Metabolite correlation networks
In general, correlations between metabolites can be used to assist in
unravelling the biological basis of variation caused either by different
environments or genetic backgrounds (Ursem et al. 2008). In order
to understand the correlation between metabolite contents within the RIL
sub-populations and how their interaction is influenced by the
nutritional maternal environment, pairwise Spearman correlation analysis
was performed between the metabolites. For each environmental condition,
correlation analysis of all 118 detected metabolites has been performed
and a correlation heatmap was generated (Figure S2, Table S5 ).
The results showed that most of the unknown metabolites are highly
correlated with annotated metabolites such as amino acids and organic
acids including TCA cycle intermediates. Only known metabolites that
showed significant correlations (FDR≤0.05) were selected for
constructing correlation networks (Figure 5, Table 2 ). By using
the network approach, the correlation between metabolites within each
sub-population as a result of similar genetic regulation can be
visualised, while different metabolic patterns in between the different
maternal environments could provide more insight into the influence of
environment and G×E on
regulation
of metabolites. Correlation networks have often been used in
metabolomics studies (Morgenthal, Weckwerth & Steuer 2006; Steueret al. 2003) to provide additional information to multivariate
approaches which have been described previously (Graffelman & van
Eeuwijk 2005). In our study, the correlation network for the HP maternal
environment contains in total 395 significant correlations (edges)
between 56 metabolites (nodes). The HP condition resulted in a network
with higher density (0.256) as compared to LN, which had in total 238
edges and 51 nodes (Table 2 ). In general, the network related
to the HP environment showed higher levels of some attributes such as
range of node degree, number of nodes and edges, network density and
average number of neighbours by possessing more metabolite connections
and correlations (Table 2 ). This higher connectivity in the
network could be related to the overall higher metabolic levels under
this specific condition. In our study dry seed metabolites were
connected more under the HP condition, in comparison with LN, which
indicates that the regulatory mechanisms under HP conditions induce
several changes in metabolism. These metabolic changes could assist
plants to cope with sub-optimal growing conditions and may result in
acclimation of the plant (Hochberg et al. 2013).
The most highly connected metabolites in each condition can be found inTable S6 . Under LN, mainly amino acids are highly correlated
with each other and thus could be predominantly involved in metabolic
changes due to LN conditions (Figure 5A ). However, under HP
maternal condition, in addition to the amino acids such as alanine,
glycine, serine and threonine, some of the TCA cycle intermediates
including malate, fumarate and succinate are also highly connected
(Figure 5B ). In both environments we observed strong
correlation between metabolites within the same category such as amino
acids. Such a consistent correlation observed in both environments
suggested that these metabolites are mainly under genetic control and
not much influenced by the environment or G×E interactions. In our
results under HP conditions glycine showed a strong correlation with
malate (one of the TCA cycle intermediates, R = 0.6, FDR = 0.00021)
while we could not find it back in the LN network. Such different
network topologies indicate a strong environmental effect on the
correlation between these metabolites. These examples show that the
correlation networks and the differences amongst them may provide
imperative information to understand the molecular basis of metabolic
changes (Schauer et al. 2006).