Chenlab

From OpenWetWare
Jump to navigationJump to search

Home         News         People         Publications         Softwares         Positions         Contact    

We are a computational biology lab combining informatic and biological techniques to study cell identity regulation. Different cell types in a healthy body share the same genetic sequence. Difference in identity of these cell types is determined by epigenetic regulation of gene expression. Meanwhile, many genetic mutations in germ line or somatic cells were found to affect epigenetic factors, and can cause disease due to cell identity dysregulation. (1) To study cell identity regulation in development, we are focused on cardiovascular endothelial lineage specification, and further work with collaborators to apply our knowledge to other cell lineages such as embryonic stem cell, cardiomyocyte, smooth muscle cells, fibroblast, and neuron cells. (2) To study cell identity dysregulation in diseases, the lab is focused on prostate cancer and heart failure, and further works with collaborators to apply our knowledge to other diseases such as Rett Syndrome, and Hutchinson-Gilford Progeria Syndrome.

Cell Identity.png

The most important discovery in the lab is that the cell identity genes as a unique group are distinct from other genes in the molecular mechanisms to regulate their expression (Xia et al, Nature Communications 2020a; Chen et al, Nature Genetics 2015). Our findings further provide insights into the molecular regulation of normal cell identities in development (Gu et al, Science 2019), molecular regulation of genetic variations (Yu et al, Nature 2018), and dysregulation of cell identity in diseases, e.g., in limb ischemia (Meng, et al, Circulation 2020) and cancers (Yi et al, Nature Cell Biology 2020; Zhao et al, Nature Communications 2020b; Dhar et al, Molecular Cell 2018). These findings build a knowledge foundation for us to develop bioinformatics techniques for discovery of cell identity regulators (Xia et al, Nature Communications 2020a). These techniques have been proved to successfully recapture known disease genes such as cancer driver genes, and further, overcome critical constraint on current technologies by successfully identifying many disease genes that were not detectable by conventional mutation analysis. These disease genes led to new therapeutic targets and diagnostic markers as have been verified in cellular, animal, and preclinical models (Zhu et al, Nature Communications 2018; Liu et al, IJC 2018; Zhu et al, Oncogene 2019) and thus, will benefit numerous cancer patients.


Major research directions in the lab include:

  • Develop new bioinformatics techniques to interpret high throughput genomic data.
  • Hi-C, ChIP-seq, ATAC-seq, RNA-seq and other additional techniques to study epigenetic regulation of transcription by 3D chromatin conformation, chromatin modifications, and chromatin-binding proteins.
  • High throughput profiling of RNA methylation, RNA binding proteins, and ribosomes to study post transcriptional regulation of gene expression.
  • Molecular, cellular, animal, and clinical models to understand mechanisms in diseases with a focus on heart failure and prostate cancer.







openwetware         Lab Intranet