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.

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.

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.

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.

Documentation system for plant transformation service and research.

BACKGROUND: In plant transformation, method compliance is critical for success. Transformation methods are complicated and tend to evolve over time. Until the complete method is published, method details are often partially orally transmitted and thus bound to a few people. Their documentation in text files are often a mixture of material and method description with many references to other sources especially to media description. These media are complex and often composed from several commercially available mixtures plus individually prepared stocks. The actual transformation experiment is generally documented in lab books, in which deviations from the methods and results are reported. Additionally, work schedules are planned in diaries. Both paper-based sources lack backup copies and miss unambiguous links to method descriptions and media recipes. DESCRIPTION: To solve the problem, we devised a standard-operation-procedure system based on a Microsoft Access database containing the interlinked modules 'Media', 'Methods' and 'Experiments'. The Media module contains all basic chemicals, stocks and complex media. In this module, complex media are composed from other elements of the Media module, thus mimicking the workflows of media preparation in the lab. The Media module is made attractive to the user by functions that generate file cards and labels. The Methods module describes each method stepwise and links the steps to the media. Copy functions allow cloning of old methods to document method evolution without alteration of the old methods. Activation and inactivation functions in the Media and the Methods module remove outdated entries from active use. The Experiments module links the method to experiment specific information. This module generates a lab-book like user interface and a work schedule, and it contains a simple result section. CONCLUSION: The system has been evolved and tested over several years in a transformation service unit, where it increased efficiency. Additionally, the system provided rapid access to data for quality control and decision making. The system can be easily modified for the use in other research environments.

Expression profiling of rice cultivars differing in their tolerance to long-term drought stress.

Understanding the molecular basis of plant performance under water-limiting conditions will help to breed crop plants with a lower water demand. We investigated the physiological and gene expression response of drought-tolerant (IR57311 and LC-93-4) and drought-sensitive (Nipponbare and Taipei 309) rice (Oryza sativa L.) cultivars to 18 days of drought stress in climate chamber experiments. Drought stressed plants grew significantly slower than the controls. Gene expression profiles were measured in leaf samples with the 20 K NSF oligonucleotide microarray. A linear model was fitted to the data to identify genes that were significantly regulated under drought stress. In all drought stressed cultivars, 245 genes were significantly repressed and 413 genes induced. Genes differing in their expression pattern under drought stress between tolerant and sensitive cultivars were identified by the genotype x environment (G x E) interaction term. More genes were significantly drought regulated in the sensitive than in the tolerant cultivars. Localizing all expressed genes on the rice genome map, we checked which genes with a significant G x E interaction co-localized with published quantitative trait loci regions for drought tolerance. These genes are more likely to be important for drought tolerance in an agricultural environment. To identify the metabolic processes with a significant G x E effect, we adapted the analysis software MapMan for rice. We found a drought stress induced shift toward senescence related degradation processes that was more pronounced in the sensitive than in the tolerant cultivars. In spite of higher growth rates and water use, more photosynthesis related genes were down-regulated in the tolerant than in the sensitive cultivars.

A plant resource and experiment management system based on the Golm Plant Database as a basic tool for omics research.

BACKGROUND: For omics experiments, detailed characterisation of experimental material with respect to its genetic features, its cultivation history and its treatment history is a requirement for analyses by bioinformatics tools and for publication needs. Furthermore, meta-analysis of several experiments in systems biology based approaches make it necessary to store this information in a standardised manner, preferentially in relational databases. In the Golm Plant Database System, we devised a data management system based on a classical Laboratory Information Management System combined with web-based user interfaces for data entry and retrieval to collect this information in an academic environment. RESULTS: The database system contains modules representing the genetic features of the germplasm, the experimental conditions and the sampling details. In the germplasm module, genetically identical lines of biological material are generated by defined workflows, starting with the import workflow, followed by further workflows like genetic modification (transformation), vegetative or sexual reproduction. The latter workflows link lines and thus create pedigrees. For experiments, plant objects are generated from plant lines and united in so-called cultures, to which the cultivation conditions are linked. Materials and methods for each cultivation step are stored in a separate ACCESS database of the plant cultivation unit. For all cultures and thus every plant object, each cultivation site and the culture's arrival time at a site are logged by a barcode-scanner based system. Thus, for each plant object, all site-related parameters, e.g. automatically logged climate data, are available. These life history data and genetic information for the plant objects are linked to analytical results by the sampling module, which links sample components to plant object identifiers. This workflow uses controlled vocabulary for organs and treatments. Unique names generated by the system and barcode labels facilitate identification and management of the material. Web pages are provided as user interfaces to facilitate maintaining the system in an environment with many desktop computers and a rapidly changing user community. Web based search tools are the basis for joint use of the material by all researchers of the institute. CONCLUSION: The Golm Plant Database system, which is based on a relational database, collects the genetic and environmental information on plant material during its production or experimental use at the Max-Planck-Institute of Molecular Plant Physiology. It thus provides information according to the MIAME standard for the component 'Sample' in a highly standardised format. The Plant Database system thus facilitates collaborative work and allows efficient queries in data analysis for systems biology research.

A quality-controlled microarray method for gene expression profiling.

Gene expression profiling on microarrays is widely used to measure the expression of large numbers of genes in a single experiment. Because of the high cost of this method, feasible numbers of replicates are limited, thus impairing the power of statistical analysis. As a step toward reducing technically induced variation, we developed a procedure of sample preparation and analysis that minimizes the number of sample manipulation steps, introduces quality control before array hybridization, and allows recovery of the prepared mRNA for independent validation of results. Sample preparation is based on mRNA separation using oligo(dT) magnetic beads, which are subsequently used for first-strand cDNA synthesis on the beads. cDNA covalently bound to the magnetic beads is used as template for second-strand cDNA synthesis, leaving the intact mRNA in solution for further analysis. The quality of the synthesized cDNA can be assessed by quantitative polymerase chain reaction using 3'- and 5'-specific primer pairs for housekeeping genes such as glyceraldehyde-3-phosphate dehydrogenase. Second-strand cDNA is chemically labeled with fluorescent dyes to avoid dye bias in enzymatic labeling reactions. After hybridization of two differently labeled samples to microarray slides, arrays are scanned and images analyzed automatically with high reproducibility. Quantile-normalized data from five biological replica display a coefficient of variation 45% for 90% of profiled genes, allowing detection of twofold changes with false positive and false negative rates of 10% each. We demonstrate successful application of the procedure for expression profiling in plant leaf tissue. However, the method could be easily adapted for samples from animal including human or from microbial origin.