primary activation induced persistent accessibility changes, and secondary activation induced secondary-specific opening of previously less accessible regions assoc ...[more]

PMID: 28317936

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Project description:Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning.We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split DNA sequences into k -mers and pre-train k -mer embedding vectors based on the co-occurrence matrix of k -mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k -mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility.The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm .tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn.Supplementary materials are available at Bioinformatics online.

Dataset Information

Priming of transcriptional memory responses via the chromatin accessibility landscape in T cells.

Publications

Priming of transcriptional memory responses via the chromatin accessibility landscape in T cells.

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