Genetic differences between individuals can greatly influence their susceptibility to disease. The information originating from the Human Genome Project (HGP), including the genome sequence and its annotation, together with projects such as the HapMap and the Human Cancer Genome Project (HCGP) have greatly accelerated our ability to find genetic variants and associate genes with a wide range of human diseases. Despite these advances, linking individual genes and their variations to disease remains a daunting challenge. Even where a causal variant has been identified, the biological insight that must precede a strategy for therapeutic intervention has generally been slow in coming. The primary reason for this is that the phenotypic effects of functional sequence variants are mediated by a dynamic network of gene products and metabolites, which exhibit emergent properties that cannot be understood one gene at a time. Our central hypothesis is that both human genetic variations and pathogens such as viruses influence local and global properties of networks to induce """"""""disease states."""""""" Therefore, we propose a general approach to understanding cellular networks based on environmental and genetic perturbations of network structure and readout of the effects using interactome mapping, proteomic analysis, and transcriptional profiling. We have chosen a defined model system with a variety of disease outcomes: viral infection. We will explore the concept that one must understand changes in complex cellular networks to fully understand the link between genotype, environment, and phenotype. We will integrate observations from network-level perturbations caused by particular viruses together with genome-wide human variation datasets for related human diseases with the goal of developing general principles for data integration and network prediction, instantiation of these in open-source software tools, and development of testable hypotheses that can be used to assess the value of our methods. Our plans to achieve these goals are summarized in the following specific aims: 1. Profile all viral-host protein-protein interactions for a group of viruses with related biological properties. 2. Profile the perturbations that viral proteins induce on the transcriptome of their host cells. 3. Combine the resulting interaction and perturbation data to derive cellular network-based models. 4. Use the developed models to interpret genome-wide genetic variations observed in human disease, 5. Integrate the bioinformatics resources developed by the various CCSG members within a Bioinformatics Core for data management and dissemination. 6. Building on existing education and outreach programs, we plan to develop a genomic and network centered educational program, with particular emphasis on providing access for underrepresented minorities to internships, workshop, and scientific meetings.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center (P50)
Project #
5P50HG004233-04
Application #
7842701
Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Feingold, Elise A
Project Start
2007-06-20
Project End
2012-03-31
Budget Start
2010-04-01
Budget End
2011-03-31
Support Year
4
Fiscal Year
2010
Total Cost
$3,375,837
Indirect Cost
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
076580745
City
Boston
State
MA
Country
United States
Zip Code
02215
Perrino, Cinzia; Barabási, Albert-Laszló; Condorelli, Gianluigi et al. (2017) Epigenomic and transcriptomic approaches in the post-genomic era: path to novel targets for diagnosis and therapy of the ischaemic heart? Position Paper of the European Society of Cardiology Working Group on Cellular Biology of the Heart. Cardiovasc Res 113:725-736
Cenik, Can; Chua, Hon Nian; Singh, Guramrit et al. (2017) A common class of transcripts with 5'-intron depletion, distinct early coding sequence features, and N1-methyladenosine modification. RNA 23:270-283
Weile, Jochen; Sun, Song; Cote, Atina G et al. (2017) A framework for exhaustively mapping functional missense variants. Mol Syst Biol 13:957
Betts, Matthew J; Wichmann, Oliver; Utz, Mathias et al. (2017) Systematic identification of phosphorylation-mediated protein interaction switches. PLoS Comput Biol 13:e1005462
Luck, Katja; Sheynkman, Gloria M; Zhang, Ivy et al. (2017) Proteome-Scale Human Interactomics. Trends Biochem Sci 42:342-354
Karras, Georgios I; Yi, Song; Sahni, Nidhi et al. (2017) HSP90 Shapes the Consequences of Human Genetic Variation. Cell 168:856-866.e12
Yang, Fan; Sun, Song; Tan, Guihong et al. (2017) Identifying pathogenicity of human variants via paralog-based yeast complementation. PLoS Genet 13:e1006779
Celaj, Albi; Schlecht, Ulrich; Smith, Justin D et al. (2017) Quantitative analysis of protein interaction network dynamics in yeast. Mol Syst Biol 13:934
Soltis, Anthony R; Kennedy, Norman J; Xin, Xiaofeng et al. (2017) Hepatic Dysfunction Caused by Consumption of a High-Fat Diet. Cell Rep 21:3317-3328
Menche, Jörg; Guney, Emre; Sharma, Amitabh et al. (2017) Integrating personalized gene expression profiles into predictive disease-associated gene pools. NPJ Syst Biol Appl 3:10

Showing the most recent 10 out of 125 publications