Variation In Metabolite Levels Research Articles

Widespread natural selection on metabolite levels in humans.

Natural selection acts ubiquitously on complex human traits, predominantly constraining the occurrence of extreme phenotypes (stabilizing selection). These constraints propagate to DNA sequence variants associated with traits under selection. The genetic signatures of such evolutionary events can thus be detected via combining effect size estimates from genetic association studies and the corresponding allele frequencies. Although this approach has been successfully applied to high-level traits, the prevalence and mode of selection acting on molecular traits remain poorly understood. Here, we estimate the action of natural selection on genetic variants associated with metabolite levels, an important layer of molecular traits. By leveraging summary statistics of published genome-wide association studies with large sample sizes, we find strong evidence of stabilizing selection for 15 out of 97 plasma metabolites, with nonessential amino acids displaying especially strong selection signatures. Mendelian randomization analysis reveals that metabolites under stronger stabilizing selection display larger effects on a range of clinically relevant complex traits, suggesting that maintaining a disease-free profile may be an important source of selective constraints on the metabolome. Metabolites under strong stabilizing selection in humans are also more conserved in their concentrations among diverse mammalian species, suggesting shared selective forces across micro- and macroevolutionary timescales. Overall, this study demonstrates that variation in metabolite levels among humans is frequently shaped by natural selection and this may act through their causal impact on disease susceptibility.

Metabolic plasma signatures in thoracic aortic aneurysm associated to tricuspid and bicuspid aortic valves

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Instituto de Salud Carlos III with co-funding from the ERDF and Comunidad de Madrid Background/Introduction Thoracic aortic aneurysm (TAA) develops asymptomatic with life-threatening consequences and its diagnosis is usually fortuitous. TAA is mostly idiopathic, although its prevalence is significantly higher in subjects with bicuspid aortic valve (BAV) for unknown reasons. We hypothesize that it is essential to investigate idiopathic TAA by differentiating between BAV and tricuspid aortic valve (TAV) patients for personalized diagnosis and treatment. Purpose We aimed to identify plasma metabolic profiles of aortic dilatation in idiopathic TAA, differentiating between subjects with BAV or TAV. Methods Plasma samples from 41 idiopathic TAA patients and 57 subjects without aortic dilatation (control, C) were collected and classified according to aortic valve type (BAV or TAV). Full metabolomics coverage was carried out by 1H RMN and LC-MS/MS untargeted analysis, including reverse phase and hydrophilic interaction chromatography and positive and negative electrospray ionization modes. Significant variation in metabolite levels were considered if FDR-adjusted p-value <0.05. Results Our analyses highlighted a metabolic signature specific for BAV and TAV subjects. BAV patients presented a marked glycerophospholipid metabolism alteration involving different lysophospholipids (LPAs, LPCs and LPEs) and phospholipids (PCs, PEs, PAs and PIs). Particularly, LPCs and LPEs were increased in BAV-TAA vs BAV-C, being an indicator of stimulation of glucose utilization through glycolysis and suppression of fatty acid biosynthesis. In contrast, carnitine-related metabolism was the most altered pathway observed in TAV subjects. In particular, we detected an increment in plasma acylcarnitines in TAV-TAA compared to TAV-C suggesting an altered β-oxidation of fatty acids and mitochondrial overload. Conclusions Idiopathic TAA development is differentially reflected in plasma from BAV and TAV patients, supporting an individualized diagnosis valve-dependent and suggesting also differential therapeutic targets.

Isolation, screening, and characterization of the newly isolated osmotolerant yeast Wickerhamomyces anomalus BKK11-4 for the coproduction of glycerol and arabitol.

This study explored the isolation and screening of an osmotolerant yeast, Wickerhamomyces anomalus BKK11-4, which is proficient in utilizing renewable feedstocks for sugar alcohol production. In batch fermentation with high initial glucose concentrations, W. anomalus BKK11-4 exhibited notable production of glycerol and arabitol. The results of the medium optimization experiments revealed that trace elements, such as H3BO3, CuSO4, FeCl3, MnSO4, KI, H4MoNa2O4, and ZnSO4, did not increase glucose consumption or sugar alcohol production but substantially increased cell biomass. Osmotic stress, which was manipulated by varying initial glucose concentrations, influenced metabolic outcomes. Elevated glucose levels promoted glycerol and arabitol production while decreasing citric acid production. Agitation rates significantly impacted the kinetics, enhancing glucose utilization and metabolite production rates, particularly for glycerol, arabitol, and citric acid. The operational pH dictated the distribution of the end metabolites, with glycerol production slightly reduced at pH 6, while arabitol production remained unaffected. Citric acid production was observed at pH 6 and 7, and acetic acid production was observed at pH 7. Metabolomic analysis using GC/MS identified 29 metabolites, emphasizing the abundance of sugar/sugar alcohols. Heatmaps were generated to depict the variations in metabolite levels under different osmotic stress conditions, highlighting the intricate metabolic dynamics occurring post-glucose uptake, affecting pathways such as the pentose phosphate pathway and glycerolipid metabolism. These insights contribute to the optimization of W. anomalus BKK11-4 as a whole-cell factory for desirable products, demonstrating its potential applicability in sustainable sugar alcohol production from renewable feedstocks.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Effects of dietary supplementation with prebiotics and Pediococcus acidilactici on gut health, transcriptome, microbiota, and metabolome in Atlantic salmon (Salmo salar L.) after seawater transfer

BackgroundGiven the importance of gut microbiota for health, growth and performance of the host, the aquaculture industry has taken measures to develop functional fish feeds aiming at modulating gut microbiota and inducing the anticipated beneficial effects. However, present understanding of the impact of such functional feeds on the fish is limited. The study reported herein was conducted to gain knowledge on performance and gut health characteristics in post-smolt Atlantic salmon fed diets varying in content of functional ingredients. Three experimental diets, a diet containing fructo-oligosaccharides (FOS), a diet with a combination of FOS and Pediococcus acidilactici (BC) and a diet containing galacto-oligosaccharides (GOS) and BC, were used in a 10-weeks feeding trial. A commercial diet without functional ingredients was also included as a control/reference. Samples of blood plasma, mucosa and digesta were subjected to microbiota, transcriptome and metabolome profiling for evaluation of the diet effects.ResultsNo significant growth differences were observed between fish fed the supplemented diets, but FOS–BC fed fish showed significantly faster growth than the control fed fish. The microbiota results showed that the BC was present in both the digesta, and the mucosa samples of fish fed the FOS–BC and GOS–BC diets. Digesta-associated microbiota was altered, while mucosa-associated microbiota was relatively unaffected by diet. Replacing FOS with GOS increased the level of metabolites linked to phospholipid, fatty acid, carnitine and sphingolipid metabolism. Variation in metabolite levels between the treatments closely correlated with genera mainly belonging to Firmicutes and Actinobacteria phyla. The transcriptome analyses indicated diet effects of exchanging FOS with GOS on immune functions, oxidative defense and stress responses. No significant diet effect was observed on intestinal inflammation in the pyloric caeca or in the distal intestine, or on lipid accumulation in the pyloric caeca.ConclusionsDietary supplementation with BC induced moderate effects on the microbiota of the digesta, while the effects of replacing FOS with GOS were more marked and was observed also for nutrient metabolism. Our data indicates therefore that the quality of a prebiotic may be of great importance for the effects of a probiotic on gut microbiota, function, and health.

MetaboVariation: Exploring Individual Variation in Metabolite Levels

To date, most metabolomics biomarker research has focused on identifying disease biomarkers. However, there is a need for biomarkers of early metabolic dysfunction to identify individuals who would benefit from lifestyle interventions. Concomitantly, there is a need to develop strategies to analyse metabolomics data at an individual level. We propose "MetaboVariation", a method that models repeated measurements on individuals to explore fluctuations in metabolite levels at an individual level. MetaboVariation employs a Bayesian generalised linear model to flag individuals with intra-individual variations in their metabolite levels across multiple measurements. MetaboVariation models repeated metabolite levels as a function of explanatory variables while accounting for intra-individual variation. The posterior predictive distribution of metabolite levels at the individual level is available, and is used to flag individuals with observed metabolite levels outside the 95% highest posterior density prediction interval at a given time point. MetaboVariation was applied to a dataset containing metabolite levels for 20 metabolites, measured once every four months, in 164 individuals. A total of 28% of individuals with intra-individual variations in three or more metabolites were flagged. An R package for MetaboVariation was developed with an embedded R Shiny web application. To summarize, MetaboVariation has made considerable progress in developing strategies for analysing metabolomics data at the individual level, thus paving the way toward personalised healthcare.

Post-acquisition water-signal removal in 3D water-unsuppressed 1 H-MR spectroscopic imaging of the prostate.

To develop a robust processing procedure of raw signals from water-unsuppressed MRSI of the prostate for the mapping of absolute tissue concentrations of metabolites. Water-unsuppressed 3D MRSI data were acquired from a phantom, from healthy volunteers, and a patient with prostate cancer. Signal processing included sequential computation of the modulus of the FID to remove water sidebands, a Hilbert transformation, and k-space Hamming filtering. For the removal of the water signal, we compared Löwner tensor-based blind source separation (BSS) and Hankel Lanczos singular value decomposition techniques. Absolute metabolite levels were quantified with LCModel and the results were statistically analyzed to compare the water removal methods and conventional water-suppressed MRSI. The post-processing algorithms successfully removed the water signal and its sidebands without affecting metabolite signals. The best water removal performance was achieved by Löwner tensor-based BSS. Absolute tissue concentrations of citrate in the peripheral zone derived from water-suppressed and unsuppressed 1 H MRSI were the same and as expected from the known physiology of the healthy prostate. Maps for citrate and choline from water-unsuppressed 3D 1 H-MRSI of the prostate showed expected spatial variations in metabolite levels. We developed a robust relatively simple post-processing method of water-unsuppressed MRSI of the prostate to remove the water signal. Absolute quantification using the water signal, originating from the same location as the metabolite signals, avoids the acquisition of additional reference data.

Metabolic profiling of night shift work – The HORMONIT study

ABSTRACT Mechanistic studies are needed to understand how rotating shift work perturbs metabolic processing. We collected plasma samples (n = 196) from 49 males, rotating car factory shift workers at the beginning and end of a night-shift (22:00–06:00 h) and day-shift (06:00 h-14:00 h). Samples underwent targeted LC-MS/MS metabolomics and concentrations of 130 metabolites were log2-transformed and pareto-scaled. An elastic net selected the most influential metabolites for linear mixed models examining within-person variation in metabolite levels at night-shift end (06:00 h) compared to day-shift start (06:00 h). Quantitative enrichment analysis explored differentially enriched biological pathways between sample time points. We included 20 metabolites (amino acids, biogenic amines, acylcarnitines, glycerophospholipids) in mixed models. Night-shift was associated with changes in concentrations of arginine (geometric mean ratio [GMR] 2.30, 95%CI 1.25, 4.23), glutamine (GMR 2.22, 95%CI 1.53, 3.24), kynurenine (GMR 3.22, 95%CI 1.05, 9.87), lysoPC18:2 (GMR 1.86, 95%CI 1.11, 3.11), lysoPC20:3 (GMR 2.48, 95%CI 1.05, 5.83), PCaa34:2 (GMR 2.27, 95%CI 1.16, 4.44), and PCae38:5 (GMR 1.66, 95%CI 1.02, 2.68). Tryptophan metabolism, glutathione metabolism, alanine metabolism, glycine and serine metabolism, and urea cycle were pathways differing between shifts. Night shift work was associated with changes in metabolites and the perturbation of metabolic and biochemical pathways related to a variety of health outcomes.

Dried Blood Spots as Matrix for Evaluation of Valproate Levels and the Immediate and Delayed Metabolomic Changes Induced by Single Valproate Dose Treatment

The immediate and delayed metabolic changes in rats treated with valproate (VPA), a drug used for the treatment of epilepsy, were profiled. An established approach using dried blood spots (DBS) as sample matrices for gas chromatography/mass spectrometry-based metabolomics profiling was modified using double solvents in the extraction of analytes. With the modified method, some of the previously undetectable metabolites were recovered and subtle differences in the metabolic changes upon exposure to a single dose of VPA between males and female rats were identified. In male rats, changes in 2-hydroxybutyric acid, pipecolic acid, tetratriacontane and stearic acid were found between the control and treatment groups at various time points from 2.5 h up to 24 h. In contrast, such differences were not observed in female rats, which could be caused by the vast inter-individual variations in metabolite levels within the female group. Based on the measured DBS drug concentrations, clearance and apparent volume of distribution of VPA were estimated and the values were found to be comparable to those estimated previously from full blood drug concentrations. The current study indicated that DBS is a powerful tool to monitor drug levels and metabolic changes in response to drug treatment.

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children.

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7–12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25–0.64), 0.50 (range: 0.33–0.62), and 0.64 (range: 0.43–0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37–0.68), 0.50 (range; 0.23–0.61), and 0.47 (range: 0.32–0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

Assessment of susceptibility to phthalate and DINCH exposure through CYP and UGT single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) of cytochrome P450 (CYPs) and UDP-glucuronosyltransferase (UGTs) genes have been proposed to influence phthalates and 1,2-cyclo-hexanedicarboxylic acid diisononyl ester (DINCH) biotransformation but have not been investigated on a populational level. We investigated the role of SNPs in CYP2C9, CYP2C19, CYP2D6, UGT2B15, and UGT1A7 genes in the biotransformation of phthalates (DEHP, DEP, DiBP, DnBP, BBzP, DiNP, DidP) and DINCH by determining their urine metabolites. From the Slovenian study population of 274 men and 289 lactating primiparous women we obtained data on phthalate and DINCH urine metabolite levels (MEHP, 5OH-MEHP, 5oxo-MEHP, 5cx-MEPP, MEP, MiBP, MnBP, MBzP, cx-MINP, OH-MiDP, MCHP, MnPeP, MnOP, 5OH-MINCH, 5oxo-MINCH), SNP genotypes (rs1057910=CYP2C9*3, rs1799853=CYP2C9*2, rs4244285=CYP2C19*2, rs12248560=CYP2C19*17, rs3892097=CYP2D6*4, rs1902023=UGT2B15*2, and rs11692021=UGT1A7*3) and questionnaires. Associations of SNPs with levels of metabolites and their ratios were assessed by multiple linear regression and ordinary logistic regression analyses. Significant associations were observed for CYP2C9*2, CYP2C9*3, CYP2C19*17, and UGT1A7*3 SNPs. The most pronounced was the influence of CYP2C9*2 and *3 on the reduced DEHP biotransformation, with lower levels of metabolites and their ratios in men and women. In contrast, carriers of CYP2C19*17 showed higher urine levels of DEHP metabolites in both genders, and in women also in higher DiNP, DiDP, and DINCH metabolite levels. The presence of UGT1A7*3 was associated with increased metabolite levels of DINCH in men and of DiBP and DBzP in women. Statistical models explained up to 27% of variability in metabolite levels or their ratios. Our observations confirm the effect of CYP2C9*2 and *3 SNPs towards reduced DEHP biotransformation. We show that CYP2C9*2, CYP2C9*3, CYP2C19*17, and UGT1A7*3 SNPs might represent biomarkers of susceptibility or resilience in phthalates and DINCH exposure that have been so far unrecognised.

Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury

Recent advances in emergency medicine and the co-ordinated delivery of trauma care mean more critically-injured patients now reach the hospital alive and survive life-saving operations. Indeed, between 2008 and 2017, the odds of surviving a major traumatic injury in the UK increased by nineteen percent. However, the improved survival rates of severely-injured patients have placed an increased burden on the healthcare system, with major trauma a common cause of intensive care unit (ICU) admissions that last ≥10 days. Improved understanding of the factors influencing patient outcomes is now urgently needed. We investigated the serum metabolomic profile of fifty-five major trauma patients across three post-injury phases: acute (days 0–4), intermediate (days 5–14) and late (days 15–112). Using ICU length of stay (LOS) as a clinical outcome, we aimed to determine whether the serum metabolome measured at days 0–4 post-injury for patients with an extended (≥10 days) ICU LOS differed from that of patients with a short (<10 days) ICU LOS. In addition, we investigated whether combining metabolomic profiles with clinical scoring systems would generate a variable that would identify patients with an extended ICU LOS with a greater degree of accuracy than models built on either variable alone. The number of metabolites unique to and shared across each time segment varied across acute, intermediate and late segments. A one-way ANOVA revealed the most variation in metabolite levels across the different time-points was for the metabolites lactate, glucose, anserine and 3-hydroxybutyrate. A total of eleven features were selected to differentiate between <10 days ICU LOS vs. >10 days ICU LOS. New Injury Severity Score (NISS), testosterone, and the metabolites cadaverine, urea, isoleucine, acetoacetate, dimethyl sulfone, syringate, creatinine, xylitol, and acetone form the integrated biomarker set. Using metabolic enrichment analysis, we found valine, leucine and isoleucine biosynthesis, glutathione metabolism, and glycine, serine and threonine metabolism were the top three pathways differentiating ICU LOS with a p < 0.05. A combined model of NISS and testosterone and all nine selected metabolites achieved an AUROC of 0.824. Differences exist in the serum metabolome of major trauma patients who subsequently experience a short or prolonged ICU LOS in the acute post-injury setting. Combining metabolomic data with anatomical scoring systems allowed us to discriminate between these two groups with a greater degree of accuracy than that of either variable alone.

Modular Evolution of the Drosophila Metabolome.

Comparative phylogenetic studies offer a powerful approach to study the evolution of complex traits. Although much effort has been devoted to the evolution of the genome and to organismal phenotypes, until now relatively little work has been done on the evolution of the metabolome, despite the fact that it is composed of the basic structural and functional building blocks of all organisms. Here we explore variation in metabolite levels across 50 My of evolution in the genus Drosophila, employing a common garden design to measure the metabolome within and among 11 species of Drosophila. We find that both sex and age have dramatic and evolutionarily conserved effects on the metabolome. We also find substantial evidence that many metabolite pairs covary after phylogenetic correction, and that such metabolome coevolution is modular. Some of these modules are enriched for specific biochemical pathways and show different evolutionary trajectories, with some showing signs of stabilizing selection. Both observations suggest that functional relationships may ultimately cause such modularity. These coevolutionary patterns also differ between sexes and are affected by age. We explore the relevance of modular evolution to fitness by associating modules with lifespan variation measured in the same common garden. We find several modules associated with lifespan, particularly in the metabolome of older flies. Oxaloacetate levels in older females appear to coevolve with lifespan, and a lifespan-associated module in older females suggests that metabolic associations could underlie 50 My of lifespan evolution.

Inter-study and time-dependent variability of metabolite abundance in cultured red blood cells

BackgroundCultured human red blood cells (RBCs) provide a powerful ex vivo assay platform to study blood-stage malaria infection and propagation. In recent years, high-resolution metabolomic methods have quantified hundreds of metabolites from parasite-infected RBC cultures under a variety of perturbations. In this context, the corresponding control samples of the uninfected culture systems can also be used to examine the effects of these perturbations on RBC metabolism itself and their dependence on blood donors (inter-study variations).MethodsTime-course datasets from five independent studies were generated and analysed, maintaining uninfected RBCs (uRBC) at 2% haematocrit for 48 h under conditions originally designed for parasite cultures. Using identical experimental protocols, quadruplicate samples were collected at six time points, and global metabolomics were employed on the pellet fraction of the uRBC cultures. In total, ~ 500 metabolites were examined across each dataset to quantify inter-study variability in RBC metabolism, and metabolic network modelling augmented the analyses to characterize the metabolic state and fluxes of the RBCs.ResultsTo minimize inter-study variations unrelated to RBC metabolism, an internal standard metabolite (phosphatidylethanolamine C18:0/20:4) was identified with minimal variation in abundance over time and across all the samples of each dataset to normalize the data. Although the bulk of the normalized data showed a high degree of inter-study consistency, changes and variations in metabolite levels from individual donors were noted. Thus, a total of 24 metabolites were associated with significant variation in the 48-h culture time window, with the largest variations involving metabolites in glycolysis and synthesis of glutathione. Metabolic network analysis was used to identify the production of superoxide radicals in cultured RBCs as countered by the activity of glutathione oxidoreductase and synthesis of reducing equivalents via the pentose phosphate pathway. Peptide degradation occurred at a rate that is comparable with central carbon fluxes, consistent with active degradation of methaemoglobin, processes also commonly associated with storage lesions in RBCs.ConclusionsThe bulk of the data showed high inter-study consistency. The collected data, quantification of an expected abundance variation of RBC metabolites, and characterization of a subset of highly variable metabolites in the RBCs will help in identifying non-specific changes in metabolic abundances that may obscure accurate metabolomic profiling of Plasmodium falciparum and other blood-borne pathogens.

Metabolism of Long-Acting Rilpivirine After Intramuscular Injection: HIV Prevention Trials Network Study 076 (HPTN 076)

A long-acting injectable formulation of rilpivirine (RPV), a non-nucleoside reverse transcriptase inhibitor, is currently under investigation for use in human immunodeficiency virus (HIV) maintenance therapy. We previously characterized RPV metabolism after oral dosing and identified seven metabolites: four metabolites resulting from mono- or dioxygenation of the 2,6-dimethylphenyl ring itself or either of the two methyl groups located on that ring, one N-linked RPV glucuronide conjugate, and two O-linked RPV glucuronides produced via glucuronidation of mono- and dihydroxymethyl metabolites. However, as is true for most drugs, the metabolism of RPV after injection has yet to be reported. The phase II clinical trial HPTN 076 enrolled 136 HIV-uninfected women and investigated the safety and acceptability of long-acting injectable RPV for use in HIV pre-exposure prophylaxis. Through the analysis of plasma samples from 80 of these participants in the active product arm of the study, we were able to detect 2 metabolites after intramuscular injection of long-acting RPV, 2-hydroxymethyl-RPV, and RPV N-glucuronide. Of the total of 80 individuals, 72 participants exhibited detectable levels of 2-hydroxymethyl-RPV in plasma samples whereas RPV N-glucuronide was detectable in plasma samples of 78 participants. In addition, RPV N-glucuronide was detectable in rectal fluid, cervicovaginal fluid, and vaginal tissue. To investigate potential genetic variation in genes encoding enzymes relevant to RPV metabolism, we isolated genomic DNA and performed next-generation sequencing of CYP3A4, CYP3A5, UGT1A1 and UGT1A4. From these analyses, four missense variants were detected for CYP3A4 whereas one missense variant and one frameshift variant were detected for CYP3A5. A total of eight missense variants of UGT1A4 were detected, whereas two variants were detected for UGT1A1; however, these variants did not appear to account for the observed interindividual variability in metabolite levels. These findings provide insight into the metabolism of long-acting RPV and contribute to an overall understanding of metabolism after oral dosing versus injection. ClinicalTrials.gov Identifier: NCT02165202.

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome.

BackgroundObesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.MethodsWe expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.ResultsWe identified 10 known metabolites that are more likely to be causes (e.g. alpha-hydroxybutyrate) or effects (e.g. valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g. glycine). Importantly, we also identified about 5 times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.ConclusionsThese findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Metabolic changes during respiratory syncytial virus infection of epithelial cells.

Viral infections induce substantial metabolic changes in infected cells to optimize viral production while cells develop countermeasures to restrict that infection. Human respiratory syncytial virus (HRSV) is an infectious pathogen that causes severe lower respiratory tract infections (LRTI) in infants, the elderly, and immunocompromised adults for which no effective treatment or vaccine is currently available. In this study, variations in metabolite levels at different time points post-HRSV infection of epithelial cells were studied by untargeted metabolomics using liquid chromatography/mass spectrometry analysis of methanol cell extracts. Numerous metabolites were significantly upregulated after 18 hours post-infection, including nucleotides, amino acids, amino and nucleotide sugars, and metabolites of the central carbon pathway. In contrast, most lipid classes were downregulated. Additionally, increased levels of oxidized glutathione and polyamines were associated with oxidative stress in infected cells. These results show how HRSV infection influences cell metabolism to produce the energy and building blocks necessary for virus reproduction, suggesting potential therapeutic interventions against this virus.

Blood-based molecular signature of Alzheimer's disease via spectroscopy and metabolomics

ObjectivesWith over 35 million cases worldwide, Alzheimer's disease (AD) represents the main cause of dementia. The differentiation of AD from other types of dementia is challenging and its early diagnosis is complicated. The established biomarkers are not only based on the invasive collection of cerebrospinal fluid, but also lack sufficient sensitivity and specificity. Therefore, much current effort is aimed at the identification of new biomarkers of AD in peripheral blood. Design and methodsWe focused on blood-based analyses using chiroptical spectroscopy (Raman optical activity, electronic circular dichroism) supplemented with conventional vibrational spectroscopy (infrared, Raman) and metabolomics (high-performance liquid chromatography with a high-resolution mass detection). ResultsThis unique approach enabled us to identify the spectral pattern of AD and variations in metabolite levels. Subsequent linear discriminant analysis of the spectral data resulted in differentiation between the AD patients and control subjects. ConclusionsIt may be stated that this less invasive approach has strong potential for the identification of disease-related changes within essential plasmatic biomolecules and metabolites.

Gestational route to healthy birth (GaRBH): protocol for an Indian prospective cohort study

IntroductionPregnancy is characterised by a high rate of metabolic shifts from early to late phases of gestation in order to meet the raised physiological and metabolic needs. This change in...

Proteomic and metabolomic approaches unveil relevant biochemical changes in carbohydrate and cell wall metabolisms of two blueberry (Vaccinium corymbosum) varieties with different quality attributes

Quality maintenance in rapidly decaying fruit such as blueberries (Vaccinium corymbosum) is of essential importance to guarantee the economic success of the crop. Fruit quality is a multifaceted subject that encompasses flavor, aroma, visual and physical issues as main factors. In this paper we report an ample characterization of different biochemical and physical aspects in two varieties (O'Neal and Emerald) of blueberries that differ in firmness, aspect, flavor and harvesting times, at two different phenological stages (fruit set vs. ripe), with the intention of unveiling how the metabolic signature of each contributes to their contrasting quality. To this effect a metabolomic, ionomic and proteomic approach was selected. The results presented here show marked differences in several variables at the two stages and between varieties. Emerald is an early variety with a large, good taste and firm fruit, while O'Neal is soft, medium sized and very sweet. Proteomic data comparison between both cultivars showed that, at fruit set, processes related with the response to inorganic compounds and small molecule metabolisms are relevant in both varieties. However, solute accumulation (mainly amino acids and organic acids), enzymes related with C: N balance, water transport and cell wall recycling are enhanced in Emerald. In ripe fruit, Emerald showed an enrichment of proteins associated with TCA, nitrogen, small molecules and cell wall in muro recycling processes, while mannitol and fatty acid metabolism were enhanced in the soft variety. The measured variation in metabolite levels gave strong support to the precedent results. This study suggests that at fruit set, a composite scenario of active metabolic recycling of the cell wall, improved C: N balance and solute accumulation give place to a more efficient carbon and water resource management. During the ripe stage, an increased and efficient in muro and metabolic recycling of the cell wall, added to enhanced inositol and secondary metabolism may be responsible for a best turgor conservation in Emerald. These findings may yield clues for improvements in fertilization practices, as well as to assist the guided development of new varieties based on biochemical quality.