Search Results (16)

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women’s Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development. Full article

Brain glucose hypometabolism is an early sign of Alzheimer’s disease (AD), and interventions which offset this deficit, such as ketogenic diets, show promise as AD therapeutics. Conversely, high-fat feeding may exacerbate AD risk. We analyzed the metabolomic profile of cerebrospinal fluid (CSF) in a pilot study of older adults who underwent saline and triglyceride (TG) infusions. Older adults (12 cognitively normal (CN), age 65.3 ± 8.1, and 9 with cognitive impairment (CI), age 70.9 ± 8.6) underwent a 5 h TG or saline infusion on different days using a random crossover design; CSF was collected at the end of infusion. Aqueous metabolites were measured using a targeted mass spectroscopy (MS) platform focusing on 215 metabolites from over 35 different metabolic pathways. Data were analyzed using MetaboAnalyst 4.0 and SAS. Of the 215 targeted metabolites, 99 were detectable in CSF. Only one metabolite significantly differed by treatment: the ketone body 3-hydroxybutyrate (HBA). Post hoc analyses showed that HBA levels were associated with age and markers of metabolic syndrome and demonstrated different correlation patterns for the two treatments. When analyzed by cognitive diagnosis group, TG-induced increases in HBA were over 3 times higher for those with cognitive impairment (change score CN +9.8 uM ± 8.3, CI +32.4 ± 7.4, p = 0.0191). Interestingly, individuals with cognitive impairment had higher HBA levels after TG infusion than those with normal cognition. These results suggest that interventions that increase plasma ketones may lead to higher brain ketones in groups at risk for AD and should be confirmed in larger intervention studies. Full article

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings. Full article

Nicotinamide adenine dinucleotide (NAD⁺) is an essential cofactor required for proper functioning of all cells and its decline is correlated with advancing age and disease. This randomized, triple-blind, placebo-controlled, crossover pilot study assessed the efficacy and safety of a combination of nicotinamide with D-ribose (RiaGev) for NAD metabolome enhancement and related benefits in healthy middle-aged adults. Supplementing with 1520 mg RiaGev twice daily for 7 days significantly increased the NAD⁺ metabolome in blood, especially NADP⁺ by 27% compared to the placebo group (p = 0.033) and over the baseline (p = 0.007). Increases in glutathione and high energy phosphates were also observed in the blood. Seven-day supplementation with RiaGev significantly (p = 0.013) reduced overall blood glucose without significant changes in insulin secretion (p = 0.796), suggesting an improved insulin sensitivity and glucose tolerance. The waking salivary cortisol of the subjects steadily and significantly decreased (p = 0.026) in the RiaGev group in contrast to the placebo. Subjects in the RiaGev group showed less fatigue, improved mental concentration and motivation over the baseline (p = 0.015, 0.018, and 0.012, respectively) as observed through the Checklist Individual Strength (CIS) questionnaire. There were no clinically relevant adverse events, or alterations in hematology, electrolytes, liver, and kidney markers pre- and post-supplementation. RiaGev appears to be safe and efficacious in increasing NAD⁺ metabolome in healthy middle-aged adults, as shown by this study. Full article

Inorganic polyphosphate (polyP) is an ancient biopolymer that is well preserved throughout evolution and present in all studied organisms. In mammals, it shows a high co-localization with mitochondria, and it has been demonstrated to be involved in the homeostasis of key processes within the organelle, including mitochondrial bioenergetics. However, the exact extent of the effects of polyP on the regulation of cellular bioenergetics, as well as the mechanisms explaining these effects, still remain poorly understood. Here, using HEK293 mammalian cells under Wild-type (Wt) and MitoPPX (cells enzymatically depleted of mitochondrial polyP) conditions, we show that depletion of polyP within mitochondria increased oxidative stress conditions. This is characterized by enhanced mitochondrial O₂⁻ and intracellular H₂O₂ levels, which may be a consequence of the dysregulation of oxidative phosphorylation (OXPHOS) that we have demonstrated in MitoPPX cells in our previous work. These findings were associated with an increase in basal peroxiredoxin-1 (Prx1), superoxide dismutase-2 (SOD2), and thioredoxin (Trx) antioxidant protein levels. Using ¹³C-NMR and immunoblotting, we assayed the status of glycolysis and the pentose phosphate pathway (PPP) in Wt and MitoPPX cells. Our results show that MitoPPX cells display a significant increase in the activity of the PPP and an increase in the protein levels of transaldolase (TAL), which is a crucial component of the non-oxidative phase of the PPP and is involved in the regulation of oxidative stress. In addition, we observed a trend towards increased glycolysis in MitoPPX cells, which corroborates our prior work. Here, for the first time, we show the crucial role played by mitochondrial polyP in the regulation of mammalian redox homeostasis. Moreover, we demonstrate a significant effect of mitochondrial polyP on the regulation of global cellular bioenergetics in these cells. Full article

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies. Full article

In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data. Full article

Plant lignans and their microbial metabolites, e.g., enterolactone (ENL), may affect bile acid (BA) metabolism through interaction with hepatic receptors. We evaluated the effects of a flaxseed lignan extract (50 mg/day secoisolariciresinol diglucoside) compared to a placebo for 60 days each on plasma BA concentrations in 46 healthy men and women (20–45 years) using samples from a completed randomized, crossover intervention. Twenty BA species were measured in fasting plasma using LC-MS. ENL was measured in 24-h urines by GC-MS. We tested for (a) effects of the intervention on BA concentrations overall and stratified by ENL excretion; and (b) cross-sectional associations between plasma BA and ENL. We also explored the overlap in bacterial metabolism at the genus level and conducted in vitro anaerobic incubations of stool with lignan substrate to identify genes that are enriched in response to lignan metabolism. There were no intervention effects, overall or stratified by ENL at FDR < 0.05. In the cross-sectional analysis, irrespective of treatment, five secondary BAs were associated with ENL excretion (FDR < 0.05). In vitro analyses showed positive associations between ENL production and bacterial gene expression of the bile acid-inducible gene cluster and hydroxysteroid dehydrogenases. These data suggest overlap in community bacterial metabolism of secondary BA and ENL. Full article

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD. Full article

Although the gut microbiome has been associated with dietary patterns linked to health, microbial metabolism is not well characterized. This ancillary study was a proof of principle analysis for a novel application of metaproteomics to study microbial protein expression in a controlled dietary intervention. We measured the response of the microbiome to diet in a randomized crossover dietary intervention of a whole-grain, low glycemic load diet (WG) and a refined-grain, high glycemic load diet (RG). Total proteins in stools from 9 participants at the end of each diet period (n = 18) were analyzed by LC MS/MS and proteins were identified using the Human Microbiome Project (HMP) human gut microbiome database and UniProt human protein databases. T-tests, controlling for false discovery rate (FDR) <10%, were used to compare the Gene Ontology (GO) biological processes and bacterial enzymes between the two interventions. Using shotgun proteomics, more than 53,000 unique peptides were identified including microbial (89%) and human peptides (11%). Forty-eight bacterial enzymes were statistically different between the diets, including those implicated in SCFA production and degradation of fatty acids. Enzymes associated with degradation of human mucin were significantly enriched in the RG diet. These results illustrate that the metaproteomic approach is a valuable tool to study the microbial metabolism of diets that may influence host health. Full article

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material^®s (SRM^®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data. Full article

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications. Full article

Cellular senescence displays a heterogeneous set of phenotypes linked to tumor suppression; however, after drug treatment, senescence may also be involved in stable or recurrent cancer. Metabolic changes during senescence can provide detailed information on cellular status and may also have implications for the development of effective treatment strategies. The metabolic response to Adriamycin (ADR) treatment, which causes senescence as well as cell death, was obtained with the aid of metabolic profiling and isotope tracing in two human breast cancer cell lines, MCF7 and MDA-MB-231. After 5 days of ADR treatment, more than 60% of remaining, intact cells entered into a senescent state, characterized by enlarged and flattened morphology and positive blue staining using SA-β-gal. Metabolic trajectory analysis showed that the two cell lines’ responses were significantly different and were divided into two distinct stages. The metabolic shift from the first stage to the second was reflected by a partial recovery of the TCA cycle, as well as amino acid and lipid metabolisms. Isotope tracing analysis indicated that the higher level of glutamine metabolism helped maintain senescence. The results suggest that the dynamic changes during senescence indicate a multi-step process involving important metabolic pathways which might allow breast cancer cells to adapt to persistent ADR treatment, while the higher level of anapleurosis may be important for maintaining the senescent state. Ultimately, a better understanding of metabolic changes during senescence might provide targets for cancer therapy and tumor eradication. Full article

The angiotensin-converting enzyme (ACE) is a peptidase that is involved in the synthesis of Angiotensin II, the bioactive component of the renin-angiotensin system. A growing body of literature argues for a beneficial impact of ACE inhibitors (ACEi) on age-associated metabolic disorders, mediated by cellular changes in reactive oxygen species (ROS) that improve mitochondrial function. Yet, our understanding of the relationship between ACEi therapy and metabolic parameters is limited. Here, we used three genetically diverse strains of Drosophila melanogaster to show that Lisinopril treatment reduces thoracic ROS levels and mitochondrial respiration in young flies, and increases mitochondrial content in middle-aged flies. Using untargeted metabolomics analysis, we also showed that Lisinopril perturbs the thoracic metabolic network structure by affecting metabolic pathways involved in glycogen degradation, glycolysis, and mevalonate metabolism. The Lisinopril-induced effects on mitochondrial and metabolic parameters, however, are genotype-specific and likely reflect the drug’s impact on nutrient-dependent fitness traits. Accordingly, we found that Lisinopril negatively affects survival under nutrient starvation, an effect that can be blunted by genotype and age in a manner that partially mirrors the drug-induced changes in mitochondrial respiration. In conclusion, our results provide novel and important insights into the role of ACEi in cellular metabolism. Full article

The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, ¹H LC-NMR suffers from pH dependent chemical shift variations, especially during urine analysis, owing to the high physiological variation of urine pH. Additionally, large solvent signals from the mobile phase in LC can obscure lower intensity signals and severely limit the number of metabolites detected. These limitations, along with sample dilution, hinder the ability to make reliable chemical shift assignments. Recently, stable isotopic labeling has been used to detect quantitatively specific classes of metabolites of interest in biofluids. Here we present a strategy that explores the combined use of two-dimensional hydrophilic interaction chromatography (HILIC) and isotope tagged NMR for the unambiguous identification of carboxyl containing metabolites present in human urine. The ability to separate structurally related compounds chromatographically, in off-line mode, followed by detection using ¹H-¹⁵N 2D HSQC (two-dimensional heteronuclear single quantum coherence) spectroscopy, resulted in the assignment of low concentration carboxyl-containing metabolites from a library of isotope labeled compounds. The quantitative nature of this strategy is also demonstrated. Full article