Fig 1.
(A) Entity relationship model of the updated version of metabolic MANET linking ancestries, SCOP, PDBsum and KEGG. (B) Screenshot of a representative subnetwork diagram describing the ‘Pyrimidine metabolism’ subnetwork of MANET. A color scale is used to assign binned ancestry values to enzyme nodes named with EC numbers.
Table 1.
MANET 3.0 database statistics.
Fig 2.
(A) The enzymatic activities (E) of the metabolic network can be dissected into a hierarchical system of subnetworks (S) and mesonetworks (M), which act as modules of metabolic activity. (B) A bipartite network describing the relationship between mesonetworks and enzymes can be dissected into its two one-mode projections, one describing how enzymes link mesonetworks to each other, the other describing how mesonetworks link enzymes to each other. (C) A bipartite network of subnetworks and enzymes can be dissected into its two one-mode projections, one describing how enzymes link subnetworks to each other, the other describing how subnetworks link enzymes to each other. (D) A bipartite network of mesonetworks and subnetworks can be dissected into its two one-mode projections, one describing how subnetworks link mesonetworks to each other, the other describing how mesonetworks link subnetworks to each other.
Fig 3.
Evolution of the mesonetwork-enzyme bipartite network.
(A) Tracing enzyme ages on the bipartite networks, facilitates studying patterns of sharing and show the evolution of networks in time (B) A bipartite graph of mesonetworks and enzymes (nd = 1.0) showing enzymes by nd distribution on a scale of red to violet representing ancestral to recent fold family domain assignments. Mesonetworks are shown as vertices in black while colored nodes denote enzymes.
Fig 4.
Run chart of enzymes in mesonetworks appearing in each nd era.
Eras are defined as nd bins of ages; the first nd bin includes enzymes appearing between nd = 0 and nd = 0.1. The inset describes the distribution of enzymes along the evolutionary timeline.
Fig 5.
Connectivity patterns among mesonetworks at different stages of the evolutionary timeline. Mesonetworks are represented by vertices while edge thickness shows the number of enzymes shared.
AAC, Amino acid metabolism; SEC, Biosynthesis of other secondary metabolites; CAR, Carbohydrate metabolism; NRG, Energy metabolism; GLY, Glycan biosynthesis and metabolism; LIP, Lipid metabolism; COF, Metabolism of cofactors and vitamins; POL, Metabolism of terpenoids and polyketides; NUC, Nucleotide metabolism; AA2, Metabolism of other amino acids; XEN, Xenobiotics biodegradation and metabolism.
Fig 6.
Average node degrees (average number of links), diameter and maximum modularity scores for each type of network (largest connected component) at each time point (0.1 nd interval).
Network sizes (total number of nodes and nodes in the largest connected component) are given in S3 Fig.
Fig 7.
Log-log plot of C(k) vs k for the one-mode enzyme (A) and subnetwork (B) projections at nd value intervals of 0.1.
Table 2.
Parameters for the power law fitting function in R for the bipartite network corresponding to the plots in S2 Fig at different nd values.
alpha: exponent for the fitted power law distribution, xmin: lower bound for the power law fitting, logLik: log-likelihood of fitted parameters, KS.stat: test statistic for the Kolmogorov-Smirnov test between fitted and sample distribution and KS.p: p-value for the Kolmogorov-Smirnov test between fitted and sample distribution. The null hypothesis is that the original data has been drawn from a fitted power-law distribution. p-values less than 0.05 imply that the null hypothesis is rejected).
Table 3.
Results of the Bartels’ test for randomness performed on each type of network at each time-point (0.1 nd interval) as well as for an equivalent Erdős–Rényi (ER) random graph.
The null hypothesis is that the underlying data has been drawn from a random distribution. p-values less than 0.05 indicate that the null hypothesis is rejected.
Fig 8.
Testing for small-world behavior in the subnetwork and enzyme one-mode networks.
(A) Comparison of clustering coefficient and average path length of the subnetwork one-mode network to that of an Erdős–Rényi (ER) network. The small-world coefficients decrease with the passage of time. (B) Comparison of clustering coefficient and average path length of the enzyme one-mode network to that of an Erdős–Rényi (ER) network. The resulting small-world coefficient increase along the evolutionary timeline.
Fig 9.
Matrix representation of subnetwork one-mode graphs by evolutionary age.
Rows represent nodes (subnetworks) with each cell indicating the number of enzymes (edges) per subnetwork in each nd interval.
Fig 10.
Evolution of metabolic networks visualized through the subnetwork one-mode projection of the subnetwork-enzyme bipartite network.
A reduced representation of the extant subnetwork one-mode projection (nd = 1.0) is shown in the middle. The reduced network projection shows major nodes (subnetworks) connecting to each other through links (shared enzymes). Greyscale values of links indicate the number of enzymes shared among the subnetworks. A full description of KEGG subnetwork labels can be found in S2 Table and S3 Table. The circle of networks describes a timeline of network growth for the subnetwork projection.
Fig 11.
Dendrogram of the subnetwork one-mode network (at nd = 1.0) resulting from hierarchical clustering.
Fig 12.
A “tapestry” of enzyme recruitment.
A heatmap based on the modularity matrix was coupled to the dendrogram obtained from hierarchical clustering of the metabolic subnetworks one-mode network (shown in Fig 11).
Fig 13.
Enzyme distribution by superkingdom at EC level 1 (N = 1924 enzymes).
(A) Enzyme distribution by superkingdom. (B). Enzymatic functions mapped along the evolutionary timeline. (C) EC level 1 breakdown by superkingdom. A, Archaea; B, Bacteria; E, Eukaryota; V, Viruses.
Fig 14.
Functional distribution of enzymes.
(A) Superkingdom makeup Distribution of each general functional category in superkingdoms and viruses. (B) Distribution of detailed functional categories along the evolutionary timeline.
Fig 15.
Survey of catalytic sites in all 543 enzymes of the M-CSA database that were mapped to a domain with an nd value.
(A) Distribution of role groups of the catalytic site residues in Venn taxonomic groups of superkingdoms and viruses. (B) Distribution of catalytic residues according to when enzymes possessing these residues appeared along the evolutionary timeline. (C) Distribution of catalytic residues based on association of parent enzymes to the superkingdoms. Highlighted background indicates the group to which the amino acids belong to: purple, basic amino acids; pink, acidic amino acids; green, polar uncharged amino acids; yellow, nonpolar amino acids.
Table 4.
Network centrality metrics for the enzyme one-mode network extracted from the subnetwork-enzyme bipartite network.
Table 5.
Enzymes with high network centrality metrics in the enzyme one-mode network that were extracted and their corresponding SCOP concise classification strings (ccs).