bound to a substrate such as a glass slide on a small scale. Differing technologies allow for
the binding of hundreds of thousands of individual sequences onto specific locations within
areas as small as 1cm^2. Generally, DNA sequences from a given species are spotted onto a
microarray, and then hybridized with labeled copies of mRNA (usually in the form of
cDNA) from a specific tissue or after some treatment, such as pathogen inoculation. If a
given mRNA is present at high levels in a treatment, then a high degree of binding to its
corresponding DNA sequence on the array will be detected. The level of binding of
transcript sequences is usually compared with levels in some untreated control tissue.
This general approach, known ascomparative gene expression, allows one to observe
the transcript profiles of tens of thousands of genes in a single experiment.
For species where genomic DNA sequence information is not as available, or where
DNA microarrays are not developed, the strategy of using expressed sequence tags
(ESTs) can also provide information on mRNA profiles. In this strategy, mRNA is collected
from the tissue of interest and then converted via reverse transcription into cDNA.
Individual clones from the collection of cDNAs, known as a “library,” are then partially
sequenced and the information is compiled in a database. The presence of a given EST
in a database then reflects the presence of its corresponding mRNA transcript in the original
tissue. By determining how often an mRNA occurs in a given tissue, and by comparing its
abundance after other treatments or in other tissues, a profile of when that particular tran-
script is present can sometimes emerge. This technique was first developed to study human
gene expression, but it is now widely applied in many types of organisms, including many
crop plants.
Ultimately, the protein products of most genes, or the metabolites that those proteins
produce, are the things that will function leading to a particular plant trait. It is therefore
useful to analyze the endproducts of gene expression. In fact, the accumulation of a given
RNA transcript measured in most gene expression studies does not always correlate with
the level or activity of the protein it encodes. This can be due to many factors, such as regu-
lation of RNA stability, protein translation rates, or posttranslational regulation of protein
stability or enzyme activity. As with genomic studies, the identification of an individual
protein from among tens of thousands can be a technical challenge.Proteomicapproaches
use different techniques to examine the large mixture of proteins present in a given tissue
or after some treatment. This usually involves separating individual proteins on the basis
of some physical characteristics such as protein size or charge. After the proteins are separ-
ated from one another their amino acid sequence can be identified using techniques such as
mass spectrometry. If the proteomic data are accompanied by a wealth of DNA sequence or
gene expression data, these data can be even more valuable, as the amino acid sequences can
be correlated with specific gene sequences in that plant. Likewise,metabolomicsis the large-
scale analysis of chemical compounds that accumulate and contribute to the characters of a
plant. These metabolites can be important not only for plant defense and physiology but also
in nutrition and food production; therefore they are valuable contributors to a number of traits
in crop plants that are of interest to farmers and consumers.
Through genomic, proteomic, and metabolomic (omics) approaches, scientists have
attempted to take a large-scale, orsystems biology, view of the events occurring at the
cellular level in an organism. The technologies developed and used in these methods
generate huge amounts of data. Trying to make sense of these data is a considerable chal-
lenge in itself, and this has given rise to a discipline calledbioinformatics, which applies
computational and mathematical methods to help scientists understand biological data
(Rhee et al. 2006).
196 GENES AND TRAITS OF INTEREST FOR TRANSGENIC PLANTS