133A Cluster Enhances Cancer Cell Migration and Invasion in Lung-Squamous Cell Carcinoma Via Regulation of Coronin1c
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Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
1 AGING Supplementary Table 2
SUPPLEMENTARY TABLES Supplementary Table 1. Details of the eight domain chains of KIAA0101. Serial IDENTITY MAX IN COMP- INTERFACE ID POSITION RESOLUTION EXPERIMENT TYPE number START STOP SCORE IDENTITY LEX WITH CAVITY A 4D2G_D 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ B 4D2G_E 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ C 6EHT_D 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ D 6EHT_E 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ E 6GWS_D 41-72 41 72 100 100 3.2Å PCNA X-RAY DIFFRACTION √ F 6GWS_E 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ G 6GWS_F 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ H 6IIW_B 2-11 2 11 100 100 1.699Å UHRF1 X-RAY DIFFRACTION √ www.aging-us.com 1 AGING Supplementary Table 2. Significantly enriched gene ontology (GO) annotations (cellular components) of KIAA0101 in lung adenocarcinoma (LinkedOmics). Leading Description FDR Leading Edge Gene EdgeNum RAD51, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, CENPW, HJURP, NDC80, CDCA5, NCAPH, BUB1, ZWILCH, CENPK, KIF2C, AURKA, CENPN, TOP2A, CENPM, PLK1, ERCC6L, CDT1, CHEK1, SPAG5, CENPH, condensed 66 0 SPC24, NUP37, BLM, CENPE, BUB3, CDK2, FANCD2, CENPO, CENPF, BRCA1, DSN1, chromosome MKI67, NCAPG2, H2AFX, HMGB2, SUV39H1, CBX3, TUBG1, KNTC1, PPP1CC, SMC2, BANF1, NCAPD2, SKA2, NUP107, BRCA2, NUP85, ITGB3BP, SYCE2, TOPBP1, DMC1, SMC4, INCENP. RAD51, OIP5, CDK1, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, ESCO2, CENPW, HJURP, TTK, NDC80, CDCA5, BUB1, ZWILCH, CENPK, KIF2C, AURKA, DSCC1, CENPN, CDCA8, CENPM, PLK1, MCM6, ERCC6L, CDT1, HELLS, CHEK1, SPAG5, CENPH, PCNA, SPC24, CENPI, NUP37, FEN1, chromosomal 94 0 CENPL, BLM, KIF18A, CENPE, MCM4, BUB3, SUV39H2, MCM2, CDK2, PIF1, DNA2, region CENPO, CENPF, CHEK2, DSN1, H2AFX, MCM7, SUV39H1, MTBP, CBX3, RECQL4, KNTC1, PPP1CC, CENPP, CENPQ, PTGES3, NCAPD2, DYNLL1, SKA2, HAT1, NUP107, MCM5, MCM3, MSH2, BRCA2, NUP85, SSB, ITGB3BP, DMC1, INCENP, THOC3, XPO1, APEX1, XRCC5, KIF22, DCLRE1A, SEH1L, XRCC3, NSMCE2, RAD21. -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Mtorc1 Controls Mitochondrial Activity and Biogenesis Through 4E-BP-Dependent Translational Regulation
Cell Metabolism Article mTORC1 Controls Mitochondrial Activity and Biogenesis through 4E-BP-Dependent Translational Regulation Masahiro Morita,1,2 Simon-Pierre Gravel,1,2 Vale´ rie Che´ nard,1,2 Kristina Sikstro¨ m,3 Liang Zheng,4 Tommy Alain,1,2 Valentina Gandin,5,7 Daina Avizonis,2 Meztli Arguello,1,2 Chadi Zakaria,1,2 Shannon McLaughlan,5,7 Yann Nouet,1,2 Arnim Pause,1,2 Michael Pollak,5,6,7 Eyal Gottlieb,4 Ola Larsson,3 Julie St-Pierre,1,2,* Ivan Topisirovic,5,7,* and Nahum Sonenberg1,2,* 1Department of Biochemistry 2Goodman Cancer Research Centre McGill University, Montreal, QC H3A 1A3, Canada 3Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 171 76, Sweden 4Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK 5Lady Davis Institute for Medical Research 6Cancer Prevention Center, Sir Mortimer B. Davis-Jewish General Hospital McGill University, Montreal, QC H3T 1E2, Canada 7Department of Oncology, McGill University, Montreal, QC H2W 1S6, Canada *Correspondence: [email protected] (J.S.-P.), [email protected] (I.T.), [email protected] (N.S.) http://dx.doi.org/10.1016/j.cmet.2013.10.001 SUMMARY ATP under physiological conditions in mammals and play a crit- ical role in overall energy balance (Vander Heiden et al., 2009). mRNA translation is thought to be the most energy- The mechanistic/mammalian target of rapamycin (mTOR) is a consuming process in the cell. Translation and serine/threonine kinase that has been implicated in a variety of energy metabolism are dysregulated in a variety of physiological processes and pathological states (Zoncu et al., diseases including cancer, diabetes, and heart 2011). -
Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing -
A Master Autoantigen-Ome Links Alternative Splicing, Female Predilection, and COVID-19 to Autoimmune Diseases
bioRxiv preprint doi: https://doi.org/10.1101/2021.07.30.454526; this version posted August 4, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. A Master Autoantigen-ome Links Alternative Splicing, Female Predilection, and COVID-19 to Autoimmune Diseases Julia Y. Wang1*, Michael W. Roehrl1, Victor B. Roehrl1, and Michael H. Roehrl2* 1 Curandis, New York, USA 2 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA * Correspondence: [email protected] or [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.30.454526; this version posted August 4, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Chronic and debilitating autoimmune sequelae pose a grave concern for the post-COVID-19 pandemic era. Based on our discovery that the glycosaminoglycan dermatan sulfate (DS) displays peculiar affinity to apoptotic cells and autoantigens (autoAgs) and that DS-autoAg complexes cooperatively stimulate autoreactive B1 cell responses, we compiled a database of 751 candidate autoAgs from six human cell types. At least 657 of these have been found to be affected by SARS-CoV-2 infection based on currently available multi-omic COVID data, and at least 400 are confirmed targets of autoantibodies in a wide array of autoimmune diseases and cancer. -
Embryonic Transcriptome and Proteome Analyses on Hepatic Lipid
Na et al. BMC Genomics (2018) 19:384 https://doi.org/10.1186/s12864-018-4776-9 RESEARCH ARTICLE Open Access Embryonic transcriptome and proteome analyses on hepatic lipid metabolism in chickens divergently selected for abdominal fat content Wei Na, Yuan-Yuan Wu, Peng-Fei Gong, Chun-Yan Wu, Bo-Han Cheng, Yu-Xiang Wang, Ning Wang, Zhi-Qiang Du* and Hui Li* Abstract Background: In avian species, liver is the main site of de novo lipogenesis, and hepatic lipid metabolism relates closely to adipose fat deposition. Using our fat and lean chicken lines of striking differences in abdominal fat content, post-hatch lipid metabolism in both liver and adipose tissues has been studied extensively. However, whether molecular discrepancy for hepatic lipid metabolism exists in chicken embryos remains obscure. Results: We performed transcriptome and proteome profiling on chicken livers at five embryonic stages (E7, E12, E14, E17 and E21) between the fat and lean chicken lines. At each stage, 521, 141, 882, 979 and 169 differentially expressed genes were found by the digital gene expression, respectively, which were significantly enriched in the metabolic, PPAR signaling and fatty acid metabolism pathways. Quantitative proteomics analysis found 20 differentially expressed proteins related to lipid metabolism, PPAR signaling, fat digestion and absorption, and oxidative phosphorylation pathways. Combined analysis showed that genes and proteins related to lipid transport (intestinal fatty acid-binding protein, nucleoside diphosphate kinase, and apolipoprotein A-I), lipid clearance (heat shock protein beta-1) and energy metabolism (NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit10andsuccinatedehydrogenase flavoprotein subunit) were significantly differentially expressed between the two lines. -
Rat Mitochondrion-Neuron Focused Microarray (Rmnchip) and Bioinfor- Matics Tools for Rapid Identification of Differential Pathways in Brain Tissues Yan A
Int. J. Biol. Sci. 2011, 7 308 International Journal of Biological Sciences 2011; 7(3):308-322 Research Paper Rat Mitochondrion-Neuron Focused Microarray (rMNChip) and Bioinfor- matics Tools for Rapid Identification of Differential Pathways in Brain Tissues Yan A. Su, Qiuyang Zhang, David M. Su, Michael X. Tang Department of Gene and Protein Biomarkers, GenProMarkers Inc., Rockville, MD 20850, USA Corresponding author: Yan A. Su, M.D., Ph.D., GenProMarkers Inc., 9700 Great Seneca Highway, Suite 182, Rockville, Maryland 20850. Phone: (301) 326-6523; Email:[email protected] © Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/ licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited. Received: 2011.01.01; Accepted: 2011.03.25; Published: 2011.03.29 Abstract Mitochondrial function is of particular importance in brain because of its high demand for energy (ATP) and efficient removal of reactive oxygen species (ROS). We developed rat mitochondrion-neuron focused microarray (rMNChip) and integrated bioinformatics tools for rapid identification of differential pathways in brain tissues. rMNChip contains 1,500 genes involved in mitochondrial functions, stress response, circadian rhythms and signal transduc- tion. The bioinformatics tool includes an algorithm for computing of differentially expressed genes, and a database for straightforward -
Genome-Wide Association Studies of LRRK2
RESEARCH ARTICLE Genomewide Association Studies of LRRK2 Modifiers of Parkinson’s Disease † † † Dongbing Lai, PhD ,1 Babak Alipanahi, PhD,2,3 Pierre Fontanillas, PhD,2 Tae-Hwi Schwantes-An, PhD,1 Jan Aasly, MD, PhD,4 Roy N. Alcalay, MD, MS,5 Gary W. Beecham, PhD,6 Daniela Berg, MD,7,8 Susan Bressman, MD,9 Alexis Brice, MD,10 Kathrin Brockman, MD ,8,11 Lorraine Clark, PhD,12 Mark Cookson, PhD,13 Sayantan Das, PhD,2 Vivianna Van Deerlin, MD, PhD,14 Jordan Follett, PhD,15 Matthew J. Farrer, PhD,15 Joanne Trinh, PhD,16 Thomas Gasser, MD,8,11 Stefano Goldwurm, MD, PhD,17 Emil Gustavsson, PhD,18 Christine Klein, MD ,16 Anthony E. Lang, MD,19 J. William Langston, MD,20 Jeanne Latourelle, DSc,21 Timothy Lynch, MD,22 Karen Marder, MD, MPH,23 Connie Marras, MD, PhD,19 Eden R. Martin, PhD,6 Cory Y. McLean, PhD,2,24 Helen Mejia-Santana, MS,25 Eric Molho, MD,26 Richard H. Myers, PhD,27 Karen Nuytemans, PhD,6 Laurie Ozelius, PhD,9 Haydeh Payami, PhD,28 Deborah Raymond, MS,9 Ekaterina Rogaeva, PhD,29 Michael P. Rogers, MD,30 Owen A. Ross, PhD ,31,32 Ali Samii, MD,33 Rachel Saunders-Pullman, MD,9 Birgitt Schüle, MD,34 Claudia Schulte, MS,8,11 William K. Scott, PhD,6 Caroline Tanner, MD,35 Eduardo Tolosa, MD, PhD,36 James E. Tomkins, PhD,37 Dolores Vilas, MD,36 John Q. Trojanowski, MD, PhD,14 The 23andMe Research Team,2 Ryan Uitti, MD,38 Jeffery M. Vance, MD, PhD,6 Naomi P. -
Lncrna SNHG8 Is Identified As a Key Regulator of Acute Myocardial
Zhuo et al. Lipids in Health and Disease (2019) 18:201 https://doi.org/10.1186/s12944-019-1142-0 RESEARCH Open Access LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis Liu-An Zhuo, Yi-Tao Wen, Yong Wang, Zhi-Fang Liang, Gang Wu, Mei-Dan Nong and Liu Miao* Abstract Background: Long noncoding RNAs (lncRNAs) are involved in numerous physiological functions. However, their mechanisms in acute myocardial infarction (AMI) are not well understood. Methods: We performed an RNA-seq analysis to explore the molecular mechanism of AMI by constructing a lncRNA-miRNA-mRNA axis based on the ceRNA hypothesis. The target microRNA data were used to design a global AMI triple network. Thereafter, a functional enrichment analysis and clustering topological analyses were conducted by using the triple network. The expression of lncRNA SNHG8, SOCS3 and ICAM1 was measured by qRT-PCR. The prognostic values of lncRNA SNHG8, SOCS3 and ICAM1 were evaluated using a receiver operating characteristic (ROC) curve. Results: An AMI lncRNA-miRNA-mRNA network was constructed that included two mRNAs, one miRNA and one lncRNA. After RT-PCR validation of lncRNA SNHG8, SOCS3 and ICAM1 between the AMI and normal samples, only lncRNA SNHG8 had significant diagnostic value for further analysis. The ROC curve showed that SNHG8 presented an AUC of 0.850, while the AUC of SOCS3 was 0.633 and that of ICAM1 was 0.594. After a pairwise comparison, we found that SNHG8 was statistically significant (P SNHG8-ICAM1 = 0.002; P SNHG8-SOCS3 = 0.031). -
Supplementary Dataset S2
mitochondrial translational termination MRPL28 MRPS26 6 MRPS21 PTCD3 MTRF1L 4 MRPL50 MRPS18A MRPS17 2 MRPL20 MRPL52 0 MRPL17 MRPS33 MRPS15 −2 MRPL45 MRPL30 MRPS27 AURKAIP1 MRPL18 MRPL3 MRPS6 MRPS18B MRPL41 MRPS2 MRPL34 GADD45GIP1 ERAL1 MRPL37 MRPS10 MRPL42 MRPL19 MRPS35 MRPL9 MRPL24 MRPS5 MRPL44 MRPS23 MRPS25 ITB ITB ITB ITB ICa ICr ITL original ICr ICa ITL ICa ITL original ICr ITL ICr ICa mitochondrial translational elongation MRPL28 MRPS26 6 MRPS21 PTCD3 MRPS18A 4 MRPS17 MRPL20 2 MRPS15 MRPL45 MRPL52 0 MRPS33 MRPL30 −2 MRPS27 AURKAIP1 MRPS10 MRPL42 MRPL19 MRPL18 MRPL3 MRPS6 MRPL24 MRPS35 MRPL9 MRPS18B MRPL41 MRPS2 MRPL34 MRPS5 MRPL44 MRPS23 MRPS25 MRPL50 MRPL17 GADD45GIP1 ERAL1 MRPL37 ITB ITB ITB ITB ICa ICr original ICr ITL ICa ITL ICa ITL original ICr ITL ICr ICa translational termination MRPL28 MRPS26 6 MRPS21 PTCD3 C12orf65 4 MTRF1L MRPL50 MRPS18A 2 MRPS17 MRPL20 0 MRPL52 MRPL17 MRPS33 −2 MRPS15 MRPL45 MRPL30 MRPS27 AURKAIP1 MRPL18 MRPL3 MRPS6 MRPS18B MRPL41 MRPS2 MRPL34 GADD45GIP1 ERAL1 MRPL37 MRPS10 MRPL42 MRPL19 MRPS35 MRPL9 MRPL24 MRPS5 MRPL44 MRPS23 MRPS25 ITB ITB ITB ITB ICa ICr original ICr ITL ICa ITL ICa ITL original ICr ITL ICr ICa translational elongation DIO2 MRPS18B MRPL41 6 MRPS2 MRPL34 GADD45GIP1 4 ERAL1 MRPL37 2 MRPS10 MRPL42 MRPL19 0 MRPL30 MRPS27 AURKAIP1 −2 MRPL18 MRPL3 MRPS6 MRPS35 MRPL9 EEF2K MRPL50 MRPS5 MRPL44 MRPS23 MRPS25 MRPL24 MRPS33 MRPL52 EIF5A2 MRPL17 SECISBP2 MRPS15 MRPL45 MRPS18A MRPS17 MRPL20 MRPL28 MRPS26 MRPS21 PTCD3 ITB ITB ITB ITB ICa ICr ICr ITL original ITL ICa ICa ITL ICr ICr ICa original -
SUPPLEMENTAL FIGURES and TABLES Figure S1
SUPPLEMENTAL FIGURES AND TABLES Figure S1. Multidimensional data model for assessing iPSCs and their neuronal derivatives. (A) Principal component analysis of fibroblasts (3 samples in red), iPSC( 7 samples in gray) and their neural derivatives NPCs ( 4 samples in blue) and neurons ( 8 samples in purple). (B, C) Model-Based Multi Class Pluripotency Score (PluriTest). (B) The lines in the plot indicated empirically determined thresholds for defining normal pluripotent lines. Score above blue lines indicate those scores that we have observed in approximately 95 percent of the pluripotent cells. All 7 iPSC lines in this chip were all above blue line. (C) Pluripotency analyses data showed all 7 iPSC lines in this chip passed PluriTest with P<0.05 Figure S2. (A) q RT-PCR analysis of gene expression on selected pluripotency markers (TDGF- 1, OCT4, Nanog and Sox2) in fibroblasts and iPSC lines. (B) Comparison analysis of gene expression of the selected pluripotency and NPC markers in iPSC and NPC lines from gene expression microarray. (C) Chromosome-specific fluorescence in-situ hybridization showed that iPSC lines maintained the normal human karyotype. Figure S3. Genotypic effects of rs9834970 on TRANK1 gene expression at baseline in iPSC Figure S4. TRANK1 expression (Winsorized mean over probe sets) in 10 postmortem human brain regions, stratified by genotype at rs9834970. Source: www.braineac.org Figure S5. Genotypic effects of rs906842 on TRANK1 gene expression in NPCs at baseline and after 72 hours of VPA treatment. Figure S6. CTCF binding sites overlapping rs906482. Binding sites were experimentally verified by ChipSeq in various cell lines. ENCODE data was summarized by (http://insulatordb.uthsc.edu), and depicted in UCSC Human Genome Browser (hg19).