Quantification of the plant terpenome: predicted versus actual emission potentials

Abstract

Plant essential oils are complex mixtures of volatile organic compounds, which play indispensable roles in communication, defense, and adaptive evolution. The complete chemical library produced by a plant is referred to as its terpenome. The potential biological information stored in essential oil composition data can provide an insight into the silent language of plants, as well as roles of terpene emissions in direct and indirect defense, and for playing a crucial role in adaptive evolution. In this work, we have attempted to measure the plant terpenome from a global perspective. One way of measuring the terpenome is to observe and record actual emissions in natural conditions, and this has been in practice for over a century through variously evolving methods of comprehensive GC–MS and HPLC. An alternative method is a knowledge-based prediction of the terpenome, and this method has gained popularity in recent years, with the advent of large-scale genome sequencing technologies. Over the past decade, our laboratory has been involved in compilation and investigation of the plant terpenome using both these methods and this has offered us the opportunity to compare and contrast data from actual and potential emissions, in order to better understand the terpenome and its roles in primary, secondary and adaptive metabolism. We have used emission data in conjunction with genomic data in order to understand how a plant creates the so-called final terpenome, specific to itself, and whether or not plants tap the complete potential for terpene biosynthesis at their disposal according to their genomes. For measuring actual emissions, we have used EssOilDB (the ESSential OIL DataBase), the largest contextual web resource for phytochemicals and for measuring the total plant potential for emissions, we have used TERZYME, an automated algorithm for identification and analysis of genes and proteins involved in isoprenoid biosynthesis.

Full text

ORIGINAL ARTICLE
Quantification of the plant terpenome: predicted versus actual
emission potentials
Piyush Priya
1
•Sangita Kumari
1
•Gitanjali Yadav
1
Received: 2 October 2016 / Accepted: 19 October 2016
ÓIndian Society for Plant Physiology 2016
Abstract Plant essential oils are complex mixtures of
volatile organic compounds, which play indispensable
roles in communication, defense, and adaptive evolution.
The complete chemical library produced by a plant is
referred to as its terpenome. The potential biological
information stored in essential oil composition data can
provide an insight into the silent language of plants, as well
as roles of terpene emissions in direct and indirect defense,
and for playing a crucial role in adaptive evolution. In this
work, we have attempted to measure the plant terpenome
from a global perspective. One way of measuring the ter-
penome is to observe and record actual emissions in natural
conditions, and this has been in practice for over a century
through variously evolving methods of comprehensive
GC–MS and HPLC. An alternative method is a knowledge-
based prediction of the terpenome, and this method has
gained popularity in recent years, with the advent of large-
scale genome sequencing technologies. Over the past
decade, our laboratory has been involved in compilation
and investigation of the plant terpenome using both these
methods and this has offered us the opportunity to compare
and contrast data from actual and potential emissions, in
order to better understand the terpenome and its roles in
primary, secondary and adaptive metabolism. We have
used emission data in conjunction with genomic data in
order to understand how a plant creates the so-called final
terpenome, specific to itself, and whether or not plants tap
the complete potential for terpene biosynthesis at their
disposal according to their genomes. For measuring actual
emissions, we have used EssOilDB (the ESSential OIL
DataBase), the largest contextual web resource for phyto-
chemicals and for measuring the total plant potential for
emissions, we have used TERZYME, an automated algo-
rithm for identification and analysis of genes and proteins
involved in isoprenoid biosynthesis.
Keywords Terpenome Phytochemicals EssOilDB 
Terzyme Essential oil emission
Introduction
Plants produce a diverse array of phytochemicals for both
general and specialized functions. Environmental factors
such as light, drought, temperature and several biotic
conditions can greatly influence the yield, composition and
emission of phytochemicals. Extensive research on phy-
tochemicals has shown these to be important protective and
signaling molecules under oxidative, thermal and pathogen
stress, and can act both as allelochemical and neighbor
detection signals, thus constituting a platform for plant–
plant interaction (Leitner et al. 2008; Vickers et al. 2009).
Herbivore-challenged plants are known to emit volatile
phytochemicals not only to invite the natural enemies of
herbivores (parasitoids and predators), but also to aid
neighboring plants by inducing defense response in them
(Pare
´and Tumlinson 1999; Kessler and Baldwin 2001;
Baldwin et al. 2006). Alongside of such roles in defense,
volatile phytochemicals have enormous applications in
pharmaceutical, industrial and agribiotechnological areas.
Since antiquity, plant-originated natural products have
been used for their medicinal and organoleptic properties.
They are extensively used in food and cosmetics industry
for generating flavor and fragrances due to their pleasant
&Gitanjali Yadav
gy@nipgr.ac.in
1
Computational Biology Laboratory, National Institute of
Plant Genome Research, New Delhi 110067, India
123
Ind J Plant Physiol.
DOI 10.1007/s40502-016-0256-x

scent (Caputi and Aprea 2011). The significant role of these
phytochemicals in the prevention and treatment of human
diseases is attributed to their specific activities against
microbes, cancer, cardiovascular problems and diabetes
(Reddy et al. 1998; Bardon et al. 1998,2002; Abdollahi
et al. 2003). Besides these properties, they have also shown
to be associated with hepatoprotective, spasmolytic and
carminative activities (Ozbek et al. 2003; Lahlou et al.
2003). Reports on the analysis of their free radical scav-
enging capacity have suggested that they can act as
promising natural antioxidants as well (Ruberto and Bar-
atta 2000; Botsoglou et al. 2003; Mimica-Dukic et al.
2003).
Among the phytochemicals, the isoprenoid family rep-
resents one of the most ancient and widespread classes of
structurally and functionally rich biomolecules known to
man. Although these natural products are synthesized in all
organisms, the plant kingdom exhibits tremendous varia-
tion in their chemistry and roles, ranging from primary
metabolism to secondary metabolism and specialized
ecological interactions with the environment. The emission
of volatile terpenes is estimated to account for more than
half of the total emission of essential oils. Terpenoid
emission mainly comprises monoterpenes and sesquiter-
penes whereas their derivatives such as aldehydes, alcohols
and ketones are present in various amounts. Biosynthesis of
terpenes results in the generation of an enormous chemical
and structural diversity of natural compounds, mainly on
account of the highly complex machinery that regulates
formation of isoprenoid products. This machinery com-
prises of the unique class of enzymes known as Terpene
synthases (TPSs). All isoprenoids are derived from the
universal C5 precursor isoprene, many units of which are
joined together via prenyl transferases that catalyze the
formation of farnesyl, geranyl or geranyl–geranyl
pyrophosphates. These pyrophosphates then act as sub-
strates for the unique TPS enzymes that are the key players
in the generation of isoprenoid diversity, catalyzing one of
the most complex reactions known to chemistry and biol-
ogy. Essentially, TPSs catalyse the ionization and highly
specific cyclization of multiple pyro-phosphate intermedi-
ates into a diverse range of cyclic molecules. Infact, TPSs
can generate such a tremendous diversity of compounds
using the regiochemical and stereochemical intricacy of
their cyclization reactions that the complete terpenoid
chemical library of a given plant has been termed as the
‘terpenome’ (Cane and Ikeda 2012; Christianson 2008).
With the availability of sequenced genomes and high
throughput data analysis tools, the automated large-scale
search and characterization for genome wide TPSs is now
becoming feasible, offering an unprecedented opportunity
for genome mining efforts toward discovery and charac-
terization of the terpenome. The complete chemical library
across the plant kingdom is highly complex consisting of
about 60,000 complex chemical structures but any given
plant species can synthesize only a fraction of it. Thus, for
each plant species, TPSs have evolved the unique capacity
to synthesize a specific array of terpenes.
In this work, an attempt has been made to quantitatively
measure the plant ‘terpenome’ at a species level through a
comprehensive analysis of available data on essential oils
(where the term ‘essential’ denotes that the oil has the fra-
grant essence of the source plant), treating them as multi-
component chemical combinations having terpenes as their
major constituents along with other non-terpene compounds.
We have used emission data in conjunction with genomic
data in order to understand how a plant creates the so-called
final terpenome, specific to itself, and whether or not plants
tap the complete potential for terpene biosynthesis at their
disposal according to their genomes. Thus, We have per-
formed comparison of actual and potential phytochemical
emissions; where the term ‘actual’ refers to the observed and
recorded plant emissions in natural conditions, a strategy that
has been in practice for over a century through variously
evolving methods of comprehensive GC–MS and HPLC.
Essential oils are generally obtained from plants using an
appropriate extraction method (Vigan 2010). Traditional
extraction methods like hydrodistillation, steam distillation,
solvent extraction and expression under pressure, are more
common although advanced techniques such as supercritical
fluid and subcritical water extraction have been used in
recent times (Edris 2007; Coelho et al. 2012). For this, we
have used an in-house repository developed over the past
decade through a systematic compilation of documented
emission profiles along with the additional information about
the respective source plants, geographical locations,
extracted compounds and their compositions, existing biotic
and abiotic factors at the time of extraction, and several
others, in order to gain insights into product diversity. The
massive data thus collected data has been integrated into an
online repository named ‘EssoilDB’ (Kumari et al. 2014).
Although more than 5000 individual compounds are recor-
ded in the database, the major constituents of essential oils
are terpenoid compounds and their derivatives. The alter-
native method of terpenome quantification involves a
knowledge-based prediction of the genomic complement of
terpenes, and this method has gained popularity in recent
years, with the advent of large-scale genome sequencing
technologies. Since the terpene biosynthetic machinery
regulates both quantity and diversity of terpenes present in a
plant, we define the ‘terpenome’ to signify the full comple-
ment of TPSs genes present in a genome. Plants genomes, on
account of repeated events of duplication, tend to have very
large sizes, and this in turn, leads to the existence of gene
families in the plant kingdom. The TPSs family is one such
example, with members that have similar sequences and
Ind J Plant Physiol.
123

structures, and yet show clear divergence in different lin-
eages (Bohlmann et al. 1998). Current knowledge of the
TPSs in plants, with the exception of the moss genome
Physcomitrella patens (which has a single functional TPS
gene), indicates that the TPS gene family is a mid-size
family, with gene numbers ranging from approximately
20–150 (Chen et al. 2011). Rapidly increasing numbers of
plant nuclear genomes have enabled us to perform a com-
prehensive identification and functional prediction of TPSs
in 42 plant genomes, and the method has been developed as a
freely available online web resource, namely, ‘Terzyme’
(Priya et al. 2015).
In the following sections, we present a comparative
assessment of predictions from Terzyme and actual
emission data from EssOilDB, for 19 plant species for
which such an assessment was feasible, based on avail-
ability of the full genome as well as availability of
emission data. We compare and contrast emission data
with genomic data in order to confirm that plants do
indeed create a so-called final terpenome, specific to each
species, and also that, plants have the unique ability to
selectively tap the potential for terpene biosynthesis at
their disposal according to their requirement. We discuss
the advantages and limitations of both methods, following
specific examples to illustrate the biological importance
of quantifying the terpenome, and also to highlight our
ongoing efforts to exploit its applicability in the current
era of comparative genomics.
Methodology
Emission data collection
Essential oil profiles were collated from EssOilDB, the
largest online contextual library of phytochemicals,
encompassing literature records spanning a century of
published reports of essential oil profiles, starting from
early 1900s to date (Kumari et al. 2014). EssOilDB con-
tains data from over 30 scientific journals and 1667 plant
species, subspecies or varieties representing about 80 dis-
tinct taxonomic families encompassing the entire range
from ancient Magnolids, lower plants like chlorophytes and
mosses, to the gymnosperms and angiosperms.
TPS data compilation
Terzyme (Priya et al. 2015) is hidden markov model-based
tool developed for identification and analysis of genes and
proteins involved in isoporenoid emissions and was run on
42 available nuclear genomes leading to identification of
more than 3000 unique terpene synthase enzymes in the
plant kingdom.
Data analysis
Comparative analysis of EssOilDB and Terzyme data was
performed for identification of common plant species for
which both actual and potential emission data would be
available for further analysis. Emissions from these species
were further categorized based on the terpene product
specificity, and the genes identified in these plants were
interactively mapped using IGMAP (Priya et al. 2015).
Product level clustering of emissions and TPS predictions
were performed using Terzyme, Pubchem classification as
well as in-house shell scripts. Monoterpene specific emis-
sion pattern visualization was done in R. Quantification of
the volatile terpenome in this manner led to a dataset of
terpene emission products and corresponding genomic
signatures for each compound in each plant, and this data
was manually compared in order to identify patterns, if
any, between actual and predicted emission potentials.
Results and discussion
Association of essential oil data: actual and potential
emissions
In all, 19 plant species were identified for which, both
emission datasets, as well as genomic datasets were
available for comparison. These 19 species are listed in
Table 1. As described in the introduction, it is important
to understand the actual volatile terpene emissions of a
plant, as much as the TPS complement within the
respective genome that is responsible for production of
the emitted volatiles. Towards this, we have compared the
two datasets, one representing the complete genomic
complement of TPS genes in a given plant (as predicted
by TERZYME described in methods), and the second
representing actual emission profiles obtained from GC/
GC–MS based essential oil compositional data from
published literature records for the corresponding plant, as
complied from EssoilDB (Kumari et al. 2014). These two
datasets are termed as the ‘potential’ and the ‘actual’
terpenome, respectively and the comparative analysis can
only be performed for plants for which detailed essential
oil compositional data is available, in addition to avail-
ability of full nuclear genome sequence. In all, the com-
parison could be performed for 19 plants where both
datasets were available. Table 1depicts the non-redundant
number of actual compounds observed to be emitted from
each given plant (observed terpenome) compared with the
total number of identified terpene biosynthetic enzyme
coding sequences in the genome (potential terpenome).
The data is further classified by the class of compounds
emitted and predicted.
Ind J Plant Physiol.
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It is clear from Table 1that the quantified terpenome
across all 19 plants shows a high degree of variability
between actual number of compounds recorded as released,
compared with the potential number of compounds that
can, in principle, be biosynthesized by the species. For
example, in case of the model dicot Arabidopsis, 36 dis-
tinct TPSs were identified in the nuclear genome, whereas
literature records contain information about 29 unique,
non-redundant terpenoid compounds known to have been
emitted by the plant. It is well known that plants do not
utilize their full phytochemical potential at a given time
and that essential oil compositions are often influenced by
bio-geological conditions at the time of extraction, such as
environmental and physiological circumstances, plant parts
used for oil extraction, stress/disease and geographical
distribution etc. Our method of quantification has been
designed to take into account any or all of these factors and
combine them before performing a proportional estimate in
terms of the genomic potential. This allows us to get an
accurate estimate and gain insights into how these factors
individually or in concert, influence phytochemical
dynamics of the species in question. In case of Eucalyptus,
the emission of only 20 monoterpenes have been recorded
in literature, whereas the genome of this plant contains
genes that code for double this number of monoterpene
synthases. This is the case with most other plants analysed
in this effort, and it may be explained by limitations of
record compilations or the unavailability of data under
stress for the plant, since these are secondary metabolites
induced for specific functions and a large number of
biosynthetic genes may lie dormant under natural condi-
tions. Similarly, Arabidopsis has been observed, under
various conditions, to emit 29 of the 36 potential terpenes.
It may be noted that the remaining seven (36–29) com-
pounds have not ‘‘yet’’ been observed by any experimental
study, and may be highly specialized compounds that are
only emitted under very specific conditions that have not
been recorded in literature till date. Future studies may be
able to shed light on the specific conditions under which
these compounds are released. A detailed perusal of these
seven compounds revealed very interesting patterns that
have been presented in the next section. In contrast, the
case of the green bean (P.vulgaris) is quite striking since it
has been known to emit only three of the 49 terpene based
compounds that its genome has the potential to biosyn-
thesize (Table 1). This is a case where a miniscule portion
of the prospective terpenome has been recorded by
experimental studies. It is quite possible that much more of
the potential terpenome is being emitted regularly by the
green bean, but has not been recorded in literature due to
lack of sufficient number of studies on essential oil com-
position. However, it is also possible that much of the
potential terpenome of the bean is latent and awaiting
specific conditions in which to be expressed. It is quite
possible that emission data from these plants may have
been collected under normal conditions lacking any stress
Table 1 List of plants used in
this study, along with quantified
phytochemical signatures of the
terpenome with predictions and
actual emissions categorized by
functional groupings
Plant species Monoterpenes O/P Diterpenes O/P Sesquiterpenes O/P Total O/P
Arabidopsis thaliana 14/07 0/6 15/23 29/36
Citrus sinensis 33/36 0/8 20/44 53/88
Eucalyptus grandis 20/40 0/19 14/52 34/111
Ricinus communis 5/25 0/13 0/21 5/59
Populus trichocarpa 12/31 0/10 10/26 22/67
Phaseolus vulgaris 3/21 0/10 0/18 3/49
Vitis vinifera 7/26 2/23 24/69 33/118
Glycine max 4/18 0/18 0/09 4/45
Fragaria vesca 33/14 2/12 6/34 41/60
Malus domestica 14/36 1/25 5/57 20/118
Oryza sativa 3/1 1/13 4/29 8/43
Citrus clementina 23/8 0/12 10/1 33/21
Medicago truncatula 3/16 0/18 9/18 12/52
Capsella rubella 11/08 1/9 6/25 18/42
Prunus persica 8/12 0/8 0/7 8/27
Cucumus sativus 3/11 0/4 1/17 4/32
Carica papaya 14/12 1/11 3/18 18/41
Gossypium raimondii 12/27 0/09 4/40 16/76
Cicer arietinum 16/2 0/6 0/11 16/19
*O/P observed (actual) terpene emission records/predicted no. of TPS genes
Ind J Plant Physiol.
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factors, such as herbivory, thereby resulting in a discor-
dance between the observed and predicted terpenome.
Emission patterns of monoterpenes
It may be noted that the present comparative analysis relies
extensively on observed/published emissions of phyto-
chemicals, and it is known that monoterpenes constitute the
major fraction of terpene volatile emission, since these are
lowest MW compounds. Accordingly, the 19 plants have
been grouped by the ‘‘kind’’ of terpene emitted, and this
enables better comprehension of individual patterns and
emission signatures for a large fraction of the data. Further
analysis of the patterns was done between corresponding
values of identified Monoterpene synthase proteins present
in these 19 plant species. The analysis of Arabidopsis
emissions, discussed earlier, presents a very interesting
case based on monoterpene emissions. Of the 36 TPS
sequences that were identified/predicted in the genome of
Arabidopsis, seven are monoTPSs, 6 are DiTPSs, and 23
are SesquiTPSs. Of these predicted TPSs, none of the
diterpenes and only half of the sesquiterpenes have been
observed in emission profile studies (see Table 1). How-
ever, a total of 14 distinct monoterpene compound emis-
sions have been observed in the plant, as compared to only
seven monoTPS sequences in the genome—clearly sug-
gesting that observed emissions are higher than the pre-
dicted number of biosynthetic proteins in the genome. In
general, based on the above considerations, the monoter-
pene emission data in Table 1can be categorized into three
groups, as depicted in Fig. 1. The group of plants in Fig. 1a
represent cases where the actual and observed emissions
are comparable in quantity. Plants in this category have
attained the full potential of terpene volatile emission, as
per available experimental records. The second group,
shown in Fig. 1b, is represented by majority of the 19
plants investigated, and this group represents cases where
the observed volatile terpenome is much smaller than
potential terpenome. It can be inferred that the genome has
the potential to produce far more terpenoids than those
actually recorded for these plants. As mentioned earlier, it
is well known that terpene emission profiles are condition
specific and spatio-temporal, often altered based on plant
developmental stage, source parts and various geomor-
phological situations. Also each plant species is known to
occupy its own unique ecological niche, and thus it aptly
evolves the ability to synthesize sets of compounds that
assist it in interaction and adaptations with its biotic and
abiotic environment (Chen et al. 2011). Finally, in plants
like Arabidopsis thaliana,Oryza sativa, Fragaria vesca,
Citrus clementina and Cicer arietinum the observed ter-
penome was found to be much higher than the potential
terpenome. This presents an ironic situation, where the
genome was found to contain fewer TPS genes than the
minimum number required to produce the spectrum of
actual emissions observed in these plants. Figure 1c depicts
this case in various plants investigated. In such cases, it
may be conjectured that although the total number of TPSs
available for each plant, viz. seven in case of Arabidopsis
thaliana and one in case of Oryza sativa, is relatively low
when compared to their respective emission profiles, these
specific TPS enzymes may possess the ability to form
multiple terpene products, thus bestowing the plant to adapt
in its specific local niche.
Most importantly, all the 19 plants in the study support
the existence of a unique species specific phytochemical
signature that appears to be independently discernable in
both datasets, i.e. genomic or emission profile data. How-
ever, it is only after a detailed comparison of both datasets,
as presented here, that one can identify patterns that sug-
gest that phytochemical fingerprints may be dynamic.
Further discussion on such a multi-faceted approach to
terpenome quantification is presented in the next sec-
tion. These examples also offer opportunities for further
research leading to new insights and a fuller understanding
of phytochemical dynamics and TPS product complexity.
Conclusion and future directions
Quantification of the plant terpenome can be done using
genomic data. The number, location, and functional classes
of TPS genes present in the nuclear genome provide a
pattern for the scope of potential isoprenoids that a given
genome may be capable of generating. However, it must be
noted that complete genomes may not be available for
species that are not model plants or agriculturally/eco-
nomically important. Furthermore, the disadvantage of
using genomic data alone is that one cannot predict what
exactly the final emitted product will be, as it depends on
several post translations modifications and activity of genes
and proteins not included in the Terpenome prediction.
Genomic data can only suggest the ‘potential’ terpenome,
without being able to specify which part of the full ter-
penome spectrum may be produced at a given time under a
given situation by a given plant. Transcriptomics and
proteome-based studies are adding to our knowledge on
this aspect and future studies should take into account
multi-omics datasets, rather than only one. A second, more
exact method of quantifying the terpenome is to actually
measure the emission profiles in the field using compre-
hensive chromatography techniques. This method of the
‘actual’ terpenome provides a very specific notion of the
terpenome, but it overlooks the fact that the plant may not
have released its entire potential spectrum of phytochem-
icals at the time of experiment, and neither does it provide
Ind J Plant Physiol.
123

any clue to what other compounds may be emitted in other
conditions.
Comparison of actual terpenome with the potential ter-
penome, as performed in this study, allowed us to exploit
the advantages of both methods to establish that plants do
indeed have phytochemical fingerprints, and this study also
proved to be particularly successful in detection of phyto-
chemical dynamics within a given species, revealing how
plants can modulate their terpenome based on condition or
environment-specific needs. In summary, our data provides
evidence that plant can intelligently engineer their actual
terpenome by driving the expression of particular TPSs
based on the specific factors like stress and other spatio-
temporal environmental conditions. Attainment of full
potential of terpenome is condition specific for most plants
and out of many potential TPSs genes, it may be that, only
few specific genes express that potentially help plants to
counter the specific condition to which plant has been
exposed. We also identified plants in which recorded ter-
pene emissions are is fairly higher than the potential pro-
duct-formation capacity of the corresponding genome,
suggesting very interesting scenarios, which might directly
relate to TPS promiscuity, and subtle modulations within
TPS active sites, resulting in a plasticity that eventually
enables the plant to generate multiple products from min-
imal substrate pool. To maximize the number of final
Fig. 1 Comparison of observed
terpenome with the potential
terpenome in 19 plants. aThe
cases in which observed
terpenome was comparable with
potential terpenome. bPlants in
which observed terpenome was
fairly lower than potential
terpenome, cCases were
observed terpenome was
comparatively higher than
potential terpenome
Ind J Plant Physiol.
123

products, they have the ability to create multiple products
from minimal/single substrate pool and minimal genetic
alterations (Degenhardt et al. 2009; Sacchettini and Poulter
1997; Keasling 2008).
Finally, although we are still far from a systems-level
modeling of phytochemical signatures or dynamics, this
work has led to the generation of the most comprehensive
and high quality atlas of the plant terpenome till date and
we have already initiated studies into large scale
chemosystematics of more than 1500 plant species, based
on variability of phytochemical emission data and its
superimposition with plant distances at the level of
molecular taxonomy (Kumari et al. 2013). We also hope
that this work will pave the way for further studies into
characterization of multi-product TPS enzymes, as pre-
dicted by our data, through metabolic engineering.
Acknowledgements Authors’ thanks are due to Director, NIPGR for
encouragement, the SERB Women’s Excellence Award Grant of
DBT, Govt of India, to GY for financial support, the Biotechnology
Information System Network (BTISNET) program of Dept of
Biotechnology (DBT), Govt of India, for computational resources,
and the Council of Scientific and Industrial Research (CSIR) for
Senior Research Fellowship (SRF) to SK and PP.
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