Suppr超能文献

脑基因表达与网络推断引擎(BrainGENIE)

BrainGENIE: The Brain Gene Expression and Network Imputation Engine.

机构信息

Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.

Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia.

出版信息

Transl Psychiatry. 2023 Mar 22;13(1):98. doi: 10.1038/s41398-023-02390-w.

Abstract

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

摘要

在体实验分析人类脑组织存在很大的挑战和伦理问题。为了解决这个问题,我们开发了一种名为 Brain Gene Expression and Network-Imputation Engine(BrainGENIE)的计算方法,该方法利用外周血转录组来预测脑组织特异性基因表达水平。利用 Genotype-Tissue Expression(GTEx)项目收集的配对血脑转录组数据,通过全血基因表达谱对 BrainGENIE 模型进行训练,以预测十个不同脑区的基因表达水平。将 BrainGENIE 的性能与 PrediXcan 进行了比较,PrediXcan 是一种从基因型推断基因表达水平的常用方法。BrainGENIE 显著预测了 2947-11816 个基因(经错误发现率校正的 p < 0.05)的脑组织特异性表达水平,包括许多无法被 PrediXcan 等转录组推断方法显著预测的转录本。BrainGENIE 比直接从血液得出的相关性和 PrediXcan 的预测更能重现自闭症、双相情感障碍和精神分裂症中与诊断相关的基因表达变化。我们开发了一个方便的 BrainGENIE 部署软件工具集,并提供了如何最好地实施模型的建议。BrainGENIE 补充并在某些方面优于现有的转录组推断工具,提供了有生物学意义的预测,并开辟了新的研究途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a14/10033657/076968886fcc/41398_2023_2390_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验