A predicted protein functional network aids in novel gene mining for characteristic secondary metabolites in tea plant (Camellia sinensis).

ABSTRACT

Modeling a protein functional network in concerned species is an efficient approach for identifying novel genes in certain biological pathways. Tea plant (Camellia sinensis) is an important commercial crop abundant in numerous characteristic secondary metabolites (e.g., polyphenols, alkaloids, alkaloids) that confer tea quality and health benefits. Decoding novel genes responsible for tea characteristic components is an important basis for applied genetic improvement and metabolic engineering. Herein, a high-quality protein functional network for tea plant (TeaPoN) was predicted using cross-species protein functional associations transferring and integration 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 topological properties such as scale-free and small-world. We revealed the modular organization of genes related to the major three tea characteristic components (theanine, caffeine, catechin) in TeaPoN, which served as strong evidence for the utility of TeaPoN in novel gene mining. Importantly, several case studies regarding gene identification for tea characteristic components were presented. To aid in the use of TeaPoN, a concise web interface for data deposit and novel gene screening was developed (http//teapon.wchoda.com). We believe that TeaPoN will serve as a useful platform for functional genomics studies associated with characteristic secondary metabolites in tea plant.