The modeling of transcription to genome proximal elements to date has revealed associations, but seldom are disruptions performed to confirm mechanistic possibilities and substantiate causality. In studying multistate cell systems, many processes important to human health are difficult to study due to low cell availability and/or dyssynchrony leading to heterogeneous cell populations. We propose to study a model of the human epidermal differentiation system, which by its intrinsic properties does not have these problems but at the same time closely simulates the native process. We plan to perform multiple next generation sequencing modalities of transcription and gene proximal components over a time course spanning the transition from progenitor to differentiated keratinocytes. A network based on boosting methods and dynamic Bayesian networks will then be generated to model transcription to the gene proximal components, and this construct will be tested with various regulatory disruptions. Moreover, additional assays of transcription and gene proximal components will be performed during intervals which the model suggests will be particularly illuminating for epidermal differentiation. With these new data, the model will be further refined, and this cycle will be repeated multiple times. Because of the tractability of our experimental system to a vast array of sequencing assays and regulatory disruptions, we will be able to achieve an understanding of how much each assay contributes to the predictive accuracy of our model. In this way, our results will have implications not only for skin biology and hundreds of skin disorders but also for network modeling of transcriptional regulation in general.

Public Health Relevance

The regulome of cells is, to date, incompletely understood. We plan to perform multiple next generation sequencing modalities on an epidermal differentiation system over a time course. A network model will then be constructed and tested with various regulatory disruptions. Because of the tractability of our experimental system to sequencing assays and regulatory disruptions due to the ease of biomaterial generation, we will be able to achieve an understanding of how much each assay contributes to the predictive accuracy of our model. In this way, our results will have implications not only for dermatology but also for network modeling of regulation in general, especially for systems which are more difficult to study experimentally due to low cell availability, etc.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HG007919-03
Application #
9178027
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Pazin, Michael J
Project Start
2015-01-05
Project End
2017-11-30
Budget Start
2016-12-01
Budget End
2017-11-30
Support Year
3
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Rubin, Adam J; Barajas, Brook C; Furlan-Magaril, Mayra et al. (2017) Lineage-specific dynamic and pre-established enhancer-promoter contacts cooperate in terminal differentiation. Nat Genet 49:1522-1528
Araya, Carlos L; Cenik, Can; Reuter, Jason A et al. (2016) Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations. Nat Genet 48:117-25
Risca, Viviana I; Greenleaf, William J (2015) Beyond the Linear Genome: Paired-End Sequencing as a Biophysical Tool. Trends Cell Biol 25:716-9
Phanstiel, Douglas H; Boyle, Alan P; Heidari, Nastaran et al. (2015) Mango: a bias-correcting ChIA-PET analysis pipeline. Bioinformatics 31:3092-8
Bao, Xiaomin; Rubin, Adam J; Qu, Kun et al. (2015) A novel ATAC-seq approach reveals lineage-specific reinforcement of the open chromatin landscape via cooperation between BAF and p63. Genome Biol 16:284
Grubert, Fabian; Zaugg, Judith B; Kasowski, Maya et al. (2015) Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions. Cell 162:1051-65
Phanstiel, Douglas H; Boyle, Alan P; Araya, Carlos L et al. (2014) Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures. Bioinformatics 30:2808-10
Heidari, Nastaran; Phanstiel, Douglas H; He, Chao et al. (2014) Genome-wide map of regulatory interactions in the human genome. Genome Res 24:1905-17
Chen, Rui; Giliani, Silvia; Lanzi, Gaetana et al. (2013) Whole-exome sequencing identifies tetratricopeptide repeat domain 7A (TTC7A) mutations for combined immunodeficiency with intestinal atresias. J Allergy Clin Immunol 132:656-664.e17