Metabolomics-based profiling for quality assessment and revealing the impact of drying of Turmeric (Curcuma longa L.).

Turmeric, the rhizomes of Curcuma longa L., is one of the top selling spices, food preservatives, and food colorants. In addition, it exhibits health promoting benefits owing to its unique phytochemical composition. Nevertheless, it is commonly subjected to heat drying, hence, the dried powder is the most used form and can easily be adulterated with allied species. Therefore, our research aimed to profile the phytochemical composition and investigate the impact of drying of turmeric. Extraction and fractionation followed by LC- and GC-MS analysis resulted in the identification of a total of 161 metabolites belonged to various phytochemical classes. Moreover, multivariate data analysis identified curcuminoids, terpecurcumins, and organic acids as potential markers for drying. Based on the applied analytical techniques in combination with chemometrics, these investigations have succeeded to provide good coverage of the metabolome of turmeric in both fresh and dried forms.

The INCREASE project: Intelligent Collections of food-legume genetic resources for European agrofood systems.

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.

The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe.

Ginger (Zingiber officinale Roscoe) is consumed for health-promoting effects and as a food condiment. Comprehensive phytochemical analysis, other than gingerols and shogaols, has not yet been deeply investigated. Hence, the current research aimed to establish a non-targeted metabolomics approach for the discrimination between fresh ginger rhizome samples collected from four different producing countries, i.e., China, India, Pakistan, and Peru. In addition, lab-dried samples were analyzed to trace drying-induced metabolites. A comprehensive extraction procedure was carried out resulting in production of polar and non-polar fractions. The polar fraction was analyzed by ultra-performance liquid chromatography coupled with Fourier transform tandem mass spectrometry (UPLC-C18-FT-MS/MS) and gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS) post derivatization. UPLC-C8-FT-MS/MS was used for analysis of non-polar fraction. Results revealed for identification of a total of 253 metabolites. In addition, multivariate data analysis (MVDA), including principal component analysis (PCA) demonstrated clustering of Asian specimens. Several metabolites with a characteristic pattern for the origin revealing the highest contents of bioactive metabolites in the Peruvian product. Moreover, chemical markers identified, including [6]-gingerol and [6]-shogaol discriminating between fresh and dried samples. Furthermore, abundances of some primary metabolites, including amino acids and cinnamic acid, have confirmed the biosynthetic pathway of gingerols and their transformation upon drying to shogaols. The proposed approach can be applied as a potential candidate for quality assessment of ginger and other medicinal plants.

Towards the Development, Maintenance, and Standardized Phenotypic Characterization of Single-Seed-Descent Genetic Resources for Common Bean.

The optimal use of legume genetic resources represents a key prerequisite for coping with current agriculture-related societal challenges, including conservation of agrobiodiversity, agricultural sustainability, food security, and human health. Among legumes, the common bean (Phaseolus vulgaris) is the most economically important for human consumption, and its evolutionary trajectories as a species have been crucial to determining the structure and level of its present and available genetic diversity. Genomic advances are considerably enhancing the characterization and assessment of important genetic variants. For this purpose, the development and availability of, and access to, well-described and efficiently managed genetic resource collections that comprise pure lines derived by single-seed-descent cycles will be paramount for the use of the reservoir of common bean variability and for the advanced breeding of legume crops. This is one of the main aims of the new and challenging European project INCREASE, which is the implementation of Intelligent Collections with appropriate standardized protocols that must be characterized, maintained, and made available, along with the related data, to users such as breeders and researchers. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Characterizing common bean seeds for seed trait descriptors Basic Protocol 2: Bean seed imaging Basic Protocol 3: Characterizing bean lines for plant trait descriptors specific for common bean Primary Seed Increase.

Flux profiling of photosynthetic carbon metabolism in intact plants.

Flux analysis has been carried out in plants for decades, but technical innovations are now enabling it to be carried out in photosynthetic tissues in a more precise fashion with respect to the number of metabolites measured. Here we describe a protocol, using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS), to resolve intracellular fluxes of the central carbon metabolism in illuminated intact Arabidopsis thaliana rosettes using the time course of the unlabeled fractions in 40 major constituents of the metabolome after switching to (13)CO2. We additionally simplify modeling assumptions, specifically to cope with the presence of multiple cellular compartments. We summarize all steps in this 8-10-week-long process, including setting up the chamber; harvesting; liquid extraction and subsequent handling of sample plant material to chemical derivatization procedures such as silylation and methoxymation (necessary for gas chromatography only); choosing instrumentation settings and evaluating the resultant chromatogram in terms of both unlabeled and labeled peaks. Furthermore, we describe how quantitative insights can be gained by estimating both benchmark and previously unknown fluxes from collected data sets.

Robin: an intuitive wizard application for R-based expression microarray quality assessment and analysis.

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.

Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents.

Numerous studies have revealed the extent of genetic and phenotypic variation between both species and cultivars of tomato. Using a series of tomato lines resulting from crosses between a cherry tomato and three independent large fruit cultivar (Levovil, VilB, and VilD), extensive profiling of both central primary metabolism and volatile organic components of the fruit was performed. In this study, it was possible to define a number of quantitative trait loci (QTLs) which determined the levels of primary metabolites and/or volatile organic components and to evaluate their co-location with previously defined organoleptic QTLs. Correlation analyses between either the primary metabolites or the volatile organic compounds and organoleptic properties revealed a number of interesting associations, including pharmaceutical aroma-guaiacol and sourness-alanine, across the data set. Considerable correlation within the levels of primary metabolites or volatile organic compounds, respectively, were also observed. However, there was relatively little association between the levels of primary metabolites and volatile organic compounds, implying that they are not tightly linked to one another. A notable exception to this was the strong association between the levels of sucrose and those of a number of volatile organic compounds. The combined data presented here are thus discussed both with respect to those obtained recently from wide interspecific crosses of tomato and within the framework of current understanding of the chemical basis of fruit taste.