Integration of metabolomics data into metabolic networks

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Non-targeted metabolomics analysis of metabolite changes in two quinoa genotypes under drought stress

BACKGROUND

Quinoa is an important economic crop, drought is one of the key factors affecting quinoa yield. Clarifying the adaptation strategy of quinoa to drought is conducive to cultivating drought-tolerant varieties. At present, the study of quinoa on drought stress-related metabolism and the identification of related metabolites are still unknown. As a direct feature of biochemical functions, metabolites can reveal the biochemical pathways involved in drought response.

RESULT

Here, we studied the physiological and metabolic responses of drought-tolerant genotype L1 and sensitive genotype HZ1. Under drought conditions, L1 had higher osmotic adjustment ability and stronger root activity than HZ1, and the relative water content of L1 was also higher than that of HZ1. In addition, the barrier-to- sea ratio of L1 is significantly higher than that of HZ1. Using untargeted metabolic analysis, a total of 523, 406, 301 and 272 differential metabolites were identified in L1 and HZ1 on day 3 and day 9 of drought stress. The key metabolites (amino acids, nucleotides, peptides, organic acids, lipids and carbohydrates) accumulated differently in quinoa leaves. and HZ1 had the most DEMs in Glycerophospholipid metabolism (ko00564) and ABC transporters (ko02010) pathways.

CONCLUSION

These results provide a reference for characterizing the response mechanism of quinoa to drought and improving the drought tolerance of quinoa.

Metabolomic analysis for disclosing nutritional and therapeutic prospective of traditional rice cultivars of Cauvery deltaic region, India

Traditional rice is gaining popularity worldwide due to its high nutritional and pharmaceutical value, as well as its high resistance to abiotic and biotic stresses. This has attracted significant attention from breeders, nutritionists, and plant protection scientists in recent years. Hence, it is critical to investigate the grain metabolome to reveal germination and nutritional importance. This research aimed to explore non-targeted metabolites of five traditional rice varieties, viz., Chinnar, Chithiraikar, Karunguruvai, Kichili samba, and Thooyamalli, for their nutritional and therapeutic properties. Approximately 149 metabolites were identified using the National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) and were grouped into 34 chemical classes. Major classes include fatty acids (31.1-56.3%), steroids and their derivatives (1.80-22.4%), dihydrofurans (8.98-11.6%), prenol lipids (0.66-4.44%), organooxygen compounds (0.12-6.45%), benzene and substituted derivatives (0.53-3.73%), glycerolipids (0.36-2.28%), and hydroxy acids and derivatives (0.03-2.70%). Significant variations in metabolite composition among the rice varieties were also observed through the combination of univariate and multivariate statistical analyses. Principal component analysis (PCA) reduced the dimensionality of 149 metabolites into five principle components (PCs), which explained 96% of the total variance. Two clusters were revealed by hierarchical cluster analysis, indicating the distinctiveness of the traditional varieties. Additionally, a partial least squares-discriminant analysis (PLS-DA) found 17 variables important in the projection (VIP) scores of metabolites. The findings of this study reveal the biochemical intricate and distinctive metabolomes of the traditional therapeutic rice varieties. This will serve as the foundation for future research on developing new rice varieties with traditional rice grain metabolisms to increase grain quality and production with various nutritional and therapeutic benefits.

A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus Strain 1A01

While Vibrio splendidus is best known as an opportunistic pathogen in oysters, Vibrio splendidus strain 1A01 was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using flux balance analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the model's biomass reaction to V. splendidus 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus 1A01 are described. IMPORTANCE A genome-scale metabolic model of V. splendidus 1A01 was reconstructed in this work. We offer solutions to the technical problems associated with model reconstruction for a marine bacterial strain like V. splendidus 1A01, which arise largely from the high salt concentration found in both seawater and culture media that simulate seawater.

Metabolomics analysis unveils important changes involved in the salt tolerance of Salicornia europaea

Salicornia europaea is one of the world's salt-tolerant plant species and is recognized as a model plant for studying the metabolism and molecular mechanisms of halophytes under salinity. To investigate the metabolic responses to salinity stress in S. europaea, this study performed a widely targeted metabolomic analysis after analyzing the physiological characteristics of plants exposed to various NaCl treatments. S. europaea exhibited excellent salt tolerance and could withstand extremely high NaCl concentrations, while lower NaCl conditions (50 and 100 mM) significantly promoted growth by increasing tissue succulence and maintaining a relatively stable K⁺ concentration. A total of 552 metabolites were detected, 500 of which were differently accumulated, mainly consisting of lipids, organic acids, saccharides, alcohols, amino acids, flavonoids, phenolic acids, and alkaloids. Sucrose, glucose, p-proline, quercetin and its derivatives, and kaempferol derivatives represented core metabolites that are responsive to salinity stress. Glycolysis, flavone and flavonol biosynthesis, and phenylpropanoid biosynthesis were considered as the most important pathways responsible for salt stress response by increasing the osmotic tolerance and antioxidant activities. The high accumulation of some saccharides, flavonoids, and phenolic acids under 50 mM NaCl compared with 300 mM NaCl might contribute to the improved salt tolerance under the 50 mM NaCl treatment. Furthermore, quercetin, quercetin derivatives, and kaempferol derivatives showed varied change patterns in the roots and shoots, while coumaric, caffeic, and ferulic acids increased significantly in the roots, implying that the coping strategies in the shoots and roots varied under salinity stress. These findings lay the foundation for further analysis of the mechanism underlying the response of S. europaea to salinity.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

From Function to Metabolome: Metabolomic Analysis Reveals the Effect of Probiotic Fermentation on the Chemical Compositions and Biological Activities of Perilla frutescens Leaves

This study aimed to investigate the impact of probiotic fermentation on the active components and functions of Perilla frutescens leaves (PFL). PFL was fermented for 7 days using six probiotics (Lactobacillus Plantarum SWFU D16, Lactobacillus Plantarum ATCC 8014, Lactobacillus Rhamnosus ATCC 53013, Streptococcus Thermophilus CICC 6038, Lactobacillus Casei ATCC 334, and Lactobacillus Bulgaricus CICC 6045). The total phenol and flavonoid contents, antioxidant abilities, as well as α-glucosidase and acetylcholinesterase inhibition abilities of PFL during the fermentation process were evaluated, and its bioactive compounds were further quantified by high-performance liquid chromatography (HPLC). Finally, non-targeted ultra-HPLC-tandem mass spectroscopy was used to identify the metabolites affected by fermentation and explore the possible mechanisms of the action of fermentation. The results showed that most of the active component contents and functional activities of PFL exhibited that it first increased and then decreased, and different probiotics had clearly distinguishable effects from each other, of which fermentation with ATCC 53013 for 1 day showed the highest enhancement effect. The same trend was also confirmed by the result of the changes in the contents of 12 phenolic acids and flavonoids by HPLC analysis. Further metabolomic analysis revealed significant metabolite changes under the best fermentation condition, which involved primarily the generation of fatty acids and their conjugates, flavonoids. A total of 574 and 387 metabolites were identified in positive ion and negative ion modes, respectively. Results of Spearman's analysis indicated that some primary metabolites and secondary metabolites such as flavonoids, phenols, and fatty acids might play an important role in the functional activity of PFL. Differential metabolites were subjected to the KEGG database and 97 metabolites pathways were obtained, of which biosyntheses of unsaturated fatty acids, flavonoid, and isoflavonoid were the most enriched pathways. The above results revealed the potential reason for the differences in metabolic and functional levels of PFL after fermentation. This study could provide a scientific basis for the further study of PFL, as well as novel insights into the action mechanism of probiotic fermentation on the chemical composition and biological activity of food/drug.

Overexpression of Pennisetum purpureum CCoAOMT Contributes to Lignin Deposition and Drought Tolerance by Promoting the Accumulation of Flavonoids in Transgenic Tobacco

Elephant grass (Pennisetum purpureum) is a fast-growing and low-nutrient demand plant that is widely used as a forage grass and potential energy crop in tropical and subtropical regions of Asia, Africa, and the United States. Transgenic tobacco with the PpCCoAOMT gene from Pennisetum purpureum produces high lignin content that is associated with drought tolerance in relation to lower accumulation of reactive oxygen species (ROS), along with higher antioxidant enzyme activities and osmotic adjustment. In this study, transgenic tobacco plants revealed no obvious cost to plant growth when expressing the PpCCoAOMT gene. Metabolomic studies demonstrated that tobacco plants tolerant to drought stress accumulated flavonoids under normal and drought conditions, which likely explains the observed tolerance phenotype in wild-type tobacco. Our results suggest that plants overexpressing PpCCoAOMT were better able to cope with water deficit than were wild-type controls; metabolic flux was redirected within primary and specialized metabolism to induce metabolites related to defense to drought stress. These results could help to develop drought-resistant plants for agriculture in the future.

Physiology and metabonomics reveal differences in drought resistance among soybean varieties

BACKGROUND

The soybean is an important food crop worldwide. Drought during the first pod stage significantly affects soybean yield, and understanding the metabolomic and physiological changes in soybeans under drought stress is crucial. This study identified the differential metabolites in initial pod stage soybean leaves under polyethylene glycol-simulated drought stress, using ultra performance liquid chromatography and tandem mass spectrometry, and the physiological indexes related to drought resistance.

RESULTS

Physiologically, drought resistance also generates enzyme and antioxidant activity; levels of superoxide dismutase, peroxidase, and catalase first increased and subsequently decreased, while those of soluble sugar, soluble protein, malondialdehyde, and proline content increased in both varieties. The contents of CAT, proline and soluble sugar in Heinong 44 (HN44) were higher than those in Heinong 65 (HN65), and the contents of MDA were lower than those in HN65. In metabolomics, the OPLS-DA model was used to screen different metabolites. KEGG analysis showed that the two varieties resisted drought through different ways. Amino acid metabolism and lipid metabolism play a key role in drought resistance of the two varieties, respectively. TCA cycle was one of the core pathways of drought resistance in two varieties. Changes in the content of L-Asparagine and citric acid may be one of the reasons for the difference in drought resistance between the two varieties.

CONCLUSIONS

We think that the reasons of drought resistance among soybean varieties are as follows: the main metabolic pathways are different under drought stress; the contents of metabolites in these metabolic pathways are different; some physiological indexes are different, such as MDA, CAT, proline content and so on. Our study enhances the understanding of the metabolomic soybean drought stress response and provides a reference for soybean drought resistance breeding.

Analysis of the Formation of Sauce-Flavored Daqu Using Non-targeted Metabolomics

Sauce-flavored Daqu exhibits different colors after being stacked and fermented at high temperatures. Heiqu (black Daqu, BQ) with outstanding functions is difficult to obtain because its formation mechanism is unclear. In this study, we compared the metabolites in different types of Daqu using ultra-high-performance liquid chromatography triple quadrupole mass spectrometry to explore the formation process of BQ. We found that 251 differential metabolites were upregulated in BQ. Metabolic pathway analysis showed that "tyrosine metabolism" was enriched, and most metabolites in this pathway were differential metabolites upregulated in BQ. The tyrosine metabolic pathway is related to enzymatic browning and melanin production. In addition, the high-temperature and high-humidity fermentation environment of sauce-flavored Daqu promoted an increase in the melanoidin content via a typical Maillard reaction; thus, the melanoidin content in BQ was much higher than that in Huangqu and Baiqu. By strengthening the Maillard reaction precursor substances, amino acids, and reducing sugars, the content of Daqu melanoidin increased significantly after simulated fermentation. Therefore, the enzymatic browning product melanin and Maillard reaction product melanoidin are responsible for BQ formation. This study revealed the difference between BQ and other types of Daqu and provides theoretical guidance for controlling the formation of BQ and improving the quality of liquor.

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data.

Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks.

Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.

Analysis of Flavonoid Metabolites in Watercress (Nasturtium officinale R. Br.) and the Non-Heading Chinese Cabbage (Brassica rapa ssp. chinensis cv. Aijiaohuang) Using UHPLC-ESI-MS/MS

Flavonoids from plants play an important role in our diet. Watercress is a special plant that is rich in flavonoids. In this study, four important watercress varieties were compared with non-heading Chinese cabbage by ultra-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS). A total of 132 flavonoid metabolites (including 8 anthocyanins, 2 dihydroflavone, 3 dihydroflavonol, 1 flavanols, 22 flavones, 11 flavonoid carbonosides, 82 flavonols, and 3 isoflavones) were detected. Flavonoid metabolites varied widely in different samples. Both the non-heading Chinese cabbage and the variety of watercress from Guangdong, China, had their own unique metabolites. This work is helpful to better understand flavonoid metabolites between the non-heading Chinese cabbage and the other four watercress varieties, and to provide a reliable reference value for further research.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (J_max) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high J_max was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.

This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

The integration of data at different levels of cellular organization is an important goal in computational biology for understanding the way the genotypic variation translates into phenotypic variation. Novel profiling technologies and accurate high-throughput phenotyping now allows genomic, transcriptomic, metabolic and proteomic characterization of a large number of individuals under various environmental conditions. However, the metabolic fluxes remain difficult to measure. In this work, we take advantage of recent advances in genome-scale functional annotation and constraint-based metabolic modeling to provide a mathematical framework that allows to estimate internal cellular fluxes from protein abundances and elucidate the biological mechanisms underlying phenotypic variation. Applied to yeast as a model system, this approach highlights that the negative correlation between production traits such as maximum population size and growth and fermentation traits is explained by a differential usage of energy production pathways. The ability to identify molecular and metabolic bases of the variation of integrated traits through population studies has a broad applicability domain.

Metabolite Analysis of Jerusalem Artichoke (Helianthus tuberosus L.) Seedlings in Response to Polyethylene Glycol-Simulated Drought Stress

Jerusalem artichokes are a perennial crop with high drought tolerance and high value as a raw material to produce biofuels, functional feed, and food. However, there are few comprehensive metabolomic studies on Jerusalem artichokes under drought conditions. Methods: Ultra-performance liquid chromatography and tandem mass spectrometry were used to identify differential metabolites in Jerusalem artichoke seedling leaves under polyethylene glycol (PEG) 6000-simulated drought stress at 0, 18, 24, and 36 h. Results: A total of 661 metabolites and 236 differential metabolites were identified at 0 vs. 18, 18 vs. 24, and 24 vs. 36 h. 146 differential metabolites and 56 common were identified and at 0 vs. 18, 24, and 36 h. Kyoto Encyclopedia of Genes and Genomes enrichment identified 236 differential metabolites involved in the biosynthesis of secondary metabolites and amino acids. Metabolites involved in glycolysis, phenolic metabolism, tricarboxylic cycle, glutamate-mediated proline biosynthesis, urea cycle, amino acid metabolism, unsaturated fatty acid biosynthesis, and the met salvage pathway responded to drought stress. Conclusion: A metabolic network in the leaves of Jerusalem artichokes under drought stress is proposed. These results will improve understanding of the metabolite response to drought stress in Jerusalem artichokes and develop a foundation for breeding drought-resistant varieties.

Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism

Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.

Environment-coupled models of leaf metabolism

The plant leaf is the main site of photosynthesis. This process converts light energy and inorganic nutrients into chemical energy and organic building blocks for the biosynthesis and maintenance of cellular components and to support the growth of the rest of the plant. The leaf is also the site of gas–water exchange and due to its large surface, it is particularly vulnerable to pathogen attacks. Therefore, the leaf's performance and metabolic modes are inherently determined by its interaction with the environment. Mathematical models of plant metabolism have been successfully applied to study various aspects of photosynthesis, carbon and nitrogen assimilation and metabolism, aided suggesting metabolic intervention strategies for optimized leaf performance, and gave us insights into evolutionary drivers of plant metabolism in various environments. With the increasing pressure to improve agricultural performance in current and future climates, these models have become important tools to improve our understanding of plant–environment interactions and to propel plant breeders efforts. This overview article reviews applications of large-scale metabolic models of leaf metabolism to study plant–environment interactions by means of flux-balance analysis. The presented studies are organized in two ways — by the way the environment interactions are modelled — via external constraints or data-integration and by the studied environmental interactions — abiotic or biotic.

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.

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.

An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments.

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Dynamics of Plant Metabolism during Cold Acclimation

Plants have evolved strategies to tightly regulate metabolism during acclimation to a changing environment. Low temperature significantly constrains distribution, growth and yield of many temperate plant species. Exposing plants to low but non-freezing temperature induces a multigenic processes termed cold acclimation, which eventually results in an increased freezing tolerance. Cold acclimation comprises reprogramming of the transcriptome, proteome and metabolome and affects communication and signaling between subcellular organelles. Carbohydrates play a central role in this metabolic reprogramming. This review summarizes current knowledge about the role of carbohydrate metabolism in plant cold acclimation with a focus on subcellular metabolic reprogramming, its thermodynamic constraints under low temperature and mathematical modelling of metabolism.

Analysis of widely targeted metabolites of the euhalophyte Suaeda salsa under saline conditions provides new insights into salt tolerance and nutritional value in halophytic species

BACKGROUND

Suaeda salsa L. (S. salsa) is an annual euhalophyte with high salt tolerance and high value as an oil crop, traditional Chinese medicine and vegetable. However, there are few comprehensive studies on the metabolomics of S. salsa under saline conditions.

RESULTS

Seedlings of S. salsa were cultured with 0, 200 and 500 mM NaCl for two days. Then, widely targeted metabolites were detected with ultra performance liquid chromatography and tandem mass spectrometry. A total of 639 metabolites were annotated. Among these, 253 metabolites were differential metabolites. Salt treatment increased the content of certain metabolites, such as nucleotide and its derivates, organic acids, the content of amino acids, lipids such as α-linolenic acid, and certain antioxidants such as quercetin. These substances may be correlated to osmotic tolerance, increased antioxidant activity, and medical and nutritional value in the species.

CONCLUSION

This study comprehensively analyzed the metabolic response of S. salsa under salinity from the perspective of omics, and provides an important theoretical basis for understanding salt tolerance and evaluating nutritional value in the species.

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

Impact of sample preparation upon intracellular metabolite measurements in 3D cell culture systems

INTRODUCTION

Interest in cell culture metabolomics has increased greatly in recent years because of its many potential applications and advantages (e.g., in toxicology). The first critical step for exploring the cellular metabolome is sample preparation. For metabolomics studies, an ideal sample preparation would extract a maximum number of metabolites and would enable reproducible, accurate analysis of a large number of samples and replicates. In addition, it would provide consistent results across several studies over a relatively long time frame.

OBJECTIVES

This study was conducted to evaluate the impact of sample preparation strategies on monitoring intracellular metabolite responses, highlighting the potential critical step(s) in order to finally improve the quality of metabolomics studies.

METHODS

The sample preparation strategies were evaluated by calculating the sample preparation effect, matrix factor, and process efficiency (PE) for 16 tobacco exposition-related metabolites, including nicotine, nicotine-derived nitrosamine ketone, their major metabolites, and glutathione, using isotopically-labelled internal standards. Samples were analyzed by liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS).

RESULTS

A sample drying step increased losses or variability for some selected metabolites. By avoiding evaporation, good sample preparation recovery was obtained for these compounds. For some metabolites, the cell or culture type impacted PE and matrix factor.

CONCLUSION

In our sample preparation protocol, the drying-reconstitution step was identified as the main cause of metabolite losses or increased data variability during metabolomics analysis by LC-HRMS. Furthermore, PE was affected by the type of matrix. Isotopologue internal standards fully compensate losses or enhancements.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways

Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology.

Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

Crop metabolomics: from diagnostics to assisted breeding

BACKGROUND

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

AIM OF REVIEW

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Translational metabolomics applied to crop breeding programs.

Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.

iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models

MOTIVATION

Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks.

RESULTS

Here we present a computationally and experimentally facile approach, termed iReMet-Flux, which integrates relative metabolomics data in a metabolic model to predict differential fluxes at a genome-scale level. Our approach and its variants complement the flux estimation methods based on radioactive tracer labeling. We employ iReMet-Flux with publically available metabolic profiles to predict reactions and pathways with altered fluxes in photo-autotrophically grown Arabidopsis and four photorespiratory mutants undergoing high-to-low CO2 acclimation. We also provide predictions about reactions and pathways which are most strongly regulated in the investigated experiments. The robustness and variability analyses, tailored to the formulation of iReMet-Flux, demonstrate that the findings provide biologically relevant information that is validated with external measurements of net CO2 exchange and biomass production. Therefore, iReMet-Flux paves the wave for mechanistic dissection of the interplay between pathways of primary and secondary metabolisms at a genome-scale.

AVAILABILITY AND IMPLEMENTATION

The source code is available from the authors upon request.

CONTACT

nikoloski@mpimp-golm.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed "unsteady-state flux balance analysis" (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through ¹³C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Metabolomics reveals significant variations in metabolites and correlations regarding the maturation of walnuts (Juglans regia L.)

The content of walnut metabolites is related to its nutritive value and physiological characteristics, however, comprehensive information concerning the metabolome of walnut kernels is limited. In this study we analyzed the metabolites of walnut kernels at five developmental stages from filling to ripening using GC-MS-based untargeted metabolomics; of a total 252 peaks identified, 85 metabolites were positively identified. Further statistical analysis revealed that these 85 metabolites covered different types of metabolism pathways. PCA scores revealed that the metabolic compositions of the embryo are different at each stage, while the metabolic composition of the endotesta could not be significantly separated into distinct groups. Additionally, 7225 metabolite-metabolite correlations were detected in walnut kernel by a Pearson correlation coefficient approach; during screening of the calculated correlations, 463 and 1047 were determined to be significant with r²≥0.49 and had a false discovery rate (FDR) ≤0.05 in endotesta and embryo, respectively. This work provides the first comprehensive metabolomic study of walnut kernels and reveals that most of the carbohydrate and protein-derived carbon was transferred into other compounds, such as fatty acids, during the maturation of walnuts, which may potentially provide the basis for further studies on walnut kernel metabolism.

Summary: This work provides a comprehensive metabolomic study of walnut kernels and reveals most of the carbohydrate and protein-derived carbons were transferred into other compounds during the maturation of walnuts.

A Caenorhabditis elegans Genome-Scale Metabolic Network Model

Caenorhabditis elegans is a powerful model to study metabolism and how it relates to nutrition, gene expression, and life history traits. However, while numerous experimental techniques that enable perturbation of its diet and gene function are available, a high-quality metabolic network model has been lacking. Here, we reconstruct an initial version of the C. elegans metabolic network. This network model contains 1,273 genes, 623 enzymes, and 1,985 metabolic reactions and is referred to as iCEL1273. Using flux balance analysis, we show that iCEL1273 is capable of representing the conversion of bacterial biomass into C. elegans biomass during growth and enables the predictions of gene essentiality and other phenotypes. In addition, we demonstrate that gene expression data can be integrated with the model by comparing metabolic rewiring in dauer animals versus growing larvae. iCEL1273 is available at a dedicated website (wormflux.umassmed.edu) and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal's physiology.

Photorespiratory Bypasses Lead to Increased Growth in Arabidopsis thaliana: Are Predictions Consistent with Experimental Evidence?

Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO₂ exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions.

Non-targeted Metabolite Profiling and Scavenging Activity Unveil the Nutraceutical Potential of Psyllium (Plantago ovata Forsk)

Non-targeted metabolomics implies that psyllium (Plantago ovata) is a rich source of natural antioxidants, PUFAs (ω-3 and ω-6 fatty acids) and essential and sulfur-rich amino acids, as recommended by the FAO for human health. Psyllium contains phenolics and flavonoids that possess reducing capacity and reactive oxygen species (ROS) scavenging activities. In leaves, seeds, and husks, about 76, 78, 58% polyunsaturated, 21, 15, 20% saturated, and 3, 7, 22% monounsaturated fatty acids were found, respectively. A range of FAs (C12 to C24) was detected in psyllium and among different plant parts, a high content of the nutritive indicators ω-3 alpha-linolenic acid CPS (57%) and ω-6 linoleic acid CPS (18%) was detected in leaves. Similarly, total content of phenolics and the essential amino acid valine were also detected utmost in leaves followed by sulfur-rich amino acids and flavonoids. In total, 36 different metabolites were identified in psyllium, out of which 26 (13 each) metabolites were detected in leaves and seeds, whereas the remaining 10 were found in the husk. Most of the metabolites are natural antioxidants, phenolics, flavonoids, or alkaloids and can be used as nutrient supplements. Moreover, these metabolites have been reported to have several pharmaceutical applications, including anti-cancer activity. Natural plant ROS scavengers, saponins, were also detected. Based on metabolomic data, the probable presence of a flavonoid biosynthesis pathway was inferred, which provides useful insight for metabolic engineering in the future. Non-targeted metabolomics, antioxidants and scavenging activities reveal the nutraceutical potential of the plant and also suggest that psyllium leaves can be used as a green salad as a dietary supplement to daily food.

Inference and Prediction of Metabolic Network Fluxes

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.

Metabolic Profiling of Alpine and Ecuadorian Lichens

Non-targeted ¹H-NMR methods were used to determine metabolite profiles from crude extracts of Alpine and Ecuadorian lichens collected from their natural habitats. In control experiments, the robustness of metabolite detection and quantification was estimated using replicate measurements of Stereocaulon alpinum extracts. The deviations in the overall metabolite fingerprints were low when analyzing S. alpinum collections from different locations or during different annual and seasonal periods. In contrast, metabolite profiles observed from extracts of different Alpine and Ecuadorian lichens clearly revealed genus- and species-specific profiles. The discriminating functions determining cluster formation in principle component analysis (PCA) were due to differences in the amounts of genus-specific compounds such as sticticin from the Sticta species, but also in the amounts of ubiquitous metabolites, such as sugar alcohols or trehalose. However, varying concentrations of these metabolites from the same lichen species e.g., due to different environmental conditions appeared of minor relevance for the overall cluster formation in PCA. The metabolic clusters matched phylogenetic analyses using nuclear ribosomal DNA (nrDNA) internal transcribed spacer (ITS) sequences of lichen mycobionts, as exemplified for the genus Sticta. It can be concluded that NMR-based non-targeted metabolic profiling is a useful tool in the chemo-taxonomy of lichens. The same approach could also facilitate the discovery of novel lichen metabolites on a rapid and systematical basis.