Introduction
The maternal environment in which seeds develop and mature have a
profound influence on seed properties such as germination vigour. The
sink-source connection between the mother plant and the seeds allows the
seeds to accumulate reserves required for seed germination and seedling
growth (Baud et al. 2008). Metabolites such as amino acids,
sugars and organic acids play a vital role in the different stages of
seed development such as maturation, desiccation and germination
(Borisjuk et al. 2004; Fait et al. 2006). During seed
maturation, the content of these metabolites in seeds decreases and
storage reserves, including starch, oil and seed storage proteins
increase (Fait et al. 2006; Galili et al. 2014). It has
also been shown that the subsequent metabolite content and composition
of dry seeds may reflect the maturation environments in which they
developed (He et al. 2016). For example, in different species it
has been reported that nitrogen related metabolites such as asparagine,
allantoin and GABA show a lower content in seeds developed under low
nitrate maternal environments (Geshnizjani et al. 2019; Heet al. 2016). Although many studies have been performed related
to the effect of maternal environments on dry seed metabolic content,
more information is required to understand the genetic and molecular
mechanisms governing the metabolic changes in response to the maternal
environment.
In general, each observed phenotype in plants is the consequence of
different cellular processes such as gene transcription, protein
translation and, finally, metabolite production (Kooke & Keurentjes
2011). Therefore, genetic variation is not only confined to phenotypic
traits such as seed and seedling quality traits. Many studies have
revealed that metabolite composition and content, which play a very
critical role in plant growth and development, is also controlled by
genetic variation within a plant species (Windsor et al. 2005).
The existing natural variation for both phenotypic traits and metabolite
content is displayed by a continuous distribution, considered as
quantitative variation. Such variations are often regulated by multiple
loci and can be detected in mapping populations like recombinant inbred
line (RIL) populations where the different loci are known as phenotypic
or metabolite quantitative trait loci (QTLs and mQTLs, respectively)
(Keurentjes & Sulpice 2009; Lisec et al. 2008). Many QTL
analyses have been performed in seeds and many QTLs that regulate
complex quantitative traits such as seed germination characteristics,
seed size, seedling traits as well as seed metabolites have been
described (Kazmi et al. 2012; Kazmi et al. 2017; Khanet al. 2012; Schauer et al. 2006).
Plants are a rich source of biochemical compounds that are mainly
contributing to plant development, adaptation and final appearance and
yield (Binder 2010). Therefore, the quantitative variation of these
metabolites may have an influence on different physiological traits like
seed germination and seedling establishment. The integrative analysis of
metabolites and genetics has provided valuable information and knowledge
on how natural variation regulates metabolite levels and their
subsequent effect on growth of plants and their adaptation and how this
knowledge can be used in plant breeding (Kliebenstein 2009).
Genetical genomics in which QTL analysis is integrated with proteomics,
transcriptomics and metabolomics has provided in-depth understanding of
molecular mechanisms regulating complex traits (Jansen & Nap 2001;
Keurentjes et al. 2006; Kliebenstein et al. 2006; Schaueret al. 2006). Nonetheless, more advanced approaches are required
for further determination of the complexity of quantitative traits. In
addition to genotype (G), molecular networks are also influenced by the
environment (E) and the interaction between genotype and the environment
(G×E). Thus, the incorporation of different environments in genetic
studies is a prerequisite for comprehensive perception of the regulation
of molecular mechanisms. Li, Breitling and Jansen (2008) proposed a new
strategy which is called generalized genetical genomics (GGG) by which
both genetic and environmental perturbations can be studied. This
approach allows QTL analysis governing the interesting molecular traits
under consideration of multiple environments. It is a cost-effective
method to not only determine the genotype but also the environmental
effects and their interaction for detected QTLs (Li, Breitling & Jansen
2008). In principle, by creating similar subpopulations of RILs and
subjecting each of these to a different environment, G, E and G×E
effects can be investigated in a cost-effective experimental design
(Joosen et al. 2013).
Although the QTLs governing dry seed metabolite content have been
previously detected in many plants, including tomato (Kazmi et
al. 2017; Toubiana et al. 2012), the effect of G×E interactions
has been studied to a much lesser extent (Albert et al. 2016;
Kazmi et al. 2017; Rosental et al. 2016). In this study we
used a RIL population derived from a cross between two tomato species:Solanum lycopersicum (cv. Moneymaker) and Solanum
pimpinellifolium (Voorrips et al. 2000). We have exploited the
existing natural variation in this population to investigate how QTLs
are influenced by the environment to which the mother plants are
exposed. Moreover, metabolic profiling of the seeds which have matured
in different environments will be useful to illustrate important
metabolic differences that regulate the development and adaptation of
plants (Joosen et al. 2013). By using a GGG approach we performed
metabolite analysis for the RIL population and their parental lines,
grown in high phosphate and low nitrate environments. By generating
metabolite correlation networks and performing mQTL analysis, genetic
and molecular aspects of seed metabolic changes in response to the
maternal environments have been discovered.