摘要
Modeling a protein functional network in concerned species is an efficient approach for identifying novel genesin certain biological pathways. Tea plant (Camellia sinensis) is an important commercial crop abundant innumerous characteristic secondary metabolites (e.g., polyphenols, alkaloids, alkaloids) that confer tea qualityand health benefits. Decoding novel genes responsible for tea characteristic components is an important basisfor applied genetic improvement and metabolic engineering. Herein, a high-quality protein functional networkfor tea plant (TeaPoN) was predicted using cross-species protein functional associations transferring andintegration combined with a stringent biological network criterion control. TeaPoN contained 31,273 nonredundant functional interactions among 6,634 tea proteins (or genes), with general network topologicalproperties such as scale-free and small-world. We revealed the modular organization of genes related to themajor three tea characteristic components (theanine, caffeine, catechin) in TeaPoN, which served as strongevidence for the utility of TeaPoN in novel gene mining. Importantly, several case studies regarding geneidentification for tea characteristic components were presented. To aid in the use of TeaPoN, a concise webinterface for data deposit and novel gene screening was developed (http://teapon.wchoda.com). We believe thatTeaPoN will serve as a useful platform for functional genomics studies associated with characteristic secondarymetabolites in tea plant.