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

Comparison of modified Matyash method to conventional solvent systems for polar metabolite and lipid extractions

In the last decade, metabolomics has experienced significant advances in the throughput and robustness of analytical methodologies. Yet the preparation of biofluids and low-mass tissue samples remains a laborious and potentially inconsistent manual process, and a significant bottleneck for high-throughput metabolomics. To address this, we have compared three different sample extraction solvent systems in three diverse sample types with the purpose of selecting an optimum protocol for subsequent automation of sample preparation. We have investigated and re-optimised the solvent ratios in the recently introduced methyl tert-butyl ether (MTBE)/methanol/water solvent system (here termed modified Matyash; 2.6/2.0/2.4, v/v/v) and compared it to the original Matyash method (10/3/2.5, v/v/v) and the conventional chloroform/methanol/water (stepwise Bligh and Dyer, 2.0/2.0/1.8, v/v/v) using two biofluids (human serum and urine) and one tissue (whole Daphnia magna). This is the first report of the use of the Matyash method for extracting metabolites from the US National Institutes of Health (NIH) model organism D. magna. Extracted samples were analysed by non-targeted direct infusion mass spectrometry metabolomics or LC-MS metabolomics. Overall, the modified Matyash method yielded a higher number of peaks and putatively annotated metabolites compared to the original Matyash method (1-29% more peaks and 1-30% more metabolites) and the Bligh and Dyer method (4-20% more peaks and 1-41% more metabolites). Additionally the modified Matyash method was superior when considering metabolite intensities. The reproducibility of the modified Matyash method was higher than other methods (in 10 out of 12 datasets, compared to the original Matyash method; and in 8 out of 12 datasets, compared to the Bligh and Dyer method), based upon the observation of a lower mRSD of peak intensities. In conclusion, the modified Matyash method tended to provide a higher yield and reproducibility for most sample types in this study compared to two widely used methods

Developing and applying state of the art molecular technologies to discover mechanism of ZnO nanoparticle perturbation in Daphnia-algae chemical signal transfer system

In light of rapid industrial progress of the 21st century, the number of chemicals, including nanomaterials, used on a daily basis and introduced into the environment is constantly increasing. Far less chemicals, however, undergo appropriate risk assessment procedures, to ensure that they are not toxic or hazardous. Therefore, there is a necessity for new high-throughput techniques and tools in order to boost risk assessment. In this study it was attempted to develop a novel risk assessment paradigm, incorporating automated sample preparation for metabolomics toxicity testing, multi-trophic level ZnO nanoparticle (NP) toxicity assessment and an adverse outcome pathway (AOP) in Daphnia-algae information transfer system. In order to enhance metabolomics toxicity screening, several extraction solvent systems were tested (using direct infusion mass spectrometry (DIMS) and liquid chromatography (LC)-MS), ultimately selecting more robotics-compatible Bligh and Dyer method for further automation (even though modified Matyash method provided higher yield and reproducibility [1]). The low-mass tissue (Daphnia) extraction employing Biomek NXp platform was then automated (and assessed) to be integrated with existing high-throughput DIMS into the metabolomics pipeline. The optimised method was capable of a fully-automated extraction for polar and non-polar metabolites (24 Daphnia samples per batch), requiring 90 minutes, with no significant contamination or sample carry-over. Furthermore, multi-trophic toxicity was assessed by studying ZnO NP-induced changes in sulfonated lipids (SLs, participate as kairomones in signalling between Daphnia and algae, inducing defences) and an early molecular mechanism leading to those changes (via multi-omics approach – RNA-seq and LC-MS). It was shown that SLs decrease rapidly after exposure (at 0.3 h) and are not preceded by the changes in sulfur/sulfonation/glutathione metabolism (unlike hypothesized), while the mechanism likely involves activation of TNF/IL1B (tumor necrosis factor/interleukin 1 beta). Ultimately, a putative AOP featuring SL-mediated perturbation in Daphnia and algae (caused by ZnO NPs) was developed to aid the risk assessment of nanomaterials in aquatic food webs, however further research is required to fill in the existing knowledge gaps

Project 1: Molecular mechanisms of neurotoxicity and genotoxicity by brominated flame retardants in sh-sy5y human neuroblastoma cells and Project 2: Whole-genome sequencing, finishing and analysis of three bacterial human pathogens

This combined research thesis, submitted to the University of Birmingham, consists of two projects. The first project addressed the neurotoxic effects by three of the most widely used flame retardants – hexabromocylododecane, tetrabromobisphenol-A and decabromodiphenyl ether in SH-SY5Y human neuroblastoma cells. The results demonstrated high toxicity potential in all three compounds even at low concentrations. Moreover, all compounds caused DNA single-strand breaks at non-cytotoxic concentrations. HBCD proved to be more potent than other two compounds tested. Therefore, it can be concluded that all three compounds are potentially neurotoxic and what more, genotoxic in human cells in-vitro. The second project attempted to finish the genome of multidrug-resistant Elizabethkingia meningoseptica 501 and start finishing the genome of a new Pseudomonas aeruginosa ST395 strain, employing whole-genome Nextera XT and Mate Pair sequencing (Illumina). Optimization of Nextera XT for organisms with different GC content was also carried out in the study using 3 species – Escherichia coli (Medium GC), Pseudomonas aeruginosa (high GC) and Elizabethkingia meningoseptica (low GC). Finally, using PCR and Sanger sequencing, the genome of E. meningoseptica was almost completed, obtaining 4 gapped fragments of the genome, what makes HTS (high-throughput sequencing) a very powerful scientific tool

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