One important area of application of imaging that has not been fully exploited, due to limitation of existing technology, is the ability to monitor the expression of specific cellular proteins in vivo. Unlike anatomical imaging, molecular imaging of specific cellular proteins would display the biochemical abnormalities underlying disease rather than the structural consequences of abnormalities. If fully developed, it could offer the opportunity to monitor the progress of clinical treatments by imaging the expression of specific marker proteins, and may even form an important platform to enable the testing and development of new therapeutic paradigms. The focus of this grant proposal is to harness the power of RNA-based sensors that will enable sensitive detection of specific molecular signatures in living cells. The RNA sensor we engineered controls the expression of a reporter gene by polyA signal-mediated cleavage. Mammalian polyA signals are exclusively located at the 3'-untranslated region (UTR). When a new polyA site is artificially created at 5'UTR, where they are never localized in normal transcriptional units, extremely efficient cleavage of that polyA signal leads to destruction of the mRNA and therefore loss of reporter gene expression. Binding of a target protein to the engineered polyA signal efficiently blocks the cleavage, resulting in preservation of the intact mRNA, thus enabling reporter expression. In turn, we have shown that the reporter signal from such a sensor exhibited extremely low leaky expression in live human cells, and upon the detection of a specific protein, the signal was effectively induced above one hundred folds. This is two orders of magnitude higher than has been previously achieved in live human cells, giving a dynamic range that would allow unprecedented applications in a variety of experimental settings. The overall objective is to create a general molecular sensor platform based on the modulation of polyA cleavage that could utilize current or next generation reporters and aptamers for the purpose of imaging a variety of specific molecules in live cells. Moreover, the established molecular sensor platform will provide a foundation for expanding the spectrum of molecular signatures that the polyA sensor can detect in vivo.

Public Health Relevance

This application aims to harness the power of RNA-based sensors that will enable sensitive detection of specific molecular signatures in living cells. The RNA sensor described offers enhanced capacities for imaging molecular signatures in vivo than is currently possible. The application directly answers the needs outlined in PAR-09-016.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB013584-03
Application #
8511364
Study Section
Special Emphasis Panel (ZRG1-SBIB-A (55))
Program Officer
Conroy, Richard
Project Start
2011-08-01
Project End
2015-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$330,993
Indirect Cost
$95,903
Name
Baylor College of Medicine
Department
Pathology
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Hsiao, Kuei-Yang; Lin, Ya-Chi; Gupta, Sachin Kumar et al. (2017) Noncoding Effects of Circular RNA CCDC66 Promote Colon Cancer Growth and Metastasis. Cancer Res 77:2339-2350
Kannan, Kalpana; Coarfa, Cristian; Chao, Pei-Wen et al. (2015) Recurrent BCAM-AKT2 fusion gene leads to a constitutively activated AKT2 fusion kinase in high-grade serous ovarian carcinoma. Proc Natl Acad Sci U S A 112:E1272-7
Kannan, Kalpana; Coarfa, Cristian; Rajapakshe, Kimal et al. (2014) CDKN2D-WDFY2 is a cancer-specific fusion gene recurrent in high-grade serous ovarian carcinoma. PLoS Genet 10:e1004216
Zhang, Hao; Lin, Wan; Kannan, Kalpana et al. (2013) Aberrant chimeric RNA GOLM1-MAK10 encoding a secreted fusion protein as a molecular signature for human esophageal squamous cell carcinoma. Oncotarget 4:2135-43
Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven et al. (2013) Identification of cancer fusion drivers using network fusion centrality. Bioinformatics 29:1174-81