Differential metabolic networks unravel the effects of silent plant phenotypes

The Anticancer Activity of Monosaccharides: Perspectives and Outlooks

A major hallmark of cancer is the reprogramming of cellular metabolism from oxidative phosphorylation (OXPHOS) to glycolysis, a phenomenon known as the Warburg effect. To sustain high rates of glycolysis, cancer cells overexpress GLUT transporters and glycolytic enzymes, allowing for the enhanced uptake and consumption of glucose. The Warburg effect may be exploited in the treatment of cancer; certain epimers and derivatives of glucose can enter cancer cells and inhibit glycolytic enzymes, stunting metabolism and causing cell death. These include common dietary monosaccharides (ᴅ-mannose, ᴅ-galactose, ᴅ-glucosamine, ʟ-fucose), as well as some rare monosaccharides (xylitol, ᴅ-allose, ʟ-sorbose, ʟ-rhamnose). This article reviews the literature on these sugars in in vitro and in vivo models of cancer, discussing their mechanisms of cytotoxicity. In addition to this, the anticancer potential of some synthetically modified monosaccharides, such as 2-deoxy-ᴅ-glucose and its acetylated and halogenated derivatives, is reviewed. Further, this article reviews how certain monosaccharides can be used in combination with anticancer drugs to potentiate conventional chemotherapies and to help overcome chemoresistance. Finally, the limitations of administering two separate agents, a sugar and a chemotherapeutic drug, are discussed. The potential of the glycoconjugation of classical or repurposed chemotherapy drugs as a solution to these limitations is reviewed.

D-allose, a typical rare sugar: properties, applications, and biosynthetic advances and challenges

D-allose, a C-3 epimer of D-glucose and an aldose-ketose isomer of D-allulose, exhibits 80% of sucrose's sweetness while being remarkably low in calories and nontoxic, making it an appealing sucrose substitute. Its diverse physiological functions, particularly potent anticancer and antitumor effects, render it a promising candidate for clinical treatment, garnering sustained attention. However, its limited availability in natural sources and the challenges associated with chemical synthesis necessitate exploring biosynthetic strategies to enhance production. This overview encapsulates recent advancements in D-allose's physicochemical properties, physiological functions, applications, and biosynthesis. It also briefly discusses the crucial role of understanding aldoketose isomerase structure and optimizing its performance in D-allose synthesis. Furthermore, it delves into the challenges and future perspectives in D-allose bioproduction. Early efforts focused on identifying and characterizing enzymes responsible for D-allose production, followed by detailed crystal structure analysis to improve performance through molecular modification. Strategies such as enzyme immobilization and implementing multi-enzyme cascade reactions, utilizing more cost-effective feedstocks, were explored. Despite progress, challenges remain, including the lack of efficient high-throughput screening methods for enzyme modification, the need for food-grade expression systems, the establishment of ordered substrate channels in multi-enzyme cascade reactions, and the development of downstream separation and purification processes.

A comprehensive review of recent advances in the characterization of L-rhamnose isomerase for the biocatalytic production of D-allose from D-allulose

D-Allose and D-allulose are two important rare natural monosaccharides found in meager amounts. They are considered to be the ideal substitutes for table sugar (sucrose) for, their significantly lower calorie content with around 80 % and 70 % of the sweetness of sucrose, respectively. Additionally, both monosaccharides have gained much attention due to their remarkable physiological properties and excellent health benefits. Nevertheless, D-allose and D-allulose are rare in nature and difficult to produce by chemical methods. Consequently, scientists are exploring bioconversion methods to convert D-allulose into D-allose, with a key enzyme, L-rhamnose isomerase (L-RhIse), playing a remarkable role in this process. This review provides an in-depth analysis of the extractions, physiological functions and applications of D-allose from D-allulose. Specifically, it provides a detailed description of all documented L-RhIse, encompassing their biochemical properties including, pH, temperature, stabilities, half-lives, metal ion dependence, molecular weight, kinetic parameters, specific activities and specificities of the substrates, conversion ratio, crystal structure, catalytic mechanism as well as their wide-ranging applications across diverse fields. So far, L-RhIses have been discovered and characterized experimentally by numerous mesophilic and thermophilic bacteria. Furthermore, the crystal forms of L-RhIses from E. coli and Stutzerimonas/Pseudomonas stutzeri have been previously cracked, together with their catalytic mechanism. However, there is room for further exploration, particularly the molecular modification of L-RhIse for enhancing its catalytic performance and thermostability through the directed evolution or site-directed mutagenesis.

Inoculation of wheat with Bacillus sp. wp-6 altered amino acid and flavonoid metabolism and promoted plant growth

Inoculation of wheat seedling with Bacillus sp. wp-6 changed amino acid metabolism and flavonoid synthesis and promoted plant growth. Plant growth-promoting rhizobacteria (PGPR), which can reduce the use of agrochemicals, is vital for the development of sustainable agriculture. In this study, proteomics and metabolomics analyses were performed to investigate the effects of inoculation with a PGPR, Bacillus sp. wp-6, on wheat (Triticum aestivum L.) seedling growth. The results showed that inoculation with Bacillus sp. wp-6 increased shoot and root fresh weights by 19% and 18%, respectively, after 40 days. The expression levels of alpha-linolenic acid metabolism-related proteins and metabolites (lipoxygenase 2, allene oxide synthase 2, jasmonic acid, 17-hydroxylinolenic acid) and flavonoid biosynthesis-related proteins and metabolites (chalcone synthase 2 and PHC 4'-O-glucoside) were up-regulated. In addition, the expression levels of amino acid metabolism-related proteins (NADH-dependent glutamate synthase, bifunctional aspartokinase/homoserine, anthranilate synthase alpha subunit 1, and 3-phosphoshikimate 1-carboxyvinyltransferase) and metabolites (L-aspartate, L-arginine, and S-glutathionyl-L-cysteine) were also significantly up-regulated. Among them, NADH-dependent glutamate synthase and bifunctional aspartokinase/homoserine could act as regulators of nitrogen metabolism. Overall, inoculation of wheat with Bacillus sp. wp-6 altered alpha-linolenic acid metabolism, amino acid metabolism, and flavonoid synthesis and promoted wheat seedling growth. This study will deepen our understanding of the mechanism by which Bacillus sp. wp-6 promotes wheat growth using proteomics and metabolomics.

A reactive oxygen species Ca2+ signalling pathway identified from a chemical screen for modifiers of sugar-activated circadian gene expression

Sugars are essential metabolites for energy and anabolism that can also act as signals to regulate plant physiology and development. Experimental tools to disrupt major sugar signalling pathways are limited. We performed a chemical screen for modifiers of activation of circadian gene expression by sugars to discover pharmacological tools to investigate and manipulate plant sugar signalling. Using a library of commercially available bioactive compounds, we identified 75 confident hits that modified the response of a circadian luciferase reporter to sucrose in dark-adapted Arabidopsis thaliana seedlings. We validated the transcriptional effect on a subset of the hits and measured their effects on a range of sugar-dependent phenotypes for 13 of these chemicals. Chemicals were identified that appear to influence known and unknown sugar signalling pathways. Pentamidine isethionate was identified as a modifier of a sugar-activated Ca²⁺ signal that acts as a calmodulin inhibitor downstream of superoxide in a metabolic signalling pathway affecting circadian rhythms, primary metabolism and plant growth. Our data provide a resource of new experimental tools to manipulate plant sugar signalling and identify novel components of these pathways.

Ectopic Expression of Arabidopsis thaliana zDof1.3 in Tomato (Solanum lycopersicum L.) Is Associated with Improved Greenhouse Productivity and Enhanced Carbon and Nitrogen Use

A large collection of transgenic tomato lines, each ectopically expressing a different Arabidopsis thaliana transcription factor, was screened for variants with alterations in leaf starch. Such lines may be affected in carbon partitioning, and in allocation to the sinks. We focused on ‘L4080’, which harbored an A. thaliana zDof (DNA-binding one zinc finger) isoform 1.3 (AtzDof1.3) gene, and which had a 2−4-fold higher starch-to-sucrose ratio in source leaves over the diel (p < 0.05). Our aim was to determine whether there were associated effects on productivity. L4080 plants were altered in nitrogen (N) and carbon (C) metabolism. The N-to-C ratio was higher in six-week-old L4080, and when treated with 1/10 N, L4080 growth was less inhibited compared to the wild-type and this was accompanied by faster root elongation (p < 0.05). The six-week-old L4080 acquired 42% more dry matter at 720 ppm CO2, compared to ambient CO2 (p < 0.05), while the wild-type (WT) remained unchanged. GC-MS-TOF data showed that L4080 source leaves were enriched in amino acids compared to the WT, and at 49 DPA, fruit had 25% greater mass, higher sucrose, and increased yield (25%; p < 0.05) compared to the WT. An Affymetrix cDNA array analysis suggested that only 0.39% of the 9000 cDNAs were altered by 1.5-fold (p < 0.01) in L4080 source leaves. 14C-labeling of fruit disks identified potential differences in 14-DPA fruit metabolism suggesting that post-transcriptional regulation was important. We conclude that AtzDof1.3 and the germplasm derived therefrom, should be investigated for their ‘climate-change adaptive’ potential.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Path-level interpretation of Gaussian graphical models using the pair-path subscore

BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.

Physiological, proteomic, and metabolomic analysis provide insights into Bacillus sp.-mediated salt tolerance in wheat

Herein, the inoculation with strain wp-6 promoted the growth of wheat seedlings by improving the energy production and conversion of wheat seedlings and alleviating salt stress. Soil salinization decreases crop productivity due to high toxicity of sodium ions to plants. Plant growth-promoting rhizobacteria (PGPR) have been demonstrated to alleviate salinity stress. However, the mechanism of PGPR in improving plant salt tolerance remains unclear. In this study, physiological analysis, proteomics, and metabolomics were applied to investigate the changes in wheat seedlings under salt stress (150 mM NaCl), both with and without plant root inoculation with wp-6 (Bacillus sp.). Under salt stress, root inoculation with strain wp-6 increased plant biomass (57%) and root length (25%). The Na⁺ content was reduced, while the K⁺ content and K⁺/Na⁺ ratio were increased. The content of malondialdehyde was decreased by 31.94% after inoculation of wp-6 under salt stress, while the content of proline, soluble sugar, and soluble protein were increased by 7.48%, 12.34%, and 4.12%, respectively. The peroxidase, catalase, and superoxide dismutase activities were increased after inoculation of wp-6 under salt stress. Galactose metabolism, phenylalanine metabolism, caffeine metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and glutathione metabolism might play an important role in promoting the growth of salt-stressed wheat seedlings after the inoculation with wp-6. Interaction analysis of differentially expressed proteins and metabolites found that energy production and transformation-related proteins and six metabolites (D-arginine, palmitoleic acid, chlorophyllide b, rutin, pheophorbide a, and vanillylamine) were mainly involved in the growth of wheat seedlings after the inoculation with wp-6 under salt stress. Furthermore, correlation analysis found that inoculation with wp-6 promotes the growth of salt-stressed wheat seedlings mainly through regulating amino acid metabolism and porphyrin and chlorophyll metabolism. This study provides an eco-friendly method to increase agricultural productivity and paves a way to sustainable agriculture.

CorDiffViz: an R package for visualizing multi-omics differential correlation networks

BACKGROUND

Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.

RESULTS

We present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz . Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html .

CONCLUSIONS

Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

Fast-forward breeding for a food-secure world

Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.

Metabolomics and Molecular Approaches Reveal Drought Stress Tolerance in Plants

Metabolic regulation is the key mechanism implicated in plants maintaining cell osmotic potential under drought stress. Understanding drought stress tolerance in plants will have a significant impact on food security in the face of increasingly harsh climatic conditions. Plant primary and secondary metabolites and metabolic genes are key factors in drought tolerance through their involvement in diverse metabolic pathways. Physio-biochemical and molecular strategies involved in plant tolerance mechanisms could be exploited to increase plant survival under drought stress. This review summarizes the most updated findings on primary and secondary metabolites involved in drought stress. We also examine the application of useful metabolic genes and their molecular responses to drought tolerance in plants and discuss possible strategies to help plants to counteract unfavorable drought periods.

Spatial distribution of proteins and metabolites in developing wheat grain and their differential regulatory response during the grain filling process

Grain filling and grain development are essential biological processes in the plant's life cycle, eventually contributing to the final seed yield and quality in all cereal crops. Studies of how the different wheat (Triticum aestivum L.) grain components contribute to the overall development of the seed are very scarce. We performed a proteomics and metabolomics analysis in four different developing components of the wheat grain (seed coat, embryo, endosperm, and cavity fluid) to characterize molecular processes during early and late grain development. In-gel shotgun proteomics analysis at 12, 15, 20, and 26 days after anthesis (DAA) revealed 15 484 identified and quantified proteins, out of which 410 differentially expressed proteins were identified in the seed coat, 815 in the embryo, 372 in the endosperm, and 492 in the cavity fluid. The abundance of selected protein candidates revealed spatially and temporally resolved protein functions associated with development and grain filling. Multiple wheat protein isoforms involved in starch synthesis such as sucrose synthases, starch phosphorylase, granule-bound and soluble starch synthase, pyruvate phosphate dikinase, 14-3-3 proteins as well as sugar precursors undergo a major tissue-dependent change in abundance during wheat grain development suggesting an intimate interplay of starch biosynthesis control. Different isoforms of the protein disulfide isomerase family as well as glutamine levels, both involved in the glutenin macropolymer pattern, showed distinct spatial and temporal abundance, revealing their specific role as indicators of wheat gluten quality. Proteins binned into the functional category of cell growth/division and protein synthesis/degradation were more abundant in the early stages (12 and 15 DAA). At the metabolome level all tissues and especially the cavity fluid showed highly distinct metabolite profiles. The tissue-specific data are integrated with biochemical networks to generate a comprehensive map of molecular processes during grain filling and developmental processes.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Metabolomics-driven gene mining and genetic improvement of tolerance to salt-induced osmotic stress in maize

The farmland of the world's main corn-producing area is increasingly affected by salt stress. Therefore, the breeding of salt-tolerant cultivars is necessary for the long-term sustainability of global corn production. Previous studies have shown that natural maize varieties display a large diversity of salt tolerance, yet the genetic variants underlying such diversity remain poorly discovered and applied, especially those mediating the tolerance to salt-induced osmotic stress (SIOS). Here we report a metabolomics-driven understanding and genetic improvement of maize SIOS tolerance. Using a LC-MS-based untargeted metabolomics approach, we profiled the metabolomes of 266 maize inbred lines under control and salt conditions, and then identified 37 metabolite biomarkers of SIOS tolerance (METO1-37). Follow-up metabolic GWAS (mGWAS) and genotype-to-phenotype modeling identified 10 candidate genes significantly associating with the SIOS tolerance and METO abundances. Furthermore, we validated that a citrate synthase, a glucosyltransferase and a cytochrome P450 underlie the genotype-METO-SIOS tolerance associations, and showed that their favorable alleles additively improve the SIOS tolerance of elite maize inbred lines. Our study provides a novel insight into the natural variation of maize SIOS tolerance, which boosts the genetic improvement of maize salt tolerance, and demonstrates a metabolomics-based approach for mining crop genes associated with this complex agronomic trait.

Integrated transcript and metabolite profiling reveals coordination between biomass size and nitrogen metabolism in Arabidopsis F1 hybrids

Heterosis refers to the improved agronomic performance of F₁ hybrids relative to their parents. Although this phenomenon is widely employed to increase biomass, yield, and stress tolerance of plants, the underlying molecular mechanisms remain unclear. To dissect the metabolic fluctuations derived from genomic and/or environmental differences contributing to the improved biomass of F₁ hybrids relative to their parents, we optimized the growth condition for Arabidopsis thaliana F₁ hybrids and their parents. Modest but statistically significant increase in the biomass of F₁ hybrids was observed. Plant samples grown under the optimized condition were also utilized for integrated omics analysis to capture specific changes in the F₁ hybrids. Metabolite profiling of F₁ hybrids and parent plants was performed using gas chromatography-mass spectrometry. Among the detected 237 metabolites, 2-oxoglutarate (2-OG) and malate levels were lower and the level of aspartate was higher in the F₁ hybrids than in each parent. In addition, microarray analysis revealed that there were 44 up-regulated and 12 down-regulated genes with more than 1.5-fold changes in expression levels in the F₁ hybrid compared to each parent. Gene ontology (GO) analyses indicated that genes up-regulated in the F₁ hybrids were largely related to organic nitrogen (N) process. Quantitative PCR verified that glutamine synthetase 2 (AtGLN2) was upregulated in the F₁ hybrids, while other genes encoding enzymes in the GS-GOGAT cycle showed no significant differences between the hybrid and parent lines. These results suggested the existence of metabolic regulation that coordinates biomass and N metabolism involving AtGLN2 in F₁ hybrids.

Rare Sugars: Recent Advances and Their Potential Role in Sustainable Crop Protection

Rare sugars are monosaccharides with a limited availability in the nature and almost unknown biological functions. The use of industrial enzymatic and microbial processes greatly reduced their production costs, making research on these molecules more accessible. Since then, the number of studies on their medical/clinical applications grew and rare sugars emerged as potential candidates to replace conventional sugars in human nutrition thanks to their beneficial health effects. More recently, the potential use of rare sugars in agriculture was also highlighted. However, overviews and critical evaluations on this topic are missing. This review aims to provide the current knowledge about the effects of rare sugars on the organisms of the farming ecosystem, with an emphasis on their mode of action and practical use as an innovative tool for sustainable agriculture. Some rare sugars can impact the plant growth and immune responses by affecting metabolic homeostasis and the hormonal signaling pathways. These properties could be used for the development of new herbicides, plant growth regulators and resistance inducers. Other rare sugars also showed antinutritional properties on some phytopathogens and biocidal activity against some plant pests, highlighting their promising potential for the development of new sustainable pesticides. Their low risk for human health also makes them safe and ecofriendly alternatives to agrochemicals.

Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation

Mechanistic or mammalian target of rapamycin complex 1 (mTORC1) is an important regulator of effector functions, proliferation, and cellular metabolism in macrophages. The biochemical processes that are controlled by mTORC1 are still being defined. Here, we demonstrate that integrative multiomics in conjunction with a data-driven inverse modeling approach, termed COVRECON, identifies a biochemical node that influences overall metabolic profiles and reactions of mTORC1-dependent macrophage metabolism. Using a combined approach of metabolomics, proteomics, mRNA expression analysis, and enzymatic activity measurements, we demonstrate that Tsc2, a negative regulator of mTORC1 signaling, critically influences the cellular activity of macrophages by regulating the enzyme phosphoglycerate dehydrogenase (Phgdh) in an mTORC1-dependent manner. More generally, while lipopolysaccharide (LPS)-stimulated macrophages repress Phgdh activity, IL-4-stimulated macrophages increase the activity of the enzyme required for the expression of key anti-inflammatory molecules and macrophage proliferation. Thus, we identify Phgdh as a metabolic checkpoint of M2 macrophages.

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Metabolomics and inverse modeling reveal a Tsc2/mTORC1-dependent checkpoint in macrophages

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M2 macrophages have high Phgdh activity

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Phgdh activity promotes M2 polarization

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Phgdh activity supports macrophage proliferation

LC-MS based metabolic fingerprinting of apricot pistils after self-compatible and self-incompatible pollinations

KEY MESSAGE

LC-MS based metabolomics approach revealed that putative metabolites other than flavonoids may significantly contribute to the sexual compatibility reactions in Prunus armeniaca. Possible mechanisms on related microtubule-stabilizing effects are provided. Identification of metabolites playing crucial roles in sexual incompatibility reactions in apricot (Prunus armeniaca L.) was the aim of the study. Metabolic fingerprints of self-compatible and self-incompatible apricot pistils were created using liquid chromatography coupled to time-of-flight mass spectrometry followed by untargeted compound search. Multivariate statistical analysis revealed 15 significant differential compounds among the total of 4006 and 1005 aligned metabolites in positive and negative ion modes, respectively. Total explained variance of 89.55% in principal component analysis (PCA) indicated high quality of differential expression analysis. The statistical analysis showed significant differences between genotypes and pollination time as well, which demonstrated high performance of the metabolic fingerprinting and revealed the presence of metabolites with significant influence on the self-incompatibility reactions. Finally, polyketide-based macrolides similar to peloruside A and a hydroxy sphingosine derivative are suggested to be significant differential metabolites in the experiment. These results indicate a strategy of pollen tubes to protect microtubules and avoid growth arrest involved in sexual incompatibility reactions of apricot.

Solanum lycopersicum Seedlings. Metabolic Responses Induced by the Alkamide Affinin

Alkamides have been observed to interact in different ways in several superior organisms and have been used in traditional medicine in many countries e.g., to relieve pain. Previous studies showed that affinin when applied to other plant species induces prominent changes in the root architecture and induces transcriptional adjustments; however, little is known about the metabolic pathways recruited by plants in response to alkamides. Previous published work with Arabidopsis seedlings treated in vitro with affinin at 50 µM significantly reduced primary root length. In tomato seedlings, that concentration did not reduce root growth but increase the number and length of lateral roots. Non-targeted metabolomic analysis by Gas Chromatography couplet to Mass Spectrometry (GC/EIMS) showed that, in tomato seedlings, affinin increased the accumulation of several metabolites leading to an enrichment of several metabolic pathways. Affinin at 100 µM alters the accumulation of metabolites such as organic acids, amino acids, sugars, and fatty acids. Finally, our results showed a response possibly associated with nitrogen, GABA shunt and serine pathways, in addition to a possible alteration in the mitochondrial electron transport chain (ETC), interesting topics to understand the molecular and metabolic mechanisms in response to alkamide in plants.

Infraspecific Chemical Variability and Biological Activity of Casearia sylvestris from Different Brazilian Biomes

Casearia sylvestris is an outstanding representative of the Casearia genus. This representability comes from its distinctive chemical profile and pharmacological properties. This species is widespread from North to South America, occurring in all Brazilian biomes. Based on their morphology, 2 varieties are recognized: C. sylvestris var. sylvestris and C. sylvestris var. lingua. Despite the existence of data about their chemical composition, a deeper understanding of the specialized metabolism correlation and variation in respect to environmental factors and its repercussion over their biological activities was still pending. In this study, an UHPLC-DAD-based metabolomics approach was employed for the investigation of the chemical variation of 12 C. sylvestris populations sampled across 4 Brazilian biomes and ecotones. The correlation between infraspecific chemical variability and the cytotoxic and antioxidant activities was achieved by multivariate data analysis. The analyses showed that C. sylvestris var. lingua prevailed at Cerrado areas, and it was correlated with lower cytotoxic activity and high level of glycosylated flavonoids. Among them, narcissin and isorhamnetin-3-O-α-L-rhamnopyranosyl-(1 → 2)-α-L-arabinopyranoside showed good correlation with the antioxidant activity. Conversely, C. sylvestris var. sylvestris prevailed at the Atlantic Forest areas, and it was associated with high cytotoxic activity and high content of clerodane diterpenoids. Different casearins showed good correlation (R2  = 0.3 - 0.70) with the cytotoxic activity. These findings highlighted the great complexity among different C. sylvestris populations, their chemical profile, and the related biological activities. Consequently, it can certainly influence the medicinal properties, as well as the quality and efficacy, of C. sylvestris phytomedicines.

Berberine alters gut microbial function through modulation of bile acids

BACKGROUND

Berberine (BBR) is a plant-based nutraceutical that has been used for millennia to treat diarrheal infections and in contemporary medicine to improve patient lipid profiles. Reduction in lipids, particularly cholesterol, is achieved partly through up-regulation of bile acid synthesis and excretion into the gastrointestinal tract (GI). The efficacy of BBR is also thought to be dependent on structural and functional alterations of the gut microbiome. However, knowledge of the effects of BBR on gut microbiome communities is currently lacking. Distinguishing indirect effects of BBR on bacteria through altered bile acid profiles is particularly important in understanding how dietary nutraceuticals alter the microbiome.

RESULTS

Germfree mice were colonized with a defined minimal gut bacterial consortium capable of functional bile acid metabolism (Bacteroides vulgatus, Bacteroides uniformis, Parabacteroides distasonis, Bilophila wadsworthia, Clostridium hylemonae, Clostridium hiranonis, Blautia producta; B4PC2). Multi-omics (bile acid metabolomics, 16S rDNA sequencing, cecal metatranscriptomics) were performed in order to provide a simple in vivo model from which to identify network-based correlations between bile acids and bacterial transcripts in the presence and absence of dietary BBR. Significant alterations in network topology and connectivity in function were observed, despite similarity in gut microbial alpha diversity (P = 0.30) and beta-diversity (P = 0.123) between control and BBR treatment. BBR increased cecal bile acid concentrations, (P < 0.05), most notably deoxycholic acid (DCA) (P < 0.001). Overall, analysis of transcriptomes and correlation networks indicates both bacterial species-specific responses to BBR, as well as functional commonalities among species, such as up-regulation of Na+/H+ antiporter, cell wall synthesis/repair, carbohydrate metabolism and amino acid metabolism. Bile acid concentrations in the GI tract increased significantly during BBR treatment and developed extensive correlation networks with expressed genes in the B4PC2 community.

CONCLUSIONS

This work has important implications for interpreting the effects of BBR on structure and function of the complex gut microbiome, which may lead to targeted pharmaceutical interventions aimed to achieve the positive physiological effects previously observed with BBR supplementation.

Genomic footprints of repeated evolution of CAM photosynthesis in a Neotropical species radiation

The adaptive radiation of Bromeliaceae (pineapple family) is one of the most diverse among Neotropical flowering plants. Diversification in this group was facilitated by shifts in several adaptive traits or "key innovations" including the transition from C₃ to CAM photosynthesis associated with xeric (heat/drought) adaptation. We used phylogenomic approaches, complemented by differential gene expression (RNA-seq) and targeted metabolite profiling, to address the mechanisms of C₃ /CAM evolution in the extremely species-rich bromeliad genus, Tillandsia, and related taxa. Evolutionary analyses of whole-genome sequencing and RNA-seq data suggest that evolution of CAM is associated with coincident changes to different pathways mediating xeric adaptation in this group. At the molecular level, C₃ /CAM shifts were accompanied by gene expansion of XAP5 CIRCADIAN TIMEKEEPER homologs, a regulator involved in sugar- and light-dependent regulation of growth and development. Our analyses also support the re-programming of abscisic acid-related gene expression via differential expression of ABF2/ABF3 transcription factor homologs, and adaptive sequence evolution of an ENO2/LOS2 enolase homolog, effectively tying carbohydrate flux to abscisic acid-mediated abiotic stress response. By pinpointing different regulators of overlapping molecular responses, our results suggest plausible mechanistic explanations for the repeated evolution of correlated adaptive traits seen in a textbook example of an adaptive radiation.

Genomic Prediction Informed by Biological Processes Expands Our Understanding of the Genetic Architecture Underlying Free Amino Acid Traits in Dry Arabidopsis Seeds

Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana. Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.

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, translational, 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.

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, translational, 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.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools

Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Age-Dependent Metabolic Profiles Unravel the Metabolic Relationships Within and Between Flax Leaves (Linum usitatissimum)

Flax for oil seed is a crop of increasing popularity, but its cultivation needs technical improvement. Important agronomic traits such as productivity and resistance to stresses are to be regarded as the result of the combined responses of individual organs and their inter-communication. Ultimately, these responses directly reflect the metabolic profile at the cellular level. Above ground, the complexity of the plant phenotype is governed by leaves at different developmental stages, and their ability to synthesise and exchange metabolites. In this study, the metabolic profile of differently-developed leaves was used firstly to discriminate flax leaf developmental stages, and secondly to analyse the allocation of the metabolites within and between leaves. For this purpose, the concentration of 52 metabolites, both primary and specialized, was followed by gas chromatography (GC-) and liquid chromatography coupled to mass spectrometry (LC-MS) in alternate pairs of flax leaves. On the basis of their metabolic content, three populations of leaves in different growth stages could be distinguished. Primary and specialized metabolites showed characteristic distribution patterns, and compounds similarly evolving with leaf age could be grouped by the aid of the Kohonen self-organising map (SOM) algorithm. Ultimately, visualisation of the correlations between metabolites via hierarchical cluster analysis (HCA) allowed the assessment of the metabolic fluxes characterising different leaf developmental stages, and the investigation of the relationships between primary and specialized metabolites.

Different "metabolomic niches" of the highly diverse tree species of the French Guiana rainforests

Tropical rainforests harbor a particularly high plant diversity. We hypothesize that potential causes underlying this high diversity should be linked to distinct overall functionality (defense and growth allocation, anti-stress mechanisms, reproduction) among the different sympatric taxa. In this study we tested the hypothesis of the existence of a metabolomic niche related to a species-specific differential use and allocation of metabolites. We tested this hypothesis by comparing leaf metabolomic profiles of 54 species in two rainforests of French Guiana. Species identity explained most of the variation in the metabolome, with a species-specific metabolomic profile across dry and wet seasons. In addition to this “homeostatic” species-specific metabolomic profile significantly linked to phylogenetic distances, also part of the variance (flexibility) of the metabolomic profile was explained by season within a single species. Our results support the hypothesis of the high diversity in tropical forest being related to a species-specific metabolomic niche and highlight ecometabolomics as a tool to identify this species functional diversity related and consistent with the ecological niche theory.

Grape berry position affects the diurnal dynamics of its metabolic profile

Solar irradiance and air temperature are characterized by dramatic circadian fluctuations and are known to significantly modulate fruit composition. To date, it remains unclear whether the abrupt, yet predictive, diurnal changes in radiation and temperature prompt direct metabolic turn-over in the fruit. We assessed the role of fruit insolation, air temperature, and source-tissue CO₂ assimilation in the diurnal compositional changes in ripening grape berries. This was performed by comparing the diurnal changes in metabolite profiles of berries positioned such that they experienced (a) contrasting diurnal solar irradiance patterns, and (b) similar irradiance but contrasting diurnal CO₂ assimilation patterns of adjacent leaves. Grape carbon levels increased during the morning and decreased thereafter. Sucrose levels decreased throughout the day and were correlated with air temperature, but not with the diurnal pattern of leaf CO₂ assimilation. Tight correlation between sucrose and glucose-6-phosphate indicated the involvement of photorespiration/glycolysis in sucrose depletion. Amino acids, polyamines, and phenylpropanoids fluctuated diurnally, and were highly responsive to the diurnal insolation pattern of the fruit. Our results fill the knowledge gap regarding the circadian pattern of source-sink assimilate-translocation in grapevine. In addition, they suggest that short-term direct solar exposure of the fruit impacts both its diurnal and nocturnal metabolism.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

Clinical Metabolomics Identifies Blood Serum Branched Chain Amino Acids as Potential Predictive Biomarkers for Chronic Graft vs. Host Disease

The allogeneic hematopoietic stem cell transplantation procedure—the only curative therapy for many types of hematological cancers—is increasing, and graft vs. host disease (GVHD) is the main cause of morbidity and mortality after transplantation. Currently, GVHD diagnosis is clinically performed. Whereas, biomarker panels have been developed for acute GVHD (aGVHD), there is a lack of information about the chronic form (cGVHD). Using nuclear magnetic resonance (NMR) and gas chromatography coupled to time-of-flight (GC-TOF) mass spectrometry, this study prospectively evaluated the serum metabolome of 18 Brazilian patients who had undergone allogeneic hematopoietic stem cell transplantation (HSCT). We identified and quantified 63 metabolites and performed the metabolomic profile on day −10, day 0, day +10 and day +100, in reference to day of transplantation. Patients did not present aGVHD or cGVHD clinical symptoms at sampling times. From 18 patients analyzed, 6 developed cGVHD. The branched-chain amino acids (BCAAs) leucine and isoleucine were reduced and the sulfur-containing metabolite (cystine) was increased at day +10 and day +100. The area under receiver operating characteristics (ROC) curves was higher than 0.79. BCAA findings were validated by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in 49 North American patients at day +100; however, cystine findings were not statistically significant in this patient set. Our results highlight the importance of multi-temporal and multivariate biomarker panels for predicting and understanding cGVHD.

McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data

Motivation: Single-cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome-wide expression analysis at single-cell resolution provides a window into dynamics of cellular phenotypes. This facilitates the characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on the development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in the separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, the performance of dimensionality reduction techniques for cell visualization, and gene distribution. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq.

Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism

Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.

Transcriptomic and proteomic responses to very low CO2 suggest multiple carbon concentrating mechanisms in Nannochloropsis oceanica

BACKGROUND

In industrial oleaginous microalgae such as Nannochloropsis spp., the key components of the carbon concentration mechanism (CCM) machineries are poorly defined, and how they are mobilized to facilitate cellular utilization of inorganic carbon remains elusive.

RESULTS

For Nannochloropsis oceanica, to unravel genes specifically induced by CO₂ depletion which are thus potentially underpinning its CCMs, transcriptome, proteome and metabolome profiles were tracked over 0 h, 3 h, 6 h, 12 h and 24 h during cellular response from high CO₂ level (HC; 50,000 ppm) to very low CO₂ (VLC; 100 ppm). The activity of a biophysical CCM is evidenced based on induction of transcripts encoding a bicarbonate transporter and two carbonic anhydrases under VLC. Moreover, the presence of a potential biochemical CCM is supported by the upregulation of a number of key C4-like pathway enzymes in both protein abundance and enzymatic activity under VLC, consistent with a mitochondria-implicated C4-based CCM. Furthermore, a basal CCM underpinned by VLC-induced upregulation of photorespiration and downregulation of ornithine-citrulline shuttle and the ornithine urea cycles is likely present, which may be responsible for efficient recycling of mitochondrial CO₂ for chloroplastic carbon fixation.

CONCLUSIONS

Nannochloropsis oceanica appears to mobilize a comprehensive set of CCMs in response to very low CO₂. Its genes induced by the stress are quite distinct from those of Chlamydomonas reinhardtii and Phaeodactylum tricornutum, suggesting tightly regulated yet rather unique CCMs. These findings can serve the first step toward rational engineering of the CCMs for enhanced carbon fixation and biomass productivity in industrial microalgae.

Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

Metabolomics in Plant Priming Research: The Way Forward?

A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.

Grape Berry Acclimation to Excessive Solar Irradiance Leads to Repartitioning between Major Flavonoid Groups

Warm viticulture regions are associated with inferior wines, resulting from the interaction between microclimate and fruit biochemistry. Solar irradiance triggers biosynthetic processes in the fruit and dominates its thermal balance. Therefore, deciphering its impact on fruit metabolism is pivotal to develop strategies for fruit protection and ameliorate its quality traits. Here, we modified light quality and intensity in the fruit-zone and integrated micrometeorology with grape and wine metabolomics, allowing a complete assessment, from field to bottle. We analyzed the dynamics of fruit's adaptation to altered conditions during ripening and constructed temporal-based metabolic networks. Micrometeorological modifications shifted the balance between the major flavonoids, associating increased solar exposure with lower levels of anthocyanins and flavan-3-ols, and higher flavonols. Differences were fixed from 2 weeks postveraison until harvest, suggesting a controlled acclimation response rather than external modulation. Differences in grape composition manifested in the wine and resulted in higher color intensity and improved wine hue under partial shading.

Recent research on the physiological functions, applications, and biotechnological production of D-allose

D-Allose is a rare monosaccharide, which rarely appears in the natural environment. D-Allose has an 80% sweetness relative to table sugar but is ultra-low calorie and non-toxic and is thus an ideal candidate to take the place of table sugar in food products. It displays unique health benefits and physiological functions in various fields, including food systems, clinical treatment, and the health care fields. However, it is difficult to produce chemically. The biotechnological production of D-allose has become a research hotspot in recent years. Therefore, an overview of recent studies on the physiological functions, applications, and biotechnological production of D-allose is presented. In this review, the physiological functions of D-allose are introduced in detail. In addition, the different types of D-allose-producing enzymes are compared for their enzymatic properties and for the biotechnological production of D-allose. To date, very little information is available on the molecular modification and food-grade expression of D-allose-producing enzymes, representing a very large research space yet to be explored.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.

Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM)

Gestational diabetes mellitus during pregnancy has severe implications for the health of the mother and the fetus. Therefore, early prediction and an understanding of the physiology are an important part of prenatal care. Metabolite profiling is a long established method for the analysis and prediction of metabolic diseases. Here, we applied untargeted and targeted metabolomic protocols to analyze plasma and urine samples of pregnant women with and without GDM. Univariate and multivariate statistical analyses of metabolomic profiles revealed markers such as 2-hydroxybutanoic acid (AHBA), 3-hydroxybutanoic acid (BHBA), amino acids valine and alanine, the glucose-alanine-cycle, but also plant-derived compounds like sitosterin as different between control and GDM patients. PLS-DA and VIP analysis revealed tryptophan as a strong variable separating control and GDM. As tryptophan is biotransformed to serotonin we hypothesized whether serotonin metabolism might also be altered in GDM. To test this hypothesis we applied a method for the analysis of serotonin, metabolic intermediates and dopamine in urine by stable isotope dilution direct infusion electrospray ionization mass spectrometry (SID-MS). Indeed, serotonin and related metabolites differ significantly between control and GDM patients confirming the involvement of serotonin metabolism in GDM. Clustered correlation coefficient visualization of metabolite correlation networks revealed the different metabolic signatures between control and GDM patients. Eventually, the combination of selected blood plasma and urine sample metabolites improved the AUC prediction accuracy to 0.99. The detected GDM candidate biomarkers and the related systemic metabolic signatures are discussed in their pathophysiological context. Further studies with larger cohorts are necessary to underpin these observations.

Proteomic Analysis Reveals Coordinated Regulation of Anthocyanin Biosynthesis through Signal Transduction and Sugar Metabolism in Black Rice Leaf

Black rice (Oryza sativa L.) is considered to be a healthy food due to its high content of anthocyanins in the pericarp. The synthetic pathway of anthocyanins in black rice grains has been identified, however, the proteomic profile of leaves during grain development is still unclear. Here, isobaric Tags Relative and Absolute Quantification (iTRAQ) MS/MS was carried out to identify statistically significant changes of leaf proteome in the black rice during grain development. Throughout three sequential developmental stages, a total of 3562 proteins were detected and 24 functional proteins were differentially expressed 3–10 days after flowering (DAF). The detected proteins are known to be involved in various biological processes and most of these proteins were related to gene expression regulatory (33.3%), signal transduction (16.7%) and developmental regulation and hormone-like proteins (12.5%). The coordinated changes were consistent with changes in regulatory proteins playing a leading role in leaves during black rice grain development. This indicated that signal transduction between leaves and grains may have an important role in anthocyanin biosynthesis and accumulation during grain development of black rice. In addition, four identified up-regulated proteins associated with starch metabolism suggested that the remobilization of nutrients for starch synthesis plays a potential role in anthocyanin biosynthesis of grain. The mRNA transcription for eight selected proteins was validated with quantitative real-time PCR. Our results explored the proteomics of the coordination between leaf and grain in anthocyanins biosynthesis of grain, which might be regulated by signal transduction and sugar metabolism in black rice leaf.

Metabolic shift in sugars and amino acids regulates sprouting in Saffron corm

Saffron is one of the most expensive spices of the world. Since this spice is triploid and meiosis is unusual, it cannot reproduce sexually like other plants; rather, it is propagated vegetatively via an underground corm, which can withstand a long dry dormant period before sprouting. Thus, corms are indispensable to saffron propagation. To identify and analyse signature metabolites associated with the ‘dormancy-sprouting’ process, non-targeted GC-MS was performed at different stages of corm development. Comparative metabolite profiling reflected dissimilar profiles among the stages as portrayed by differential cluster patterns of metabolites in the PCA and PLS-DA analysis. Correlation analysis revealed the interdependencies of individual metabolites and metabolic pathway. At the onset of stage 2, characterized by the initiation and differentiation of leaf primordia, a shift from dormancy to active metabolism occurred as derived from the increased abundance of sugars and other metabolites involved in the tricarboxylic acid cycle, glycolytic, amino acid and fatty acid pathways. These changes contribute to sprouting and vegetative growth of the corm. The present study provides new insights into saffron corm composition and metabolite changes associated with various stages of corm development and may pave the way for achieving agronomical improvements in this economically important spice.

Effects of Combined Low Glutathione with Mild Oxidative and Low Phosphorus Stress on the Metabolism of Arabidopsis thaliana

Plants possess highly sensitive mechanisms that monitor environmental stress levels for a dose-dependent fine-tuning of their growth and development. Differences in plant responses to severe and mild abiotic stresses have been recognized. Although many studies have revealed that glutathione can contribute to plant tolerance to various environmental stresses, little is known about the relationship between glutathione and mild abiotic stress, especially the effect of stress-induced altered glutathione levels on the metabolism. Here, we applied a systems biology approach to identify key pathways involved in the gene-to-metabolite networks perturbed by low glutathione content under mild abiotic stress in Arabidopsis thaliana. We used glutathione synthesis mutants (cad2-1 and pad2-1) and plants overexpressing the gene encoding γ-glutamylcysteine synthetase, the first enzyme of the glutathione biosynthetic pathway. The plants were exposed to two mild stress conditions-oxidative stress elicited by methyl viologen and stress induced by the limited availability of phosphate. We observed that the mutants and transgenic plants showed similar shoot growth as that of the wild-type plants under mild abiotic stress. We then selected the synthesis mutants and performed multi-platform metabolomics and microarray experiments to evaluate the possible effects on the overall metabolome and the transcriptome. As a common oxidative stress response, several flavonoids that we assessed showed overaccumulation, whereas the mild phosphate stress resulted in increased levels of specific kaempferol- and quercetin-glycosides. Remarkably, in addition to a significant increased level of sugar, osmolytes, and lipids as mild oxidative stress-responsive metabolites, short-chain aliphatic glucosinolates over-accumulated in the mutants, whereas the level of long-chain aliphatic glucosinolates and specific lipids decreased. Coordinated gene expressions related to glucosinolate and flavonoid biosynthesis also supported the metabolite responses in the pad2-1 mutant. Our results suggest that glutathione synthesis mutants accelerate transcriptional regulatory networks to control the biosynthetic pathways involved in glutathione-independent scavenging metabolites, and that they might reconfigure the metabolic networks in primary and secondary metabolism, including lipids, glucosinolates, and flavonoids. This work provides a basis for the elucidation of the molecular mechanisms involved in the metabolic and transcriptional regulatory networks in response to combined low glutathione content with mild oxidative and nutrient stress in A. thaliana.

Investigating the Role of the Photorespiratory Pathway in Non-photosynthetic Tissues

Whilst photorespiration represents one of the dominant pathway fluxes in photosynthetic tissues there are hints from publically available gene expression data such as that housed in the bioarray resource (BAR; www.bar.utoronto.ca) that several of the constituent enzymes are present in roots and other heterotrophic tissues. Here we describe a protocol based on modification of the gaseous environment surrounding individual tissues of mutant and wild type Arabidopsis and evaluation of the consequences. This method could additionally easily be used for larger plants.

Metabolomics Identifies Metabolic Markers of Maturation in Human Pluripotent Stem Cell-Derived Cardiomyocytes

Cardiovascular disease is a leading cause of death worldwide. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) hold immense clinical potential and recent studies have enabled generation of virtually pure hPSC-CMs with high efficiency in chemically defined and xeno-free conditions. Despite these advances, hPSC-CMs exhibit an immature phenotype and are arrhythmogenic in vivo, necessitating development of strategies to mature these cells. hPSC-CMs undergo significant metabolic alterations during differentiation and maturation. A detailed analysis of the metabolic changes accompanying maturation of hPSC-CMs may prove useful in identifying new strategies to expedite hPSC-CM maturation and also may provide biomarkers for testing or validating hPSC-CM maturation. In this study we identified global metabolic changes which take place during long-term culture and maturation of hPSC-CMs derived from three different hPSC lines. We have identified several metabolic pathways, including phospholipid metabolism and pantothenate and Coenzyme A metabolism, which showed significant enrichment upon maturation in addition to fatty acid oxidation and metabolism. We also identified increases in glycerophosphocholine and the glycerophosphocholine:phosphocholine ratio as potential metabolic biomarkers of maturation. These biomarkers were also affected in a similar manner during murine heart development in vivo. These results support that hPSC-CM maturation is associated with extensive metabolic changes in metabolic network utilization and understanding the roles of these metabolic changes has the potential to develop novel approaches to monitor and expedite hPSC-CM maturation.