Team Members & Projects

Team Leader


Mark Viant

Mark Viant

Professor of Metabolomics

Professor Mark Viant’s expertise lies predominantly in the field of metabolomics. His research interests span from method development in analytical chemistry and bioinformatics through to the application of metabolomics to both human and environmental toxicology, focusing extensively on finding novel solutions for industry and regulators in chemical safety science.


        

Postdoctoral Fellows


Sophia Whitlock

Sophia Whitlock

Project: Towards an integrated approach in defining the MoA / KE of chemicals
Funder: Unilever

The principal aim of our research collaboration with Unilever is to optimise and apply multi-omics and computational approaches to discover and subsequently characterise metabolic and transcriptional key events that are predictive of apical (toxicological) endpoints, focusing on Daphnia magna.

Objectives:
1. Determine optimal experimental designs for multi-omics and phenotyping time-course studies to provide us the ability to discover putative molecular key events in organisms exposed to baseline narcotic and specific acting chemicals.
2. Subsequently develop optimal experimental designs to characterise the quantitative relationships between these putative key events including the adverse outcome.
3. To explore how to integrate such findings with existing in vitro and in silico approaches to inform on a chemical’s Mode-of-Action as part of a weight of evidence approach for risk assessment.

Tom Lawson

Tom Lawson

Project: MetaboFlow - the development of standardised workflows for processing metabolomics data to aid reproducible data sharing and big data initiatives
Funder: Wellcome Trust (grant no. 202952/C/16/Z)

The MetaboFlow project seeks to address an urgent need within the metabolomics research community for reproducible and scalable metabolomics data analysis workflows.

Objectives:
1. Working with collaborators from institutions in the UK and Europe, we aim to integrate a wide range of existing and novel metabolomics data processing algorithms, feature extraction and metabolite identification tools into this open-source and cloud-based Galaxy research platform. This will enable data analyses to be conducted within a highly structured framework, ensuring that all stages of a given workflow, including the inputs, outputs and parameters applied, are recorded. Users will also be able to easily share all, or parts, of an analysis history with chosen collaborators, making reproducible and open metabolomics research both viable and simple.
2. To provide training and outreach in MetaboFlow.

PhD Students


Stefan Schade

Stefan Schade

2015 - 2019 BBSRC iCASE with Unilever

Project: An integrative approach for understanding the adverse outcome pathways in algae

Objectives:
1. Optimise methods for the culturing, sampling, extraction and measurement of metabolites and lipids from Chlamydomonas reinhardtii (a single-cell green algae used in toxicity testing).
2. Develop and conduct time-course gene expression, metabolomics and lipidomics studies to deeply probe the molecular responses of C. reinhardtii to chemical exposures.
3. Construct gene regulatory networks and metabolic networks, encapsulating both the temporal changes and dose-response surface, to help to identify and characterise molecular key events in toxicity pathways, as well as the “tipping points” to adverse (apical) endpoints within an AOP framework

Judith Ngere

Judith Ngere

2016 - 2020 NERC iCASE with Thermo Fisher Scientific

Project : Novel approaches to characterise the toxicokinetics and toxicodynamics of chemicals using metabolomics approaches

Objectives:
1. Evaluate the applicability of mass spectrometry based metabolomics methods to measure the exposome.
2. Optimise and apply direct infusion mass spectrometry (DIMS) and LC-MS metabolomics to simultaneously characterise the internal dose of a chemical, its potential biotransformation products, and the effects of the chemical on the metabolome.
3. Utilising these analytical methods, to evaluate the effectiveness of metabolomics for toxicokinetic and toxicodynamic studies in chemical exposed environmental organisms, including bee and Daphnia.

Julia Malinowska

Julia Malinowska

2017 - 2021 NERC iCASE with Thermo Fisher Scientific

Project: Ultrahigh sensitivity metabolomics: using mass spectrometry to open a window into discovering novel molecular responses to chemical toxicants.

Objectives:
1. Develop low-flow LC-MS methods for untargeted metabolomics analyses of low biomass cell samples using in vitro toxicology.
2. Utilise these analytical methods to evaluate the effectiveness of metabolomics for biologically-driven chemical grouping and read-across (a variation on the widely used method in chemical risk assessment).
3. Evaluate the potential of metabolomics to simultaneously generate toxicokinetic information (alongside the toxicodynamics) from in vitro toxicology studies in order to further support read-across case studies.

Matthew Smith

Matthew Smith

2017 - 2021 BBSRC iCASE with Waters

Project: Mass spectrometry imaging of the model organism Daphnia: a tool for localising molecular stress responses

Objectives:
1. Optimise Desorption Electrospray Ionisation (DESI) imaging mass spectrometry to study the spatial distribution of polar metabolites and lipids in small model organisms.
2. Apply DESI to build a spatial atlas of the metabolome of a model organism, focusing on Daphnia, benefitting from this species’ Deep Metabolome Annotation project in our Metabolomics & Systems Toxicology Laboratory.
3. Apply DESI in the context of Daphnia toxicology to localise the uptake, distribution and metabolism of chemical pollutants alongside measurements of changes in endogenous metabolism (spatial metabolomics).

Tara Bowen

Tara Bowen

2018 - 2022 BBSRC iCASE with AstraZeneca

Project: Novel metabolomics strategies to understand drug-induced perturbations in cardiac microtissues

Objectives:
1. Optimise microtissue sample preparation for DIMS-based untargeted metabolomics analysis.
2. Characterise the metabolome of cardiac microtissues following drug (cardiotoxicant) perturbation.
3. Use data from metabolomics, and complementary techniques, to generate hypotheses on the molecular mechanisms of drug-induced cardiotoxicity.
4. Validate hypothesised modes of action using appropriate techniques.

Hanna Gruszczynska

Hanna Gruszczynska

2018 - 2022 NERC iCASE with Thermo Fisher Scientific

Project: Developing and implementing metabolomics approaches for environmental risk assessment of chemicals

Objectives:
1. Establish and optimise novel high-throughput sampling and non-targeted direct infusion mass spectrometry (DIMS) metabolomics for low biomass samples.
2. Evaluate the capabilities of multi-omics and computational approaches for biologically-based chemical grouping and read-across in single and multiple OECD test species, Daphnia magna and potentially fish, studying ‘dirty’ and specific-acting chemicals.
3. Assess the potential of metabolomics to generate untargeted toxicokinetics data to further enhance robust grouping and read-across of ‘dirty’ and specific-acting chemicals.
4. Disseminate and translate findings into regulatory practice through strategic partnership and international engagement with regulatory and government bodies, including the European Chemicals Agency (ECHA), the US Environmental Protection Agency (EPA) and Health Canada.

Anne Freier

Anne Freier

2018 - 2022 BBSRC MIBTP iCASE with the Health and Safety Laboratory

Project: Integrating metabolomics and physiological modelling to ensure food safety

Objectives:
1. Develop, conduct and subsequently optimise the experimental design and metabolomics approaches required to generate in vitro metabolomics data describing the mechanistic responses of cells, focusing on pesticides in the food chain that are of human concern. The studies will be designed with the central purpose of deriving ‘benchmark doses’, i.e. the pesticide doses corresponding to low, but measurable metabolic perturbations that precede higher order cellular damage.
2. Investigate and subsequently optimise the computational strategies for extracting the relevant information from the metabolomics dose-response datasets to derive robust ‘points of departure’ for each chemical. This builds upon related work recently published using transcriptomics data. We will explore data and information derived at a range of levels, from individual metabolites to metabolic pathways.
3. Apply biologically-based mathematical models, such as PBPK models, that can be used to extrapolate from the quantitative in vitro data derived above to in vivo, for the purposes of human risk assessment for food-borne pesticides. Specifically, we will seek to incorporate mechanistic biology into the PBPK models to provide quantitative, biologically-based chemical risk assessment, for example using, RVis, a free to use, open access PBPK modelling software package.