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.

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.

Evaluating Cefoperazone-Induced Gut Metabolic Functional Changes in MR1-Deficient Mice.

Mucosal-associated invariant T cells are activated following the recognition of bacterial antigens presented by the major histocompatibility complex class I-related molecule (MR1). Previous metagenomics data showed that MR1-/- knock-out (KO) mice had distinct microbiota and displayed a resistance to Clostridioides difficile (CDI) colonization vs. wild-type (WT) mice. In the present study, LC/MS-based untargeted metabolomics are applied to evaluate the changes in metabolic activities, in accordance with the changes in gut microbiota caused by cefoperazone (Cef) treatment. Adult C57Bl/6J WT and MR1-/- KO mice were given sterile drinking water or spiked with 0.5 mg/mL Cef ad libitum for five days. Fecal pellets were collected daily, and both small intestinal and cecal contents were harvested at sacrifice. The PLS-DA score plots of the metabolomic data indicate that the microbiota is relatively less disturbed by Cef treatment in KO mice, which is consistent with the metagenomics data. The most noticeable differences in the metabolome of KO and WT mice were the increases in carbohydrates in the WT mice, but not in the KO mice. Metabolic functional biomarkers were identified through the correlation analysis of gamma-aminobutyric acid (GABA) and riboflavin. These detected metabolic functional biomarkers could provide information complementary to metagenomics data.

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.

Emerging technologies and their impact on regulatory science.

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

Discovery of Novel Proteomic Biomarkers for the Prediction of Kidney Recovery from Dialysis-Dependent AKI Patients.

BACKGROUND: AKI requiring dialysis (AKI-D) is associated with prolonged hospitalization, mortality, and progressive CKD among survivors. Previous studies have examined only select urine or serum biomarkers for predicting kidney recovery from AKI. METHODS: Serum samples collected on day 8 of randomized RRT from 72 patients enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were analyzed by the SOMAscan proteomic platform to profile 1305 proteins in each sample. Of these patients, 38 recovered kidney function and dialysis was discontinued, whereas another 34 patients remained on dialysis by day 28. RESULTS: Differential serum levels of 119 proteins, with 53 higher and 66 lower, were detected in samples from patients who discontinued dialysis, compared with patients who remained on dialysis by day 28. Patients were classified into tertiles on the basis of SOMAscan protein measurements for the 25 proteins most differentially expressed. The association of serum levels of each protein with kidney recovery was further evaluated using logistic regression analysis. Higher serum levels of CXCL11, CXCL2/CXCL3, CD86, Wnt-7a, BTK, c-Myc, TIMP-3, CCL5, ghrelin, PDGF-C, survivin, CA2, IL-9, EGF, and neuregulin-1, and lower levels of soluble CXCL16, IL1RL1, stanniocalcin-1, IL-6, and FGF23 when classified in tertiles were significantly associated with better kidney recovery. This significant association persisted for each of these proteins after adjusting for potential confounding risk factors including age, sex, cardiovascular SOFA score, congestive heart failure, diabetes, modality of intensive dialysis treatment, cause of AKI, baseline serum creatinine, day 8 urine volume, and estimated 60-day mortality risk. CONCLUSIONS: These results suggest concerted changes between survival-related proteins and immune-regulatory chemokines in regulating angiogenesis, endothelial and epithelial remodeling, and kidney cell regeneration, illustrating potential mechanisms of kidney recovery. Thus, this study identifies potential novel predictive biomarkers of kidney recovery in patients with AKI-D.

Serum metabolite profiles predict outcomes in critically ill patients receiving renal replacement therapy.

Acute kidney injury (AKI) requiring renal replacement therapy (RRT) is associated with increased incidence of dialysis dependence and portends high mortality in critically ill patients. At the early stage of RRT, serum metabolic biomarkers might differntiate patients with a high risk of mortality or permanent kidney injury from those who can recover. Serum samples from participants enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were collected on day 1 (n = 97) and day 8 (n = 105) of randomized RRT. The samples were further evaluated using LC/MS-based metabolic profiling. A model predicting mortality by day 8 was built from samples collected on day 1 and based on four metabolites with an area under the curve (AUC) of 0.641. A model most predictive of mortality by day 28 was built from the levels of 11 serum metabolites from day 8 with an AUC of 0.789. Both day 1 and day 8 samples had lower serum levels of 1-arachidonoyl-lysoPC and 1-eicosatetraenoyl-lysoPC (involved in anti-inflammatory processes) in the critically ill patients who died by day 8 or day 28. Higher levels of amino acids and amino acid metabolites in the day 8 model predicting < day 28 mortality may be indicative of muscle wasting. A kidney recovery biomarker panel based on the serum levels of three metabolites from day 8 samples with an AUC of 0.70 was devised. Serum metabolic profiling of AKI critically ill patients requiring RRT revealed predictive models of mortality based on observed differences in four serum metabolites at day 1 and 11 metabolites at day 8 which were predictive of mortality. Significant changes in the levels of these metabolites suggest links to inflammatory processes and/or muscle wasting.

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.

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.

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.

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.

Bile Acid Profile and its Changes in Response to Cefoperazone Treatment in MR1 Deficient Mice.

Mucosal associated invariant T-cells (MAIT cells) are activated following recognition of bacterial antigens (riboflavin intermediates) presented on major histocompatibility complex class I-related molecule (MR1). Our previous study showed that MR1-/- knock-out (KO) mice (lacking MAIT cells) harbor a unique microbiota that is resistant to antibiotic disruption and Clostridioides difficile colonization. While we have characterized the microbiota of this mouse strain, changes in global metabolic activity in these KO mice have not been assessed. Here, LC/MS-based untargeted metabolomics was applied to investigate the differences in the metabolome, specifically in the bile acid (BA) profile of wild-type (WT) and MR1-/- KO mice, as well as how antibiotics change these profiles. BA changes were evaluated in the intestinal content, cecum content, and stool samples from MR1-/- mice and WT mice treated with cefoperazone (Cef). Fecal pellets were collected daily and both intestinal and cecal contents were harvested at predetermined endpoints on day 0 (D0), day 1 (D1), day 3 (D3), and day 5 (D5). KO mice exhibited no changes in 6-hydroxymethyl-8-D-ribityllumazine (rRL-6-CH2OH; an MR1-restricted riboflavin derivative) in the stool samples at either time point vs. D0, while WT mice showed significant decreases in rRL-6-CH2OH in the stool samples on all treatment days vs. D0. Metabolomics analysis from cecal and stool samples showed that KO mice had more total BA intensity (KO/WT = ~1.7 and ~3.3 fold higher) than that from WT mice prior to Cef treatment, while the fold change difference (KO/WT = ~4.5 and ~4.4 fold) increased after five days of Cef treatment. Both KO and WT mice showed decreases in total BA intensity in response to Cef treatment, however, less dramatic decreases were present in KO vs. WT mice. Increases in taurocholic acid (TCA) intensity and decreases in deoxycholic acid (DCA) intensity in the stool samples from WT mice were associated with the depletion of certain gut bacteria, which was consistent with the previously reported microbiome data. Furthermore, the non-detected TCA and relatively higher DCA intensity in the KO mice might be related to Clostridioides difficile infection resistance, although this needs further investigation.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine.

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual's metabolic profile has been referred to as his or her "metabotype." As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual's pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

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.

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.

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.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite.

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

Microbiota of MR1 deficient mice confer resistance against Clostridium difficile infection.

Clostridium difficile (Cd) infection (CDI) typically occurs after antibiotic usage perturbs the gut microbiota. Mucosa-associated invariant T cells (MAIT) are found in the gut and their development is dependent on Major histocompatibility complex-related protein 1 (MR1) and the host microbiome. Here we were interested in determining whether the absence of MR1 impacts resistance to CDI. To this end, wild-type (WT) and MR1-/- mice were treated with antibiotics and then infected with Cd spores. Surprisingly, MR1-/- mice exhibited resistance to Cd colonization. 16S rRNA gene sequencing of feces revealed inherent differences in microbial composition. This colonization resistance was transferred from MR1-/- to WT mice via fecal microbiota transplantation, suggesting that MR1-dependent factors influence the microbiota, leading to CDI susceptibility.