Memorial Sloan Kettering Cancer Center
New York, US
Decoding epigenetic programs in cellular differentiation
In order to differentiate into distinct lineages, multipotent cells must undergo large-scale remodeling of chromatin and orchestrate dramatic gene expression changes. How do multipotent cells encode the potential for multiple cell fates, and how can we decipher the transcriptional programs that carry out cell state transitions in commitment to specific fates? To address these questions, we carried out an integrative computational analysis of enhancer landscape and gene expression dynamics in hematopoietic differentiation using DNase-seq, histone mark ChIP-seq, and RNA-seq. We examined how early establishment of enhancers and complex regulatory locus control together govern gene expression changes in cell state transitions. We found that high complexity genes – i.e. those with a large total number of DNase-mapped enhancers across the lineage – differ architecturally and functionally from low complexity genes, achieve larger expression changes, and are enriched for both cell-type specific and “transition” enhancers, which are established in hematopoietic stem and progenitor cells (HSPCs) and maintained in one differentiated cell fate but lost in others. We then developed a quantitative model to predict gene expression changes from the DNA sequence content and lineage history of active enhancers. Our method accurately predicts expression changes for high complexity genes during differentiation, suggests a novel mechanistic role for PU.1 at transition peaks in B cell specification, and can be used to improve assignment of enhancers to genes. We are using these methods to decode normal cell state transitions in T lymphocyte differentiation and aberrant cell states in cancer.
Christina Leslie did her undergraduate degree in Pure and Applied Mathematics at the University of Waterloo in Canada. She was awarded an NSERC 1967 Science and Engineering Fellowship for graduate study and did a PhD in Mathematics at the University of California, Berkeley, where her thesis work dealt with differential geometry and representation theory. She won an NSERC Postdoctoral Fellowship and did her postdoctoral training in the Mathematics Department at Columbia University in 1999-2000. She then joined the faculty of the Computer Science Department and later the Center for Computational Learning Systems at Columbia University, where she began to work in computational biology and machine learning and became the principal investigator leading the Computational Biology Group. In 2007, she moved her lab to the Computational Biology program of Memorial Sloan Kettering Cancer Center, where she is currently an Associate Member. Dr. Leslie’s research group uses computational methods to study the regulation of gene expression in mammalian cells and the dysregulation of expression programs in cancer. She is well known for developing machine learning approaches – algorithms for learning predictive models from data – for analysis of high-throughput biological data, especially from next-generation sequencing. Focus areas in the lab include dissecting transcriptional and epigenetic programs in differentiation, microRNA-mediated gene regulation, alternative cleavage and polyadenylation, and integrative analysis of tumor data sets.