2. Introduction
• Plants are the richest with secondary metabolites among different organisms (5.000
– 25.000 per plant).
• Plants provide the source material for over half the drugs currently prescribed.
• Often, bioactive compounds in plants are produced in very small quantities.
10,000 kg of Pacific yew bark yields less than 1 kg of the potent anti-cancer
compound paclitaxel!
Chandra et al. Biotechnology for Medicinal Plants. Springer, 2014: P275 1
3. Introduction
• Shikimic acid pathway, non-mevalonate (MEP) pathway and mevalonate (MVA)
pathway lead to diverse classes of compounds, which include the terpenoids,
monoterpene indole alkaloids, isoquinoline alkaloids, flavonoids and anthocyanins.
Wilson et al. Current opinion in biotechnology 26 (2014): 174-182. 2
7. ME approaches theory
STOP!
We must first gain knowledge about the plant’s
metabolism.
Remember!
The use of term “engineering” implies that there
is some precise understanding of the system that
is being modified.
6
8. Understanding plant metabolism:
A systems biological approach
• To be able to manipulate plant metabolism, one must first create a metabolic model.
• Constructing a metabolic model using a systems biological approach requires the
four steps:
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
7
9. Understanding plant metabolism:
A systems biological approach
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
Step 1 Find enzymes/reactions:
- Literature search and databases
- Gene annotation
- Experimental procedures
Step 2 Identification of inter-compartmental transport
reactions:
- The most challenging step as information in this
area remains, for the most part, uncharacterized.
Step 3 Determine the reversibility of reactions
- There are some obvious reactions
- Some others can be concluded thermodynamically
- Some algorithms do exist
8
10. Major Databases Containing Metabolic Information on Plants
Baghalian et al. The Plant Cell Online 26.10 (2014): 3847-3866. 9
13. Compartmentation makes plant genome-scale
reconstruction challenging: even for Arabidopsis,
the most intensively studied plant species, this
knowledge is far from complete.
de Oliveira et al. Current opinion in biotechnology24.2 (2013): 271-277. 12
14. Understanding plant metabolism:
A systems biological approach
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
• While step one provides a static view of the metabolic
network, mathematical methods are required in order
to process and integrate heterogeneous omics data and
to build a comprehensive metabolic model.
• Computational platforms have been developed to
make the mathematical analysis and visualization
convenient.
• Mathematical modeling methods:
- Network Models
- Stoichiometric Models
- Genome-Scale Metabolic Models
- Metabolic flux analysis (MFA) (moghayesash ba
gene essentiality data)
- Kinetic Models
13
15. Definitions of Terms Used in Metabolic Modeling
Baghalian et al. The Plant Cell Online 26.10 (2014): 3847-3866. 14
17. Understanding plant metabolism:
A systems biological approach
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
• A valid model faithfully reflects the biologically realistic
behavior of the metabolic network.
• Lack of concordance between observed and predicted
behavior requires re-elaboration of the model to
remove inconsistencies.
• A model becomes acceptable as soon as the outcome
of future experiments can be predicted.
• It is also an overriding priority to continuously update
the validated model by reference to new findings.
• 70 to 90% similarity between experiment and
prediction is a good result.
16
18. Understanding plant metabolism:
A systems biological approach
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
• A valid model can be regarded as a virtual laboratory,
so predictions can be made much faster and more
cheaply than by conducting the necessary wet lab
experiments.
• The intention is to manipulate a given pathway to
produce a specific outcome, applying the model can
potentially generate a variety of alternative strategies,
which can lead to a directed experimental validation.
• NFA is one of the important part of systems biology and
has the potential to make predictions on the basis of a
functional understanding.
17
19. flux balance analysis of
large networks
de Oliveira et al. Current opinion in biotechnology24.2 (2013): 271-277. 18
24. • GGPP, the precursor for both diterpenoids and tetraterpenoids, was
accumulated by suppressing the pathway to carotenoids.
• Two genes for the pathway, phytoene synthase (PSY) and phytoene
desaturase (PDS) in the first and second step for the pathway of carotenoid
synthesis respectively, were silenced
• How?
Partial products of PSY and PDS were prepared by PCR and used to
produce the pTRV2:PSY and pTRV2:PDS plasmid.
Transformation of A.tumefaciens strain GV2260 with the
plasmids was acomplished by the freez-thaw method.
To confirm successful silencing of the PSY and PDS gene, total
RNA was isolated and visualized on agarose gel.
CASE STUDY 1
Hasan et al. Plant cell reports 33.6 (2014): 895-904. 23
25. • Identification and quantification of the amounts of taxadiene were
conducted by GC–MS.
• Metabolic pathway shunting by suppression of the phytoene synthase gene
expression which resulted in accumulation of increased taxadiene
accumulation by 1.4- or 1.9- fold, respectively. In
CASE STUDY 1
Hasan et al. Plant cell reports 33.6 (2014): 895-904. 24
26. 1. Knocking out gene function by targeted RNA degradation.
2. Interfering with protein function using specific inhibitors
or antibodies.
Other methods for gene silencing??
Wurtzel et al. Encyclopedia of Chemical Processing (2006): 2191-2200. 25
28. CASE STUDY 2
• The medicinal plant Catharanthus roseus is of enormous pharmaceutical interest
because it contains more than 120 terpenoid indole alkaloids (TIAs):
Ajmalicine antihypertensive
vinblastine and vincristine antineoplastic
They are produced only in very low amounts in C. roseus plants (2) and,
despite significant efforts, cell cultures are not yet a valid alternative
for production.
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 27
29. CASE STUDY 2
Metabolite Profiling Transcipt profiling
Identify the conditions where
differential accumulation of
the desired metabolites can be
observed.
TIA-targeted metabolite
profiling
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
LC/MS Peak filtering 178
peaks
Using an internal library of
masses and retention times,
TIAs were identified
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 28
30. CASE STUDY 2
Metabolite Profiling Transcipt profiling
Identify the conditions where
differential accumulation of
the desired metabolites can be
observed.
TIA-targeted metabolite
profiling
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
LC/MS Peak filtering 178
peaks
Using an internal library of
masses and retention times,
TIAs were identified
cDNA-AFLP technique
Sequencing BLAST with a
library (European Molecular
Biology Laboratory)
Less than 10% gave a perfect
match. Thus, the vast majority
of the tags
are undescribed.
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 28
31. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
• The accumulation profiles of the 178 metabolite
peaks retained were combined with the expression
profiles of the 417 transcripts for integrated
analysis.
• principal component analysis (PCA) was performed
first to explore the variability structure of the data.
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 29
32. Rischer et al. Proceedings of the National Academy of
Sciences 103.14 (2006): 5614-5619.
30
34. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
• Correlation network analysis was used to establish
gene-to-gene and gene-to-metabolite co-regulation
patterns
• The Pearson correlation coefficient between each
pair of variables (either gene or metabolite) across
the profiles, including all time points and
conditions, was calculated.
• TOM SAWYER VISUALIZATION 6.0 visualization
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 32
35. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
Rischer et
al. Proceedings of the
National Academy of
Sciences 103.14
(2006): 5614-5619.
33
36. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
Rischer et
al. Proceedings of the
National Academy of
Sciences 103.14
(2006): 5614-5619.
34
37. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 35
38. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 36
39. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
Rischer et al. Proceedings of the National Academy of Sciences 103.14 (2006): 5614-5619. 37
40. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
• The power of this approach was also successfully
demonstrated by other studies of taxol biosynthesis
and by the author’s own study of tobacco nicotine
biosynthesis.
38
41. CASE STUDY 2
Establishment of
the Metabolic
Network
Convert
reconstruction to
Mathematical
Model and
Visualization
Validation
Analytical
Investigation
A publishable full research data
provided
39
43. CASE STUDY 2
Zhou et al. Biotechnology and applied biochemistry 52.4 (2009): 313-323. 41
44. CASE STUDY 2
• Genes encoding rate-limiting enzymes and some key transcription factors can be
used to improve TIA production by overexpressing them in transgenic C. roseus
cultures.
• Tdc cDNA driven by CaMV 35S (cauliflower-mosaic-virus 35 S promoter) was
introduced into C. roseus by means of A. tumefaciens-mediated gene
transformation.
.
Zhou et al. Biotechnology and applied biochemistry 52.4 (2009): 313-323. 42
45. CASE STUDY 2
Zhou et al. Biotechnology and applied biochemistry 52.4 (2009): 313-323. 43
46. CASE STUDY 2
• Genes encoding rate-limiting enzymes and some key transcription factors can
be used to improve TIA production by overexpressing them in transgenic C.
roseus cultures.
• Tdc cDNA driven by CaMV 35S (cauliflower-mosaic-virus 35 S promoter) was
introduced into C. roseus by means of A. tumefaciens-mediated gene
transformation.
• Overexpression of Tdc was not necessary to achieve high levels of alkaloid
accumulation, but only enhanced tryptamine levels, and constitutively high
TDC activity seemed to be detrimental to C. roseus growth.
• The Str-overexpressing cultures showed a 10-fold higher STR activity than
wildtype, resulting in higher TIA accumulation.
Zhou et al. Biotechnology and applied biochemistry 52.4 (2009): 313-323. 44
47. Other methods for increasing expression??
1. Introduce genes into the plant.
2. Promoters to direct gene expression in the appropriate spatial
and temporal landscape.
Wurtzel et al. Encyclopedia of Chemical Processing (2006): 2191-2200. 45
48. • Although most early examples of metabolic engineering involved single-gene
interventions, this approach suffers from limitations such as the inability to
increase the activity of multimeric enzymes with heterologous subunits, and the
inability to target multiple metabolites simultaneously.
Multigene transfer
Farré et al. Annual review of plant biology 65 (2014): 187-223. 46
50. Increasing metabolic diversity
• The common building blocks of the diterpenoid pathways include various
diterpene synthases, cytochrome P450 monooxygenases,
glycosyltransferases, acyltransferases, methyltransferases, and
oxidoreductases that act specifically on different parts of the skeleton.
• Combining these enzymes in a common background should make it possible
to generate novel forms of decoration (or at least novel combinations of
decoration) on a single diterpene skeleton.
• Similar building blocks are required for the synthesis of triterpenes.
• For example, the production of avenacin 1 requires many enzymes with
different roles, but only five of the corresponding genes have been isolated.
• These genes have been expressed in different combinations in heterologous
plants—e.g., the expression of AsMT1, AsUGT74H5, and AsSCPL showed that it
was possible to achieve the acetylation of the triterpene backbone.
Farré et al. Annual review of plant biology 65 (2014): 187-223. 48
52. Summary
Metabolic engineering is in principle a simple process of enhancing the capacity
of existing pathways, controlling the distribution of flux, and, if necessary,
bolting on additional functionalities so that novel compounds can be produced.
In practice, there are at least four major challenges that need to be overcome:
- Gaining enough knowledge of the endogenous pathways to know the
best intervention points.
- Identifying and sourcing the genes that can be used to modify the
targeted metabolic pathway.
- Expressing those genes in such a way as to produce a functional enzyme
in a relevant context (such as the correct subcellular compartment).
- Achieving the primary goals of the metabolic engineering strategy
without affecting endogenous metabolism to the extent that the plant
cannot grow and develop normally.
50
53. Summary
The valuable pharmacological and nutritional properties of many
secondary metabolites combined with their low levels of production in
nature mean that active research into novel strategies for metabolic
engineering will become increasingly important.
The evolution of metabolic engineering from single- to multistep
approaches, along with high-throughput methods for gene discovery and
functional analysis and novel platforms for combinatorial testing of
heterologous pathways in plants, will increase the predictive accuracy of
early development stages.
Thus helping to refine engineering strategies and reduce the need for
trial-and-error testing.
51
First line: Metabolomics in Medicinal PlantResearch p 275 az tork
Metabolic engineering volume 27 issue 2015
Az : Metabolic engineering approaches for production of biochemicals in food and medicinal plants
Due to their high degree of structural diversity, these compounds have significant commercial value as pharmaceuticals, nutraceuticals, dyes, fragrances, flavors and pesticides. In addition to their commercial applications, many dietary health benefits can be attributed to specific plant natural products in popular food crops. For instance, the com- pounds seen in Figure 1 are anti-cancer agents (vinblastine and paclitaxel), analgesics (morphine), antioxidants (nar- ingenin and delphinidin) and provitamins (b-carotene).
Anuual review of plant biology volume 65
The flux through a target pathway can be enhanced by increasing the expression
of enzymes in upstream pathways to ensure a sufficient supply of precursors, by increasing the
expression of the first committed enzyme in the target compound pathway so that flux is directed
specifically toward the target metabolite, and by increasing the activity of rate-limiting enzymes
that create bottlenecks and cause the buildup of inhibitory intermediates. Additional strategies
that can be used instead of or in combination with the above include the suppression of competing
pathways downstream of branch points to avoid intermediates being diverted, the suppression of
catabolic steps so that a target metabolite is not degraded or converted into other molecules, and
the creation of sink compartments that store the target metabolite, all of which shift the chemical
equilibrium toward increased production.
(a) Increase in enzyme activity Fraser PD, Römer S, Shipton CA, Mills PB, Kiano JW, et al. 2002. Evaluation of transgenic tomato plants expressing an additional phytoene synthase in a fruit-specific manner. Proc. Natl. Acad. Sci. USA 99:1092–97. http://www.ncbi.nlm.nih.gov/pubmed/11805345 Römer S, Fraser PD, Kiano JW, Shipton CA, Misawa N, et al. 2000. Elevation of the provitamin A content of transgenic tomato plants. Nat. Biotechnol. 18:666–69. http://www.ncbi.nlm.nih.gov/pubmed/10835607 (b) Upstream precursor overexpression Enfissi EM, Fraser PD, Lois LM, Boronat A, Schuch W, Bramley PM. 2005. Metabolic engineering of the mevalonate and non-mevalonate isopentenyl diphosphate-forming pathways for the production of health-promoting isoprenoids in tomato. Plant Biotechnol. J. 3:17–27. http://www.ncbi.nlm.nih.gov/pubmed/17168896 Morris WL, Ducreux LJ, Hedden P, Millam S, Taylor MA. 2006. Overexpression of a bacterial 1-deoxy-D-xylulose 5-phosphate synthase gene in potato tubers perturbs the isoprenoid metabolic network: implications for the control of the tuber life cycle. J. Exp. Bot. 57:3007–18. http://www.ncbi.nlm.nih.gov/pubmed/16873449 (c) Blocking by gene silencing Diretto G, Tavazza R, Welsch R, Pizzichini D, Mourgues F, et al. 2006. Metabolic engineering of potato tuber carotenoids through tuber-specific silencing of lycopene epsilon cyclase. BMC Plant Biol. 6:13. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1570464/ Diretto G, Welsch R, Tavazza R, Mourgues F, Pizzichini D, et al. 2007. Silencing of betacarotene hydroxylase increases total carotenoid and beta-carotene levels in potato tubers. BMC Plant Biol. 7:11. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1828156/ Römer S, Lübeck J, Kauder F, Steiger S, Adomat C, Sandmann G. 2002. Genetic engineering of a zeaxanthin-rich potato by antisense inactivation and co-suppression of carotenoid epoxidation. Metab. Eng. 4:263–72. http://www.ncbi.nlm.nih.gov/pubmed/12646321 Yu B, Lydiate DJ, Young LW, Schäfer UA, Hannoufa A. 2008. Enhancing the carotenoid content of Brassica napus seeds by downregulating lycopene epsilon cyclase. Transgenic Res. 17:573–85. http://www.ncbi.nlm.nih.gov/pubmed/17851775 Supplemental Material: Annu. Rev. Plant Biol. 2014. 65:187–223 doi: 10.1146/annurev-arplant-050213-035825 Engineering Complex Metabolic Pathways in Plants Farré et al. 2 (d) Generation of a metabolic sink Lopez AB, Van Eck J, Conlin BJ, Paolillo DJ, O'Neill J, Li L. 2008. Effect of the cauliflower Or transgene on carotenoid accumulation and chromoplast formation in transgenic potato tubers. J. Exp. Bot. 59:213–23. http://www.ncbi.nlm.nih.gov/pubmed/18256051 Lu S, Van Eck J, Zhou X, Lopez AB, O'Halloran DM, et al. 2006. The cauliflower Or gene encodes a DnaJ cysteine-rich domain-containing protein that mediates high levels of β-carotene accumulation. Plant Cell 18:3594–605. http://www.ncbi.nlm.nih.gov/pubmed/17172359
target product, andis the result of the target product conversion. (a) Modification of the activity of
enzymes implicated in rate-limiting steps in the target pathway by modulation of one or two key enzymes or
multiple enzymes. (b) Upstream precursor enhancement by increasing flux through the pathway by
overexpressing the enzyme(s) that catalyze(s) the first committed step of the pathway. (c) Blocked pathway
branch points by RNA interference or antisense RNA. (d ) Enhanced accumulation of the target metabolite
by increasing sink compartments. Adapted from Reference 177.
Be ebarate dige:::::: Block a metabolic flux (re-channel)
Channel a metabolic flux into new cell compartments
Induce a metabolic flux (can lead to unexpected results)
Introduce a new metabolic pathway into organism (the most successful way)
Systems biology has been defined as the study of the interactions
between genes, metabolites, proteins, and regulatory elements
and seeks to elaborate integrative models and/or networks az the plat cell volume issue 2014
Genome scale metabolic reconstruction yields a model
describing the entire metabolic and transport network
that a genome is capable of producing
The construction
of networks in microbes is becoming semi-automated,
whereas in plants it is a more labor-intensive
process which has been carried out most thoroughly for
Arabidopsis and maize
Literature sources or online databases (Table 1) provide much
of the information required for establishing the structure of
a metabolic network
Nevertheless, there are usually gaps in which neither the literature
nor databases can provide the required information. This is
especially common when it comes to inter/intracompartmental
transport reactions along with their related transport systems,
as information in this area remains, for the most part,
uncharacterized.
A “pathway mapping” tool developed for
the KEGG database produces a graphical display of the location
of various metabolites in the relevant metabolic network
(Fiehn et al., 2011).
and the Plant Metabolic Network,
which contains links to plant metabolic pathway databases
(Zhang et al., 2010).
A “pathway mapping” tool developed for
the KEGG database produces a graphical display of the location
of various metabolites in the relevant metabolic network
(Fiehn et al., 2011).
and the Plant Metabolic Network,
which contains links to plant metabolic pathway databases
(Zhang et al., 2010).
A “pathway mapping” tool developed for
the KEGG database produces a graphical display of the location
of various metabolites in the relevant metabolic network
(Fiehn et al., 2011).
and the Plant Metabolic Network,
which contains links to plant metabolic pathway databases
(Zhang et al., 2010).
Simplified representation of the reconstruction process. Step A: Metabolic network components such as metabolites (circles), enzymatic reactions (E)
and transporters (T) are extracted from genome annotation and metabolic pathway databases. The green circle (S) represents the substrate into the
network and the blue circles represent the products out of the network (P1–P5). Note that the enzymatic reactions are shown in different colours
indicating multiple gene calls for a particular enzyme. The elements in red are examples of common gaps found in the flat metabolic reconstruction that
needs further gap filing to allow in silico metabolic functionality. Step B: Simplified representation of a compartmented metabolic reconstruction
showing the cytosolic and plastidic glycolysis to the TCA cycle in mitochondria. More curation efforts are needed during this step to carefully allocate
enzymes to different organelles. A number of step reactions and transporters are suggested during the curation process to allow cell metabolic
function (elements in red) based on biochemical evidences. Az current opinion in biotechnology volume 24 issue 2 2013
A well established platform to interrogate the metabolic reconstruction is the constraint-based reconstruction and analysis (COBRA), methods.
In plants, however, the complexity of the metabolic network has
held back the development of computer-aided synthesis
methods (Mendes, 2002), but this sort of bioinformatics platform
is quite widely used for modeling in microorganisms.
Fortunately,
the mathematical approaches are developing rapidly
and efforts have been intensified to develop plant-specific platforms, allowing us to analyze and model plant metabolic
pathways (Libourel and Shachar-Hill, 2008; Stitt et al., 2010).
KINETIC: In this approach metabolic networks are modeled in
detail with differential equations representing reaction
rates that depend on metabolite levels and enzyme properties.
The approach generally applies to
small-sized and mid-sized networks because each reaction
involves multiple model parameters which greatly
expands the possible operational space and computational
requirements compared to simplified representations
in the steady state. This approach relies on literature
values for the kinetic parameters of enzymes involved
and/or additional in vitro enzymology to obtain estimates
of enzyme parameters (Km, Vmax, and so on). parameter values obtained in vitro under conditions may
not represent those in the intracellular state.
Figure 1. Steady State Modeling Approaches.
(A) A model reaction consisting of three metabolites (M), three exchanges (b), and three internal reactions (v).
(B) The reaction network represented in a stoichiometric matrix.
(C) The network rewritten in matrix form based on the equations.
(D) In a metabolic steady state, the product of the stoichiometric matrix (S) and the flux vector (v) returns a null vector (i.e., S.v = 0). Mass balance
equations for each metabolite have been represented here.
(E) Constraints (shown in gray) and solution space. With no constraints, the flux distribution of a biological network reconstruction may lie at any point in
a solution space (i). FBA (ii) solves the equation S.v = 0 by calculating intracellular fluxes from the measurement of a limited number of input and output
fluxes. The solution (black dot) requires the definition of an objective function. By applying EMA and EPA (iii), the irreversible fluxes are constrained to be
non-negative (v $ 0), then the resulting space of flux distributions is a convex polyhedral cone, which represents the flux space of the metabolic system,
containing all allowable flux distributions. MFA (iv) provides information concerning the contribution of a measured reaction (m) to the operational state
of overall unmeasured fluxes and computes a metabolic flux vector specific for a particular growth condition (black dot).
In plants, however, the complexity of the metabolic network has
held back the development of computer-aided synthesis
methods (Mendes, 2002), but this sort of bioinformatics platform
is quite widely used for modeling in microorganisms.
Fortunately,
the mathematical approaches are developing rapidly
and efforts have been intensified to develop plant-specific platforms, allowing us to analyze and model plant metabolic
pathways (Libourel and Shachar-Hill, 2008; Stitt et al., 2010).
Such findings may
include new locations for specific processes or the discovery of
formerly unknown reactions (Kruger et al., 2012). An example in
which a validated model was revised and updated occurred during a recent study by Wang et al. (2012). Using the validated
model of C4GEM (de Oliveira Dal’Molin et al., 2010b) for their
study, they realized that there were missing reactions in the
xylose pathway and that the model could be improved by including
them.
The basis for flux balance analysis of large networks. With no constraints, the flux distribution of a biological network reconstruction may lie at any
point in a solution space. When mass balance constraints imposed by the stoichiometric matrix S and capacity constraints imposed by reversibilities
are applied to a network, it defines an allowable or feasible solution space. The network may acquire any flux distribution within this cone, but points
outside this space violate the mass conservation in the system and therefore are denied by the imposed constraints. Through optimisation of an
objective function, FBA can identify a single optimal flux distribution that lies on the edge of the allowable solution space. Az current opinion in biotechnology volume 24 issue 2 2013
Anuual review of plant biology volume 65
The flux through a target pathway can be enhanced by increasing the expression
of enzymes in upstream pathways to ensure a sufficient supply of precursors, by increasing the
expression of the first committed enzyme in the target compound pathway so that flux is directed
specifically toward the target metabolite, and by increasing the activity of rate-limiting enzymes
that create bottlenecks and cause the buildup of inhibitory intermediates. Additional strategies
that can be used instead of or in combination with the above include the suppression of competing
pathways downstream of branch points to avoid intermediates being diverted, the suppression of
catabolic steps so that a target metabolite is not degraded or converted into other molecules, and
the creation of sink compartments that store the target metabolite, all of which shift the chemical
equilibrium toward increased production.
(a) Increase in enzyme activity Fraser PD, Römer S, Shipton CA, Mills PB, Kiano JW, et al. 2002. Evaluation of transgenic tomato plants expressing an additional phytoene synthase in a fruit-specific manner. Proc. Natl. Acad. Sci. USA 99:1092–97. http://www.ncbi.nlm.nih.gov/pubmed/11805345 Römer S, Fraser PD, Kiano JW, Shipton CA, Misawa N, et al. 2000. Elevation of the provitamin A content of transgenic tomato plants. Nat. Biotechnol. 18:666–69. http://www.ncbi.nlm.nih.gov/pubmed/10835607 (b) Upstream precursor overexpression Enfissi EM, Fraser PD, Lois LM, Boronat A, Schuch W, Bramley PM. 2005. Metabolic engineering of the mevalonate and non-mevalonate isopentenyl diphosphate-forming pathways for the production of health-promoting isoprenoids in tomato. Plant Biotechnol. J. 3:17–27. http://www.ncbi.nlm.nih.gov/pubmed/17168896 Morris WL, Ducreux LJ, Hedden P, Millam S, Taylor MA. 2006. Overexpression of a bacterial 1-deoxy-D-xylulose 5-phosphate synthase gene in potato tubers perturbs the isoprenoid metabolic network: implications for the control of the tuber life cycle. J. Exp. Bot. 57:3007–18. http://www.ncbi.nlm.nih.gov/pubmed/16873449 (c) Blocking by gene silencing Diretto G, Tavazza R, Welsch R, Pizzichini D, Mourgues F, et al. 2006. Metabolic engineering of potato tuber carotenoids through tuber-specific silencing of lycopene epsilon cyclase. BMC Plant Biol. 6:13. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1570464/ Diretto G, Welsch R, Tavazza R, Mourgues F, Pizzichini D, et al. 2007. Silencing of betacarotene hydroxylase increases total carotenoid and beta-carotene levels in potato tubers. BMC Plant Biol. 7:11. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1828156/ Römer S, Lübeck J, Kauder F, Steiger S, Adomat C, Sandmann G. 2002. Genetic engineering of a zeaxanthin-rich potato by antisense inactivation and co-suppression of carotenoid epoxidation. Metab. Eng. 4:263–72. http://www.ncbi.nlm.nih.gov/pubmed/12646321 Yu B, Lydiate DJ, Young LW, Schäfer UA, Hannoufa A. 2008. Enhancing the carotenoid content of Brassica napus seeds by downregulating lycopene epsilon cyclase. Transgenic Res. 17:573–85. http://www.ncbi.nlm.nih.gov/pubmed/17851775 Supplemental Material: Annu. Rev. Plant Biol. 2014. 65:187–223 doi: 10.1146/annurev-arplant-050213-035825 Engineering Complex Metabolic Pathways in Plants Farré et al. 2 (d) Generation of a metabolic sink Lopez AB, Van Eck J, Conlin BJ, Paolillo DJ, O'Neill J, Li L. 2008. Effect of the cauliflower Or transgene on carotenoid accumulation and chromoplast formation in transgenic potato tubers. J. Exp. Bot. 59:213–23. http://www.ncbi.nlm.nih.gov/pubmed/18256051 Lu S, Van Eck J, Zhou X, Lopez AB, O'Halloran DM, et al. 2006. The cauliflower Or gene encodes a DnaJ cysteine-rich domain-containing protein that mediates high levels of β-carotene accumulation. Plant Cell 18:3594–605. http://www.ncbi.nlm.nih.gov/pubmed/17172359
target product, andis the result of the target product conversion. (a) Modification of the activity of
enzymes implicated in rate-limiting steps in the target pathway by modulation of one or two key enzymes or
multiple enzymes. (b) Upstream precursor enhancement by increasing flux through the pathway by
overexpressing the enzyme(s) that catalyze(s) the first committed step of the pathway. (c) Blocked pathway
branch points by RNA interference or antisense RNA. (d ) Enhanced accumulation of the target metabolite
by increasing sink compartments. Adapted from Reference 177.
Plant cell reports volume 33
product of paclitaxel synthesis
is cyclized from geranylgeranyl diphosphate
(GGPP), and further complex hydroxylation and acylation
processes of the unique taxane core skeleton produce
paclitaxel.
To accomplish de novo production of taxadiene,
we transformed Nicotiana benthamiana with a taxadiene
synthase (TS) gene. The introduced TS gene under the
transcriptional control of the CaMV 35S promoter was
constitutively expressed in N. benthamiana, and the de
novo production of taxadiene was confirmed by mass
spectroscopy profiling. Transformed N. benthamiana
homozygous lines produced 11–27 lg taxadiene/g of dry
weight. The highest taxadiene production line TSS-8 was
further treated with an elicitor, methyl jasmonate, and
metabolic pathway shunting by suppression of the phytoene
synthase gene expression which resulted in accumulation
of increased taxadiene accumulation by 1.4- or 1.9-
fold, respectively. In summary, we report that the production
of taxadiene in N. benthamiana was possible by the
ectopic expression of the TS gene, and higher accumulation
of taxadiene could be achieved by elicitor treatment or
metabolic pathway shunting of the terpenoid pathway.
Anuual review of plant biology volume 65
The flux through a target pathway can be enhanced by increasing the expression
of enzymes in upstream pathways to ensure a sufficient supply of precursors, by increasing the
expression of the first committed enzyme in the target compound pathway so that flux is directed
specifically toward the target metabolite, and by increasing the activity of rate-limiting enzymes
that create bottlenecks and cause the buildup of inhibitory intermediates. Additional strategies
that can be used instead of or in combination with the above include the suppression of competing
pathways downstream of branch points to avoid intermediates being diverted, the suppression of
catabolic steps so that a target metabolite is not degraded or converted into other molecules, and
the creation of sink compartments that store the target metabolite, all of which shift the chemical
equilibrium toward increased production.
(a) Increase in enzyme activity Fraser PD, Römer S, Shipton CA, Mills PB, Kiano JW, et al. 2002. Evaluation of transgenic tomato plants expressing an additional phytoene synthase in a fruit-specific manner. Proc. Natl. Acad. Sci. USA 99:1092–97. http://www.ncbi.nlm.nih.gov/pubmed/11805345 Römer S, Fraser PD, Kiano JW, Shipton CA, Misawa N, et al. 2000. Elevation of the provitamin A content of transgenic tomato plants. Nat. Biotechnol. 18:666–69. http://www.ncbi.nlm.nih.gov/pubmed/10835607 (b) Upstream precursor overexpression Enfissi EM, Fraser PD, Lois LM, Boronat A, Schuch W, Bramley PM. 2005. Metabolic engineering of the mevalonate and non-mevalonate isopentenyl diphosphate-forming pathways for the production of health-promoting isoprenoids in tomato. Plant Biotechnol. J. 3:17–27. http://www.ncbi.nlm.nih.gov/pubmed/17168896 Morris WL, Ducreux LJ, Hedden P, Millam S, Taylor MA. 2006. Overexpression of a bacterial 1-deoxy-D-xylulose 5-phosphate synthase gene in potato tubers perturbs the isoprenoid metabolic network: implications for the control of the tuber life cycle. J. Exp. Bot. 57:3007–18. http://www.ncbi.nlm.nih.gov/pubmed/16873449 (c) Blocking by gene silencing Diretto G, Tavazza R, Welsch R, Pizzichini D, Mourgues F, et al. 2006. Metabolic engineering of potato tuber carotenoids through tuber-specific silencing of lycopene epsilon cyclase. BMC Plant Biol. 6:13. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1570464/ Diretto G, Welsch R, Tavazza R, Mourgues F, Pizzichini D, et al. 2007. Silencing of betacarotene hydroxylase increases total carotenoid and beta-carotene levels in potato tubers. BMC Plant Biol. 7:11. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1828156/ Römer S, Lübeck J, Kauder F, Steiger S, Adomat C, Sandmann G. 2002. Genetic engineering of a zeaxanthin-rich potato by antisense inactivation and co-suppression of carotenoid epoxidation. Metab. Eng. 4:263–72. http://www.ncbi.nlm.nih.gov/pubmed/12646321 Yu B, Lydiate DJ, Young LW, Schäfer UA, Hannoufa A. 2008. Enhancing the carotenoid content of Brassica napus seeds by downregulating lycopene epsilon cyclase. Transgenic Res. 17:573–85. http://www.ncbi.nlm.nih.gov/pubmed/17851775 Supplemental Material: Annu. Rev. Plant Biol. 2014. 65:187–223 doi: 10.1146/annurev-arplant-050213-035825 Engineering Complex Metabolic Pathways in Plants Farré et al. 2 (d) Generation of a metabolic sink Lopez AB, Van Eck J, Conlin BJ, Paolillo DJ, O'Neill J, Li L. 2008. Effect of the cauliflower Or transgene on carotenoid accumulation and chromoplast formation in transgenic potato tubers. J. Exp. Bot. 59:213–23. http://www.ncbi.nlm.nih.gov/pubmed/18256051 Lu S, Van Eck J, Zhou X, Lopez AB, O'Halloran DM, et al. 2006. The cauliflower Or gene encodes a DnaJ cysteine-rich domain-containing protein that mediates high levels of β-carotene accumulation. Plant Cell 18:3594–605. http://www.ncbi.nlm.nih.gov/pubmed/17172359
target product, andis the result of the target product conversion. (a) Modification of the activity of
enzymes implicated in rate-limiting steps in the target pathway by modulation of one or two key enzymes or
multiple enzymes. (b) Upstream precursor enhancement by increasing flux through the pathway by
overexpressing the enzyme(s) that catalyze(s) the first committed step of the pathway. (c) Blocked pathway
branch points by RNA interference or antisense RNA. (d ) Enhanced accumulation of the target metabolite
by increasing sink compartments. Adapted from Reference 177.
The PCR
products were then visualized by 1.2 % agarose gel
electrophoresis. Marhale sevom ba kit rt bad agarose
The PCR
products were then visualized by 1.2 % agarose gel
electrophoresis. Marhale sevom ba kit rt bad agarose
Az metabolic eng seminar (2)
1: Double-stranded RNA interference
(dsRNAi) today provides probably the
preferred method to knock out gene function.[3]
An emerging alternative to this is the use of
RNase P-mediated RNA degradation.[4] Both
of these approaches are dependent on the availability
of methods for introducing and expressing
RNA in the host plant
2: There are many protein
inhibitors of metabolic enzymes that, when
overexpressed, could have the potential to inhibit
specific enzymatic steps (e.g., Ref.[5]). Nonprotein
inhibitors of metabolic enzymes are
also extensively used, resulting in some potent
plant herbicides for which resistance can be
easily manipulated, or in the formation of new
compounds, when nonessential pathways are
inhibited.[6,7]
are already in clinical use
Proceeding of national academy of science volume 103
Remember: literature and dabase was done they wanted to fil the gaps so experimental kardan.
Before initiating functional genomics-driven
gene discovery for TIA metabolism in periwinkle cells, we
needed to identify the conditions in which differential accumulation
of the desired metabolites can be observed (10). In the
case of C. roseus, the literature suggests that TIA accumulation
is strongly influenced by the complex interaction of phytohormones
such as auxins and jasmonates (11). Therefore, it seemed
most promising to investigate the combined effects of these two
hormones on TIA accumulation in C. roseus cells, applying
growth and elicitation conditions similar to those described in ref. 11.
ajmalicine, tabersonine, catharanthine, yohimbine,
cathenamine, secologanine, lochnerinine, 16-methoxy-2,3-
dihydro-3-hydroxytabersonine, and desacetoxyvindoline.
The remaining peaks contained
metabolites most abundantly in the range of 300–400 mz, the
expected range for monomeric TIA metabolites. Some also
displayed retention times similar to the TIAs identified. More
detailed analytical investigation would be required for conclusive
chemical characterization.
The negative
effect of auxins on ajmalicine levels has also been observed in
ref. 11, but the levels of other monomeric TIAs were not assessed
in that study.
Most
importantly, however, not all monomeric TIAs identified are
found within the same auxin-modulated subcluster, indicating
divergent regulation of metabolite biosynthesis and accumulation
within the TIA pathway. For instance, auxins enhanced the
accumulation of tabersonine and catharanthine, the two building
blocks for bisindole alkaloid biosynthesis, whereas they repressed
ajmalicine, a monomeric TIA not involved in the synthesis
of bisindole alkaloids. This result corroborates the findings
obtained by TIA-targeted metabolite analysis and underscores
the reliability of our C. roseus metabolome data set. Albate vase vin ha in be nafe mast
PEAK FILT: To better match the size
of the transcriptional data set, the peaks from the metabolic
profile data set were filtered based on coefficient of variation
and retention time. Peaks eluting within the first 3 min were
excluded, because retention is unstable within this period.
Furthermore, peaks with too low signal-to-noise ratio, low
intensity, low variability, and natural isotope peaks were removed.
BLAST searches with the sequences from the C. roseus cDNAAFLP
tags revealed that 37% of the CR tags displayed no
sequence similarity to any known plant genes.
The CR tags could be divided into different subclusters, based
either on their expression profiles or their annotation
RT-PCR was performed for all of the remaining known
but undetected TIA biosynthesis genes and showed that all but
two (T16H and TDC) were either not differentially expressed or
not transcribed at all (Fig. 6B).
The principal component analysis (14)
was performed by using PLS Toolbox package (Eigenvector
Research, Wenatchee, WA) and MATLAB (Mathworks, Gouda,
The Netherlands).
The first two principal components (PCs), accounting
for 64% of the total variability, revealed clear separations:
the auxin-treated cells from the nontreated cells by the
first PC and each group across the time domain by the second
PC (Fig. 7 and Table 2, which are published as supporting
information on the PNAS web site).
Integration of transcriptomics and metabolomics
data will be crucial for the study of gene-to-metabolite networks
for (secondary) metabolism in plants, both at the regulatory and
catalytic levels. We have performed a linear correlation network
analysis of transcripts and nontargeted metabolites from elicited
periwinkle cells to create previously undescribed gene-to-gene
and gene-to-metabolite networks and, thereby, discover previously
undescribed genes involved in TIAbiosynthesis. In contrast
to previous studies, in which long-term effects on metabolism
(days after application of nutritional stress; ref. 22) or steadystate
situations (transgenic plants; ref. 23) were evaluated, we
focused on inducible short-term effects (gene expression and
metabolite accumulation within hours after MeJA application).
Nevertheless the correlation networks constructed allowed us
to identify those genes most likely to be involved in TIA
metabolism and, therefore, locate possible candidates coding for
some of the missing links in the biosynthesis of monomeric TIAs.
Fig. 1. Biosynthesis of C. roseus TIAs. Metabolites are given as full names in
lowercase and enzymes as abbreviations in capitals. Full and dashed arrows
mark single and multiple conversion steps between intermediates, respectively.
In the upper left corner, transcription factors binding to promoters of
TIA biosynthetic genes are indicated. In addition, a graphical snapshot shows
the relative transcript (boxes) and metabolite (circles) accumulation levels in
samples harvested 12 h after elicitation. The left and right boxes or circles
reflect the influence of jasmonate and auxin, respectively. Red, induced
accumulation compared with control without the phytohormone; green,
repressed accumulation compared with control without the phytohormone;
white, no effect of the phytohormone on accumulation; crossed, no transcript
or metabolite accumulation detected. Enzymes and transcription factors
listed: ORCA, octadecanoid-responsive Catharanthus AP2-domain; BPF, box
P-binding factor; GBF, G-box binding factor; ZCT, zinc finger Catharanthus transcription factor; DXS, 1-deoxy-D-xylulose-5-phosphate synthase; DXR,
1-deoxy-D-xylulose-5-phosphate reductoisomerase; CMS, 4-diphosphocytidyl-
2C-methyl-D-erythrol 4-phosphate synthase; CMK, 4-diphosphocytidyl-2Cmethyl-
D-erythrol kinase; MECS, 2C-methyl-D-erythrol-2,4-cyclodiphosphate
synthase; HDS, GCPE, 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase;
HDR, 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase; IPPI,
isopentenylpyrophosphate isomerase; G10H, geraniol 10-hydroxylase; CPR,
cytochrome P450 reductase; 10HGO, 10-hydroxygeraniol oxidoreductase; SLS,
secologanin synthase; STR, strictosidine synthase; SGD, strictosidine -Dglucosidase;
AS, anthranilate synthase; TDC, tryptophan decarboxylase; T16H,
tabersonine 16-hydroxylase; OMT,O-methyltransferase; NMT,N-methyltransferase;
D4H, desacetoxyvindoline 4-hydroxylase; DAT, deacetylvindoline 4-Oacetyltransferase;
MAT, acetyl-CoA:minovincine-O-acetyltransferase.
To best visualize the complex networks of secondary
metabolism in C. roseus cells, two different subsets of the profiles
were analyzed. The correlation network analysis was first performed
for a select subset of identified metabolites and genes
(Fig. 2A) chosen to include the 9 TIAmetabolite peaks identified
and the 34 gene tags identical to or with close sequence similarity
to genes that encode proteins catalyzing either jasmonate or TIA
biosynthesis. Given their importance in TIA biosynthesis, all
putative cytochrome P450 (CYP450) and AP2 transcription
factors also were included.
With the
exception of three tags corresponding to an AP2 factor
(CRG20), a lipoxygenase possibly involved in jasmonate biosynthesis
(CRG48), and a CYP450 (CRG96), all of the CR tags
belonging to the gene classes used in this subset could be
integrated into the network
An unbiased subset was then visualized, subtracted from the
complete network across all transcript and metabolite profiles,
and centered on the tabersonine node by using the cutoff for
absolute value of correlation coefficient C 0.8 (Fig. 2B).
The
gene-to-metabolite network around the tabersonine node consisted
of 11 metabolites and 13 genes (with a BLAST hit)
representing the nearest neighbors. Many of the unassigned
metabolites included in this cluster displayed masses in the range
of 300–400 mz, the expected range for monomeric TIA metabolites
and, thus, might constitute yet-unknown intermediates
or side products of TIA metabolism. Accordingly, this network
centered on tabersonine includes numerous tags corresponding
to enzymes with currently unidentified activity and substrate
specificity that are, thus, likely to code for some of the missing
links in the biosynthesis of tabersonine or other monomeric
TIAs.
Fig. 4. Metabolome analysis of elicited C. roseus cells. Nontargeted metabolite profiling represented by average linkage hierarchical clustering of metabolite accumulation profiles. Two subclusters of the full cluster, grouping metabolites whose accumulation is affected by auxin treatments, are shown. The treatments, the presence and absence of auxin (NA) and MeJA (MJ) and the time points (in hours) are indicated at the top, and the retention time and the m/z value are on the right. AR, auxin repressed; AS, auxin stimulated. Blue and yellow boxes correspond to stimulated or repressed accumulation of metabolites, relative to the average accumulation level of all samples, respectively.
Listed in order of impact, these forces are the
addition of MeJA (irrespective of auxin presence), growth
(independent of the exogenous application of hormones or
elicitors), and the presence of auxin (irrespective of MeJA
presence). These factors modulate the expression of 42.8%,
33.8%, and 28.5% of the CR tags, respectively. Only for 5.2% of
the CR tags was the expression affected both by MeJA and auxin.
According to the Functional Catalog of the Munich Information
According to the Functional Catalog of the Munich Information
Center for Protein Sequences (http:mips.gsf.deprojects
funcat), the CR tags can be classified into eight broadly defined
functional groups. The functional category ‘‘Metabolism and
Energy’’ in particular is one of the major groups (Fig. 5) and
includes, as anticipated, a large number of TIA genes. The tags
that corresponded to genes reported to be associated with TIA
biosynthesis, and isolated as differentially expressed by cDNAAFLP,
include tags for genes associated with biosynthesis of the
terpenoid moiety, biosynthesis of the indole moiety, biosynthesis
of monomer TIAs, and transcription factors regulating TIA
biosynthesis (Fig. 6A, which is published as supporting information
on the PNAS web site). In all these cases, the tags showed
a (near) perfect match with the gene sequences encoding the
isoforms reported to catalyze (or regulate) the known enzymatic
reactions.
Moreover, the cDNA-AFLP set also includes tags with
close similarity (70%) to 10HGO, STR, T16H, and DAT but
whose sequence does not perfectly match that of the isoenzyme
reported to catalyze the corresponding enzymatic reactions.
Because in plant secondary metabolism even close sequence
similarity is not sufficient to indicate a correct functional annotation,
further analysis will be required to ascertain whether the
gene products corresponding to these tags catalyze identical
reactions or whether they possibly generate structurally related
alkaloids or other types of secondary metabolites. As can be seen
in Fig. 6A, the expression of all these genes can be induced by
elicitation with MeJA and either stimulated or repressed by
auxin. RT-PCR was performed for all of the remaining known
but undetected TIA biosynthesis genes and showed that all but
two (T16H and TDC) were either not differentially expressed or
not transcribed at all (Fig. 6B).
Fig. 5. Transcriptome analysis of elicited C. roseus cells. (A) Average linkage hierarchical clustering of CR gene tags. The treatments [auxin (NA) presence or absence, MeJA (MJ) presence or absence] and the time points (in hours) are indicated at the top. Red and green boxes reflect transcriptional activation and repression, respectively, relative to the average expression level of all samples. Gray boxes correspond to missing time points. JR, Jasmonate Repressed; JS, Jasmonate Stimulated; JSAR, Jasmonate Stimulated Auxin Repressed; JSAS, Jasmonate Stimulated Auxin Stimulated; AR, Auxin Repressed; AS, Auxin Stimulated; GD, Growth Down-regulated; GU, Growth Up-regulated. (B) Pie representation of the distribution of the different expression clusters as established by average linkage hierarchical clustering of CR gene tags. (C) Pie representation of the distribution of the CR gene tags according to the classification in MIPS functional categories.
Fig. 2. Gene-to-metabolite networks in elicited C. roseus cells. Metabolites
are represented by circles and transcripts by squares. (A) Correlation network
for a select subset of identified metabolites and genes consisting of nine TIA
metabolite peaks and gene tags identical to or with close sequence similarity
to genes, encoding for proteins catalyzing jasmonate and TIA biosynthesis,
respectively, and all putative cytochrome P450 and AP2 transcription factors.
Correlations between the variables are calculated from the complete profiles
across all conditions and time points, and edges are drawn when the linear
correlation coefficient is0.55. Tags corresponding to AP2TF (CRG20), CYP450
(CRG96), and LOX (CRG48) also were included in the gene subset, but these
data points have been removed from the figure because no correlation was
observed. (B) Correlation network for the complete set of metabolites and
genes (Upper), with a zoom-in on the tabersonine node and 24 of its nearest
neighbors (Lower). Red lines represent positive correlations (C 0.8).
Listed in order of impact, these forces are the
addition of MeJA (irrespective of auxin presence), growth
(independent of the exogenous application of hormones or
elicitors), and the presence of auxin (irrespective of MeJA
presence). These factors modulate the expression of 42.8%,
33.8%, and 28.5% of the CR tags, respectively. Only for 5.2% of
the CR tags was the expression affected both by MeJA and auxin.
According to the Functional Catalog of the Munich Information
According to the Functional Catalog of the Munich Information
Center for Protein Sequences (http:mips.gsf.deprojects
funcat), the CR tags can be classified into eight broadly defined
functional groups. The functional category ‘‘Metabolism and
Energy’’ in particular is one of the major groups (Fig. 5) and
includes, as anticipated, a large number of TIA genes. The tags
that corresponded to genes reported to be associated with TIA
biosynthesis, and isolated as differentially expressed by cDNAAFLP,
include tags for genes associated with biosynthesis of the
terpenoid moiety, biosynthesis of the indole moiety, biosynthesis
of monomer TIAs, and transcription factors regulating TIA
biosynthesis (Fig. 6A, which is published as supporting information
on the PNAS web site). In all these cases, the tags showed
a (near) perfect match with the gene sequences encoding the
isoforms reported to catalyze (or regulate) the known enzymatic
reactions.
Moreover, the cDNA-AFLP set also includes tags with
close similarity (70%) to 10HGO, STR, T16H, and DAT but
whose sequence does not perfectly match that of the isoenzyme
reported to catalyze the corresponding enzymatic reactions.
Because in plant secondary metabolism even close sequence
similarity is not sufficient to indicate a correct functional annotation,
further analysis will be required to ascertain whether the
gene products corresponding to these tags catalyze identical
reactions or whether they possibly generate structurally related
alkaloids or other types of secondary metabolites. As can be seen
in Fig. 6A, the expression of all these genes can be induced by
elicitation with MeJA and either stimulated or repressed by
auxin. RT-PCR was performed for all of the remaining known
but undetected TIA biosynthesis genes and showed that all but
two (T16H and TDC) were either not differentially expressed or
not transcribed at all (Fig. 6B).
Fig. 5. Transcriptome analysis of elicited C. roseus cells. (A) Average linkage hierarchical clustering of CR gene tags. The treatments [auxin (NA) presence or absence, MeJA (MJ) presence or absence] and the time points (in hours) are indicated at the top. Red and green boxes reflect transcriptional activation and repression, respectively, relative to the average expression level of all samples. Gray boxes correspond to missing time points. JR, Jasmonate Repressed; JS, Jasmonate Stimulated; JSAR, Jasmonate Stimulated Auxin Repressed; JSAS, Jasmonate Stimulated Auxin Stimulated; AR, Auxin Repressed; AS, Auxin Stimulated; GD, Growth Down-regulated; GU, Growth Up-regulated. (B) Pie representation of the distribution of the different expression clusters as established by average linkage hierarchical clustering of CR gene tags. (C) Pie representation of the distribution of the CR gene tags according to the classification in MIPS functional categories.
RT-PCR was performed for all of the remaining known
but undetected TIA biosynthesis genes and showed that all but
two (T16H and TDC) were either not differentially expressed or
not transcribed at all (Fig. 6B).
Fig. 6. Expression pattern of TIA genes.(A) Average linkage hierarchical clustering of CR cDNA-amplified fragment length polymorphism (AFLP) tags with full sequence identity or close similarity to genes involved in TIA biosynthesis. The treatments and time points are indicated at the top (as in Fig. 5), and the tag codes and gene abbreviations (as in Fig. 1) on the right. Tags without a (near) perfect sequence match with the previously released TIA gene sequences are indicated as "-like." Red and green boxes reflect transcriptional activation and repression, respectively, relative to the average expression level of all samples. (B) RT-PCR analysis with gene specific primers for the genes indicated on the left. RT-PCR did not detect the transcripts for the genes encoding for ORCA1, D4H, DAT, MAT, ZCT1, and ZCT2, suggesting these genes were not transcribed under our experimental conditions. Treatments and time points are indicated at the top (as in Fig. 5). Gene abbreviations are the same as in Fig. 1. (C) Sections of cDNA-AFLP showing constitutive expression of the genes indicated on the left. Arrowheads mark the corresponding gene tag. HMGR, 3-hydroxy-3-methylglutaryl CoA reductase.
RT-PCR was performed for all of the remaining known
but undetected TIA biosynthesis genes and showed that all but
two (T16H and TDC) were either not differentially expressed or
not transcribed at all (Fig. 6B).
Fig. 6. Expression pattern of TIA genes.(A) Average linkage hierarchical clustering of CR cDNA-amplified fragment length polymorphism (AFLP) tags with full sequence identity or close similarity to genes involved in TIA biosynthesis. The treatments and time points are indicated at the top (as in Fig. 5), and the tag codes and gene abbreviations (as in Fig. 1) on the right. Tags without a (near) perfect sequence match with the previously released TIA gene sequences are indicated as "-like." Red and green boxes reflect transcriptional activation and repression, respectively, relative to the average expression level of all samples. (B) RT-PCR analysis with gene specific primers for the genes indicated on the left. RT-PCR did not detect the transcripts for the genes encoding for ORCA1, D4H, DAT, MAT, ZCT1, and ZCT2, suggesting these genes were not transcribed under our experimental conditions. Treatments and time points are indicated at the top (as in Fig. 5). Gene abbreviations are the same as in Fig. 1. (C) Sections of cDNA-AFLP showing constitutive expression of the genes indicated on the left. Arrowheads mark the corresponding gene tag. HMGR, 3-hydroxy-3-methylglutaryl CoA reductase.
RT-PCR was performed for all of the remaining known
but undetected TIA biosynthesis genes and showed that all but
two (T16H and TDC) were either not differentially expressed or
not transcribed at all (Fig. 6B).
Fig. 6. Expression pattern of TIA genes.(A) Average linkage hierarchical clustering of CR cDNA-amplified fragment length polymorphism (AFLP) tags with full sequence identity or close similarity to genes involved in TIA biosynthesis. The treatments and time points are indicated at the top (as in Fig. 5), and the tag codes and gene abbreviations (as in Fig. 1) on the right. Tags without a (near) perfect sequence match with the previously released TIA gene sequences are indicated as "-like." Red and green boxes reflect transcriptional activation and repression, respectively, relative to the average expression level of all samples. (B) RT-PCR analysis with gene specific primers for the genes indicated on the left. RT-PCR did not detect the transcripts for the genes encoding for ORCA1, D4H, DAT, MAT, ZCT1, and ZCT2, suggesting these genes were not transcribed under our experimental conditions. Treatments and time points are indicated at the top (as in Fig. 5). Gene abbreviations are the same as in Fig. 1. (C) Sections of cDNA-AFLP showing constitutive expression of the genes indicated on the left. Arrowheads mark the corresponding gene tag. HMGR, 3-hydroxy-3-methylglutaryl CoA reductase.
Biotechnology and applied biochemistry volume 52
It was surprising that those transgenic cell lines of C. roseus overexpressing tdc and str lost their capacity to produce high levels of alkaloids after 2 years of subculture, although the enzymes of both transgenes remained at high activity [82]. Whitmer et al. [83] concluded that a strategy of indirect selection, such as the use of antibiotic-resistance genes, is insufficient to maintain the concerted expression of TIApathway elements necessary for high productivity. Transient overexpression of Tdc and Str1 genes in intact leaves was reported to promote the accumulation of TIAs in C. roseus
Az ,metabolic eng seminar (2)
1: Agrobacterium-mediated transformation
provides a facile method and usually results in a
low number of integration events, minimizing
problems of cosuppression of endogenous genes,
introduction of genes by particle bombardment
has none of the host plant limitations imposed
by the host preference of Agrobacterium and
has been used in a broad range of plants.[8]
2: A
large number of plant promoters capable of
directing robust gene expression in almost every
plant tissue have been identified.[9,10] An emerging
alternative to using natural promoters is
the generation of ‘‘artificial’’ or ‘‘synthetic’’
promoters that would provide the desired
expression patterns.[11]
annual
The need to introduce and control multiple genes in plants has also benefited from synthetic biology, which uses a palette of synthetic DNA sequences to generate expression constructs that behave in a predictable manner
Annual
Schematic summary of the principles of different methods for multigene transfer. Each panel shows a different method and charts the
origin, fate, and activity of two different transgenes (blue and red blocks, with promoters shown as sideways arrows). The corresponding
products at the level of mRNA (wavy lines) and polypeptides (discs) are shown in matching colors. (a) In the gene-stacking approach,
plants already carrying transgenes 1 and 2 are crossed to bring both genes into the same line. The genes are integrated and expressed
independently (diagonal line) and therefore may segregate in later generations. A backcross program is thus needed to bring the two
transgenes to homozygosity. (b) In the retransformation approach, plants already carrying transgene 1 are transformed with transgene
2, to bring both genes into the same line. The genes are integrated and expressed independently (diagonal line) and therefore may
segregate in later generations. A backcross program is thus also needed in this case to bring the two transgenes to homozygosity. (c) In
the unlinked-transgene approach, transgenes 1 and 2 are introduced into wild-type plants using separate vectors. All genes tend to
integrate at homozygosity, which is random and may integrate in tandem (as shown here) or in head-to-head or tail-to-tail
conformations, occasionally with intervening genomic DNA sequences. Although panels a–c show individual transgenes as blue and red
blocks, the same principles of integration and segregation also apply to groups of linked transgenes. (d ) In the linked-transgene
approach, the transgenes are arranged in tandem on a single vector. The entire construct tends to integrate so that the integrated
transgenes are arranged in the same order as on the vector. This becomes increasingly difficult with more transgenes unless
high-capacity binary bacterial artificial chromosome (BIBAC)/transformation-competent artificial chromosome (TAC) vectors are
employed. (e) In the split reading frame approach, two genes are expressed as a fusion protein linked by the 2A peptide from
food-and-mouth-disease virus (black disc). This results in the expression of polycistronic mRNA and a polyprotein, which is self-cleaved
into proteins 1 and 2, although each retains part of the 2A peptide. ( f ) In the operon approach, two or more genes are expressed as an
operon, yielding a polycistronic mRNA, but the proteins are translated independently via internal ribosome entry sites. This approach
is feasible only for genes expressed in plastids, and it is therefore suitable for plants such as tobacco and a small number of other species
that are amenable to plastid transformation (as shown) but not currently for cereal crops such as maize (shown in the other panels).
Adapted from Reference 178. In the online PDF of this article (available at http://www.annualreviews.org), click the underlined text
to go directly to the associated online reference for each method.
Another example of end-product metabolic engineering is the production of glucosinolates
(81). These are defense compounds in the Brassicaceae family, and they can act as antinutritional
factors in food, but they also protect humans against some forms of cancer
The biosynthesis of glucosinolates (Figure 4) involves amino acid sidechain
elongation, glucone biosynthesis in five steps, and then further side-chain modifications that
depend on the glucosinolate type, with the reactions taking place in different organelles (10, 143).
Metabolic engineering in Arabidopsis, tobacco, and mustard has been achieved through intervention
during side-chain elongation, core-structure formation, and/or side-chain modification to
generate specific derivatives, but multigene engineering is necessary to avoid the accumulation of
intermediates that have a negative impact on the taste and nutritional value of food (10).
CYP83A1 and CYP83B1, two nonredundant cytochrome P450 enzymes metabolizing oximes in the biosynthesis of glucosinolates in Arabidopsis: The ability of CYP83A1 to metabolize aromatic oximes, albeit at small levels, explains the presence of indole glucosinolates at various levels in different developmental stages of the CYP83B1 knockout mutant, rnt1-1. Plants overexpressing CYP83B1 contain elevated levels of aliphatic glucosinolates derived from methionine homologs, whereas the level of indole glucosinolates is almost constant in the overexpressing lines.
Inability to Identify the Genes: Even in plants with complete genome sequences, such as rice (73), potato
(119), maize (136), and banana (34), the lack of functional annotations makes it difficult to identify
the components of complete metabolic pathways.
The lack of data is being addressed through the development of large-scale sequencing and
proteomics projects targeting metabolically relevant tissues such as the trichome, which produces
a range of important secondary metabolites (83, 149).
Inability to Express the Functional Enzyme: Once a candidate gene has been selected, the next challenge is to achieve the adequate expression
of a functional enzyme in an appropriate background. Promoter choice is important to ensure
the gene is expressed robustly, but even when a strong promoter is used, the level of active
enzyme accumulating in the cell may be low because of feedback regulation and/or epigenetic
phenomena that lead to transgene silencing either immediately or in subsequent generations
Intermediates Shuttled into Different Pathways or Spontaneously Broken Down:
The accumulation of intermediates in a metabolic pathway can in some cases spill over into other
metabolic pathways and can also trigger changes in the wider metabolic network, leading to the
production of unwanted compounds. This can be addressed by secondary modifications to block
these other pathways, but it is difficult to predict the impact this may have on plant health and
metabolism in general. Therefore, to avoid shuttling of intermediates to other pathways, it is
preferable to force the intermediates into the desired pathway by boosting downstream enzymatic
steps as well as accessory proteins that help to increase flux, e.g., transporters whose normal role
is to shuttle intermediates between compartments.
Species-Dependent Bottlenecks and Regulatory Mechanisms:For example, a twogene
strategy was successfully used for folate biofortification in tomato (37) and rice (145) but
appeared to be inadequate in Arabidopsis plants and potato tubers: Although the precursors PABA
and pterin accumulated in the latter plants, folate levels remained low (16).This demonstrates that
engineering strategies cannot be applied universally, reflecting the existence of species-dependent
bottlenecks and regulatory mechanisms.
Unpredicted Interactions with Endogenous Metabolism:
One example of the above phenomenon is the transfer of the dhurrin biosynthesis pathway from
sorghum to Arabidopsis. Dhurrin is a cyanogenic glucoside derived from tyrosine, and its production
in Arabidopsis therefore depleted the endogenous tyrosine pool and restricted flux toward other
tyrosine-dependent pathways such as alkaloids, phenols, and pigments. The impact on the host
plant was not restricted to the metabolic profile, but the disruption to the overall abundance
and distribution of metabolites had a significant impact on gene expression and also affected the
morphology of the transgenic plants (82). Similarly, tomato plants overexpressing PSY1 to increase
carotenoid production were characterized by dwarfing, low levels of chlorophyll, and the inhibition
of carotenoid synthesis (54). These pleiotropic effects resulted from the diversion of GGPP into
the carotenoid pathway, thus removing an important precursor of gibberellin synthesis. The effects
were overcome by restricting the expression of CRTB to the fruits (52).
These challenges should be addressed by applying the most advanced molecular biology tools
to each problem, e.g., high-throughput screening of DNA-seq and RNA-seq data to identify
missing steps in metabolic pathways, including a focus on high-throughput assay development to
identify relevant genes even in obscure species. Synthetic biology approaches should be used to
systematically screen conditions that allow the expression of functional enzymes, e.g., by testing
panels of constitutive, tissue-specific, and inducible promoters to find those that achieve expression
without toxicity, as well as targeting sequences to ensure that the subcellular location of the
enzymes is optimized. Novel approaches such as combinatorial metabolic engineering, selecting
from a library of plants carrying diverse collections of metabolic transgenes, can be used to test
for unproductive interactions with endogenous pathways and to identify bottlenecks that can be
alleviated by further interventions.