Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is one of the most commonly altered tumor suppressor genes in human cancers. It has been shown that tumor susceptibility is highly sensitive to cellular PTEN levels, thereby highlighting the importance of molecular mechanisms for PTEN regulation. Recent research has uncovered a novel mechanism for post-transcriptional regulation of PTEN via a network of microRNAs (miRNAs) and competing endogenous RNAs (ceRNAs). Currently known ceRNAs that regulate PTEN are limited to protein-coding mRNAs and a quantitative framework for modeling PTEN regulation by ceRNAs is lacking. The project proposes to develop computational and experimental approaches for the discovery and analysis of both non-coding and coding ceRNAs of PTEN. The proposed research will lead to the identification of multiple ceRNAs of PTEN and focus on their role in controlling cellular PTEN levels and their impact on tumor susceptibility. The long-term objective is to develop novel therapeutic approaches for cancer based on elevating cellular PTEN levels using ceRNA-based regulation.
The specific aims are to: 1) Develop a quantitative model for kinetics and regulation of PTEN by ceRNAs; 2) Develop quantitative models of post-transcriptional regulation of PTEN by ceRNA networks; and 3) Develop protocols for controlling PTEN using ceRNAs and determine the impact on tumor susceptibility. This project involves the innovative integration of approaches from different disciplines and tools such as single-cell assays, stochastic modeling, machine learning, and bioinformatics to analyze regulation of PTEN via ceRNAs. The development of quantitative models and tools for analysis of ceRNA-based regulation, will significantly impact current and future research aimed at understanding its role in diverse cellular processes, thereby significantly impacting the field beyond PTEN function. The project will strengthen collaboration between different departments at UMass Boston and DF/HCC, and will provide excellent interdisciplinary training and mentorship for students and researchers involved. Project Co-Leads, Zarringhalam and Kulkarni, are mentoring several undergraduate students at UMass Boston who are currently working on the project. Students have written Honors theses, given talks based on their research at undergraduate conferences, and are co-authors on multiple presentations at prestigious international conferences. Providing ample opportunity for research experiences for students from underrepresented backgrounds is a top priority of the project, and the project Co-Leads will work diligently with the U54 Research Education Core to facilitate these experiences. Besides the positives arising from interdisciplinary training for students in cutting-edge research, the wide dissemination of the results will also contribute positively to the UMass Boston-DF/HCC U54 Partnership, attract external funding and lead to the development of a strong cancer research program at UMass Boston.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
2U54CA156734-06A1
Application #
9211683
Study Section
Special Emphasis Panel (ZCA1-SRB-X (A1))
Project Start
Project End
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
6
Fiscal Year
2016
Total Cost
$178,425
Indirect Cost
$61,425
Name
University of Massachusetts Boston
Department
Type
DUNS #
808008122
City
Boston
State
MA
Country
United States
Zip Code
02125
Bellinger, Stephanie; Hatamimoslehabadi, Maryam; Borg, Raymond E et al. (2018) Characterization of a NIR absorbing thienyl curcumin contrast agent for photoacoustic imaging. Chem Commun (Camb) 54:6352-6355
Kumar, Niraj; Zarringhalam, Kourosh; Kulkarni, Rahul V (2018) Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit. Biophys J 114:2530-2539
Zarringhalam, Kourosh; Degras, David; Brockel, Christoph et al. (2018) Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes. Sci Rep 8:1237
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Fakhry, Carl T; Zarringhalam, Kourosh; Kulkarni, Rahul V (2018) Bioinformatic Approach for Prediction of CsrA/RsmA-Regulating Small RNAs in Bacteria. Methods Mol Biol 1737:47-56
Borg, Raymond E; Rochford, Jonathan (2018) Molecular Photoacoustic Contrast Agents: Design Principles & Applications. Photochem Photobiol 94:1175-1209
Borg, Raymond E; Hatamimoslehabadi, Maryam; Bellinger, Stephanie et al. (2018) Photophysical and Photoacoustic Properties of ?-Extended Curcumin Dyes. Effects of the Terminal Dimethylamino Electron-donor and the Bridging Aryl Ring. Photochem Photobiol :
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Kang, Tianyu; Ding, Wei; Zhang, Luoyan et al. (2017) A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data. BMC Bioinformatics 18:565
Horowitz, Jordan M; Kulkarni, Rahul V (2017) Stochastic gene expression conditioned on large deviations. Phys Biol 14:03LT01

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