Answering biological questions by analysis of the strawberry metabolome

Background

The qualitative and quantitative analysis of all low molecular weight metabolites within a biological sample, known as the metabolome, provides powerful insights into their roles in biological systems and processes. The study of all the chemical structures, concentrations, and interactions of the thousands of metabolites is called metabolomics. However present state of the art methods and equipment can only analyse a small portion of the numerous, structurally diverse groups of chemical substances found in biological samples, especially with respect to samples of plant origin with their huge diversity of secondary metabolites. Nevertheless, metabolite profiling and fingerprinting techniques have been applied to the analysis of the strawberry metabolome since their early beginnings.

Aim

The application of metabolomics and metabolite profiling approaches within strawberry research was last reviewed in 2011. Here, we aim to summarize the latest results from research of the strawberry metabolome since its last review with a special emphasis on studies that address specific biological questions.

Key scientific concepts

Analysis of strawberry, and other fruits, requires a plethora of analytical methods and approaches encompassing the analysis of primary and secondary metabolites, as well as capturing and quantifying volatile compounds that are related to aroma as well as fruit development, function and plant-to-plant communication. The success and longevity of metabolite and volatile profiling approaches in fruit breeding relies upon the ability of the approach to uncover biologically meaningful insights. The key concepts that must be addressed and are reviewed include: gene function analysis and genotype comparison, analysis of environmental effects and plant protection, screening for bioactive compounds for food and non-food uses, fruit development and physiology as well as fruit sensorial quality. In future, the results will facilitate fruit breeding due to the identification of metabolic QTLs and candidate genes for fruit quality and consumer preference.

     

Metabolic profiling of strawberry (Fragaria x ananassa Duch.) during fruit development and maturation

Strawberry (Fragaria × ananassa Duch), a fruit of economic and nutritional importance, is also a model species for fleshy fruits and genomics in Rosaceae. Strawberry fruit quality at different harvest stages is a function of the fruit's metabolite content, which results from physiological changes during fruit growth and ripening. In order to investigate strawberry fruit development, untargeted (GC-MS) and targeted (HPLC) metabolic profiling analyses were conducted. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to explore the non-polar and polar metabolite profiles from fruit samples at seven developmental stages. Different cluster patterns and a broad range of metabolites that exerted influence on cluster formation of metabolite profiles were observed. Significant changes in metabolite levels were found in both fruits turning red and fruits over-ripening in comparison with red-ripening fruits. The levels of free amino acids decreased gradually before the red-ripening stage, but increased significantly in the over-ripening stage. Metabolite correlation and network analysis revealed the interdependencies of individual metabolites and metabolic pathways. Activities of several metabolic pathways, including ester biosynthesis, the tricarboxylic acid cycle, the shikimate pathway, and amino acid metabolism, shifted during fruit growth and ripening. These results not only confirmed published metabolic data but also revealed new insights into strawberry fruit composition and metabolite changes, thus demonstrating the value of metabolomics as a functional genomics tool in characterizing the mechanism of fruit quality formation, a key developmental stage in most economically important fruit crops.     

The light-hyperresponsive high pigment-2dg mutation of tomato: alterations in the fruit metabolome

Overall metabolic modifications between fruit of light-hyperresponsive high-pigment (hp) tomato (Lycopersicon esculentum) mutant plants and isogenic nonmutant (wt) control plants were compared. Targeted metabolite analyses, as well as large-scale nontargeted mass spectrometry (MS)-based metabolite profiling, were used to phenotype the differences in fruit metabolite composition. Targeted high-performance liquid chromatography with photodiode array detection (HPLC-PDA) metabolite analyses showed higher levels of isoprenoids and phenolic compounds in hp-2dg fruit. Nontargeted GC-MS profiling of red fruits produced 25 volatile compounds that showed a 1.5-fold difference between the genotypes. Analyses of red fruits using HPLC coupled to high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in both ESI-positive and ESI-negative mode generated, respectively, 6168 and 5401 mass signals, of which 142 and 303 showed a twofold difference between the genotypes. hp-2dg fruits are characterized by overproduction of many metabolites, several of which are known for their antioxidant or photoprotective activities. These metabolites may now be more closely implicated as resources recruited by plants to respond to and manage light stress. The similarity in metabolic alterations in fruits of hp-1 and hp-2 mutant plants helps us to understand how hp mutations affect cellular processes.     

Metabolite annotations based on the integration of mass spectral information

A large number of metabolites are found in each plant, most of which have not yet been identified. Development of a methodology is required to deal systematically with unknown metabolites, and to elucidate their biological roles in an integrated 'omics' framework. Here we report the development of a 'metabolite annotation' procedure. The metabolite annotation is a process by which structures and functions are inferred for metabolites. Tomato ( Solanum lycopersicum cv. Micro-Tom) was used as a model for this study using LC-FTICR-MS. Collected mass spectral features, together with predicted molecular formulae and putative structures, were provided as metabolite annotations for 869 metabolites. Comparison with public databases suggests that 494 metabolites are novel. A grading system was introduced to describe the evidence supporting the annotations. Based on the comprehensive characterization of tomato fruit metabolites, we identified chemical building blocks that are frequently found in tomato fruit tissues, and predicted novel metabolic pathways for flavonoids and glycoalkaloids. These results demonstrate that metabolite annotation facilitates the systematic analysis of unknown metabolites and biological interpretation of their relationships, which provide a basis for integrating metabolite information into the system-level study of plant biology.     

Evaluation of automated electrospray-TOF mass spectrometryfor metabolic fingerprinting of the plant metabolome

Metabolic fingerprinting is increasingly employed in microbial and plant metabolomics. Identification and evaluation of analytical factors that influence mass spectra produced with automated electrospray time of flight mass spectrometry to support metabolic fingerprinting are described. Instrument resolution of 4000 (FWHM) at mass 200 Da provided detection of ions of the same nominal mass but different monoisotopic masses. Complex mass spectra were obtained from polar extracts of tomato fruit in positive and negative ion mode. These spectra consist of metabolite ions (molecular, adduct and fragment) and those derived from the extraction medium, largely in the form of [M+H]⁺, [M–H]^–, [M+Na]⁺, [M+K]⁺, [2M+H]⁺, [M+Cl]^– and [2M–H]^–. Ionisation suppression reduced sensitivity, although its effect was consistent for a wide range of metabolite concentrations. Variability in ion signal intensity was lower in analytical (2.2–30.1%) compared to biological (within fruit 9.6–27.6%; between-fruit 13.2–34.4%) replicates. The method is applicable to high throughput metabolic fingerprinting and, with accurate mass measurements, is able to provide reductions in data complexity and preliminary identification of metabolites.     

Reconfiguration of the achene and receptacle metabolic networks during strawberry fruit development

The anatomy of strawberry (Fragaria x ananassa) fruit, in which the achene is found on the outer part of the fruit, makes it an excellent species for studying the regulation of fruit development. It can provide a model for the cross talk between primary and secondary metabolism, whose role is of pivotal importance in the process. By combining gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry with the aim of addressing the metabolic regulation underlying fruit seed development, we simultaneously analyzed the composition of primary and secondary metabolites, separately, in achene and receptacle during fruit ripening of strawberry cultivar Herut. The results from these analyses suggest that changes in primary and secondary metabolism reflect organ and developmental specificities. For instance, the receptacle was characterized by increases in sugars and their direct derivatives, while the achene was characterized by a major decrease in the levels of carbon- and nitrogen-rich compounds, with the exception of storage-related metabolites (e.g. raffinose). Furthermore, the receptacle, and to a lesser extent the achene, exhibited dynamic fluctuations in the levels and nature of secondary metabolites across the ripening process. In the receptacle, proanthocyanidins and flavonol derivatives characterized mainly early developmental stages, while anthocyanins were abundant in the mature red stage; in the achene, ellagitannin and flavonoids were abundant during early and late development, respectively. Correlation-based network analysis suggested that metabolism is substantially coordinated during early development in either organ. Nonetheless, a higher degree of connectivity within and between metabolic pathways was measured in the achenes. The data are discussed within the context of current models both of the interaction of primary and secondary metabolism and of the metabolic interaction between the different plant organs.     

Cloning and molecular characterization of a strawberry fruit ripening-related cDNA corresponding a mRNA for a low-molecular-weight heat-shock protein

We have isolated and characterized a cDNA from a strawberry fruit subtractive library that shows homology to class-I low-molecular-weight (LMW) heat-shock protein genes from other higher plants. The strawberry cDNA (clone njjs4) was a 779 bp full-length cDNA with a single open reading frame of 468 bp that is expected to encode a protein of ca. 17.4 kDa with a pI of 6.57. Southern analysis with genomic DNA showed several high-molecular-weight hybridization bands, indicating that the corresponding njjs4 gene is not present as a single copy in the genome. This strawberry gene was not expressed in roots, leaves, flowers and stolons but in fruits at specific stages of elongation and ripening. However, a differential pattern of mRNA expression was detected in the fruit tissues achenes and receptacle. The njjs4 gene expression increased in achenes accompanying the process of seed maturation whereas in the receptacle, a high mRNA expression was detected in the W2 stage, during which most of the metabolic changes leading to the fruit ripening are occurring. Our results clearly show a specific relationship of this njjs4 strawberry gene with the processes of seed maturation and fruit ripening, and strongly support that at least some of the class-I LMW heat-shock protein-like genes have a heat-stress-independent role in plant development, including fruit ripening.     

Tissue specialization at the metabolite level is perceived during the development of tomato fruit

Fruit maturation and tissue differentiation are important topics in plant physiology. These biological phenomena are accompanied by specific alterations in the biological system, such as differences in the type and concentration of metabolites. The secondary metabolism of tomato (Solanum lycopersicum) fruit was monitored by using liquid chromatography (LC) coupled to photo-diode array (PDA) detection, fluorescence detection (FD), and mass spectrometry (MS). Through this integrated approach different classes of compounds were analysed: carotenoids, xanthophylls, chlorophylls, tocopherols, ascorbic acid, flavonoids, phenolic acids, glycoalkaloids, saponins, and other glycosylated derivatives. Related metabolite profiles of peel and flesh were found between several commercial tomato cultivars indicating similar metabolite trends despite the genetic background. For a single tomato cultivar, metabolite profiles of different fruit tissues (vascular attachment region, columella and placenta, epidermis, pericarp, and jelly parenchyma) were examined at the green, breaker, turning, pink, and red stages of fruit development. Unrelated to the chemical nature of the metabolites, behavioural patterns could be assigned to specific ripening stages or tissues. These findings suggest spatio-temporal specificity in the accumulation of endogenous metabolites from tomato fruit.     

Spectral trees as a robust annotation tool in LC–MS based metabolomics

The identification of large series of metabolites detectable by mass spectrometry (MS) in crude extracts is a challenging task. In order to test and apply the so-called multistage mass spectrometry (MS ⁿ ) spectral tree approach as tool in metabolite identification in complex sample extracts, we firstly performed liquid chromatography (LC) with online electrospray ionization (ESI)?CMS ⁿ , using crude extracts from both tomato fruit and Arabidopsis leaf. Secondly, the extracts were automatically fractionated by a NanoMate LC-fraction collector/injection robot (Advion) and selected LC-fractions were subsequently analyzed using nanospray-direct infusion to generate offline in-depth MS ⁿ spectral trees at high mass resolution. Characterization and subsequent annotation of metabolites was achieved by detailed analysis of the MS ⁿ spectral trees, thereby focusing on two major plant secondary metabolite classes: phenolics and glucosinolates. Following this approach, we were able to discriminate all selected flavonoid glycosides, based on their unique MS ⁿ fragmentation patterns in either negative or positive ionization mode. As a proof of principle, we report here 127 annotated metabolites in the tomato and Arabidopsis extracts, including 21 novel metabolites. Our results indicate that online LC?CMS ⁿ fragmentation in combination with databases of in-depth spectral trees generated offline can provide a fast and reliable characterization and annotation of metabolites present in complex crude extracts such as those from plants.     

The origin of correlations in metabolomics data

A phenomenon observed earlier in the development of metabolomics as a systems biology methodology, consists of a small but significant number of metabolites whose levels are highly correlated between biological replicates. Contrary to initial interpretations, these correlations are not necessarily only between neighboring metabolites in the metabolic network. Most metabolites that participate in common reactions are not correlated in this way, while some non-neighboring metabolites are highly correlated. Here we investigate the origin of such correlations using metabolic control analysis and computer simulation of biochemical networks. A series of cases is identified which lead to high correlation between metabolite pairs in replicate measurement. These are (1) chemical equilibrium, (2) mass conservation, (3) asymmetric control distribution, and (4) unusually high variance in the expression of a single gene. The importance of identifying metabolite correlations within a physiological state and changes of correlation between different states is discussed in the context of systems biology.     

Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is becoming the dominant technology in metabolomics, involving the comprehensive analysis of small molecules in biological systems. However, its use is still limited mainly by challenges in global high-throughput identification of metabolites: LC-MS data is highly complex, particularly due to the formation of multiple ionization products from individual metabolites. To address the limitation in metabolite identification, we developed a principled approach, designed to exploit the multi-dimensional information hidden in the data. The workflow first clusters candidate ionization products of the same metabolite together which typically have similar retention time, then searches for mass relationships among them in order to determine their ion types and metabolite identity. The robustness of our approach was demonstrated by its application to the LC-MS profiles of cell culture supernatant, which accurately predicted most of the known media components in the samples. Compared to conventional methods, our approach was able to generate significantly fewer candidate metabolites without missing out valid ones, thus reducing false-positive matches. Additionally, improved confidence in identification is achieved since each prediction comes with a probable combination of known ion types. Hence, our integrative workflow provides precursor mass predictions with high confidence by identifying various ionization products which account for a large proportion of detected peaks, thus minimizing false positives.     

A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography-mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ～12 h from metabolite extraction to peak integration for a data set containing 15 total samples (～6 h for a single sample).     

The ABRF Metabolomics Research Group 2013 Study: Investigation of Spiked Compound Differences in a Human Plasma Matrix

Metabolomics is an emerging field that involves qualitative and quantitative measurements of small molecule metabolites in a biological system. These measurements can be useful for developing biomarkers for diagnosis, prognosis, or predicting response to therapy. Currently, a wide variety of metabolomics approaches, including nontargeted and targeted profiling, are used across laboratories on a routine basis. A diverse set of analytical platforms, such as NMR, gas chromatography-mass spectrometry, Orbitrap mass spectrometry, and time-of-flight-mass spectrometry, which use various chromatographic and ionization techniques, are used for resolution, detection, identification, and quantitation of metabolites from various biological matrices. However, few attempts have been made to standardize experimental methodologies or comparative analyses across different laboratories. The Metabolomics Research Group of the Association of Biomolecular Resource Facilities organized a “round-robin” experiment type of interlaboratory study, wherein human plasma samples were spiked with different amounts of metabolite standards in 2 groups of biologic samples (A and B). The goal was a study that resembles a typical metabolomics analysis. Here, we report our efforts and discuss challenges that create bottlenecks for the field. Finally, we discuss benchmarks that could be used by laboratories to compare their methodologies.     

A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.     

Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption

Through an HPLC-Q-TOF-MS-driven nontargeted metabolomics approach, we aimed to discriminate changes in the urinary metabolome of subjects with metabolic syndrome (MetS), following 12 weeks of mixed nuts consumption (30 g/day), compared to sex- and age-matched individuals given a control diet. The urinary metabolome corresponding to the nut-enriched diet clearly clustered in a distinct group, and the multivariate data analysis discriminated relevant mass features in this separation. Metabolites corresponding to the discriminating ions (MS features) were then subjected to multiple tandem mass spectrometry experiments using LC-ITD-FT-MS, to confirm their putative identification. The metabolomics approach revealed 20 potential markers of nut intake, including fatty acid conjugated metabolites, phase II and microbial-derived phenolic metabolites, and serotonin metabolites. An increased excretion of serotonin metabolites was associated for the first time with nut consumption. Additionally, the detection of urinary markers of gut microbial and phase II metabolism of nut polyphenols confirmed the understanding of their bioavailability and bioactivity as a priority area of research in the determination of the health effects derived from nut consumption. The results confirmed how a nontargeted metabolomics strategy may help to access unexplored metabolic pathways impacted by diet, thereby raising prospects for new intervention targets.     

Non-targeted analysis of spatial metabolite composition in strawberry (Fragariaxananassa) flowers

Formation of flower organs and the subsequent pollination process require a coordinated spatial and temporal regulation of particular metabolic pathways. In this study a comparison has been made between the metabolite composition of individual flower organs of strawberry (Fragaria × ananassa) including the petal, sepal, stamen, pistil and the receptacle that gives rise to the strawberry fruit. Non-targeted metabolomics analysis of the semi-polar secondary metabolites by the use of UPLC-qTOF-MS was utilized in order to localize metabolites belonging to various chemical classes (e.g. ellagitannins, proanthocyanidins, flavonols, terpenoids, and spermidine derivatives) to the different flower organs. The vast majority of the tentatively identified metabolites were ellagitannins that accumulated in all five parts of the flower. Several metabolite classes were detected predominantly in certain flower organs, as for example spermidine derivatives were present uniquely in the stamen and pistil, and the proanthocyanidins were almost exclusively detected in the receptacle and sepals. The latter organ was also rich in terpenoids (i.e. triterpenoid and sesquiterpenoid derivatives) whereas phenolic acids and flavonols were the predominant classes of compounds detected in the petals. Furthermore, we observed extensive variation in the accumulation of metabolites from the same class in a single organ, particularly in the case of ellagitannins, and the flavonols quercetin, kaempferol and isorhamnetin. These results allude to spatially-restricted production of secondary metabolite classes and specialized derivatives in flowers that take part in implementing the unique program of individual organs in the floral life cycle.