PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, ranslational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding

Integrated Physiological, Proteomic, and Metabolomic Analysis of Ultra Violet (UV) Stress Responses and Adaptation Mechanisms in Pinus radiata

Globally expected changes in environmental conditions, especially the increase of UV irradiation, necessitate extending our knowledge of the mechanisms mediating tree species adaptation to this stress. This is crucial for designing new strategies to maintain future forest productivity. Studies focused on environmentally realistic dosages of UV irradiation in forest species are scarce. Pinus spp. are commercially relevant trees and not much is known about their adaptation to UV. In this work, UV treatment and recovery of Pinus radiata plants with dosages mimicking future scenarios, based on current models of UV radiation, were performed in a time-dependent manner. The combined metabolome and proteome analysis were complemented with measurements of + physiological parameters and gene expression. Sparse PLS analysis revealed complex molecular interaction networks of molecular and physiological data. Early responses prevented phototoxicity by reducing photosystem activity and the electron transfer chain together with the accumulation of photoprotectors and photorespiration. Apart from the reduction in photosynthesis as consequence of the direct UV damage on the photosystems, the primary metabolism was rearranged to deal with the oxidative stress while minimizing ROS production. New protein kinases and proteases related to signaling, coordination, and regulation of UV stress responses were revealed. All these processes demonstrate a complex molecular interaction network extending the current knowledge on UV-stress adaptation in pine

PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor

The PhosPhAt database provides a resource consolidating our current knowledge of mass spectrometry-based identified phosphorylation sites in Arabidopsis and combines it with phosphorylation site prediction specifically trained on experimentally identified Arabidopsis phosphorylation motifs. The database currently contains 1187 unique tryptic peptide sequences encompassing 1053 Arabidopsis proteins. Among the characterized phosphorylation sites, there are over 1000 with unambiguous site assignments, and nearly 500 for which the precise phosphorylation site could not be determined. The database is searchable by protein accession number, physical peptide characteristics, as well as by experimental conditions (tissue sampled, phosphopeptide enrichment method). For each protein, a phosphorylation site overview is presented in tabular form with detailed information on each identified phosphopeptide. We have utilized a set of 802 experimentally validated serine phosphorylation sites to develop a method for prediction of serine phosphorylation (pSer) in Arabidopsis. An analysis of the current annotated Arabidopsis proteome yielded in 27 782 predicted phosphoserine sites distributed across 17 035 proteins. These prediction results are summarized graphically in the database together with the experimental phosphorylation sites in a whole sequence context. The Arabidopsis Protein Phosphorylation Site Database (PhosPhAt) provides a valuable resource to the plant science community and can be accessed through the following link http://phosphat.mpimp-golm.mpg.d

Recurrent allopolyploidizations diversify ecophysiological traits in marsh orchids (Dactylorhiza majalis s.l.).

Whole-genome duplication has shaped the evolution of angiosperms and other organisms, and is important for many crops. Structural reorganization of chromosomes and repatterning of gene expression are frequently observed in allopolyploids, with physiological and ecological consequences. Recurrent origins from different parental populations are widespread among polyploids, resulting in an array of lineages that provide excellent models to uncover mechanisms of adaptation to divergent environments in early phases of polyploid evolution. We integrate here transcriptomic and ecophysiological comparative studies to show that sibling allopolyploid marsh orchid species (Dactylorhiza, Orchidaceae) occur in different habitats (low nutrient fens vs. meadows with mesic soils) and are characterized by a complex suite of intertwined, pronounced ecophysiological differences between them. We uncover distinct features in leaf elemental chemistry, light-harvesting, photoprotection, nutrient transport and stomata activity of the two sibling allopolyploids, which appear to match their specific ecologies, in particular soil chemistry differences at their native sites. We argue that the phenotypic divergence between the sibling allopolyploids has a clear genetic basis, generating ecological barriers that maintain distinct, independent lineages, despite pervasive interspecific gene flow. This suggests that recurrent origins of polyploids bring about a long-term potential to trigger and maintain functional and ecological diversity in marsh orchids and other groups

Time-resolved impulse response of the magnetoplasmon resonance in a two-dimensional electron gas

We have used optically excited ultrashort electrical pulses to measure the magnetoplasmon resonance of a two-dimensional electron gas formed in an AlGaAs/GaAs heterostructure at frequencies up to 200 gigahertz. This is accomplished by incorporating the sample into a guided wave probe operating in a pumped (^{3}He) system. We are able to detect the resonance by launching a stimulus pulse in the guide, and monitoring the system response in a time resolved pump-probe arrangement. Data obtained from measurements yield resonant frequencies that agree with the magnetoplasmon dispersion relation.Comment: 4 pages, 4 figure

Radar climatology of the COPS region

A climatology of convection initiation (CI) and convective enhancements (CE) has been developed using radar reflectivity data in southwestern Germany and eastern France over the period of May�August of 2000�2006 and 2008. The study region included the Vosges Mountains of France, the Rhine Valley which straddles France and Germany, the Black ForestMountains and the SwabianMountains of Germany. Convection occurred frequently during the summer months throughout the study region. The CI density (number of initiations per square km) illustrates preferential formation in the mountain regions while the CE events spanned both mountains and valleys nearly equally. There is a strongmid-day peak of the CI events suggesting that diurnal heating is critical for CI in the region. The very strong thunderstorms (>46 dBZ) first occurred in the mountains and �2 h later in the Rhine Valley. During the summer of 2007, the Convective and Orographically-induced Precipitation Study (COPS) field campaign was conducted with the objective of obtaining improved understanding of convective processes and short-term quantitative precipitation forecasting in low-mountain regions. Comparisons were made between the radar climatology results and the COPS summer. The COPS summer exhibited preferential CI density in the mountainous regions but not as pronounced as the climatology. The COPS summer had a similar diurnal peak of CI events as climatology but the ratio of daytime to nighttime CI (1.7), or amplitude of the diurnal cycle, was less than that of climatology (3.0). While both the 8-year climatology and COPS summer were dominated by daytime, locally-forced CI occurrences, the broad distribution of daytime CI events and increase in nighttime events observed during COPS indicate a more active synoptic pattern in 2007

Consistency analysis of metabolic correlation networks

<p>Abstract</p> <p>Background</p> <p>Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network).</p> <p>Results</p> <p>Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network.</p> <p>Conclusion</p> <p>The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles.</p

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment