Mapping a plant’s chemical vocabularyIvan R Baxter & Justin O Borevitz

Plants generate an amazing variety of small molecules and are arguably nature’s finest chemists. A new study identifies over 2,000 small molecule mass peaks in the model plant Arabidopsis thaliana and defines both the genetic diversity and genetic architecture controlling the production of these compounds.

Various colors, smells and tastes drive plant-animal coevolution through a biochemical courtship played daily and seasonally for mil-lennia. These chemical ballads take on new verses, becoming new vocabularies and ulti-mately new dialects and languages. On page 842 of this issue1, the ‘vocal trace’ of the wild mustard Arabidopsis thaliana’s chemical song reveals incredible intraspecies variation con-trolled by simple and complex genetic deter-minants. Thus, the source of the wild mustard potpourri is revealed.

The full complement of metabolites has yet to be identified for any plant species and pres-ents a supreme challenge. Metabolites can be divided into two main classes: primary metabo-lites involved in the normal cellular processes for growth, development and reproduction, and secondary metabolites that comprise the rest of the plant’s metabolome and include molecules involved in defense against predators and para-sites, colorants and volatiles to attract pollina-tors, and signals to other plants (allelopathy). Primary metabolites are expected be conserved across most species, whereas secondary metab-olites were generally thought to be specific to species or genera2.

Incredible diversityIn their new study, Keurentjes et al. describe the incredible diversity of metabolites pres-ent in a handful of wild strains of the model plant Arabidopsis thaliana (Fig. 1). Included among these are the well-known mustard fla-

vors, widely variable in the Brassicaceae fam-ily, which includes the daikon radish and the hot wasabi. Each species, and now clearly each strain within a species, has evolved a different chemical repertoire. These signatures of dif-ferentially modified primary and secondary metabolites likely coevolved under the selective pressures of herbivores, pathogens and pollina-tors3 and have adapted to abiotic factors such as temperature and light (anthocyanins for ultraviolet tolerance). In crop plants, humans have selected for reduced ‘bitterness’ or other flavors attractive to us, with crop breeders act-ing as natural product chemists.

Methodological and computational advances have made large-scale unbiased metabolite pro-filing possible, thus allowing their genetic dis-section through the analysis of mapping lines. In an elegant demonstration of the power genetics brings to metabolomics, the authors profiled both wild strains and recombinant inbred lines (RIL) segregating variation between two strains.

Keurentjes et al. report that half of the 2,500 mass peaks were unique to three or fewer of the 14 strains. The 331 mass peaks detected in all 14 accessions are likely to include a core of primary metabolites required for survival, although sur-veys of additional strains may find that some of these are nonessential. The large diversity of metabolites found is even more striking given that a single healthy juvenile stage was sampled. Seedlings growing on sterile plates are unlikely to be responding to pathogens, predators, tem-perature or nutrient stress and are far from starting reproductive growth. Yet even these highly sheltered (until their immersion in liquid nitrogen) plants are producing vastly different complements of metabolites.

Hidden variationTo begin their genetic dissection, Keurentjes et al. employed the widely used Cvi/Ler RIL set. Using state-of-the-art methods to map both presence or absence as well as

Ivan R. Baxter is at the Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, USA, and Justin O. Borevitz is in the Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, Illinois 60637, USA.e-mail: borevitz@uchicago.edu

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Figure 1 Wild Arabidopsis thaliana accessions show dramatic genetic variation in their metabolite profiles. An individual plant’s unique chemical code communicates with the environment and may provide selective advantage. Seedlng images and mass spectrometry traces courtesy of Matthieu Reymond and Joost Keurentjes, respectively.

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quantitative variation, they found quantita-tive trait loci (QTL) for ∼75% of the more than 2,000 mass signals. One-third of the mass peaks segregating were transgressive, i.e., not present in the parental lines and thus caused by new allelic combinations. This demonstrates that genetic recombina-tion within a single species is able to produce many new compounds, implicating selection on standing variation. This hidden varia-tion, now revealed to plant breeders, opens the door for fine manipulation of nutri-tional traits in crop plants by interbreed-ing wild relatives with domesticated lines4. Researchers will be able to use the identified loci to begin deconstructing the complex interactions that control the metabolome of this model plant and, by extension, the crop plants that feed the world.

The approach of Keurentjes et al. to metabo-lomic profiling of RIL populations could also be applied to other high-throughput method-ologies, including proteomics5, ionomics6 and expression profiling7, potentially allowing each individual component of the cell to be treated as a trait to be mapped. Questions regard-ing genetic architecture (the number of QTL controlling a trait) and pleiotropy (the genetic clustering of QTL across traits) are certainly comparable, and the results suggest similar dis-tributions for genetic control of expression and metabolism. Just as many expression traits are

caused by a single QTL, many metabolites have a simple genetic basis. Few traits are controlled by many QTL, with the caveat that their poly-genic nature is harder to detect. Pleiotropy is also prominent: some QTL control many mass traits, while others may regulate just a hand-ful. This is expected for candidate genes acting upstream or downstream in a regulatory or biosynthetic pathway.

An issue not addressed by Keurentjes et al. is dominance and maternal effects, which could be modeled globally by including reciprocal F1s. Those masses showing present/absent variation may be more likely to show dominant modes of inheritance. The authors’ elegant use of second-order genetic correlations to rebuild pathways reveals the power of genetical metabolomics. Future high-powered studies and standardized analysis methods are now needed to support this initial result.

Scratching the surfaceObviously, we are just scratching the surface of the metabolomic diversity that exists in Arabidopsis thaliana, let alone the rest of the plant kingdom. In this impressive study, the authors only profiled a single RIL popula-tion, under a single growth condition, at a single stage, with a single extraction, and they ran their mass spectrometer only in negative mode. Changing any one of these variables would likely add hundreds, if not

thousands, of new mass peaks to investigate. Importantly, new environments and devel-opmental stages will allow the global gene-by-environment dynamic of metabolism to be revealed. Already, the developmental variability in the glucosinolate pathways suggests that plants exquisitely regulate global metabolism in a tissue- and environ-ment-specific manner.

The metabolomic diversity displayed by the seedlings profiled here undoubtedly affects developmental morphology and physiological responses in mature plants. Thus, these chemical cues contain the direct and indirect phenotypes ultimately under selection in nature. They are the birdsongs or idioms on the front lines of phenotypic variation within the species. This study reveals the exquisite genetic control of plant metabolites, which are integrated with tran-scripts, proteins and ions to trigger cellular events leading to phenotypic variation.

1. Keurentjes, J.J.B. et al. Nat. Genet. 38, 842–849 (2006).

2. D’Auria, J.C. & Gershenzon, J. Curr. Opin. Plant Biol. 8, 308–316 (2005).

3. Erlich, P.H. & Raven, P.H. Evolution 18, 586–608 (1964).

4. Tanksley, S.D. & McCouch, S.R. Science 277, 1063–1066 (1997).

5. Peck, S.C. Plant Physiol. 138, 591–599 (2005).6. Salt, D.E. Plant Physiol. 136, 2451–2456 (2004).7. West, M.A.L. et al. Genome Res., published online 15

May 2006 (doi: 10.1101/gr.5011206).

Kornel Schuebel, Wei Chen and Stephen B. Baylin are in the Division of Cancer Biology, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University, Baltimore, Maryland, USA.e-mail: sbaylin@jhmi.edu

CIMPle origin for promoter hypermethylation in colorectal cancer?Kornel Schuebel, Wei Chen & Stephen B Baylin

The proposal that a distinct subset of colorectal cancers show a marked propensity for promoter CpG island DNA methylation and associated gene silencing has been hotly debated. A new study takes an unbiased approach and not only strongly concludes that a CpG island methylator phenotype exists, but also offers new markers to define a concept that could teach much about the origins of cancer.

A controversy among cancer epigeneticists has touched on key questions in the field: what causes DNA promoter hypermethylation and gene silencing? Is it a stochastic phenomenon, and does it involve a programmed series of events? Issa and colleagues originally pro-

posed that a subset of colorectal cancers have a promoter CpG island methylator phenotype (CIMP)1. Association of CIMP with tumors having a more proximal location in the colon, signs of mucinous cell differentiation and a high frequency of microsatellite instability (MSI) potentially links epigenetic events to fundamental defects associated with cancer cell origins. All of these key issues quickly took a back seat, however, to controversies about CIMP itself, and whether modes of data analy-sis and/or choice of hypermethylated markers determined whether one saw the phenotype or

not2. On page 787 of this issue, Peter Laird and colleagues3 wrestle squarely with the problem using a clever, unbiased approach and cast a strong vote for the legitimacy of CIMP and suggest means to better define it.

CIMP enters the stageIncreasing interest in promoter hypermeth-ylation and gene silencing has pushed tumor biologists to employ not only candidate gene discovery approaches but also random screens of the cancer ‘epigenome’ to identify new hypermethylated genes4. Through this

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