College of Animal Science and Technology, School of Information and Computer, Anhui Agricultural University, Hefei, 230036, Anhui Province, China.
Parasit Vectors. 2023 Mar 14;16(1):98. doi: 10.1186/s13071-023-05698-0.
Apicomplexa consist of numerous pathogenic parasitic protistan genera that invade host cells and reside and replicate within the parasitophorous vacuole (PV). Through this interface, the parasite exchanges nutrients and affects transport and immune modulation. During the intracellular life-cycle, the specialized secretory organelles of the parasite secrete an array of proteins, among which dense granule proteins (GRAs) play a major role in the modification of the PV. Despite this important role of GRAs, a large number of potential GRAs remain unidentified in Apicomplexa.
A multi-view attention graph convolutional network (MVA-GCN) prediction model with multiple features was constructed using a combination of machine learning and genomic datasets, and the prediction was performed on selected Neospora caninum protein data. The candidate GRAs were verified by a CRISPR/Cas9 gene editing system, and the complete NcGRA64(a,b) gene knockout strain was constructed and the phenotypes of the mutant were analyzed.
The MVA-GCN prediction model was used to screen N. caninum candidate GRAs, and two novel GRAs (NcGRA64a and NcGRA64b) were verified by gene endogenous tagging. Knockout of complete genes of NcGRA64(a,b) in N. caninum did not affect the parasite's growth and replication in vitro and virulence in vivo.
Our study showcases the utility of the MVA-GCN deep learning model for mining Apicomplexa GRAs in genomic datasets, and the prediction model also has certain potential in mining other functional proteins of apicomplexan parasites.
顶复门生物由许多入侵宿主细胞并存在于吞噬小泡（PV）内并复制的致病性寄生原生动物属组成。通过这个界面，寄生虫可以交换营养物质并影响运输和免疫调节。在细胞内生命周期中，寄生虫的特殊分泌细胞器分泌一系列蛋白质，其中致密颗粒蛋白（GRAs）在 PV 的修饰中起主要作用。尽管 GRA 具有重要作用，但顶复门生物中的大量潜在 GRA 仍未被识别。
使用机器学习和基因组数据集的组合构建了具有多种特征的多视图注意力图卷积网络（MVA-GCN）预测模型，并对选定的刚地弓形虫蛋白数据进行了预测。候选 GRA 由 CRISPR/Cas9 基因编辑系统验证，并构建了完整的 NcGRA64(a,b)基因敲除株系，分析了突变体的表型。
使用 MVA-GCN 预测模型筛选刚地弓形虫候选 GRA，并用基因内源标记验证了两个新的 GRA（NcGRA64a 和 NcGRA64b）。刚地弓形虫 NcGRA64(a,b)的完整基因敲除不影响寄生虫在体外的生长和复制以及体内的毒力。
我们的研究展示了 MVA-GCN 深度学习模型在挖掘基因组数据集中顶复门生物 GRA 方面的实用性，该预测模型在挖掘其他顶复门寄生虫的功能蛋白方面也具有一定的潜力。
