Dual-omics profiling and ensemble machine learning for mapping cell state dynamics and exposure thresholds in response to nanoplastic–biotoxin co-exposures

About the Project

The human exposome is defined by the totality of environmental exposures across a lifetime, which represents one of the most complex determinants of human health (IHEN). Although, there are growing awareness of environmental contaminants in non-communicable diseases, the connections between specific exposome inputs and the cell state dynamics and responses are poorly characterised. In this project, we aim to integrate dual-omics profiling with ensemble machine learning and biosensing technologies to study how human cells remodel their molecular states in response to biotic and abiotic contaminants, for examples, biotoxins and nanoplastics (NPs), which are the emerging contaminants of concern. Climate changes accelerate the production and redistribution of biotoxins. At the same time, climate changes also increase the physical fragmentation of plastics into NPs due to increased frequency and intensity of erratic weather patterns. The NPs are known to create a Trojan Horse effect that delivers the payloads when they infiltrate food chains. Nevertheless, it is unknown if NPs are also the vectors for biotoxins, and emerge as potentially synergistic exposure class with substantial risks to humans. By using well-formulated human cell models, the project will apply dual-omics in proteo- and metabolomics to delineate the canonical cellular states and dynamics, and study molecular mechanisms of NPs-biotoxins. The dual-omic datasets will be examined by ensemble machine learning for the training and development of predictive toxicology models of exposure phenotypes. We will also employ existing biosensing protocols developed in our lab to quantify the NPs-biotoxins exposure. The quantifiable exposure data are combined with cell state profiles to construct dose-response models. The project is anticipated to produce the first systematic mapping of contaminant exposure thresholds against defined cellular state transitions, and integrate into human exposome models that inform toxicological outcomes, regulatory decisions, and science-to-policy translation.

Main Supervisor: Dr. Michelle Yap Khai Khun, Monash University

Associate Supervisors: Dr. Ong Huey Fang, Dr. Krystle Angelique Santiago, Dr. Faddrine Jang