Epigenomics and Computational Biology Laboratory

Capelluto Research Group (2018)

We study epigenetic regulatory networks associated with cell specification and disease development. Toward this goal, we emphasize the development of novel high-throughput experimental approaches and the implementation of computational tools for -omics data analysis. Over the last decade, we have investigated the epigenetic programming mechanisms with stem cells and for tissues of the brain and blood.

Our group is particularly interested in strategies to assess epigenetic variation within and between cell populations, and to reveal transcription factors and gene networks controlling epigenetic dynamics during normal development and diseases. The term “epigenetic” mainly refers to histone modifications and DNA methylation, which are alternative ways to control gene expression while maintaining the nucleotide sequence of the genome. In other words, epigenetic changes allow cells with identical genomic content to demonstrate distinct phenotypes.

Adaptor Proteins in Endosomal Protein Trafficking

Ubiquitylation is a highly controlled post-translational modification of proteins, in which proteins are conjugated either with monoubiquitin or polyubiquitin chains. Ubiquitin modifications on target proteins are recognized by ubiquitin-binding domains, which are found in several effector proteins. We study the function and structure of the Toll-interacting protein (Tollip), which contains the C2 and CUE ubiquitin-binding domains and participates in the innate immune signaling pathway and endosomal protein trafficking.

Lipid-binding Events in the Wnt Signaling Pathway

Recently, our group has implemented genome-wide hairpin bisulfite sequencing to assess asymmetric DNA methylation between the double strands of DNA. This approach enables the assessment of the fidelity of DNA methylation inheritance. Since the challenges in epigenomic studies are not limited to data generation but also reside in interpretation of such enormous amounts of data, we have developed computational methods to decode bisulfite sequencing data: 1) the analysis of “methylation entropy” to quantitatively assess the variation of DNA methylation patterns in a given cell population; 2) nonparametric Bayesian clustering to detect bipolar methylated genomic loci; and 3) a computational pipeline to decipher the heterogeneity in DNA methylation patterns and to infer cell-type specific methylated loci.

Modulators of Platelet Aggregation

Platelets form a clump at the site of vascular injury to stop bleeding. One negative regulator of platelet aggregation is Disabled-2 (Dab2), a protein released to the extracellular surface upon platelet activation. Dab2 inhibits platelet aggregation by competing with fibrinogen for alphaIIb-beta3 integrin receptor binding. The inhibitory role of Dab2 depends on its recognition to sulfatides, sphingolipids found on the platelet surface, which interact with coagulation proteins, playing a major role in haemostasis.

DMEAS is the user-friendly tool dedicated to analyze methylation entropy

DMEAS is the first user-friendly tool dedicated to analyze methylation entropy for the quantification of epigenetic variation. The DMEAS program, user guide, and all the testing data are freely available

HBS Analyzer

HBS analyzer is the first command-line-based open-source tool to process genome-wide hairpin bisulfite sequencing data. It accepts Illumina paired-end sequencing reads as input, performs alignment to recover the original (pre-bisulfite-converted) DNA sequences, and calls methylation status for cytosines on both DNA strands. The HBS analyzer program, user guide, and all the testing data are freely available.

Lab Members


Alajoleen, Razan

Visiting Student

Armstrong, Nicole

BREU Student

Banerjee, Sharmi

Visiting Student

Murray, Alexander

Graduate Research Assistant

Wei, Xiaoran

Visiting Student

Xu, Xiguang

Visiting Student


Xu X, Wei X, Xie H. Advances in methods and software for RNA cytosine methylation analysis. Genomics. 2019 Oct 30. pii: S0888-7543(19)30516-6. https://doi.org/10.1016/j.ygeno.2019.10.017

Sun Z, Xu X, He J, Murray A, Sun MA, Wei X, Wang X, McCoig E, Xie E, Jiang X, Li L, Zhu J, Chen J, Morozov A, Pickrell AM, Theus MH, Xie H. EGR1 recruits TET1 to shape the brain methylome during development and upon neuronal activity. Nat Commun. 2019 Aug 29;10(1):3892. https://doi.org/10.1038/s41467-019-11905-3


Hazy A, Bochicchio L, Oliver A, Xie E, Geng S, Brickler T, Xie H, Li L, Allen IC, Theus MH. Divergent age-dependent peripheral immune transcriptomic profile following traumatic brain injury. Sci Rep. 2019 Jun 12;9(1):8564. https://doi.org/10.1038/s41598-019-45089-z

Banerjee S, Wei X, Xie H. Recursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modules. Comput Struct Biotechnol J. 2019 Apr 9;17:507-515. https://doi.org/10.1016/j.csbj.2019.04.003

He J, Xu X, Monavarfeshani A, Banerjee S, Fox MA, Xie H. Retinal-input-induced epigenetic dynamics in the developing mouse dorsal lateral geniculate nucleus. Epigenetics Chromatin. 2019 Feb 14;12(1):13. https://doi.org/10.1186/s13072-019-0257-x

Banerjee S, Zhu H, Tang M, Feng WC, Wu X, Xie H. Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells. Front Genet. 2019 Jan 15;9:731. https://doi.org/10.3389/fgene.2018.00731

Ma S, de la Fuente Revenga M, Sun Z, Sun C, Murphy TW, Xie H, González-Maeso J, Lu C. Cell-type-specific brain methylomes profiled via ultralow-input microfluidics. Nat Biomed Eng. 2018 Mar;2(3):183-194. https://doi.org/10.1038/s41551-018-0204-3

Luo Y, He J, Xu X, Sun MA, Wu X, Lu X, Xie H. Integrative single-cell omics analyses reveal epigenetic heterogeneity in mouse embryonic stem cells. PLoS Comput Biol. 2018 Mar 21;14(3):e1006034. https://doi.org/10.1371/journal.pcbi.1006034

Oestreich K, Xie H. Interviewed by Tech V. Scientist awarded $2 million grant from the NIH to study the body's immune memory response. EurekAlert. 2018. https://www.eurekalert.org/pub_releases/2018-06/vt-sa062718.php


Tran H, Wu X, Tithi S, Sun M-A, Xie H, Zhang L. A Bayesian Assignment Method for Ambiguous Bisulfite Short Reads. PLOS ONE. 2016;11(3). https://doi.org/10.1371/journal.pone.0151826.

Sharif J, Endo TA, Nakayama M, Karimi MM, Shimada M, Katsuyama K, Goyal P, Brind’Amour J, Sun M-A, Sun Z, Ishikura T, Mizutani-Koseki Y, Ohara O, Shinkai Y, Nakanishi M, Xie H, Lorincz MC, Koseki H. Activation of Endogenous Retroviruses in Dnmt1 −/− ESCs Involves Disruption of SETDB1-Mediated Repression by NP95 Binding to Hemimethylated DNA. Cell Stem Cell. 2016;19(1):81–94. https://doi.org/10.1016/j.stem.2016.03.013.

Okyere B, Giridhar K, Hazy A, Chen M, Keimig D, Bielitz RC, Xie H, He J-Q, Huckle WR, Theus MH. Endothelial-Specific EphA4 Negatively Regulates Native Pial Collateral Formation and Re-Perfusion following Hindlimb Ischemia. PLOS ONE. 2016;11(7). https://doi.org/10.1371/journal.pone.0159930.

von Meyenn F, Iurlaro M, Habibi E, Liu NQ, Salehzadeh-Yazdi A, Santos F, Petrini E, Milagre I, Yu M, Xie Z, Kroeze LI, Nesterova TB, Jansen JH, Xie H, He C, Reik W, Stunnenberg HG. Impairment of DNA Methylation Maintenance Is the Main Cause of Global Demethylation in Naive Embryonic Stem Cells. Molecular Cell. 2016;62(6):848–861. https://doi.org/10.1016/j.molcel.2016.04.025.

Sun M-A, Sun Z, Wu X, Rajaram V, Keimig D, Lim J, Zhu H, Xie H. Mammalian Brain Development is Accompanied by a Dramatic Increase in Bipolar DNA Methylation. SCIENTIFIC REPORTS. 2016;6. https://doi.org/10.1038/srep32298.


Porter J, Sun M-A, Xie H, Zhang L. Investigating bisulfite short-read mapping failure with hairpin bisulfite sequencing data. Presented at the 4th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Miami Beach, FL; 2015. https://doi.org/10.1186/1471-2164-16-S11-S2

He J, Sun M-A, Wang Z, Wang Q, Li Q, Xie H. Characterization and machine learning prediction of allele-specific DNA methylation. GENOMICS. 2015;106(6):331–339. https://doi.org/10.1016/j.ygeno.2015.09.007

Wu X, Sun M-A, Zhu H, Xie H. Nonparametric Bayesian clustering to detect bipolar methylated genomic loci. BMC BIOINFORMATICS. 2015;16. https://doi.org/10.1186/s12859-014-0439-2

Sun M-A, Velmurugan KR, Keimig D, Xie H. HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis. Advances in Bioinformatics. 2015;2015:1–4. https://doi.org/10.1155/2015/760423


Zhao L, Sun M-A, Li Z, Bai X, Yu M, Wang M, Liang L, Shao X, Arnovitz S, Wang Q, He C, Lu X, Chen J, Xie H. The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation. GENOME RESEARCH. 2014;24(8):1296–1307. https://doi.org/10.1101/gr.163147.113.

Shao X, Zhang C, Sun M-A, Lu X, Xie H. Deciphering the heterogeneity in DNA methylation patterns during stem cell differentiation and reprogramming. BMC Genomics. 2014;15(1):978–978. https://doi.org/10.1186/1471-2164-15-978.

Luo Y, Lu X, Xie H. Dynamic Alu methylation during normal development, aging, and tumorigenesis. BioMed research international. 2014;2014:784706. https://doi.org/10.1155/2014/784706.

McErlean P, Favoreto S, Costa FF, Shen J, Quraishi J, Biyasheva A, Cooper JJ, Scholtens DM, Vanin EF, Bonaldo MF de, Xie H, Soares MB, Avila PC. Human rhinovirus infection causes different DNA methylation changes in nasal epithelial cells from healthy and asthmatic subjects. BMC Medical Genomics. 2014;7(1). https://doi.org/10.1186/1755-8794-7-37.

Tran H, Porter J, Sun M-A, Sun M-A, Xie H, Zhang L. Objective and comprehensive evaluation of bisulfite short read mapping tools. Advances in bioinformatics. 2014;2014:472045. https://doi.org/10.1155/2014/472045


He J, Sun X, Shao X, Liang L, Xie H. DMEAS: DNA methylation entropy analysis software. Bioinformatics. 2013;29(16):2044–2045. https://doi.org/10.1093/bioinformatics/btt332


Hoxha E, Lambers E, Xie H, De Andrade A, Krishnamurthy P, Wasserstrom JA, Ramirez V, Thal M, Verma SK, Soares MB, Kishore R. Histone Deacetylase 1 Deficiency Impairs Differentiation and Electrophysiological Properties of Cardiomyocytes Derived from Induced Pluripotent Cells. STEM CELLS. 2012;30(11):2412–2422. https://doi.org/10.1002/stem.1209.

Wang M, Xie H, Shrestha S, Sredni S, Morgan GA, Pachman LM. Methylation alterations of WT1 and homeobox genes in inflamed muscle biopsy samples from patients with untreated juvenile dermatomyositis suggest self-renewal capacity. Arthritis & Rheumatism. 2012;64(10):3478–3485. https://doi.org/10.1002/art.34573.

Zhang X, Wallace AD, Du P, Kibbe WA, Jafari N, Xie H, Lin S, Baccarelli A, Soares MB, Hou L. DNA methylation alterations in response to pesticide exposure in vitro. Environmental and Molecular Mutagenesis. 2012;53(7):542–549. https://doi.org/10.1002/em.21718.