The effect of black cohosh extract and risedronate coadministration on bone health in an ovariectomized rat model.

Preparations of black cohosh extract are sold as dietary supplements marketed to relieve the vasomotor symptoms of menopause, and some studies suggest it may protect against postmenopausal bone loss. Postmenopausal women are also frequently prescribed bisphosphonates, such as risedronate, to prevent osteoporotic bone loss. However, the pharmacodynamic interactions between these compounds when taken together is not known. To investigate possible interactions, 6-month-old, female Sprague-Dawley rats underwent bilateral ovariectomy or sham surgery and were treated for 24 weeks with either vehicle, ethinyl estradiol, risedronate, black cohosh extract or coadministration of risedronate and black cohosh extract, at low or high doses. Bone mineral density (BMD) of the femur, tibia, and lumbar vertebrae was then measured by dual-energy X-ray absorptiometry (DEXA) at weeks 0, 8, 16, and 24. A high dose of risedronate significantly increased BMD of the femur and vertebrae, while black cohosh extract had no significant effect on BMD individually and minimal effects upon coadministration with risedronate. Under these experimental conditions, black cohosh extract alone had no effect on BMD, nor did it negatively impact the BMD-enhancing properties of risedronate.

Metabolomics as a Truly Translational Tool for Precision Medicine.

Metabolomics is unique among omics technologies in being applicable to metabolism and toxicity studies broadly across organisms (e.g., humans, other mammals, model organisms, and even bacteria) and across biological materials (e.g., blood, urine, saliva, biopsy, and stool), including cultured cells and subcellular fractions. Metabolomics can be used to characterize biologic response patterns in humans as well as to support mechanistic studies in model systems and ex vivo studies. A broad range of resources are available, including publicly accessible data repositories (e.g., Metabolomics Workbench), tools for biostatistics and bioinformatics (e.g., MetaboAnalyst), metabolite identification (e.g., Metlin), and pathway analysis (e.g., Kyoto Encyclopedia of Genes and Genomes). Thus, metabolomics is more than a promise of the future; metabolomics is already available as a translational approach to facilitate precision medicine. This ACT Symposium review will contain an introduction to metabolomics in toxicity studies followed by sections on translational metabolic networks, translational metabolite biomarkers of acetaminophen-induced acute liver injury, translational framework using high-resolution metabolomics for integrated pharmacokinetics and pharmacodynamics, and precision medicine applications: extracting actionable targets from untargeted metabolomics data following one year in space.

Distinct lipid signatures are identified in the plasma of rats with chronic inflammation induced by estradiol benzoate and sex hormones.

INTRODUCTION: Prostatitis is likely to occur in younger or middle-aged men, while prostate cancer is likely to occur in older men. Although amino acids and lipids as biomarkers of prostate cancer have been examined using prostate cancer cell lines/tissues, no previous studies have evaluated amino acids or lipids as potential chronic prostatitis biomarkers. OBJECTIVES: The study's aim was to identify amino acids and lipids that could serve as potential biomarkers of chronic prostatitis. METHODS: We profiled the amino acids and lipids found in plasma from rats collected in a previous study. In brief, a total of 148 Sprague-Dawley rats (offspring) were dosed with estradiol benzoate (EB) on postnatal days (PNDs) 1, 3 and 5, and subsequently dosed with testosterone (T)/estradiol (E) tubes via subcutaneous implants from PND 90 to 200. Plasma was collected on PNDs 30, 90, 100, 145 and 200. Analysis was conducted with a Xevo TQ-S triple-quadrupole mass spectrometer using a Biocrates AbsoluteIDQ p180 kit. RESULTS: Plasma acylcarnitines [(C2, C16:1, C18, C18:1, C18:1-OH, and C18:2)], glycerophospholipids (lysophosphatidylcholine-acyl, -di-acyl, and -di-acyl acyl-alkyl) and sphingomyelins [SM (OH) C16:1, SM C18:0, SM C18:1, and SM C20:2] significantly increased on PND 145, when chronic inflammation was observed in the dorsolateral prostate of rats dosed with EB, T, and E. No statistical significances of amino acid levels were observed in the EB + T + E group on PND 145. CONCLUSION: Exposure to EB, T, and E altered lipid levels in rat plasma with chronic prostate inflammation. These findings suggest that the identified lipids may be predictive chronic prostatitis biomarkers. The results require confirmation through additional nonclinical and human studies.

Pharmacometabolomic Pathway Response of Effective Anticancer Agents on Different Diets in Rats with Induced Mammary Tumors.

Metabolomics is an effective approach to characterize the metabotype which can reflect the influence of genetics, physiological status, and environmental factors such as drug intakes, diet. Diet may change the chemopreventive efficacy of given agents due to the altered physiological status of the subject. Here, metabolomics response to a chemopreventive agent targretin or tamoxifen, in rats with methylnitrosourea-induced tumors on a standard diet (4% fat, CD) or a high fat diet (21% fat, HFD) was evaluated, and found that (1) the metabolome was substantially affected by diet and/or drug treatment; (2) multiple metabolites were identified as potential pharmacodynamic biomarkers related to targretin or tamoxifen regardless of diet and time; and (3) the primary bile acid pathway was significantly affected by targretin treatment in rats on both diets, and the lysolipid pathway was significantly affected by tamoxifen treatment in rats on the high fat diet.

An Integrated Analysis of Metabolites, Peptides, and Inflammation Biomarkers for Assessment of Preanalytical Variability of Human Plasma.

Discrepancies in blood sample collection and processing could have a significant impact on levels of metabolites, peptides, and protein biomarkers of inflammation in the blood; thus, sample quality control is critical for successful biomarker identification and validation. In this study, we analyzed the effects of several preanalytical processing conditions, including different storage times and temperatures for blood or plasma samples and different centrifugation forces on the levels of metabolites, peptides, and inflammation biomarkers in human plasma samples using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. Temperature was found to be the major factor for metabolite variation, and both time and temperature were identified as major factors for peptide variation. For inflammation biomarkers, temperature played different roles depending on the sample type (blood or plasma). Low temperature affected inflammation biomarkers in blood, while room temperature impacted inflammation biomarkers in plasma.

Towards quality assurance and quality control in untargeted metabolomics studies.

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day 'Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies' held at the National Cancer Institute's Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.

Comparison of Effects of Diet on Mammary Cancer: Efficacy of Various Preventive Agents and Metabolomic Changes of Different Diets and Agents.

To determine the effects of diet, rats were placed on a standard diet (4% fat) or on a modified Western (high-fat diet, HFD) diet (21% fat) at 43 days of age (DOA) and administered methylnitrosourea (MNU) at 50 DOA. Rats were administered effective (tamoxifen, vorozole, and Targretin) or ineffective (metformin and Lipitor) chemopreventive agents either by daily gavage or in the diet beginning at 57 DOA and continuing until sacrifice (190 DOA). Latency period of the tumors was determined by palpation, and multiplicity and cancer weights per rat were determined at final sacrifice. Rats on the HFD versus standard diet had: (i) a 6% increase in final body weights; (ii) significant decreases in tumor latency; and (iii) significant increases in final tumor multiplicity and average tumor weight. Tamoxifen, vorozole, and Targretin were highly effective preventive agents, whereas Lipitor and metformin were ineffective in rats on either diet. Serum was collected at 78 DOA and at sacrifice (190 DOA), and metabolomics were determined to identify the metabolite changes due to diets and effective agents. Rats given the HFD had increased levels of saturated free fatty acids (including myristate) and decreased levels of 2-aminooctanoate. Furthermore, rats on the HFD diet had increased levels of 2-aminobutyrate and decreases in glycine markers previously identified as indicators of prediabetes. Targretin increased long-chain glycophospholipids (e.g., oleyl-linoleoyl-glycerophosphocholine) and decreased primary bile acids (e.g., taurocholate). Tamoxifen increased palmitoyl-linoleoyl-glycophosphocholine and decreased stearoyl-arachidonyl glycophosphocholine. Finally, increased levels of methylated nucleotides (5-methylcytidine) and decreased levels of urea cycle metabolites (N-acetylcitrulline) were associated with the presence of mammary cancers.

Immune response proteins as predictive biomarkers of doxorubicin-induced cardiotoxicity in breast cancer patients.

Cancer treatment with doxorubicin (DOX) can induce cumulative dose-dependent cardiotoxicity. Currently, there are no specific biomarkers that can identify patients at risk during the initial doses of chemotherapy. The aim of this study was to examine plasma cytokines/chemokines and potential cardiovascular biomarkers for the prediction of DOX-induced cardiotoxicity. Plasma samples were collected before (T0), and after the first (T1) and the second (T2) cycles of DOX-based chemotherapy of 27 breast cancer patients, including five patients who presented with >10% decline of left ventricular ejection fraction (LVEF), five patients with LVEF decline of 5-10%, and 17 patients who maintained normal LVEF at the end of chemotherapy (240 mg/m2 cumulative dose of DOX from four cycles of treatment). Multiplex immunoassays were used to screen plasma samples for 40 distinct chemokines, nine matrix metalloproteinases, 33 potential markers of cardiovascular diseases, and the fourth-generation cardiac troponin T assay. The results showed that the patients with abnormal decline of LVEF (>10%) had lower levels of CXCL6 and sICAM-1 and higher levels of CCL23 and CCL27 at T0; higher levels of CCL23 and lower levels of CXCL5, CCL26, CXCL6, GM-CSF, CXCL1, IFN-γ, IL-2, IL-8, CXCL11, CXCL9, CCL17, and CCL25 at T1; and higher levels of MIF and CCL23 at T2 than the patients who maintained normal LVEF. Patients with LVEF decline of 5-10% had lower plasma levels of CXCL1, CCL3, GDF-15, and haptoglobin at T0; lower levels of IL-16, FABP3, and myoglobin at T1; and lower levels of myoglobin and CCL23 at T2 as compared to the patients who maintained normal LVEF. This pilot study identified potential biomarkers that may help predict which patients are vulnerable to DOX-induced cardiotoxicity although further validation is needed in a larger cohort of patients. Impact statement Drug-induced cardiotoxicity is one of the major concerns in drug development and clinical practice. It is critical to detect potential cardiotoxicity early before onset of symptomatic cardiac dysfunction or heart failure. Currently there are no qualified clinical biomarkers for the prediction of cardiotoxicity caused by cancer treatment such as doxorubicin (DOX). By using multiplex immunoassays, we identified proteins with significantly changed plasma levels in a group of breast cancer patients who were treated with DOX-based chemotherapy and produced cardiotoxicity. These proteins were associated with immune response and were identified before DOX treatment or at early doses of treatment, thus they could be potential predictive biomarkers of cardiotoxicity although further validation is required to warrant their clinical values.

Dose-response analysis of epigenetic, metabolic, and apical endpoints after short-term exposure to experimental hepatotoxicants.

Identification of sensitive and novel biomarkers or endpoints associated with toxicity and carcinogenesis is of a high priority. There is increasing interest in the incorporation of epigenetic and metabolic biomarkers to complement apical data; however, a number of questions, including the tissue specificity, dose-response patterns, early detection of those endpoints, and the added value need to be addressed. In this study, we investigated the dose-response relationship between apical, epigenetic, and metabolomics endpoints following short-term exposure to experimental hepatotoxicants, clofibrate (CF) and phenobarbital (PB). Male F344 rats were exposed to PB (0, 5, 25, and 100 mg/kg/day) or CF (0, 10, 50, and 250 mg/kg/day) for seven days. Exposure to PB or CF resulted in dose-dependent increases in relative liver weights, hepatocellular hypertrophy and proliferation, and increases in Cyp2b1 and Cyp4a1 transcripts. These changes were associated with altered histone modifications within the regulatory units of cytochrome genes, LINE-1 DNA hypomethylation, and altered microRNA profiles. Metabolomics data indicated alterations in the metabolism of bile acids. This study provides the first comprehensive analysis of the apical, epigenetic and metabolic alterations, and suggests that the latter two occur within or near the dose response curve of apical endpoint alterations following exposure to experimental hepatotoxicants.

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification.

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the "true" external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6ppm×6ppm×1Å in the C-C region, 6ppm×30ppm×1Å in the C-N region, and 30ppm×30ppm×1Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model - a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.

Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction.

The estrogenic potential (expressed as a score composite of 18 high throughput screening bioassays) of 1528 compounds from the ToxCast database was modeled by a 3-dimensional spectral data activity relationship approach (3D-SDAR). Due to a lack of 17 O nuclear magnetic resonance (NMR) simulation software, the most informative carbon-carbon 3D-SDAR fingerprints were augmented with indicator variables representing oxygen atoms from carbonyl and carboxamide, ester, sulfonyl, nitro, aliphatic hydroxyl, and phenolic hydroxyl groups. To evaluate the true predictive performance of the authors' model the United States Environmental Protection Agency provided them with a blind test set consisting of 2008 compounds. Of these, 543 had available literature data-their binding affinity served to estimate the external classification accuracy of the developed model: predictive accuracy of 0.62, sensitivity of 0.71, and specificity of 0.53 were obtained. Compared with alternative modeling techniques, the authors' model displayed very little reduction in performance between the modeling and the prediction set. A 3D-SDAR mapping technique allowed identification of structural features essential for estrogenicity: 1) the presence of a phenolic OH group or cyclohexenone, 2) a second aromatic or phenolic ring at a distance of 6 Što 8 Šfrom the oxygen of the first phenol ring, 3) the presence of a methyl group approximately 6 Šaway from the centroid of a phenol ring, and 4) a carbonyl group in close proximity (∼4 Šmeasured to the centroid) to 1 of the phenol rings. Environ Toxicol Chem 2017;36:823-830. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Metabolomics enables precision medicine: "A White Paper, Community Perspective".

INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. OBJECTIVES OF WHITE PAPER­EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Metabolomics evaluation of the impact of smokeless tobacco exposure on the oral bacterium Capnocytophaga sputigena.

The association between exposure to smokeless tobacco products (STP) and oral diseases is partially due to the physiological and pathological changes in the composition of the oral microbiome and its metabolic profile. However, it is not clear how STPs affect the physiology and ecology of oral microbiota. A UPLC/QTof-MS-based metabolomics study was employed to analyze metabolic alterations in oral bacterium, Capnocytophaga sputigena as a result of smokeless tobacco exposure and to assess the capability of the bacterium to metabolize nicotine. Pathway analysis of the metabolome profiles indicated that smokeless tobacco extracts caused oxidative stress in the bacterium. The metabolomics data also showed that the arginine-nitric oxide pathway was perturbed by the smokeless tobacco treatment. Results also showed that LC/MS was useful in identifying STP constituents and additives, including caffeine and many flavoring compounds. No significant changes in levels of nicotine and its major metabolites were found when C. sputigena was cultured in a nutrient rich medium, although hydroxylnicotine and cotinine N-oxide were detected in the bacterial metabolites suggesting that nicotine metabolism might be present as a minor degradation pathway in the bacterium. Study results provide new insights regarding the physiological and toxicological effects of smokeless tobacco on oral bacterium C. sputigena and associated oral health as well as measuring the ability of the oral bacterium to metabolize nicotine.

Early metabolomics changes in heart and plasma during chronic doxorubicin treatment in B6C3F1 mice.

The present study aimed to identify molecular markers of early stages of cardiotoxicity induced by a potent chemotherapeutic agent, doxorubicin (DOX). Male B6C3F1 mice were dosed with 3 mg kg(-1) DOX or saline via tail vein weekly for 2, 3, 4, 6 or 8 weeks (cumulative DOX doses of 6, 9, 12, 18 or 24 mg kg(-1) , respectively) and euthanized a week after the last dose. Mass spectrometry-based and nuclear magnetic resonance spectrometry-based metabolic profiling were employed to identify initial biomarkers of cardiotoxicity before myocardial injury and cardiac pathology, which were not noted until after the 18 and 24 mg kg(-1) cumulative doses, respectively. After a cumulative dose of 6 mg kg(-1) , 18 amino acids and four biogenic amines (acetylornithine, kynurenine, putrescine and serotonin) were significantly increased in cardiac tissue; 16 amino acids and two biogenic amines (acetylornithine and hydroxyproline) were significantly altered in plasma. In addition, 16 acylcarnitines were significantly increased in plasma and five were significantly decreased in cardiac tissue compared to saline-treated controls. Plasma lactate and succinate, involved in the Krebs cycle, were significantly altered after a cumulative dose of 6 mg kg(-1) . A few metabolites remained altered at higher cumulative DOX doses, which could partly indicate a transition from injury processes at 2 weeks to repair processes with additional injury happening concurrently before myocardial injury at 8 weeks. These altered metabolic profiles in mouse heart and plasma during the initial stages of injury progression due to DOX treatment may suggest these metabolites as candidate early biomarkers of cardiotoxicity. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. CITATION: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.

Translational biomarkers of acetaminophen-induced acute liver injury.

Acetaminophen (APAP) is a commonly used analgesic drug that can cause liver injury, liver necrosis and liver failure. APAP-induced liver injury is associated with glutathione depletion, the formation of APAP protein adducts, the generation of reactive oxygen and nitrogen species and mitochondrial injury. The systems biology omics technologies (transcriptomics, proteomics and metabolomics) have been used to discover potential translational biomarkers of liver injury. The following review provides a summary of the systems biology discovery process, analytical validation of biomarkers and translation of omics biomarkers from the nonclinical to clinical setting in APAP-induced liver injury.

Genetic associations with micronutrient levels identified in immune and gastrointestinal networks.

The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6-14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein-protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene-nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions.

Methylation potential associated with diet, genotype, protein, and metabolite levels in the Delta Obesity Vitamin Study.

Micronutrient research typically focuses on analyzing the effects of single or a few nutrients on health by analyzing a limited number of biomarkers. The observational study described here analyzed micronutrients, plasma proteins, dietary intakes, and genotype using a systems approach. Participants attended a community-based summer day program for 6-14 year old in 2 years. Genetic makeup, blood metabolite and protein levels, and dietary differences were measured in each individual. Twenty-four-hour dietary intakes, eight micronutrients (vitamins A, D, E, thiamin, folic acid, riboflavin, pyridoxal, and pyridoxine) and 3 one-carbon metabolites [homocysteine (Hcy), S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH)], and 1,129 plasma proteins were analyzed as a function of diet at metabolite level, plasma protein level, age, and sex. Cluster analysis identified two groups differing in SAM/SAH and differing in dietary intake patterns indicating that SAM/SAH was a potential marker of nutritional status. The approach used to analyze genetic association with the SAM/SAH metabolites is called middle-out: SNPs in 275 genes involved in the one-carbon pathway (folate, pyridoxal/pyridoxine, thiamin) or were correlated with SAM/SAH (vitamin A, E, Hcy) were analyzed instead of the entire 1M SNP data set. This procedure identified 46 SNPs in 25 genes associated with SAM/SAH demonstrating a genetic contribution to the methylation potential. Individual plasma metabolites correlated with 99 plasma proteins. Fourteen proteins correlated with body mass index, 49 with group age, and 30 with sex. The analytical strategy described here identified subgroups for targeted nutritional interventions.

Targeted liquid chromatography-mass spectrometry analysis of serum acylcarnitines in acetaminophen toxicity in children.

AIM: Long-chain acylcarnitines have been postulated to be sensitive biomarkers of acetaminophen (APAP)-induced hepatotoxicity in mouse models. In the following study, the relationship of acylcarnitines with other known indicators of APAP toxicity was examined in children receiving low-dose (therapeutic) and high-dose ('overdose' or toxic ingestion) exposure to APAP. MATERIALS & METHODS: The study included three subject groups: group A (therapeutic dose, n = 187); group B (healthy controls, n = 23); and group C (overdose, n = 62). Demographic, clinical and laboratory data were collected for each subject. Serum samples were used for measurement of APAP protein adducts, a biomarker of the oxidative metabolism of APAP and for targeted metabolomics analysis of serum acylcarnitines using ultra performance liquid chromatography-triple-quadrupole mass spectrometry. RESULTS: Significant increases in oleoyl- and palmitoyl-carnitines were observed with APAP exposure (low dose and overdose) compared with controls. Significant increases in serum ALT, APAP protein adducts and acylcarnitines were observed in overdose children that received delayed treatment (time to treatment from overdose >24 h) with the antidote N-acetylcysteine. Time to peak APAP protein adducts in serum was shorter than that of the acylcarnitines and serum ALT. CONCLUSION: Perturbations in long-chain acylcarnitines in children with APAP toxicity suggest that mitochrondrial injury and associated impairment in the ß-oxidation of fatty acids are clinically relevant as biomarkers of APAP toxicity.

Metabolomics evaluation of the effects of green tea extract on acetaminophen-induced hepatotoxicity in mice.

Green tea has been purported to have beneficial health effects including protective effects against oxidative stress. Acetaminophen (APAP) is a widely used analgesic drug that can cause acute liver injury in overdose situations. These studies explored the effects of green tea extract (GTE) on APAP-induced hepatotoxicity in liver tissue extracts using ultra performance liquid chromatography/quadrupole time-of-flight mass spectrometry and nuclear magnetic resonance spectroscopy. Mice were orally administered GTE, APAP or GTE and APAP under three scenarios. APAP alone caused a high degree of hepatocyte necrosis associated with increases in serum transaminases and alterations in multiple metabolic pathways. The time of GTE oral administration relative to APAP either protected against or potentiated the APAP-induced hepatotoxicity. Dose dependent decreases in histopathology scores and serum transaminases were noted when GTE was administered prior to APAP; whereas, the opposite occurred when GTE was administered after APAP. Similarly, metabolites altered by APAP alone were less changed when GTE was given prior to APAP. Significantly altered pathways included fatty acid metabolism, glycerophospholipid metabolism, glutathione metabolism, and energy pathways. These studies demonstrate the complex interaction between GTE and APAP and the need to employ novel analytical strategies to understand the effects of dietary supplements on pharmaceutical compounds.