Knapp J.S., Cabrera W.L. Metabolomics: Metabolites, Metabonomics, and Analytical Technologies

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Plant Environmental Metabolomics 167

levels of isoprene emissions but Globularia alypum and Pinus halepensis increased terpene

emissions.

Metals and Soil Pollution

There is also potential to use a metabolomics approach in identifying the mechanisms and

selective factors that evolved traits involved in metal accumulation in plants such as Thlaspi

caerulescens (Assuncao, Schat et al. 2003). Also, Bailey, Oven et al. (2003) have studied

cadmium exposure in Silene cucubalus by NMR and PCA and found changes in abundance of

a variety of metabolites, such as malic acid and glutamine. There is also a need to assess the

impact that persistent organic pollutants have on plant metabolism. Phytoremediation is one

way in which such pollutants could be removed from the environment, and by taking a

metabolomics approach, it is possible to identify possible mechanisms in which

phytoremediation could work (Narasimhan, Basheer et al. 2003; Ott, Aranibar et al. 2003;

Van Aken 2008).

Atmospheric Carbon Dioxide (CO

)

There has been much work on assessing how elevated concentrations of atmospheric CO

will alter plant growth and plant chemistry (Long, Ainsworth et al. 2004). In a world where

there will be more C available to plants, changes in the concentration of phenolics, terpenes

and structural polysaccharides may have knock-on affects at an ecosystem level as levels and

rates of herbivory and litter decomposition are altered (Penuelas and Estiarte 1998).

However, such changes in plant chemistry are dependent on the species inherent ability to

acclimate to such new conditions, alongside the availability of other nutrients. Studies using

LC-PDA-MS have identified changes in secondary metabolite concentrations and

lignification in semi-natural Solardome experimental conditions (Davey, Bryant et al. 2004).

The use of FACE (Free-Air CO

Enrichment) experiments allows plants to be grown in open-

air fields at controlled elevated atmospheric CO

concentrations. Such large scale sites will

allow more plant species, and material, to be obtained for metabolomics and other

physiological measurements (Long, Ainsworth et al. 2004). Li, Sioson et al. (2006) have

already studied the metabolic responses to elevated CO2 using FACE rings. They found

differences in the metabolome and transcriptome of Arabidopsis grown at elevated CO

. Not

only were global metabolome differences observed, the metabolic response differed

according to its ecotype implying some level of evolutionary adaptation at a species level to

concentrations.

Ozone

Low level tropospheric ozone (O

) can severely damage, or even kill, plants by causing

serious oxidative stress in the plant cells (Mittler 2002). Sensitivity to ozone is plant species

specific, with some species in a community being resistant whilst others are not (Penuelas,

Llusia et al. 1999). Such interspecific differences in resistance to ozone with the heightened

Matthew P. Davey 168

ability to deal with oxidative stress, must rest with differences in genetic adaptation and the

subsequent metabolic changes (Smirnoff 1998). One of the first publications on the effect of

ozone pollution on plant tissues using a metabolomics approach was by Kontunen-Soppela,

Ossipov et al. (2007). They studied the effect of ozone on white birch (Betula pendula) in

field conditions and out of 339 metabolites identified by GC-MS and HPLC, 98 metabolites

(such as increases in quercetins and decreases in triterpenoid concentrations) were associated

with ozone treatments. More recently, (Cho 2008) investigated the effect that ozone had on

rice plants and identified changes in amino acid concentrations in conjunction with alterations

in gene transcript and protein expression.

Photoperiod

Day length differs across the globe and shifts annually so causing many phenological

changes in nature. The subtle metabolic changes that occur when plants are exposed to

different periods of light have started to be identified by Goodacre, York et al. (2003). They

directly injected the leaf sap of Pharbitis into a mass spectrometer (Direct Injection Mass

Spectrometry DIMS), and after DFA of the spectra were able to discriminate plants that were

subjected to different photoperiods. Photoperiod also affects when buds burst. The subtle

changes in C and N assimilation of developing leaves of Quaking Aspen (Populus

tremuloides) have been analysed by GC-MS and PCA and HCA (Jeong, Jiang et al. 2004).

This study is particularly nice as they incorporated other physiological measurements, such as

leaf gas exchange. Identifying such changes in metabolism during photoperiod is important

when trying to decipher the mechanisms involved in processes such as floral induction and

the effect of climate change, especially warming.

UltraViolet Radiation

Ultra-Violet-B radiation (280-320nm) (UV-B) is an important abiotic stressor throughout

the world, especially in polar regions where this has particularly adverse affects on plant

growth (Day, Ruhland et al. 2001). UV-B does affect plant chemistry, in particular there has

been much research on how this affects UV-absorbing compounds (mainly phenolics) (Lois

1994). Lake, Field et al. (2009) have successfully used a mixture of DIMS, HPLC and ms-ms

to identify temporal changes in the metabolite fingerprints and phenylpropanoid and

flavonoid metabolism in Arabidopsis thaliana exposed to elevated UV-B. However, there is

still much work to be carried out to prove that increases in a variety of metabolites provide a

protective function by blocking UV-B light before it reaches plant cells and to assess the role

of metabolites in the consequences of UV-B exposure such as repairing damaged cells and

other structures such as DNA (Lois 1994; Smirnoff 1998). Outside field conditions, cell

cultures have also been used to study the changes in secondary metabolite profiles of the

legume Medicago truncatula (Broeckling, Huhman et al. 2005) but there is also a call for

metabolic research on UV-B to also be carried out in the field as the phenotype observed in

controlled growth rooms may be different to those observed in field experiments

(Kliebenstein 2004). There has also been discussion on whether UV-B induced changes in

Plant Environmental Metabolomics 169

the metabolome of crop species may be beneficial to human health as many of the compounds

that increase in abundance are phenolics and other antioxidants (Jansen 2008).

Temperature

Thermotolerance is an important, and somewhat expensive, trait for a plant and is likely

to be a limiting factor in plant distribution (Woodward 1987; Browse and Lange 2004).

There is significant variation in freezing tolerance even within a plant species (Zhen and

Ungerer 2008) and changes in the metabolite content of the plant during cold temperatures

may play an advantageous role in cell cryoprotection prior to freezing temperatures (Stitt and

Hurry 2002). This process is known as cold acclimation (Thomashow 1999; Hurry, Strand et

al. 2000). Such metabolic changes are likely to differ according to a plant species’ inherent

ability to adapt or acclimate to cold temperatures. Guy, Kaplan et al. (2008) have recently

published a comprehensive review on the metabolomics of temperature stress. One of the

first studies to assess the metabolome of temperature stressed plants was by Kaplan, Kopka et

al. (2004). They used GC-MS, followed by PCA, to identify 143 and 311 metabolites in

Arabidopsis thaliana that responded to heat or cold shock, respectively. They even identified

changes in metabolite abundances that were previously not associated with temperature stress.

Cook, Fowler et al. (2004) also reported the exploration of the metabolome of two contrasting

ecotypes of Arabidopsis thaliana and Hannah, Wiese et al. (2006) have also analysed the

metabolome, and transcriptome, of nine geographically diverse ecotypes of Arabidopsis.

Again, both studies reported significant natural variation for freezing tolerances and the

preceding acclamatory processes within the metabolome.

Outside the model species, we have identified significant metabolic changes, using

DIMS, GC and HPLC in Arabidopsis lyrata spp. petraea grown from natural populations

across Europe (Davey, Burrell et al. 2008; Davey, Woodward et al. 2009). In a similar study

to assess the cold acclimated metabolome Arabidopsis thaliana using DIMS, Gray and Heath

(2005) found 1187 masses (DIMS-Fourier Transform-Ion Cyclotron Resonance) of which

about 8% significantly increased or decreased in intensity after seven days cold treatment.

Such a large-scale assessment of the changes that plants make in metabolism to temperature

enables insights into alterations in the different metabolic pools, the associated changes in

gene transcripts and the possibility of identifying temperature related biomarkers (Browse and

Lange 2004).

Ecology

Plant populations are affected by a variety of biotic interactions (Arany, de Jong et al.

2005) and metabolites, especially secondary metabolites, play a key role in surviving a

multitude of ecological processes. The identification of such metabolites is likely to increase

as metabolomics becomes a useable tool to assess ecological questions (Kliebenstein 2004;

D'Auria and Gershenzon 2005). Most, if not all, plants are at risk of herbivory, pathogen and

fungal attack and are in competition for resources from neighbouring plants. The effect that

such biotic influences can have on the plants metabolome is reviewed below.

Matthew P. Davey 170

Herbivory

Herbivory is an important ecological and economical process. The induction and precise

function of a wide variety of metabolites in non-commercial plant species remains to be

obtained. The allocation of metabolites to either defence or growth functions has been the

centre of physiological and ecological research and debate for a few decades (Herms and

Mattson 1992; Hamilton, Zangerl et al. 2001). Allocation of carbon and nitrogen to classes of

compounds such as phenolics for defence or amino acids for growth is complex, species-

specific and resource limited (Jones and Hartley 1999; Davey, Harmens et al. 2007).

Metabolomics approaches have started to be used in assessing the metabolic alternations

occurring in plants that were subjected to either a generalist or a specialist herbivore (Jansen,

Allwood et al. 2009). Arany, de Jong et al. (2008) studied an inland and a coastal natural

population of Arabidopsis thaliana. They detected differences in the metabolome of each

species but more interestingly they detected that the differences in the metabolome, mainly in

glucosinolate concentrations, affected the growth of specialist or generalist herbivores so

implying chemical adaptation to herbivory type at a population level. Also, the study by

Riipi, Haukioja et al. (2004) nicely show the within-season and between-year variation in leaf

chemistry of mountain birch (Betula pubescens). They were able to detect temporal changes

in leaf metabolites that may be used for anti-herbivory purposes, such as hydrolysable tannins

and proanthocyanidins. This outlines the importance of assessing temporal scales when

assessing metabolomic changes in the field, especially for ecological studies. Kant, Ament et

al. (2004) have also used GC-MS screening techniques to detect volatile compounds that were

emitted from tomato plants that were infested with spider mites and genetically-modified

Aspen trees that over express sucrose phosphate synthetase in order to increase cell sucrose

concentration and biomass was shown to change the metabolic composition of secondary

metabolites associated with anti-herbivory (Hjalten, Lindau et al. 2007).

Competition

Plant competition in the field will be regulated by resources such as light, CO

and

nutrients; allelopathy and the plants inherent capacity to compete. Such inter-specific

competition is considered to influence the metabolome of plants, as shown by Gidman et al.

(2003). They were able to identify chemical differences in the FT-IR spectra of

Brachypodium distachyion when grown in competition with Arabidopsis thaliana.

Interestingly, there were no detectable changes in the FT-IR spectra of A. thaliana, implying a

species-specific response to competition.

Floral Scents

Floral scents are also important in agriculture, horticulture and in studying ecosystem

function. Already, metabolomic approaches have been applied and it is hoped that the

metabolomics approach will lead to a more detailed understanding of the underlying

processes involved in floral scents, such as circadian rhythms, and the evolutionary ecology

Plant Environmental Metabolomics 171

between plant and pollinator (Vainstein, Lewinsohn et al. 2001; Verdonk, de Vos et al. 2003;

Fridman and Pichersky 2005).

Populations, Evolution and Genetics

Natural selection acts on variation in phenotypes, and understanding the origins and

maintenance of this variation is the focus of ecological genetics (Jackson, Linder et al. 2002)

and metabolic fingerprinting and profiling is currently being utilised for environmental

genomics research to identify ecologically important genes and traits (Benfey and Mitchell-

Olds 2008). To truly assign function to genes from metabolites, the best current approach is to

use plant crossings and using single nucleotide polymorphism (SNP); quantitative trait loci

(QTL) and amplified fragment length polymorphism (AFLP) techniques (Jansen and Nap

2001). Jansen and Nap (2001) have already successfully used the model tree Populus to

identify candidate genes for regulated flavonoid biosynthesis, a class of compounds that have

many ecological functions in plants (Morreel, Goeminne et al. 2006). Metabolomic

approaches can also be used to link plant genotypes and phenotypes using either forward or

reverse genetic approaches, however, in natural systems, the forward genetic approach is

likely to take precedence over reverse genetics (Fiehn 2002). Therefore, metabolomics could

help in assessing the evolutionary history of a plant species and it may also help to assess the

evolution of the actual metabolites and pathways (Schwab 2003). For example, the metabolite

profiles, mainly cyclitols by GC-MS, of eucalypts were obtained alongside related ecological

data of each species, to assess the evolution of this genus in arid environments (Merchant,

Richter et al. 2006). Also, the geographical and evolutionary diversity of glucosinolates, an

anti-herbivore class of compounds in Brassicaceae (Windsor, Reichelt et al. 2005; Clauss,

Dietel et al. 2006; Keurentjes, Fu et al. 2006). Intra and interspecific diversity in

glucosinolates structures, or any other class of metabolites, may provide clues as to whether

species and populations within a genus were evolved by parallel evolution or by a common

ancestral phenotype (Windsor, Reichelt et al. 2005).

Genetic versus Environmental Influences on the Metabolome

The amount of influence that genetics or the environment has over metabolism is difficult

to measure and interpret. A field trial to assess the influence of environment and genetics in

metabolic variation in a number of Douglas-fir trees with a known genetical family history

was carried out by Robinson, Ukrainetz et al. (2007). Here, metabolites in the tree xylem

were examined by GC-MS and multivariate discriminant analysis. The metabolite

phenotypes were largely associated with environmental site information rather than any

associations with the known genetic family structure. However, more recently, Ossipova et

al. (2008) set out to study how metabolomics and the associated chemometrics could be used

to recognise two different genotypes and the metabolic phenotypes of field grown birch trees

(Betula pendula) under elevated ozone treatments. From the GC-MS and LC-MS

fingerprinting, they were able to discriminate the different genotypes and even able to

discriminate which field the trees were grown in. However, there was less metabolic

variation in the ozone treated plants when compared to the differences in genotype. Such

Matthew P. Davey 172

interesting results showing the influence that genetics and environment have on the

metabolome needs to be investigated further.

Populations

A major application of metabolomics in ecology is the understanding of why plants only

grow in restricted areas. Plant adaptation to the local environment should result in traits that

are relevant to the abiotic and biotic conditions of the plants realised niche (Hoffmann 2005).

Plant environmental metabolomics can help us understand the adaptive significance of traits

and gene functions as it is likely that many genes are expressed only in the realised niche,

which may be difficult to replicate in the laboratory environment (Jackson, Linder et al.

2002). An example of the ecological application of metabolomics is the study of Arabidopsis

lyrata ssp. petraea. Across Europe, this species occurs in small isolated populations in Wales,

Scotland, Germany, Norway, Sweden and Iceland, usually growing on rocky or stony cliffs

and shores. Genetic differences between the populations have been obtained, however,

metabolomic fingerprinting using DIMS, HPLC and GC, followed by PCA, nicely

differentiate the Welsh and Swedish populations, which also differ from the closely related

Arabidopsis thaliana (Davey, Burrell et al. 2008). Along a similar vein, NMR has been used

to characterise nine different ecotypes of Arabidopsis thaliana by NMR followed by PCA

(Ward, Harris et al. 2003). Also, the different genotypes the same species of Populus trees

can also be discriminated by GC-MS metabolite profiling (Robinson, Gheneim et al. 2005).

Such approaches will ultimately aid the understanding of the complex genetic and

environmental factors underlying plant metabolism and plant distribution.

A benefit of being able to identify a species ecotype, or genotype, by metabolic

fingerprinting is that by using the correct statistical techniques it is possible to determine the

geographical origin of the plant sample. The correct identification of a plants geographical

origin can be made using techniques such as pyrolysis mass spectrometry and artificial neural

networks (Salter, Lazzari et al. 1997). Here, olive oils were analysed by such methods and by

assessing the spectra using training and test sets, oils were correctly assigned to its origin of

growth in different regions of Italy. Such work on assessing and predicting the geographical

origin of olive oils in Greece has also been carried out NMR fingerprinting (Petrakis,

Agiomyrgianaki et al. 2008). Supervised modelling techniques, such as Partial Least Squares

– Discriminant Analysis (PLS-DA) have already been successful in correctly classifying the

Country of origin of wine samples using its chemical component (Capron, Smeyers-Verbeke

et al. 2007). Another example of identifying metabolic differences between populations of

the same plant species is in the medicinal herb Ephedra sinica. (Schaneberg, Crockett et al.

2003) showed that they were able to identify the geographical origin on a cross continental

scale of plant extracts from this species using chemical fingerprinting. Even the origin of tea

(Camellia sinensis) can be discriminated using metabolite profiling (Sultana, Stecher et al.

2008) and different types of tropical hardwoods can be characterised by FT-IR and NMR

combined with processing data by PCA (Nuopponen, Wikberg et al. 2006).

Plant Environmental Metabolomics 173

Conclusion

It is becoming clear that plant environmental metabolomics will play an important part in

the understanding and manipulation of the natural world (Wollenweber, Porter et al. 2005;

Dixon, Gang et al. 2006; Schauer and Fernie 2006; Bundy, Davey et al. 2009). Such

alterations in the metabolite fingerprints and phenotypes of plant populations in response to

abiotic and biotic interactions need to be investigated. This will ultimately aid our

understanding of the complex genetic and environmental factors underlying plant metabolism

and plant distribution which is important for assessing how plants may respond to climatic

change (Thomas, Cameron et al. 2004; Jump and Penuelas 2005). There is recognition that

field-based measurements, alongside other traditional measurements in plant physiology, is

required (Blanchard 2004). It is also clear that plant metabolomics will increasingly become

integrated into the other omic approaches, after which the true power of the technology will

become apparent (Fridman and Pichersky 2005; Usadel, Blasing et al. 2008).

Abbreviations

DFA : Discriminant Function Analysis;

DIMS : Direct Injection Mass Spectrometry;

FT-IR : FourierTransform-InfraRed;

GC : Gas Chromatography;

GC-MS : Gas Chromatography-Mass Spectrometry;

HPLC : High Performance Liquid Chromatography;

MS : Mass Spectrometry;

NMR : Nuclear Magnetic Resonance;

PCA : Principal Component Analysis;

PDA : Photo Diode Array;

PLS-DA : Partial Least Squares-Discriminant Analysis

Some text within this article has been reproduced with kind permission from Springer

Science+Business Media from the article by the author: Bundy, J., M. Davey, et al. (2009).

"Environmental metabolomics: a critical review and future perspectives." Metabolomics 5(1):

3-21.

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