Matthew W. Mitchell

Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems.

To address the challenges associated with metabolomics analyses, such as identification of chemical structures and elimination of experimental artifacts, we developed a platform that integrated the chemical analysis, including identification and relative quantification, data reduction, and quality assurance components of the process. The analytical platform incorporated two separate ultrahigh performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS(2)) injections; one injection was optimized for basic species, and the other was optimized for acidic species. This approach permitted the detection of 339 small molecules, a total instrument analysis time of 24 min (two injections at 12 min each), while maintaining a median process variability of 9%. The resulting MS/MS(2) data were searched against an in-house generated authentic standard library that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as their associated MS/MS spectra for all molecules in the library. The library allowed the rapid and high-confidence identification of the experimentally detected molecules based on a multiparameter match without need for additional analyses. This integrated platform enabled the high-throughput collection and relative quantitative analysis of analytical data and identified a large number and broad spectrum of molecules with a high degree of confidence.

α-Hydroxybutyrate Is an Early Biomarker of Insulin Resistance and Glucose Intolerance in a Nondiabetic Population

Background Insulin resistance is a risk factor for type 2 diabetes and cardiovascular disease progression. Current diagnostic tests, such as glycemic indicators, have limitations in the early detection of insulin resistant individuals. We searched for novel biomarkers identifying these at-risk subjects. Methods Using mass spectrometry, non-targeted biochemical profiling was conducted in a cohort of 399 nondiabetic subjects representing a broad spectrum of insulin sensitivity and glucose tolerance (based on the hyperinsulinemic euglycemic clamp and oral glucose tolerance testing, respectively). Results Random forest statistical analysis selected α-hydroxybutyrate (α–HB) as the top-ranked biochemical for separating insulin resistant (lower third of the clamp-derived MFFM = 33 [12] µmol·min−1·kgFFM −1, median [interquartile range], n = 140) from insulin sensitive subjects (MFFM = 66 [23] µmol·min−1·kgFFM −1) with a 76% accuracy. By targeted isotope dilution assay, plasma α–HB concentrations were reciprocally related to MFFM; and by partition analysis, an α–HB value of 5 µg/ml was found to best separate insulin resistant from insulin sensitive subjects. α–HB also separated subjects with normal glucose tolerance from those with impaired fasting glycemia or impaired glucose tolerance independently of, and in an additive fashion to, insulin resistance. These associations were also independent of sex, age and BMI. Other metabolites from this global analysis that significantly correlated to insulin sensitivity included certain organic acid, amino acid, lysophospholipid, acylcarnitine and fatty acid species. Several metabolites are intermediates related to α-HB metabolism and biosynthesis. Conclusions α–hydroxybutyrate is an early marker for both insulin resistance and impaired glucose regulation. The underlying biochemical mechanisms may involve increased lipid oxidation and oxidative stress.

Analysis of the adult human plasma metabolome.

OBJECTIVE It is well established that disease states are associated with biochemical changes (e.g., diabetes/glucose, cardiovascular disease/cholesterol), as are responses to chemical agents (e.g., medications, toxins, xenobiotics). Recently, nontargeted methods have been used to identify the small molecules (metabolites) in a biological sample to uncover many of the biochemical changes associated with a disease state or chemical response. Given that these experimental results may be influenced by the composition of the cohort, in the present study we assessed the effects of age, sex and race on the relative concentrations of small molecules (metabolites) in the blood of healthy adults. METHODS Using gas- and liquid-chromatography in combination with mass spectrometry, a nontargeted metabolomic analysis was performed on plasma collected from an age- and sex-balanced cohort of 269 individuals. RESULTS Of the more than 300 unique compounds that were detected, significant changes in the relative concentration of more than 100 metabolites were associated with age. Many fewer differences were associated with sex and fewer still with race. Changes in protein, energy and lipid metabolism, as well as oxidative stress, were observed with increasing age. Tricarboxylic acid intermediates, creatine, essential and nonessential amino acids, urea, ornithine, polyamines and oxidative stress markers (e.g., oxoproline, hippurate) increased with age. Compounds related to lipid metabolism, including fatty acids, carnitine, beta-hydroxybutyrate and cholesterol, were lower in the blood of younger individuals. By contrast, relative concentrations of dehydroepiandrosterone-sulfate (a proposed antiaging androgen) were lowest in the oldest age group. Certain xenobiotics (e.g., caffeine) were higher in older subjects, possibly reflecting decreases in hepatic cytochrome P450 activity. CONCLUSIONS Our nontargeted analytical approach detected a large number of metabolites, including those that were found to be statistically altered with age, sex or race. Age-associated changes were more pronounced than those related to differences in sex or race in the population group we studied. Age, sex and race can be confounding factors when comparing different groups in clinical studies. Future studies to determine the influence of diet, lifestyle and medication are also warranted.

Discovery of Metabolomics Biomarkers for Early Detection of Nephrotoxicity

Drug-induced nephrotoxicity is a major concern, since many pharmacological compounds are filtered through the kidneys for excretion into urine. To discover biochemical biomarkers useful for early identification of nephrotoxicity, metabolomic experiments were performed on Sprague-Dawley Crl:CD (SD) rats treated with the nephrotoxins gentamicin, cisplatin, or tobramycin. Using a combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS), a global, nontargeted metabolomics analysis was performed on urine and kidney samples collected after one, five, and twenty-eight dosing days. Increases in polyamines and amino acids were observed in urine from drug-treated rats after a single dose, and prior to observable histological kidney damage and conventional clinical chemistry indications of nephrotoxicity. Thus, these metabolites are potential biomarkers for the early detection of drug-induced nephrotoxicity. Upon prolonged dosing, nephrotoxin-induced changes included a progressive loss of amino acids in urine, concomitant with a decrease in amino acids and nucleosides in kidney tissue. A nephrotoxicity prediction model, based on the levels of branched-chain amino acids in urine, distinguished nephrotoxin-treated samples from vehicle-control samples, with 100%, 93%, and 70% accuracy at day 28, day 5, and day 1, respectively. Thus, this panel of biomarkers may provide a noninvasive method to detect kidney injury long before the onset of histopathological kidney damage.

Untargeted Metabolomic Profiling as an Evaluative Tool of Fenofibrate-Induced Toxicology in Fischer 344 Male Rats

Peroxisome proliferator-activated receptor-α (PPARα) agonists such as fenofibrate are used to treat dyslipidemia. Although fenofibrate is considered safe in humans, it is known to cause hepatocarcinogenesis in rodents. To evaluate untargeted metabolic profiling as a tool for gaining insight into the underlying pharmacology and hepatotoxicology, Fischer 344 male rats were dosed with 300 mg/kg/day of fenofibrate for 14 days and the urine and plasma were analyzed on days 2 and 14. A combination of liquid and gas chromatography mass spectrometry returned the profiles of 486 plasma and 932 urinary metabolites. Aside from known pharmacological effects, such as accelerated fatty acid β-oxidation and reduced plasma cholesterol, new observations on the drug’s impact on cellular metabolism were generated. Reductions in TCA cycle intermediates and biochemical evidence of lactic acidosis demonstrated that energy metabolism homeostasis was altered. Perturbation of the glutathione biosynthesis and elevation of oxidative stress markers were observed. Furthermore, tryptophan metabolism was up-regulated, resulting in accumulation of tryptophan metabolites associated with reactive oxygen species generation, suggesting the possibility of oxidative stress as a mechanism of nongenotoxic carcinogenesis. Finally, several metabolites related to liver function, kidney function, cell damage, and cell proliferation were altered by fenofibrate-induced toxicity at this dose.

The Small Molecule GMX1778 Is a Potent Inhibitor of NAD+ Biosynthesis: Strategy for Enhanced Therapy in Nicotinic Acid Phosphoribosyltransferase 1-Deficient Tumors

ABSTRACT GMX1777 is a prodrug of the small molecule GMX1778, currently in phase I clinical trials for the treatment of cancer. We describe findings indicating that GMX1778 is a potent and specific inhibitor of the NAD+ biosynthesis enzyme nicotinamide phosphoribosyltransferase (NAMPT). Cancer cells have a very high rate of NAD+ turnover, which makes NAD+ modulation an attractive target for anticancer therapy. Selective inhibition by GMX1778 of NAMPT blocks the production of NAD+ and results in tumor cell death. Furthermore, GMX1778 is phosphoribosylated by NAMPT, which increases its cellular retention. The cytotoxicity of GMX1778 can be bypassed with exogenous nicotinic acid (NA), which permits NAD+ repletion via NA phosphoribosyltransferase 1 (NAPRT1). The cytotoxicity of GMX1778 in cells with NAPRT1 deficiency, however, cannot be rescued by NA. Analyses of NAPRT1 mRNA and protein levels in cell lines and primary tumor tissue indicate that high frequencies of glioblastomas, neuroblastomas, and sarcomas are deficient in NAPRT1 and not susceptible to rescue with NA. As a result, the therapeutic index of GMX1777 can be widended in the treatment animals bearing NAPRT1-deficient tumors by coadministration with NA. This provides the rationale for a novel therapeutic approach for the use of GMX1777 in the treatment of human cancers.

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Evidence from Cameroon reveals differences in the genetic structure and histories of chimpanzee populations

The history of the genus Pan is a topic of enduring interest. Chimpanzees (Pan troglodytes) are often divided into subspecies, but the population structure and genetic history of chimpanzees across Africa remain unclear. Some population genetics studies have led to speculation that, until recently, this species constituted a single population with ongoing gene flow across its range, which resulted in a continuous gradient of allele frequencies. Chimpanzees, designated here as P. t. ellioti, occupy the Gulf of Guinea region that spans southern Nigeria and western Cameroon at the center of the distribution of this species. Remarkably, few studies have included individuals from this region, hindering the examination of chimpanzee population structure across Africa. Here, we analyzed microsatellite genotypes of 94 chimpanzees, including 32 designated as P. t. ellioti. We find that chimpanzees fall into three major populations: (i) Upper Guinea in western Africa (P. t. verus); (ii) the Gulf of Guinea region (P. t. ellioti); and (iii) equatorial Africa (P. t. troglodytes and P. t. schweinfurthii). Importantly, the Gulf of Guinea population is significantly different genetically from the others, sharing a last common ancestor with the populations in Upper Guinea ~0.46 million years ago (mya) and equatorial Africa ~0.32 mya. Equatorial chimpanzees are subdivided into up to three populations occupying southern Cameroon, central Africa, and eastern Africa, which may have constituted a single population until ~0.10–0.11 mya. Finally, occasional hybridization may be occurring between the Gulf of Guinea and southern Cameroon populations.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health

Significance Metabolomics has been increasingly recognized as a powerful functional tool that integrates the impacts from genetics, environment, microbiota, and xenobiotics. We used a broad-spectrum metabolomics platform to analyze plasma samples from 80 adults of normal health. The comprehensive metabolic profiles provided a functional readout to assess the penetrance of gene mutations identified by whole-exome sequencing on these individuals. Conversely, metabolic abnormalities identified by statistical analysis uncovered potential damaging mutations that were previously unappreciated. Additionally, we found metabolic signatures consistent with early signs of disease conditions and drug effects associated with efficacy and toxicity. Our findings demonstrate that metabolomics could be an effective tool in precision medicine for disease risk assessment and customized drug therapy in clinics. Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

Effects of mainstream cigarette smoke on the global metabolome of human lung epithelial cells.

Metabolomics is a technology for identifying and quantifying numerous biochemicals across metabolic pathways. Using this approach, we explored changes in biochemical profiles of human alveolar epithelial carcinoma (A549) cells following in vitro exposure to mainstream whole smoke (WS) aerosol as well as to wet total particulate matter (WTPM) or gas/vapor phase (GVP), the two constituent phases of WS from 2R4F Kentucky reference cigarettes. A549 cells were exposed to WTPM or GVP (expressed as WTPM mass equivalent GVP volumes) at 0, 5, 25, or 50 microg/mL or to WS from zero, two, four, and six cigarettes for 1 or 24 h. Cell pellets were analyzed for perturbations in biochemical profiles, with named biochemicals measured, analyzed, and reported in a heat map format, along with biochemical and physiological interpretations (mSelect, Metabolon Inc.). Both WTPM and GVP exposures likely decreased glycolysis (based on decreased glycolytic intermediaries) and increased oxidative stress and cell damage. Alterations in the Krebs cycle and the urea cycle were unique to WTPM exposure, while induction of hexosamines and alterations in lipid metabolism were unique to GVP exposure. WS altered glutathione (GSH) levels, enhanced polyamine and pantothenate levels, likely increased beta-oxidation of fatty acids, and increased phospholipid degradation marked by an increase in phosphoethanolamine. GSH, glutamine, and pantothenate showed the most significant changes with cigarette smoke exposure in A549 cells based on principal component analysis. Many of the changed biochemicals were previously reported to be altered by cigarette exposure, but the global metabolomic approach offers the advantage of observing changes to hundreds of biochemicals in a single experiment and the possibility for new discoveries. The metabolomic approach may thus be used as a screening tool to evaluate conventional and novel tobacco products offering the potential to reduce risks of smoking.