On a daily basis, the population is exposed to environmental chemicals. This exposure has been associated with many human diseases ranging from heart disease to Parkinson's disease, and even several cancers. The liver is an essential organ to study for understanding how environmental toxicants affect the body due to its capability of detoxifying these foreign chemicals, among other key responsibilities. The liver contains multiple cell types, each having distinct functionality, and investigating how these cell types communicate is imperative to understanding physiological functions of the liver. However, in vivo studies of inter-cellular communications in the liver are difficult for both humans and model organisms. Emerging as an attractive alternative, in vitro bioengineered liver models have been developed that represent the most important cell types (hepatocytes, liver sinusoidal endothelial cells (LSECs), and Kupffer cells) and maintain cell-type-specific phenotypes and behaviors as a surrogate for costly experiments. Using these liver models, in combination with genomic and computational methods, promises to improve our understanding of the cellular signaling that occurs within the liver models under both normal conditions and after an exposure to environmental toxicants, thereby resulting in more impactful and biologically meaningful insights. Developing novel computational methods seek to quantitatively identify the normal inter-cellular communication patterns between the liver cell types and utilize transcriptomic and proteomic data for predicting how environmental chemicals perturb the normal hepatic inter-cellular communication patterns. This research proposal will develop novel network based computational methods related to inter-cellular signaling and communication in the liver. Computational methods will be tested, benchmarked and validated using experimental data from both toxicant response assays and bioengineered liver models. These approaches will make predictions and prioritizations that are biologically informative and relevant, thus driving future experiments. To this end, a database of rat-related liver-specific protein-protein and regulatory interactions and signaling pathways derived from published literature as a starting framework for computing liver-specific signaling networks will be created. This network will be used to predict dysregulated signaling patterns from high-throughput response assays. The liver-specific background network will be extended to include inter-cellular interactions and integrate transcriptomic and proteomic data from 3D bioengineered liver models. This data integration will result in the identification of inter-cellular communication patterns specific for bioengineered liver systems. Additionally, toxicant-specific signaling network as a result of the liver models'exposure to various environmental chemicals will be computed. From these toxicant-specific networks, differential network analysis will be performed to identify chemical specific signaling patterns, which subsequently will prioritize future experiments. Taken together, this proposal will characterize and propose mechanisms in which toxicants alter the natural inter-cellular communication patterns.

Public Health Relevance

Everyday, humans are exposed to environmental chemicals that are both synthetic and naturally occurring. The liver is pivotal when determining how the body responds to environmental exposures, due to its capability in detoxifying foreign substances. Understanding how the liver specific cell types communicate after exposure will shed light on how the liver processes these chemicals.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZRG1)
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Shreffler, Carol K
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Virginia Polytechnic Institute and State University
Biostatistics & Other Math Sci
Biomed Engr/Col Engr/Engr Sta
United States
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Tegge, Allison N; Sharp, Nicholas; Murali, T M (2016) Xtalk: a path-based approach for identifying crosstalk between signaling pathways. Bioinformatics 32:242-51