Levels Of Individual Metabolites Research Articles

Optimization of pre-enrichment strategies for mouse hematopoietic stem cell isolation and metabolomic analysis

Blood cell production arises from the activity of hematopoietic stem cells (HSCs), defined by their self-renewal capacity and ability to give rise to all mature blood cell types. The mouse remains one of the most studied species in hematological research, and markers to define and isolate mouse HSCs are well-established. Given the very low frequency of HSCs in the bone marrow, stem cell pre-enrichment by red blood cell lysis and magnetic cell separation is often performed as part of the isolation process to reduce sorting times. Several pre-enrichment strategies are available, differing in their speed, degree of enrichment, final cell yield, and cost. In the current study, we performed a side-by-side comparison and provide a decision tree to help researchers select a pre-enrichment strategy for mouse HSC isolation depending on their downstream application. We then compared different pre-enrichment techniques in combination with metabolomics analysis of HSCs, where speed, yield and temperature during pre-enrichment are crucial factors, and found that the choice of pre-enrichment strategy significantly impacts the number of metabolites detected and levels of individual metabolites in HSCs.

Comprehensive blood metabolomics profiling of Parkinson’s disease reveals coordinated alterations in xanthine metabolism

Parkinson’s disease (PD) is a highly heterogeneous disorder influenced by several environmental and genetic factors. Effective disease-modifying therapies and robust early-stage biomarkers are still lacking, and an improved understanding of the molecular changes in PD could help to reveal new diagnostic markers and pharmaceutical targets. Here, we report results from a cohort-wide blood plasma metabolic profiling of PD patients and controls in the Luxembourg Parkinson’s Study to detect disease-associated alterations at the level of systemic cellular process and network alterations. We identified statistically significant changes in both individual metabolite levels and global pathway activities in PD vs. controls and significant correlations with motor impairment scores. As a primary observation when investigating shared molecular sub-network alterations, we detect pronounced and coordinated increased metabolite abundances in xanthine metabolism in de novo patients, which are consistent with previous PD case/control transcriptomics data from an independent cohort in terms of known enzyme-metabolite network relationships. From the integrated metabolomics and transcriptomics network analysis, the enzyme hypoxanthine phosphoribosyltransferase 1 (HPRT1) is determined as a potential key regulator controlling the shared changes in xanthine metabolism and linking them to a mechanism that may contribute to pathological loss of cellular adenosine triphosphate (ATP) in PD. Overall, the investigations revealed significant PD-associated metabolome alterations, including pronounced changes in xanthine metabolism that are mechanistically congruent with alterations observed in independent transcriptomics data. The enzyme HPRT1 may merit further investigation as a main regulator of these network alterations and as a potential therapeutic target to address downstream molecular pathology in PD.

Principles of metabolome conservation in animals

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Tryptophan metabolism determines outcome in tuberculous meningitis: a targeted metabolomic analysis.

Cellular metabolism is critical for the host immune function against pathogens, and metabolomic analysis may help understand the characteristic immunopathology of tuberculosis. We performed targeted metabolomic analyses in a large cohort of patients with tuberculous meningitis (TBM), the most severe manifestation of tuberculosis, focusing on tryptophan metabolism. We studied 1069 Indonesian and Vietnamese adults with TBM (26.6% HIV-positive), 54 non-infectious controls, 50 with bacterial meningitis, and 60 with cryptococcal meningitis. Tryptophan and downstream metabolites were measured in cerebrospinal fluid (CSF) and plasma using targeted liquid chromatography-mass spectrometry. Individual metabolite levels were associated with survival, clinical parameters, CSF bacterial load and 92 CSF inflammatory proteins. CSF tryptophan was associated with 60-day mortality from TBM (hazard ratio [HR] = 1.16, 95% confidence interval [CI] = 1.10-1.24, for each doubling in CSF tryptophan) both in HIV-negative and -positive patients. CSF tryptophan concentrations did not correlate with CSF bacterial load nor CSF inflammation but were negatively correlated with CSF interferon-gamma concentrations. Unlike tryptophan, CSF concentrations of an intercorrelating cluster of downstream kynurenine metabolites did not predict mortality. These CSF kynurenine metabolites did however correlate with CSF inflammation and markers of blood-CSF leakage, and plasma kynurenine predicted death (HR 1.54, 95% CI = 1.22-1.93). These findings were mostly specific for TBM, although high CSF tryptophan was also associated with mortality from cryptococcal meningitis. TBM patients with a high baseline CSF tryptophan or high systemic (plasma) kynurenine are at increased risk of death. These findings may reveal new targets for host-directed therapy. This study was supported by National Institutes of Health (R01AI145781) and the Wellcome Trust (110179/Z/15/Z and 206724/Z/17/Z).

Extended steroid profiling in H295R cells provides deeper insight into chemical-induced disturbances of steroidogenesis: Exemplified by prochloraz and anabolic steroids

Human adrenocortical H295R cells have been validated by the OECD Test Guideline 456 to detect chemicals disrupting testosterone and 17β-estradiol (estradiol) biosynthesis. This study evaluated a novel approach to detect disturbances of steroidogenesis in H295R cells, exemplified by prochloraz and five anabolic steroids. Steroid profiles were assessed by an untargeted LC-MS-based method, providing a relative quantification of 57 steroids annotated according to their accurate masses and retention times. Such a panel of steroids included several mineralocorticoids, glucocorticoids, progestins and adrenal androgens. The coverage of a high number of metabolites in this extended steroid profiling facilitated grouping of chemicals with similar effects and detecting subtler differences between chemicals. It allowed, for example, distinguishing between the effects of turinabol and oxymetholone, supposed to act similarly in a previous characterization including only nine adrenal steroids. Furthermore, the results revealed that product/substrate ratios can provide superior information on altered enzyme activities compared to individual metabolite levels. For example, the 17α-hydroxypregnenolone/pregnenolone ratio was found to be a more sensitive marker for detecting 17α-hydroxylase inhibition by prochloraz than the corresponding individual steroids. These results illustrate that chemical grouping and calculation of product/substrate ratios can provide valuable information on mode-of-action and help prioritizing further experimental work.

MP04-10 MACHINE LEARNING PREDICTS METABOLIC BIOMARKERS OF RESPONSE TO ANTI-VEGF DRUG SUNITINIB IN CCRCC

You have accessJournal of UrologyCME1 Apr 2023MP04-10 MACHINE LEARNING PREDICTS METABOLIC BIOMARKERS OF RESPONSE TO ANTI-VEGF DRUG SUNITINIB IN CCRCC Katiana Vazquez-Rivera, X. Amy Xie, Stephen Reese, Ritesh Kotecha, Martin Voss, Robert Motzer, Wesley Tansey, Ari Hakimi, and Ed Reznik Katiana Vazquez-RiveraKatiana Vazquez-Rivera More articles by this author , X. Amy XieX. Amy Xie More articles by this author , Stephen ReeseStephen Reese More articles by this author , Ritesh KotechaRitesh Kotecha More articles by this author , Martin VossMartin Voss More articles by this author , Robert MotzerRobert Motzer More articles by this author , Wesley TanseyWesley Tansey More articles by this author , Ari HakimiAri Hakimi More articles by this author , and Ed ReznikEd Reznik More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003215.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Clear cell renal cell carcinoma (ccRCC) is a metabolic disease with multiple mutated genes involved in the regulation of cellular metabolic processes, however it remains unknown how ccRCC metabolites are associated with response to anti-VEGF or immunotherapy agents. Metabolomic signatures may serve as a biomarker of response or serve as future points of intervention for targeted agents. Thus, we sought to characterize metabolite signatures associated with response to anti-VEGF therapy in patients with ccRCC. METHODS: A machine learning algorithm called T-MIRTH (Transcriptomics-Metabolite Imputation via Rank-Transformation and Harmonization) was developed to identify key metabolites. In short, this algorithm imputes metabolite abundances from RNA sequencing data by modeling metabolite-RNA covariation across datasets with paired metabolomics and transcriptomics data. 262 well-predicted metabolites were used to test the association between imputed levels of individual metabolites by T-MIRTH and progression free survival (PFS) in 7 published sunitinib clinical trials in advanced ccRCC. Using a multivariable Cox proportional hazard model, metabolomic signatures were correlated with progression-free survival and regression results were aggregated using a random effects meta-analysis model. RESULTS: Our T-MIRTH algorithm was validated using 3 ccRCC datasets with paired metabolomics and transcriptomics data and we demonstrated that T-MIRTH can accurately predict metabolite levels using RNA sequencing data. We then demonstrated using the ccRCC TCGA that T-MIRTH predicted metabolic differences between both tumor/normal samples and high/low-stage samples. Our algorithm was able to identify 262 validated metabolites. The results from our meta-analysis across 7 clinical trial demonstrated 7 metabolites significantly associated with improved PFS in the sunitinib arm (FDR < 0.05). In particular, high levels of 1-methylimidazole acetate had improved PFS within the sunitinib arm across all trials. CONCLUSIONS: Using a novel algorithm, we identified 7 metabolites that were associated with improved PFS in patients treated with sunitinib across 7 clinical trials. These metabolites may serve as biomarkers of response and may also be important targets for future therapeutics. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e37 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Katiana Vazquez-Rivera More articles by this author X. Amy Xie More articles by this author Stephen Reese More articles by this author Ritesh Kotecha More articles by this author Martin Voss More articles by this author Robert Motzer More articles by this author Wesley Tansey More articles by this author Ari Hakimi More articles by this author Ed Reznik More articles by this author Expand All Advertisement PDF downloadLoading ...

Rectal swabs as a viable alternative to faecal sampling for the analysis of gut microbiota functionality and composition

Faecal or biopsy samples are frequently used to analyse the gut microbiota, but issues remain with the provision and collection of such samples. Rectal swabs are widely-utilised in clinical practice and previous data demonstrate their potential role in microbiota analyses; however, studies to date have been heterogenous, and there are a particular lack of data concerning the utility of swabs for the analysis of the microbiota’s functionality and metabolome. We compared paired stool and rectal swab samples from healthy individuals to investigate whether rectal swabs are a reliable proxy for faecal sampling. There were no significant differences in key alpha and beta diversity measures between swab and faecal samples, and inter-subject variability was preserved. Additionally, no significant differences were demonstrated in abundance of major annotated phyla. Inferred gut functionality using Tax4Fun2 showed excellent correlation between the two sampling techniques (Pearson’s coefficient r = 0.9217, P < 0.0001). Proton nuclear magnetic resonance (1H NMR) spectroscopy enabled the detection of 20 metabolites, with overall excellent correlation identified between rectal swab and faecal samples for levels all metabolites collectively, although more variable degrees of association between swab and stool for levels of individual metabolites. These data support the utility of rectal swabs in both compositional and functional analyses of the gut microbiota.

The sex-specific metabolic signature of C57BL/6NRj mice during aging

Due to intact reactive oxygen species homeostasis and glucose metabolism, C57BL/6NRj mice are especially suitable to study cellular alterations in metabolism. We applied Nuclear Magnetic resonance spectroscopy to analyze five different tissues of this mouse strain during aging and included female and male mice aged 3, 6, 12, and 24 months. Metabolite signatures allowed separation between the age groups in all tissues, and we identified the most prominently changing metabolites in female and male tissues. A refined analysis of individual metabolite levels during aging revealed an early onset of age-related changes at 6 months, sex-specific differences in the liver, and a biphasic pattern for various metabolites in the brain, heart, liver, and lung. In contrast, a linear decrease of amino acids was apparent in muscle tissues. Based on these results, we assume that age-related metabolic alterations happen at a comparably early aging state and are potentially associated with a metabolic switch. Moreover, identified differences between female and male tissues stress the importance of distinguishing between sexes when studying age-related changes and developing new treatment approaches. Besides, metabolomic features seem to be highly dependent on the genetic background of mouse strains.

Evaluation of Urea Cycle Activity by Metabolic Flux Analysis Using Mass Spectrometry.

Hepatocytes play an important role in maintaining homeostasis in living organisms by carrying out various metabolic functions. The urea cycle, one of the metabolic pathways taking place in hepatocytes, is an important metabolic pathway that converts toxic ammonia to nontoxic urea. Performing quantitative assessments of individual metabolite levels using a mass spectrometer is useful for assessing the metabolic state of the urea cycle in hepatocytes. In addition, metabolic flux analysis using stable isotopes and a mass spectrometer is a new technique for measuring the metabolic state. It enables conducting specific, objective, and quantitative measurement of the activated state of the target metabolic pathway regardless of external disturbing factors. This section describes the technical background and methodology of performing metabolic flux analysis of the urea cycle by mass spectrometry.

Genetic overlap and causality between blood metabolites and migraine

The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.

Urinary Concentrations of Dialkylphosphate Metabolites of Organophosphate pesticides in the Study of Asian Women and their Offspring’s Development and Environmental Exposures (SAWASDEE)

BackgroundMeasurements of urinary dialkyl phosphate (DAP) metabolites are often used to characterize exposures to organophosphate (OP) insecticides; however, some challenges to using urinary DAP metabolites as an exposure measure exist. OP insecticides have short biological half-lives with measurement in a single urine sample typically only reflecting recent exposure within the last few days. Because of the field staff and participant burden of longitudinal sample collection and the high cost of multiple measurements, typically only one or two urine samples have been used to evaluate OP insecticide exposure during pregnancy, which is unlikely to capture an accurate picture of prenatal exposure. MethodsWe recruited pregnant farmworker women in Chom Thong and Fang, two districts of Chiang Mai province in northern Thailand (N = 330) into the Study of Asian Women and their Offspring’s Development and Environmental Exposures (SAWASDEE) from 2017 to 2019. We collected up to 6 serial urine samples per participant during gestation and composited the samples to represent early, mid, and late pregnancy. We measured concentrations of urinary DAP metabolites in the composited urine samples and evaluated the within- and between-participant variability of these levels. We also investigated predictors of OP insecticide exposure. ResultsDAP metabolite concentrations in serial composite samples were weakly to moderately correlated. Spearman correlations indicated that composite urine samples were more highly correlated in Fang participants than in Chom Thong participants. The within-person variances (0.064–0.65) exceeded the between-person variances for DETP, DEP, ∑DEAP, DMP, DMTP, ∑DMAP, ∑DAP. The intraclass correlations (ICCs) for the volume-based individual metabolite levels (ng/mL) ranged from 0.10 to 0.66. For ∑DEAP, ∑DMAP, and ∑DAP the ICCs were, 0.47, 0.17, 0.45 respectively. We observed significant differences between participants from Fang compared to those from Chom Thong both in demographic and exposure characteristics. Spearman correlations of composite samples from Fang participants ranged from 0.55 to 0.66 for the ∑DEAP metabolite concentrations in Fang indicating moderate correlation between pregnancy periods. The ICCs were higher for samples from Fang participants, which drove the overall ICCs. ConclusionsCollecting multiple (∼6) urine samples during pregnancy rather than just 1 or 2 improved our ability to accurately assess exposure during the prenatal period. By compositing the samples, we were able to still obtain trimester-specific information on exposure while keeping the analytic costs and laboratory burden low. This analysis also helped to inform how to best conduct future analyses within the SAWASDEE study. We observed two different exposure profiles in participants in which the concentrations and variability in data were highly linked to the residential location of the participants.

Exploring the metabolomic profile of cerebellum after exposure to acute stress

Psychological stress and stress-related disorders constitute a major health problem in modern societies. Although the brain circuits involved in emotional processing are intensively studied, little is known about the implication of cerebellum in stress responses whereas the molecular changes induced by stress exposure in cerebellum remain largely unexplored. Here, we investigated the effects of acute stress exposure on mouse cerebellum. We used a forced swim test (FST) paradigm as an acute stressor. We then analyzed the cerebellar metabolomic profiles of stressed (n = 11) versus control (n = 11) male CD1 mice by a Nuclear Magnetic Resonance (NMR)-based, untargeted metabolomics approach. Our results showed altered levels of 19 out of the 47 annotated metabolites, which are implicated in neurotransmission and N-acetylaspartic acid (NAA) turnover, as well as in energy and purine/pyrimidine metabolism. We also correlated individual metabolite levels with FST behavioral parameters, and reported associations between FST readouts and levels of 4 metabolites. This work indicates an altered metabolomic signature after acute stress in the cerebellum and highlights a previously unexplored involvement of cerebellum in stress responses.

Abstract MP07: Metabolic Signature Improves Heart Failure Risk Prediction In Older Adults

Introduction: Circulating metabolome profiling holds promise in predicting HF risk, but its prediction performance among older adults is not well established. Hypothesis: We hypothesize that metabolic signatures are associated with the risk of HF and its subtypes (HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF)), and they can improve HF risk prediction beyond established risk factors. Methods: We measured 828 serum metabolites among 4,030 African and European Americans free of HF from the Atherosclerosis Risk in Communities (ARIC) study visit 5 (2011-2013). We regressed incident HF on each metabolite using Cox proportional hazards models. A metabolite risk score (MRS) was derived by summing individual metabolite levels weighted by beta coefficients estimated from least absolute shrinkage and selection operator (LASSO) regularized regressions. We regressed incident HF, HFpEF and HFrEF on the MRS. Harrell’s C-statistics were calculated to evaluate risk discrimination. We replicated the association between MRS and HF in 3,697 independent ARIC participants with metabolite measured at visit 1 (1987-1989). Results: Among 4,030 participants, the mean (SD) age was 76 (5) years. Adjusting for HF risk factors, 302 metabolites were associated with incident HF (false discovery rate &lt; 0.05). One SD increase of the MRS, constructed from 51 metabolites selected by LASSO, was associated with two to three-fold high risk of HF, HFpEF and HFrEF in the fully adjusted models ( Table ). Five-year risk prediction analysis showed that C statistics improved from 0.850 to 0.884 by adding MRS over ARIC HF risk factors, kidney function and NT-proBNP (ΔC (95%CI) = 0.034 (0.017,0.052)). In the replication analysis, a more parsimonious MRS constructed using 15 metabolites, was associated with incident HF ( Table ). Conclusions: We identified a metabolic signature that was associated with the risk of HF and improved HF risk prediction. Our findings may shed light on pathways in HF development and at-risk populations.

Neurochemistry of response inhibition and interference in gambling disorder: a preliminary study of γ-aminobutyric acid (GABA+) and glutamate-glutamine (Glx).

Neurobehavioral research on the role of impulsivity in gambling disorder (GD) has produced heterogeneous findings. Impulsivity is multifaceted with different experimental tasks measuring different subprocesses, such as response inhibition and distractor interference. Little is known about the neurochemistry of inhibition and interference in GD. We investigated inhibition with the stop signal task (SST) and interference with the Eriksen Flanker task, and related performance to metabolite levels in individuals with and without GD. We employed magnetic resonance spectroscopy (MRS) to record glutamate-glutamine (Glx/Cr) and inhibitory, γ-aminobutyric acid (GABA+/Cr) levels in the dorsal ACC (dACC), right dorsolateral prefrontal cortex (dlPFC), and an occipital control voxel. We found slower processing of complex stimuli in the Flanker task in GD (P<.001, η2p=0.78), and no group differences in SST performance. Levels of dACC Glx/Cr and frequency of incongruent errors were correlated positively in GD only (r=0.92, P=.001). Larger positive correlations were found for those with GD between dACC GABA+/Cr and SST Go error response times (z=2.83, P=.004), as well as between dACC Glx/Cr and frequency of Go errors (z=2.23, P=.03), indicating general Glx-related error processing deficits. Both groups expressed equivalent positive correlations between posterror slowing and Glx/Cr in the right dlPFC (GD: r=0.74, P=.02; non-GD: r=.71, P=.01). Inhibition and interference impairments are reflected in dACC baseline metabolite levels and error processing deficits in GD.

Serum d- and l-lactate, pyruvate and glucose levels in individuals with at-risk mental state and correlations with clinical symptoms.

Little information exists on the peripheral metabolite levels in individuals with at-risk mental state who meet the criteria for a high-risk state of psychosis. Here, we aimed to investigate serum levels of glucose, pyruvate and d- and l-lactate, which may act as a signalling molecule for learning and memory in neuronal cells. High performance liquid chromatography or commercial kits were used to assess serum metabolites in individuals with attenuated psychosis symptoms of at-risk mental state (n = 24, men = 12) who were not receiving antipsychotics. The metabolite levels of these individuals were compared with those of age- and sex-matched healthy individuals (controls, n = 23, men = 11). Correlations between the metabolites and clinical symptoms of at-risk mental state were also examined. Individuals with at-risk mental state had higher serum glucose levels than did controls (P = 2.18 × 10-3 ), while no significant difference in pyruvate levels were observed between the groups. Individuals with at-risk mental state had significantly lower serum l-lactate levels than did controls (P = 6.31 × 10-5 ), while no differences in d-lactate levels were observed. Furthermore, a negative correlation was identified between serum l-lactate levels and Positive and Negative Syndrome Scale negative symptoms scores (r = -0.5651, P = 4.01 × 10-3 ) in individuals with at-risk mental state. We found that, compared with controls, individuals with at-risk mental state have reduced serum l-lactate levels, which may predate psychosis onset, and may be involved in the related negative symptoms.

Coronary artery disease, genetic risk and the metabolome in young individuals.

Background: Genome-wide association studies have identified genetic variants associated with coronary artery disease (CAD) in adults - the leading cause of death worldwide. It often occurs later in life, but variants may impact CAD-relevantphenotypesearlyandthroughoutthelife-course. Cohorts with longitudinal and genetic data on thousands of individuals are letting us explore the antecedents of this adult disease. Methods: 148 metabolites, with a focus on the lipidome, measured using nuclear magnetic resonance ( 1H-NMR) spectroscopy, and genotype data were available from 5,907 individuals at ages 7, 15, and 17 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Linear regression was used to assess the association between the metabolites and an adult-derived genetic risk score (GRS) of CAD comprising 146 variants. Individual variant-metabolite associations were also examined. Results: The CAD-GRS associated with 118 of 148 metabolites (false discovery rate [FDR] < 0.05), the strongest associations being with low-density lipoprotein (LDL) and atherogenic non-LDL subgroups. Nine of 146 variants in the GRS associated with one or more metabolites (FDR < 0.05). Seven of these are within lipid loci: rs11591147 PCSK9, rs12149545 HERPUD1-CETP, rs17091891 LPL, rs515135 APOB, rs602633 CELSR2-PSRC1, rs651821 APOA5, rs7412 APOE-APOC1. All associated with metabolites in the LDL or atherogenic non-LDL subgroups or both including aggregate cholesterol measures. The other two variants identified were rs112635299 SERPINA1 and rs2519093 ABO. Conclusions: Genetic variants that influence CAD risk in adults are associated with large perturbations in metabolite levels in individuals as young as seven. The variants identified are mostly within lipid-related loci and the metabolites they associated with are primarily linked to lipoproteins.Along with further research, this knowledge could allow for preventative measures, such as increased monitoring of at-risk individuals and perhaps treatment earlier in life, to be taken years before any symptoms of the disease arise.

The effect of apolipoprotein E polymorphism on serum metabolome \u2013 a population-based 10-year follow-up study

Apolipoprotein E (apoE) is the key regulator of plasma lipids, mediating altered functionalities in lipoprotein metabolism – affecting the risk of coronary artery (CAD) and Alzheimer’s diseases, as well as longevity. Searching pathways influenced by apoE prior to adverse manifestations, we utilized a metabolome dataset of 228 nuclear-magnetic-resonance-measured serum parameters with a 10-year follow-up from the population-based Young Finns Study cohort of 2,234 apoE-genotyped (rs7412, rs429358) adults, aged 24–39 at baseline. At the end of our follow-up, by limiting FDR-corrected p < 0.05, regression analyses revealed 180/228 apoE-polymorphism-related associations with the studied metabolites, in all subjects – without indications of apoE x sex interactions. Across all measured apoE- and apoB-containing lipoproteins, ε4 allele had consistently atherogenic and ε2 protective effect on particle concentrations of free/esterified cholesterol, triglycerides, phospholipids and total lipids. As novel findings, ε4 associated with glycoprotein acetyls, LDL-diameter and isoleucine – all reported biomarkers of CAD-risk, inflammation, diabetes and total mortality. ApoE-subgroup differences persisted through our 10-year follow-up, although some variation of individual metabolite levels was noticed. In conclusion, apoE polymorphism associate with a complex metabolic change, including aberrations in multiple novel biomarkers related to elevated cardiometabolic and all-cause mortality risk, extending our understanding about the role of apoE in health and disease.

Coronary artery disease, genetic risk and the metabolome in young individuals

Background: Genome-wide association studies have identified genetic variants associated with coronary artery disease (CAD) in adults – the leading cause of death worldwide. It often occurs later in life, but variants may impact CAD-relevant phenotypes early and throughout the life-course. Cohorts with longitudinal and genetic data on thousands of individuals are letting us explore the antecedents of this adult disease. Methods: 148 metabolites, with a focus on the lipidome, measured using nuclear magnetic resonance ( 1H-NMR) spectroscopy, and genotype data were available from 5,907 individuals at ages 7, 15, and 17 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Linear regression was used to assess the association between the metabolites and an adult-derived genetic risk score (GRS) of CAD comprising 146 variants. Individual variant-metabolite associations were also examined. Results: The CAD-GRS associated with 118 of 148 metabolites (false discovery rate [FDR] < 0.05), the strongest associations being with low-density lipoprotein (LDL) and atherogenic non-LDL subgroups. Nine of 146 variants in the GRS associated with one or more metabolites (FDR < 0.05). Seven of these are within lipid loci: rs11591147 PCSK9, rs12149545 HERPUD1-CETP, rs17091891 LPL, rs515135 APOB, rs602633 CELSR2-PSRC1, rs651821 APOA5, rs7412 APOE-APOC1. All associated with metabolites in the LDL or atherogenic non-LDL subgroups or both including aggregate cholesterol measures. The other two variants identified were rs112635299 SERPINA1 and rs2519093 ABO. Conclusions: Genetic variants that influence CAD risk in adults are associated with large perturbations in metabolite levels in individuals as young as seven. The variants identified are mostly within lipid-related loci and the metabolites they associated with are primarily linked to lipoproteins. Along with further research, this knowledge could allow for preventative measures, such as increased monitoring of at-risk individuals and perhaps treatment earlier in life, to be taken years before any symptoms of the disease arise.

Early metabolic characterization of brain tissues after whole body radiation based on gas chromatography-mass spectrometry in a rat model.

Radiation-induced brain injury involves acute, early delayed and late delayed damage based on the time-course and clinical manifestations. The acute symptoms are mostly transient and reversible, whereas the late delayed radiation-induced changes are progressive and irreversible. Therefore, evaluation of the organ-specific early response to ionizing radiation exposure is necessary for improving treatment strategies and minimizing possible damage at an early stage after radiation exposure. In the current study, the gas chromatography-mass spectrometry technique based on metabolomics coupled with metabolic correlation network was applied to investigate the early metabolic characterization of rat brain tissues following irradiation. Our findings showed that the metabolic response to irradiation was not just limited to the variations of individual metabolite levels, but also accompanied by alterations of network correlations among various metabolites. Metabolite clustering indicated that energy metabolism disorder and inflammation response were induced following radiation exposure. The correlation networks revealed that the strong positive correlations of differential metabolites were highly reduced and significant negative linkages were highlighted in irradiated groups even without statistical changes in metabolic levels. Our findings provided new insights into our understanding of the radiation-induced acute brain injury mechanism and clues as to the therapy target for clinical applications.

Coronary artery disease, genetic risk and the metabolome in young individuals

Background: Genome-wide association studies have identified genetic variants associated with coronary artery disease (CAD) in adults – the leading cause of death worldwide. It often occurs later in life, but variants may impact CAD-relevant phenotypes early and throughout the life-course. Cohorts with longitudinal and genetic data on thousands of individuals are letting us explore the antecedents of this adult disease. Methods: 149 metabolites, with a focus on the lipidome, measured using nuclear magnetic resonance (1H-NMR) spectroscopy, and genotype data were available from 5,905 individuals at ages 7, 15, and 17 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Linear regression was used to assess the association between the metabolites and an adult-derived genetic risk score (GRS) of CAD comprising 146 variants. Individual variant-metabolite associations were also examined. Results: The CAD-GRS associated with 118 of 149 metabolites (false discovery rate [FDR] &lt; 0.05), the strongest associations being with low-density lipoprotein (LDL) and atherogenic non-LDL subgroups. Nine of 146 variants in the GRS associated with one or more metabolites (FDR &lt; 0.05). Seven of these are within lipid loci: rs11591147 PCSK9, rs12149545 HERPUD1-CETP, rs17091891 LPL, rs515135 APOB, rs602633 CELSR2-PSRC1, rs651821 APOA5, rs7412 APOE-APOC1. All associated with metabolites in the LDL or atherogenic non-LDL subgroups or both including aggregate cholesterol measures. The other two variants identified were rs112635299 SERPINA1 and rs2519093 ABO. Conclusions: Genetic variants that influence CAD risk in adults are associated with large perturbations in metabolite levels in individuals as young as seven. The variants identified are mostly within lipid-related loci and the metabolites they associated with are primarily linked to lipoproteins. This knowledge could allow for preventative measures, such as increased monitoring of at-risk individuals and perhaps treatment earlier in life, to be taken years before any symptoms of the disease arise.