Correlation between metabolites and seed phenotypic
traits
In order to assess the relationship between metabolites and seed
phenotypic traits, seed performance phenotypes which were previously
assessed for the same seeds (Geshnizjani et al. 2020), were
integrated into the metabolic correlation matrix (Figure 7 ). We
found many positive and negative correlations between metabolites and
phenotypic traits. The results revealed that seed size and weight are
positively correlated with most of the amino acids and TCA cycle
intermediates such as succinate, citrate and malate (Figure 7,
Table S8 ). The strongest positive correlation was found between seed
size and amino acids including pyroglutamate, leucine and isoleucine
(R≥0.4, p -value<0.0001). Among all the seed germination
traits maximum germination percentage (Gmax) showed the
highest number of significant correlations with metabolites of which
most are negative. Gmax under osmotic stress (mannitol
and NaCl) has a significant positive correlation with 2-ketoglutarate
which is one of the TCA cycle intermediates, involved in supplying the
required energy for seed germination (Table S8 ).
Gmax under optimal and sub-optimal germination
environments showed strong negative correlation with many of the amino
acids (such as pyroglutamate, GABA, methionine and leucine), organic
acids (glycerate and malonate) and TCA cycle intermediates (malate and
succinate).
Amino acids, are the precursors of protein synthesis and also precursors
of some TCA cycle intermediates (e.g. citrate and succinate), serve as
energy generation units for embryo growth as well as radicle protrusion
(Lehmann & Ratajczak 2008; Ratajczak et al. 1996; Rosental,
Nonogaki & Fait 2014). Since energy and proteins are two elements
supporting germination, such a negative correlation between them and
germination of tomato seeds is not expected. However, our results are in
accordance with several foregoing studies which reported that
accumulation of amino acids, such as methionine, lysine and GABA, may
cause inhibition of seed germination (Amir 2010; Angelovici et
al. 2011). In some other reports, amino acids were considered as one of
the biological methods to control weeds since the external application
of many amino acids decreased the seed germination percentage for some
species such as broomrape (Vurro et al. 2006; Wilson & Bell
1978). Such a negative effect of amino acids on seed germination could
be related to accumulation of certain amino acids in the seeds and
subsequent reduction of some other metabolites such as TCA cycle
intermediates which may play vital roles in seed germination (Angeloviciet al. 2011; Rosental et al. 2016). For example, the
biosynthetic pathway of lysine uses pyruvate which is the central
component of the TCA cycle. Depletion of pyruvate from the TCA cycle
will ultimately result in a decrease in the production of TCA cycle
intermediates. Hence, such a decrease in TCA cycle input results in
declined levels of available energy, which in turn negatively affects
seed germination (Angelovici et al. 2011; Day et al. 1994;
Shedlarski & Gilvarg 1970). A strong negative correlation was found
between Gmax in water and methionine content of the
seeds (R=0.42, p -value<0.001). Similar results have
been found in different species such as lettuce (Wilson & Bell 1978)
and tomato (Rosental et al. 2016). Feedback inhibition of
increased methionine on the upstream enzymes activity such as
cystathionine γ-synthase (CGS) has been reported before (Chiba et
al. 2003; Rosental et al. 2016). Hence high methionine content
of seeds may limit the synthesis of sulfur-rich proteins which
subsequently results in the reduction of seed germination (Amir 2010).
However, our findings seem in contrast with a few other studies in which
a high level of methionine did not lead to a decrease of germination
which indicated that methionine was not negatively correlated with
germination (Amir, Han & Ma 2012; Gallardo et al. 2002).
We also performed a correlation analysis between metabolites and seed
phenotypic traits within each tomato RIL sub-population and two
correlation heatmaps were generated (Figure S3 ). In general,
substantial differences were not observed between the two maternal
environments (HP and LN); however, correlations appeared stronger within
HP as compared to LN conditions and some correlations were specifically
observed in one of the environments. For example, the positive
correlation observed between many amino acids and seed size and weight
were either lost at LN or were not as strong as what was observed at HP
(Figure S3 ). In addition, a limited number of metabolites (e.g.
galactarate) showed a significant positive correlation with most of the
phenotypic traits in LN; however, the same metabolite showed a weak
negative correlation with the same seed phenotypic traits in HP
(Figure S3 ). An association of germination percentage and
metabolic content of the dry seeds may raise the possibility to predict
germination behaviour using the metabolic signature of the dry seeds
(Rosental, Nonogaki & Fait 2014).