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Your Environment. Your Health.

Progress Reports: Massachusetts Institute of Technology: Systems Toxicology of Environmental Contaminants

Superfund Research Program

Systems Toxicology of Environmental Contaminants

Project Leader: Forest M. White
Co-Investigator: Douglas A. Lauffenburger
Grant Number: P42ES027707
Funding Period: 2017-2022
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Progress Reports

Year:   2020  2019  2018  2017 

Over the past year the research team has continued to explore the systems-level response to exposure to single-agent environmental contaminants, while beginning to design experiments to explore response to complex mixtures of environmental contaminants. The team has quantified the signaling response to acute exposure to benzo[a]pyrene (BaP), an exemplar PAH, in lung and liver cancer cell lines at one, three, and six days of treatment. The cellular signaling response to BaP exposure is highly sensitive, with changes in DNA damage response (DDR) signaling resulting from exposure to as low as 2.5 ppb BaP for one day. Exposure to BaP also resulting in altered tyrosine phosphorylation (pTyr) at each time point, with mitogenic and cell migration signaling pathways increasing in exposed cells; altered signaling was correlated with increased cell migration in response to BaP exposure. In collaboration with the Assessment of Genotoxic Health Risks at Superfund Sites through Mutational Signatures Project and the NDMA and DNA Alkylation Repair in the Liver: Impact of Gene-Environment Interactions on Cellular Responses, Mutations and Cancer Project, the team has quantified the systems-level response to exposure to NDMA in mouse livers in vivo, across wild-type, Aag-/-, AagTg (overexpressing Aag), and Mgmt-/- mice, and have now correlated this data to tumor development at ten months following exposure. DDR and pTyr signaling at one day following exposure are both strongly predictive of tumor development at ten months; this model highlights signaling nodes most strongly correlated with oncogenesis. Finally, the team has developed new methodology to enable analysis of formalin fixed samples with much higher reproducibility.

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