Stefan Bekiranov

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Primary Appointment

Associate Professor, Biochemistry and Molecular Genetics

Education

  • BS, Microwave Engineering, University of California, Los Angeles
  • PhD, Statistical Physics, University of California, Santa Barbara
  • Postdoc, Statistical Physics, University of Maryland
  • Postdoc, Computational Biology, The Rockefeller University

Research Disciplines

Bioinformatics and Genomics, Biophysics, Biophysics & Structural Biology, Cancer Biology, Computational Biology, Epigenetics, Molecular Biology, Translational Science

Research Interests

Physical Modeling of Microarray Hybridization; Analysis of Genomic Tiling Array Data; Bioinformatics; Computational Biology; Regulatory Networks

Research Description

My laboratory develops and applies computational statistics methods to functional genomic data with a focus on epigenetic data. Epigenetics is the study of heritable phenotypic traits not coded in the primary DNA sequence, which can be modified by environmental factors and comprise a complex regulatory network that controls a number of processes on DNA including transcription, replication and repair. A central element of this control network is chemical groups (e.g., acetyl or methyl) that are added or removed from DNA and histones, which package DNA in a cell's nucleus. Control of processes such as transcription is achieved by making DNA sequences available or unavailable for factors including General Transcription Factors and Pol II that initiate transcription as well as recruitment of factors that facilitate each stage of transcription (i.e., promotion, elongation, etc.). Similarly, accessibility of DNA regulates the binding of the Origin Recognition Complex and subsequent initiation of DNA replication. Epigenetic factors have been shown to play a fundamental role in regulating cell differentiation, and the dysregulation of epigenetic processes have been implicated in a number of diseases including cancer, diabetes, and neurological disorders.
We are interested in characterizing the complex epigenetic regulatory network of histone modifications and DNA methylation, which exhibits extensive cross-talk. We apply machine-learning methods including Multivariate Regression Splines (MARS) and Bayesian Networks (BN) to epigenomic data in order to uncover this network and understand how it regulates transcription and DNA replication. We analyze both publicly available data sets (e.g., epigenomic data generated by K. Zhao's lab and the ENCODE, modENCODE and Epigenomics Mapping Consortia) as well as data generated by colleagues here at UVa. In our collaborations, we are studying (1) preinitiation complex (PIC) and transcription factor dynamics and their role in regulation of transcription level and precision (2) epigenomic regulation of the epithelial to mesenchymal transition -- a model of how cells are reprogrammed during metastasis (3) origins of DNA replication in the human genome and the epigenetic factors that drive them (4) the role of histone deacetylaces including Sir2 in aging and (5) the impact of chemicals in our environment (e.g., Bisphenol A) on our epigenome and subsequent phenotypic outcomes. An exciting aspect of the collaborative environment at UVa is that we are able to experimentally test our predicted epigenetic regulatory network models at the biochemical and phenotypic level.

Personal Statement

My laboratory develops and applies computational statistics methods to functional genomic data with a focus on epigenetic data.  Epigenetics is the study of heritable phenotypic traits not coded in the primary DNA sequence, which can be modified by environmental factors and comprise a complex regulatory network that controls a number of processes on DNA including transcription, replication and repair.  A central element of this control network is chemical groups (e.g., acetyl or methyl) that are added or removed from DNA and histones, which package DNA in a cellâs nucleus.  Control of processes such as transcription is achieved by making DNA sequences available or unavailable for factors including General Transcription Factors and Pol II that initiate transcription as well as recruitment of factors that facilitate each stage of transcription (i.e., promotion, elongation, etc.). Similarly, accessibility of DNA regulates the binding of the Origin Recognition Complex and subsequent initiation of DNA replication.  Epigenetic factors have been shown to play a fundamental role in regulating cell differentiation, and the dysregulation of epigenetic processes have been implicated in a number of diseases including cancer, diabetes, and neurological disorders.
    
We are interested in characterizing the complex epigenetic regulatory network of histone modifications and DNA methylation, which exhibits extensive cross-talk.  We apply machine-learning methods including Multivariate Regression Splines (MARS) and Bayesian Networks (BN) to epigenomic data in order to uncover this network and understand how it regulates transcription and DNA replication.  We analyze both publicly available data sets (e.g., epigenomic data generated by K. Zhaoâs lab and the ENCODE, modENCODE and Epigenomics Mapping Consortia) as well as data generated by colleagues here at UVa.  In our collaborations, we are studying (1) preinitiation complex (PIC) and transcription factor dynamics and their role in regulation of transcription level and precision (2) epigenomic regulation of the epithelial to mesenchymal transitionâa model of how cells are reprogrammed during metastasis (3) origins of DNA replication in the human genome and the epigenetic factors that drive them (4) the role of histone deacetylaces including Sir2 in aging and (5) the impact of chemicals in our environment (e.g., Bisphenol A) on our epigenome and subsequent phenotypic outcomes.  An exciting aspect of the collaborative environment at UVa is that we are able to experimentally test our predicted epigenetic regulatory network models at the biochemical and phenotypic level.

Training

  • Training in Cell and Molecular Biology
  • Training in Molecular Biophysics

Selected Publications

Zaidi HA, Auble DT, Bekiranov S, RNA synthesis is associated with multiple TBP-chromatin binding events., 2017; Scientific reports. 7() 39631. PMID: 28051102 | PMCID: PMC5209698

Chen X, Poorey K, Carver MN, Müller U, Bekiranov S, Auble DT, Brow DA, Transcriptomes of six mutants in the Sen1 pathway reveal combinatorial control of transcription termination across the Saccharomyces cerevisiae genome., 2017; PLoS genetics. 13(6) e1006863. PMID: 28665995 | PMCID: PMC5513554

Zaidi H, Hoffman EA, Shetty SJ, Bekiranov S, Auble DT, Second-generation method for analysis of chromatin binding with formaldehyde crosslinking kinetics., 2017; The Journal of biological chemistry. () . PMID: 28972159

Anaya J, Reon B, Chen WM, Bekiranov S, Dutta A, A pan-cancer analysis of prognostic genes., 2016; PeerJ. 3() e1499. PMID: 27047702 | PMCID: PMC4815555

Cherepanova OA, Gomez D, Shankman LS, Swiatlowska P, Williams J, Sarmento OF, Alencar GF, Hess DL, Bevard MH, Greene ES, Murgai M, Turner SD, Geng YJ, Bekiranov S, Connelly JJ, Tomilin A, Owens GK, Activation of the pluripotency factor OCT4 in smooth muscle cells is atheroprotective., 2016; Nature medicine. 22(6) 657-65. PMID: 27183216 | PMCID: PMC4899256

True JD, Muldoon JJ, Carver MN, Poorey K, Shetty SJ, Bekiranov S, Auble DT, The Modifier of Transcription 1 (Mot1) ATPase and Spt16 Histone Chaperone Co-regulate Transcription through Preinitiation Complex Assembly and Nucleosome Organization., 2016; The Journal of biological chemistry. 291(29) 15307-19. PMID: 27226635 | PMCID: PMC4946942

Roller DG, Capaldo B, Bekiranov S, Mackey AJ, Conaway MR, Petricoin EF, Gioeli D, Weber MJ, Combinatorial drug screening and molecular profiling reveal diverse mechanisms of intrinsic and adaptive resistance to BRAF inhibition in V600E BRAF mutant melanomas., 2015; Oncotarget. 7(3) 2734-53. PMID: 26673621 | PMCID: PMC4823068

Wierman MB, Matecic M, Valsakumar V, Li M, Smith DL, Bekiranov S, Smith JS, Functional genomic analysis reveals overlapping and distinct features of chronologically long-lived yeast populations., 2015; Aging. 7(3) 177-94. PMID: 25769345 | PMCID: PMC4394729

Capaldo BJ, Roller D, Axelrod MJ, Koeppel AF, Petricoin EF, Slingluff CL, Weber MJ, Mackey AJ, Gioeli D, Bekiranov S, Systems Analysis of Adaptive Responses to MAP Kinase Pathway Blockade in BRAF Mutant Melanoma., 2015; PloS one. 10(9) e0138210. PMID: 26405815 | PMCID: PMC4583389

CieÅlik M, Bekiranov S, Genome-wide predictors of NF-κB recruitment and transcriptional activity., 2015; BioData mining. 8() 37. PMID: 26617673 | PMCID: PMC4661973

Cohen JN, Tewalt EF, Rouhani SJ, Buonomo EL, Bruce AN, Xu X, Bekiranov S, Fu YX, Engelhard VH, Tolerogenic properties of lymphatic endothelial cells are controlled by the lymph node microenvironment., 2014; PloS one. 9(2) e87740. PMID: 24503860 | PMCID: PMC3913631

Viswanathan R, Hoffman EA, Shetty SJ, Bekiranov S, Auble DT, Analysis of chromatin binding dynamics using the crosslinking kinetics (CLK) method., 2014; Methods (San Diego, Calif.). 70(2) 97-107. PMID: 25448301 | PMCID: PMC4267959

Poorey K, Viswanathan R, Carver MN, Karpova TS, Cirimotich SM, McNally JG, Bekiranov S, Auble DT, Measuring chromatin interaction dynamics on the second time scale at single-copy genes., 2013; Science (New York, N.Y.). 342(6156) 369-72. PMID: 24091704 | PMCID: PMC3997053

McCullough SD, Xu X, Dent SY, Bekiranov S, Roeder RG, Grant PA, Reelin is a target of polyglutamine expanded ataxin-7 in human spinocerebellar ataxia type 7 (SCA7) astrocytes., 2012; Proceedings of the National Academy of Sciences of the United States of America. 109(52) 21319-24. PMID: 23236151 | PMCID: PMC3535616

Smith SC, Havaleshko DM, Moon K, Baras AS, Lee J, Bekiranov S, Burke DJ, Theodorescu D, Use of yeast chemigenomics and COXEN informatics in preclinical evaluation of anticancer agents., 2011; Neoplasia (New York, N.Y.). 13(1) 72-80. PMID: 21253455 | PMCID: PMC3023847

Hoang SA, Xu X, Bekiranov S, Quantification of histone modification ChIP-seq enrichment for data mining and machine learning applications., 2011; BMC research notes. 4() 288. PMID: 21834981 | PMCID: PMC3170335

Matecic M, Smith DL, Pan X, Maqani N, Bekiranov S, Boeke JD, Smith JS, A microarray-based genetic screen for yeast chronological aging factors., 2010; PLoS genetics. 6(4) e1000921. PMID: 20421943 | PMCID: PMC2858703

Xu X, Hoang S, Mayo MW, Bekiranov S, Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression., 2010; BMC bioinformatics. 11() 396. PMID: 20653935 | PMCID: PMC2928206

Wade SL, Poorey K, Bekiranov S, Auble DT, The Snf1 kinase and proteasome-associated Rad23 regulate UV-responsive gene expression., 2009; The EMBO journal. 28(19) 2919-31. PMID: 19680226 | PMCID: PMC2760106

Kim Y, Bekiranov S, Lee JK, Park T, Double error shrinkage method for identifying protein binding sites observed by tiling arrays with limited replication., 2009; Bioinformatics (Oxford, England). 25(19) 2486-91. PMID: 19667080 | PMCID: PMC2800349

Park T, Kim Y, Bekiranov S, Lee JK, Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays., 2007; Nucleic acids research. 35(9) e69. PMID: 17430969 | PMCID: PMC1888820