Source of nicotinamide governs its metabolic fate in cultured cells, mice, and humans.

Metabolic routing of nicotinamide (NAM) to NAD+ or 1-methylnicotinamide (MeNAM) has impacts on human health and aging. NAM is imported by cells or liberated from NAD+. The fate of 2H4-NAM in cultured cells, mice, and humans was determined by stable isotope tracing. 2H4-NAM is an NAD+ precursor via the salvage pathway in cultured A549 cells and human PBMCs and in A549 cell xenografts and PBMCs from 2H4-NAM-dosed mice and humans, respectively. 2H4-NAM is a MeNAM precursor in A549 cell cultures and xenografts, but not isolated PBMCs. NAM released from NAD+ is a poor MeNAM precursor. Additional A549 cell tracer studies yielded further mechanistic insight. NAMPT activators promote NAD+ synthesis and consumption. Surprisingly, NAM liberated from NAD+ in NAMPT activator-treated A549 cells is also routed toward MeNAM production. Metabolic fate mapping of the dual NAM sources across the translational spectrum (cells, mice, humans) illuminates a key regulatory node governing NAD+ and MeNAM synthesis.

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.

BACKGROUND: Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. METHODS: We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks. RESULTS: We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC. CONCLUSIONS: Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

Glutamine-derived 2-hydroxyglutarate is associated with disease progression in plasma cell malignancies.

The production of the oncometabolite 2-hydroxyglutarate (2-HG) has been associated with c-MYC overexpression. c-MYC also regulates glutamine metabolism and drives progression of asymptomatic precursor plasma cell (PC) malignancies to symptomatic multiple myeloma (MM). However, the presence of 2-HG and its clinical significance in PC malignancies is unknown. By performing 13C stable isotope resolved metabolomics (SIRM) using U[13C6]Glucose and U[13C5]Glutamine in human myeloma cell lines (HMCLs), we show that 2-HG is produced in clonal PCs and is derived predominantly from glutamine anaplerosis into the TCA cycle. Furthermore, the 13C SIRM studies in HMCLs also demonstrate that glutamine is preferentially utilized by the TCA cycle compared with glucose. Finally, measuring the levels of 2-HG in the BM supernatant and peripheral blood plasma from patients with precursor PC malignancies such as smoldering MM (SMM) demonstrates that relatively elevated levels of 2-HG are associated with higher levels of c-MYC expression in the BM clonal PCs and with a subsequent shorter time to progression (TTP) to MM. Thus, measuring 2-HG levels in BM supernatant or peripheral blood plasma of SMM patients offers potential early identification of those patients at high risk of progression to MM, who could benefit from early therapeutic intervention.

Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS). METHODS: Twenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses. RESULTS: This multiethnic, obese sample was matched by age (PCOS, 37±6; MetS, 40±6years) and body mass index (BMI) (PCOS, 34.6±5.1; MetS, 33.7±5.2kg/m2). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P=.02), essential amino acids (P=.03), the essential amino acid lysine (P=.02), and the lysine metabolite α-aminoadipic acid (P=.02) in models adjusted for surrogate variables representing technical variation in metabolites. No significant differences between groups were observed in concentrations of free fatty acids or vitamin D metabolites. Evaluation of the relationship of metabolites with clinical characteristics showed 1) negative associations of essential and BCAA with insulin sensitivity and sex hormone-binding globulin and 2) positive associations with homeostasis model of insulin resistance and free testosterone; metabolites were not associated with BMI or percent body fat. CONCLUSIONS: PCOS was associated with significant metabolic alterations not attributed exclusively to androgen-related pathways, obesity, or MetS. Concentrations of essential amino acids and BCAA are increased in PCOS, which might result from or contribute to their insulin resistance.

Precision medicine of aneurysmal subarachnoid hemorrhage, vasospasm and delayed cerebral ischemia.

INTRODUCTION: Precision medicine provides individualized treatment of diseases through leveraging patient-to-patient variation. Aneurysmal subarachnoid hemorrhage carries tremendous morbidity and mortality with cerebral vasospasm and delayed cerebral ischemia proving devastating and unpredictable. Lack of treatment measures for these conditions could be improved through precision medicine. Areas covered: Discussed are the pathophysiology of CV and DCI, treatment guidelines, and evidence for precision medicine used for prediction and prevention of poor outcomes following aSAH. A PubMed search was performed using keywords cerebral vasospasm or delayed cerebral ischemia and either biomarkers, precision medicine, metabolomics, proteomics, or genomics. Over 200 peer-reviewed articles were evaluated. The studies presented cover biomarkers identified as predictive markers or therapeutic targets following aSAH. Expert commentary: The biomarkers reviewed here correlate with CV, DCI, and neurologic outcomes after aSAH. Though practical use in clinical management of aSAH is not well established, using these biomarkers as predictive tools or therapeutic targets demonstrates the potential of precision medicine.

Impact of Long-Term Poor and Good Glycemic Control on Metabolomics Alterations in Type 1 Diabetic People.

CONTEXT: Poor glycemic control in individuals with type 1 diabetes (T1D) is associated with both micro- and macrovascular complications, but good glycemic control does not fully prevent the risk of these complications. OBJECTIVE: The objective of the study was to determine whether T1D with good glycemic control have persistent abnormalities of metabolites and pathways that exist in T1D with poor glycemic control. DESIGN: We compared plasma metabolites in T1D with poor (glycated hemoglobin ≥ 8.5%, T1D[-] and good (glycated hemoglobin < 6.5%, T1D[+]) glycemic control with nondiabetic controls (ND). SETTING: The study was conducted at the clinical research unit. PATIENTS OR OTHER PARTICIPANTS: T1D with poor (n = 14), T1D(-) and good, T1D(+) (n = 15) glycemic control and matched (for age, sex, and body mass index) ND participants were included in the study. INTERVENTION(S): There were no intervention. MAIN OUTCOME MEASURE(S): Comparison of qualitative and quantitative profiling of metabolome was performed. RESULTS: In T1D(-), 347 known metabolites belonging to 38 metabolic pathways involved in cholesterol, vitamin D, tRNA, amino acids (AAs), bile acids, urea, tricarboxylic acid cycle, immune response, and eicosanoids were different from ND. In T1D(+),154 known metabolites belonging to 26 pathways including glycolysis, gluconeogenesis, bile acids, tRNA biosynthesis, AAs, branch-chain AAs, retinol, and vitamin D metabolism remained altered from ND. Targeted measurements of AA metabolites, trichloroacetic acid, and free fatty acids showed directional changes similar to the untargeted metabolomics approach. CONCLUSIONS: Comprehensive metabolomic profiling identified extensive metabolomic abnormalities in T1D with poor glycemic control. Chronic good glycemic control failed to normalize many of these perturbations, suggesting a potential role for these persistent abnormalities in many complications in T1D.

Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics.

Alzheimer's Disease (AD) currently affects more than 5 million Americans, with numbers expected to grow dramatically as the population ages. The pathophysiological changes in AD patients begin decades before the onset of dementia, highlighting the urgent need for the development of early diagnostic methods. Compelling data demonstrate that increased levels of amyloid-beta compromise multiple cellular pathways; thus, the investigation of changes in various cellular networks is essential to advance our understanding of early disease mechanisms and to identify novel therapeutic targets. We applied a liquid chromatography/mass spectrometry-based non-targeted metabolomics approach to determine global metabolic changes in plasma and cerebrospinal fluid (CSF) from the same individuals with different AD severity. Metabolic profiling detected a total of significantly altered 342 plasma and 351 CSF metabolites, of which 22% were identified. Based on the changes of >150 metabolites, we found 23 altered canonical pathways in plasma and 20 in CSF in mild cognitive impairment (MCI) vs. cognitively normal (CN) individuals with a false discovery rate <0.05. The number of affected pathways increased with disease severity in both fluids. Lysine metabolism in plasma and the Krebs cycle in CSF were significantly affected in MCI vs. CN. Cholesterol and sphingolipids transport was altered in both CSF and plasma of AD vs. CN. Other 30 canonical pathways significantly disturbed in MCI and AD patients included energy metabolism, Krebs cycle, mitochondrial function, neurotransmitter and amino acid metabolism, and lipid biosynthesis. Pathways in plasma that discriminated between all groups included polyamine, lysine, tryptophan metabolism, and aminoacyl-tRNA biosynthesis; and in CSF involved cortisone and prostaglandin 2 biosynthesis and metabolism. Our data suggest metabolomics could advance our understanding of the early disease mechanisms shared in progression from CN to MCI and to AD.

Concordance of changes in metabolic pathways based on plasma metabolomics and skeletal muscle transcriptomics in type 1 diabetes.

Insulin regulates many cellular processes, but the full impact of insulin deficiency on cellular functions remains to be defined. Applying a mass spectrometry-based nontargeted metabolomics approach, we report here alterations of 330 plasma metabolites representing 33 metabolic pathways during an 8-h insulin deprivation in type 1 diabetic individuals. These pathways included those known to be affected by insulin such as glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses and those hitherto unknown to be altered including prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and anti-inflammatory responses. A significant concordance of metabolome and skeletal muscle transcriptome-based pathways supports an assumption that plasma metabolites are chemical fingerprints of cellular events. Although insulin treatment normalized plasma glucose and many other metabolites, there were 71 metabolites and 24 pathways that differed between nondiabetes and insulin-treated type 1 diabetes. Confirmation of many known pathways altered by insulin using a single blood test offers confidence in the current approach. Future research needs to be focused on newly discovered pathways affected by insulin deficiency and systemic insulin treatment to determine whether they contribute to the high morbidity and mortality in T1D despite insulin treatment.

Electron spray ionization mass spectrometry and 2D 31P NMR for monitoring 18O/16O isotope exchange and turnover rates of metabolic oligophosphates.

A new method was here developed for the determination of (18)O-labeling ratios in metabolic oligophosphates, such as ATP, at different phosphoryl moieties (α-, ß-, and γ-ATP) using sensitive and rapid electrospray ionization mass spectrometry (ESI-MS). The ESI-MS-based method for monitoring of (18)O/(16)O exchange was validated with gas chromatography-mass spectrometry and 2D (31)P NMR correlation spectroscopy, the current standard methods in labeling studies. Significant correlation was found between isotopomer selective 2D (31)P NMR spectroscopy and isotopomer less selective ESI-MS method. Results demonstrate that ESI-MS provides a robust analytical platform for simultaneous determination of levels, (18)O-labeling kinetics and turnover rates of α-, ß-, and γ-phosphoryls in ATP molecule. Such method is advantageous for large scale dynamic phosphometabolomic profiling of metabolic networks and acquiring information on the status of probed cellular energetic system.