Use cases, best practice and reporting standards for metabolomics in regulatory toxicology

SOLA: dissecting dose-response patterns in multi-omics data using a semi-supervised workflow

An increasing number of ecotoxicological studies have used omics-data to understand the dose-response patterns of environmental stressors. However, very few have investigated complex non-monotonic dose-response patterns with multi-omics data. In the present study, we developed a novel semi-supervised network analysis workflow as an alternative to benchmark dose (BMD) modelling. We utilised a previously published multi-omics dataset generated from Daphnia magna after chronic gamma radiation exposure to obtain novel knowledge on the dose-dependent effects of radiation. Our approach combines 1) unsupervised co-expression network analysis to group genes with similar dose responses into modules; 2) supervised classification of these modules by relevant response patterns; 3) reconstruction of regulatory networks based on transcription factor binding motifs to reveal the mechanistic underpinning of the modules; 4) differential co-expression network analysis to compare the discovered modules across two datasets with different exposure periods; and 5) pathway enrichment analysis to integrate transcriptomics and metabolomics data. Our method unveiled both known and novel effects of gamma radiation, provide insight into shifts in responses from low to high dose rates, and can be used as an alternative approach for multi-omics dose-response analysis in future. The workflow SOLA (Semi-supervised Omics Landscape Analysis) is available at https://gitlab.com/wanxin.lai/SOLA.git.

Wildfire Smoke: Health Effects, Mechanisms, and Mitigation

Wildfires are becoming more frequent and intense on a global scale, raising concerns about their acute and long-term effects on human health. We conducted a systematic review of the current epidemiological evidence on wildfire health risks and a meta-analysis to investigate the association between wildfire smoke exposure and various health outcomes. We discovered that wildfire smoke increases the risk of premature deaths and respiratory morbidity in the general population. Meta-analysis of cause-specific mortality and morbidity revealed that wildfire smoke had the strongest associations with cardiovascular mortality (RR: 1.018, 95% CI: 1.014-1.021), asthma hospitalization (RR: 1.054, 95% CI: 1.026-1.082), and asthma emergency department visits (RR: 1.117, 95% CI: 1.035-1.204) in the general population. Subgroup analyses of age found that adults and elderly adults were more susceptible to the cardiopulmonary effects of wildfire smoke. Next, we systematically addressed the toxicological mechanisms of wildfire smoke, including direct toxicity, oxidative stress, inflammatory reactions, immune dysregulation, genotoxicity and mutations, skin allergies, inflammation, and others. We discuss wildfire smoke risk mitigation strategies including public health interventions, regulatory measures, and personal actions. We conclude by highlighting current research limitations and future directions for wildfire research, such as elucidating the complex interactions of wildfire smoke components on human health, developing personalized risk assessment tools, and improving resilience and adaptation strategies to mitigate the health effects of wildfires in changing climate.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

INTRODUCTION

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.

OBJECTIVES

This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.

METHODS

An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).

RESULTS

Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.

CONCLUSION

This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Feature-agnostic metabolomics for determining effective subcytotoxic doses of common pesticides in human cells

Although classical molecular biology assays can provide a measure of cellular response to chemical challenges, they rely on a single biological phenomenon to infer a broader measure of cellular metabolic response. These methods do not always afford the necessary sensitivity to answer questions of subcytotoxic effects, nor do they work for all cell types. Likewise, boutique assays such as cardiomyocyte beat rate may indirectly measure cellular metabolic response, but they too, are limited to measuring a specific biological phenomenon and are often limited to a single cell type. For these reasons, toxicological researchers need new approaches to determine metabolic changes across various doses in differing cell types, especially within the low-dose regime. The data collected herein demonstrate that LC-MS/MS-based untargeted metabolomics with a feature-agnostic view of the data, combined with a suite of statistical methods including an adapted environmental threshold analysis, provides a versatile, robust, and holistic approach to directly monitoring the overall cellular metabolomic response to pesticides. When employing this method in investigating two different cell types, human cardiomyocytes and neurons, this approach revealed separate subcytotoxic metabolomic responses at doses of 0.1 and 1 µM of chlorpyrifos and carbaryl. These findings suggest that this agnostic approach to untargeted metabolomics can provide a new tool for determining effective dose by metabolomics of chemical challenges, such as pesticides, in a direct measurement of metabolomic response that is not cell type-specific or observable using traditional assays.

Evaluating the performance of multi-omics integration: a thyroid toxicity case study

Multi-omics data integration has been repeatedly discussed as the way forward to more comprehensively cover the molecular responses of cells or organisms to chemical exposure in systems toxicology and regulatory risk assessment. In Canzler et al. (Arch Toxicol 94(2):371-388. https://doi.org/10.1007/s00204-020-02656-y ), we reviewed the state of the art in applying multi-omics approaches in toxicological research and chemical risk assessment. We developed best practices for the experimental design of multi-omics studies, omics data acquisition, and subsequent omics data integration. We found that multi-omics data sets for toxicological research questions were generally rare, with no data sets comprising more than two omics layers adhering to these best practices. Due to these limitations, we could not fully assess the benefits of different data integration approaches or quantitatively evaluate the contribution of various omics layers for toxicological research questions. Here, we report on a multi-omics study on thyroid toxicity that we conducted in compliance with these best practices. We induced direct and indirect thyroid toxicity through Propylthiouracil (PTU) and Phenytoin, respectively, in a 28-day plus 14-day recovery oral rat toxicity study. We collected clinical and histopathological data and six omics layers, including the long and short transcriptome, proteome, phosphoproteome, and metabolome from plasma, thyroid, and liver. We demonstrate that the multi-omics approach is superior to single-omics in detecting responses at the regulatory pathway level. We also show how combining omics data with clinical and histopathological parameters facilitates the interpretation of the data. Furthermore, we illustrate how multi-omics integration can hint at the involvement of non-coding RNAs in post-transcriptional regulation. Also, we show that multi-omics facilitates grouping, and we assess how much information individual and combinations of omics layers contribute to this approach.

Modeling HepaRG metabolome responses to pyrrolizidine alkaloid exposure for insight into points of departure and modes of action

The new challenges in toxicology demand novel and innovative in vitro approaches for deriving points of departure (PODs) and determining the mode of action (MOA) of chemicals. Therefore, the aim of this original study was to couple in vitro studies with untargeted metabolomics to model the concentration-response of extra- and intracellular metabolome data on human HepaRG cells treated for 48 h with three pyrrolizidine alkaloids (PAs): heliotrine, retrorsine and lasiocarpine. Modeling revealed that the three PAs induced various monotonic and, importantly, biphasic curves of metabolite content. Based on unannotated metabolites, the endometabolome was more sensitive than the exometabolome in terms of metabolomic effects, and benchmark concentrations (BMCs) confirmed that lasiocarpine was the most hepatotoxic PA. Regarding its MOA, impairment of lipid metabolism was highlighted at a very low BMC (first quartile, 0.003 µM). Moreover, results confirmed that lasiocarpine targets bile acids, as well as amino acid and steroid metabolisms. Analysis of the endometabolome, based on coupling concentration-response and PODs, gave encouraging results for ranking toxins according to their hepatotoxic effects. Therefore, this novel approach is a promising tool for next-generation risk assessment, readily applicable to a broad range of compounds and toxic endpoints.

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.

Mass spectrometry-based metabolomics for the investigation of antibiotic-bacterial interactions

With the development of analytical technologies especially mass spectrometry, metabolomics is becoming increasingly hot in the field of studying antibiotic-bacterial interactions. On the one hand, metabolomics can reveal metabolic perturbations in bacteria in the presence of antibiotics and expose metabolic mechanisms. On the other hand, through in-depth analysis of bacterial metabolic profiles, biomarkers and bioactive secondary metabolites with great potential as drug precursors can be discovered. This review focuses on the experimental workflow of bacterial metabolomics and its application to study the interaction between bacteria and antibiotics. Metabolomics improves the understanding of antibiotic lethality, reveals metabolic perturbations in antibiotic-resistant bacteria, guides the diagnosis and antibiotic treatment of infectious diseases, and aids in the exploration of antibacterial metabolites in nature. Furthermore, current limitations and directions for future developments in this area are discussed.

Nanometabolomics Elucidated Biological Prospective of Mo4/3B2-x Nanosheets: Toward Metabolic Reprogramming of Amino Acid Metabolism

Mo4/3B2-x nanosheets are newly developed, and 2D transition metal borides (MBene) were reported in 2021, but there is no report on their further applications and modification; hence, this article sheds light on the significance of potential biological prospects for future biomedical applications. Therefore, elucidation of the biocompatibility, biotoxicology, and bioactivity of Mo4/3B2-x nanosheets has been an urgent need to be fulfilled. Nanometabolomics (also referred as nanomaterials-based metabolomics) was first proposed and utilized in our previous work, which specialized in interpreting nanomaterials-induced metabolic reprogramming through aqueous metabolomics and lipidomics approach. Hence, nanometabolomics could be considered as a novel concept combining nanoscience and metabolomics to provide bioinformation on nanomaterials' biomedical applications. In this work, the safe range of concentration (<50 mg/L) with good biosafety toward human umbilical vein endothelial cells (HUVECs) was discovered. The low concentration (5 mg/L) and high concentration (50 mg/L) of Mo4/3B2-x nanosheets were utilized for the in vitro Mo4/3B2-x-cell interaction. Nanometabolomics has elucidated the biological prospective of Mo4/3B2-x nanosheets via monitoring its biocompatibility and metabolic shift of HUVECs. The results revealed that 50 mg/L Mo4/3B2-x nanosheets could lead to a stronger alteration of amino acid metabolism with disturbance of the corresponding amino acid-related pathways (including amino acid metabolism, amino acid degradation, fatty acid biosynthesis, and lipid biosynthesis and metabolism). These interesting results were closely involved with the oxidative stress and production of excess ROS. This work could be regarded as a pathbreaking study on Mo4/3B2-x nanosheets at a biological level, which also designates their further biochemical, medical, and industrial application and development based on nanometabolomics bioinformation.

Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of Cannabis Species

The Cannabis species is one of the potent ancient medicinal plants acclaimed for its medicinal properties and recreational purposes. The plant parts are used and exploited all over the world for several agricultural and industrial applications. For many years Cannabis spp. has proven to present a highly diverse metabolomic profile with a pool of bioactive metabolites used for numerous pharmacological purposes ranging from anti-inflammatory to antimicrobial. Cannabis sativa has since been an extensive subject of investigation, monopolizing the research. Hence, there are fewer studies with a comprehensive understanding of the composition of bioactive metabolites grown in different environmental conditions, especially C. indica and a few other Cannabis strains. These pharmacological properties are mostly attributed to a few phytocannabinoids and some phytochemicals such as terpenoids or essential oils which have been tested for antimicrobial properties. Many other discovered compounds are yet to be tested for antimicrobial properties. These phytochemicals have a series of useful properties including anti-insecticidal, anti-acaricidal, anti-nematicidal, anti-bacterial, anti-fungal, and anti-viral properties. Research studies have reported excellent antibacterial activity against Gram-positive and Gram-negative multidrug-resistant bacteria as well as methicillin-resistant Staphylococcus aureus (MRSA). Although there has been an extensive investigation on the antimicrobial properties of Cannabis, the antimicrobial properties of Cannabis on phytopathogens and aquatic animal pathogens, mostly those affecting fish, remain under-researched. Therefore, the current review intends to investigate the existing body of research on metabolomic profile and anti-microbial properties whilst trying to expand the scope of the properties of the Cannabis plant to benefit the health of other animal species and plant crops, particularly in agriculture.

Metabolomic profiling of deep vein thrombosis

Deep vein thrombosis (DVT) of the lower extremities is one of the most common peripheral vascular diseases, with significant complications and sequelae. Metabolomics aims to identify small molecules in biological samples. It can serve as a promising method for screening compounds that can be used for early disease detection, diagnosis, treatment response prediction, and prognosis. In addition, high-throughput metabolomics screening can yield significant insights into the pathophysiological pathways of DVT. Currently, the metabolomic profiles of DVT have yielded inconsistent expression patterns. This article examines the recent advancements in metabolomic studies of DVT and analyzes the factors that may influence the results.

Demonstrating the reliability of in vivo metabolomics based chemical grouping: towards best practice

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.

Utility of in vivo metabolomics to support read-across for UVCB substances under REACH

Structure-based grouping of chemicals for targeted testing and read-across is an efficient way to reduce resources and animal usage. For substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs), structure-based grouping is virtually impossible. Biology-based approaches such as metabolomics could provide a solution. Here, 15 steam-cracked distillates, registered in the EU through the Lower Olefins Aromatics Reach Consortium (LOA), as well as six of the major substance constituents, were tested in a 14-day rat oral gavage study, in line with the fundamental elements of the OECD 407 guideline, in combination with plasma metabolomics. Beyond signs of clinical toxicity, reduced body weight (gain), and food consumption, pathological investigations demonstrated the liver, thyroid, kidneys (males only), and hematological system to be the target organs. These targets were confirmed by metabolome pattern recognition, with no additional targets being identified. While classical toxicological parameters did not allow for a clear distinction between the substances, univariate and multivariate statistical analysis of the respective metabolomes allowed for the identification of several subclusters of biologically most similar substances. These groups were partly associated with the dominant (> 50%) constituents of these UVCBs, i.e., indene and dicyclopentadiene. Despite minor differences in clustering results based on the two statistical analyses, a proposal can be made for the grouping of these UVCBs. Both analyses correctly clustered the chemically most similar compounds, increasing the confidence that this biological approach may provide a solution for the grouping of UVCBs.

Big Question to Developing Solutions: A Decade of Progress in the Development of Aquatic New Approach Methodologies from 2012 to 2022

In 2012, 20 key questions related to hazard and exposure assessment and environmental and health risks of pharmaceuticals and personal care products in the natural environment were identified. A decade later, this article examines the current level of knowledge around one of the lowest-ranking questions at that time, number 19: "Can nonanimal testing methods be developed that will provide equivalent or better hazard data compared with current in vivo methods?" The inclusion of alternative methods that replace, reduce, or refine animal testing within the regulatory context of risk and hazard assessment of chemicals generally faces many hurdles, although this varies both by organism (human-centric vs. other), sector, and geographical region or country. Focusing on the past 10 years, only works that might reasonably be considered to contribute to advancements in the field of aquatic environmental risk assessment are highlighted. Particular attention is paid to methods of contemporary interest and importance, representing progress in (1) the development of methods which provide equivalent or better data compared with current in vivo methods such as bioaccumulation, (2) weight of evidence, or (3) -omic-based applications. Evolution and convergence of these risk assessment areas offer the basis for fundamental frameshifts in how data are collated and used for the protection of taxa across the breadth of the aquatic environment. Looking to the future, we are at a tipping point, with a need for a global and inclusive approach to establish consensus. Bringing together these methods (both new and old) for regulatory assessment and decision-making will require a concerted effort and orchestration. Environ Toxicol Chem 2024;43:559-574. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Host-gut microbiota interactions shape parasite infections in farmed Atlantic salmon

Animals and their associated microbiota share long evolutionary histories. However, it is not always clear how host genotype and microbiota interact to affect phenotype. We applied a hologenomic approach to explore how host-microbiota interactions shape lifetime growth and parasite infection in farmed Atlantic salmon (Salmo salar). Multi-omics data sets were generated from the guts of 460 salmon, 82% of which were naturally infected with an intestinal cestode. A single Mycoplasma bacterial strain, MAG01, dominated the gut metagenome of large, non-parasitized fish, consistent with previous studies showing high levels of Mycoplasma in the gut microbiota of healthy salmon. While small and/or parasitized salmon also had high abundance of MAG01, we observed increased alpha diversity in these individuals, driven by increased frequency of low-abundance Vibrionaceae and other Mycoplasma species that carried known virulence genes. Colonization by one of these cestode-associated Mycoplasma strains was associated with host individual genomic variation in long non-coding RNAs. Integrating the multi-omic data sets revealed coordinated changes in the salmon gut mRNA transcriptome and metabolome that correlated with shifts in the microbiota of smaller, parasitized fish. Our results suggest that the gut microbiota of small and/or parasitized fish is in a state of dysbiosis that partly depends on the host genotype, highlighting the value of using a hologenomic approach to incorporate the microbiota into the study of host-parasite dynamics.IMPORTANCEStudying host-microbiota interactions through the perspective of the hologenome is gaining interest across all life sciences. Intestinal parasite infections are a huge burden on human and animal health; however, there are few studies investigating the role of the hologenome during parasite infections. We address this gap in the largest multi-omics fish microbiota study to date using natural cestode infection of farmed Atlantic salmon. We find a clear association between cestode infection, salmon lifetime growth, and perturbation of the salmon gut microbiota. Furthermore, we provide the first evidence that the genetic background of the host may partly determine how the gut microbiota changes during parasite-associated dysbiosis. Our study therefore highlights the value of a hologenomic approach for gaining a more in-depth understanding of parasitism.

Reporting and reproducibility: Proteomics of fish models in environmental toxicology and ecotoxicology

Environmental toxicology and ecotoxicology research efforts are employing proteomics with fish models as New Approach Methodologies, along with in silico, in vitro and other omics techniques to elucidate hazards of toxicants and toxins. We performed a critical review of toxicology studies with fish models using proteomics and reported fundamental parameters across experimental design, sample preparation, mass spectrometry, and bioinformatics of fish, which represent alternative vertebrate models in environmental toxicology, and routinely studied animals in ecotoxicology. We observed inconsistencies in reporting and methodologies among experimental designs, sample preparations, data acquisitions and bioinformatics, which can affect reproducibility of experimental results. We identified a distinct need to develop reporting guidelines for proteomics use in environmental toxicology and ecotoxicology, increased QA/QC throughout studies, and method optimization with an emphasis on reducing inconsistencies among studies. Several recommendations are offered as logical steps to advance development and application of this emerging research area to understand chemical hazards to public health and the environment.

Establishing a framework for best practices for quality assurance and quality control in untargeted metabolomics

BACKGROUND

Quality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities.

AIM OF REVIEW

A present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Community engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a "living guidance" document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field.

Contaminants of Concern and Spatiotemporal Metabolomic Changes in Quagga Mussels (Dreissena bugensis rostriformis) from the Milwaukee Estuary (Wisconsin, USA)

Environmental metabolomics has emerged as a promising technique in the field of biomonitoring and as an indicator of aquatic ecosystem health. In the Milwaukee Estuary (Wisconsin, USA), previous studies have used a nontargeted metabolomic approach to distinguish between zebra mussels (Dreissena polymorpha) collected from sites of varying contamination. To further elucidate the potential effects of contaminants on bivalve health in the Milwaukee Estuary, the present study adopted a caging approach to study the metabolome of quagga mussels (Dreissena bugensis rostriformis) deployed in six sites of varying contamination for 2, 5, or 55 days. Caged mussels were co-deployed with two types of passive sampler (polar organic chemical integrative samplers and semipermeable membrane devices) and data loggers. In conjunction, in situ quagga mussels were collected from the four sites studied previously and analyzed for residues of contaminants and metabolomics using a targeted approach. For the caging study, temporal differences in the metabolomic response were observed with few significant changes observed after 2 and 5 days, but larger differences (up to 97 significantly different metabolites) to the metabolome in all sites after 55 days. A suite of metabolic pathways were altered, including biosynthesis and metabolism of amino acids, and upmodulation of phospholipids at all sites, suggesting a potential biological influence such as gametogenesis. In the caging study, average temperatures appeared to have a greater effect on the metabolome than contaminants, despite a large concentration gradient in polycyclic aromatic hydrocarbons residues measured in passive samplers and mussel tissue. Conversely, significant differences between the metabolome of mussels collected in situ from all three contaminated sites and the offshore reference site were observed. Overall, these findings highlight the importance of contextualizing the effects of environmental conditions and reproductive processes on the metabolome of model organisms to facilitate the wider use of this technique for biomonitoring and environmental health assessments. Environ Toxicol Chem 2024;43:307-323. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

The exometabolome of microbial communities inhabiting bare ice surfaces on the southern Greenland Ice Sheet

Microbial blooms colonize the Greenland Ice Sheet bare ice surface during the ablation season and significantly reduce its albedo. On the ice surface, microbes are exposed to high levels of irradiance, freeze-thaw cycles, and low nutrient concentrations. It is well known that microorganisms secrete metabolites to maintain homeostasis, communicate with other microorganisms, and defend themselves. Yet, the exometabolome of supraglacial microbial blooms, dominated by the pigmented glacier ice algae Ancylonema alaskanum and Ancylonema nordenskiöldii, remains thus far unstudied. Here, we use a high-resolution mass spectrometry-based untargeted metabolomics workflow to identify metabolites in the exometabolome of microbial blooms on the surface of the southern tip of the Greenland Ice Sheet. Samples were collected every 6 h across two diurnal cycles at 5 replicate sampling sites with high similarity in community composition, in terms of orders and phyla present. Time of sampling explained 46% (permutational multivariate analysis of variance [PERMANOVA], pseudo-F = 3.7771, p = 0.001) and 27% (PERMANOVA, pseudo-F = 1.8705, p = 0.001) of variance in the exometabolome across the two diurnal cycles. Annotated metabolites included riboflavin, lumichrome, tryptophan, and azelaic acid, all of which have demonstrated roles in microbe-microbe interactions in other ecosystems and should be tested for potential roles in the development of microbial blooms on bare ice surfaces.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium

BACKGROUND

The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies.

METHOD

We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure.

RESULT

Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle).

CONCLUSION

These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

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.

Evaluation of amino acid profile by targeted metabolomics in the eukaryotic model under exposure of benzo[a]pyrene as the exclusive stressor

Amino acids (AAs) are a class of important metabolites in metabolomics methodology that investigates metabolite changes in a cell, tissue, or organism for early diagnosis of diseases. Benzo[a]pyrene (BaP) is considered a priority contaminant by different environmental control agencies because it is a proven carcinogenic compound for humans. Therefore, it is important to evaluate the BaP interference in the metabolism of amino acids. In this work, a new amino acid extraction procedure (derivatized with propyl chloroformate/propanol) using functionalized magnetic carbon nanotubes was developed and optimized. A hybrid nanotube was used followed by desorption without heating, and excellent extraction of analytes was obtained. After exposure of Saccharomyces cerevisiae, the BaP concentration of 25.0 μmol L-1 caused changes in cell viability, indicating metabolic changes. A fast and efficient GC/MS method using a Phenomenex ZB-AAA column was optimized, enabling the determination of 16 AAs in yeasts exposed or not to BaP. A comparison of AA concentrations obtained in the two experimental groups showed that glycine (Gly), serine (Ser), phenylalanine (Phe), proline (Pro), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), tyrosine (Tyr), and leucine (Leu) statistically differentiated, after subsequent application of ANOVA with Bonferroni post-hoc test, with a confidence level of 95%. This amino acid pathway analysis confirmed previous studies that revealed the potential of these AAs as toxicity biomarker candidates.

Metabolomics 2022 workshop report: state of QA/QC best practices in LC-MS-based untargeted metabolomics, informed through mQACC community engagement initiatives

INTRODUCTION

The Metabolomics Quality Assurance and Quality Control Consortium (mQACC) organized a workshop during the Metabolomics 2022 conference.

OBJECTIVES

The goal of the workshop was to disseminate recent findings from mQACC community-engagement efforts and to solicit feedback about a living guidance document of QA/QC best practices for untargeted LC-MS metabolomics.

METHODS

Four QC-related topics were presented.

RESULTS

During the discussion, participants expressed the need for detailed guidance on a broad range of QA/QC-related topics accompanied by use-cases.

CONCLUSIONS

Ongoing efforts will continue to identify, catalog, harmonize, and disseminate QA/QC best practices, including outreach activities, to establish and continually update QA/QC guidelines.

Toxicometabolomics as a tool for next generation environmental risk assessment

Traditionally applied methodology in environmental risk assessment (ERA) has fallen out of step with technological advancements and regulatory requirements, challenging effectiveness and accuracy of the assessments. Extensive efforts have been focused towards a transition to a more data-driven and mechanistically-based next generation risk assessment. Metabolomics can produce detailed and comprehensive molecular insight into affected biochemical processes. Combining metabolomics with environmental toxicology can help to understand the mechanisms and/or modes of action underlying toxicity of environmental pollutants and inform adverse outcome pathways, as well as facilitate identification of biomarkers to quantify effects and/or exposure. This Technical Report describes the activities and work performed within the frame of the European Food Risk Assessment Fellowship Programme (EU-FORA), implemented at the section 'Environmental Chemistry and Toxicology' at the Department of Environmental Science at Aarhus University in Denmark with synergies to an ongoing H2020 RIA project 'EndocRine Guideline Optimisation' (ERGO). In accordance with the 'training by doing' principles of the EU-FORA, the fellowship project combined the exploration of the status of scientific discussion on methodology in ERA through literature study with hands-on training, using the metabolomics analysis pipeline established at Aarhus University. For the hands-on training, an amphibian metamorphosis assay (OECD test no.231) was used as a proof-of-concept toxicometabolomics study case. Both a targeted biomarker - and an untargeted metabolomics approach was applied.

Application of high throughput in vitro metabolomics for hepatotoxicity mode of action characterization and mechanistic-anchored point of departure derivation: a case study with nitrofurantoin

Omics techniques have been increasingly recognized as promising tools for Next Generation Risk Assessment. Targeted metabolomics offer the advantage of providing readily interpretable mechanistic information about perturbed biological pathways. In this study, a high-throughput LC-MS/MS-based broad targeted metabolomics system was applied to study nitrofurantoin metabolic dynamics over time and concentration and to provide a mechanistic-anchored approach for point of departure (PoD) derivation. Upon nitrofurantoin exposure at five concentrations (7.5 µM, 15 µM, 20 µM, 30 µM and 120 µM) and four time points (3, 6, 24 and 48 h), the intracellular metabolome of HepG2 cells was evaluated. In total, 256 uniquely identified metabolites were measured, annotated, and allocated in 13 different metabolite classes. Principal component analysis (PCA) and univariate statistical analysis showed clear metabolome-based time and concentration effects. Mechanistic information evidenced the differential activation of cellular pathways indicative of early adaptive and hepatotoxic response. At low concentrations, effects were seen mainly in the energy and lipid metabolism, in the mid concentration range, the activation of the antioxidant cellular response was evidenced by increased levels of glutathione (GSH) and metabolites from the de novo GSH synthesis pathway. At the highest concentrations, the depletion of GSH, together with alternations reflective of mitochondrial impairments, were indicative of a hepatotoxic response. Finally, a metabolomics-based PoD was derived by multivariate PCA using the whole set of measured metabolites. This approach allows using the entire dataset and derive PoD that can be mechanistically anchored to established key events. Our results show the suitability of high throughput targeted metabolomics to investigate mechanisms of hepatoxicity and derive point of departures that can be linked to existing adverse outcome pathways and contribute to the development of new ones.

Plant-Root Exudate Analogues Influence Activity of the 1-Aminocyclopropane-1-Carboxylate (ACC) Deaminase Gene in Pseudomonas hormoni G20-18T

Plants exposed to abiotic stress such as drought and salinity produce 1-aminocyclopropane-1-carboxylic acid (ACC) that is converted into the stress hormone ethylene. However, plant growth-promoting bacteria (PGPB), which synthesize the enzyme ACC deaminase, may lower the ACC concentration thereby reducing the concentration of ethylene and alleviating the abiotic stress. The PGPB Pseudomonas hormoni G20-18T (previously named P. fluorescens G20-18) harbors the genes acdR and acdS that encode regulation and synthesis of ACC deaminase, respectively. Regulation of the acdS gene has been investigated in several studies, but so far, it has been an open question whether plants can regulate microbial synthesis of ACC deaminase. In this study, small molecules in wheat root exudates were identified using untargeted metabolomics, and compounds belonging to amino acids, organic acids, and sugars were selected for evaluation of their influence on the expression of the acdS and acdR genes in P. hormoni G20-18T. acdS and acdR promoters were fused to the fluorescence reporter gene mCherry enabling the study of acdS and acdR promoter activity. In planta studies in wheat seedlings indicated an induced expression of acdS in association with the roots. Exudate molecules such as aspartate, alanine, arginine, and fumarate as well as glucose, fructose, and mannitol actively induced the acdS promoter, whereas the plant hormone indole-3-acetic acid (IAA) inhibited expression. Here, we present a model for how stimulatory and inhibitory root exudate molecules influence acdS promoter activity in P. hormoni G20-18T.

Unveiling potentially convergent key events related to adverse outcome pathways induced by silver nanoparticles via cross-species omics-scale analysis

The adverse effects of silver nanoparticles (AgNPs) have been studied in various models. However, there has been discordance between molecular responses across the literature, attributed to methodological biases and the physicochemical variability of AgNPs. In this study, a gene pathway meta-analysis was conducted to identify convergent and divergent key events (KEs) associated with AgNPs and explore common patterns of these KEs across species. We performed a cross-species analysis of transcriptomic data from multiple studies involving various AgNPs exposure. Pathway enrichment analysis revealed a set of pathways linked to oxidative stress, apoptosis, and metabolite and lipid metabolism, which are considered potentially conserved KEs across species. Subsequently, experiments confirmed that oxidative stress responses could be early KEs in both Caenorhabditis elegans and HepG2 cells. Moreover, AgNPs preferentially impaired the mitochondria, as evidenced by mitochondrial fragmentation and dysfunction. Furthermore, disruption of amino acids, nucleotides, sulfur compounds, glycerolipids, and glycerophospholipids metabolism were in good agreement with gene pathway shreds of evidence. Our findings imply that, although there may be organism-specific responses, potentially conserved events could exist regardless of species and physicochemical factors. These results provide valuable insights into the development of adverse outcome pathways of AgNPs across species and the regulatory toxicity of AgNPs.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Systems toxicology of complex wood combustion aerosol reveals gaseous carbonyl compounds as critical constituents

Epidemiological studies identified air pollution as one of the prime causes for human morbidity and mortality, due to harmful effects mainly on the cardiovascular and respiratory systems. Damage to the lung leads to several severe diseases such as fibrosis, chronic obstructive pulmonary disease and cancer. Noxious environmental aerosols are comprised of a gas and particulate phase representing highly complex chemical mixtures composed of myriads of compounds. Although some critical pollutants, foremost particulate matter (PM), could be linked to adverse health effects, a comprehensive understanding of relevant biological mechanisms and detrimental aerosol constituents is still lacking. Here, we employed a systems toxicology approach focusing on wood combustion, an important source for air pollution, and demonstrate a key role of the gas phase, specifically carbonyls, in driving adverse effects. Transcriptional profiling and biochemical analysis of human lung cells exposed at the air-liquid-interface determined DNA damage and stress response, as well as perturbation of cellular metabolism, as major key events. Connectivity mapping revealed a high similarity of gene expression signatures induced by wood smoke and agents prompting DNA-protein crosslinks (DPCs). Indeed, various gaseous aldehydes were detected in wood smoke, which promote DPCs, initiate similar genomic responses and are responsible for DNA damage provoked by wood smoke. Hence, systems toxicology enables the discovery of critical constituents of complex mixtures i.e. aerosols and highlights the role of carbonyls on top of particulate matter as an important health hazard.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

Simultaneously discovering the fate and biochemical effects of pharmaceuticals through untargeted metabolomics

Untargeted metabolomics is an established approach in toxicology for characterising endogenous metabolic responses to xenobiotic exposure. Detecting the xenobiotic and its biotransformation products as part of the metabolomics analysis provides an opportunity to simultaneously gain deep insights into its fate and metabolism, and to associate the internal relative dose directly with endogenous metabolic responses. This integration of untargeted exposure and response measurements into a single assay has yet to be fully demonstrated. Here we assemble a workflow to discover and analyse pharmaceutical-related measurements from routine untargeted UHPLC-MS metabolomics datasets, derived from in vivo (rat plasma and cardiac tissue, and human plasma) and in vitro (human cardiomyocytes) studies that were principally designed to investigate endogenous metabolic responses to drug exposure. Our findings clearly demonstrate how untargeted metabolomics can discover extensive biotransformation maps, temporally-changing relative systemic exposure, and direct associations of endogenous biochemical responses to the internal dose.

Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.

Perspectives on the Drosophila melanogaster Model for Advances in Toxicological Science

The use of Drosophila melanogaster for studies of toxicology has grown considerably in the last decade. The Drosophila model has long been appreciated as a versatile and powerful model for developmental biology and genetics because of its ease of handling, short life cycle, low cost of maintenance, molecular genetic accessibility, and availability of a wide range of publicly available strains and data resources. These features, together with recent unique developments in genomics and metabolomics, make the fly model especially relevant and timely for the development of new approach methodologies and movements toward precision toxicology. Here, we offer a perspective on how flies can be leveraged to identify risk factors relevant to environmental exposures and human health. First, we review and discuss fundamental toxicologic principles for experimental design with Drosophila. Next, we describe quantitative and systems genetics approaches to resolve the genetic architecture and candidate pathways controlling susceptibility to toxicants. Finally, we summarize the current state and future promise of the emerging field of Drosophila metabolomics for elaborating toxic mechanisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Soil water stress alters differentially relative metabolic pathways affecting growth performance and metal uptake efficiency in a cadmium hyperaccumulator ecotype of Sedum alfredii

Modeling plants for biomass production and metal uptake from surrounding environment is strongly dependent on the moisture content of soil. Therefore, experiments were conducted to find out how soil moisture affects the phenotypic traits, photosynthetic efficiency, metabolic profile, and metal accumulation in the hyperaccumulating ecotype of Sedum alfredii (S. alfredii). A total of six water potential gradients were set: 0 ~ -15 kPa (T1), -15 ~ -30 kPa (T2), -30 ~ -45 kPa (T3), -45 ~ -60 kPa (T4), -60 ~ -75 kPa (T5), and -75 ~ -90 kPa (T6). Different water potential treatments had a significant effect on plant growth and metal uptake efficiency. Compared to T3, T2 was more effective in promoting plant growth and development, with an increase in biomass of 23% and 17% in both fresh weight (FW) and dry weight (DW), respectively. T2 and T3 had the highest cadmium (Cd) content in the shoot (280.2 mg/kg) and (283.3 mg/kg), respectively, whereas T1 had the lowest values (204.7 mg/kg). Cd availability for plants in the soil was affected by moving soil moisture cycles. Changes in soil moisture that were either too high or too low compared to the ideal soil water content for S. alfredii growth resulted in a significant reduction in Cd accumulation in shoots. Tryptophan, phenylalanine, and other amino acids were accumulated in T5, whereas only tryptophan and phenylalanine slightly increased in T1. Sugars and alcohols such as sucrose, trehalose, mannitol, galactinol, and mannobiose increased in T5, while they decreased significantly in T1. Interestingly, in contrast to T1, the two impaired metabolic pathways in T5 (galactose and starch metabolism) were identified to be glucose metabolic pathways. These findings provide scientific information (based on experiments) to improve biomass production and metal uptake efficiency in hyperaccumulating ecotype of S. alfredii for phytoremediation-contaminated agricultural fields.

Impact of nanopesticide CuO-NPs and nanofertilizer CeO2-NPs on wheat Triticum aestivum under global warming scenarios

Concurrent effect of nanomaterials (NMs) and warming on plant performance remains largely unexplored. In this study, the effects of nanopesticide CuO and nanofertilizer CeO₂ on wheat (Triticum aestivum) under optimal (22 °C) and suboptimal (30 °C) temperatures were evaluated. CuO-NPs exerted a stronger negative effect on plant root systems than CeO₂-NPs at tested exposure levels. The toxicity of both NMs could be attributed to altered nutrient uptake, induced membrane damage, and raised disturbance of antioxidative related biological pathways. Warming significantly inhibited root growth, which was mainly linked to the disturbance of energy metabolism relevant biological pathways. The toxicity of NMs was enhanced upon warming, with a stronger inhibition of root growth and Fe and Mn uptake. Increasing temperature increased the accumulation of Ce upon CeO₂-NP exposure, while the accumulation of Cu was not affected. The relative contribution of NMs and warming to their combined effects was evaluated by comparing disturbed biological pathways under single and multiple stressors. CuO-NPs was the dominant factor inducing toxic effects, while both CeO₂-NPs and warming contributed to the mixed effect. Our study revealed the importance of carefully considering global warming as a factor in risk assessment of agricultural applications of NMs.

MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation

INTRODUCTION

Metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification.

OBJECTIVES

Develop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community.

METHODS

MetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference.

RESULTS

MetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples.

CONCLUSION

We offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.

Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups having similar structures and similar toxicological properties. Here we review case studies of chemical grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACH and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria, and boundaries of the group; adequacy of methods used to justify the group; and inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management.

Distinct profiles of oxylipid mediators in liver, lung, and placenta after maternal nano-TiO2 nanoparticle inhalation exposure

Nano-titanium dioxide (nano-TiO2) is a widely used nanomaterial found in several industrial and consumer products, including surface coatings, paints, sunscreens and cosmetics, among others. Studies have linked gestational exposure to nano-TiO2 with negative maternal and fetal health outcomes. For example, maternal pulmonary exposure to nano-TiO2 during gestation has been associated not only with maternal, but also fetal microvascular dysfunction in a rat model. One mediator of this altered vascular reactivity and inflammation is oxylipid signaling. Oxylipids are formed from dietary lipids through several enzyme-controlled pathways as well as through oxidation by reactive oxygen species. Oxylipids have been linked to control of vascular tone, inflammation, pain and other physiological and disease processes. In this study, we use a sensitive UPLC-MS/MS based analysis to probe the global oxylipid response in liver, lung, and placenta of pregnant rats exposed to nano-TiO2 aerosols. Each organ presented distinct patterns in oxylipid signaling, as assessed by principal component and hierarchical clustering heatmap analysis. In general, pro-inflammatory mediators, such as 5-hydroxyeicosatetraenoic acid (1.6 fold change) were elevated in the liver, while in the lung, anti-inflammatory and pro-resolving mediators such as 17-hydroxy docosahexaenoic acid (1.4 fold change) were elevated. In the placenta the levels of oxylipid mediators were generally decreased, both inflammatory (e.g. PGE2, 0.52 fold change) and anti-inflammatory (e.g. Leukotriene B4, 0.49 fold change). This study, the first to quantitate the levels of these oxylipids simultaneously after nano-TiO2 exposure, shows the complex interplay of pro- and anti-inflammatory mediators from multiple lipid classes and highlights the limitations of monitoring the levels of oxylipid mediators in isolation.

Mode of Action Hypothesis Testing in Chemical Safety Assessments Using Metabolomics as Supporting Evidence: Phenobarbital and Cyclobutrifluram Metabolomics Profile Comparison

In long term rodent studies administering Cyclobutrifluram (TYMIRIUM® Technology), a new agrochemical, there was a slight elevation of incidence of hepatocellular carcinomas in male CD-1 mice that was within the historical control range but appeared to be dose responsive. Cyclobutrifluram's ability to activate mouse constitutive androstane receptor (CAR) mediated gene transcription was confirmed in vitro, therefore a 28-day dietary toxicity study was conducted in vivo in male CD-1 mice to assess the CAR activation mode of action hypothesis of Cyclobutrifluram along with phenobarbital, a known CAR activator. In addition to other end points comprehensive (polar and lipidomic) hybrid metabolomics analyses were performed on terminal plasma and liver samples following 2-, 7- and 28-days dietary exposure to cyclobutrifluram and phenobarbital. The data generation and quality assessments were performed in line with the principles of the MEtabolomics standaRds Initiative in Toxicology (MERIT). First the full annotated feature set was used to compare the metabolomic changes induced by the administration of the two test substances using Shared and Unique Structures plots. This gave a comprehensive overview of the similarity of the two effect profiles showing good correlation and demonstrated that no other, alternative effect signatures were detected. Then the phenobarbital induced differentially abundant metabolites were selected, compared to the literature and their direction of change was assessed in cyclobutrifluram profiles, finding good agreement. Both approaches concluded that the metabolomics data supports the CAR activation hypothesis. Comparison of the metabolomic effect profiles can be a line of evidence in mode of action hypothesis testing in the chemical risk assessment process.

The metabolome of pink-footed goose: Heavy metals and lipid metabolism

Wildlife is exposed to mixtures of environmental contaminants that affect health and population dynamics. Exposure to toxic heavy metals originating from anthropogenic sources may exert metabolic effects at even low exposure concentrations. Here we investigated the relationships between heavy metal exposure and metabolic changes in the migratory bird pink-footed goose (Anser brachyrhynchus). We used blood pellet and blood plasma samples from 27 free-ranging pink-footed geese to study heavy metal (Cd, Cr, Hg, and Pb) exposure in relation to the metabolome. The results relate blood concentrations of Cd (range: 0.218-1.09 ng/g), Cr (range: 0.299-5.60 ng/g), and Hg (range: 2.63-6.00 ng/g) to signal areas of fatty acids and other lipids, while no correlations were identified for Pb level (range: 21.0-64.2 ng/g) exposure. Lipid signal areas were negatively associated with concentrations of Cr and positively associated with Hg exposure (both p < 0.05). α-Linolenic acid and 9-oxononanoic acid were negatively correlated to Cr exposure (both p < 0.05) and were related in the α-linolenic acid metabolism pathway. Compared to known thresholds for aviary species, the heavy metal concentrations are below levels of toxicity, which may explain the low number of metabolites that significantly change. Nevertheless, the heavy metal exposure is still correlated to changes in the lipid metabolism that may reduce migrating birds' breeding success and increase mortality for an exposed part of the population.

Model-driven data curation pipeline for LC-MS-based untargeted metabolomics

INTRODUCTION

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing.

OBJECTIVES

The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review.

METHODS

Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package.

RESULTS

The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations.

CONCLUSION

The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation

While adverse biological effects of acute high-dose ionizing radiation have been extensively investigated, knowledge on chronic low-dose effects is scarce. The aims of the present study were to identify hazards of low-dose ionizing radiation to Daphnia magna using multiomics dose-response modeling and to demonstrate the use of omics data to support an adverse outcome pathway (AOP) network development for ionizing radiation. Neonatal D. magna were exposed to γ radiation for 8 days. Transcriptomic analysis was performed after 4 and 8 days of exposure, whereas metabolomics and confirmative bioassays to support the omics analyses were conducted after 8 days of exposure. Benchmark doses (BMDs, 10% benchmark response) as points of departure (PODs) were estimated for both dose-responsive genes/metabolites and the enriched KEGG pathways. Relevant pathways derived using the BMD modeling and additional functional end points measured by the bioassays were overlaid with a previously published AOP network. The results showed that several molecular pathways were highly relevant to the known modes of action of γ radiation, including oxidative stress, DNA damage, mitochondrial dysfunction, protein degradation, and apoptosis. The functional assays showed increased oxidative stress and decreased mitochondrial membrane potential and ATP pool. Ranking of PODs at the pathway and functional levels showed that oxidative damage related functions had relatively low PODs, followed by DNA damage, energy metabolism, and apoptosis. These were supportive of causal events in the proposed AOP network. This approach yielded promising results and can potentially provide additional empirical evidence to support further AOP development for ionizing radiation.

A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK)

Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.

Mechanistic Insights into the Biological Effects of Engineered Nanomaterials: A Focus on Gold Nanoparticles

Nanotechnology has great potential to significantly advance the biomedical field for the benefit of human health. However, the limited understanding of nano-bio interactions leading to unknowns about the potential adverse health effects of engineered nanomaterials and to the poor efficacy of nanomedicines has hindered their use and commercialization. This is well evidenced considering gold nanoparticles, one of the most promising nanomaterials for biomedical applications. Thus, a fundamental understanding of nano-bio interactions is of interest to nanotoxicology and nanomedicine, enabling the development of safe-by-design nanomaterials and improving the efficacy of nanomedicines. In this review, we introduce the advanced approaches currently applied in nano-bio interaction studies-omics and systems toxicology-to provide insights into the biological effects of nanomaterials at the molecular level. We highlight the use of omics and systems toxicology studies focusing on the assessment of the mechanisms underlying the in vitro biological responses to gold nanoparticles. First, the great potential of gold-based nanoplatforms to improve healthcare along with the main challenges for their clinical translation are presented. We then discuss the current limitations in the translation of omics data to support risk assessment of engineered nanomaterials.

Advancing metabolic networks and mapping updated urinary metabolic fingerprints after exposure to typical carcinogenic heterocyclic aromatic amines

Heterocyclic aromatic amines (HAAs) were not only present in cooked foods and cigarette smoke, but also measured in airborne particles and diesel-exhaust particles. Typical HAAs have been reported to induce carcinogenicity and metabolic disturbances, but how these hazardous compounds interfere with metabolic networks by regulating metabolic pathways and fingerprinting signature metabolites as biomarkers remains ambiguous. We developed an advanced strategy that adopted chemical isotope labeling ultrahigh-performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry for urinary nontargeted metabolomics analysis to gain new insight into in vivo physiological responses stimulated by exposure to typical HAAs. Rats were orally administered with a single dose of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) or 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) (1 and 10 mg/kg bw) and their D₃-isotopic compounds, respectively, and urine samples were then continuously collected within 36 h. Metabolomics data were acquired and processed by classical multivariate statistical analysis, while urinary metabolites were further identified and characterized according to mass spectrometric fragmentation rules, time- and dose-dependent profiles, and calibration of synthesized standards. We monitored 23 and 37 urinary metabolites as the biotransformation products of PhIP and MeIQx, respectively, and first identified demethylated metabolites of PhIP, tentatively named 2-amino-6-phenylimidazo[4,5-b]pyridine, and dihydroxylation products of classical HAAs as short-term biomarkers of exposure to further unravel the metabolic networks. In addition, our findings revealed that both HAAs significantly disturb histidine metabolism, arginine and proline metabolism, tryptophan metabolism, pyrimidine metabolism, tricarboxylic acid cycle, etc. Furthermore, we found that histamine, methionine, alanine, and 4-guanidinobutanoic acid could be considered potential characteristic biomarkers for the oncogenicity or carcinogenicity of both PhIP and MeIQx and screened their specific key pivotal metabolites. The current metabolomics approach is applicable in mapping updated urinary metabolic fingerprints and identifying potential specific biomarkers for HAAs-induced early tumorigenesis.

Relationship between serum thyroid hormones and their associated metabolites, and gene expression bioindicators in the back skin of Rana [Lithobates] catesbeiana tadpoles and frogs during metamorphosis

Anuran metamorphosis is characterized by profound morphological changes including remodeling of tissues and organs. This transition is initiated by thyroid hormones (THs). However, the current knowledge of changing levels of THs during metamorphosis relies on pooled samples using methods known for high variability with sparse reporting of measured variation. Moreover, establishing a clear linkage between key gene expression bioindicators and TH levels throughout the metamorphic process is needed. Using state-of-the-art ultra-high performance liquid chromatography isotope-dilution tandem mass spectrometry, we targeted 12 THs and metabolites in the serum of Rana [Lithobates] catesbeiana (n=5-10) across seven distinct postembryonic stages beginning with premetamorphic tadpoles (Gosner stage 31-33) and continuing through metamorphosis to a juvenile frog (Gosner stage 46). TH levels were related to TH-relevant gene transcripts (thra, thrb, and thibz) in back skin of the same individual animals. Significant increases from basal levels were observed for thyroxine (T4) and 3,3',5-triiodothyronine (T3) at Gosner stage 41, reaching maximal levels at Gosner stage 44 (28 ± 10 and 2.3 ± 0.5 ng/mL, respectively), and decreasing to basal levels in juvenile frogs. In contrast, 3,5-diiodothyronine (T2) increased significantly at Gosner stage 40 and was maintained elevated until stage 44. While thra transcript levels remained constant and then decreased at the end of metamorphic climax, thrb and thibz were induced to maximal levels at Gosner stage 41, followed by a decrease to basal levels in the froglet. This exemplifies the exquisite timing of events during metamorphosis as classic early response genes are transcribed in anticipation of peak TH concentrations. The distinct T2 concentration profile suggests a biological role of this biomolecule in anuran postembryonic development and an additional aspect that may be a target of anthropogenic chemicals that can disrupt anuran metamorphosis and TH signalling. Hence, as a second aim of the study, we set out to find additional bioindicators of metamorphosis, which can aid future investigations of developmental disruption. Using a sensitive nanoLC-Orbitrap system an untargeted analysis workflow was applied. Among 6,062 endogenous metabolites, 421 showed metamorphosis-dependent concentration dynamics. These potential bioindicators included several carnitines, prostaglandins and some steroid hormones.