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Final Progress Reports: Michigan State University: An Integrated Experimental and Computational Approach to Understand the Effects of Population Variability on the Shape of the Dose-Response Curve

Superfund Research Program

An Integrated Experimental and Computational Approach to Understand the Effects of Population Variability on the Shape of the Dose-Response Curve

Project Leader: John J. LaPres
Grant Number: P42ES004911
Funding Period: 2006-2021

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Final Progress Reports

Year:   2020  2012 

Contaminants that bind and activate the aryl-hydrocarbon receptor (AhR) are frequently present at Superfund sites and are among the top contaminants with measureable routes of human exposure. Exposure to these contaminants produces a diverse range of effects including altered function of β-cells in the immune system and liver cancer. The primary goal of this project has been to understand how β-cell differentiation and proliferation are disrupted by AhR agonists and through the course of this project this goal has been expanded to include understanding the AhR signaling network across multiple tissues. The research will utilize 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) as a prototypical AhR agonist.

In the last progress update, collaborative interactions with the A Proteomic Analysis of the AHR signaling Network project led by Dr. LaPres identified an important role for the AhR in the mitochondria via protein interactions. In a project funded through other resources, the research team generated a knock-in mouse model that contains a tag on the end of the AhR protein. This model was used to explore novel protein interactions that may be involved in TCDD toxicity. Initial studies with these animals showed that known interacting partners such as Hsp90, Ara9 (Aip), and Arnt in the mouse liver could be identified. However, additional interactions with novel proteins were difficult to reproducibly obtain. The incorporation of a cross-linking step and switching to a new proteomics core facility dramatically improved the experimental results.

The knock-in mice were either treated with TCDD or the vehicle for 2 hours and liver was analyzed for AhR protein interactions. In vehicle treated mice, a number of protein interactions were identified that were significant over background including the known AHR binding proteins, Hsp90 and Ara9. Interestingly, the interacting proteins were enriched in those located in the mitochondria. This supports the earlier observation that the AhR is located within the mitochondria. In addition, bioinformatic analysis of the interacting proteins showed enrichment in valine, leucine and isoleucine degradation and fatty acid metabolism.

In TCDD-treated liver samples, additional protein interactions were identified together with the known AhR dimerization partner, Arnt. The interacting proteins were enriched in those located in the mitochondria and peroxisomes. Only a minority of the interacting proteins were of nuclear origin. The interaction with Arnt suggests that nuclear proteins were present, but following ligand binding, these interactions are relatively transient compared with interactions in other parts of the cell. Bioinformatic analysis of the interacting proteins showed enrichment in valine, leucine, and isoleucine degradation, fatty acid metabolism, propanoate metabolism, alanine, aspartate, and glutamate metabolism, PPAR signaling, and arginine and proline metabolism.

In summary, the research on the Dissecting the Signaling Network for Ah Receptor-mediated β-cell Toxicity project has led to the development of an in vivo approach for identifying protein-protein interaction with the AhR. The investigation identified both known and novel AhR interacting partners. A significant number of the interacting partners were localized to the mitochondria and peroxisomes and have a functional role in cellular metabolic processes. The results suggest that the AhR may play a functional role in these metabolic processes through mechanisms that may be unrelated to its role as a transcription factor (i.e., non-genomic).

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