Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Metabolomics evaluation of the photochemical impact of violet-blue light (405 nm) on ex vivo platelet concentrates.

INTRODUCTION: Microbicidal violet-blue light in the visible spectrum (405 nm) has been under evaluation for pathogen inactivation in ex vivo human plasma and platelets (PLTs) stored in plasma. Results to date have demonstrated that several blood-borne infectious disease-causing pathogens can be successfully reduced to significantly low levels in the light-treated plasma and PLTs. METHOD: In order to evaluate whether the microbicidal 405 nm light is safe for the treatment of PLT concentrates for pathogen inactivation, LC/MS-based metabolomics analyses were performed to evaluate the overall impact of 405 nm violet-blue light treatment on ex vivo PLT concentrates suspended in plasma and on plasma itself, and to identify metabolome changes in intra-platelet and extra-cellular medium (i.e., plasma). RESULTS: The metabolomics data identified that platelet activating factors (PAFs), agonists and prostaglandins, which can influence PLT basic functions such as integrity, activation, and aggregation potential were unaltered, suggesting that 405 nm light illumination is safe regarding PLT basic functions. Distinct increases in hydroxyl fatty acids and aldehydes, as well as decreases in antioxidant metabolites indicated that reactive oxygen species (ROS) were generated at high levels after only one hour of exposure to 405 nm light. Distinctly changed endogenous photosensitizer metabolites after 1 h of light exposure provided good evidence that 405 nm light was an effective microbicide acting through ROS mechanism and no external additive photosensitizers were required.

Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management.

BACKGROUND: Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. OBJECTIVE: The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. KEY REPORTING PRACTICES: Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES: This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS: The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS: The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

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.

Evaluation of the Performance of Lipidyzer Platform and Its Application in the Lipidomics Analysis in Mouse Heart and Liver.

Lipids play important roles in cell signaling, energy storage, and as major structural components of cell membranes. To date, little work has been conducted to show the extent of tissue specificity of lipid compositions. Here, the recently acquired Lipidyzer platform was employed in this pilot study: (i) to assess the performance of the Lipidyzer platform, (ii) to explore lipid profiles in liver and cardiac tissue in mice, (iii) to examine sex-specific differences in lipids in the liver tissue, and (iv) to evaluate biological variances in lipidomes present in animals. In total, 787 lipid species from 13 lipid classes were measured in the liver and heart. Lipidomics data from the Lipidyzer platform were very reproducible with the coefficient of variations of the quality control (QC) samples, ∼10%. The total concentration of the cholesterol esters (CE) lipid class, and specifically CE(16:1) and CE(18:1) species, showed sex differences in the liver. Cardiac tissue had higher levels of phospholipids containing docosahexaenoic acid, which could be related to heart health status and function. Our results demonstrate the usefulness of the Lipidyzer platform in identifying differences in lipid profile at the tissue level and between male and female mice in specific tissues.

Hepatic Transcript Profiles of Cytochrome P450 Genes Predict Sex Differences in Drug Metabolism.

Safety assessments of new drug candidates are an important part of the drug development and approval process. Often, possible sex-associated susceptibilities are not adequately addressed, and better assessment tools are needed. We hypothesized that hepatic transcript profiles of cytochrome P450 (P450) enzymes can be used to predict sex-associated differences in drug metabolism and possible adverse events. Comprehensive hepatic transcript profiles were generated for F344 rats of both sexes at nine ages, from 2 weeks (preweaning) to 104 weeks (elderly). Large differences in the transcript profiles of 29 drug metabolizing enzymes and transporters were found between adult males and females (8-52 weeks). Using the PharmaPendium data base, 41 drugs were found to be metabolized by one or two P450 enzymes encoded by sexually dimorphic mRNAs and thus were candidates for evaluation of possible sexually dimorphic metabolism and/or toxicities. Suspension cultures of primary hepatocytes from three male and three female adult rats (10-13 weeks old) were used to evaluate the metabolism of 11 drugs predicted to have sexually dimorphic metabolism. The pharmacokinetics of the drug or its metabolite was analyzed by liquid chromatography/tandem mass spectrometry using multiple reaction monitoring. Of those drugs with adequate metabolism, the predicted significant sex-different metabolism was found for six of seven drugs, with half-lives 37%-400% longer in female hepatocytes than in male hepatocytes. Thus, in this rat model, transcript profiles may allow identification of potential sex-related differences in drug metabolism. SIGNIFICANCE STATEMENT: The present study showed that sex-different expression of genes coding for drug metabolizing enzymes, specifically cytochrome P450s, could be used to predict sex-different drug metabolism and, thus, provide a new tool for protecting susceptible subpopulations from possible adverse drug events.

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.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners.

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. OBJECTIVES: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics. METHODS: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. RESULTS: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). CONCLUSIONS: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

Determination of structural factors affecting binding to mu, kappa and delta opioid receptors.

Addiction is a complex behavioral phenomenon in which naturally occurring or synthetic chemicals modulate the response of the reward system through their binding to a variety of neuroreceptors, resulting in compulsive substance-seeking and use despite harmful consequences to the individual. Among these, the opioid receptor (OR) family and more specifically, the mu-opioid receptor (MOR) subtype plays a critical role in the addiction to powerful prescription and illicit drugs such as hydrocodone, oxycodone, fentanyl, cocaine, and methamphetamine (Contet et al. in Curr Opin Neurobiol 14(3):370-378, 2004). Conversely, agonists binding to kappa (KOR) and antagonists binding to delta opioid receptors (DOR) have been reported to induce negative reinforcing effects. As more than 700 new psychoactive substances were illegally sold between 2009 and 2016 (DEA-DCT-DIR-032-18), most of them lacking basic toxicological and pharmacological profiles, molecular modeling approaches that could quickly and reliably fill the gaps in our knowledge would be highly desirable tools for determining the effects of these synthetics. Here, we report accurate 3D-spectrometric data-activity relationship classification models for large and diverse datasets of MOR, KOR and DOR binders with areas under the receiver operating characteristic curve for the "blind" prediction sets exceeding 0.88. Structural features associated with (selective) binding to MOR, KOR and/or DOR were identified. These models could assist regulatory agencies in evaluating the health risks associated with the use of unprofiled substances as well as to help the pharmaceutical industry in its search for new drugs to combat addiction.

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.

Stability of the Human Plasma Proteome to Pre-analytical Variability as Assessed by an Aptamer-Based Approach.

Variable processing and storage of whole blood and/or plasma are potential confounders in biomarker development and clinical assays. The goal of the study was to investigate how pre-analytical variables impact the human plasma proteome. Whole blood obtained from 16 apparently healthy individuals was collected in six EDTA tubes and processed randomly under six pre-analytical variable conditions including blood storage at 0 °C or RT for 6 h (B6h0C or B6hRT) before processing to plasma, plasma storage at 4 °C or RT for 24 h (P24h4C or P24hRT), low centrifugal force at 1300 × g, (Low×g), and immediate processing to plasma under 2500 × g (control) followed by plasma storage at -80 °C. An aptamer-based proteomic assay was performed to identify significantly changed proteins (fold change ≥1.2, P < 0.05, and false discovery rate < 0.05) relative to the control from a total of 1305 proteins assayed. Pre-analytical conditions Low×g and B6h0C resulted in the most plasma proteome changes with 200 and 148 proteins significantly changed, respectively. Only 36 proteins were changed under B6hRT. Conditions P24h4C and P24hRT yielded changes of 28 and 75 proteins, respectively. The complement system was activated in vitro under the conditions B6hRT, P24h4C, and P24hRT. The results suggest that particular pre-analytical variables should be controlled for clinical measurement of specific biomarkers.

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.

Interest is high in improving quality control for clinical metabolomics: setting the path forward for community harmonization of quality control standards.

Up to now, quality assurance (QA) and quality control (QC) in metabolomics are procedures that most labs did using their own in-house developed procedures and rules since there was no consensus or minimum requirement. Now there is a lot of enthusiasm for developing standardization of QA and QC procedures.

Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation.

A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated 13C and 15N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nitrothiophene groups, and aromatic hydroxylamine moieties. 3D-SDAR was also able to capture "true" negative contributions that are particularly difficult to detect through alternative methods. These include sulphonamide, acetamide, and other functional groups, which not only lack contributions to the overall mutagenic potential, but are known to actively lower it, if present in the chemical structures of what otherwise would be potential mutagens.

Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans.

Acetaminophen (APAP) overdose is the leading cause of acute liver failure. Yet the mechanisms underlying adaptive tolerance toward APAP-induced liver injury are not fully understood. To better understand molecular mechanisms contributing to adaptive tolerance to APAP is an underpinning foundation for APAP-related precision medicine. In the current study, the mRNA and microRNA (miRNA) expression profiles derived from next generation sequencing data for APAP-treated (5 and 10 mM) HepaRG cells and controls were analyzed systematically. Putative miRNAs targeting key dysregulated genes involved in APAP hepatotoxicity were selected using in silico prediction algorithms, un-biased gene ontology, and network analyses. Luciferase reporter assays, RNA electrophoresis mobility shift assays, and miRNA pull-down assays were performed to investigate the role of miRNAs affecting the expression of dysregulated genes. Levels of selected miRNAs were measured in serum samples obtained from children with APAP overdose (58.6-559.4 mg/kg) and from healthy controls. As results, 2758 differentially expressed genes and 47 miRNAs were identified. Four of these miRNAs (hsa-miR-224-5p, hsa-miR-320a, hsa-miR-449a, and hsa-miR-877-5p) suppressed drug metabolizing enzyme (DME) levels involved in APAP-induced liver injury by downregulating HNF1A, HNF4A and NR1I2 expression. Exogenous transfection of these miRNAs into HepaRG cells effectively rescued them from APAP toxicity, as indicated by decreased alanine aminotransferase levels. Importantly, hsa-miR-320a and hsa-miR-877-5p levels were significantly elevated in serum samples obtained from children with APAP overdose compared to health controls. Collectively, these data indicate that hsa-miR-224-5p, hsa-miR-320a, hsa-miR-449a, and hsa-miR-877-5p suppress DME expression involved in APAP-induced hepatotoxicity and they contribute to an adaptive response in hepatocytes.

Evaluation of metabolism of azo dyes and their effects on Staphylococcus aureus metabolome.

Dyes containing one or more azo linkages are widely applied in cosmetics, tattooing, food and drinks, pharmaceuticals, printing inks, plastics, leather, as well as paper industries. Previously we reported that bacteria living on human skin have the ability to reduce some azo dyes to aromatic amines, which raises potential safety concerns regarding human dermal exposure to azo dyes such as those in tattoo ink and cosmetic colorant formulations. To comprehensively investigate azo dye-induced toxicity by skin bacteria activation, it is very critical to understand the mechanism of metabolism of the azo dyes at the systems biology level. In this study, an LC/MS-based metabolomics approach was employed to globally investigate metabolism of azo dyes by Staphylococcus aureus as well as their effects on the metabolome of the bacterium. Growth of S. aureus in the presence of Sudan III or Orange II was not affected during the incubation period. Metabolomics results showed that Sudan III was metabolized to 4-(phenyldiazenyl) aniline (48%), 1-[(4-aminophenyl) diazenyl]-2-naphthol (4%) and eicosenoic acid Sudan III (0.9%). These findings indicated that the azo bond close to naphthalene group of Sudan III was preferentially cleaved compared with the other azo bond. The metabolite from Orange II was identified as 4-aminobenzene sulfonic acid (35%). A much higher amount of Orange II (~90×) was detected in the cell pellets from the active viable cells compared with those from boiled cells incubated with the same concentration of Orange II. This finding suggests that Orange II was primarily transported into the S. aureus cells for metabolism, instead of the theory that the azo dye metabolism occurs extracellularly. In addition, the metabolomics results showed that Sudan III affected energy pathways of the S. aureus cells, while Orange II had less noticeable effects on the cells. In summary, this study provided novel information regarding azo dye metabolism by the skin bacterium, the effects of azo dyes on the bacterial cells and the important role on the toxicity and/or inactivation of these compounds due to microbial metabolism.

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.

Potential of extracellular microRNAs as biomarkers of acetaminophen toxicity in children.

UNLABELLED: Developing biomarkers for detecting acetaminophen (APAP) toxicity has been widely investigated. Recent studies of adults with APAP-induced liver injury have reported human serum microRNA-122 (miR-122) as a novel biomarker of APAP-induced liver injury. The goal of this study was to examine extracellular microRNAs (miRNAs) as potential biomarkers for APAP liver injury in children. Global levels of serum and urine miRNAs were examined in three pediatric subgroups: 1) healthy children (n=10), 2) hospitalized children receiving therapeutic doses of APAP (n=10) and 3) children hospitalized for APAP overdose (n=8). Out of 147 miRNAs detected in the APAP overdose group, eight showed significantly increased median levels in serum (miR-122, -375, -423-5p, -30d-5p, -125b-5p, -4732-5p, -204-5p, and -574-3p), compared to the other groups. Analysis of urine samples from the same patients had significantly increased median levels of four miRNAs (miR-375, -940, -9-3p and -302a) compared to the other groups. Importantly, correlation of peak serum APAP protein adduct levels (an indicator of the oxidation of APAP to the reactive metabolite N-acetyl-para-quinone imine) with peak miRNA levels showed that the highest correlation was observed for serum miR-122 (R=0.94; p<0.01) followed by miR-375 (R=0.70; p=0.05). CONCLUSION: Our findings demonstrate that miRNAs are increased in children with APAP toxicity and correlate with APAP protein adducts, suggesting a potential role as biomarkers of APAP toxicity.