Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1

Abstract

Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c-type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.

Keywords: active protein; extracellular electron transfer; network-based methods; protein–protein interaction; transcriptional regulatory interaction.

Figure 1

The protein expression patterns identified by MFuzz. Membership values are color-encoded from red (high values) to green (low values), S1–S6 represent six groups of sequential samples under different O₂ levels (S1–S3 for high-O₂ conditions, and S4–S6 for low-O₂ conditions).

Figure 2

KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment for the active proteins involved in activating the extracellular electron transfer (EET) process. False discovery rate < 0.05.

Figure 3

The proteins involved in network motifs 5, 6, and 8. (A) The protein domain enrichment, false discovery rate < 0.05. (B) The EET proteins and signal proteins (%) in network motifs 5, 6, and 8 vs. those in the whole genome of Shewanella oneidensis MR-1.

Figure 4

The functional modules in the proteins involved in network motifs 5, 6, and 8. (A) The protein interaction network generated by the R package igraph [23]; green nodes refer to the down-regulated proteins and red nodes refer to the up-regulated proteins. (B) The functional modules in the largest connected part of the protein interaction network; drawn by the Pajek program [61], modules are shown in distinct colors. (C) Module 8: multiheme cytochromes, and (D) Module 6: multisignal processing, which are generated by using the default parameters for the k-means cluster of these two modules in STRING (Search Tool for Recurring Instances of Neighbouring Genes) online tool [20,21], clusters are shown in distinct colors.