Erythropoiesis is a fundamental process in vertebrate animals and has long served as a paradigm for molecular investigations of developmental regulation. It has been well established that a small number of transcription factors, also known as master regulators, play an essential role in the maintenance of cell-identity and/or regulate the cell differentiation process. However, it is not fully elucidated how they work in combination with each other or various cofactors, particularly at different developmental stages. Since there are at least a thousand transcriptional regulators in mammals, the number of possible combinations is astronomical. Using experimental methods alone to dissect such complexity remains a daunting task as it demands prohibitively high cost and labor. To overcome this challenge, we propose a systems biology approach to be carried out by a team of experienced experimental and computational biologists. Using human primary erythroid precursor cells as the model system, we will generate extensive experimental data by genomic, epigenomic, and transcriptomic profiling, develop data-integrative and predictive computational methods, and perturb the systems by disrupting the normal regulatory activities. Our preliminary work has identified important regulatory network differences between adult and fetal erythroid precursors and suggested that collaboration between master regulators and cofactors plays an important role in driving developmental stage-specific transcriptional changes through acting upon enhancer elements. This will be extended by focusing on the following specific aims: (1) Predict and validate the developmental stage-specific gene regulatory networks in human primary erythroid precursors by integrating genomic and epigenomic data-types;(2) Perturb the gene regulatory networks using molecular and genetic experiments and further integrate such information to refine our network model;(3) Characterize the role of genetic variants influencing chromatin state, gene expression, and erythroid traits. In the end our results will greatly expand our current mechanistic understanding of combinatorial control in establishing cell-type and developmental stage-specificity and provide functional insights into erythroid trait- associated genetic variants.

Public Health Relevance

Red blood cells provide vital life-support by supplying tissues and organs with oxygen. The regulatory mechanisms for their developmental processes have yet to be fully elucidated. We propose to develop a systems biology approach to identify developmental stage-specific gene regulatory networks and then use these networks as a guide to characterize the biological function of disease-associated genetic variants identified by genome-wide association studies.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL119099-01A1
Application #
8734667
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Qasba, Pankaj
Project Start
2014-09-01
Project End
2019-05-31
Budget Start
2014-09-01
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02215
Tsoucas, Daphne; Yuan, Guo-Cheng (2017) Recent progress in single-cell cancer genomics. Curr Opin Genet Dev 42:22-32
Marco, Eugenio; Meuleman, Wouter; Huang, Jialiang et al. (2017) Multi-scale chromatin state annotation using a hierarchical hidden Markov model. Nat Commun 8:15011
Beyaz, Semir; Kim, Ji Hyung; Pinello, Luca et al. (2017) The histone demethylase UTX regulates the lineage-specific epigenetic program of invariant natural killer T cells. Nat Immunol 18:184-195
Canver, Matthew C; Lessard, Samuel; Pinello, Luca et al. (2017) Variant-aware saturating mutagenesis using multiple Cas9 nucleases identifies regulatory elements at trait-associated loci. Nat Genet 49:625-634
Giecold, Gregory; Marco, Eugenio; Garcia, Sara P et al. (2016) Robust lineage reconstruction from high-dimensional single-cell data. Nucleic Acids Res 44:e122
Huang, Jialiang; Liu, Xin; Li, Dan et al. (2016) Dynamic Control of Enhancer Repertoires Drives Lineage and Stage-Specific Transcription during Hematopoiesis. Dev Cell 36:9-23
Pinello, Luca; Canver, Matthew C; Hoban, Megan D et al. (2016) Analyzing CRISPR genome-editing experiments with CRISPResso. Nat Biotechnol 34:695-7
Jiang, Lan; Chen, Huidong; Pinello, Luca et al. (2016) GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol 17:144
Saadatpour, Assieh; Lai, Shujing; Guo, Guoji et al. (2015) Single-Cell Analysis in Cancer Genomics. Trends Genet 31:576-86
Canver, Matthew C; Smith, Elenoe C; Sher, Falak et al. (2015) BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527:192-7

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