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

Abstract

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.

Figure 1

Analysis of the genome-scale metabolic network and identification of functionally important (FI) genes. The in silico knock-out simulations were conducted for all genes in the network (j = 1 to 706) under 42 expression-guided growth simulations (i = 1 to 42) to compare fluxes between the parent and knock-out networks as estimated via E-Fmin and MOMA, respectively.

Figure 2

Graphical illustration of centrality concepts using a mock network. The nodes and edges, which represent genes and their co-expression relationships, respectively, are shown as an adjacency matrix (A). The graph is disconnected and the individual subgraphs are referred to as components. As centralities are zeros for isolated genes 7 and 8 (shaded in the table on the right), they can be optionally excluded from the adjacency matrix.

Figure 3

Distribution of gene centrality: (a) degree; (b) eigenvector; (c) betweenness; (d) closeness. In each panel, genes were sorted out in a descending order of the corresponding centrality measure.

Figure 4

Principle component analysis (PCA) of estimated flux vectors in Synechococcus 7002 under 42 growth conditions. PCA identified only five different sets of conditions that lead to the difference in flux distribution. The first two principal components (PC1 and PC2) accounted for 99.4% of the variance.

Figure 5

The relationship between topological and functional importance of genes: (a) degree; (b) eigenvector; (c) betweenness; (d) closeness; and (e) overall centrality. The overall centrality combines four individual centrality values. For normalization, each centrality measure was divided by its maximum value.

Figure 6

Distribution of genes with distinct functional importance along the combined centrality measure normalized by its maximum: (a) Group 1 (blue); (b) Group 2 (green); (c) Group 3 (red). Dashed line in each panel represents top 50% threshold.

Figure 7

Classified gene groups by their functional and topological importance. 706 genes are divided into FI (i.e., Group 2 and Group 3) and unimportant genes (Group 1). Each group contains topologically important (TI) genes and isolated genes. Three sets of particular interest include TI but not FI (Tf), FI but not TI (Ft), and intersection of TI and FI (TF) genes as highlighted in bold. The numbers in parentheses represent the number of genes belonging to each set.