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ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii reveals novel virulence factors implicated in host cell invasion
bioRxiv - Microbiology Pub Date : 2021-09-15 , DOI: 10.1101/2021.09.14.460186
Lakshmipuram S. Swapna , Grant C. Stevens , Aline Sardinha da Silva , Lucas Zhongming Hu , Verena Brand , Daniel D. Fusca , Xuejian Xiong , Jon P. Boyle , Michael E. Grigg , Andrew Emili , John Parkinson

The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen with significant impact in children and during pregnancy. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we utilize a biochemical fractionation strategy coupled with mass spectrometry to predict interactions by correlation profiling. Key to this approach is the integration of additional datasets based on gene co-expression as well as phylogenetic profiles that eliminate poorly supported interactions and reduce the number of false positive interactions. In addition to a supervised machine learning strategy, we employed an unsupervised approach in data integration, based on similarity network fusion, to overcome the deficit of high-quality training data in non-model organisms. The resulting high confidence network, we term ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering of this network identifies 93 protein complexes, predicting both novel complexes as well as new components for previously known complexes. In particular, we identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community.

中文翻译:

ToxoNet:弓形虫中蛋白质-蛋白质相互作用的高可信度图揭示了与宿主细胞侵袭有关的新型毒力因子

顶复体细胞内寄生虫弓形虫是一种主要的食源性病原体,对儿童和怀孕期间有重大影响。大多数弓形虫蛋白质组仍未表征,蛋白质复合物的组织尚不清楚。为了克服这一知识差距,我们利用生化分级策略与质谱分析相结合,通过相关分析来预测相互作用。这种方法的关键是整合基于基因共表达和系统发育谱的额外数据集,这些数据集消除了支持不足的相互作用并减少了假阳性相互作用的数量。除了有监督的机器学习策略外,我们在数据集成中采用了基于相似性网络融合的无监督方法,以克服非模式生物中高质量训练数据的不足。由此产生的高置信度网络,我们称之为 ToxoNet,包含连接 652 个蛋白质的 2,063 个相互作用。该网络的聚类识别了 93 个蛋白质复合物,预测了新复合物以及先前已知复合物的新成分。特别是,我们确定了富含线粒体机制的簇,其中包括以前未表征的蛋白质,这些蛋白质可能代表对氧化磷酸化的新适应。此外,富含定位于分泌细胞器和内膜复合物的蛋白质的复合物,预测了代表新目标的其他新成分,以进行详细的功能表征。我们将 ToxoNet 作为公开可用的资源提供,期望它有助于推动研究界的未来假设。我们确定了富含线粒体机制的簇,其中包括以前未表征的蛋白质,这些蛋白质可能代表对氧化磷酸化的新适应。此外,富含定位于分泌细胞器和内膜复合物的蛋白质的复合物,预测了代表新目标的其他新成分,以进行详细的功能表征。我们将 ToxoNet 作为公开可用的资源提供,期望它有助于推动研究界的未来假设。我们确定了富含线粒体机制的簇,其中包括以前未表征的蛋白质,这些蛋白质可能代表对氧化磷酸化的新适应。此外,富含定位于分泌细胞器和内膜复合物的蛋白质的复合物,预测了代表新目标的其他新成分,以进行详细的功能表征。我们将 ToxoNet 作为公开可用的资源提供,期望它有助于推动研究界的未来假设。预测代表新目标的其他新组件,以进行详细的功能表征。我们将 ToxoNet 作为公开可用的资源提供,期望它有助于推动研究界的未来假设。预测代表新目标的其他新组件,以进行详细的功能表征。我们将 ToxoNet 作为公开可用的资源提供,期望它有助于推动研究界的未来假设。
更新日期:2021-09-16
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