Construction And Analysis Of Tea Plant Protein Functional Interaction Network

Tea plant(Camellia sinensis)is an important commercial crop abundant in its characteristic secondary metabolites conferring tea quality and health benefits.Tea plant is rich in characteristic secondary metabolites such as theanine,polyphenols,alkaloids,vitamins and minerals,etc.The decoding of genes responsible for these characteristic components is an important basis for applied studies in genetic improvement and metabolic engineering.However,at present only a small number of genes related to tea plant metabolites have been functionally identified,and the functional analysis of a large number of other tea plant agronomic traits(cold resistance,pest resistance)is still being explored.At present,the main method of studying the function of tea plant genes(proteins)is biological experiments,and these processes are more time-consuming and labor-intensive.At this stage,the construction of biomolecular networks using large-scale omics data,such as protein physical interaction networks,genetic networks,and gene co-expression networks,and the prediction of gene or protein function from this has become an effective and important application Functional genomics tools for different species.In the research of this subject,we used the vertical translation of cross-species protein function interaction between model species and tea plant,integrated a combined pipeline controlled by strict different biological network rules,and predicted the high-quality protein function interaction network of tea(Tea Po N),and network analysis and tea plant gene function prediction application.The main research content and research results of the paper:(1)Construction of high-quality tea plant protein function interaction network.Use the prediction algorithm based on homology mapping and the protein network of model organisms to predict the functional interaction of tea plant proteins to obtain the corresponding score,and then select the threshold through the small world and scale-free characteristics of the real biological network,and then establish tea plant protein interaction network with high confidence.Finally,a high-quality tea plant protein functional interaction network containing 6,634 tea plant proteins and 31,273 non-redundant functional interactions was obtained.(2)The obtained tea plant protein functional interaction network was analyzed based on the topology characteristics of complex network theory.Calculate the degree distribution of the network,clustering coefficient,characteristic path length,topological coefficient,intermediary,etc.The results found that most of the top 20 hinge protein nodes belong to the ribosomal protein family,which indicates that ribosomal proteins play an important role in the functional interaction group of tea plant proteins.(3)The protein function module of the network was analyzed and a network interface for new gene mining was established.Using the MCODE algorithm to analyze the network,the functional modules of protein correlation are obtained,and the cluster analysis of protein functional modules with the number of nodes greater than 100 is performed.The results show that there is a strong connection between the functional modules and the three major metabolites of tea plants.The correlations obtained by further analysis have been well proved in previous studies.In summary,the predicted TeaPoN can be used as a useful data resource platform for new gene mining of characteristic secondary metabolites in tea plants.At the same time,we have developed an easy-to-use data retrieval platform(http://teapon.wchoda.com)for tea researchers in this field.When querying by inputting genes of interest,the platform will provide users with relevant module information and function-related genes,functional interaction strength,and detailed gene descriptions.