Diabetes without previous coronary heart disease (CHD) carries a lifetime risk of vascular death as high as that for people with CHD. The goal of this project is to explore and discover novel and more complete regulation mechanisms of atherogenic pathways in diabetes by developing advanced statistical analysis and novel pathway modeling capabilities and techniques based on microarray data collected from animal disease progression models and combined with various functional and genomic database resources. Microarray profiling experiments that follow different diabetic progression conditions of well-established animal models will be cost-effectively executed to obtain genome-wide gene expression information for their atherogenic pathway conditions (measured from visceral fat tissue-derived adipocytes and macrophages). We will carefully examine the atherogenic pathway mechanisms, especially those associated with 12/15-LO and PPARg, which have been targeted by our primary pathway investigation due to their critical physiological roles in atherosclerosis. In particular, the latter has been identified as one of main pharmacological targets for treating cardiovascular complications among type II diabetes patients.
Our specific aims are to: 1) discover atherogenic pathway genes, especially relevant to 12/15-LO and PPARg at different stages of diabetic atherosclerosis progression by developing and applying advanced statistical analysis approaches to microarray data on different diabetic animal-model conditions with a small number of replicates, 2) develop a formal language (FL) framework and its genomic information database for various expression and functional information of 12/15-LO, PPARg and other atherogenic genes that will be identified from the time-course microarray data of diabetic animal models and for the genes known for their functions and mechanisms in atherosclerosis, and 3) discover novel atherogenic pathway mechanisms in diabetes, especially those associated with 12/15-LO and PPARg by developing and applying genome integrative pathway modeling (GIPaM) technology. Novel findings from these investigations will directly benefit the diagnosis and treatment of atherosclerotic cardiovascular disease. Our GIPaM technology will also greatly enhance pathway discovery/modeling capability in other fields of the biomedical science.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL081690-05
Application #
7893777
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Ershow, Abby
Project Start
2006-08-01
Project End
2012-07-31
Budget Start
2010-08-01
Budget End
2012-07-31
Support Year
5
Fiscal Year
2010
Total Cost
$287,867
Indirect Cost
Name
University of Virginia
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
065391526
City
Charlottesville
State
VA
Country
United States
Zip Code
22904
Muthiah, Annamalai; Keller, Susanna R; Lee, Jae K (2017) Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism. Int J Genomics 2017:8514071
Cheng, Feng; Keeley, Ellen C; Lee, Jae K (2012) Molecular prediction for atherogenic risks across different cell types of leukocytes. BMC Med Genomics 5:2
Cheng, Feng; Theodorescu, Dan; Schulman, Ira G et al. (2011) In vitro transcriptomic prediction of hepatotoxicity for early drug discovery. J Theor Biol 290:27-36
Williams, Paul D; Owens, Charles R; Dziegielewski, Jaroslaw et al. (2011) Cyclophilin B expression is associated with in vitro radioresistance and clinical outcome after radiotherapy. Neoplasia 13:1122-31
Lee, Seungmook; Kwon, Min-Seok; Lee, Hyoung-Joo et al. (2011) Enhanced peptide quantification using spectral count clustering and cluster abundance. BMC Bioinformatics 12:423
Cheng, Feng; Cho, Sang-Hoon; Lee, Jae K (2010) Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses. Methods Mol Biol 620:471-84
Lee, Jae K; Coutant, Charles; Kim, Young-Chul et al. (2010) Prospective comparison of clinical and genomic multivariate predictors of response to neoadjuvant chemotherapy in breast cancer. Clin Cancer Res 16:711-8
Nagji, Alykhan S; Cho, Sang-Hoon; Liu, Yuan et al. (2010) Multigene expression-based predictors for sensitivity to Vorinostat and Velcade in non-small cell lung cancer. Mol Cancer Ther 9:2834-43
Williams, Paul D; Cheon, Sooyoung; Havaleshko, Dmytro M et al. (2009) Concordant gene expression signatures predict clinical outcomes of cancer patients undergoing systemic therapy. Cancer Res 69:8302-9
Overdevest, Jonathan B; Theodorescu, Dan; Lee, Jae K (2009) Utilizing the molecular gateway: the path to personalized cancer management. Clin Chem 55:684-97

Showing the most recent 10 out of 16 publications