The effect of pollen monodiets on fat body morphology parameters and energy substrate levels in the fat body and hemolymph of Apis mellifera L. workers.

Human activities associated with large-scale farms and the monocultures expose honey bees to one type of food. Moreover, there is an ongoing decline of plant species producing pollen and nectar in Europe. A poorly balanced diet affects a number of processes occurring in a bee's body. The fat body and hemolymph are the tissues that participate in all of them. Therefore, the aim of our study was to determine the effect of hazel, pine, rapeseed, buckwheat, phacelia and goldenrod pollen on the morphological parameters of fat body trophocytes, the diameters of cell nuclei in oenocytes and the concentrations of compounds involved in energy metabolism (glucose, glycogen, triglycerides and protein). In the cage tests, the bees were fed from the first day of life with sugar candy (control group) or candy with a 10% addition of one of the 6 pollen types. Hemolymph and fat body from various locations were collected from 1-, 7- and 14-day-old workers. Pollen produced by plant species such as hazel and pine increased glucose concentrations in the bee tissues, especially in the hemolymph. It can therefore be concluded that they are valuable sources of energy (in the form of simple carbohydrates) which are quickly used by bees. Pollen from plants blooming in the summer and autumn increased the concentrations of proteins, glycogen and triglycerides in the fat body, especially that from the third tergite. The accumulation of these compounds was associated with an increased the length and width of trophocytes as well as with enhanced metabolic activity, which was evidenced in the increasing diameter of oenocyte cell nuclei. It seems a balanced multi-pollen diet is more valuable for bees, but it is important to understand the effects of the particular pollen types in the context of a mono-diet. In the future, this will make it possible to produce mixtures that can ensure homeostasis in the apian body.

Investigating the Physiological Mechanisms between Resistance Training and Pain Relief in the Cancer Population: A Literature Review.

This literature review examines the mechanisms of how exercise, specifically in the form of resistance training, may lead to pain relief in the cancer population. Primary data from three different cancer populations: breast, prostate, and lung, will be examined. A number of experimental studies have been conducted to confirm the effectiveness of resistance training on pain relief as well as the biochemical pathways that relate to this process. In this review, we will examine 5 randomized controlled trials. For the purposes of this review, pain is defined as physical suffering or discomfort associated with illness. Pain is the body's natural signal, bringing attention to damage that has been sustained by tissues. However, chronic pain is common in the cancer population, and often serves no good purpose but instead will negatively impact both physical and mental health. The three types of pain: nociceptive, neuropathic, and inflammatory pathways have been investigated, and the knowledge of pain mechanisms allows for the understanding of how it is associated with pain. The purpose of this exploratory literature review is to give insight on how to maximize pain-relieving effects of resistance training. Research has indicated that resistance training modulates pain pathways by upregulating the release of pain-relieving substances including beta-endorphins, anti-inflammatory cytokines, and endocannabinoids. Understanding of the benefits of resistance training may be useful in relieving cancer pain, and reproducing effects of pain-relieving strategies while minimizing the symptoms related to cancer and its treatment.

CBD Supplementation Has a Positive Effect on the Activity of the Proteolytic System and Biochemical Markers of Honey Bees (Apis mellifera) in the Apiary.

We examined how CBD extract influences the activity of the immune system in the hemolymph of honey bees in the hive test. The bees were divided into 3 groups: (CSy) bees fed with CBD in sugar syrup with glycerin; (CSt) cotton strip with CBD placed in hive bees fed pure sugar syrup, (C) control bees fed sugar syrup with glycerin. CBD extract increased the total protein concentrations, proteases and their inhibitor activities in each age (the except for acidic protease activities in the 21st and 28th day and alkaline protease inhibitor activities in the 28th day in CSt group) in comparison with group C. In the groups with the extract there was also an increase in the enzymatic marker activities: ALP, AST (decrease on day 28 for CSt), ALT; and non-enzymatic marker concentrations: glucose; triglycerides; cholesterol and creatinine. The urea acid and albumin concentrations were lower in CSy and CSt groups compared to the C group (higher concentration of albumin was displayed by control bees). Higher activities/concentrations of most of biochemical parameters were obtained in the CSy compared to the CSt and C. CBD supplementation can positively influence workers' immune system.

Polysaccharide metabolism regulates structural colour in bacterial colonies.

The brightest colours in nature often originate from the interaction of light with materials structured at the nanoscale. Different organisms produce such coloration with a wide variety of materials and architectures. In the case of bacterial colonies, structural colours stem for the periodic organization of the cells within the colony, and while considerable efforts have been spent on elucidating the mechanisms responsible for such coloration, the biochemical processes determining the development of this effect have not been explored. Here, we study the influence of nutrients on the organization of cells from the structurally coloured bacteria Flavobacterium strain IR1. By analysing the optical properties of the colonies grown with and without specific polysaccharides, we found that the highly ordered organization of the cells can be altered by the presence of fucoidans. Additionally, by comparing the organization of the wild-type strain with mutants grown in different nutrient conditions, we deduced that this regulation of cell ordering is linked to a specific region of the IR1 chromosome. This region encodes a mechanism for the uptake and metabolism of polysaccharides, including a polysaccharide utilization locus (PUL operon) that appears specific to fucoidan, providing new insight into the biochemical pathways regulating structural colour in bacteria.

Environment-driven shifts in interindividual variation and phenotypic integration within subnetworks of the mussel transcriptome and proteome.

The environment can alter the magnitude of phenotypic variation among individuals, potentially influencing evolutionary trajectories. However, environmental influences on variation are complex and remain understudied. Populations in heterogeneous environments might exhibit more variation, the amount of variation could differ between benign and stressful conditions, and/or variation might manifest in different ways among stages of the gene-to-protein expression cascade or among physiological functions. Here, we explore these three issues by quantifying patterns of inter-individual variation in both transcript and protein expression levels among California mussels, Mytilus californianus Conrad. Mussels were exposed to five ecologically relevant treatments that varied in the mean and interindividual heterogeneity of body temperature. To target a diverse set of physiological functions, we assessed variation within 19 expression subnetworks, including canonical stress-response pathways and empirically derived coexpression clusters that represent a diffuse set of cellular processes. Variation in expression was particularly pronounced in the treatments with high mean and heterogeneous body temperatures. However, with few exceptions, environment-dependent shifts of variation in the transcriptome were not reflected in the proteome. A metric of phenotypic integration provided evidence for a greater degree of constraint on relative expression levels (i.e., stronger correlation) within expression subnetworks in benign, homogeneous environments. Our results suggest that environments that are more stressful on average - and which also tend to be more heterogeneous - can relax these expression constraints and reduce phenotypic integration within biochemical subnetworks. Context-dependent "unmasking" of functional variation may contribute to interindividual differences in physiological phenotype and performance in stressful environments.

Salicylic Acid's Impact on Growth, Photosynthesis, and Antioxidant Enzyme Activity of Triticum aestivum When Exposed to Salt.

Recently, the application of salicylic acid (SA) for improving a plant's resistance to abiotic stresses has increased. A large part of the irrigated land (2.1% out of 19.5%) is severely affected by salinity stress worldwide. In 2020, total production of wheat (Triticum aestivum) was 761 million tons, representing the second most produced cereal after maize; therefore, research on its salinity tolerance is of world concern. Photosynthetic attributes such as net photosynthetic rate (PN), stomatal conductance (gs), intercellular CO2 concentration (Ci), and transpiration rate (E) were increased significantly by the application of SA. Salt stress increased antioxidant enzyme activity; however, SA further boosted their activity along with proline level. We conclude that SA interacts with meristematic cells, thereby triggering biochemical pathways conductive to the increment in morphological parameters. Further research is required to dissect the mechanisms of SA within the wheat plants under stress.

Cannabis Extract Has a Positive-Immunostimulating Effect through Proteolytic System and Metabolic Compounds of Honey Bee (Apis mellifera) Workers.

In the study, we assessed the effect of hemp extract on activities of resistance parameters and the metabolic compound concentration in adult workers' hemolymph. Bees were divided into the following groups: (1) control group fed with mixture of sugar and water-glycerine solution, (2) experimental group with pure sugar syrup and inside with cotton strips soaked with hemp extract, (3) experimental group with a mixture of sugar syrup with hemp extract. Hemp extracts caused an increase in the protein concentrations and reduced the protease activities regardless of the administration method. The protease inhibitor activities were decreased only in the group that received hemp extract on the strips. The biomarker activities (ALP, ALT, AST) increased from the control group and workers feeding extract in syrup and decreased in workers supplemented with the extract on strips. In young, 2-day-old workers, the glucose concentration was higher in the groups feeding with the extract than in the control. Hemp extract influenced an increase in urea concentrations in workers' hemolymph in comparison with the control. The hemp supplementation positively influences the immune system of workers, and the appropriate method of administration may be adapted to the health problems of bees.

Individual based models exhibiting Lévy-flight type movement induced by intracellular noise.

We use an individual-based model and its associated kinetic equation to study the generation of long jumps in the motion of E. coli. These models relate the run-and-tumble process to the intracellular reaction where the intrinsic noise plays a central role. Compared with previous work in Perthame et al. (Z Angew Math Phys 69(3):1-15, 2018), in which the parametric assumptions are mainly targeted for mathematical convenience but not well-suited for numerical simulations or comparison with experimental results, our current paper makes use of biologically meaningful pathways and tumbling kernels. The main contribution of this current work is bridging the gap between the theoretical results and experimentally available data. Some particular forms of how the tumbling frequency depends on the internal variable are proposed. Moreover, we propose two individual-based models, one for the tumbling frequency and the other for the receptor activity, and perform numerical simulations. Power-law decay of the run length distribution, which corresponds to Lévy-type motions, is observed in our numerical results. The particular decay rate agrees quantitatively with the analytical result.

Metabolite identification of ibuprofen biodegradation by Patulibacter medicamentivorans under aerobic conditions.

Ibuprofen (IBU) is a non-steroidal anti-inflammatory drug that is becoming increasingly recognized as an important micropollutant to be monitored in wastewater treatment plants (WWTP), since it has been detected in effluents at the µg L-1 level. The IBU metabolites from biological degradation are not completely understood and can represent a threat to natural aquatic systems. P. medicamentivorans was previously isolated from WWTP sludge and found to be capable of IBU degradation. The aerobic biodegradation of ibuprofen by this organism was investigated in a batch lab-scale reactor for the identification of the metabolites formed. The metabolites were analysed and putatively identified by HPLC-DAD-MS/MS and GC-MS and biodegradation pathways were proposed. The toxicity and the biodegradability potential of the metabolites were also investigated. The results showed that IBU biotransformation was achieved by hydroxylation followed by the formation of a carboxylic acid in the IBU molecule and by the formation of a catechol, allowing the aromatic ring cleavage. Two biodegradation pathways were proposed: in one, the metabolites generated from the enzymatic action correspond to a less biodegradable chemical structure of the intermediate products (isobutylbenzene and 3-isobutylphenol), with comparatively higher toxicity; in the other mechanism, more oxidable chemical structures were formed with less toxicity and higher biodegradability. This suggests that the biodegradation of IBU by P. medicamentivorans can take place by more than one mechanism regarding the enzymes formed by this Gram-positive bacterium, with subsequent oxidation of the parent compound to overall more soluble and less toxic compounds to fish, daphnia and green algae.

Gene and Biochemical Pathway Evaluation of Burns Injury via Protein-Protein Interaction Network Analysis.

BACKGROUND: Severe burns injury can affect several vital systems in the body and can cause inflammation in organs such as the heart, liver, and kidney. Many inflammatory mediators and regulatory hormones related to burn injuries are recognized. In this study, the genes related to burn injury interacted via network analysis, and the central nodes were enriched through gene ontology (GO). MATERIALS AND METHODS: Disease query of STRING database was used for data gathering, and the network was constructed using Cytoscape software version 3.6.0. After gene screening, the central nodes were enriched via GO analysis by ClueGO. The highlighted genes and pathways were clustered and analyzed in detail. RESULTS: Among 1067 genes, 35 critical genes that are involved in the 14 highlighted biochemical pathways were recognized. Interpretation of the finding indicates that a number of central genes can be considered as potential biomarkers related to burn injury. CONCLUSION: Can we revise to "Burn injuries have features that are common to several diseases and increases their risk.

NMR Analysis Reveals a Wealth of Metabolites in Root-Knot Nematode Resistant Roots of Citrullus amarus Watermelon Plants.

Citrullus amarus ( CA ) (previously known as Citrullus lanatus var. citroides ) accessions collected in southern Africa are known to have resistance to root-knot nematodes (RKN) and are suitable rootstocks for grafted watermelon. The objective of this study was to conduct a comparative metabolomics analysis and identify unique metabolites in roots of CA accessions versus roots of watermelon cultivars ( Citrullus lanatus (Thunb.) Matsum. and Nakai var. lanatus; CL ). Nuclear magnetic resonance (NMR) technology and principal component analysis (PCA) were used to analyze and compare metabolic profiles of seven CA accessions resistant to RKN along with two RKN-susceptible watermelon cultivars (Charleston Gray and Crimson Sweet). Calculation of the Mahalanobis distance revealed that the CA United States Plant Introduction (PI) 189225 (Line number 1832) and PI 482324 (1849) have the most distinct metabolic profiles compared with the watermelon cultivars Charleston Gray and Crimson Sweet, respectively. Several amino acids identified in the CA accessions were reported in previous studies to have a nematicidal effect. The results in this study indicate that roots of watermelon accessions collected in the wild are rich in metabolic compounds. These metabolic compounds may have been diminished in watermelon cultivars as a consequence of many years of cultivation and selection for desirable fruit qualities.

Stepwise evolution of floral pigmentation predicted by biochemical pathway structure.

Developmental pathways play a major role in influencing the distribution of naturally occurring phenotypes. For example, pathway structure and regulation could make some phenotypes inaccessible or restrict the routes through which phenotypes evolve. In this study, we examine floral anthocyanin pigments across the Solanaceae family and test whether patterns of phenotypic variation are consistent with predicted constraints based on the structure of the flavonoid biosynthetic pathway. We find that anthocyanin evolution occurs in a stepwise manner whereby transitions between the production of red mono hydroxylated pelargonidin pigments and blue trihydroxylated delphinidin pigments first passes through an intermediate step of producing purple dihydroxylated cyanidin pigments. Although the transitions between these three pigment types differ in frequency, we infer that these shifts are often reversible, suggesting that the functionality of the underlying biochemical pathway is generally conserved. Furthermore, our study finds that some pigment combinations are never observed, pointing to additional constraints on naturally occurring phenotypes. Overall, our findings provide insights into how the structure of an angiosperm-wide biochemical pathway has shaped macroevolutionary variation in floral pigmentation.

Multifragment DNA Assembly of Biochemical Pathways via Automated Ligase Cycling Reaction.

The microbial production of commodity, fine, and specialty chemicals is a driving force in biotechnology. An essential requirement is to introduce biosynthetic pathways to the target compound(s) into chassis organisms. First suitable enzymes must be selected and characterized, and then genetic pathways must be designed and assembled into suitable expression vectors. The design of these pathways is crucial for balancing the pathway for efficient in vivo activity. This can be achieved through optimization of the pathway regulation by altering transcription and translation rates. The possible permutations of a multigene pathway create a vast design space which is intractable to explore using traditional time-consuming and laborious pathway assembly methods. The advent of multifragment DNA assembly technologies has enabled simultaneous, multiplexed pathway construction allowing an increased capability to sample the design space. Furthermore, the implementation of laboratory automation allows error-reduced, high-throughput (HTP) construction of pathways. In this chapter, we present a workflow that combines automated in silico design of DNA parts followed by pathway assembly using the ligase cycling reaction on robotics platforms, to allow multiplexed assembly of plasmid-borne gene pathways with high efficiency. Details and considerations in designing DNA parts for expression bacterial chassis are discussed followed by laboratory protocols for HTP pathway assembly and screening using robotics platforms. This workflow is employed in the SYNBIOCHEM Synthetic Biology Research Center, providing the capability to assemble over 96 plasmids simultaneously, with over 40% of clones from each assembly harboring the correctly assembled plasmids. This workflow is easy to modify for use in other laboratories and will help to accelerate synthetic biology projects with diverse applications.

Biochemical pathway analysis of gastric atrophy.

AIM: Pathway analysis of gastric atrophy to find new molecular prospective of disease. BACKGROUND: Gastric atrophy as a process which is accompanied with "loss of glans" in stomach can be considered as a risk factor of gastric cancer. Here, the correlated biochemical pathways to the disorder have been analyzed via protein-protein interaction (PPI) network analysis. METHODS: The genes related to gastric atrophy were retrieved by STRING database and organized in a network by Cytoscape. Three significant clusters were determined by ClusterONE plug-in of Cytoscape. The elements of cluster-2 which contained all central nodes of the network were enriched by ClueGO and the biochemical pathways discussed in details. RESULTS: The number of seven central nodes (which are included in cluster-2); INS, TP53, IL6, TNF, SRC, MYC, and IL8 were identified. The biochemical pathways related to the elements of cluster-2 were determined and clustered in nine groups. The pathways were discussed in details. CONCLUSION: Pathway analysis indicates that the introduced central genes of the network can be considered as biomarkers of gastric atrophy.

Characterisation of a putative glutamate 5-kinase from Leishmania donovani.

Previous metabolic studies have demonstrated that leishmania parasites are able to synthesise proline from glutamic acid and threonine from aspartic acid. The first committed step in both biosynthetic pathways involves an amino acid kinase, either a glutamate 5-kinase (G5K; EC2.7.2.11) or an aspartokinase (EC2.7.2.4). Bioinformatic analysis of multiple leishmania genomes identifies a single amino acid-kinase gene (LdBPK 262740.1) variously annotated as either a putative glutamate or aspartate kinase. To establish the catalytic function of this Leishmania donovani gene product, we have determined the physical and kinetic properties of the recombinant enzyme purified from Escherichia coli. The findings indicate that the enzyme is a bona fide G5K with no activity as an aspartokinase. Tetrameric G5K displays kinetic behaviour similar to its bacterial orthologues and is allosterically regulated by proline, the end product of the pathway. The structure-activity relationships of proline analogues as inhibitors are broadly similar to the bacterial enzyme. However, unlike G5K from E. coli, leishmania G5K lacks a C-terminal PUA (pseudouridine synthase and archaeosine transglycosylase) domain and does not undergo higher oligomerisation in the presence of proline. Gene replacement studies are suggestive, but not conclusive that G5K is essential. ENZYMES: Glutamate 5-kinase (EC2.7.2.11); aspartokinase (EC2.7.2.4).

Metabolic Profiling: Status, Challenges, and Perspective.

Metabolic profiling has advanced greatly in the past decade and evolved from the status of a research topic of a small number of highly specialized laboratories to the status of a major field applied by several hundreds of laboratories, numerous national centers, and core facilities. The present chapter provides our view on the status of the remaining challenges and a perspective of this fascinating research area.

Comparative metabolite profiling of drought stress in roots and leaves of seven Triticeae species.

BACKGROUND: Drought is a lifestyle disease. Plant metabolomics has been exercised for understanding the fine-tuning of the potential pathways to surmount the adverse effects of drought stress. A broad spectrum of morphological and metabolic responses from seven Triticeae species including wild types with different drought tolerance/susceptibility level was investigated under control and water scarcity conditions. RESULTS: Significant morphological parameters measured were root length, surface area, average root diameter and overall root development. Principal Component Analysis, Partial Least-Squares-Discriminant Analysis and Hierarchical Cluster Analysis were applied to the metabolomic data obtained by Gas Chromatography-Mass Spectrometry technique in order to determine the important metabolites of the drought tolerance across seven different Triticeae species. The metabolites showing significant accumulation under the drought stress were considered as the key metabolites and correlated with potential biochemical pathways, enzymes or gene locations for a better understanding of the tolerance mechanisms. In all tested species, 45 significantly active metabolites with possible roles in drought stress were identified. Twenty-one metabolites out of forty-five including sugars, amino acids, organic acids and low molecular weight compounds increased in both leaf and root samples of TR39477, IG132864 and Bolal under the drought stress, contrasting to TTD-22, Tosunbey, Ligustica and Meyeri samples. Three metabolites including succinate, aspartate and trehalose were selected for further genome analysis due to their increased levels in TR39477, IG132864, and Bolal upon drought stress treatment as well as their significant role in energy producing biochemical pathways. CONCLUSION: These results demonstrated that the genotypes with high drought tolerance skills, especially wild emmer wheat, have a great potential to be a genetic model system for experiments aiming to validate metabolomics-genomics networks.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.

Computational analysis of interactions of oxidative stress and tetrahydrobiopterin reveals instability in eNOS coupling.

In cardiovascular and neurovascular diseases, an increase in oxidative stress and endothelial dysfunction has been reported. There is a reduction in tetrahydrobiopterin (BH4), which is a cofactor for the endothelial nitric oxide synthase (eNOS), resulting in eNOS uncoupling. Studies of the enhancement of BH4 availability have reported mixed results for improvement in endothelial dysfunction. Our understanding of the complex interactions of eNOS uncoupling, oxidative stress and BH4 availability is not complete and a quantitative understanding of these interactions is required. In the present study, we developed a computational model for eNOS uncoupling that considers the temporal changes in biopterin ratio in the oxidative stress conditions. Using the model, we studied the effects of cellular oxidative stress (Qsupcell) representing the non-eNOS based oxidative stress sources and BH4 synthesis (QBH4) on eNOS NO production and biopterin ratio (BH4/total biopterins (TBP)). Model results showed that oxidative stress levels from 0.01 to 1nM·s-1 did not affect eNOS NO production and eNOS remained in coupled state. When the Qsupcell increased above 1nM·s-1, the eNOS coupling and NO production transitioned to an oscillatory state. Oxidative stress levels dynamically changed the biopterin ratio. When Qsupcell increased from 1 to 100nM·s-1, the endothelial cell NO production, TBP levels and biopterin ratio reduced significantly from 26.5 to 2nM·s-1, 3.75 to 0.002µM and 0.99 to 0.25, respectively. For an increase in BH4 synthesis, the improvement in NO production rate and BH4 levels were dependent on the extent of cellular oxidative stress. However, a 10-fold increase in QBH4 at higher oxidative stresses did not restore the NO-production rate and the biopterin ratio. Our mechanistic analysis reveals that a combination of enhancing tetrahydrobiopterin level with a reduction in cellular oxidative stress may result in significant improvement in endothelial dysfunction.

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies.

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.