Light acclimation interacts with thylakoid ion transport to govern the dynamics of photosynthesis in Arabidopsis.

Understanding photosynthesis in natural, dynamic light environments requires knowledge of long-term acclimation, short-term responses, and their mechanistic interactions. To approach the latter, we systematically determined and characterized light-environmental effects on thylakoid ion transport-mediated short-term responses during light fluctuations. For this, Arabidopsis thaliana wild-type and mutants of the Cl- channel VCCN1 and the K+ exchange antiporter KEA3 were grown under eight different light environments and characterized for photosynthesis-associated parameters and factors in steady state and during light fluctuations. For a detailed characterization of selected light conditions, we monitored ion flux dynamics at unprecedented high temporal resolution by a modified spectroscopy approach. Our analyses reveal that daily light intensity sculpts photosynthetic capacity as a main acclimatory driver with positive and negative effects on the function of KEA3 and VCCN1 during high-light phases, respectively. Fluctuations in light intensity boost the accumulation of the photoprotective pigment zeaxanthin (Zx). We show that KEA3 suppresses Zx accumulation during the day, which together with its direct proton transport activity accelerates photosynthetic transition to lower light intensities. In summary, both light-environment factors, intensity and variability, modulate the function of thylakoid ion transport in dynamic photosynthesis with distinct effects on lumen pH, Zx accumulation, photoprotection, and photosynthetic efficiency.

Genome-Wide Approach to Identify Quantitative Trait Loci for Drought Tolerance in Tetraploid Potato (Solanum tuberosum L.).

Drought represents a major abiotic stress factor negatively affecting growth, yield and tuber quality of potatoes. Quantitative trait locus (QTL) analyses were performed in cultivated potatoes for drought tolerance index DRYM (deviation of relative starch yield from the experimental median), tuber starch content, tuber starch yield, tuber fresh weight, selected transcripts and metabolites under control and drought stress conditions. Eight genomic regions of major interest for drought tolerance were identified, three representing standalone DRYM QTL. Candidate genes, e.g., from signaling pathways for ethylene, abscisic acid and brassinosteroids, and genes encoding cell wall remodeling enzymes were identified within DRYM QTL. Co-localizations of DRYM QTL and QTL for tuber starch content, tuber starch yield and tuber fresh weight with underlying genes of the carbohydrate metabolism were observed. Overlaps of DRYM QTL with metabolite QTL for ribitol or galactinol may indicate trade-offs between starch and compatible solute biosynthesis. Expression QTL confirmed the drought stress relevance of selected transcripts by overlaps with DRYM QTL. Bulked segregant analyses combined with next-generation sequencing (BSAseq) were used to identify mutations in genes under the DRYM QTL on linkage group 3. Future analyses of identified genes for drought tolerance will give a better insight into drought tolerance in potatoes.

Metabolite and transcript markers for the prediction of potato drought tolerance.

Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker-assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT-PCR and GC-MS profiling, respectively. Transcript marker candidates were selected from a published RNA-Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.

Getting back to nature: a reality check for experiments in controlled environments.

Irradiance from sunlight changes in a sinusoidal manner during the day, with irregular fluctuations due to clouds, and light-dark shifts at dawn and dusk are gradual. Experiments in controlled environments typically expose plants to constant irradiance during the day and abrupt light-dark transitions. To compare the effects on metabolism of sunlight versus artificial light regimes, Arabidopsis thaliana plants were grown in a naturally illuminated greenhouse around the vernal equinox, and in controlled environment chambers with a 12-h photoperiod and either constant or sinusoidal light profiles, using either white fluorescent tubes or light-emitting diodes (LEDs) tuned to a sunlight-like spectrum as the light source. Rosettes were sampled throughout a 24-h diurnal cycle for metabolite analysis. The diurnal metabolite profiles revealed that carbon and nitrogen metabolism differed significantly between sunlight and artificial light conditions. The variability of sunlight within and between days could be a factor underlying these differences. Pairwise comparisons of the artificial light sources (fluorescent versus LED) or the light profiles (constant versus sinusoidal) showed much smaller differences. The data indicate that energy-efficient LED lighting is an acceptable alternative to fluorescent lights, but results obtained from plants grown with either type of artificial lighting might not be representative of natural conditions.

The drought response of potato reference cultivars with contrasting tolerance.

Systems responses to drought stress of four potato reference cultivars with differential drought tolerance (Solanum tuberosum L.) were investigated by metabolome profiling and RNA sequencing. Systems analysis was based on independent field and greenhouse trials. Robust differential drought responses across all cultivars under both conditions comprised changes of proline, raffinose, galactinol, arabitol, arabinonic acid, chlorogenic acid and 102 transcript levels. The encoded genes contained a high proportion of heat shock proteins and proteins with signalling or regulatory functions, for example, a homolog of abscisic acid receptor PYL4. Constitutive differences of the tolerant compared with the sensitive cultivars included arbutin, octopamine, ribitol and 248 transcripts. The gene products of many of these transcripts were pathogen response related, such as receptor kinases, or regulatory proteins, for example, a homolog of the Arabidopsis FOUR LIPS MYB-regulator of stomatal cell proliferation. Functional enrichment analyses imply heat stress as a major acclimation component of potato leaves to long-term drought stress. Enhanced heat stress during drought can be caused by loss of transpiration cooling. This effect and CO2 limitation are the main consequences of drought-induced or abscisic acid-induced stomatal closure. Constitutive differences in metabolite and transcript levels between tolerant and sensitive cultivars indicate interactions of drought tolerance and pathogen resistance in potato.

Whole-genome sequencing of tetraploid potato varieties reveals different strategies for drought tolerance.

Climate changes leading to increasingly longer seasonal drought periods in large parts of the world increase the necessity for breeding drought-tolerant crops. Cultivated potato (Solanum tuberosum), the third most important vegetable crop worldwide, is regarded as drought-sensitive due to its shallow root architecture. Two German tetraploid potato cultivars differing in drought tolerance and their F1-progeny were evaluated under various drought scenarios. Bulked segregant analyses were combined with whole-genome sequencing (BSA-Seq) using contrasting bulks of drought-tolerant and drought-sensitive F1-clones. Applying QTLseqr, 15 QTLs comprising 588,983 single nucleotide polymorphisms (SNPs) in 2325 genes associated with drought stress tolerance were identified. SeqSNP analyses in an association panel of 34 mostly starch potato varieties using 1-8 SNPs for each of 188 selected genes narrowed the number of candidate genes down to 10. In addition, ent-kaurene synthase B was the only gene present under QTL 10. Eight of the identified genes (StABP1, StBRI1, StKS, StLEA, StPKSP1, StPKSP2, StYAB5, and StZOG1) address plant development, the other three genes (StFATA, StHGD and StSYP) contribute to plant protection under drought stress. Allelic variation in these genes might be explored in future breeding for drought-tolerant potato varieties.

Elemental formula annotation of polar and lipophilic metabolites using (13) C, (15) N and (34) S isotope labelling, in combination with high-resolution mass spectrometry.

The unbiased and comprehensive analysis of metabolites in any organism presents a major challenge if proper peak annotation and unambiguous assignment of the biological origin of the peaks are required. Here we provide a comprehensive multi-isotope labelling-based strategy using fully labelled (13) C, (15) N and (34) S plant tissues, in combination with a fractionated metabolite extraction protocol. The extraction procedure allows for the simultaneous extraction of polar, semi-polar and hydrophobic metabolites, as well as for the extraction of proteins and starch. After labelling and extraction, the metabolites and lipids were analysed using a high-resolution mass spectrometer providing accurate MS and all-ion fragmentation data, providing an unambiguous readout for every detectable isotope-labelled peak. The isotope labelling assisted peak annotation process employed can be applied in either an automated database-dependent or a database-independent analysis of the plant polar metabolome and lipidome. As a proof of concept, the developed methods and technologies were applied and validated using Arabidopsis thaliana leaf and root extracts. Along with a large repository of assigned elemental compositions, which is provided, we show, using selected examples, the accuracy and reliability of the developed workflow.

Unravelling Differences in Candidate Genes for Drought Tolerance in Potato (Solanum tuberosum L.) by Use of New Functional Microsatellite Markers.

Potato is regarded as drought sensitive and most vulnerable to climate changes. Its cultivation in drought prone regions or under conditions of more frequent drought periods, especially in subtropical areas, requires intensive research to improve drought tolerance in order to guarantee high yields under limited water supplies. A candidate gene approach was used to develop functional simple sequence repeat (SSR) markers for association studies in potato with the aim to enhance breeding for drought tolerance. SSR primer combinations, mostly surrounding interrupted complex and compound repeats, were derived from 103 candidate genes for drought tolerance. Validation of the SSRs was performed in an association panel representing 34 mainly starch potato cultivars. Seventy-five out of 154 SSR primer combinations (49%) resulted in polymorphic, highly reproducible banding patterns with polymorphic information content (PIC) values between 0.11 and 0.90. Five SSR markers identified allelic differences between the potato cultivars that showed significant associations with drought sensitivity. In all cases, the group of drought-sensitive cultivars showed predominantly an additional allele, indicating that selection against these alleles by marker-assisted breeding might confer drought tolerance. Further studies of these differences in the candidate genes will elucidate their role for an improved performance of potatoes under water-limited conditions.

Can Metabolite- and Transcript-Based Selection for Drought Tolerance in Solanum tuberosum Replace Selection on Yield in Arid Environments?

Climate models predict an increased likelihood of drought, demanding efficient selection for drought tolerance to maintain yield stability. Classic tolerance breeding relies on selection for yield in arid environments, which depends on yield trials and takes decades. Breeding could be accelerated by marker-assisted selection (MAS). As an alternative to genomic markers, transcript and metabolite markers have been suggested for important crops but also for orphan corps. For potato, we suggested a random-forest-based model that predicts tolerance from leaf metabolite and transcript levels with a precision of more than 90% independent of the agro-environment. To find out how the model based selection compares to yield-based selection in arid environments, we applied this approach to a population of 200 tetraploid Solanum tuberosum ssp. tuberosum lines segregating for drought tolerance. Twenty-four lines were selected into a phenotypic subpopulation (PPt) for superior tolerance based on relative tuber starch yield data from three drought stress trials. Two subpopulations with superior (MPt) and inferior (MPs) tolerance were selected based on drought tolerance predictions based on leaf metabolite and transcript levels from two sites. The 60 selected lines were phenotyped for yield and drought tolerance in 10 multi-environment drought stress trials representing typical Central European drought scenarios. Neither selection affected development or yield potential. Lines with superior drought tolerance and high yields under stress were over-represented in both populations selected for superior tolerance, with a higher number in PPt compared to MPt. However, selection based on leaf metabolites may still be an alternative to yield-based selection in arid environments as it works on leaves sampled in breeder's fields independent of drought trials. As the selection against low tolerance was ineffective, the method is best used in combination with tools that select against sensitive genotypes. Thus, metabolic and transcript marker-based selection for drought tolerance is a viable alternative to the selection on yield in arid environments.

13C isotope-labeled metabolomes allowing for improved compound annotation and relative quantification in liquid chromatography-mass spectrometry-based metabolomic research.

Metabolomics is rapidly becoming an integral part of many life science studies ranging from disease diagnostics to systems biology. However, a number of problems such as the discrimination of biological from non-biological signals, efficient compound annotation, and reliable quantification are still not satisfactorily solved in untargeted LC-MS-based metabolomics research. Extending our previous work on direct infusion-based metabolomics, we here describe a (13)C isotope labeling strategy in combination with an Ultra Performance Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry-based approach (UPLC-FTICR MS) which provides a technological platform offering solutions to a number of the above-mentioned problems. We further demonstrate that the use of a fully labeled metabolome is not only beneficial for high end mass spectrometers, such as that used in this study but also provides a considerable improvement to every other mass spectrometry-based metabolomic platform.

Teaching (and learning from) metabolomics: the 2006 PlantMetaNet ETNA Metabolomics Research School.

Under the auspices of the European Training and Networking Activity programme of the European Union, a 'Metabolic Profiling and Data Analysis' Plant Genomics and Bioinformatics Summer School was hosted in Potsdam, Germany between 20 and 29 September 2006. Sixteen early career researchers were invited from the European Union partner nations and the so-called developing nations (Appendix). Lectures from invited leading European researchers provided an overview of the state of the art of these fields and seeded discussion regarding major challenges for their future advancement. Hands-on experience was provided by an example experiment - that of defining the metabolic response of Arabidopsis to treatment of a commercial herbicide of defined mode of action. This experiment was performed throughout the duration of the course in order to teach the concepts underlying extraction and machine handling as well as to provide a rich data set with which the required computation and statistical skills could be illustrated. Here we review the state of the field by describing both key lectures given at and practical aspects taught at the summer school. In addition, we disclose results that were obtained using the four distinct technical platforms at the different participating institutes. While the effects of the chosen herbicide are well documented, this study looks at a broader number of metabolites than in previous investigations. This allowed, on the one hand, not only to characterise further effects of the herbicide than previously observed but also to detect molecules other than the herbicide that were obviously present in the commercial formulation. These data and the workshop in general are all discussed in the context of the teaching of metabolomics.

Performance of Arabidopsis thaliana under different light qualities: comparison of light-emitting diodes to fluorescent lamp.

For precise phenotyping, Arabidopsis thaliana (L.) Heynh. is grown under controlled conditions with fluorescent lamps as the predominant light source. Replacement by systems based on light emitting diodes (LED) could improve energy efficiency and stability of light quality and intensity. To determine whether this affects the reproducibility of results obtained under fluorescent lamps, four Arabidopsis accessions and a phytochrome mutant were grown and phenotyped under two different LED types or under fluorescent lamps. All genotypes had significantly higher rosette weight and seed mass and developed faster under LED light than under fluorescent lamps. However, differences between genotypes were reproducible independent of the light source. Chlorophyll content, photosynthetic complex accumulation and light response curves of chlorophyll fluorescence parameters were indistinguishable under LED and fluorescent light. Principal component analysis of leaf metabolite concentrations revealed that the effect of a change from fluorescent light to LED light was small compared with the diurnal effect, which explains 74% of the variance and the age effect during vegetative growth (12%). Altogether, the replacement of fluorescent lamps by LED allowed Arabidopsis cultivation and reproduction of results obtained under fluorescent light.

Salt stress responses in a geographically diverse collection of Eutrema/Thellungiella spp. accessions.

Salinity strongly impairs plant growth and development. Natural genetic variation can be used to dissect complex traits such as plant salt tolerance. We used 16 accessions of the halophytic species Eutrema salsugineum (previously called Thellungiella salsuginea (Pallas) O.E.Schulz, Thellungiella halophila (C.A.Meyer) O.E. Schulz and Thellungiella botschantzevii D.A.German to investigate their natural variation in salinity tolerance. Although all accessions showed survival and growth up to 700mM NaCl in hydroponic culture, their relative salt tolerance varied considerably. All accessions accumulated the compatible solutes proline, sucrose, glucose and fructose and the polyamines putrescine and spermine. Relative salt tolerance was not correlated with the content of any of the investigated solutes. We compared the metabolomes and transcriptomes of Arabidopsis thaliana (L. Heynh.) Col-0 and E. salsugineum Yukon under control and salt stress conditions. Higher content of several metabolites in Yukon compared with Col-0 under control conditions indicated metabolic pre-adaptation to salinity in the halophyte. Most metabolic salt responses in Yukon took place at 200mM NaCl, whereas few additional changes were observed between 200 and 500mM. The opposite trend was observed for the transcriptome, with only little overlap between salt-regulated genes in the two species. In addition, only about half of the salt-regulated Yukon unigenes had orthologues in Col-0.

A software tool for the input and management of phenotypic data using personal digital assistants and other mobile devices.

BACKGROUND: Plant breeding and genetics demand fast, exact and reproducible phenotyping. Efficient statistical evaluation of phenotyping data requires standardised data storage ensuring long-term data availability while maintaining intellectual property rights. This is state of the art at phenomics centres, which, however, are unavailable for most scientists. For them we developed a simple and cost-efficient system, the Phenotyper, which employs mobile devices or personal digital assistants (PDA) for on-site data entry and open-source software for data management. RESULTS: A graphical user interface (GUI) on a PDA replaces paper-based form sheet and data entry on a desktop. The user can define his phenotyping schemes in a web tool without in-depth knowledge of the system and thus adjust it more easily to new research aspects than in a classical laboratory information management system (LIMS). In the Phenotyper, schemes are built from controlled vocabulary gained from published ontologies. Vocabulary and schemes are stored in a database that also manages the user access. From the web page, schemes are downloaded as extended markup language (XML) files for the transfer to the PDA and the exchange between users. On the PDA, the GUI displays the schemes and stores data in comma separated value format and XML format. After manual quality control, data are uploaded via a web page to an independently hosted results database, in which data are stored in an entity-attribute-value structure to provide maximum flexibility. Datasets are linked to the original and curated data files stored on a file server. The ownership stamp, project affiliation and date stamp of a dataset are used to regulate data access, which is restricted to data belonging to the user or to his projects and data, for which the embargo period has ended. By export of standardised ASCII reports to long-term data storage facility, long-term accessibility allows searching, citing and use of raw data beyond the lifetime of the database. The Phenotyper is available to the scientific community for use and further development. CONCLUSIONS: The Phenotyper provides a well-structured, but flexible data acquisition and management structure for mobile on-site measurements for efficient evaluation and shared use of data.

Assessment of drought tolerance and its potential yield penalty in potato.

Climate models predict an increased likelihood of seasonal droughts for many areas of the world. Breeding for drought tolerance could be accelerated by marker-assisted selection. As a basis for marker identification, we studied the genetic variance, predictability of field performance and potential costs of tolerance in potato (Solanum tuberosum L.). Potato produces high calories per unit of water invested, but is drought-sensitive. In 14 independent pot or field trials, 34 potato cultivars were grown under optimal and reduced water supply to determine starch yield. In an artificial dataset, we tested several stress indices for their power to distinguish tolerant and sensitive genotypes independent of their yield potential. We identified the deviation of relative starch yield from the experimental median (DRYM) as the most efficient index. DRYM corresponded qualitatively to the partial least square model-based metric of drought stress tolerance in a stress effect model. The DRYM identified significant tolerance variation in the European potato cultivar population to allow tolerance breeding and marker identification. Tolerance results from pot trials correlated with those from field trials but predicted field performance worse than field growth parameters. Drought tolerance correlated negatively with yield under optimal conditions in the field. The distribution of yield data versus DRYM indicated that tolerance can be combined with average yield potentials, thus circumventing potential yield penalties in tolerance breeding.

Growing evening primroses (Oenothera).

The model plant Oenothera has contributed significantly to the biological sciences and it dominated the early development of plant genetics, cytogenetics, and evolutionary biology. The great advantage of using Oenothera as a model system is a large body of genetic, cytological, morphological, and ecological information collected over more than a century. The Oenothera system offers a well-studied taxonomy, population structure, and ecology. Cytogenetics and formal genetics at the population level are extensively developed, providing an excellent basis to study evolutionary questions. Further, Oenothera is grown as an oil seed crop for the production of essential fatty acids (gamma-linoleic acid) and is considered to be a medicinal plant due to its many pharmaceutically active secondary metabolites, such as ellagitannins. Although Oenothera has been cultivated as a laboratory organism since the end of the 19th century, there is a substantial lack of literature dealing with modern greenhouse techniques for the genus. This review compiles an overview about the growth requirements for the genus Oenothera, with a special focus on its genetically best-studied subsections Oenothera and Munzia. Requirements for greenhouse, field, and agronomic cultures are presented, together with information on substrate types, pest control, as well as vegetative and seed propagation, cross pollination, harvest, and seed storage. Particular aspects like germination, bolting, and flowering induction in taxonomically diverse material are reviewed. Methods recommended are supported by ecological and experimental data. An overview of the possibilities for wide hybridization and polyploidy induction in the genus is given. Germplasm resources are referenced. In summary, a comprehensive guideline for successful laboratory cultivation of Oenothera species is provided.

SILAC and alternatives in studying cellular proteomes of plants.

Quantitative proteomics by metabolic labeling has a high impact on the growing field of plant systems biology. SILAC has been pioneered and optimized for plant cell culture systems allowing for SILAC-based quantitative experiments in specialized experimental setups. In comparison to other model organisms, the application of SILAC to whole plants is challenging. As autotrophic organisms, plants under their natural growth conditions can hardly be fully labeled with stable isotope-coded amino acids. The metabolic labeling with inorganic nitrogen is therefore the method of choice for most whole-plant physiological questions. Plants can easily metabolize different inorganic nitrogen isotopes. The incorporation of the labeled inorganic nitrogen then results in proteins and metabolites with distinct molecular mass, which can be detected on a mass spectrometer. In comparative quantitative experiments, similarly as in SILAC experiments, treated and untreated samples are differentially labeled by nitrogen isotopes and jointly processed, thereby minimizing sample-to-sample variation. In recent years, heavy nitrogen labeling has become a widely used strategy in quantitative proteomics and novel approaches were developed for metabolite identification. Here we present a typical hydroponics setup, the workflow for processing of samples, mass spectrometry and data analysis for large-scale metabolic labeling experiments of whole plants.

Identification of drought tolerance markers in a diverse population of rice cultivars by expression and metabolite profiling.

Rice provides about half of the calories consumed in Asian countries, but its productivity is often reduced by drought, especially when grown under rain-fed conditions. Cultivars with increased drought tolerance have been bred over centuries. Slow selection for drought tolerance on the basis of phenotypic traits may be accelerated by using molecular markers identified through expression and metabolic profiling. Previously, we identified 46 candidate genes with significant genotype × environment interaction in an expression profiling study on four cultivars with contrasting drought tolerance. These potential markers and in addition GC-MS quantified metabolites were tested in 21 cultivars from both indica and japonica background that varied in drought tolerance. Leaf blades were sampled from this population of cultivars grown under control or long-term drought condition and subjected to expression analysis by qRT-PCR and metabolite profiling. Under drought stress, metabolite levels correlated mainly negatively with performance parameters, but eight metabolites correlated positively. For 28 genes, a significant correlation between expression level and performance under drought was confirmed. Negative correlations were predominant. Among those with significant positive correlation was the gene coding for a cytosolic fructose-1,6-bisphosphatase. This enzyme catalyzes a highly regulated step in C-metabolism. The metabolic and transcript marker candidates for drought tolerance were identified in a highly diverse population of cultivars. Thus, these markers may be used to select for tolerance in a wide range of rice germplasms.

Dissecting rice polyamine metabolism under controlled long-term drought stress.

A selection of 21 rice cultivars (Oryza sativa L. ssp. indica and japonica) was characterized under moderate long-term drought stress by comprehensive physiological analyses and determination of the contents of polyamines and selected metabolites directly related to polyamine metabolism. To investigate the potential regulation of polyamine biosynthesis at the transcriptional level, the expression of 21 genes encoding enzymes involved in these pathways were analyzed by qRT-PCR. Analysis of the genomic loci revealed that 11 of these genes were located in drought-related QTL regions, in agreement with a proposed role of polyamine metabolism in rice drought tolerance. The cultivars differed widely in their drought tolerance and parameters such as biomass and photosynthetic quantum yield were significantly affected by drought treatment. Under optimal irrigation free putrescine was the predominant polyamine followed by free spermidine and spermine. When exposed to drought putrescine levels decreased markedly and spermine became predominant in all cultivars. There were no correlations between polyamine contents and drought tolerance. GC-MS analysis revealed drought-induced changes of the levels of ornithine/arginine (substrate), substrates of polyamine synthesis, proline, product of a competing pathway and GABA, a potential degradation product. Gene expression analysis indicated that ADC-dependent polyamine biosynthesis responded much more strongly to drought than the ODC-dependent pathway. Nevertheless the fold change in transcript abundance of ODC1 under drought stress was linearly correlated with the drought tolerance of the cultivars. Combining metabolite and gene expression data, we propose a model of the coordinate adjustment of polyamine biosynthesis for the accumulation of spermine under drought conditions.

Conducting molecular biomarker discovery studies in plants.

Molecular biomarkers are molecules whose concentrations in a biological system inform about the current phenotypical state and, more importantly, may also be predictive of future phenotypic trait endpoints. The identification of biomarkers has gained much attention in targeted plant breeding since technologies have become available that measure many molecules across different levels of molecular organization and at decreasing costs. In this chapter, we outline the general strategy and workflow of conducting biomarker discovery studies. Critical aspects of study design as well as the statistical data analysis and model building will be highlighted.