metabolites are produced by plants. These metabolites can be used for therapeutic
purposes, fragrances, flavors, and pesticides, among others (Goossens et al. 2003).
However, the plant extracts from which secondary metabolites come are insufficient to satisfy the market demand because plants grow slowly and with difficulty.
Initially, chemical synthesis was able to satisfy the demand of secondary metabolites; however, the complexity of the molecular structures and stereochemical
requirements of some secondary metabolites limit the use of this technology
(Miralpeix et al. 2013).
Metabolic engineering provides tools that enable large-scale production of
secondary metabolites with high-added value, in heterologous systems such as
microorganisms, cell cultures, and plant organs. Genomics, transcriptomics, proteomics, and metabolomics can be used to determine the function of a gene at the
transcript, protein, or metabolite level and establish new metabolic pathways in
organisms. The genomic sequence contains the code of proteins, enzymes, and
transcription factors involved in the biosynthetic pathway. Until now 25 plant
genomes have been published, but only the genomes of Arabidopsis thaliana and
Oryza sativa are almost identified. The genetic information of these model plants
can be used to associate physiological processes in related plants, like Medicago
truncatula and its pathway for nitrogen fixation in legumes, tomato as a model of
development of fruit, and rice as a development model for grains (Moon-Yoon
et al. 2013). The sequencing of model plants can help to establish new metabolic
pathways and complete the existing ones. These developments provide more
efficient methods to increase yields of secondary metabolites with high-added
value (Hirsch and Robin-Buell 2013).
Microarray analysis allows obtaining a transcriptome profile of a plant under
certain conditions and can identify many candidate genes involved in the
production of secondary metabolites, based on their expression. However, this
technology is being displaced by transcriptome sequencing (RNA-seq) which is
more versatile for determining metabolic pathways in plants. This type of analysis
provides the function of a gene but not its functionality. To implement this it
requires further analyses, which include the isolation of cDNA, the construction of
vectors for genetic transformation, determination of their metabolic profile, and
quantitative analysis of the desired compound (Yonekura-Sakakibara et al. 2013).
The general problem for characterizing a plant metabolome is the complexity
and chemical diversity of compounds. Analytical techniques like GC–MS can
detect only a portion of a wide variety of compounds. It is estimated that the
approximate number of metabolites in a plant can vary from 5,000 to 25,000
different compounds according to the species (Hegeman 2010). Only in A. thaliana
2,613 different compounds (PMN 2013) were detected. As no analytical technology is able to detect all the massive catalog of secondary metabolites in plant
species, it is advisable to combine multiple analytical technologies to obtain a
more accurate metabolomic profile, for example, the combination of chromatography-mass spectrometry (MS) and nuclear magnetic resonance (NMR). Currently,
the MS technique is usually the choice for metabolomic studies when the sample
volume is too limited for using NMR (Kueger et al. 2012).
430
L. A. Cruz-Ramírez et al.
purposes, fragrances, flavors, and pesticides, among others (Goossens et al. 2003).
However, the plant extracts from which secondary metabolites come are insufficient to satisfy the market demand because plants grow slowly and with difficulty.
Initially, chemical synthesis was able to satisfy the demand of secondary metabolites; however, the complexity of the molecular structures and stereochemical
requirements of some secondary metabolites limit the use of this technology
(Miralpeix et al. 2013).
Metabolic engineering provides tools that enable large-scale production of
secondary metabolites with high-added value, in heterologous systems such as
microorganisms, cell cultures, and plant organs. Genomics, transcriptomics, proteomics, and metabolomics can be used to determine the function of a gene at the
transcript, protein, or metabolite level and establish new metabolic pathways in
organisms. The genomic sequence contains the code of proteins, enzymes, and
transcription factors involved in the biosynthetic pathway. Until now 25 plant
genomes have been published, but only the genomes of Arabidopsis thaliana and
Oryza sativa are almost identified. The genetic information of these model plants
can be used to associate physiological processes in related plants, like Medicago
truncatula and its pathway for nitrogen fixation in legumes, tomato as a model of
development of fruit, and rice as a development model for grains (Moon-Yoon
et al. 2013). The sequencing of model plants can help to establish new metabolic
pathways and complete the existing ones. These developments provide more
efficient methods to increase yields of secondary metabolites with high-added
value (Hirsch and Robin-Buell 2013).
Microarray analysis allows obtaining a transcriptome profile of a plant under
certain conditions and can identify many candidate genes involved in the
production of secondary metabolites, based on their expression. However, this
technology is being displaced by transcriptome sequencing (RNA-seq) which is
more versatile for determining metabolic pathways in plants. This type of analysis
provides the function of a gene but not its functionality. To implement this it
requires further analyses, which include the isolation of cDNA, the construction of
vectors for genetic transformation, determination of their metabolic profile, and
quantitative analysis of the desired compound (Yonekura-Sakakibara et al. 2013).
The general problem for characterizing a plant metabolome is the complexity
and chemical diversity of compounds. Analytical techniques like GC–MS can
detect only a portion of a wide variety of compounds. It is estimated that the
approximate number of metabolites in a plant can vary from 5,000 to 25,000
different compounds according to the species (Hegeman 2010). Only in A. thaliana
2,613 different compounds (PMN 2013) were detected. As no analytical technology is able to detect all the massive catalog of secondary metabolites in plant
species, it is advisable to combine multiple analytical technologies to obtain a
more accurate metabolomic profile, for example, the combination of chromatography-mass spectrometry (MS) and nuclear magnetic resonance (NMR). Currently,
the MS technique is usually the choice for metabolomic studies when the sample
volume is too limited for using NMR (Kueger et al. 2012).
430
L. A. Cruz-Ramírez et al.