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.

Corrigendum: High-throughput discovery of novel developmental phenotypes.

This corrects the article DOI: 10.1038/nature19356.

High-throughput discovery of novel developmental phenotypes.

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.

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.

A bioimage informatics platform for high-throughput embryo phenotyping.

High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene-phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encompass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for themselves without the need for highly specialized software or technical expertise. In this article, we present a platform of software tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC. Over 750 reconstructions from 80 embryonic lethal and subviable lines have been captured to date, all of which are openly accessible at mousephenotype.org. Although designed for the IMPC, all software is available under an open-source licence for others to use and develop further. Ongoing developments aim to increase throughput and improve the analysis and dissemination of image data. Furthermore, we aim to ensure that images are searchable so that users can locate relevant images associated with genes, phenotypes or human diseases of interest.

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.

A Scoping Review of the Incidence, Predictors, and Outcomes of Delirium Among Critically Ill Stroke Patients.

ABSTRACT: BACKGROUND: Delirium is a common, often iatrogenically induced syndrome that may impede the physical, cognitive, and psychological recovery of critically ill adults. The effect delirium has on outcomes of intensive care unit patients having acute neurologic injury remains unclear because previous studies frequently exclude this vulnerable population. The aim of this scoping review was to describe the incidence, predictors, and outcomes of delirium among adults admitted to an intensive care unit experiencing an acute ischemic stroke, intracerebral hemorrhage, or aneurysmal subarachnoid hemorrhage. METHODS: PubMed, CINAHL, Web of Science, EMBASE, and Scopus were searched with the terms (1) stroke, (2) critical care, and (3) delirium. Inclusion criteria were original peer-reviewed research reporting the incidence, outcomes, or predictors of delirium after acute stroke among critically ill adults. Editorials, reviews, posters, conference proceedings, abstracts, and studies in which stroke was not the primary reason for admission were excluded. Title and abstract screening, full-text review, and data extraction were performed by 2 authors, with disagreements adjudicated by a third author. RESULTS: The initial search yielded 1051 results. Eighteen studies met eligibility criteria and were included in the review. Stroke type was not mutually exclusive and included persons given a diagnosis of acute ischemic stroke (11), intracerebral hemorrhage (12), aneurysmal subarachnoid hemorrhage (8), and other (1) strokes. Incidence of delirium among stroke patients ranged from 12% to 75%. Predictors of delirium included older age, preexisting dementia, higher severity of illness, and physical restraint use. Outcomes associated with delirium included higher mortality, longer length of stay, worse cognition and quality of life, and lower functional status. CONCLUSIONS: Current findings are limited by heterogenous populations, assessments, and measurement parameters. Detection and management of delirium among critically ill stroke patients requires an approach with specific considerations to the complexities of acute neurological injury and concomitant critical illness.

Knowledge-Driven Approaches to Create the MTox700+ Metabolite Panel for Predicting Toxicity.

Endogenous metabolite levels describe the molecular phenotype that is most downstream from chemical exposure. Consequently, quantitative changes in metabolite levels have the potential to predict mode-of-action and adversity, with regulatory toxicology predicated on the latter. However, toxicity-related metabolic biomarker resources remain highly fragmented and incomplete. Although development of the S1500+ gene biomarker panel has accelerated the application of transcriptomics to toxicology, a similar initiative for metabolic biomarkers is lacking. Our aim was to define a publicly available metabolic biomarker panel, equivalent to S1500+, capable of predicting pathway perturbations and/or adverse outcomes. We conducted a systematic review of multiple toxicological resources, yielding 189 proposed metabolic biomarkers from existing assays (BASF, Bowes-44, and Tox21), 342 biomarkers from databases (Adverse Outcome Pathway Wiki, Comparative Toxicogenomics Database, QIAGEN Ingenuity Pathway Analysis, and Toxin and Toxin-Target Database), and 435 biomarkers from the literature. Evidence mapping across all 8 resources generated a panel of 722 metabolic biomarkers for toxicology (MTox700+), of which 462 (64%) are associated with molecular pathways and 575 (80%) with adverse outcomes. Comparing MTox700+ and S1500+ revealed that 418 (58%) metabolic biomarkers associate with pathways shared across both panels, with further metabolites mapping to unique pathways. Metabolite reference standards are commercially available for 646 (90%) of the panel metabolites, and assays exist for 578 (80%) of these biomarkers. This study has generated a publicly available metabolic biomarker panel for toxicology, which through its future laboratory deployment, is intended to help build foundational knowledge to support the generation of molecular mechanistic data for chemical hazard assessment.

Incidence and prevalence of coma in the UK and the USA.

The epidemiology of coma is unknown because case ascertainment with traditional methods is difficult. Here, we used crowdsourcing methodology to estimate the incidence and prevalence of coma in the UK and the USA. We recruited UK and US laypeople (aged ≥18 years) who were nationally representative (i.e. matched for age, gender and ethnicity according to census data) of the UK and the USA, respectively, utilizing a crowdsourcing platform. We provided a description of coma and asked survey participants if they-'right now' or 'within the last year'-had a family member in coma. These participants (UK n = 994, USA n = 977) provided data on 30 387 family members (UK n = 14 124, USA n = 16 263). We found more coma cases in the USA (n = 47) than in the UK (n = 20; P = 0.009). We identified one coma case in the UK (0.007%, 95% confidence interval 0.00-0.04%) on the day of the survey and 19 new coma cases (0.13%, 95% confidence interval 0.08-0.21%) within the preceding year, resulting in an annual incidence of 135/100 000 (95% confidence interval 81-210) and a point prevalence of 7 cases per 100 000 population (95% confidence interval 0.18-39.44) in the UK. We identified five cases in the USA (0.031%, 95% confidence interval 0.01-0.07%) on the day of the survey and 42 new cases (0.26%, 95% confidence interval 0.19-0.35%) within the preceding year, resulting in an annual incidence of 258/100 000 (95% confidence interval 186-349) and a point prevalence of 31 cases per 100 000 population (95% confidence interval 9.98-71.73) in the USA. The five most common causes were stroke, medically induced coma, COVID-19, traumatic brain injury and cardiac arrest. To summarize, for the first time, we report incidence and prevalence estimates for coma across diagnosis types and settings in the UK and the USA using crowdsourcing methods. Coma may be more prevalent in the USA than in the UK, which requires further investigation. These data are urgently needed to expand the public health perspective on coma and disorders of consciousness.

ISA API: An open platform for interoperable life science experimental metadata.

BACKGROUND: The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. RESULTS: In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. CONCLUSIONS: The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

Predictors of New-Onset Physical Restraint Use in Critically Ill Adults.

BACKGROUND: Physical restraints are frequently used for intensive care patients and are associated with substantial morbidity. The effects of common evidence-based critical care interventions on use of physical restraints remain unclear. OBJECTIVE: To identify independent predictors of new-onset use of physical restraints in critically ill adults. METHODS: Secondary analysis of a prospective cohort study involving 5 adult intensive care units in a tertiary care medical center in the United States. Use of physical restraints was determined via daily in-person assessments and medical record review. Mixed-effects logistic regression analysis was used to examine factors associated with new-onset use of physical restraints, adjusting for covariates and within-subject correlation among intensive care unit days. RESULTS: Of 145 patients who were free of physical restraints within 48 hours of intensive care unit admission, 24 (16.6%) had restraints newly applied during their stay. In adjusted models, delirium (odds ratio [OR], 5.09; 95% CI, 1.83-14.14), endotracheal tube presence (OR, 3.47; 95% CI, 1.22-9.86), and benzodiazepine administration (OR, 3.17; 95% CI, 1.28-7.81) significantly increased the odds of next-day use of physical restraints. Tracheostomy was associated with significantly lowered odds of next-day restraint use (OR, 0.13; 95% CI, 0.02-0.73). Compared with patients with a target sedation level, patients who were in a coma (OR, 2.56; 95% CI, 0.80-8.18) or deeply sedated (OR, 2.53; 95% CI, 0.91-7.08) had higher odds of next-day use of physical restraints, and agitated patients (OR, 0.08; 95% CI, 0.00-2.07) were less likely to experience restraint use. CONCLUSION: Several potentially modifiable risk factors are associated with next-day use of physical restraints.

Metabolomic method to detect a metabolite corona on amino-functionalized polystyrene nanoparticles.

Protein coronas on nanoparticles (NPs) affect their physicochemical properties, cellular uptake, and toxicity, and have been described extensively. To date, studies of the occurrence of small molecule (metabolite) coronas are limited. We sought to determine whether a metabolite corona forms on NPs, using high-sensitivity metabolomics combined with a model system for freshwater ecotoxicology (Daphnia magna feeding on Chlorella vulgaris). Using amino-functionalized polystyrene NPs (NH2-pNPs), we showed the impact of this material on Daphnia feeding to provide a rationale for the detailed molecular investigations. We then employed a targeted LC-MS/MS approach for sodium dodecyl sulfate (SDS) as an analog to signaling molecules known to occur in our freshwater model system and optimized a corona extraction method for this representative metabolite. Next, we performed an untargeted discovery-based metabolomics study - using high-sensitivity nanoelectrospray direct infusion mass spectrometry (DIMS) - to enable an unbiased assessment of the metabolite corona of NH2-pNPs in the freshwater model system. Our results demonstrate that SDS was successfully recovered from NH2-pNPs, confirming that the extraction protocol was fit-for-purpose. Untargeted DIMS metabolomics reproducibly detected 100 s of small molecule peaks extracted from NH2-pNPs exposed to conditioned media from the D. magna-C. vulgaris model system. Attempts to annotate these extracted metabolites, including by using van Krevelen and Kendrick Mass Defect plots, indicate a diverse range of metabolites that were not clustered into any particular class. Overall we demonstrate the existence of an ecologically relevant metabolite corona on the surface of NPs through application of a high-sensitivity, untargeted mass spectrometry metabolomics workflow.

Diagnostic Reasoning and Cognitive Biases of Nurse Practitioners.

BACKGROUND: Diagnostic reasoning is often used colloquially to describe the process by which nurse practitioners and physicians come to the correct diagnosis, but a rich definition and description of this process has been lacking in the nursing literature. METHOD: A literature review was conducted with theoretical sampling seeking conceptual insight into diagnostic reasoning. RESULTS: Four common themes emerged: Cognitive Biases and Debiasing Strategies, the Dual Process Theory, Diagnostic Error, and Patient Harm. Relevant cognitive biases are discussed, followed by debiasing strategies and application of the dual process theory to reduce diagnostic error and harm. CONCLUSION: The accuracy of diagnostic reasoning of nurse practitioners may be improved by incorporating these items into nurse practitioner education and practice. [J Nurs Educ. 2018;57(4):203-208.].

Illness representation of patients with systolic heart failure.

Studies have shown that individuals influence their health outcomes, both positively and negatively, through their illness representation. To date, no studies describe the illness representation of persons with systolic heart failure, a significant contributor of morbidity and mortality in older adults. The purpose of this study was to describe illness representation in heart failure. Twenty-two subjects with New York Heart Association class II or III systolic heart failure were recruited at a university-based heart failure clinic. Illness representation was measured using the revised Illness Perception Questionnaire. The means on each of the 9 subscales were found to be significantly different from the neutral point of 3. The results suggest that participants believed that their heart failure was a chronic, cyclic disease with serious consequences that they could control through treatment. In addition, participants believed that they understood their heart failure and did not have a negative affective response to their heart failure.