Novel approaches to characterise the exposome: Enabling discovery of associations between pollutants and environmental health

Lead Research Organisation: University of Birmingham
Department Name: Sch of Biosciences

Abstract

In 2005, Chris Wild at UC-Berkeley introduced the concept of the EXPOSOME - representing all environmental exposures from conception onwards - as a quantity of critical interest to health, launching major international research programs. With
>70,000 synthetic chemicals used by industry, from pharmaceuticals to agrichemicals to consumer products, the challenge to measure the exposome is colossal. Since many of these chemicals enter our environment it is essential to understand the potential impacts of complex chemical mixtures, typically at low levels, on environmental and human health.
Specifically, to what extent do these mixtures perturb organism health, and which chemicals are predominantly responsible? Knowing this is essential to strengthen regulation under the Water Framework Directive. METABOLOMICS is a transformative technology that measures 1000's of metabolites in organisms, yielding information-rich molecular signatures that describe the responses to pollution. Building on our decade-long track record in metabolomics, we willadapt mass spectrometry metabolomics approaches to measure both the exposome ('exposure') and metabolome ('effect').
We will then undertake the first ever ecological Exposome Wide Association Study (EWAS) to discover associations between environmental pollution and health of a sentinel freshwater species, Daphnia magna. This species has multiple benefits: it is a sentinel organism in freshwater ecology and ecotoxicology; lifetime exposures are feasible due to the rapid life cycle; chemically unexposed lab populations exist as negative controls; and all components of the Daphnia's environment can be controlled, including nutrition and pollutant exposure. Overall, this studentship will provide the
community with novel methods to characterise the exposome, and build weight of evidence to support the application of EWAS approaches to discover links between pollution and health.

Our CASE PARTNER, Thermo Fisher Scientific (TFS), is a world leader in the development of liquid chromatography and mass spectrometry (LC-MS) and has identified metabolomics as a priority area. In 2013, TFS formed a Technology Alliance Partnership (TAP) with the University of Birmingham, the first such partnership within Europe. This proposal builds upon four existing iCASE awards with TFS as part of their commitment to graduate training. Collectively this team will
provide SPECIALIST TRAINING: in LC-MS, including unparalleled access to current and pre-released metabolomics technologies (TFS); in metabolomics, toxicology and analytical sciences (Viant lab; the largest group in environmental metabolomics nationally).

TRANSFERABLE SKILLS will be taught at both Birmingham, through the extensive courses in the Biosciences Graduate Research School, and at TFS, including business awareness, project management and financial training. This training will be truly MULTIDISCIPLINARY to enrich the student experience. Furthermore the main supervisor is highly experienced, having completed 9 PhDs in the past five years and with 4 current students, several of which are/were NERC CASE.

The student will be integrated within three pre-existing communities of PhD students and researchers, providing an exceptional TRAINING ENVIRONMENT: the growing Environmental Systems Biology network at Birmingham, comprising 7 research groups; the Computational Toxicology community, a network of several research groups from Birmingham and internationally; and the community of ca. 30 scientists who constitute the TAP with TFS.

The IMPACTS of this research will be great: economically to TFS through developing and marketing their technologies to study the exposome; socio-economically by developing EWAS approaches that enable a more rigorous assessment of the effects of chemicals on health, of relevance to risk regulation; and by training a scientist who is competent in molecular and computational 'Big Data' science.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/N008820/1 06/02/2017 05/08/2021
1844318 Studentship NE/N008820/1 06/02/2017 05/08/2020