Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

Life

Hyun-Seob Song¹  Ryan S. McClure²  Hans C. Bernstein²  Christopher C. Overall²  Eric A. Hill²  Alexander S. Beliaev¹  John C. Meeks² 

[1] Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA;

关键词: cyanobacteria;    Synechococcus 7002;    gene co-expression network;    genome-scale metabolic network;    node centrality;    gene knock-out simulation;    randomization test;   

DOI  :  10.3390/life5021127

来源: mdpi

【 摘 要 】

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.