mQTL profiling of the tomato RIL population
The calculation of the coefficient of variation (CV) showed that most of
the metabolites possess a CV value higher than 40%, which indicates
that there is considerable variation within the RIL population for the
metabolite levels in the dry seeds (Figure 8, Table S9 ). In
order to investigate if such a high level of variability within
metabolites could be explained by differences in alleles and genetic
factors, a metabolic quantitative trait locus (mQTL) analysis was
performed with the obtained metabolite data. Each metabolite is in
general controlled by several pathways and regulators. Thus, as
expected, we hardly identified metabolites for which a single genetic
locus significantly explained the metabolite levels.
In our study we performed mQTL analysis for each maternal environment to
evaluate the genetic variation within each sub-population. Furthermore
we used the whole set of RILs to detect mQTLs explained by a genetic
component (G) and the genotype by environment interaction (G×E).
We identified mQTLs across all conditions (Table 3 ). Regarding
the sub-populations 66 and 129 mQTLs were detected for seeds from LN and
HP environments, respectively. The heatmap of the LOD profiles and
characteristics of the mQTLs in each environment are presented inFigure S4 and Table S10 , respectively. In both
maternal environments several mQTLs were detected which were hardly
detected for other metabolites. For example, in the seeds developed
under HP conditions a single strong QTL on chromosome 9 was detected,
regulating asparagine. Another independent significant QTL was
identified on the top of chromosome 11 for phenylalanine under the same
environmental conditions (Figure S4A, Table S10 ). Detection of
such specific mQTLs in our data reveals the tight and independent
genetic regulation of metabolite biosynthesis in seeds (Keurentjeset al. 2008). Under the same maternal condition some organic
acids such as benzoate, gluconate, glycerate and glycolate mapped to a
similar position on chromosome 5 (Figure S4A, Table S10 ). On
chromosome 9, we detected mQTLs for TCA cycle intermediates including
citrate and malate which were co-locating with the one regulating F6P as
one of the precursors of the TCA cycle. There is also a QTL on the top
of chromosome 1 affecting amino acids in seeds from the HP environment.
Despite the strong correlation that has been found between amino acids
and TCA cycle intermediates in seeds from HP conditions, no co-located
QTLs were identified for them. This might be due to several smaller QTLs
regulating variation of the metabolites, each of them explaining a small
part of the variation and therefore not reaching the threshold LOD
score. Regarding the seeds grown in the LN maternal environment we found
more than one QTL for some of the metabolites such as GABA, citrate and
malate. The vital role of these metabolites has been reported in
relation with the alleviation of environmental stress effects (Kaplanet al. 2004; Kinnersley & Turano 2000; Krasensky & Jonak 2012;
Obata & Fernie 2012). For the LN environment many of the amino acids
have co-locating QTLs at the bottom of chromosome 4 and in the middle of
chromosome 5 (Figure S4B ). Such strong co-locating QTLs for
amino acids was expected since they showed a high connection in the
correlation network of the LN environment (Figure 5A ). In
general, such co-localizing QTLs for metabolites suggest that, in
addition to the single independent QTLs regulating metabolite contents,
some general regulatory loci and genes are involved in the regulation of
metabolite synthesis (Keurentjes et al. 2008).
Combining the sub-populations and using the whole set of RILs leads to
an increase in the number of detected QTLs with 382 and 146 QTLs for G
and G×E effects, respectively. An overview of the detected QTLs is
provided by the heatmap of the LOD profiles (Figure 9 ). On the
top and bottom of chromosome 4 there are two QTLs that explain the
variation for many amino acids such as aspartate, GABA, glutamine,
methionine, serine and threonine. Similarly, a co-located QTL was
detected for galactarate and malate on chromosome 10 (Figure
9 ). Co-localization of these mQTLs is not surprising since galactarate
is the precursor of 2-oxoglutarate and 2-oxoglutarate is one of the
intermediates of the TCA cycle and is generally converted to malate in a
couple of subsequent reactions. Our results show that myo-inositol and
galactinol are highly associated with each other and closely grouped
together. Therefore, it is not surprising that they both have a
co-locating QTL on chromosome two (Figure 6, Figure 9 ). The
robust correlation between raffinose pathway metabolites including
galactinol and myo-inositol has also been reported for seeds of other
species that developed under environmental stress (Cook et al.2004; He et al. 2016). These metabolites are known for their
protective role for cellular structures of embryos during seed
development and desiccation (Taji et al. 2002). Furthermore, they
are able to play a key role in protecting plants from the effects of
stress resulting from reactive oxygen species (ElSayed, Rafudeen &
Golldack 2014). Some of the organic acids including gluconate, glycerate
and glycolate, together with two of the TCA cycle intermediates (malate
and succinate), had a co-locating QTL on chromosome 9 (Figure
9 ). Glutamate and GABA showed a shared QTL on chromosome 4 which has
previously been detected in the same population developed under standard
conditions (Kazmi et al. 2017). Metabolites belonging to the same
functional class are often highly correlated and can have co-locating
mQTLs (Kazmi et al. 2017). Although several mQTLs were detected
at similar positions, in general more co-located mQTLs would be expected
due to the strong correlation that has been observed between the
metabolites. This could be related to the fact that several small QTLs
are involved in regulation of the metabolites and each of them is
explaining only a small part of their variation. Such small QTLs are
likely to escape the QTL significant threshold in the QTL analysis
(Keurentjes et al. 2008).
A few mQTLs co-located with the phenotypic QTLs that have been detected
in a previous study (Geshnizjani et al. 2020). For instance, the
QTLs on the middle of chromosome 10 affecting galactarate and malate
co-located with ones influencing uniformity of germination
(U8416) at different germination conditions, such as
high temperature, mannitol, water and NaCl. In addition, the QTL on
chromosome 9, which is specifically regulating methionine, is located at
the same position as QTLs affecting seed size, seed weight and fresh and
dry weight of the seedlings. Despite the many strong correlations
between metabolites and phenotypic traits (Figure 9 ), we could
hardly detect co-locating QTLs for them. This might be due to the fact
that each of the phenotypes may not be correlated with a specific
metabolite but with a group of metabolites and thus the final metabolic
balance between the groups of metabolites could affect phenotypic traits
such as Gmax.