Lipidome characterisation and sex-specific differences in type 1 and type 2 diabetes mellitus.

BACKGROUND: In this study, we evaluated the lipidome alterations caused by type 1 diabetes (T1D) and type 2 diabetes (T2D), by determining lipids significantly associated with diabetes overall and in both sexes, and lipids associated with the glycaemic state. METHODS: An untargeted lipidomic analysis was performed to measure the lipid profiles of 360 subjects (91 T1D, 91 T2D, 74 with prediabetes and 104 controls (CT)) without cardiovascular and/or chronic kidney disease. Ultra-high performance liquid chromatography-electrospray ionization mass spectrometry (UHPLC-ESI-MS) was conducted in two ion modes (positive and negative). We used multiple linear regression models to (1) assess the association between each lipid feature and each condition, (2) determine sex-specific differences related to diabetes, and (3) identify lipids associated with the glycaemic state by considering the prediabetes stage. The models were adjusted by sex, age, hypertension, dyslipidaemia, body mass index, glucose, smoking, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, alternate Mediterranean diet score (aMED) and estimated glomerular filtration rate (eGFR); diabetes duration and glycated haemoglobin (HbA1c) were also included in the comparison between T1D and T2D. RESULTS: A total of 54 unique lipid subspecies from 15 unique lipid classes were annotated. Lysophosphatidylcholines (LPC) and ceramides (Cer) showed opposite effects in subjects with T1D and subjects with T2D, LPCs being mainly up-regulated in T1D and down-regulated in T2D, and Cer being up-regulated in T2D and down-regulated in T1D. Also, Phosphatidylcholines were clearly down-regulated in subjects with T1D. Regarding sex-specific differences, ceramides and phosphatidylcholines exhibited important diabetes-associated differences due to sex. Concerning the glycaemic state, we found a gradual increase of a panel of 1-deoxyceramides from normoglycemia to prediabetes to T2D. CONCLUSIONS: Our findings revealed an extensive disruption of lipid metabolism in both T1D and T2D. Additionally, we found sex-specific lipidome changes associated with diabetes, and lipids associated with the glycaemic state that can be linked to previously described molecular mechanisms in diabetes.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay.

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes.

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this 'omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line.

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B1, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B1, benzo[a]pyrene, cyclosporin A: from 0.2048 µM to 50 µM; rotenone: from 0.04096 to 10 µM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond.

SUMMARY: Implementing and combining methods from a diverse range of R/Bioconductor packages into 'omics' data analysis workflows represents a significant challenge in terms of standardization, readability and reproducibility. Here, we present an R/Bioconductor package, named struct (Statistics in R using Class-based Templates), which defines a suite of class-based templates that allows users to develop and implement highly standardized and readable statistical analysis workflows. Struct integrates with the STATistics Ontology to ensure consistent reporting and maximizes semantic interoperability. We also present a toolbox, named structToolbox, which includes an extensive set of commonly used data analysis methods that have been implemented using struct. This toolbox can be used to build data-analysis workflows for metabolomics and other omics technologies. AVAILABILITY AND IMPLEMENTATION: struct and structToolbox are implemented in R, and are freely available from Bioconductor (http://bioconductor.org/packages/struct and http://bioconductor.org/packages/structToolbox), including documentation and vignettes. Source code is available and maintained at https://github.com/computational-metabolomics.

Integrating in vitro metabolomics with a 96-well high-throughput screening platform.

INTRODUCTION: High-throughput screening (HTS) is emerging as an approach to support decision-making in chemical safety assessments. In parallel, in vitro metabolomics is a promising approach that can help accelerate the transition from animal models to high-throughput cell-based models in toxicity testing. OBJECTIVE: In this study we establish and evaluate a high-throughput metabolomics workflow that is compatible with a 96-well HTS platform employing 50,000 hepatocytes of HepaRG per well. METHODS: Low biomass cell samples were extracted for metabolomics analyses using a newly established semi-automated protocol, and the intracellular metabolites were analysed using a high-resolution spectral-stitching nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) method that was modified for low sample biomass. RESULTS: The method was assessed with respect to sensitivity and repeatability of the entire workflow from cell culturing and sampling to measurement of the metabolic phenotype, demonstrating sufficient sensitivity (> 3000 features in hepatocyte extracts) and intra- and inter-plate repeatability for polar nESI-DIMS assays (median relative standard deviation < 30%). The assays were employed for a proof-of-principle toxicological study with a model toxicant, cadmium chloride, revealing changes in the metabolome across five sampling times in the 48-h exposure period. To allow the option for lipidomics analyses, the solvent system was extended by establishing separate extraction methods for polar metabolites and lipids. CONCLUSIONS: Experimental, analytical and informatics workflows reported here met pre-defined criteria in terms of sensitivity, repeatability and ability to detect metabolome changes induced by a toxicant and are ready for application in metabolomics-driven toxicity testing to complement HTS assays.

Characterization of Monophasic Solvent-Based Tissue Extractions for the Detection of Polar Metabolites and Lipids Applying Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry Clinical Metabolic Phenotyping Assays.

Metabolic phenotyping of tissues uses metabolomics and lipidomics to measure the relative polar and nonpolar (lipid) metabolite levels in biological samples. This approach aims to understand disease biochemistry and identify biochemical markers of disease. Sample preparation methods must be reproducible, sensitive (high metabolite and lipid yield), and ideally rapid. We evaluated three biphasic methods for polar and nonpolar compound extraction (chloroform/methanol/water, dichloromethane/methanol/water, and methyl tert-butyl ether [MTBE]/methanol/water), a monophasic method for polar compound extraction (acetonitrile/methanol/water), and a monophasic method for nonpolar compound extraction (isopropanol/water). All methods were applied to mammalian heart, kidney, and liver tissues. Polar extracts were analyzed by hydrophilic interaction chromatography (HILIC) ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) and nonpolar extracts by C18 reversed-phase UHPLC-MS. Method reproducibility and yield were assessed using multiple annotated endogenous compounds (putatively and MS/MS annotated). Monophasic methods had the highest yield and high reproducibility for both polar (positive ion: median relative standard deviation (RSD) < 18%; negative ion: median RSD < 28%) and nonpolar (positive and negative ion: median RSD < 15%) extractions for heart, kidneys, and liver. The polar monophasic method extracted higher levels of lipid than biphasic polar extractions, and these lipids caused minimal detection suppression for other compounds during HILIC UHPLC-MS. The nonpolar monophasic method had similar or greater detection responses of all detected lipid classes compared to biphasic methods (including increased phosphatidylinositol, phosphatidylserine, and cardiolipin responses). Monophasic methods are quicker and simpler than biphasic methods and are therefore most suited for future automation.

Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening.

Incorporating safety data early in the drug discovery pipeline is key to reducing costly lead candidate failures. For a single drug development project, we estimate that several thousand samples per day require screening (<10 s per acquisition). While chromatography-based metabolomics has proven value at predicting toxicity from metabolic biomarker profiles, it lacks sufficiently high sample throughput. Acoustic mist ionization mass spectrometry (AMI-MS) is an atmospheric pressure ionization approach that can measure metabolites directly from 384-well plates with unparalleled speed. We sought to implement a signal processing and data analysis workflow to produce high-quality AMI-MS metabolomics data and to demonstrate its application to drug safety screening. An existing direct infusion mass spectrometry workflow was adapted, extended, optimized, and tested, utilizing three AMI-MS data sets acquired from technical and biological replicates of metabolite standards and HepG2 cell lysates and a toxicity study. Driven by criteria to minimize variance and maximize feature counts, an algorithm to extract the pulsed scan data was designed; parameters for signal-to-noise-ratio, replicate filter, sample filter, missing value filter, and RSD filter were all optimized; normalization and batch correction strategies were adapted; and cell phenotype filtering was implemented to exclude high cytotoxicity samples. The workflow was demonstrated using a highly replicated HepG2 toxicity data set, comprising 2772 samples from exposures to 16 drugs across 9 concentrations and generated in under 5 h, revealing metabolic phenotypes and individual metabolite changes that characterize specific modes of action. This AMI-MS workflow opens the door to ultrahigh-throughput metabolomics screening, increasing the rate of sample analysis by approximately 2 orders of magnitude.

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.

Environmentally Relevant Iron Oxide Nanoparticles Produce Limited Acute Pulmonary Effects in Rats at Realistic Exposure Levels.

Iron is typically the dominant metal in the ultrafine fraction of airborne particulate matter. Various studies have investigated the toxicity of inhaled nano-sized iron oxide particles (FeOxNPs) but their results have been contradictory, with some indicating no or minor effects and others finding effects including oxidative stress and inflammation. Most studies, however, did not use materials reflecting the characteristics of FeOxNPs present in the environment. We, therefore, analysed the potential toxicity of FeOxNPs of different forms (Fe3O4, α-Fe2O3 and γ-Fe2O3) reflecting the characteristics of high iron content nano-sized particles sampled from the environment, both individually and in a mixture (FeOx-mix). A preliminary in vitro study indicated Fe3O4 and FeOx-mix were more cytotoxic than either form of Fe2O3 in human bronchial epithelial cells (BEAS-2B). Follow-up in vitro (0.003, 0.03, 0.3 µg/mL, 24 h) and in vivo (Sprague-Dawley rats, nose-only exposure, 50 µg/m3 and 500 µg/m3, 3 h/d × 3 d) studies therefore focused on these materials. Experiments in vitro explored responses at the molecular level via multi-omics analyses at concentrations below those at which significant cytotoxicity was evident to avoid detection of responses secondary to toxicity. Inhalation experiments used aerosol concentrations chosen to produce similar levels of particle deposition on the airway surface as were delivered in vitro. These were markedly higher than environmental concentrations. No clinical signs of toxicity were seen nor effects on BALF cell counts or LDH levels. There were also no significant changes in transcriptomic or metabolomic responses in lung or BEAS-2B cells to suggest adverse effects.

Metabolic characterisation of disturbances in the APOC3/triglyceride-rich lipoprotein pathway through sample-based recall by genotype.

INTRODUCTION: High plasma triacylglyceride levels are known to be associated with increased risk of atherosclerotic cardiovascular disease. Apolipoprotein C-III (apoC-III) is a key regulator of plasma triacylglyceride levels and is associated with hypertriglyceridemia via a number of pathways. There is consistent evidence for an association of cardiovascular events with blood apoC-III level, with support from human genetic studies of APOC3 variants. As such, apoC-III has been recognised as a potential therapeutic target for patients with severe hypertriglyceridaemia with one of the most promising apoC-III-targeting drugs, volanesorsen, having recently progressed through Phase III trials. OBJECTIVES: To exploit a rare loss of function variant in APOC3 (rs138326449) to characterise the potential long-term treatment effects of apoC-III targeting interventions on the metabolome. METHODS: In a recall-by-genotype study, 115 plasma samples were analysed by UHPLC-MS to acquire non-targeted metabolomics data. The study included samples from 57 adolescents and 33 adults. Overall, 12 985 metabolic features were tested for an association with APOC3 genotype. RESULTS: 161 uniquely annotated metabolites were found to be associated with rs138326449(APOC3). The highest proportion of associated metabolites belonged to the acyl-acyl glycerophospholipid and triacylglyceride metabolite classes. In addition to the anticipated (on-target) reduction of metabolites in the triacylglyceride and related classes, carriers of the rare variant exhibited previously unreported increases in levels of a number of metabolites from the acyl-alkyl glycerophospholipid class. CONCLUSION: Overall, our results suggest that therapies targeting apoC-III may potentially achieve a broad shift in lipid profile that favours better metabolic health.

Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping.

Clinical metabolic phenotyping employs metabolomics and lipidomics to detect and measure hundreds to thousands of metabolites and lipids within human samples. This approach aims to identify metabolite and lipid changes between phenotypes (e.g. disease status) that aid understanding of biochemical mechanisms driving the phenotype. Sample preparation is a critical step in clinical metabolic phenotyping: it must be reproducible and give a high extraction yield of metabolites and lipids, and in high-throughput studies it needs to be rapid. Here, we assessed the extraction of polar metabolites from human urine and polar metabolites and lipids from human plasma for analysis by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomics and lipidomics. We evaluated several monophasic (urine and plasma) and biphasic (plasma) extractions, and we also tested alterations to (a) solvent-biofluid incubation time and temperature during monophasic extraction, and (b) phase partitioning time during biphasic extraction. Extracts were analysed by three UHPLC-MS assays: (i) hydrophilic interaction chromatography (HILIC) for urine and plasma, (ii) C18 aqueous reversed phase for urine, and (iii) C18 reversed phase for plasma lipids, and the yield and reproducibility of each method was assessed. We measured UHPLC-MS injection reproducibility as well as sample preparation reproducibility to assess sample solvent composition compatibility with UHPLC-MS and to pinpoint the origin of variance within the methods. For HILIC UHPLC-MS plasma and urine analysis, monophasic 50 : 50 methanol : acetonitrile had the most detected putatively-identified polar metabolites with high method reproducibility. This method had the highest lipid yield for plasma extracts analysed by the HILIC method. If lipid removal from the plasma polar HILIC extract is required, then the biphasic methanol/chloroform/water method is recommended. For C18 (aqueous) UHPLC-MS urine analysis, 50 : 50 methanol : water had high reproducibility and yield. For C18 UHPLC-MS plasma lipidomics, monophasic 100% isopropanol had the highest detection response of all annotated lipid classes with high reproducibility. Increasing monophasic incubation time and temperature had little benefit on metabolite and lipid yield and reproducibility for all methods.

Multiple metabolic pathways are predictive of ricin intoxication in a rat model.

INTRODUCTION: Exposure to ricin can be lethal and treatments that are under development have short windows of opportunity for administration after exposure. It is therefore essential to achieve early detection of ricin exposure to provide the best prognosis for exposed individuals. Ricin toxin can be detected in clinical samples via several antibody-based techniques, but the efficacy of these can be limited due to the rapid processing and cellular uptake of toxin in the body and subsequent low blood ricin concentrations. Other diagnostic tools that perform, in an orthogonal manner, are therefore desirable. OBJECTIVES: To determine time-dependent metabolic changes in Sprague-Dawley rats following intravenous exposure to ricin. METHODS: Sprague-Dawley rats were intravenously exposed to ricin and multiple blood samples were collected from each animal for up to 48 h following exposure in two independent studies. Plasma samples were analysed applying HILIC and C18 reversed phase UHPLC-MS assays followed by univariate and multivariate analysis. RESULTS: In Sprague-Dawley rats we have demonstrated that metabolic changes measured in blood can distinguish between rats exposed intravenously to ricin and controls prior to the onset of behavioral signs of intoxication after 24 h. A total of 37 metabolites were significantly altered following exposure to ricin when compared to controls. The arginine/proline, bile acid and triacylglyceride metabolic pathways were highlighted as being important with two triacylglycerides at 8 h post exposure giving an AUROC score of 0.94. At 16 h and 24 h the AUROC score increased to 0.98 and 1.0 with the number of metabolites in the panel increasing to 5 and 7, respectively. CONCLUSIONS: These data demonstrate that metabolites may be a useful tool to diagnose and detect ricin exposure, thus increasing the effectiveness of supportive therapy and future ricin-specific medical treatments.

mzML2ISA & nmrML2ISA: generating enriched ISA-Tab metadata files from metabolomics XML data.

SUMMARY: Submission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically generate ISA-Tab metadata file stubs from raw XML metabolomics data files. The parsing packages are separated into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). Overall, the use of mzML2ISA & nmrML2ISA reduces the time needed to capture metadata substantially (capturing 90% of metadata on assay and sample levels), is much less prone to user input errors, improves compliance with minimum information reporting guidelines and facilitates more finely grained data exploration and querying of datasets. AVAILABILITY AND IMPLEMENTATION: mzML2ISA & nmrML2ISA are available under version 3 of the GNU General Public Licence at https://github.com/ISA-tools. Documentation is available from http://2isa.readthedocs.io/en/latest/. CONTACT: reza.salek@ebi.ac.uk or isatools@googlegroups.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics.

Tandem mass spectrometry (MS/MS or MS2) is a widely used approach for structural annotation and identification of metabolites in complex biological samples. The importance of assessing the contribution of the precursor ion within an isolation window for MS2 experiments has been previously detailed in proteomics, where precursor ion purity influences the quality and accuracy of matching to mass spectral libraries, but to date, there has been little attention to this data-processing technique in metabolomics. Here, we present msPurity, a vendor-independent R package for liquid chromatography (LC) and direct infusion (DI) MS2 that calculates a simple metric to describe the contribution of the selected precursor. The precursor purity metric is calculated as "intensity of a selected precursor divided by the summed intensity of the isolation window". The metric is interpolated at the recorded point of MS2 acquisition using bordering full-scan spectra. Isotopic peaks of the selected precursor can be removed, and low abundance peaks that are believed to have limited contribution to the resulting MS2 spectra are removed. Additionally, the isolation efficiency of the mass spectrometer can be taken into account. The package was applied to Data Dependent Acquisition (DDA)-based MS2 metabolomics data sets derived from three metabolomics data repositories. For the 10 LC-MS2 DDA data sets with > ±1 Da isolation windows, the median precursor purity score ranged from 0.67 to 0.96 (scale = 0 to +1). The R package was also used to assess precursor purity of theoretical isolation windows from LC-MS data sets of differing sample types. The theoretical isolation windows being the same width used for an anticipated DDA experiment (±0.5 Da). The most complex sample had a median precursor purity score of 0.46 for the 64,498 XCMS determined features, in comparison to the less spectrally complex sample that had a purity score of 0.66 for 5071 XCMS features. It has been previously reported in proteomics that a purity score of <0.5 can produce unreliable spectra matching results. With this assumption, we show that for complex samples there will be a large number of metabolites where traditional DDA approaches will struggle to provide reliable annotations or accurate matches to mass spectral libraries.

Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop.

Prevailing knowledge gaps in linking specific molecular changes to apical outcomes and methodological uncertainties in the generation, storage, processing, and interpretation of 'omics data limit the application of 'omics technologies in regulatory toxicology. Against this background, the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) convened a workshop Applying 'omics technologies in chemicals risk assessment that is reported herein. Ahead of the workshop, multi-expert teams drafted frameworks on best practices for (i) a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) the processing of 'omics data; and (iii) weight-of-evidence approaches for integrating 'omics data. The workshop participants confirmed the relevance of these Frameworks to facilitate the regulatory applicability and use of 'omics data, and the workshop discussions provided input for their further elaboration. Additionally, the key objective (iv) to establish approaches to connect 'omics perturbations to phenotypic alterations was addressed. Generally, it was considered promising to strive to link gene expression changes and pathway perturbations to the phenotype by mapping them to specific adverse outcome pathways. While further work is necessary before gene expression changes can be used to establish safe levels of substance exposure, the ECETOC workshop provided important incentives towards achieving this goal.

HAMMER: automated operation of mass frontier to construct in silico mass spectral fragmentation libraries.

SUMMARY: Experimental MS(n) mass spectral libraries currently do not adequately cover chemical space. This limits the robust annotation of metabolites in metabolomics studies of complex biological samples. In silico fragmentation libraries would improve the identification of compounds from experimental multistage fragmentation data when experimental reference data are unavailable. Here, we present a freely available software package to automatically control Mass Frontier software to construct in silico mass spectral libraries and to perform spectral matching. Based on two case studies, we have demonstrated that high-throughput automation of Mass Frontier allows researchers to generate in silico mass spectral libraries in an automated and high-throughput fashion with little or no human intervention required. AVAILABILITY AND IMPLEMENTATION: Documentation, examples, results and source code are available at http://www.biosciences-labs.bham.ac.uk/viant/hammer/.

The Lipidomic Profile Is Associated with the Dietary Pattern in Subjects with and without Diabetes Mellitus from a Mediterranean Area.

Lipid functions can be influenced by genetics, age, disease states, and lifestyle factors, particularly dietary patterns, which are crucial in diabetes management. Lipidomics is an expanding field involving the comprehensive exploration of lipids from biological samples. In this cross-sectional study, 396 participants from a Mediterranean region, including individuals with type 1 diabetes (T1D), type 2 diabetes (T2D), and non-diabetic individuals, underwent lipidomic profiling and dietary assessment. Participants completed validated food frequency questionnaires, and lipid analysis was conducted using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC/MS). Multiple linear regression models were used to determine the association between lipid features and dietary patterns. Across all subjects, acylcarnitines (AcCa) and triglycerides (TG) displayed negative associations with the alternate Healthy Eating Index (aHEI), indicating a link between lipidomic profiles and dietary habits. Various lipid species (LS) showed positive and negative associations with dietary carbohydrates, fats, and proteins. Notably, in the interaction analysis between diabetes and the aHEI, we found some lysophosphatidylcholines (LPC) that showed a similar direction with respect to aHEI in non-diabetic individuals and T2D subjects, while an opposite direction was observed in T1D subjects. The study highlights the significant association between lipidomic profiles and dietary habits in people with and without diabetes, particularly emphasizing the role of healthy dietary choices, as reflected by the aHEI, in modulating lipid concentrations. These findings underscore the importance of dietary interventions to improve metabolic health outcomes, especially in the context of diabetes management.

Bilateral remote ischemic conditioning in children: A two-center, double-blind, randomized controlled trial in young children undergoing cardiac surgery.

Objective: The study objective was to determine whether adequately delivered bilateral remote ischemic preconditioning is cardioprotective in young children undergoing surgery for 2 common congenital heart defects with or without cyanosis. Methods: We performed a prospective, double-blind, randomized controlled trial at 2 centers in the United Kingdom. Children aged 3 to 36 months undergoing tetralogy of Fallot repair or ventricular septal defect closure were randomized 1:1 to receive bilateral preconditioning or sham intervention. Participants were followed up until hospital discharge or 30 days. The primary outcome was area under the curve for high-sensitivity troponin-T in the first 24 hours after surgery, analyzed by intention-to-treat. Right atrial biopsies were obtained in selected participants. Results: Between October 2016 and December 2020, 120 eligible children were randomized to receive bilateral preconditioning (n = 60) or sham intervention (n = 60). The primary outcome, area under the curve for high-sensitivity troponin-T, was higher in the preconditioning group (mean: 70.0 ± 50.9 µg/L/h, n = 56) than in controls (mean: 55.6 ± 30.1 µg/L/h, n = 58) (mean difference, 13.2 µg/L/h; 95% CI, 0.5-25.8; P = .04). Subgroup analyses did not show a differential treatment effect by oxygen saturations (pinteraction = .25), but there was evidence of a differential effect by underlying defect (pinteraction = .04). Secondary outcomes and myocardial metabolism, quantified in atrial biopsies, were not different between randomized groups. Conclusions: Bilateral remote ischemic preconditioning does not attenuate myocardial injury in children undergoing surgical repair for congenital heart defects, and there was evidence of potential harm in unstented tetralogy of Fallot. The routine use of remote ischemic preconditioning cannot be recommended for myocardial protection during pediatric cardiac surgery.