65derivative with relative concentrations depending on the derivatization conditions
and time. Thus, the derivatization step itself can introduce biases affecting each
metabolite in a different way depending on its structure, concentration, and relative
affinity for the derivatizing agent. If these biases are not identified and properly ac-
counted for, they will skew the measured metabolite concentration profile providing
a faulty perspective of the metabolic state of the samples. Kanani and Klapa ( 2007 )
proposed a normalization method for this type of biases. Proper normalization of
the omic profiles at each molecular level before data analysis is quite crucial to es-
tablish comparability between the samples and avoid assigning biological meaning
to experimental biases.
In addition, in integrated transcriptomic and metabolomic analysis of ( combined)
stresses in plants, the final interpretation of the results should take into consideration
the different specifics of the two analyses. Transcriptomic analyses are based on the
comparison of the concentration profile of mRNA transcripts in an equal amount
of total mRNA between for all samples. It is considered that the cells of the same
species produce equal amount of total mRNA independently of their physiologi-
cal state. Thus, the transcriptomic analysis of a biological sample among different
physiological states provide a measure of the change in the composition of the total
mRNA of this sample among different physiological conditions. On the other hand,
the amount of the acquired metabolite extract of a biological sample can change
between states. The internal standard is added to allow for sample normalization
per unit of mass of the investigated biological system. In this way, it is mainly the
change in the amounts and to a lesser extent the change in the relative concentra-
tions of the metabolites in the extract that governs the observed differences among
the metabolic profiles of the various samples (Kanani et al. 2010 ).
3.8 Conclusions
In the post-genomic era, high-throughput biomolecular (omic) analyses have been
used to gain insight into the molecular response of the plants to abiotic stresses.
Among the mostly investigated abiotic stresses are the high soil or water salinity
and the elevated CO 2 in the growth environment of the plants. The most popular
omic analyses for this type of stresses have been the MS metabolomics and the
transcriptomics based on DNA microarrays. While physiological studies have in-
dicated that the elevated CO 2 can alleviate the negative effect of the salinity stress
in plants, only one time-series omic analysis study has been reported so far for
the combined implementation of elevated CO 2 and salinity stresses on plants. The
particular study on A. thaliana plant liquid cultures indicated that the elevated CO 2
provides additional resources to the plants allowing them to produce the required
osmoprotectants to counteract the salinity stress without having to sacrifice their
growth (Kanani et al. 2010 ). However, for the insights garnered from this model
system study to be directly applicable in crop improvement and production, species
and cultivars of commercial value for the food industry and/or agro-biotechnology
3 Investigating the Effect of Elevated CO 2 in the Growth Environment ...