Showing papers on "Metabolome published in 2018"

HMDB 4.0: the human metabolome database for 2018.

Abstract: The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

The fecal metabolome as a functional readout of the gut microbiome.

Abstract: The human gut microbiome plays a key role in human health 1 , but 16S characterization lacks quantitative functional annotation 2 . The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host–microbiome interactions 3 . In this comprehensive description of the fecal metabolome, examining 1,116 metabolites from 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (heritability (H2) = 17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition, explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral-fat mass, thereby illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling thus is a novel tool to explore links among microbiome composition, host phenotypes, and heritable complex traits. Comprehensive fecal metabolic profiling in 786 individuals from TwinsUK provides insights into the influence of host genetics and gut microbial composition on metabolites that may mediate microbiome-associated phenotypes.

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics

Abstract: Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives.

Metabolomics activity screening for identifying metabolites that modulate phenotype.

Abstract: Metabolomics activity screening can identify small-molecule metabolites that are readily available and can be used to alter phenotypes, including stem cell differentiation, T-cell survival, oligodendrocyte maturation, and insulin signaling. Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype1,2,3. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

Arachidonic Acid Metabolites in Cardiovascular and Metabolic Diseases.

Abstract: Lipid and immune pathways are crucial in the pathophysiology of metabolic and cardiovascular disease. Arachidonic acid (AA) and its derivatives link nutrient metabolism to immunity and inflammation, thus holding a key role in the emergence and progression of frequent diseases such as obesity, diabetes, non-alcoholic fatty liver disease, and cardiovascular disease. We herein present a synopsis of AA metabolism in human health, tissue homeostasis, and immunity, and explore the role of the AA metabolome in diverse pathophysiological conditions and diseases.

Root exudate metabolomes change under drought and show limited capacity for recovery

Abstract: Root exudates comprise a large variety of compounds released by plants into the rhizosphere, including low-molecular-weight primary metabolites (particularly saccharides, amino acids and organic acids) and secondary metabolites (phenolics, flavonoids and terpenoids). Changes in exudate composition could have impacts on the plant itself, on other plants, on soil properties (e.g. amount of soil organic matter), and on soil organisms. The effects of drought on the composition of root exudates, however, have been rarely studied. We used an ecometabolomics approach to identify the compounds in the exudates of Quercus ilex (holm oak) under an experimental drought gradient and subsequent recovery. Increasing drought stress strongly affected the composition of the exudate metabolome. Plant exudates under drought consisted mainly of secondary metabolites (71% of total metabolites) associated with plant responses to drought stress, whereas the metabolite composition under recovery shifted towards a dominance of primary metabolites (81% of total metabolites). These results strongly suggested that roots exude the most abundant root metabolites. The exudates were changed irreversibly by the lack of water under extreme drought conditions, and the plants could not recover.

Metabolomics and proteomics reveal drought-stress responses of leaf tissues from spring-wheat.

Abstract: To reveal the integrative biochemical networks of wheat leaves in response to water deficient conditions, proteomics and metabolomics were applied to two spring-wheat cultivars (Bahar, drought-susceptible; Kavir, drought-tolerant). Drought stress induced detrimental effects on Bahar leaf proteome, resulting in a severe decrease of total protein content, with impairments mainly in photosynthetic proteins and in enzymes involved in sugar and nitrogen metabolism, as well as in the capacity of detoxifying harmful molecules. On the contrary, only minor perturbations were observed at the protein level in Kavir stressed leaves. Metabolome analysis indicated amino acids, organic acids, and sugars as the main metabolites changed in abundance upon water deficiency. In particular, Bahar cv showed increased levels in proline, methionine, arginine, lysine, aromatic and branched chain amino acids. Tryptophan accumulation via shikimate pathway seems to sustain auxin production (indoleacrylic acid), whereas glutamate reduction is reasonably linked to polyamine (spermine) synthesis. Kavir metabolome was affected by drought stress to a less extent with only two pathways significantly changed, one of them being purine metabolism. These results comprehensively provide a framework for better understanding the mechanisms that govern plant cell response to drought stress, with insights into molecules that can be used for crop improvement projects.

Connection between BMI-Related Plasma Metabolite Profile and Gut Microbiota

Abstract: Context Emerging evidence has related the gut microbiome and circulating metabolites to human obesity. Gut microbiota is responsible for several metabolic functions, and altered plasma metabolome might reflect differences in the gut microbiome. Objective To identify a plasma metabolite profile associated with body mass index (BMI) in a general population and investigate whether such metabolite profile is associated with distinct composition of the gut microbiota. Design Targeted profiling of 48 plasma metabolites was performed in a population of 920 Swedish adults (mean age, 39 years; 53% women) from the ongoing Malmo Offspring Study using targeted liquid chromatography-mass spectrometry. Gut microbiota was analyzed by sequencing the 16S ribosomal RNA gene (V1-V3 region) in fecal samples of 674 study participants. Results BMI was associated with 19 metabolites (P < 0.001 for all), of which glutamate provided the strongest direct association (P = 5.2e-53). By orthogonal partial least squares regression, a metabolite principal component predictive of BMI was constructed (PC BMI). In addition to glutamate, PC BMI was dominated by branched-chain amino acids (BCAAs) and related metabolites. Four gut microbiota genera (Blautia, Dorea, Ruminococcus, and SHA-98) were associated with both BMI and PC BMI (P < 8.0e-4 for all). When simultaneously regressing PC BMI and metabolite-associated gut bacteria against BMI, only PC BMI remained statistically significant. Conclusions We discovered associations between four gut microbiota genera (Blautia, Dorea, Ruminococcus, and SHA-98) and BMI-predictive plasma metabolites, including glutamate and BCAAs. Thus, these metabolites could be mediators between gut microbiota and obesity, pointing to potential future opportunities for targeting the gut microbiota in prevention of obesity. (Less)

Morphine induces changes in the gut microbiome and metabolome in a morphine dependence model

Abstract: Opioid analgesics are frequently prescribed in the United States and worldwide. However, serious comorbidities, such as dependence, tolerance, immunosuppression and gastrointestinal disorders limit their long-term use. In the current study, a morphine-murine model was used to investigate the role of the gut microbiome and metabolome as a potential mechanism contributing to the negative consequences associated with opioid use. Results reveal a significant shift in the gut microbiome and metabolome within one day following morphine treatment compared to that observed after placebo. Morphine-induced gut microbial dysbiosis exhibited distinct characteristic signatures, including significant increase in communities associated with pathogenic function, decrease in communities associated with stress tolerance and significant impairment in bile acids and morphine-3-glucuronide/morphine biotransformation in the gut. Moreover, expansion of Enterococcus faecalis was strongly correlated with gut dysbiosis following morphine treatment, and alterations in deoxycholic acid (DCA) and phosphatidylethanolamines (PEs) were associated with opioid-induced metabolomic changes. Collectively, these results indicate that morphine induced distinct alterations in the gut microbiome and metabolome, contributing to negative consequences associated with opioid use. Therapeutics directed at maintaining microbiome homeostasis during opioid use may reduce the comorbidities associated with opioid use for pain management.

Sorting cells alters their redox state and cellular metabolome.

Abstract: A growing appreciation of the metabolic artifacts of cell culture has generated heightened enthusiasm for performing metabolomics on populations of cells purified from tissues and biofluids. Fluorescence activated cell sorting, or FACS, is a widely used experimental approach to purify specific cell types from complex heterogeneous samples. Here we show that FACS introduces oxidative stress and alters the metabolic state of cells. Compared to unsorted controls, astrocytes subjected to FACS prior to metabolomic analysis showed altered ratios of GSSG to GSH, NADPH to NADP+, and NAD+ to NADH. Additionally, a 50% increase in reactive oxygen species was observed in astrocytes subjected to FACS relative to unsorted controls. At a more comprehensive scale, nearly half of the metabolomic features that we profiled by liquid chromatography/mass spectrometry were changed by at least 1.5-fold in intensity due to cell sorting. Some specific metabolites identified to have significantly altered levels as a result of cell sorting included glycogen, nucleosides, amino acids, central carbon metabolites, and acylcarnitines. Although the addition of fetal bovine serum to the cell-sorting buffer decreased oxidative stress and attenuated changes in metabolite concentrations, fetal bovine serum did not preserve the metabolic state of the cells during FACS. We conclude that, irrespective of buffer components and data-normalization strategies we examined, metabolomic results from sorted cells do not accurately reflect physiological conditions prior to sorting.

Butyrate Protects Mice Against Methionine-Choline-Deficient Diet-Induced Non-alcoholic Steatohepatitis by Improving Gut Barrier Function, Attenuating Inflammation and Reducing Endotoxin Levels.

Abstract: Butyrate exerts protective effects against non-alcoholic steatohepatitis (NASH), but the underlying mechanisms are unclear. We aimed to investigate the role of butyrate-induced gut microbiota and metabolism in NASH development. Sixty-five C57BL/6J mice were divided into four groups (n = 15-17 per group) and were fed either a methionine-choline-sufficient (MCS) diet or methionine-choline-deficient (MCD) diet with or without sodium butyrate (SoB; 0.6 g/kg body weight) supplementation for 6 weeks. Liver injury, systematic inflammation, and gut barrier function were determined. Fecal microbiome and metabolome were analyzed using 16S rRNA deep sequencing and gas chromatography-mass spectrometry (GC-MS). The results showed that butyrate alleviated the MCD diet-induced microbiome dysbiosis, as evidenced by a significantly clustered configuration separate from that of the MCD group and by the depletion of Bilophila and Rikenellaceae and enrichment of promising probiotic genera Akkermansia, Roseburia, Coprococcus, Coprobacillus, Delftia, Sutterella, and Coriobacteriaceae genera. The fecal metabolomic profile was also substantially improved by butyrate; several butyrate-responsive metabolites involved in lipid metabolism and other pathways, such as stearic acid, behenic acid, oleic acid, linoleic acid, squalene, and arachidonic acid, were identified. Correlation analysis of the interaction matrix indicated that the modified gut microbiota and fecal metabolites induced by butyrate were strongly correlated with the alleviation of hepatic injury, fibrosis progression, inflammation, and lipid metabolism and intestinal barrier dysfunction. In conclusion, our results demonstrated that butyrate exerts protective effects against NASH development, and these effects may be driven by the protective gut microbiome and metabolome induced by butyrate. This study thus provides new insights into NASH prevention.

Combined Analysis of the Fruit Metabolome and Transcriptome Reveals Candidate Genes Involved in Flavonoid Biosynthesis in Actinidia arguta.

Abstract: To assess the interrelation between the change of metabolites and the change of fruit color, we performed a combined metabolome and transcriptome analysis of the flesh in two different Actinidia arguta cultivars: “HB” (“Hongbaoshixing”) and “YF” (“Yongfengyihao”) at two different fruit developmental stages: 70d (days after full bloom) and 100d (days after full bloom). Metabolite and transcript profiling was obtained by ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer and high-throughput RNA sequencing, respectively. The identification and quantification results of metabolites showed that a total of 28,837 metabolites had been obtained, of which 13,715 were annotated. In comparison of HB100 vs. HB70, 41 metabolites were identified as being flavonoids, 7 of which, with significant difference, were identified as bracteatin, luteolin, dihydromyricetin, cyanidin, pelargonidin, delphinidin and (−)-epigallocatechin. Association analysis between metabolome and transcriptome revealed that there were two metabolic pathways presenting significant differences during fruit development, one of which was flavonoid biosynthesis, in which 14 structural genes were selected to conduct expression analysis, as well as 5 transcription factor genes obtained by transcriptome analysis. RT-qPCR results and cluster analysis revealed that AaF3H, AaLDOX, AaUFGT, AaMYB, AabHLH, and AaHB2 showed the best possibility of being candidate genes. A regulatory network of flavonoid biosynthesis was established to illustrate differentially expressed candidate genes involved in accumulation of metabolites with significant differences, inducing red coloring during fruit development. Such a regulatory network linking genes and flavonoids revealed a system involved in the pigmentation of all-red-fleshed and all-green-fleshed A. arguta, suggesting this conjunct analysis approach is not only useful in understanding the relationship between genotype and phenotype, but is also a powerful tool for providing more valuable information for breeding.

Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients

Abstract: The implementation of whole-exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single-platform, high-resolution liquid chromatography quadrupole time of flight (LC-QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM-suspected patients. This method, which we termed “next-generation metabolic screening” (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the “various forms of chromatography mass spectrometry (XCMS)” software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex, untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of IEM-related metabolites to filter untargeted metabolomics data. Based on this IEM-panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term “open the metabolome” analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs.

Oral Administration of Porphyromonas gingivalis Alters the Gut Microbiome and Serum Metabolome.

Abstract: Periodontal disease induced by periodontopathic bacteria like Porphyromonas gingivalis is demonstrated to increase the risk of metabolic, inflammatory, and autoimmune disorders. Although precise mechanisms for this connection have not been elucidated, we have proposed mechanisms by which orally administered periodontopathic bacteria might induce changes in gut microbiota composition, barrier function, and immune system, resulting in an increased risk of diseases characterized by low-grade systemic inflammation. Accumulating evidence suggests a profound effect of altered gut metabolite profiles on overall host health. Therefore, it is possible that P. gingivalis can affect these metabolites. To test this, C57BL/6 mice were administered with P. gingivalis W83 orally twice a week for 5 weeks and compared with sham-inoculated mice. The gut microbial communities were analyzed by pyrosequencing the 16S rRNA genes. Inferred metagenomic analysis was used to determine the relative abundance of KEGG pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance (NMR)-based metabolomics coupled with multivariate statistical analyses. Oral administration of P. gingivalis induced a change in gut microbiota composition. The distributions of metabolic pathways differed between the two groups, including those related to amino acid metabolism and, in particular, the genes for phenylalanine, tyrosine, and tryptophan biosynthesis. Also, alanine, glutamine, histidine, tyrosine, and phenylalanine were significantly increased in the serum of P. gingivalis-administered mice. In addition to altering immune modulation and gut barrier function, oral administration of P. gingivalis affects the host's metabolic profile. This supports our hypothesis regarding a gut-mediated systemic pathology resulting from periodontal disease.IMPORTANCE Increasing evidence suggest that alterations of the gut microbiome underlie metabolic disease pathology by modulating gut metabolite profiles. We have shown that orally administered Porphyromonas gingivalis, a representative periodontopathic bacterium, alters the gut microbiome; that may be a novel mechanism by which periodontitis increases the risk of various diseases. Given the association between periodontal disease and metabolic diseases, it is possible that P. gingivalis can affect the metabolites. Metabolite profiling analysis demonstrated that several amino acids related to a risk of developing diabetes and obesity were elevated in P. gingivalis-administered mice. Our results revealed that the increased risk of various diseases by P. gingivalis might be mediated at least in part by alteration of metabolic profiles. The findings should add new insights into potential links between periodontal disease and systemic disease for investigators in periodontal disease and also for investigators in the field of other diseases, such as metabolic diseases.

Profiling of metabolome and bacterial community dynamics in ensiled Medicago sativa inoculated without or with Lactobacillus plantarum or Lactobacillus buchneri.

Abstract: Using gas chromatography mass spectrometry and the PacBio single molecule with real-time sequencing technology (SMRT), we analyzed the detailed metabolomic profiles and microbial community dynamics involved in ensiled Medicago sativa (alfalfa) inoculated without or with the homofermenter Lactobacillus plantarum or heterofermenter Lactobacillus buchneri. Our results revealed that 280 substances and 102 different metabolites were present in ensiled alfalfa. Inoculation of L. buchneri led to remarkable up-accumulation in concentrations of 4-aminobutyric acid, some free amino acids, and polyols in ensiled alfalfa, whereas considerable down-accumulation in cadaverine and succinic acid were observed in L. plantarum-inoculated silages. Completely different microbial flora and their successions during ensiling were observed in the control and two types of inoculant-treated silages. Inoculation of the L. plantarum or L. buchneri alters the microbial composition dynamics of the ensiled forage in very different manners. Our study demonstrates that metabolomic profiling analysis provides a deep insight in metabolites in silage. Moreover, the PacBio SMRT method revealed the microbial composition and its succession during the ensiling process at the species level. This provides information regarding the microbial processes underlying silage formation and may contribute to target-based regulation methods to achieve high-quality silage production.

Maize multi-omics reveal roles for autophagic recycling in proteome remodelling and lipid turnover

Abstract: The turnover of cytoplasmic material by autophagic encapsulation and delivery to vacuoles is essential for recycling cellular constituents, especially under nutrient-limiting conditions. To determine how cells/tissues rely on autophagy, we applied in-depth multi-omic analyses to study maize (Zea mays) autophagy mutants grown under nitrogen-replete and -starvation conditions. Broad alterations in the leaf metabolome were evident in plants missing the core autophagy component ATG12, even in the absence of stress, particularly affecting products of lipid turnover and secondary metabolites, which were underpinned by substantial changes in the transcriptome and/or proteome. Cross-comparison of messenger RNA and protein abundances allowed for the identification of organelles, protein complexes and individual proteins targeted for selective autophagic clearance, and revealed several processes controlled by this catabolism. Collectively, we describe a facile multi-omic strategy to survey autophagic substrates, and show that autophagy has a remarkable influence in sculpting eukaryotic proteomes and membranes both before and during nutrient stress. It has been well established that nutrient starvation induces cell autophagy. Now, researchers present large-scale multi-omics analyses of maize autophagy mutants under nitrogen starvation, and show that autophagy could play more housekeeping roles in plants.

Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells

Abstract: Liver toxicity is a leading systemic toxicity of drugs and chemicals demanding more human-relevant, high throughput, cost effective in vitro solutions. In addition to contributing to animal welfare, in vitro techniques facilitate exploring and understanding the molecular mechanisms underlying toxicity. New 'omics technologies can provide comprehensive information on the toxicological mode of action of compounds, as well as quantitative information about the multi-parametric metabolic response of cellular systems in normal and patho-physiological conditions. Here, we combined mass-spectroscopy metabolomics with an in vitro liver toxicity model. Metabolite profiles of HepG2 cells treated with 35 test substances resulted in 1114 cell supernatants and 3556 intracellular samples analyzed by metabolomics. Control samples showed relative standard deviations of about 10-15%, while the technical replicates were at 5-10%. Importantly, this procedure revealed concentration-response effects and patterns of metabolome changes that are consistent for different liver toxicity mechanisms (liver enzyme induction/inhibition, liver toxicity and peroxisome proliferation). Our findings provide evidence that identifying organ toxicity can be achieved in a robust, reliable, human-relevant system, representing a non-animal alternative for systemic toxicology.

Endogenous metabolites of vitamin E limit inflammation by targeting 5-lipoxygenase.

Abstract: Systemic vitamin E metabolites have been proposed as signaling molecules, but their physiological role is unknown. Here we show, by library screening of potential human vitamin E metabolites, that long-chain ω-carboxylates are potent allosteric inhibitors of 5-lipoxygenase, a key enzyme in the biosynthesis of chemoattractant and vasoactive leukotrienes. 13-((2R)-6-hydroxy-2,5,7,8-tetramethylchroman-2-yl)-2,6,10-trimethyltridecanoic acid (α-T-13′-COOH) can be synthesized from α-tocopherol in a human liver-on-chip, and is detected in human and mouse plasma at concentrations (8–49 nM) that inhibit 5-lipoxygenase in human leukocytes. α-T-13′-COOH accumulates in immune cells and inflamed murine exudates, selectively inhibits the biosynthesis of 5-lipoxygenase-derived lipid mediators in vitro and in vivo, and efficiently suppresses inflammation and bronchial hyper-reactivity in mouse models of peritonitis and asthma. Together, our data suggest that the immune regulatory and anti-inflammatory functions of α-tocopherol depend on its endogenous metabolite α-T-13′-COOH, potentially through inhibiting 5-lipoxygenase in immune cells.

Determinants of the urinary and serum metabolome in children from six European populations.

Abstract: Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project ( http://www.projecthelix.eu ). Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput 1H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit). We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum. We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children.

JMorp: Japanese Multi Omics Reference Panel

Abstract: We developed jMorp, a new database containing metabolome and proteome data for plasma obtained from >5000 healthy Japanese volunteers from the Tohoku Medical Megabank Cohort Study, which is available at https://jmorp.megabank.tohoku.ac.jp. Metabolome data were measured by proton nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC-MS), while proteome data were obtained by nanoLC-MS. We released the concentration distributions of 37 metabolites identified by NMR, distributions of peak intensities of 257 characterized metabolites by LC-MS, and observed frequencies of 256 abundant proteins. Additionally, correlation networks for the metabolites can be observed using an interactive network viewer. Compared with some existing databases, jMorp has some unique features: (i) Metabolome data were obtained using a single protocol in a single institute, ensuring that measurement biases were significantly minimized; (ii) The database contains large-scale data for healthy volunteers with various health records and genome data and (iii) Correlations between metabolites can be easily observed using the graphical viewer. Metabolites data are becoming important intermediate markers for evaluating the health states of humans, and thus jMorp is an outstanding resource for a wide range of researchers, particularly those in the fields of medical science, applied molecular biology, and biochemistry.

Intermittent Hypoxia and Hypercapnia, a Hallmark of Obstructive Sleep Apnea, Alters the Gut Microbiome and Metabolome

Abstract: Obstructive sleep apnea (OSA) is a common disorder characterized by episodic obstruction to breathing due to upper airway collapse during sleep. Because of the episodic airway obstruction, intermittently low O2 (hypoxia) and high CO2 (hypercapnia) ensue. OSA has been associated with adverse cardiovascular and metabolic outcomes, although data regarding potential causal pathways are still evolving. As changes in inspired O2 and CO2 can affect the ecology of the gut microbiota and the microbiota has been shown to contribute to various cardiometabolic disorders, we hypothesized that OSA alters the gut ecosystem, which, in turn, exacerbates the downstream physiological consequences. Here, we model human OSA and its cardiovascular consequence using Ldlr-/- mice fed a high-fat diet and exposed to intermittent hypoxia and hypercapnia (IHH). The gut microbiome and metabolome were characterized longitudinally (using 16S rRNA amplicon sequencing and untargeted liquid chromatography-tandem mass spectrometry [LC-MS/MS]) and seen to covary during IHH. Joint analysis of microbiome and metabolome data revealed marked compositional changes in both microbial (>10%, most remarkably in Clostridia) and molecular (>22%) species in the gut. Moreover, molecules that altered in abundance included microbe-dependent bile acids, enterolignans, and fatty acids, highlighting the impact of IHH on host-commensal organism cometabolism in the gut. Thus, we present the first evidence that IHH perturbs the gut microbiome functionally, setting the stage for understanding its involvement in cardiometabolic disorders. IMPORTANCE Intestinal dysbiosis mediates various cardiovascular diseases comorbid with OSA. To understand the role of dysbiosis in cardiovascular and metabolic disease caused by OSA, we systematically study the effect of intermittent hypoxic/hypercapnic stress (IHH, mimicking OSA) on gut microbes in an animal model. We take advantage of a longitudinal study design and paired omics to investigate the microbial and molecular dynamics in the gut to ascertain the contribution of microbes on intestinal metabolism in IHH. We observe microbe-dependent changes in the gut metabolome that will guide future research on unrecognized mechanistic links between gut microbes and comorbidities of OSA. Additionally, we highlight novel and noninvasive biomarkers for OSA-linked cardiovascular and metabolic disorders.

Shifts in the Gut Metabolome and Clostridium difficile Transcriptome throughout Colonization and Infection in a Mouse Model.

Abstract: Antibiotics alter the gut microbiota and decrease resistance to Clostridium difficile colonization; however, the mechanisms driving colonization resistance are not well understood. Loss of resistance to C. difficile colonization due to antibiotic treatment is associated with alterations in the gut metabolome, specifically, with increases in levels of nutrients that C. difficile can utilize for growth in vitro . To define the nutrients that C. difficile requires for colonization and pathogenesis in vivo , we used a combination of mass spectrometry and RNA sequencing (RNA Seq) to model the gut metabolome and C. difficile transcriptome throughout an acute infection in a mouse model at the following time points: 0, 12, 24, and 30 h. We also performed multivariate-based integration of the omics data to define the signatures that were most important throughout colonization and infection. Here we show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time in the mouse cecum and that C. difficile gene expression is consistent with their utilization in vivo . This was also reinforced by the multivariate-based integration of the omics data where we were able to discriminate the metabolites and transcripts that support C. difficile physiology between the different time points throughout colonization and infection. This report illustrates how important the availability of amino acids and other nutrients is for the initial stages of C. difficile colonization and progression of disease. Future studies identifying the source of the nutrients and engineering bacteria capable of outcompeting C. difficile in the gut will be important for developing new targeted bacterial therapeutics. IMPORTANCE Clostridium difficile is a bacterial pathogen of global significance that is a major cause of antibiotic-associated diarrhea. Antibiotics deplete the indigenous gut microbiota and change the metabolic environment in the gut to one favoring C. difficile growth. Here we used metabolomics and transcriptomics to define the gut environment after antibiotics and during the initial stages of C. difficile colonization and infection. We show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time and that C. difficile gene expression is consistent with their utilization by the bacterium in vivo . We employed an integrated approach to analyze the metabolome and transcriptome to identify associations between metabolites and transcripts. This highlighted the importance of key nutrients in the early stages of colonization, and the data provide a rationale for the development of therapies based on the use of bacteria that specifically compete for nutrients that are essential for C. difficile colonization and disease.

Transcriptomic and metabolomic analyses reveal that bacteria promote plant defense during infection of soybean cyst nematode in soybean

Abstract: Soybean cyst nematode (SCN) is the most devastating pathogen of soybean. Our previous study showed that the plant growth-promoting rhizobacterium Bacillus simplex strain Sneb545 promotes soybean resistance to SCN. Here, we conducted a combined metabolomic and transcriptomic analysis to gain information regarding the biological mechanism of defence enhancement against SCN in Sneb545-treated soybean. To this end, we compared the transcriptome and metabolome of Sneb545-treated and non-treated soybeans under SCN infection. Transcriptomic analysis showed that 6792 gene transcripts were common in Sneb545-treated and non-treated soybeans. However, Sneb545-treated soybeans showed a higher concentration of various nematicidal metabolites, including 4-vinylphenol, methionine, piperine, and palmitic acid, than non-treated soybeans under SCN infection. Overall, our results validated and expanded the existing models regarding the co-regulation of gene expression and metabolites in plants, indicating the advantage of integrated system-oriented analysis.

Towards predicting the environmental metabolome from metagenomics with a mechanistic model.

Abstract: The environmental metabolome and metabolic potential of microorganisms are dominant and essential factors shaping microbial community composition. Recent advances in genome annotation and systems biology now allow us to semiautomatically reconstruct genome-scale metabolic models (GSMMs) of microorganisms based on their genome sequence 1 . Next, growth of these models in a defined metabolic environment can be predicted in silico, mechanistically linking the metabolic fluxes of individual microbial populations to the community dynamics. A major advantage of GSMMs is that no training data is needed, besides information about the metabolic capacity of individual genes (genome annotation) and knowledge of the available environmental metabolites that allow the microorganism to grow. However, the composition of the environment is often not fully determined and remains difficult to measure 2 . We hypothesized that the relative abundance of different bacterial species, as measured by metagenomics, can be combined with GSMMs of individual bacteria to reveal the metabolic status of a given biome. Using a newly developed algorithm involving over 1,500 GSMMs of human-associated bacteria, we inferred distinct metabolomes for four human body sites that are consistent with experimental data. Together, we link the metagenome to the metabolome in a mechanistic framework towards predictive microbiome modelling.

Metabolomics in Plant Priming Research: The Way Forward?

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

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

Abstract: A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

Microbiome-metabolome signatures in mice genetically prone to develop dementia, fed a normal or fatty diet

Abstract: Cognitive decline, obesity and gut dysfunction or microbial dysbiosis occur in association. Our aim was to identify gut microbiota-metabolomics signatures preceding dementia in genetically prone (3xtg) mice, with and without superimposed high-fat diet. We examined the composition and diversity of their gut microbiota, and serum and faecal metabolites. 3xtg mice showed brain hypometabolism typical of pre-demented stage, and lacked the physiological bacterial diversity between caecum and colon seen in controls. Cluster analyses revealed distinct profiles of microbiota, and serum and fecal metabolome across groups. Elevation in Firmicutes-to-Bacteroidetes abundance, and exclusive presence of Turicibacteraceae, Christensenellaceae, Anaeroplasmataceae and Ruminococcaceae, and lack of Bifidobacteriaceae, were also observed. Metabolome analysis revealed a deficiency in unsaturated fatty acids and choline, and an overabundance in ketone bodies, lactate, amino acids, TMA and TMAO in 3xtg mice, with additive effects of high-fat diet. These metabolic alterations were correlated with high prevalence of Enterococcaceae, Staphylococcus, Roseburia, Coprobacillus and Dorea, and low prevalence of S24.7, rc4.4 and Bifidobacterium, which in turn related to cognitive impairment and cerebral hypometabolism. Our results indicate an effect of transgenic background on gut microbiome-metabolome, enhanced by high-fat diet. The resulting profiles may precede overt cognitive impairment, suggesting their predictive or risk-stratifying potential.