Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

Abstract

Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein–protein interaction network. Proteins with statistically higher topological overlap in the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.

Systematic and functional identification of small non-coding RNAs associated with exogenous biofuel stress in cyanobacterium Synechocystis sp. PCC 6803

“…According to our previously weighted gene co-expression network analysis (WGCNA) with the Synechocystis proteomic data [ 48 ], photosynthesis antenna proteins, porphyrin and chlorophyll metabolism and photosynthesis were identified as the top three significant biofuel-specific responsive pathways after cells were treated with exogenous biofuels [ 48 ]. Therefore, considering the association of responsive sRNA with these three pathways (Fig.…”

“…The genetic locations of nc33 , nc65 , and nc85 sRNA genes are provided in Table 1 . Interestingly, target enrichment analysis showed that Nc65 and Nc85 were enriched in porphyrin and chlorophyll metabolism and Nc33 in photosynthesis; both metabolic pathways were identified as biofuel-specific responsive pathways in our previous study [ 48 ]. This was consistent with a previous study that showed that proteins related to photosynthesis and chlorophyll concentration were up-regulated upon ethanol exposure in Synechocystis cells [ 23 ], indicating that the responses of sRNAs and protein to biofuels could point to similar mechanisms.…”

Four New Genes of Cyanobacterium Synechococcus elongatus PCC 7942 Are Responsible for Sensitivity to 2-Nonanone

“…The obtained results allow us to set new tasks for the upcoming research of the molecular mechanisms of action of ketones on bacterial cells. [47,48]).…”

“… Modes of 2-nonanone action on cyanobacteria cell, (some figure elements are adapted from [ 47 , 48 ]). …”

A global network-based protocol for functional inference of hypothetical proteins in Synechocystis sp. PCC 6803

“…The procedure to construct co-expression network includes:i) data normalization was performed with the raw proteomic or transcriptomic data converted into a ratio of condition versus its control, and then log2 transformed; ii) correlation values were calculated for all possible pairs, in which correlation was defined as the Pearson correlation coefficient for all pairwise genes/proteins; iii) normalized the correlation coefficient using a Min-Max linear normalization algorithm developed previously (Guo et al, 2012), and then the co-expression network was constructed in which the weight of edge represents Pearson correlation coefficients, and the node of network represents genes/proteins (Pei et al, 2014); iv) a correlation coefficient cutoff was applied to the coexpression network, where only gene/protein pairs with a correlation coefficient higher than the cutoff were considered connected. As the biological networks behave like a scale-free network (Tsoi et al, 2014), the distribution of connections follows power-law relationship.…”