Metabolomic Networks Research Articles

Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses

BackgroundEndurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise.For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition.ResultsWe obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways.Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels.In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition.ConclusionsTo the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses.

Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia.

Preeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive. This study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms. Metabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods. In total, 72 (0.9%) metabolite features were associated (p<0.01) with preeclampsia after adjustment for maternal age, race, and gestational age. These features had moderate to good discriminatory ability; in ROC curve analyses a summary score based on these features displayed an area under the curve (AUC) of 0.794 (95%CI 0.700, 0.888). This profile retained the ability to distinguish preeclamptic from healthy pregnancies in the third trimester (AUC:0.762 (95% CI 0.663, 0.860)). Additionally, metabolite set enrichment analysis identified common pathways, including glycerophospholipid metabolism, at the two time-points. Integration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system. These findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required.

Transcriptomic and Metabolomic Networks in the Grape Berry Illustrate That it Takes More Than Flavonoids to Fight Against Ultraviolet Radiation.

Plants are constantly challenged by environmental fluctuations. In response, they have developed a wide range of morphological and biochemical adaptations committed to ameliorate the effects of abiotic stress. When exposed to higher solar radiation levels, plants activate the synthesis of a large set of enzymes and secondary metabolites as part of a complex sunscreen and antioxidant defense mechanism. Grapevine (Vitis vinifera L.) has become a widely used system for studying adaptive responses to this type of stress since changes in berry composition, positively influenced by increased ultraviolet (UV) radiation levels, improve the quality of wines subsequently produced. Despite the fact that most of the attention has been directed toward the synthesis of flavonoids, recent transcriptomic and metabolomic studies have shown that stilbenoids and isoprenoids (e.g., terpenes and carotenoids) are also an important part of the grape UV-response machinery. This minireview focuses on the latest findings referring to the metabolic responses of grapes to UV radiation and proposes a model for its transcriptional control. Depending on the berry developmental stage and the type of radiation (i.e., irradiance level, exposure length), increased UV levels activate different metabolic pathways through the activity of master regulators belonging to the basic Leucine Zipper Domain (bZIP) and R2R3-MYB transcription factor families. This transcriptional control is influenced by the interaction of other environmental factors such as light, temperature or soil water availability. In grapevine, phenylpropanoids are part of, but are not the whole story, in the fight against radiation damage.

Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool.

BackgroundColorectal cancers (CRC) are associated with perturbations in cellular amino acids, nucleotides, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates. A non-targeted global metabolome approach was utilized for exploring human CRC, adjacent mucosa, and stool. In this pilot study, we identified metabolite profile differences between CRC and adjacent mucosa from patients undergoing colonic resection. Metabolic pathway analyses further revealed relationships between complex networks of metabolites.MethodsSeventeen CRC patients participated in this pilot study and provided CRC, adjacent mucosa ~10 cm proximal to the tumor, and stool. Metabolomes were analyzed by gas chromatography-mass spectrometry (GC/MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). All of the library standard identifications were confirmed and further analyzed via MetaboLyncTM for metabolic network interactions.ResultsThere were a total of 728 distinct metabolites identified from colonic tissue and stool matrices. Nineteen metabolites significantly distinguished CRC from adjacent mucosa in our patient-matched cohort. Glucose-6-phosphate and fructose-6-phosphate demonstrated 0.64-fold and 0.75-fold lower expression in CRC compared to mucosa, respectively, whereas isobar: betaine aldehyde, N-methyldiethanolamine, and adenylosuccinate had 2.68-fold and 1.88-fold higher relative abundance in CRC. Eleven of the 19 metabolites had not previously been reported for CRC relevance. Metabolic pathway analysis revealed significant perturbations of short-chain fatty acid metabolism, fructose, mannose, and galactose metabolism, and glycolytic, gluconeogenic, and pyruvate metabolism. In comparison to the 500 stool metabolites identified from human CRC patients, only 215 of those stool metabolites were also detected in tissue. This CRC and stool metabolome investigation identified novel metabolites that may serve as key small molecules in CRC pathogenesis, confirmed the results from previously reported CRC metabolome studies, and showed networks for metabolic pathway aberrations. In addition, we found differences between the CRC and stool metabolomes.ConclusionsStool metabolite profiles were limited for direct associations with CRC and adjacent mucosa, yet metabolic pathways were conserved across both matrices. Larger patient-matched CRC, adjacent non-cancerous colonic mucosa, and stool cohort studies for metabolite profiling are needed to validate these small molecule differences and metabolic pathway aberrations for clinical application to CRC control, treatment, and prevention.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0151-y) contains supplementary material, which is available to authorized users.

Metabolomic Networks Associated with Changes in Cytokine Expression Differ in Multiple Sclerosis Patients and Healthy Controls Following Vitamin D Supplementation (P5.297)

Metabolomic Networks Associated with Changes in Cytokine Expression Differ in Multiple Sclerosis Patients and Healthy Controls Following Vitamin D Supplementation (P5.297)

Secondary Metabolic Pathway-Targeted Metabolomics.

This chapter provides step-by-step methods for building secondary metabolic pathway-targeted molecular networks to assess microbial natural product biosynthesis at a systems level and to aid in downstream natural product discovery efforts. Methods described include high-resolution mass spectrometry (HRMS)-based comparative metabolomics, pathway-targeted tandem MS (MS/MS) molecular networking, and isotopic labeling for the elucidation of natural products encoded by orphan biosynthetic pathways. The metabolomics network workflow covers the following six points: (1) method development, (2) bacterial culture growth and organic extraction, (3) HRMS data acquisition and analysis, (4) pathway-targeted MS/MS data acquisition, (5) mass spectral network building, and (6) network enhancement. This chapter opens with a discussion on the practical considerations of natural product extraction, chromatographic processing, and enhanced detection of the analytes of interest within complex organic mixtures using liquid chromatography (LC)-HRMS. Next, we discuss the utilization of a chemometric platform, focusing on Agilent Mass Profiler Professional software, to run MS-based differential analysis between sample groups and controls to acquire a unique set of molecular features that are dependent on the presence of a secondary metabolic pathway. Using this unique list of molecular features, the chapter then details targeted MS/MS acquisition for subsequent pathway-dependent network clustering through the online Global Natural Products Social Molecular Networking (GnPS) platform. Genetic information, ionization intensities, isotopic labeling, and additional experimental data can be mapped onto the pathway-dependent network, facilitating systems biosynthesis analyses. The finished product will provide a working molecular network to assess experimental perturbations and guide novel natural product discoveries.

Cumulative metabolic effects of low-dose benzo(a)pyrene exposure on human cells.

Benzo(a)pyrene (B[a]P) is a common environmental and foodborne pollutant which has been identified as a Group I carcinogen. Although the carcinogenicity of B[a]P has been illustrated, its comprehensive influence on metabolism and further relevance in adverse health outcomes are not well understood. To investigate the global metabolic effects of long-term B[a]P exposure at environmental dosage, we utilized the human SMMC-7721 cell-based B[a]P exposure models to perform a metabolomics study and network analysis. A total of 316 biochemicals were identified and 104 metabolites were found to be significantly altered. Bioinformatics analysis showed that the amino acid, carbohydrate, and lipid metabolism pathways and the nucleotide metabolism pathway were influenced by prolonged B[a]P exposure. Notably, the metabolic effects of B[a]P varied with different dosages. In addition, B[a]P exposure caused a decline in the glycolysis process but enhanced the glycolytic capability of SMMC-7721 cells in vitro. These findings establish the overall B[a]P-induced metabolic network, characterize the metabolic effects of chronic and environmental B[a]P exposure on human-relevant cells, and enhance the understanding of the adverse outcome pathway frame of B[a]P.

OP26DELINEATING INTRA-TUMOUR METABOLOMIC AND PHOSPHO-PROTEOMIC HETEROGENEITY IN PATIENT GLIOBLASTOMAS THROUGH ADVANCED ANALYTICAL METHODS

BACKGROUND: Molecularly targeted approaches informing next-generation glioblastoma (GBM) chemotherapy must account for the intra-heterogeneous genome landscape and aim to eradicate invasive residual sub-populations. We outline an analytical strategy that forms a functional genomic corollary to existing genome-wide mutation and gene expression studies. METHODS: Biopsies from spatially distinct intra-GBM regions were surgically resected via Stealth and 5-ALA fluorescence guidance, including core, enhancing and invasive margin regions. Cellular intra-tumour heterogeneity (ITH) was confirmed by immunohistochemistry and qPCR arrays, whilst molecular analytics were conducted using liquid chromatography-mass spectrometry (metabolomic/lipidomic) magnetic resonance spectroscopy (metabolic) and SDS PAGE phosphoprotein enrichment (proteomic). RESULTS: Cellular ITH was confirmed via distinct neural stem cell expression in medial and posterior rims relative to the central enhancement region. Metabolites present in the invasive margin (5-ALA-determined), central enhancement and peri-necrotic core were distinct within each GBM. Controlling for the high proportion of normal brain cells surrounding the invasive margin, high tumour-specific glutamine levels were consistently present. Relative abundance of cytosolic phosphoproteins distinguished active protein pathways within intra-GBM regions, including invasive margin. Metabolomic network analyses confirmed that GBM residual disease is distinct from core ITH regions with respect to metabolic dependence. CONCLUSIONS: This innovative concept is the first to address ITH with multi-region biopsies using advanced analytical bioscience techniques. Identification of metabolites, lipids and phospho-proteins that are functional determinants of GBM infiltration reveal putative molecular targets on residual disease cells and represent a route to expedite new chemotherapy agents to clinic within a precision medicine framework.

The wondrous cycles of polyploidy in plants.

The wondrous cycles of polyploidy in plants.

Discovering and deciphering the pathogenic and probiotic activities from the bacterial colibactin pathway

Select strains of E. coli in our gut encode the “colibactin” pathway, a nonribosomal peptide synthetase-polyketide synthase hybrid pathway phenotypically linked to inflammatory bowel disease and colorectal cancer patients. A variety of cell biology and animal model studies have previously been reported for the pathway, but the responsible small molecules driving the phenotypes have remained enigmatic. Employing molecular networking tools, we developed a “pathway-targeted” molecular networking approach to map the colibactin pathway from both the meningitis pathogen E. coli IHE3034 and the probiotic E. coli Nissle 1917. A combination of bacterial genetics, metabolomic network analyses, system-wide isotopic labeling studies, 1- and 2D-NMR, bioinformatics, and/or synthesis supported the structures and biosynthesis of at least 32 molecules from the colibactin pathway. In vitro activity studies – 5-hydroxytryptamine-7 receptor antagonist, bacterial growth-inhibitory, and DNA interstrand crosslinking assays – for some of these molecules provide functional insights and pave the way for connecting bacterial small molecule chemistry to host physiology in animal model systems.

Metabolomics Society’s International Affiliations

In 2012 the Metabolomics Society established a more formal system for national and regional metabolomics initiatives, interest groups, societies and networks to become an International Affiliate of the Society. A number of groups (http://metabolomicssociety.org/international-affilia tions/benefits-of-affiliation) have taken up the opportunity to sign a Memorandum of Affiliation building a bridge of collaboration with the Society. The Society is very proud to have established these relationships extending our network and bringing the Society closer to the researchers and metabolomics friends. At the 11th Annual Conference of the Metabolomics Society in San Francisco (http://metabolomics2015.org/) we will be holding a meeting of representatives of all present affiliations and will establish an Affiliates Task Group which will build the basis for affiliations to communicate better with each other and share ideas, success stories and also discuss issues of any concern. If any regional metabolomics group is interested to become an affiliate, please contact the president and you are invited to our first face-to-face meeting in June. We aim to inform our community with regular updates of existing affiliations, not only through MetaboNews, but also through our society journal Metabolomics, allowing affiliations to present their missions and activities to the wider community. In this issue three affiliations have provided an update; the French Metabolomics and Fluxomics Network, The Netherlands Metabolomics Centre and the newly established Swiss Metabolomics Society.

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns.

Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/. ofiehn@ucdavis.edu.

Metabolomic Derangements Are Associated with Mortality in Critically Ill Adult Patients

ObjectiveTo identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults.RationaleComprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible.MethodsWe performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study.ResultsWe tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations).ConclusionBoth individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.

Metabolomics of ApcMin/+ mice genetically susceptible to intestinal cancer.

BackgroundTo determine how diets high in saturated fat could increase polyp formation in the mouse model of intestinal neoplasia, Apc Min/+ , we conducted large-scale metabolome analysis and association study of colon and small intestine polyp formation from plasma and liver samples of Apc Min/+ vs. wild-type littermates, kept on low vs. high-fat diet. Label-free mass spectrometry was used to quantify untargeted plasma and acyl-CoA liver compounds, respectively. Differences in contrasts of interest were analyzed statistically by unsupervised and supervised modeling approaches, namely Principal Component Analysis and Linear Model of analysis of variance. Correlation between plasma metabolite concentrations and polyp numbers was analyzed with a zero-inflated Generalized Linear Model.ResultsPlasma metabolome in parallel to promotion of tumor development comprises a clearly distinct profile in Apc Min/+ mice vs. wild type littermates, which is further altered by high-fat diet. Further, functional metabolomics pathway and network analyses in Apc Min/+ mice on high-fat diet revealed associations between polyp formation and plasma metabolic compounds including those involved in amino-acids metabolism as well as nicotinamide and hippuric acid metabolic pathways. Finally, we also show changes in liver acyl-CoA profiles, which may result from a combination of Apc Min/+ -mediated tumor progression and high fat diet. The biological significance of these findings is discussed in the context of intestinal cancer progression.ConclusionsThese studies show that high-throughput metabolomics combined with appropriate statistical modeling and large scale functional approaches can be used to monitor and infer changes and interactions in the metabolome and genome of the host under controlled experimental conditions. Further these studies demonstrate the impact of diet on metabolic pathways and its relation to intestinal cancer progression. Based on our results, metabolic signatures and metabolic pathways of polyposis and intestinal carcinoma have been identified, which may serve as useful targets for the development of therapeutic interventions.

Metabolomics Network Characterization of Resuscitation after Normocapnic Hypoxia in a Newborn Piglet Model Supports the Hypothesis That Room Air Is Better

Perinatal asphyxia is attributed to hypoxia and/or ischemia around the time of birth and may lead to multiorgan dysfunction. Aim of this research article is to investigate whether different metabolomic profiles occurred according to oxygen concentration administered at resuscitation. In order to perform the experiment, forty newborn piglets were subjected to normocapnic hypoxia and reoxygenation and were randomly allocated in 4 groups resuscitated with different oxygen concentrations, 18%, 21%, 40%, and 100%, respectively. Urine metabolic profiles at baseline and at hypoxia were analysed by 1H-NMR spectroscopy and metabolites were also identified by multivariate statistical analysis. Metabolic pathways associations were also built up by ingenuity pathway analysis (IPA). Bioinformatics analysis of metabolites characterized the effect of metabolism in the 4 groups; it showed that the 21% of oxygen is the most “physiological” and appropriate concentration to be used for resuscitation. Our data indicate that resuscitation with 21% of oxygen seems to be optimal in terms of survival, rapidity of resuscitation, and metabolic profile in the present animal model. These findings need to be confirmed with metabolomics in human and, if so, the knowledge of the perinatal asphyxia condition may significantly improve.

Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome.

Understanding genome to phenotype linkages has been greatly enabled by genomic sequencing. However, most genome analysis is typically confined to the nuclear genome. We conducted a metabolomic QTL analysis on a reciprocal RIL population structured to examine how variation in the organelle genomes affects phenotypic variation. This showed that the cytoplasmic variation had effects similar to, if not larger than, the largest individual nuclear locus. Inclusion of cytoplasmic variation into the genetic model greatly increased the explained phenotypic variation. Cytoplasmic genetic variation was a central hub in the epistatic network controlling the plant metabolome. This epistatic influence manifested such that the cytoplasmic background could alter or hide pairwise epistasis between nuclear loci. Thus, cytoplasmic genetic variation plays a central role in controlling natural variation in metabolomic networks. This suggests that cytoplasmic genomes must be included in any future analysis of natural variation. DOI: http://dx.doi.org/10.7554/eLife.00776.001.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several “-omic” layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.

Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions

Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.

A Non-Targeted Approach Unravels the Volatile Network in Peach Fruit

Volatile compounds represent an important part of the plant metabolome and are of particular agronomic and biological interest due to their contribution to fruit aroma and flavor and therefore to fruit quality. By using a non-targeted approach based on HS-SPME-GC-MS, the volatile-compound complement of peach fruit was described. A total of 110 volatile compounds (including alcohols, ketones, aldehydes, esters, lactones, carboxylic acids, phenolics and terpenoids) were identified and quantified in peach fruit samples from different genetic backgrounds, locations, maturity stages and physiological responses. By using a combination of hierarchical cluster analysis and metabolomic correlation network analysis we found that previously known peach fruit volatiles are clustered according to their chemical nature or known biosynthetic pathways. Moreover, novel volatiles that had not yet been described in peach were identified and assigned to co-regulated groups. In addition, our analyses showed that most of the co-regulated groups showed good intergroup correlations that are therefore consistent with the existence of a higher level of regulation orchestrating volatile production under different conditions and/or developmental stages. In addition, this volatile network of interactions provides the ground information for future biochemical studies as well as a useful route map for breeding or biotechnological purposes.

Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

Author SummaryIt is now possible to score variations in DNA across whole genomes, RNA levels and alternative isoforms, metabolite levels, protein levels and protein state information, protein–protein interactions, and protein–DNA interactions, in a comprehensive fashion in populations of individuals. Interactions among these molecular entities define the complex web of biological processes that give rise to all higher order phenotypes, including disease. The development of analytical approaches that simultaneously integrate different dimensions of data is essential if we are to extract the meaning from large-scale data to elucidate the complexity of living systems. Here, we use a novel Bayesian network reconstruction algorithm that simultaneously integrates DNA variation, RNA levels, metabolite levels, protein–protein interaction data, protein–DNA binding data, and protein–small-molecule interaction data to construct molecular networks in yeast. We demonstrate that these networks can be used to infer causal relationships among genes, enabling the identification of novel genes that modulate cellular regulation. We show that our network predictions either recapitulate known biology or can be prospectively validated, demonstrating a high degree of accuracy in the predicted network.