Transcript Slide 1

Genomics and
metabolomics converge for
tomato flavor improvement
Presented by:
J. Erron Haggard
To: HRT 221
April 28, 2008
Flavor in tomato results from the combination of sugars
(glucose and fructose) and acids (citric and malic)
perceived by receptors in the tongue and ~400 volatile
compounds perceived by olfactory receptors
~30 of these volatiles contribute significantly , some
positively, some negatively
Many of these compounds are derived from amino acids,
lipids, and carotenoids
Many of the synthesis pathways are known, but some are
mysteries, with no genes identified for synthesis or
accumulation.
In this study, the authors attempt to identify loci affecting
the composition of chemicals related to flavor in a
genetically diverse, but well-defined introgression line
population: 75 lines, each with a single introgression from
Solanum pennellii in an otherwise fixed S. esculentum
background.
Obviously, variation does exist.
Nine lines exhibited significant season x line interactions
(i.e. were higher or lower in spring than in autumn)
Overall, volatiles were generally lower in autumn than in
spring, with a few exceptions
107 bins
Additional points:
• In most cases, except for some apocarotenoids, the
introgressions produced more of the metabolite than M82
• Correlations between loci controlling certain compounds
are indicative of a putative pathway
• While citrate-associated loci were identified, no loci were
consistently significantly associated with malate
concentration
• The precursor-product relationship in carotenoidapocarotenoid biosynthesis was confirmed by these data
GC/MS used to quantify 74
metabolites
Identified 889 singlemetabolite QTL, but cannot
establish causality
Figure 1: Heat map of metabolite
profiles by introgression line
Red = increase
Blue = decrease
Purple = increase in one
harvest and decrease in
another (9.9% of results)
Whole-plant
phenotypic traits
and phosphorylated
intermediates
Figure 2: Cartographic
representation of the
combined metabolic and
morphological network
of the tomato
Amino Acids
Figure 2: Cartographic
representation of the
combined metabolic and
morphological network
of the tomato
Sugars and
organic acids
Figure 2: Cartographic
representation of the
combined metabolic and
morphological network
of the tomato
Additional points:
• Harvest index had the greatest number of significant
associations (n=20), and a high degree of connectivity to the
other modules (n=7)
• Only Brix showed a similar pattern of relation outside its
module, all others maintained their connections within their
module
• Most traits defined as “whole plant phenotype associated”
belong to central metabolic pathways and seem to be more
stable
• It seems that the traditional breeding goal, Brix, is negatively
correlated with the assimilation of photosynthate toward
nutritional compounds, such as vitamins
Figure 3:
Morphologically
associated and
independent
metabolites
Red = correlated
significantly to at
least one true
morphological
trait (p < 0.005)
Orange =
correlated
significantly to at
least one true
morphological
trait (p < 0.05)
Gray = no
significant
correlation
Pale = not tested
Figure 4: Genomic
regions containing QTL
a – malate is morphologically independent (fig. 3) – recall the previous authors’
failure to locate malate QTL
b – IL6-3 -> 50% reduction in harvest index
Concluding Thoughts
The utility of the IL approach lies in negating all epistatic
effects to elucidate only additive gene action. Will this
approach necessarily bring about the “best” tomato? Is
there some benefit to epistasis and heterosis with regard to
breeding for tomato flavor?
This approach also removes the aspect of human
preference. It provides mechanisms, but doesn’t address
desires. How much malate/α-tocopherol/2-methylbutanal
do you like in your tomatoes?
Concluding Thoughts
What is the real benefit of these QTL? There exist a
plethora of QTL mapping papers. Wouldn’t it be more
interesting to see these results applied in a MAS program
to produce an actual crop with some improvement?
Until they are applied to some advantage, QTL remain a
statistical fiction.
References:
Giovannoni, JJ (2006) Breeding new life into plant
metabolism. Nature Biotech 24:418-9
Schauer et al. (2006) Comprehensive metabolic profiling
and phenotyping of interspecific introgression lines for
tomato improvement Nature Biotech 24:447-54
Tieman et al. (2006) Identification of loci affecting flavour
volatiles emissions in tomato fruits. J. Exp Bot 57:887896