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Kai Tan, PhD
Kai Tan
Professor of Pediatrics

Dr. Tan studies transcriptional regulation during normal development and disease. This involves the interplay of multiple transcription and epigenetic factors in a 3D chromosomal environment. Using experimental genomics and computational modeling, Dr. Tan investigates transcriptional regulatory networks underlying embryonic hematopoiesis, T cell differentiation, and pediatric leukemia.

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Bio

Dr. Tan’s research encompasses four areas: transcriptional regulation of embryonic hematopoiesis and T-cell differentiation; the role the three-dimensional chromosome organization in hematopoiesis and pediatric cancer; the role of noncoding sequence variants in pediatric cancers; and single-cell analysis of embryonic hematopoiesis and tumor microenvironment.

The ontogeny of hematopoietic stem cells (HSCs) and T-cell differentiation are driven by a series of gene expression programs over time. Dr. Tan is developing cutting-edge genomic and computational tools to profile and model the gene regulatory circuitry in these two developmental systems. His studies have revealed a repertoire of highly dynamic transcriptional regulatory sequences as well as transcriptional regulators that are previously uncharacterized. Additional studies are underway to experimentally test novel regulatory DNA sequences and transcriptional regulators.

In addition, Dr. Tan has developed a couple of computational methods for identifying large-scale and hierarchically organized chromosome domains; and long-distance enhancer-promoter interactions. In ongoing work, Dr. Tan and his team are combining experimental assays and our computational methods to study the role of 3D genome re-organization in HSC maturation and leukemogenesis.

Dr. Tan has developed a general computational framework for identifying noncoding mutations that confer disease risk. He has applied the method to nominate candidate causal noncoding mutations in five major pediatric cancers. In ongoing work, Dr. Tan and his colleagues are experimentally testing several recurrent structural variants predicted to disrupt enhancer activities in B-cell acute lymphoblastic leukemia.

In ongoing projects, Dr. Tan is using single-cell RNA-Seq to identify novel precursor cell populations of embryonic HSCs. Additionally, as a member of the NCI-funded Human Tumor Atlas Network, his research team has established the Center for Pediatric Tumor Cell Atlas to better understand clonal evolution and tumor heterogeneity at single-cell level for three types of high-risk pediatric cancers.

Education and Training

BS, Beloit College (Biochemistry), 1997

PhD, Washington University (Computational Biology), 2004

Fellowship, University of California, San Diego (Systems Biology), 2008

Titles and Academic Titles

Professor of Pediatrics

Professional Memberships

International Society for Computational Biology, 2000-

International Society for Systems Biology, 2005-

International Society for Stem Cell Research, 2008-

Professional Awards

NSF Computing Innovation Fellows Award, 2009

PhRMA Foundation Research Starter Award in Informatics, 2009

March of Dime Basil O'Connor Research Award, 2011

Kavli Frontiers of Science Fellow, US National Academy of Sciences, 2016

Publication Highlights

Pubmed:
Zhu Q, Gao P, Tober J, Bennett L, Chen C, Uzun Y, Li Y, Mumau M, Yu W, He B, Speck NA, Tan K. Developmental trajectory of pre-hematopoietic stem cell formation from endothelium. Blood. 2020 May; blood.2020004801; PMID: 32392346
Yu, W., Uzun, Y., Zhu, Q., Chen, C., & Tan, K. scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data. Genome Bio. 2020 Apr; 21(1):94. PMC7169039
Chen C, Yu W, Tober J, Gao P, He B, Lee K, Trieu T, Blobel GA, Speck NA, Tan K. Spatial genome re-organization between fetal and adult hematopoietic stem cells. Cell Rep. 2019 Dec; 29(12):4200-4211. PMC7262670
Hu Y, Gao L, Chen C, Ding Y, Wen X, Wang B, Tan K. Optimal control nodes in disease-perturbed networks as targets for combination therapy. Nat. Comm. 2019 May; 10(1):2180. PMC6522545
He B, Chen C, Teng L, and Tan K. Global view of enhancer-promoter interactome in human cells. Proc Natl Acad Sci USA. 2014 May; 111(21):E2191-9. PMID: 24821768

Active Grants/Contracts

Mechanisms of endothelial-to-hemogenic transition mediated by Runx1. NIH/NICHD, August 2017-May 2022. The goal of this project is to apply systems biology approaches to study the transcriptional regulatory network controlling hemogenic endothelial specification, mediated by Runx1 and additional transcription factors that cooperate with Runx1. Role: PI

A systems approach to the genetic study of alcohol dependence. NIH/NIAAA, March 2017-February 2021.The goal of this project is to identify genetic variants and gene networks underlying alcohol dependence. Role: Co-PI

Biochemistry of leukemia virus core binding factor. NIH/NHLBI, September 017-June 2021. The goal of this project is to determine which toll-like receptors and pathways contribute to embryonic HSC formation, in which cells toll-like receptor signaling is active, and if toll-like receptor signaling in the embryo contributes to the properties of embryonic and adult HSCs. Role: Co-I

A toolkit for identifying causal variants in transcriptional enhancers. NIH/NIGMS, July 2014-June 2019. The goal of this project is to integrate genomic and epigenomic data to identify causal genetic variants in transcriptional enhancers. Role: PI

Computational methods for unraveling combinatorial gene regulation. NIH/NIGMS July 2014-June 2019. The goal of this project is to develop a suite of computational methods that enable genome-scale identification of combinatorial interactions at enhancers and construction of predictive models of combinatorial regulation in mammalian cells. Role: PI

Tools for annotating mutations in the 3D cancer genome. NIH/NCI, April 2018-March 2021.

The goal of this project is to develop a suite of bioinformatics tools to predict the hierarchy of 3D genome organization and use such information to interpret and identify causal noncoding mutations. Role: PI