AtIPD: A Curated Database of Arabidopsis Isoprenoid Pathway Models and Genes for Isoprenoid Network Analysis

Article (PDF Available)inPlant physiology 156(4):1655-60 · May 2011with66 Reads
DOI: 10.1104/pp.111.177758 · Source: PubMed
Isoprenoid biosynthesis is one of the essential metabolic pathways in plants and other organisms. Despite the importance of isoprenoids for plant functions, not much is known about the regulation of isoprenoid synthesis. Quantitative technologies and systems approaches are now increasingly used to investigate the regulation of metabolic pathways and networks. Prerequisite for systems approaches is the knowledge of network elements and topologies. Information that can be extracted from the public metabolic pathway databases such as AraCyc and KEGG is often not sufficiently comprehensive and current. Therefore we have built a database of manually curated isoprenoid pathway models and genes, the Arabidopsis thaliana Isoprenoid Pathway Database (AtIPD; The database was compiled using information on pathways and pathway genes from BioPathAt (Lange and Ghassemian, 2003, 2005), KEGG (, AraCyc (, SUBA (, and from the literature. AtIPD can be searched or browsed to extract data and external links related to isoprenoid pathway models, enzyme activities or subcellular enzyme localizations. To display quantitative gene-related data on curated pathway models, we created image annotation and mapping files for integrated use with the MapMan tool ( Additionally, we built SBML XML files of the isoprenoid pathway images using the Cell DesignerTM tool ( Users can download all image and annotation files for customization, e.g., adding pathway structural and regulatory network elements or modifying pathway images to visualize other quantitative protein or metabolite data. AtIPD therefore represents a valuable resource for isoprenoid network analysis.
Update on Arabidopsis Isoprenoid Gene Network
AtIPD: A Curated Database of Arabidopsis Isoprenoid
Pathway Models and Genes for Isoprenoid
Network Analysis
Eva Vranova
, Matthias Hirsch-Hoffmann, and Wilhelm Gruissem*
Department of Biology, Eidgeno
ssisch Technische Hochschule Zurich, 8129 Zurich, Switzerland
Isoprenoid biosynthesis is one of the essential met-
abolic pathways in plants and other organisms. De-
spite the importance of isoprenoids for plant functions,
not much is known about the regulation of isoprenoid
synthesis. Quantitative technologies and systems ap-
proaches are now increasingly used to investigate the
regulation of metabolic pathways and networks. A
prerequisite for systems approaches is the knowledge
of network elements and topologies. Information that
can be extracted from the public metabolic pathway
databases such as AraCyc (http://www.arabidopsis.
org/biocyc) and Kyoto Encyclopedia of Genes and
Genomes (KEGG; often
is not sufficiently comprehensive and current. There-
fore, we built a database of manually curated iso-
prenoid pathway models and genes, the Arabidopsis
thaliana Isoprenoid Pathway Database (AtIPD; http:// The database was compiled us-
ing information on pathways and pathway genes
from BioPathAt (Lange and Ghassemian, 2003, 2005),
KEGG, AraCyc, SUBA (http://suba.plantenergy.uwa., and from the literature. AtIPD can be searched
or browsed to extract data and external links related to
isoprenoid pathway models, enzyme activities, or sub-
cellular enzyme localizations. To display quantitative
gene-related data on curated pathway models, we
created image annotation and mapping files for inte-
grated use with the MapMan tool (http://mapman. Additionally, we
built SBML XML files of the isoprenoid pathway
images using the Cell Designer tool (http://www. Users can download all image and
annotation files for customization, such as adding
pathway structural and regulatory network elements
or modifying pathway images to visualize other quan-
titative protein or metabolite data. AtIPD therefore
represents a valuable resource for isoprenoid network
Isoprenoid compounds are required in primary and
secondary metabolic processes. As primary metabo-
lites, they function in photosynthesis (carotenoids, chlo-
rophylls, and plastoquinone), respiration (ubiquinone),
membrane fluidity (sterols), and regulation of growth
and development (cytokinins, brassinosteroids, GAs,
abscisic acid, and strigolactones). As secondary metab-
olites, they have roles in plant protection against path-
ogens, in attracting pollinators and seed-dispersing
animals, and in allelopathic interactions (Rodrı
n, 2006). Despite the importance of isopren-
oids for plant functions, relatively little is known about
the regulation of isoprenoid synthesis.
During the last few years, systems approaches to
build biochemical pathway networks have become
possible based on available large-scale gen ome, tran-
scriptome, and metabolome data. Such stu dies reveal
the complexity of pathway regulation and flux dy-
namics. Prerequisite for systems analysis of metabolic
pathway regulation is the knowledge of network ele-
ments and topologies. At present, genome sequences
provide the most comprehensive information to define
the biological system and its metabolic pathway com-
ponents. For Arabidopsis (Arabidopsis thaliana), there
are currently three databases available from which
information on isoprenoid pathway genes can be
obtained: BioPathAt, AraCyc, and KEGG. These three
databases differ in their level of comprehensiveness
and annotation quality.
BioPathAt has the best-annotated isoprenoid path-
ways and genes (Lange and Ghassemian, 2003, 2005)
because the database was curated by experts in the
field, but unfortunately it has not been updated since
2005. AraCyc (
and KEGG ( are public
online databases that were created automatically by
homology searches between Arabidopsis enzymes
and those from existing reference pathways. AraCyc
used MetaCyc, a database of curated metabolic path-
ways obtained from the literature, as a source of
reference pathways (Mueller et al., 2003). Reference
metabolic pathways in KEGG were primarily orga-
nized from the compilations of the Japanese Biochem-
ical Society (Nishizuka, 1980, 1997) and the wall chart
of Boehringer Mannheim (Gerhard, 1992).
This work was supported by Eidgeno
ssisch Technische Hoch-
schule Zurich (grant no. TH–51 06–1) and TiMet (European Union
Framework Program 7 Project 245143 CP-IP).
* Corresponding author; e-mail
The online version of this article contains Web-only data.
Plant Physiology
, August 2011, Vol. 156, pp. 1655–1660, Ó 2011 American Society of Plant Biologists 1655
AraCyc was originally built by matching gene
annotations (names of gene products) with enzyme
names in reference pathways. The genes were then
assigned to the reference pathway (Mueller et al.,
2003). In KEGG, the Arabidopsis enzyme genes were
annotated based on sequence similarity and positional
correlation of genes. Enzyme Commission numbers
and KEGG orthology codes were assigne d to anno-
tated genes. The pathw ay was then constructed com-
putationally by correlating genes/sequences in the
genome with gene products (enzymes) in the reference
pathways according to the matching KEGG orthology
code (Masoudi-Nejad et al., 2007). In both AraCyc and
KEGG databases, pathway gaps (reactions with no
Arabidopsis enzyme identified) were filled computa-
tionally using different algorithms (Ogata et al., 1999;
Green and Karp, 2004).
Initially, the reference pathway databases used to
build AraCyc and KEGG contained mainly metabolic
pathways from bacteria, yeast, a nd mammals. As a
consequence, both Arabidopsis databases primaril y
contained annotate d genes and maps of primary me-
tabolism. Later, AraCyc and KEGG unde rwent several
rounds of computational and human curations, result-
ing in a substantial increase in the number of path-
ways and genes present in the metabolic pathway
database. Although comprehensiveness and annota-
tion quality of both databases improved dramatically,
the list of well-annotated pathways and genes is not
complete and requires additional curation prior to use.
For example, certain pathways are still not annotated,
such as the diterpenoid biosynthetic pathway, the
apocarotenoid biosynthetic pathway, and the plasto-
chromanol biosynthetic pathway. In case of annotated
pathways, many steps lack enzymes for a given reac-
tion. Not all reactions represent current pathway
models and not all enzymes, such as prenyltransfer-
ases that share a high degree of sequence similarity, are
correctly annotated to corresponding reactions and
pathways. Many annotated pathway genes are pseu-
dogenes or code for nonfunctional proteins. Often
there is a redundancy in pathway models (especially
in the AraCyc database) as different models are
retained in the database. For example, three alternative
pathways for brassinosteroid biosynthesis exist in the
AraCyc database (Ephritikhine et al., 1999; Noguchi
et al., 1999; Noguchi et al., 2000) instead of one
preferred model (Ohnishi et al., 2006). This redundant
information is confusing rather than informative. An-
other source of redundancy is inherent to the philos-
ophy that is behind the construction of both databases.
The majority of the pathways found in AraCyc and
KEGG have some measure of experimental support,
but additional computationally predicted pathways or
pathway routes are also included to maximize the
hypothesis-generating power of the database. These
pathways or pathway routes are known to exist in
other species or in nonplant organisms but have not
been reported in Arabidopsis. Although this type of
redundancy is advantageous in discovery-oriented
research, it does not help system-oriented research in
which a system and its elements should reflect reality
as closely as possible.
As a first step toward systems analysis of isoprenoid
pathways and their integration into the ce llular bio-
chemical network, we manually curated inform ation
from public databases and from the literature to con-
struct AtIPD, a high-quality database of Arabidopsis
isoprenoid pathways and genes. AtIPD is available
online at and allows users
to search and browse information related to isopren-
oid pathways and genes and to download the genes
for relation-based network analysis and pathway im-
ages. Annotation files and the mapping file can be
used with the MapMan tool (http://mapman.gabipd.
org/web/guest/mapman; Thimm et al., 2004) to vi-
sualize gene expression data. In addition, SBML XML
files of pathway images can be downloaded and used
by other programs or computer tools.
To obtain a list of isoprenoid pathway genes (Sup-
plemental Table S1), we compiled info rmation on
pathways and pathway genes from BioPathAt (Lange
and Ghassemian, 2003, 2005), from the KEGG (http://
www.geno and AraCyc (http://www. databases, and from the liter-
ature. We used BioPathAt as a starting point and
replaced old models when more updated pathway
models were found in the literature or in other data-
bases. Novel genes were added when their function or
their homology to functional proteins had been dem-
onstrated either in the literature or in case of genes
annotated by homology, at least in The Arabidopsis
Information Resource ( In
the last case, sequence alignments were inspected man-
ually for the quality of hits. Activity of all gene products
was verified in the literature, and the most relevant
refere nces are listed in Supplemental Table S1. As the
most relevant evidence for gene function, we considered
in vitro activity assays, followed by genetic complemen-
tation, metabolic profiles of mutant or overexpression
lines supplemented with labeled precursors, and meta-
bolic profiles of mutant or overexpr ess ion lines. Accord-
ingly, genes annotated by The Arabidopsis Information
Resource as expressed pseudogenes or genes encoding
proteins whose function could not be confirmed exper-
imentally were removed from the database.
Plant cells synthesize isoprenoids in the cytoplasm
and different organelles. Information on the subcellular
localization of individual enzymes is therefore essential
to understand pathway topologies. Subcellular locali-
zation was assigned based on published in vivo and in
vitro localization data (GFP fusion proteins and organ-
elle import) and on SUBA predictions (Arabidopsis
Subcellular Database; http://suba.plantenergy.uwa.; Heazlewood et al., 2007). In case of contradic-
tory results from various resources, localization was
et al.
1656 Plant Physiol. Vol. 156, 2011
assigned in the following order of priority: in vivo and
in vitro assays, mass spectrometry data, and in silico
predicted localization based on a majority vote from
different prediction tools in SUBA. When enzyme lo-
calization did not comply with the predicted pathway
localization and could not be confirmed by either GFP
fusion protein or organelle import assays, the enzyme
was assigned to the compartment in which the other
pathway enzymes were localized (Supplemental Table
S1, subcellular localization).
To store and visualize information on isoprenoid
metabolic pathways and genes, we created a compre-
hensive online database that can be accessed via (Fig. 1). All information
on genes and pathways can be searched and browsed
online but can be also exported in TXT format. Path-
way images were created with the Cell Designer 4.0.1
software, producing SBML XML files that can be
downloaded from the download section of the online
database (, Download, Path-
way name_CD.xml). For integrated use with the Map-
Man tool (
mapman), pathway images were saved as JPG files
and annotated with the ImageAnnotator tool in Map-
Man (version 3.1.1). Genes were assigned to individual
reactions using BINCodes from self-made Mapping
File. Pathway images (Pathway_name.jpg), image an-
notations (Pathway_name.xml), and mapping file
(Mapping File.xls) can be downloaded from the down-
load section of the online database (http://www., Download).
Our database of isoprenoid pathways and genes
improves and outperforms already existin g databases
in two important aspects (Table I). First, our da tabase
Figure 1. The AtIPD database ( provides access to isoprenoid pathway models and genes. From
the home page, one can navigate either to the Gene List (A) or to the Pathway List with pathway models (B). From each A and B, a
link to Gene Details (C) is provided. In a Download section, pathway images (Pathway_name.jpg), image annotations
(Pathway_name.xml), and a mapping file (Mapping File.xls) can be downloaded and used with the MapMan tool. Additionally,
pathway images can be downloaded as SBML XML files (Pathway_name_CD.xml) and customized by the Cell Designer tool.
AtIPD: Arabidopsis Isoprenoid Pathway Database
Plant Physiol. Vol. 156, 2011 1657
has 279 correctly annotated isoprenoid pathway genes
compared to 197 in BioPathAt, 205 in AraCyc, and 178
in KEGG. Second, we improved pathway annotati on,
including the annotation of three novel isoprenoid
pathways. Plastochromanol biosynthesis was only re-
cently described in Arabidopsis (Zbierzak et al., 2009).
Plastochromanol is synthesized from plastoquinone
and may regulate the antioxidant content in thylakoids
and in the plastoquinone pool that is available for
Except for GA biosynthesis, a second pathway for
diterpenoid biosynthesis was not predicted to exist
in Arabidopsis (Aubourg et al., 2002; Lange and
Ghassemian, 2003). Recently, however, geranyllinalool
synthase was identified in Arabidopsis that synthe-
sizes geranyllinalool from geranylgeranyl diphos-
phate (Herde et al., 2008). Based on a phylogenetic
tree generated from the alignment of 32 Arabidopsis
terpenoid synthases (TPSs) and 43 documented TPS s
from 25 other plant species, diterpenoid synthase
belongs to the same family as functional sesquiterpe-
noid synthases. It is therefore not possible to annotate
diterpenoid synthase genes solely based on amino
acid sequence similarity. We have therefore annotated
predicted enzymes targeted to the cytosol or the mito-
chondria as both sesqui- and diterpenoid synthases,
while those targeted to plastids were annotated as di-
terpenoid synthases. No farnesyl diphosphate synthase
enzymes have been found in Arabidopsis plastids;
therefore, it is unlikely that plastid TPSs have sesqui-
terpenoid synthase activity. Functional sesqui- and
diterpenoid synthases were assigned to their respec-
tive pathways (Supplemental Table S1).
The apocarotenoid pathway was also newly anno-
tated and besides ABA synthesis now also includes
strigolactone biosynthesis and synthesis of other apo-
carotenoids. Strigolactones are a novel class of plant
hormones derived from carotenoids that are synthe-
sized via the MAX pathway and are known to regulate
shoot branching (Bennett et al., 2006) and root architec-
ture (Ruyter-Spira et al., 2011). Other apocarotenoids
are less characterized and functionally mainly involved
in flower scent and fruit flavor (Floss and Walter, 2009).
Our curation effort has allowed us to compile the
most comprehensive isoprenoid pathway enzyme list
available to date and also improved the quality of the
database of isoprenoid pathw ays and genes. We used
most recent pathway models based mainly on the
literature data to define pathway topology. As a result,
redundancy in pathway models and/or pathway
routes that is intrinsic to AraCyc and KEG G databases
has been removed. We have also improved the quality
of gene annotation to avoid misannotation of gene s
that share a high degree of sequence similarity but
catalyze different reactions, which is often the case
based on computational prediction alone. By assign-
ing enzymes to their correct compartments, we have
further improved annotation of the isoprenoid bio-
Table I. Comparison of different metabolic pathway gene databases
Genes annotated as isoprenoid pathway genes in the BioPathAt database were extracted from (
and from Lange and Ghassemian (2003). AraCyc isoprenoid pathway genes were extracted from the AraCyc
pathway database (AraCyc version 7.0; KEGG isopren-
oid pathway genes were extracted from the KEGG PATHWAY maps (
No. of Genes
BioPathAt AraCyc KEGG AtIPD
Mevalonic acid pathway 9 7 8 9
Prenyl diphosphate biosynthesis 27 20 30 31
Sterol biosynthesis 20 22 19 25
Brassinosteroid biosynthesis 3 6 6 8
Triterpenoid biosynthesis 11 14 4 14
Sesquiterpenoid biosynthesis 16 3 0 16
Diterpenoid biosynthesis 19 0 0 19
Protein prenylation 7 2 0 7
Cytokinin biosynthesis and catabolism 0 17 17 25
Ubiquinone biosynthesis 4 2 4 4
D-erythritol 4-phosphate pathway 7 6 7 7
Chlorophyll biosynthesis and catabolism 29 35 30 37
Carotenoid biosynthesis 11 13 11 15
Apocarotenoid biosynthesis 0 2 0 5
Abscisic acid biosynthesis and catabolism 1 12 11 12
Plastoquinone, plastochromanol, tocopherol biosynthesis 5 7 7 7
Phylloquinone biosynthesis 7 9 5 9
GA biosynthesis and catabolism 16 22 15 23
Monoterpenoid biosynthesis 5 6 4 6
Total 197 205 178 279
et al.
1658 Plant Physiol. Vol. 156, 2011
synthesis pathways. Isoprenoid biosynthe sis is com-
partmentalized in the cytosol, mitochondria, and plas-
tids, and each compartment produces different end
products. Since plant isoprenoids are derived from
prenyl diphosphates that are synthesized by prenyl
transferases in all three compartments, manual cura-
tion was necessary to clarify the association of prenyl
transferases with different isop renoid pathways de-
pending on their subcellular localization.
There are many examples in metabolic pathway re-
search for which well-annotated pathways and pathway
genes are required. Basically, any type of relation-based
gene network analysis requires well-curated input data.
For example, coexpression analysis for identification of
novel pathway elements, such as novel structural genes,
regulatory genes, or transporters, relies on correctly an-
notated genes. Similarly, a search for common promoter
motifs within a subset of genes is only successful if path-
way genes are correctly annotated. A correctly curated
metabolic pathway network is also prerequisite for any
type of metabolic flux analysis.
Together with a curated set of isoprenoid pathway
models and genes, we also provide an online access to
the data allowing easily search and browse the data,
export the data, and access the information related to
the isoprenoid pathway models and genes. Com-
pared with other available pathway databases, AtIP D
is the best-annotated isoprenoid gene database that is
comprehensive b ut very user friendly and provides
intuitive pathway maps and embedded information
on subcellular localization and modularity. Another
advantage is that both pathway images and MapMan
annotation files are generated by free online tools
(Cell Designer a nd MapMan, respectively) and can be
customized by the user, thus providing an excellent
tool fo r scientists studying the isoprenoid pathway in
plants (Fig. 2). The user thus can select the pathway of
interest and add pathway compo nents, such as struc-
tural or regulatory proteins (Fig. 2A), and maps can
be modified to visualize also other quantitative data,
such as protein or metabolite data (Fig. 2B). Such a
level of modularity is not possible with existing
publicly available metabolic pathway databases.
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Table S1. List of isoprenoid pathways and pathway genes.
Supplemental Literature Cited S1. Literature cited in Supplemental Table S1.
Figure 2. Example of data visualization on pathway images and customization of pathway images. A, Visualization of gene
expression data (supplemental table II in Peschke and Kretsch, 2011; list of genes that exhibit significantly altered transcript
levels at 4 h under continuous far-red light; induced +1, repressed 21) on pathway image “Carotenoid_Apocarotenoid_
ABA_biosynthesis_and_catabolism.jpg” using MapMan tool and attached “Mapping_File.txt.” B, The same pathway image
customized by the Cell Designer tool to add elements of a putative regulatory pathway (in frame). Additionally, images can be
customized to express other gene expression data, as exemplified by visualizing metabolites (arrows). This type of customization
requires annotation of other pathway elements than structural genes with the ImageAnnotator tool in MapMan and updating a
mapping file (Mapping File.xls) with these new annotated elements.
AtIPD: Arabidopsis Isoprenoid Pathway Database
Plant Physiol. Vol. 156, 2011 1659
We thank Diana Coman, Gilles Beck, and Sean Walsh for discussions
and feedback on the manuscript. Furthermore, we sincerely apologize to
all colleagues whose work could not be cited because of space constraints.
Received April 11, 2011; accepted May 24, 2011; published May 26, 2011.
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et al.
    • "Then, prenyl diphosphate synthases catalyze chain elongation reactions by coupling IPP to DMAPP producing allylic prenyl diphosphates of different length (Vranová et al., 2013). Most of the essential plant isoprenoids are derived from the C15 and C20 allylic prenyl diphosphates farnesyl-PP (FPP) and geranylgeranyl-PP (GGPP), whose pools represent nodes of the major metabolic branch points in the isoprenoid synthesis (Vranová et al., 2011). In plants, the enzymes catalyzing the steps upstream of GGPP biosynthesis are encoded either by single copy genes or by pairs of genes (Goldstein and Brown, 1990; Rodríguez-Concepción and Boronat, 2002; Closa et al., 2010; Vranová et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: Multiple geranylgeranyl diphosphate synthases (GGPPS) for biosynthesis of geranylgeranyl diphosphate (GGPP) exist in plants. GGPP is produced in the isoprenoid pathway and is a central precursor for various primary and specialized plant metabolites. Therefore, its biosynthesis is an essential regulatory point in the isoprenoid pathway. We selected 119 GGPPSs from 48 species representing all major plant lineages, based on stringent homology criteria. After the diversification of land plants, the number of GGPPS paralogs per species increases. Already in the moss Physcomitrella patens, GGPPS appears to be encoded by multiple paralogous genes. In gymnosperms, neofunctionalization of GGPPS may have enabled optimized biosynthesis of primary and specialized metabolites. Notably, lineage-specific expansion of GGPPS occurred in land plants. As a representative species we focused here on Arabidopsis thaliana, which retained the highest number of GGPPS paralogs (twelve) among the 48 species we considered in this study. Our results show that the A. thaliana GGPPS gene family is an example of evolution involving neo- and subfunctionalization as well as pseudogenization. We propose subfunctionalization as one of the main mechanisms allowing the maintenance of multiple GGPPS paralogs in A. thaliana genome. Accordingly, the changes in the expression patterns of the GGPPS paralogs occurring after gene duplication led to developmental and/or condition specific functional evolution.
    Full-text · Article · May 2014
    • "Expression of the remaining paralogs, which encode six plastid and two cytosolic isozymes, was Subcellular compartmentalization of isoprenoid biosynthesis in A. thaliana. Based on the pathway network constructed by Vranová et al. (2011). Enzymes are shown in grey and isoprenoids in black. "
    [Show abstract] [Hide abstract] ABSTRACT: Geranylgeranyl diphosphate (GGPP) is a key precursor of various isoprenoids that have diverse functions in plant metabolism and development. The annotation of the Arabidopsis thaliana genome predicts 12 genes to encode geranylgeranyl diphosphate synthases (GGPPS). In this study we analyzed GGPPS activity as well as the subcellular localization and tissue-specific expression of the entire protein family in A. thaliana. GGPPS2 (At2g18620), GGPPS3 (At2g18640), GGPPS6 (At3g14530), GGPPS7 (At3g14550), GGPPS8 (At3g20160), GGPPS9 (At3g29430), GGPPS10 (At3g32040) and GGPPS11 (At4g36810) showed GGPPS activity in Escherichia coli, similar to activities reported earlier for GGPPS1 (At1g49530) and GGPPS4 (At2g23800) (Zhu et al. in Plant Cell Physiol 38(3):357-361, 1997a; Plant Mol Biol 35(3):331-341, b). GGPPS12 (At4g38460) did not produce GGPP in E. coli. Based on DNA sequence analysis we propose that GGPPS5 (At3g14510) is a pseudogene. GGPPS-GFP (green fluorescent protein) fusion proteins of the ten functional GGPP synthases localized to plastids, mitochondria and the endoplasmic reticulum, with the majority of the enzymes located in plastids. Gene expression analysis using quantitative real time-PCR, GGPPS promoter-GUS (β-glucuronidase) assays and publicly available microarray data revealed a differential spatio-temporal expression of GGPPS genes. The results suggest that plastids and mitochondria are key subcellular compartments for the synthesis of ubiquitous GGPP-derived isoprenoid species. GGPPS11 and GGPPS1 are the major isozymes responsible for their biosynthesis. All remaining paralogs, encoding six plastidial isozymes and two cytosolic isozymes, were expressed in specific tissues and/or at specific developmental stages, suggesting their role in developmentally regulated isoprenoid biosynthesis. Our results show that of the 12 predicted GGPPS encoded in the A. thaliana genome 10 are functional proteins that can synthesize GGPP. Their specific subcellular location and differential expression pattern suggest subfunctionalization in providing GGPP to specific tissues, developmental stages, or metabolic pathways.
    Full-text · Article · Jun 2013
    • "ynthetic genes, enzyme functions, and regulation of isoprenoid biosynthetic pathway is available in bacteria, yeast, and plants [37]. Plant web resources have recently been developed such as the public online databases AraCyc (http://www. and KEGG ( and more recently AtIPD (, [153]). These tools provide necessary knowledge for the integrative analysis of isoprenoid metabolism including gene, metabolite, and protein information as well as the subcellular organization of the pathway. All these efforts have contributed to give a boost to research on the engineering of isoprenoid pathways with the aim to industrializi"
    [Show abstract] [Hide abstract] ABSTRACT: Isoprenoids constitute one of the largest families of natural compounds. They play essential functions in plant growth and development and furnish compounds of high interest for humans. Here, we present the current knowledge on isoprenoid metabolism before describing the strategies that have been used for isoprenoid metabolic engineering. We discuss the advantages and drawbacks of using microorganisms and plants as cell platform for the production of isoprenoids of interest.
    Full-text · Chapter · May 2013 · Plant Molecular Biology
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