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Changing and stable chromatin accessibility supports transcriptional overhaul during neural stem cell activation and is altered with age

ABSTRACT: Abstract Neural stem cells (NSCs) in the adult and aged brain are largely quiescent, and require transcriptional reprogramming to re‐enter the cell cycle. However, the mechanisms underlying these changes and how they are altered with age remain undefined. Here, we identify the chromatin accessibility differences between primary neural stem/progenitor cells in quiescent and activated states. These distinct cellular states exhibit shared and unique chromatin profiles, both associated with gene regulation. Accessible chromatin states specific to activation or quiescence are active enhancers bound by key pro‐neurogenic and quiescence factors. In contrast, shared sites are enriched for core promoter elements associated with translation and metabolism. Unexpectedly, through integrated analysis, we find that many sites that become accessible during NSC activation are linked to gene repression and associated with pro‐quiescence factors, revealing a novel mechanism that may preserve quiescence re‐entry. Furthermore, we report that in aged NSCs, chromatin regions associated with metabolic and transcriptional functions bound by key pro‐quiescence transcription factors lose accessibility, suggesting a novel mechanism of age‐associated NSC dysfunction. Together, our findings reveal how accessible chromatin states regulate the transcriptional switch between NSC quiescence and activation, and how this switch is affected with age. Quiescent neural stem cells in the adult mammalian brain overhaul their transcriptome as they activate for proliferation and neurogenesis. We report that distinct modes of chromatin dynamics at promoters and enhancers support stem cell activation, and that key metabolic genes become less accessible with age.

SUBMITTER: Maybury‐Lewis S

PROVIDER: S-EPMC8590101 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Motivation:A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications in a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it still remains a big challenge to accurately annotate regulatory elements in the context of a specific cell type via automatic learning of the DNA sequence code from large-scale sequencing data. Indeed, the development of an accurate and interpretable model to learn the DNA sequence signature and further enable the identification of causative genetic variants has become essential in both genomic and genetic studies. Results:We proposed Deopen, a hybrid framework mainly based on a deep convolutional neural network, to automatically learn the regulatory code of DNA sequences and predict chromatin accessibility. In a series of comparison with existing methods, we show the superior performance of our model in not only the classification of accessible regions against background sequences sampled at random, but also the regression of DNase-seq signals. Besides, we further visualize the convolutional kernels and show the match of identified sequence signatures and known motifs. We finally demonstrate the sensitivity of our model in finding causative noncoding variants in the analysis of a breast cancer dataset. We expect to see wide applications of Deopen with either public or in-house chromatin accessibility data in the annotation of the human genome and the identification of non-coding variants associated with diseases. Availability and implementation:Deopen is freely available at https://github.com/kimmo1019/Deopen. Contact:ruijiang@tsinghua.edu.cn. Supplementary information:Supplementary data are available at Bioinformatics online.

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Changing and stable chromatin accessibility supports transcriptional overhaul during neural stem cell activation and is altered with age

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