獣医学部研究者が遺伝子機能研究のためのバーチャルツールを開発(Vet School Researchers Develop Virtual Tool For Studying Gene Function)

2024-03-13

2024-03-12 テキサス A&M大学

テキサスA&M大学獣医医学・生命科学スクールの研究者たちは、新しいバーチャルツール「Gene Knockout Inference(GenKI)」を開発しました。このツールは、科学者が遺伝子の機能を効率的に研究し、遺伝子研究で使用される動物モデルの数を削減することを目指しています。GenKIは、個々の細胞内での遺伝子の関係をシミュレートし、細胞機能に影響を与える遺伝子を研究できるようにします。このツールはマウスモデルを使わずに遺伝子を研究でき、遺伝子同士の相互作用をより簡単に見ることができます。

<関連情報>

単一細胞の遺伝子制御ネットワークを学習する変分グラフオートエンコーダーによる遺伝子ノックアウト推論 Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

Yongjian Yang, Guanxun Li, Yan Zhong, Qian Xu, Bo-Jia Chen, Yu-Te Lin, Robert S Chapkin, James J Cai
Nucleic Acids Research Published:29 May 2023
DOI:https://doi.org/10.1093/nar/gkad450

Abstract

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene—the gene to be knocked out for functional study—from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.