Mass spectrometers of ultra-high definition, such as Orbitrap and Fourier
Transform Ion Cyclotron Resonance-Mass Spectrometry (FT-ICR-MS), allow an
extremely precise metabolomic profile of a sample. The resolving power and
sensitivity of these techniques in molecules that contain abundant natural isotopes
(e.g.,
13 C,
41 K,
15 N,
17 S, and
37 Cl) is visible in their results. Its precision allows
accurate calculation in the elemental composition of unknown compounds and can
define which of these secondary metabolites are new (Allwood et al. 2012; Pollier
and Goossens 2013).
Complex metabolic regulation by the gene expression and enzymatic activity is
manifested in the metabolic flux analysis (MFA). MFA is the quantification of the
flow of a metabolite and the rate at which the enzymes act in the biosynthetic
pathway. Traditionally, this analysis is made with mass balance; however, it
presents some disadvantages such as not distinguishing a cyclic reaction, parallel
metabolic cycles that are not measurable coupled flows cannot be solved, it is
possible to solve reversible stages in a cycle, it is not possible to determine for
certain the flow of energy metabolites such as ATP, NADH, and NADPH
(Wiechert 2001), also shows no information on metabolite biosynthesis site of
interest in the cell or in a particular tissue, which is a constraint to understand how
there is a metabolite spreading and for the design of extraction methods.
Fusion proteins provide us with a simple approach but is limited to the compartmentalization of enzymatic interactions in a metabolic pathway. It involves the
generation of chimeric proteins attached via a linker region. This approach has
been widely used and is implemented by the fusion of two or more genes with a
linker region, which generates a single polypeptide with two or more features.
Linker region reduces interference in the folding, which allows proteins to retain
their native activity and spatially separate domains allow them to interact as
necessary. This methodology has been applied to proteins which are used to
increase the local concentration of intermediate products, and has also been
applied in systems where the protein–protein interaction is necessary for their
functionality (Woolston et al. 2013).
Transcriptomic data can define the possible reactions that occur in a particular
physiological condition and together with the data provided by genomic sequencing
and stoichiometry of metabolic reactions, genome-scale models can be designed.
These models have been successfully established for many microorganisms, but are
more complicated in plants because of their cellular and tissue structure. Currently,
scale modeling has been established for leaves and cells of A. thaliana in suspension,
and also for barley, Brassica, and C4 plants such as maize, sorghum, and sugarcane.
There are many challenges to modeling plant genome in scale, as many genes and
proteins have unknown functions. Central metabolism has been completely elucidated, but not the secondary metabolism because a lack of genetic sequence data of
medicinal plants, few information of metabolite production at tissue level, and
limited ability to identify the interactions of large numbers of plant species with
infinite numbers of internal and external conditions (Collakova et al. 2012).
Other bioinformatic tools have been developed to support a wide range of
experimental techniques and analyses. These tools are used to extract and interpret
15 Plants as Bioreactors for Human Health Nutrients
431
Transform Ion Cyclotron Resonance-Mass Spectrometry (FT-ICR-MS), allow an
extremely precise metabolomic profile of a sample. The resolving power and
sensitivity of these techniques in molecules that contain abundant natural isotopes
(e.g.,
13 C,
41 K,
15 N,
17 S, and
37 Cl) is visible in their results. Its precision allows
accurate calculation in the elemental composition of unknown compounds and can
define which of these secondary metabolites are new (Allwood et al. 2012; Pollier
and Goossens 2013).
Complex metabolic regulation by the gene expression and enzymatic activity is
manifested in the metabolic flux analysis (MFA). MFA is the quantification of the
flow of a metabolite and the rate at which the enzymes act in the biosynthetic
pathway. Traditionally, this analysis is made with mass balance; however, it
presents some disadvantages such as not distinguishing a cyclic reaction, parallel
metabolic cycles that are not measurable coupled flows cannot be solved, it is
possible to solve reversible stages in a cycle, it is not possible to determine for
certain the flow of energy metabolites such as ATP, NADH, and NADPH
(Wiechert 2001), also shows no information on metabolite biosynthesis site of
interest in the cell or in a particular tissue, which is a constraint to understand how
there is a metabolite spreading and for the design of extraction methods.
Fusion proteins provide us with a simple approach but is limited to the compartmentalization of enzymatic interactions in a metabolic pathway. It involves the
generation of chimeric proteins attached via a linker region. This approach has
been widely used and is implemented by the fusion of two or more genes with a
linker region, which generates a single polypeptide with two or more features.
Linker region reduces interference in the folding, which allows proteins to retain
their native activity and spatially separate domains allow them to interact as
necessary. This methodology has been applied to proteins which are used to
increase the local concentration of intermediate products, and has also been
applied in systems where the protein–protein interaction is necessary for their
functionality (Woolston et al. 2013).
Transcriptomic data can define the possible reactions that occur in a particular
physiological condition and together with the data provided by genomic sequencing
and stoichiometry of metabolic reactions, genome-scale models can be designed.
These models have been successfully established for many microorganisms, but are
more complicated in plants because of their cellular and tissue structure. Currently,
scale modeling has been established for leaves and cells of A. thaliana in suspension,
and also for barley, Brassica, and C4 plants such as maize, sorghum, and sugarcane.
There are many challenges to modeling plant genome in scale, as many genes and
proteins have unknown functions. Central metabolism has been completely elucidated, but not the secondary metabolism because a lack of genetic sequence data of
medicinal plants, few information of metabolite production at tissue level, and
limited ability to identify the interactions of large numbers of plant species with
infinite numbers of internal and external conditions (Collakova et al. 2012).
Other bioinformatic tools have been developed to support a wide range of
experimental techniques and analyses. These tools are used to extract and interpret
15 Plants as Bioreactors for Human Health Nutrients
431