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

Computational Biology; Bioinformatics; Precision Medicine; Machine Learning/AI; Quantum Computing

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

2024

Cho, H. J., Wang, Z., Cong, Y., Bekiranov, S., Zhang, A., & Zang, C. (2024). DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery. GENES, 15(2). doi:10.3390/genes15020144

2022

Kim, J., & Bekiranov, S. (2022). Generalization Performance of Quantum Metric Learning Classifiers. BIOMOLECULES, 12(11). doi:10.3390/biom12111576

Young, A., Bradley, L. A., Farrar, E., Bilcheck, H. O., Tkachenko, S., Saucerman, J. J., . . . Wolf, M. J. (2022). Inhibition of DYRK1a Enhances Cardiomyocyte Cycling After Myocardial Infarction. CIRCULATION RESEARCH, 130(9), 1345-1361. doi:10.1161/CIRCRESAHA.121.320005

2021

Kwak, M., Erdag, G., Leick, K. M., Bekiranov, S., Engelhard, V. H., & Slingluff, C. L. (2021). Associations of immune cell homing gene signatures and infiltrates of lymphocyte subsets in human melanomas: discordance with CD163+ myeloid cell infiltrates. JOURNAL OF TRANSLATIONAL MEDICINE, 19(1). doi:10.1186/s12967-021-03044-5

Lehman, C. E., Spencer, A., Hall, S., Shaw, J. J. P., Wulfkuhle, J., Petricoin, E. F., . . . Gioeli, D. (2021). IGF1R and Src inhibition induce synergistic cytotoxicity in HNSCC through inhibition of FAK. SCIENTIFIC REPORTS, 11(1). doi:10.1038/s41598-021-90289-1

Rapaport, F., Neelamraju, Y., Baslan, T., Hassane, D., Gruszczynska, A., Robert de Massy, M., . . . Garrett-Bakelman, F. E. (2021). Genomic and evolutionary portraits of disease relapse in acute myeloid leukemia. LEUKEMIA, 35(9), 2688-2692. doi:10.1038/s41375-021-01153-0

Emani, P. S., Warrell, J., Anticevic, A., Bekiranov, S., Gandal, M., McConnell, M. J., . . . Harrow, A. W. (2021). Quantum computing at the frontiers of biological sciences. NATURE METHODS, 18(7), 701-709. doi:10.1038/s41592-020-01004-3

2020

Pattarabanjird, T., Cress, C., Nguyen, A., Taylor, A., Bekiranov, S., & McNamara, C. (2020). A Machine Learning Model Utilizing a Novel SNP Shows Enhanced Prediction of Coronary Artery Disease Severity. GENES, 11(12). doi:10.3390/genes11121446

Lawson, J. T., Smith, J. P., Bekiranov, S., Garrett-Bakelman, F. E., & Sheffield, N. C. (2020). COCOA: coordinate covariation analysis of epigenetic heterogeneity. GENOME BIOLOGY, 21(1). doi:10.1186/s13059-020-02139-4

Alencar, G. F., Owsiany, K. M., Karnewar, S., Sukhavasi, K., Mocci, G., Nguyen, A. T., . . . Owens, G. K. (2020). Stem Cell Pluripotency Genes Klf4 and Oct4 Regulate Complex SMC Phenotypic Changes Critical in Late-Stage Atherosclerotic Lesion Pathogenesis. CIRCULATION, 142(21), 2045-2059. doi:10.1161/CIRCULATIONAHA.120.046672

Grabski, D. F., Ratan, A., Gray, L. R., Bekiranov, S., Rekosh, D., Hammarskjold, M. -L., & Rasmussen, S. K. (2021). Upregulation of human endogenous retrovirus-K (HML-2) mRNAs in hepatoblastoma: Identification of potential new immunotherapeutic targets and biomarkers. JOURNAL OF PEDIATRIC SURGERY, 56(2), 286-292. doi:10.1016/j.jpedsurg.2020.05.022

Singh, S., Szlachta, K., Manukyan, A., Raimer, H. M., Dinda, M., Bekiranov, S., & Wang, Y. -H. (2020). Pausing sites of RNA polymerase II on actively transcribed genes are enriched in DNA double-stranded breaks. JOURNAL OF BIOLOGICAL CHEMISTRY, 295(12), 3990-4000. doi:10.1074/jbc.RA119.011665

Kathuria, K., Ratan, A., McConnell, M., & Bekiranov, S. (2020). Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne. QUANTUM MACHINE INTELLIGENCE, 2(1). doi:10.1007/s42484-020-00017-7

2019

Gray, L. R., Jackson, R. E., Jackson, P. E. H., Bekiranov, S., Rekosh, D., & Hammarskjold, M. -L. (2019). HIV-1 Rev interacts with HERV-K RcREs present in the human genome and promotes export of unspliced HERV-K proviral RNA. RETROVIROLOGY, 16(1). doi:10.1186/s12977-019-0505-y

Lehman, C. E., Khalil, A. A., Axelrod, M. J., Dougherty, M. I., Schoeff, S. S., Taniguchi, L. E., . . . Jameson, M. J. (2020). Antitumor effect of insulin-like growth factor-1 receptor inhibition in head and neck squamous cell carcinoma. LARYNGOSCOPE, 130(6), 1470-1478. doi:10.1002/lary.28236

Li, M., Fine, R. D., Dinda, M., Bekiranov, S., & Smith, J. S. (2019). A Sir2-regulated locus control region in the recombination enhancer of Saccharomyces cerevisiae specifies chromosome III structure. PLOS GENETICS, 15(8). doi:10.1371/journal.pgen.1008339

Szymura, S. J., Zaemes, J. P., Allison, D. F., Clift, S. H., D'Innocenzi, J. M., Gray, L. G., . . . Mayo, M. W. (2019). NF-κB upregulates glutamine-fructose-6-phosphate transaminase 2 to promote migration in non-small cell lung cancer. CELL COMMUNICATION AND SIGNALING, 17. doi:10.1186/s12964-019-0335-5

Chronister, W. D., Burbulis, I. E., Wierman, M. B., Wolpert, M. J., Haakenson, M. F., Smith, A. C. B., . . . McConnell, M. J. (2019). Neurons with Complex Karyotypes Are Rare in Aged Human Neocortex. CELL REPORTS, 26(4), 825-+. doi:10.1016/j.celrep.2018.12.107

Leick, K. M., Obeid, J. M., Bekiranov, S., & Slingluff, C. L. J. (2019). Systems analysis of barrier molecule and ARNT-related gene expression regulation in melanoma. ONCOIMMUNOLOGY, 8(12). doi:10.1080/2162402X.2019.1665978

2018

Woo, L. A., Tkachenko, S., Ding, M., Plowright, A. T., Engkvist, O., Andersson, H., . . . Saucerman, J. J. (2019). High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 127, 204-214. doi:10.1016/j.yjmcc.2018.12.015

Uchiyama, R., Kupkova, K., Shetty, S. J., Linford, A. S., Pray-Grant, M. G., Wagar, L. E., . . . Auble, D. T. (2018). Histone H3 lysine 4 methylation signature associated with human undernutrition. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 115(48), E11264-E11273. doi:10.1073/pnas.1722125115

Szlachta, K., Thys, R. G., Atkin, N. D., Pierce, L. C. T., Bekiranov, S., & Wang, Y. -H. (2018). Alternative DNA secondary structure formation affects RNA polymerase II promoter-proximal pausing in human. GENOME BIOLOGY, 19. doi:10.1186/s13059-018-1463-8

Hoffman, E. A., Zaidi, H., Shetty, S. J., Bekiranov, S., & Auble, D. T. (2018). An Improved Method for Measuring Chromatin-binding Dynamics Using Time-dependent Formaldehyde Crosslinking. BIO-PROTOCOL, 8(4). doi:10.21769/BioProtoc.2905

2017

Zaidi, H., Hoffman, E. A., Shetty, S. J., Bekiranov, S., & Auble, D. T. (2017). Second-generation method for analysis of chromatin binding with formaldehyde-cross-linking kinetics. JOURNAL OF BIOLOGICAL CHEMISTRY, 292(47), 19338-19355. doi:10.1074/jbc.M117.796441

Jayappa, K. D., Portell, C. A., Gordon, V. L., Capaldo, B. J., Bekiranov, S., Axelrod, M. J., . . . Weber, M. J. (2017). Microenvironmental agonists generate de novo phenotypic resistance to combined ibrutinib plus venetoclax in CLL and MCL (vol 14, pg 933, 2017). BLOOD ADVANCES, 1(19), 1537. doi:10.1182/bloodadvances.2017011148

Jayappa, K. D., Portell, C. A., Gordon, V. L., Capaldo, B. J., Bekiranov, S., Axelrod, M. J., . . . Weber, M. J. (2017). Microenvironmental agonists generate de novo phenotypic resistance to combined ibrutinib plus venetoclax in CLL and MCL. BLOOD ADVANCES, 1(14), 933-946. doi:10.1182/bloodadvances.2016004176

Chen, X., Poorey, K., Carver, M. N., Mueller, U., Bekiranov, S., Auble, D. T., & Brow, D. A. (2017). Transcriptomes of six mutants in the Sen1 pathway reveal combinatorial control of transcription termination across the Saccharomyces cerevisiae genome. PLOS GENETICS, 13(6). doi:10.1371/journal.pgen.1006863

Zaidi, H. A., Auble, D. T., & Bekiranov, S. (2017). RNA synthesis is associated with multiple TBP-chromatin binding events. SCIENTIFIC REPORTS, 7. doi:10.1038/srep39631

2016

True, J. D., Muldoon, J. J., Carver, M. N., Poorey, K., Shetty, S. J., Bekiranov, S., & Auble, D. T. (2016). The Modifier of Transcription 1 (Mot1) ATPase and Spt16 Histone Chaperone Co-regulate Transcription through Preinitiation Complex Assembly and Nucleosome Organization.. The Journal of biological chemistry, 291(29), 15307-15319. doi:10.1074/jbc.m116.735134

Cherepanova, O. A., Gomez, D., Shankman, L. S., Swiatlowska, P., Williams, J., Sarmento, O. F., . . . Owens, G. K. (2016). Activation of the pluripotency factor OCT4 in smooth muscle cells is atheroprotective. NATURE MEDICINE, 22(6), 657-+. doi:10.1038/nm.4109

True, J. D., Muldoon, J. J., Carver, M. N., Poorey, K., Shetty, S. J., Bekiranov, S., & Auble, D. T. (2016). The Modifier of Transcription 1 (Mott) ATPase and Spt16 Histone Chaperone Co-regulate Transcription through Preinitiation Complex Assembly and Nucleosome Organization. JOURNAL OF BIOLOGICAL CHEMISTRY, 291(29), 15307-15319. doi:10.1074/jbc.M116.735134

2015

Roller, D. G., Capaldo, B., Bekiranov, S., Mackey, A. J., Conaway, M. R., Petricoin, E. F., . . . Weber, M. J. (2016). Combinatorial drug screening and molecular profiling reveal diverse mechanisms of intrinsic and adaptive resistance to BRAF inhibition in V600E BRAF mutant melanomas. ONCOTARGET, 7(3), 2734-2753. doi:10.18632/oncotarget.6548

Wierman, M. B., Matecic, M., Valsakumar, V., Li, M., Smith, D. L. J., Bekiranov, S., & Smith, J. S. (2015). Functional genomic analysis reveals overlapping and distinct features of chronologically long-lived yeast populations. AGING-US, 7(3), 177-194. doi:10.18632/aging.100729

Anaya, J., Reon, B., Chen, W. -M., Bekiranov, S., & Duna, A. (2016). A pan-cancer analysis of prognostic genesl. PEERJ, 4. doi:10.7717/peerj.1499

Cieslik, M., & Bekiranov, S. (2015). Genome-wide predictors of NF-κB recruitment and transcriptional activity. BIODATA MINING, 8. doi:10.1186/s13040-015-0071-3

Capaldo, B. J., Roller, D., Axelrod, M. J., Koeppel, A. F., Petricoin, E. F., Slingluff, C. L. J., . . . Bekiranov, S. (2015). Systems Analysis of Adaptive Responses to MAP Kinase Pathway Blockade in BRAF Mutant Melanoma. PLOS ONE, 10(9). doi:10.1371/journal.pone.0138210

2014

Wamsley, J. J., Kumar, M., Allison, D. F., Clift, S. H., Holzknecht, C. M., Szymura, S. J., . . . Mayo, M. W. (2015). Activin Upregulation by NF-κB Is Required to Maintain Mesenchymal Features of Cancer Stem-like Cells in Non-Small Cell Lung Cancer. CANCER RESEARCH, 75(2), 426-435. doi:10.1158/0008-5472.CAN-13-2702

Viswanathan, R., Hoffman, E. A., Shetty, S. J., Bekiranov, S., & Auble, D. T. (2014). Analysis of chromatin binding dynamics using the crosslinking kinetics (CLK) method. METHODS, 70(2-3), 97-107. doi:10.1016/j.ymeth.2014.10.029

Cieslik, M., & Bekiranov, S. (2014). Combinatorial epigenetic patterns as quantitative predictors of chromatin biology. BMC GENOMICS, 15. doi:10.1186/1471-2164-15-76

Cohen, J. N., Tewalt, E. F., Rouhani, S. J., Buonomo, E. L., Bruce, A. N., Xu, X., . . . Engelhard, V. H. (2014). Tolerogenic Properties of Lymphatic Endothelial Cells Are Controlled by the Lymph Node Microenvironment. PLOS ONE, 9(2). doi:10.1371/journal.pone.0087740

2013

Poorey, K., Viswanathan, R., Carver, M. N., Karpova, T. S., Cirimotich, S. M., McNally, J. G., . . . Auble, D. T. (2013). Measuring Chromatin Interaction Dynamics on the Second Time Scale at Single-Copy Genes. SCIENCE, 342(6156), 369-372. doi:10.1126/science.1242369

Cieslik, M., Hoang, S. A., Baranova, N., Chodaparambil, S., Kumar, M., Allison, D. F., . . . Bekiranov, S. (2013). Epigenetic coordination of signaling pathways during the epithelial-mesenchymal transition. EPIGENETICS & CHROMATIN, 6. doi:10.1186/1756-8935-6-28

Mesner, L. D., Valsakumar, V., Cieslik, M., Pickin, R., Hamlin, J. L., & Bekiranov, S. (2013). Bubble-seq analysis of the human genome reveals distinct chromatin-mediated mechanisms for regulating early- and late-firing origins. GENOME RESEARCH, 23(11), 1774-1788. doi:10.1101/gr.155218.113

Li, M., Valsakumar, V., Poorey, K., Bekiranov, S., & Smith, J. S. (2013). Genome-wide analysis of functional sirtuin chromatin targets in yeast. GENOME BIOLOGY, 14(5). doi:10.1186/gb-2013-14-5-r48

Wolstenholme, J. T., Rissman, E. F., & Bekiranov, S. (2013). Sexual differentiation in the developing mouse brain: contributions of sex chromosome genes. GENES BRAIN AND BEHAVIOR, 12(2), 166-180. doi:10.1111/gbb.12010

Kumar, M., Allison, D. F., Baranova, N. N., Wamsley, J. J., Katz, A. J., Bekiranov, S., . . . Mayo, M. W. (2013). NF-κB Regulates Mesenchymal Transition for the Induction of Non-Small Cell Lung Cancer Initiating Cells. PLOS ONE, 8(7). doi:10.1371/journal.pone.0068597

Hoang, S. A., & Bekiranov, S. (2013). The Network Architecture of the Saccharomyces cerevisiae Genome. PLOS ONE, 8(12). doi:10.1371/journal.pone.0081972

2012

McCullough, S. D., Xu, X., Dent, S. Y. R., Bekiranov, S., Roeder, R. G., & Grant, P. A. (2012). Reelin is a target of polyglutamine expanded ataxin-7 in human spinocerebellar ataxia type 7 (SCA7) astrocytes. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 109(52), 21319-21324. doi:10.1073/pnas.1218331110

Hoang, S., CieÅlik, M., Chodaparambil, S., Baranova, N., Kumar, M., Allison, D., . . . Bekiranov, S. (2012). Epigenetic reprogramming in the epithelial-to-mesenchymal transition. BMC Proceedings, 6(S6). doi:10.1186/1753-6561-6-s6-o28

2011

Hoang, S. A., Xu, X., & Bekiranov, S. (2011). Quantification of histone modification ChIP-seq enrichment for data mining and machine learning applications.. BMC research notes, 4, 288. doi:10.1186/1756-0500-4-288

Gioeli, D., Wunderlich, W., Sebolt-Leopold, J., Bekiranov, S., Wulfkuhle, J. D., Petricoin, E. F. I. I. I., . . . Weber, M. J. (2011). Compensatory Pathways Induced by MEK Inhibition Are Effective Drug Targets for Combination Therapy against Castration-Resistant Prostate Cancer. MOLECULAR CANCER THERAPEUTICS, 10(9), 1581-1590. doi:10.1158/1535-7163.MCT-10-1033

Hyland, E. M., Molina, H., Poorey, K., Jie, C., Xie, Z., Dai, J., . . . Boeke, J. D. (2011). An evolutionarily 'young' lysine residue in histone H3 attenuates transcriptional output in Saccharomyces cerevisiae. GENES & DEVELOPMENT, 25(12), 1306-1319. doi:10.1101/gad.2050311

Smith, S. C., Havaleshko, D. M., Moon, K., Baras, A. S., Lee, J., Bekiranov, S., . . . Theodorescu, D. (2011). Use of Yeast Chemigenomics and COXEN Informatics in Preclinical Evaluation of Anticancer Agents. NEOPLASIA, 13(1), 72-U101. doi:10.1593/neo.101214

2010

Mesner, L. D., Valsakumar, V., Karnani, N., Dutta, A., Hamlin, J. L., & Bekiranov, S. (2011). Bubble-chip analysis of human origin distributions demonstrates on a genomic scale significant clustering into zones and significant association with transcription. GENOME RESEARCH, 21(3), 377-389. doi:10.1101/gr.111328.110

Stolzenberg, D. S., Grant, P. A., & Bekiranov, S. (2011). Epigenetic methodologies for behavioral scientists. HORMONES AND BEHAVIOR, 59(3), 407-416. doi:10.1016/j.yhbeh.2010.10.007

Poorey, K., Sprouse, R. O., Wells, M. N., Viswanathan, R., Bekiranov, S., & Auble, D. T. (2010). RNA synthesis precision is regulated by preinitiation complex turnover. GENOME RESEARCH, 20(12), 1679-1688. doi:10.1101/gr.109504.110

Park, J. H., Bonthius, P. J., Tsai, H. -W., Bekiranov, S., & Rissman, E. F. (2010). Amyloid β Precursor Protein Regulates Male Sexual Behavior. JOURNAL OF NEUROSCIENCE, 30(30), 9967-9972. doi:10.1523/JNEUROSCI.1988-10.2010

Xu, X., Hoang, S., Mayo, M. W., & Bekiranov, S. (2010). Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression. BMC BIOINFORMATICS, 11. doi:10.1186/1471-2105-11-396

Li, M., Petteys, B. J., McClure, J. M., Valsakumar, V., Bekiranov, S., Frank, E. L., & Smith, J. S. (2010). Thiamine Biosynthesis in Saccharomyces cerevisiae Is Regulated by the NAD+- Dependent Histone Deacetylase Hst1. MOLECULAR AND CELLULAR BIOLOGY, 30(13), 3329-3341. doi:10.1128/MCB.01590-09

Amos, P. J., Kapur, S. K., Stapor, P. C., Shang, H., Bekiranov, S., Khurgel, M., . . . Katz, A. J. (2010). Human Adipose-Derived Stromal Cells Accelerate Diabetic Wound Healing: Impact of Cell Formulation and Delivery. TISSUE ENGINEERING PART A, 16(5), 1595-1606. doi:10.1089/ten.tea.2009.0616

Matecic, M., Smith, D. L. J., Pan, X., Maqani, N., Bekiranov, S., Boeke, J. D., & Smith, J. S. (2010). A Microarray-Based Genetic Screen for Yeast Chronological Aging Factors. PLOS GENETICS, 6(4). doi:10.1371/journal.pgen.1000921