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恒河猴基因组中增强子的机器学习识别

Machine learning identification of enhancers in the rhesus macaque genome.

作者信息

He Jing, Phan BaDoi N, Kerkhoff Willa G, Alikaya Aydin, Hong Tao, Brull Olivia R, Fredericks J Megan, Sedorovitz Morgan, Srinivasan Chaitanya, Leone Michael J, Wirfel Olivia M, Brown Ashley, Dauby Samuel, Tittle Rachel K, Lin Meng K, Hooks Bryan M, Bostan Andreea C, Gharbawie Omar A, Byrne Leah C, Pfenning Andreas R, Stauffer William R

机构信息

Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; University of Pittsburgh-Carnegie Mellon University Medical Scientist Training Program, Pittsburgh, PA 15213, USA.

出版信息

Neuron. 2025 May 21;113(10):1548-1561.e8. doi: 10.1016/j.neuron.2025.04.030.

DOI:10.1016/j.neuron.2025.04.030
PMID:40403706
Abstract

Nonhuman primate (NHP) neuroanatomy and cognitive complexity make NHPs ideal models to study human neurobiology and disease. However, NHP circuit-function investigations are limited by the availability of molecular reagents that are effective in NHPs. This calls for reagent development approaches that prioritize NHPs. Therefore, we derived enhancers from the NHP genome. We defined cell-type-specific open chromatin regions (OCRs) in single-cell data from rhesus macaques. We trained machine-learning models to rank those OCRs according to their potential as cell-type-specific enhancers for cells in the dorsolateral prefrontal cortex (DLPFC). We packaged the top-ranked layer-3-pyramidal-neuron enhancer into AAV and injected it into the macaque DLPFC. Expression was mostly restricted to layers 2 and 3 and confirmed with light-driven activation of channelrhodopsin. These results provide a crucial tool for studying the causal functions of DLPFC and provide a roadmap for optimized gene delivery in primates.

摘要

非人灵长类动物(NHP)的神经解剖结构和认知复杂性使其成为研究人类神经生物学和疾病的理想模型。然而,NHP的电路功能研究受到在NHP中有效的分子试剂可用性的限制。这就需要优先考虑NHP的试剂开发方法。因此,我们从NHP基因组中获得了增强子。我们在恒河猴的单细胞数据中定义了细胞类型特异性开放染色质区域(OCR)。我们训练机器学习模型,根据这些OCR作为背外侧前额叶皮层(DLPFC)中细胞的细胞类型特异性增强子的潜力对其进行排名。我们将排名最高的第3层锥体神经元增强子包装到腺相关病毒(AAV)中,并将其注入猕猴的DLPFC。表达主要局限于第2层和第3层,并通过光驱动的视紫红质激活得到证实。这些结果为研究DLPFC的因果功能提供了关键工具,并为灵长类动物中优化的基因传递提供了路线图。

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