Precision environmental health - an omics-based whole-mixture approach

Li, Xiaojing (2022). Precision environmental health - an omics-based whole-mixture approach. University of Birmingham. Ph.D.

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Abstract

In the natural waters, hundreds to thousands of chemicals co-exist as complex mixtures, which needs a holistic assessment of their health effects. Identifying and testing each individual chemicals in the environment is undoubtably an insurmountable challenge to ecotoxicological studies and an unrealistic approach to reveal mixture effect at environmental relevant concentration, which may require insight from toxicogenomic studies. In this thesis, a new way of understanding and potentially discovering solutions to the mixture effect problem of safeguarding the health of human populations and the environment from the unknown effects of real-world chemical mixtures, specifically targeting pollutants of inland waters.
In Chapter 1, the current status of environmental monitoring, its challenges and limitations by highlighting environmental sample classification and harmful chemical component prioritisation are described and discussed as the major issues.

The conceptual framework of Precision Environmental Health is then proposed in Chapter 2, emphasising the importance of chemical mixture modes of action in the view of multi-omics. The Precision Environmental Health framework applies an omics-based bioassay approach to comprehensively characterise the effect of environmental chemical mixtures. The core of this framework focuses on the identification and interpretation of the molecular key event (mKE), which is responsive of foreign chemical exposure and indicative of potential adverse outcome. The mKEs are subsequently applied to classify the mixture effect and identify associated chemical components. This conceptual framework aims at I integrating the data-driven biological signatures generated by omics profiles and prior knowledge of gene functions and pathways of counterpart genetic model species.

Chapter 3 explains and verifies the mathematical basis of the framework, which relies on multi-block correlation analysis. Two case studies are included to demonstrate this framework in action, and two chemical components (caffeine and carbamazepine) are selected as prove-of-concept. The Data-driven biological features are compared with prior knowledge and compared between two case, in order to prove the effectiveness and robustness of the mathematical assumption behind this framework.

Derived from PEH framework, the mKE was used to group and classify the mixture effects of chemicals at environmentally relevant concentrations in two case studies, as gene clusters of highly variable genes in the transcriptomic profiles were identified and grouping pattern of gene clusters associated with chemical responses in Chapter 4 and further identify chemical component associated signatures that may reflect the chemicals’ modes of action in Chapter 5. In Chapter 4, expressionbased clustering analysis of five gene clusters revealed that the environmental chemical mixture of a single site (M16) induced relatively higher expression levels in stress response and cellular homeostasis, and these differences are significantly related to Dibenz[a,h]anthracene, Erythromycin and Trimethoprim in the Chaobai case study. In Chapter 5, similarity analysis of chemical profiles and transcriptomic profiles reveal similar grouping pattern, as expression-based clustering analysis of gene clusters revealed that distinctive transcriptomic profiles of two sites (D11 and D12) reveal down-regulation of xenobiotic biodegradation and antioxidative response pathways.

This thesis ends by highlighting in Chapter 6 the promise of Precision Environmental Health to address harm caused by real world chemical pollutants based on my findings and discusses need for future verification.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):