Gene expression is coordinated by multiple processes that dynamically adjust the concentration of mRNAs and proteins. During environmental stress, for example, translation is generally attenuated, while activities of specifi transcription factors and proteasomal degradation increase. Since a given gene can be affected by multiple processes acting at different time points, resolving the exact dynamics and interactions of regulatory processes is crucial. To fully capture such dynamics from all angles, transcriptomic and proteomic data need to be collected simultaneously in time course experiments. To meet the current need for a rigorous statistical method to extract regulatory information from these concentration data, a statistical framework, called Protein Expression Control Analysis (PECA), was developed by our team. PECA provides a basic platform for significance analysis of regulation changes at the mRNA- and protein-levels in dynamic systems. Here, the development of several new statistical modules within the PECA framework is proposed, which will be readily usable for quantitative, multi-level gene expression studies. The work includes extensive experiments for output validation using cutting edge molecular technologies, and their application to the oxidative stress response in mammalian cells - a prominent environmental stress with relevance for carcinogenesis and neurodegenerative diseases.
Aim 1 will integrate detailed transcriptomics and proteomics data from a human cell line subjected to oxidative stress with protein interaction network data to confer modularity to PECA that enables detection of concurrent or missing regulatory changes for members of protein complexes (PECA-N).
Aim 2 will focus on estimating properly scaled rates of synthesis and degradation which are essential constituents of gene expression regulation (PECA-R). Protein translation and degradation rate changes under oxidative stress will be monitored to calibrate and validate the results.
Aim 3 will simultaneously model post-translational modifications with concentration data (PECA-M) and map them to the specific regulatory pathway of protein ubiquitination affecting proteasomal degradation (UBICON). Thanks to expertise in proteomics, gene expression analysis, and statistical modeling, this team is ideal for these efforts.

Public Health Relevance

Time-resolved protein and mRNA expression data are now becoming available and are crucial to describe the dynamics of disease progression. PECA and its new modules represent the first comprehensive statistical toolbox to study gene regulation at multiple levels and to identify important mRNAs or proteins with detailed mechanistic insight for dynamic regulation, directly applicable to challenging problems such as biomarker discovery and development of therapeutics.

National Institute of Health (NIH)
Research Project (R01)
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Special Emphasis Panel (ZGM1)
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Bender, Michael T
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New York University
Schools of Arts and Sciences
New York
United States
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Tchourine, Konstantine; Poultney, Christopher S; Wang, Li et al. (2014) One third of dynamic protein expression profiles can be predicted by a simple rate equation. Mol Biosyst 10:2850-62