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Genome-wide identification and characterization of functional neuronal activity-dependent enhancers.

ABSTRACT: Experience-dependent gene transcription is required for nervous system development and function. However, the DNA regulatory elements that control this program of gene expression are not well defined. Here we characterize the enhancers that function across the genome to mediate activity-dependent transcription in mouse cortical neurons. We find that the subset of enhancers enriched for monomethylation of histone H3 Lys4 (H3K4me1) and binding of the transcriptional coactivator CREBBP (also called CBP) that shows increased acetylation of histone H3 Lys27 (H3K27ac) after membrane depolarization of cortical neurons functions to regulate activity-dependent transcription. A subset of these enhancers appears to require binding of FOS, which was previously thought to bind primarily to promoters. These findings suggest that FOS functions at enhancers to control activity-dependent gene programs that are critical for nervous system function and provide a resource of functional cis-regulatory elements that may give insight into the genetic variants that contribute to brain development and disease.

SUBMITTER: Malik AN

PROVIDER: S-EPMC4297619 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Genome-wide identification and characterization of functional neuronal activity-dependent enhancers.

Malik Athar N AN Vierbuchen Thomas T Hemberg Martin M Rubin Alex A AA Ling Emi E Couch Cameron H CH Stroud Hume H Spiegel Ivo I Farh Kyle Kai-How KK Harmin David A DA Greenberg Michael E ME

Nature neuroscience 20140907 10

Experience-dependent gene transcription is required for nervous system development and function. However, the DNA regulatory elements that control this program of gene expression are not well defined. Here we characterize the enhancers that function across the genome to mediate activity-dependent transcription in mouse cortical neurons. We find that the subset of enhancers enriched for monomethylation of histone H3 Lys4 (H3K4me1) and binding of the transcriptional coactivator CREBBP (also called ...[more]

PMID: 25195102

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Project description:Enhancers are short non-coding DNA sequences outside of the target promoter regions that can be bound by specific proteins to increase a gene's transcriptional activity, which has a crucial role in the spatiotemporal and quantitative regulation of gene expression. However, enhancers do not have a specific sequence motifs or structures, and their scattered distribution in the genome makes the identification of enhancers from human cell lines particularly challenging. Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. Specifically, to characterize the hierarchical relationships of enhancer sequences, multi-source biological information and dynamic semantic information are fused to represent regulatory DNA enhancer sequences. Then, we implement a deep learning-based sequence network to learn the feature representation of the enhancer sequences comprehensively and to extract the implicit relationships in the dynamic semantic information. Ultimately, an ensemble machine learning classifier is trained based on the refined multi-source features and dynamic implicit relations obtained from the deep learning-based sequence network. Benchmarking experiments demonstrated that SMFM significantly outperforms other existing methods using several evaluation metrics. In addition, an independent test set was used to validate the generalization performance of SMFM by comparing it to other state-of-the-art enhancer identification methods. Moreover, we performed motif analysis based on the contribution scores of different bases of enhancer sequences to the final identification results. Besides, we conducted interpretability analysis of the identified enhancer sequences based on attention weights of EnhancerBERT, a fine-tuned BERT model that provides new insights into exploring the gene semantic information likely to underlie the discovered enhancers in an interpretable manner. Finally, in a human placenta study with 4,562 active distal gene regulatory enhancers, SMFM successfully exposed tissue-related placental development and the differential mechanism, demonstrating the generalizability and stability of our proposed framework.

Dataset Information

Genome-wide identification and characterization of functional neuronal activity-dependent enhancers.

Publications

Genome-wide identification and characterization of functional neuronal activity-dependent enhancers.

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