Researchers used systems biology to model the metabolic networks in tomato
fruit development.
When tomatoes ripen in our gardens, we watch them turn gradually from hard,
green globules to brightly colored, aromatic, and tasty fruits. This
familiar and seemingly commonplace transformation masks a seething mass of
components interacting in a well-regulated albeit highly complex manner. For
generations, agriculturalists and scientists have bred tomatoes for size,
shape, texture, flavor, shelf-life, and nutrient composition, more or less,
one trait at a time. With the advent of molecular biology, mutagenesis and
genetic transformation could produce tomatoes that were more easily
harvested or transported or turned into tomato paste. Frequently, however,
optimizing for one trait led to deterioration in another. For example,
improving flavor could have a negative effect on yield.

The revolution in genomics, with a wealth of data emerging from sequencing
and simultaneous expression analysis of thousands of genes, has made it
possible to study the numerous pathways and regulatory
networks?systems--that operate to produce a desirable fruit. This systems
approach in the new fields of metabolic and functional genomics is producing
the tools, information, and biological materials needed for screening and
breeding efforts in tomato and other members of the Solanaceae.

Dr. Fernando Carrari and his colleagues, Laura Kamenetzky, Ramon Asis, Luisa
Bermudez, Ariel Bazzini, Sebastian Asurmendi, Marie-Anne Van Sluys, Jim
Giovannoni, Alisdair Fernie, and Magdalena Rossi use a systems approach that
integrates genomic, genetic, and biochemical tools to model the metabolic
networks that interact in the process of tomato fruit development. Dr.
Carrari, of the Instituto de Biotecnologia, (INTA), Argentina, will be
presenting this work at a symposium on the Biology of Solanaceous Species at
the annual meeting of the American Society of Plant Biologists in Mérida,
Mexico (June 29, 9:10 AM).

Tomato (Solanum lycopersicum) is a member of the Solanaceae or nightshade
family, which also includes potato, eggplant, tobacco, and chili peppers.
The center of origin and diversity of tomato species is in the northern
Andes, where endemic populations of wild tomato species still grow. These
wild populations represent considerable genetic diversity, whereas
cultivated tomatoes are genetically very narrow. The Tomato Genome
Consortium is an international collaboration that is sequencing, mapping and
analyzing the genomes of both wild and cultivated varieties. Carrari and his
co-workers, as well as other scientists, have begun to make use of this
wealth of sequence data in functional and metabolic analyses of tomato and
other crops.

Plants produce an immense variety of chemical compounds for growth,
metabolism, signaling, defense, and reproduction. These metabolites function
in complex networks and pathways in which they regulate and are regulated by
parallel networks of genes. It is not possible to realistically model these
metabolic systems one compound or gene at a time. Moreover, many, if not
most traits in tomato, are not the result of one gene, but of many genes
located together in chromosomal regions called quantitative trait loci
(QTLs), because they produce a range of values in fruit or plant size or
color, rather than just two extremes. Thus metabolites, enzymes, and genes
must be analyzed simultaneously and in parallel in order to capture their
dynamic relationships. To accomplish this, Carrari and his colleagues made
use of the high genetic diversity of an ancestral tomato species, Solanum
pennellii.

Through crosses, chromosomal segments of S. pennellii were introgressed into
the genome of the cultivar Solanum lycopersicum var. Roma. Different lines
of the cultivar were then created that differed only in the chromosomal
segment received from the wild species. In this way, over 1200 metabolic
QTLs or quantitative metabolic loci (QMLs) were identified and analyzed.
Almost 900 of these QMLs were found to be associated with fruit metabolism.

The scientists then sampled a number of metabolites such as carbohydrates,
pigments, and hormones, among others, throughout flower and fruit
development. They also used microarrays to determine which genes were
expressed at those same times. Pairwise comparisons and network analyses
were then made to determine which of those genes and metabolites are
associated in possible functional networks. These associations do not
establish causality or regulatory direction, because they are only
correlational. Expression of certain genes may regulate metabolite activity,
but metabolites may also have a regulatory effect on gene expression. To
begin to define causal direction, Carrari and his colleagues perturbed these
systems by treatment with external metabolites and followed the transmission
of information from metabolite to gene. In continuing research, Carrari and
co-workers are using these methods, as well as RNA interference and
transgenesis to map QMLs and to identify and utilize candidate genes that
function at network nodes.

These systems approaches make it possible to model the whole organism
throughout its development. Moreover, an understanding of metabolic networks
will make it possible to alter metabolic pathways to produce fruits with
different secondary compounds that influence texture, taste, aroma, and
nutrition, as well as to improve yield. Metabolite analysis also has
possible applications in drug discovery, nutrient enhancement and biofuel
production. One important goal is the use of ancestral genetic resources in
place of simplistic genetic modification to avoid possible deleterious
environmental effects as well as resistance by consumers to genetically
modified food.

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