Investigating Protein Interaction Network And The Function Of Electron Transfer For Electricigens

Electricigens refer to microbes capable of extracellular electron transfer(EET) and electricity production. On the basis of EET processes, electricigens can transfer electron to artificial electrode and constitute a Microbial Fuel Cell(MFC). MFC has great potential in many applications, such as renewable energy, wastewater treatment, purification and so on. Currently, the efficiency of MFC is too low to be used in application for large-scale. Understanding EET mechanism is the key step to improve the efficiency of MFC. Numerous studies have suggested that biological networks can be used to efficiently predict the biological pathways, and thus it might be used to study the EET pathways. In this paper, we presented a genome-wide electron transfer related protein interaction network for Shewanella oneidensis MR-1, an important electricigen, to investigate the EET pathways. The main results are listed as follows:1. A genome-wide electron transfer related protein interaction network of S. oneidensis MR-1 was constructed. By means of relational databases, after integrating genome annotation, protein interaction and literature information, a genome-scale electron transfer related protein interaction network for S. oneidensis MR-1 was eventually built, which consists of 682 proteins and 5,854 interactions.2. We found the key EET proteins by analyzing the the network structure. Topology analysis showed that the network is scale-free and small-world. Additionally, several proteins were found to be the key nodes of the network by centrality analysis, most of them are involved in electron transport and few participate in EET. Using the k-shell decomposition method, we found that electron transfer related protein interaction network of S. oneidensis MR-1 has different functional parts to achieve EET.3. We predicted and reconstructed EET pathways. With modularity analysis, the network can be divided into several modules of which one module includes two classic EET pathways. We identified three potential EET pathways from this module according to protein subcellular localization, fold structure, gene expression data and other resource data. Finally, we reconstructed EET pathways by combining these three potential EET pathways with three known EET pathways.In summary, we use bioinformatics methods to systematically study EET pathways of S. oneidensis MR-1, from the levels of the gene sequence, gene expression, protein structure and protein interaction network. The results showed that our methods showed great potential in explaining the EET mechanism and may be helpful for increasing the current output of MFC.