Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea mays

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes

The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.

Limitations and advantages of using metabolite-based genome-wide association studies: focus on fruit quality traits

In the last decades, linkage mapping has help in the location of metabolite quantitative trait loci (QTL) in many species; however, this approach shows some limitations. Recently, thanks to the most recent advanced in high-throughput genotyping technologies like next-generation sequencing, metabolite genome-wide association study (mGWAS) has been proposed a powerful tool to identify the genetic variants in polygenic agrinomic traits. Fruit flavor is a complex interaction of aroma volatiles and taste being sugar and acid ratio key parameter for flavor acceptance. Here, we review recent progress of mGWAS in pinpoint gene polymorphisms related to flavor-related metabolites in fruits. Despite clear successes in discovering novel genes or regions associated with metabolite accumulation affecting sensory attributes in fruits, GWAS incurs in several limitations summarized in this review. In addition, in our own work, we performed mGWAS on 194 Citrus grandis accessions to investigate the genetic control of individual primary and lipid metabolites in ripe fruit. We have identified a total of 667 associations for 14 primary metabolites including amino acids, sugars, and organic acids, and 768 associations corresponding to 47 lipids. Furthermore, candidate genes related to important metabolites related to fruit quality such as sugars, organic acids and lipids were discovered.

Mitochondria in photosynthetic cells: Coordinating redox control and energy balance

In photosynthetic tissues in the light, the function of energy production is associated primarily with chloroplasts, while mitochondrial metabolism adjusts to balance ATP supply, regulate the reduction level of pyridine nucleotides, and optimize major metabolic fluxes. The tricarboxylic acid cycle in the light transforms into a noncyclic open structure (hemicycle) maintained primarily by the influx of malate and the export of citrate to the cytosol. The exchange of malate and citrate forms the basis of feeding redox energy from the chloroplast into the cytosolic pathways. This supports the level of NADPH in different compartments, contributes to the biosynthesis of amino acids, and drives secondary metabolism via a supply of substrates for 2-oxoglutarate-dependent dioxygenase and for cytochrome P450-catalyzed monooxygenase reactions. This results in the maintenance of redox and energy balance in photosynthetic plant cells and in the formation of numerous bioactive compounds specific to any particular plant species. The noncoupled mitochondrial respiration operates in coordination with the malate and citrate valves and supports intensive fluxes of respiration and photorespiration. The metabolic system of plants has features associated with the remarkable metabolic plasticity of mitochondria that permit the use of energy accumulated during photosynthesis in a way that all anabolic and catabolic pathways become optimized and coordinated.

Enzyme activity profiling for physiological phenotyping within functional phenomics: plant growth and stress responses

High-throughput profiling of key enzyme activities of carbon, nitrogen, and antioxidant metabolism is emerging as a valuable approach to integrate cell physiological phenotyping into a holistic functional phenomics approach. However, the analyses of the large datasets generated by this method represent a bottleneck, often keeping researchers from exploiting the full potential of their studies. We address these limitations through the exemplary application of a set of data evaluation and visualization tools within a case study. This includes the introduction of multivariate statistical analyses that can easily be implemented in similar studies, allowing researchers to extract more valuable information to identify enzymatic biosignatures. Through a literature meta-analysis, we demonstrate how enzyme activity profiling has already provided functional information on the mechanisms regulating plant development and response mechanisms to abiotic stress and pathogen attack. The high robustness of the distinct enzymatic biosignatures observed during developmental processes and under stress conditions underpins the enormous potential of enzyme activity profiling for future applications in both basic and applied research. Enzyme activity profiling will complement molecular -omics approaches to contribute to the mechanistic understanding required to narrow the genotype-to-phenotype knowledge gap and to identify predictive biomarkers for plant breeding to develop climate-resilient crops.

Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis

In Northern Europe, sowing maize one-month earlier than current agricultural practices may lead to moderate chilling damage. However, studies of the metabolic responses to low, non-freezing, temperatures remain scarce. Here, genetically-diverse maize hybrids (Zea mays, dent inbred lines crossed with a flint inbred line) were cultivated in a growth chamber at optimal temperature and then three decreasing temperatures for 2 days each, as well as in the field. Leaf metabolomic and proteomic profiles were determined. In the growth chamber, 50% of metabolites and 18% of proteins changed between 20 and 16°C. These maize responses, partly differing from those of Arabidopsis to short-term chilling, were mapped on genome-wide metabolic maps. Several metabolites and proteins showed similar variation for all temperature decreases: seven MS-based metabolite signatures and two proteins involved in photosynthesis decreased continuously. Several increasing metabolites or proteins in the growth-chamber chilling conditions showed similar trends in the early-sowing field experiment, including trans-aconitate, three hydroxycinnamate derivatives, a benzoxazinoid, a sucrose synthase, lethal leaf-spot 1 protein, an allene oxide synthase, several glutathione transferases and peroxidases. Hybrid groups based on field biomass were used to search for the metabolite or protein responses differentiating them in growth-chamber conditions, which could be of interest for breeding.

Comparative Analysis Based on Transcriptomics and Metabolomics Data Reveal Differences between Emmer and Durum Wheat in Response to Nitrogen Starvation

Mounting evidence indicates the key role of nitrogen (N) on diverse processes in plant, including development and defense. Using a combined transcriptomics and metabolomics approach, we studied the response of seedlings to N starvation of two different tetraploid wheat genotypes from the two main domesticated subspecies: emmer and durum wheat. We found that durum wheat exhibits broader and stronger response in comparison to emmer as seen from the expression pattern of both genes and metabolites and gene enrichment analysis. They showed major differences in the responses to N starvation for transcription factor families, emmer showed differential reduction in the levels of primary metabolites while durum wheat exhibited increased levels of most of them to N starvation. The correlation-based networks, including the differentially expressed genes and metabolites, revealed tighter regulation of metabolism in durum wheat in comparison to emmer. We also found that glutamate and γ-aminobutyric acid (GABA) had highest values of centrality in the metabolic correlation network, suggesting their critical role in the genotype-specific response to N starvation of emmer and durum wheat, respectively. Moreover, this finding indicates that there might be contrasting strategies associated to GABA and glutamate signaling modulating shoot vs. root growth in the two different wheat subspecies.

Identification and characterization of apocarotenoid modifiers and carotenogenic enzymes for biosynthesis of crocins in Buddleja davidii flowers

Crocetin biosynthesis in Buddleja davidii flowers proceeds through a zeaxanthin cleavage pathway catalyzed by two carotenoid cleavage dioxygenases (BdCCD4.1 and BdCCD4.3), followed by oxidation and glucosylation reactions that lead to the production of crocins. We isolated and analyzed the expression of 12 genes from the carotenoid pathway in B. davidii flowers and identified four candidate genes involved in the biosynthesis of crocins (BdALDH, BdUGT74BC1, BdUGT74BC2, and BdUGT94AA3). In addition, we characterized the profile of crocins and their carotenoid precursors, following their accumulation during flower development. Overall, seven different crocins, crocetin, and picrocrocin were identified in this study. The accumulation of these apocarotenoids parallels tissue development, reaching the highest concentration when the flower is fully open. Notably, the pathway was regulated mainly at the transcript level, with expression patterns of a large group of carotenoid precursor and apocarotenoid genes (BdPSY2, BdPDS2, BdZDS, BdLCY2, BdBCH, BdALDH, and BdUGT Genes) mimicking the accumulation of crocins. Finally, we used comparative correlation network analysis to study how the synthesis of these valuable apocarotenoids diverges among B. davidii, Gardenia jasminoides, and Crocus sativus, highlighting distinctive differences which could be the basis of the differential accumulation of crocins in the three species.

Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel

BACKGROUND

Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds-namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined.

RESULTS

Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson's |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson's |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson's |r|≥ 0.84.

CONCLUSIONS

Our analysis corrected the previously stated Pearson's correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.

Morphological and metabolic profiling of a tropical-adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor

Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water-deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation-based network analysis was employed, constructing one network for normal irrigation and one network for water-recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome-wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change.

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected.

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics. Methods: The key challenge we address here is investigating the capability of stochastic block models (SBMs) for representing and analyzing different types of biomolecular networks. Fitting them to SBMs both delivers modules of the networks and enables the derivation of edge confidence scores, and it has not yet been investigated for analyzing biomolecular networks. We apply SBM-based analysis independently to three correlation-based networks of breast cancer data originating from high-throughput measurements of different molecular layers: either transcriptomics, proteomics, or metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness.  Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biologically and phenotypically relevant functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. We conclude that biomolecular networks can be appropriately represented and analyzed by fitting SBMs. As the SBM-derived edge confidence scores are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are considered, they could be used as additional, integrated features in network-based data comparisons.

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics.  Methods: We propose to fit the networks to stochastic block models (SBM), a method that has not yet been investigated for the analysis of biomolecular networks. This procedure both delivers modules of the networks and enables the derivation of edge confidence scores. We apply it to correlation-based networks of breast cancer data originating from high-throughput measurements of diverse molecular layers such as transcriptomics, proteomics, and metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness.  Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biological meaning according to functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. As they are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are taken into account, they could be used as additional, integrated features in network-based data comparisons. Their tight relationship to edge existence probabilities can be exploited to predict missing or spurious edges in order to improve the network representation of the underlying biological system.

NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems

The berry of Physalis peruviana L. (Solanaceae) represents an important socio-economical commodity for Latin America. The absence of a clear phenotype renders it difficult to trace its place of origin. In this study, Cape gooseberries from eight different regions within the Peruvian Andes were profiled for their metabolism implementing a NMR platform. Twenty-four compounds could be unequivocally identified and sixteen quantified. One-way ANOVA and post-hoc Tukey test revealed that all of the quantified metabolites changed significantly among regions: Bambamarca I showed the most accumulated significant differences. The coefficient of variation demonstrated high phenotypic plasticity for amino acids, while sugars displayed low phenotypic plasticity. Correlation analysis highlighted the closely coordinated behavior of the amino acid profile. Finally, PLS-DA revealed a clear separation among the regions based on their metabolic profiles, accentuating the discriminatory capacity of NMR in establishing significant phytochemical differences between producing regions of the fruit of P. peruviana L.

The Glycerate and Phosphorylated Pathways of Serine Synthesis in Plants: The Branches of Plant Glycolysis Linking Carbon and Nitrogen Metabolism

Serine metabolism in plants has been studied mostly in relation to photorespiration where serine is formed from two molecules of glycine. However, two other pathways of serine formation operate in plants and represent the branches of glycolysis diverging at the level of 3-phosphoglyceric acid. One branch (the glycerate - serine pathway) is initiated in the cytosol and involves glycerate formation from 3-phosphoglycerate, while the other (the phosphorylated serine pathway) operates in plastids and forms phosphohydroxypyruvate as an intermediate. Serine formed in these pathways becomes a precursor of glycine, formate and glycolate accumulating in stress conditions. The pathways can be linked to GABA shunt via transamination reactions and via participation of the same reductase for both glyoxylate and succinic semialdehyde. In this review paper we present a hypothesis of the regulation of redox balance in stressed plant cells via participation of the reactions associated with glycerate and phosphorylated serine pathways. We consider these pathways as important processes linking carbon and nitrogen metabolism and maintaining cellular redox and energy levels in stress conditions.

Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis

Central metabolism is a coordinated network that is regulated at multiple levels by resource availability and by environmental and developmental cues. Its genetic architecture has been investigated by mapping metabolite quantitative trait loci (QTL). A more direct approach is to identify enzyme activity QTL, which distinguishes between cis-QTL in structural genes encoding enzymes and regulatory trans-QTL. Using genome-wide association studies, we mapped QTL for 24 enzyme activities, nine metabolites, three structural components, and biomass in Arabidopsis thaliana We detected strong cis-QTL for five enzyme activities. A cis-QTL for UDP-glucose pyrophosphorylase activity in the UGP1 promoter is maintained through balancing selection. Variation in acid invertase activity reflects multiple evolutionary events in the promoter and coding region of VAC-INVcis-QTL were also detected for ADP-glucose pyrophosphorylase, fumarase, and phosphoglucose isomerase activity. We detected many trans-QTL, including transcription factors, E3 ligases, protein targeting components, and protein kinases, and validated some by knockout analysis. trans-QTL are more frequent but tend to have smaller individual effects than cis-QTL. We detected many colocalized QTL, including a multitrait QTL on chromosome 4 that affects six enzyme activities, three metabolites, protein, and biomass. These traits are coordinately modified by different ACCELERATED CELL DEATH6 alleles, revealing a trade-off between metabolism and defense against biotic stress.

New insights into the impacts of elevated CO2, nitrogen, and temperature levels on the regulation of C and N metabolism in durum wheat using network analysis

The use of correlation networks and hierarchical cluster analysis provides a framework to organize and study the coordination of parameters such as genes, metabolites, proteins and physiological parameters. We have analyzed 142 traits from primary C and N metabolism, including biochemical and gene expression analyses, in a range of 32 different growth conditions (various [CO₂] levels, temperatures, N supplies, growth stages and experimental methods). To test the integration of primary metabolism, particularly under climate change, we investigated which C and N metabolic traits and transcript levels are correlated in durum wheat flag leaves using a correlation network and a hierarchical cluster analysis. There was a high amount of positive correlation between traits involved in a wide range of biological processes, suggesting a close and intricate coordination between C-N metabolisms at the biochemical and transcriptional levels. Transcript levels for genes related to N uptake and assimilation were especially coexpressed with genes belonging to the respiratory pathway, highlighting the coordination between the synthesis of organic N compounds and provision of energy and C skeletons. Also involved in this coordination were Rubisco and nitrate reductase activities, which play a key role in the regulation of plant metabolism. Carbohydrate accumulation was linked with a down-regulation of photosynthetic and N metabolism genes and nitrate reductase activity. Based on the degree of connectivity between nodes, network exploration facilitated the identification of some traits that may be biologically relevant during plant abiotic stress tolerance, as most of them are involved in limiting steps of plant metabolism.

Metabolomic analysis of tomato seed germination

INTRODUCTION

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

OBJECTIVE

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

METHODS

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

RESULTS

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

CONCLUSIONS

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.