five

Identifying Novel Cell Cycle Proteins in Apicomplexa Parasites through Co-Expression Decision Analysis

收藏
NIAID Data Ecosystem2026-03-08 收录
下载链接:
https://figshare.com/articles/dataset/_Identifying_Novel_Cell_Cycle_Proteins_in_Apicomplexa_Parasites_through_Co_Expression_Decision_Analysis_/1030811
下载链接
链接失效反馈
官方服务:
资源简介:
Hypothetical proteins comprise roughly half of the predicted gene complement of Toxoplasma gondii and Plasmodium falciparum and represent the largest class of uniquely functioning proteins in these parasites. Following the idea that functional relationships can be informed by the timing of gene expression, we devised a strategy to identify the core set of apicomplexan cell division cycling genes with important roles in parasite division, which includes many uncharacterized proteins. We assembled an expanded list of orthologs from the T. gondii and P. falciparum genome sequences (2781 putative orthologs), compared their mRNA profiles during synchronous replication, and sorted the resulting set of dual cell cycle regulated orthologs (744 total) into protein pairs conserved across many eukaryotic families versus those unique to the Apicomplexa. The analysis identified more than 100 ortholog gene pairs with unknown function in T. gondii and P. falciparum that displayed co-conserved mRNA abundance, dynamics of cyclical expression and similar peak timing that spanned the complete division cycle in each parasite. The unknown cyclical mRNAs encoded a diverse set of proteins with a wide range of mass and showed a remarkable conservation in the internal organization of ordered versus disordered structural domains. A representative sample of cyclical unknown genes (16 total) was epitope tagged in T. gondii tachyzoites yielding the discovery of new protein constituents of the parasite inner membrane complex, key mitotic structures and invasion organelles. These results demonstrate the utility of using gene expression timing and dynamic profile to identify proteins with unique roles in Apicomplexa biology.
创建时间:
2014-05-19
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作