Construction and analysis of an interologous protein-protein interaction network of Camellia sinensis leaf (TeaLIPIN) from RNA-Seq data sets.

Abstract

An interologous PPI network of tea leaf is designed by developing reference transcriptome assembly and using experimentally validated PPIs in plants. Key regulatory proteins are proposed and potential TFs are predicted. Worldwide, tea (Camellia sinensis) is the most consumed beverage primarily due to the taste, flavour, and aroma of its newly formed leaves; and has been used as an important ingredient in several traditional medicinal systems because of its antioxidant properties. For this medicinally and commercially important plant, design principles of gene-regulatory and protein-protein interaction (PPI) networks at sub-cellular level are largely un-characterized. In this work, we report a tea leaf interologous PPI network (TeaLIPIN) consisting of 11,208 nodes and 197,820 interactions. A reference transcriptome assembly was first developed from all the 44 samples of 6 publicly available leaf transcriptomes (1,567,288,290 raw reads). By inferring the high-confidence interactions among potential proteins coded by these transcripts using known experimental information about PPIs in 14 plants, an interologous PPI network was constructed and its modular architecture was explored. Comparing this network with 10,000 realizations of two types of corresponding random networks (Erdős-Rényi and Barabási-Albert models) and examining over three network centrality metrics, we predict 2750 bottleneck proteins (having p values < 0.01). 247 of these are deduced to have transcription factor domains by in-house developed HMM models of known plant TFs and these were also mapped to the draft tea genome for searching their probable loci of origin. Co-expression analysis of the TeaLIPIN proteins was also performed and top ranking modules are elaborated. We believe that the proposed novel methodology can easily be adopted to develop and explore the PPI interactomes in other plant species by making use of the available transcriptomic data.