Potential New Epigenetic Targets for HIV Cure Strategies and Beyond
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Phd Thesis Valentina Buemi.Pdf
Index 1. INTRODUCTION 1 1.1 TELOMERES 1 1.1.1 HISTORIC BACKGROUND 1 1.1.2 SEQUENCE AND STRUCTURAL MOTIFS 4 1.2 TELOMERE HOMEOSTASIS 6 1.2.1 THE SHELTERIN COMPLEX 6 1.2.2 TELOMERE IN AGING, DISEASE AND CANCER 9 1.3 DNA DAMAGE CONTROL AT TELOMERES 10 1.4 THE DNA DAMAGE REPAIR AT TELOMERES 12 1.4.1 NON HOMOLOGOUS END JOINING (NHEJ) 12 1.4.2 HOMOLOGY-DIRECTED REPAIR (HDR) 13 1.4.2.1 T-loop homologous recombination (T-loop HR) 13 1.4.2.2 Telomeric sister chromatid exchange (T-SCE) 14 1.4.2.3 Homologous recombination with interstitial sites 15 1.5 REGULATION OF TELOMERE MAINTENANCE 15 1.5.1 TELOMERASE: COMPONENTS AND MATURATION 16 1.5.1.1 hTERT structure and function 17 1.5.1.2 Telomerase RNA component structure and function 18 1.5.1.3 Telomerase RNA biogenesis and processing 20 1.5.1.4 Telomerase action at telomeres 21 1.5.1.5 Cajal Bodies and telomere homeostasis 23 1.5.1.6 Regulation of telomerase activity 24 1.5.2 ALT : ALTERNATIVE LENGTHENING OF TELOMERES 27 1.5.2.1 Mechanisms of Alternative Lengthening of Telomeres 27 1.5.2.2 ALT-associated PML nuclear bodies 30 1.6 MICRORNAS-DEPENDENT REGULATION OF TELOMERASE EXPRESSION 31 1.6.1 MICRORNAS BIOGENESIS 31 1.6.2 MIRNA DEREGULATION IN CANCER 33 1.6.3 REGULATION OF TELOMERASE EXPRESSION BY MIRNAS IN HUMAN CANCER 34 I Index 2. AIM OF THE THESIS 38 3. MATERIALS AND METHODS 39 3.1 CELL LINES AND CULTURE 39 3.2 CLONING OF SPECIFIC 3’-UTR INTO PSICHECK-2 EXPRESSION VECTOR 39 3.3 GENERATION OF MUTANT 3’-UTR OF TERT 40 3.4 MIRNA LIBRARY VECTORS 41 3.5 LUCIFERASE REPORTER SCREENING 41 3.6 TRANSFECTION -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Genome-Wide Analysis of Androgen Receptor Binding and Gene Regulation in Two CWR22-Derived Prostate Cancer Cell Lines
Endocrine-Related Cancer (2010) 17 857–873 Genome-wide analysis of androgen receptor binding and gene regulation in two CWR22-derived prostate cancer cell lines Honglin Chen1, Stephen J Libertini1,4, Michael George1, Satya Dandekar1, Clifford G Tepper 2, Bushra Al-Bataina1, Hsing-Jien Kung2,3, Paramita M Ghosh2,3 and Maria Mudryj1,4 1Department of Medical Microbiology and Immunology, University of California Davis, 3147 Tupper Hall, Davis, California 95616, USA 2Division of Basic Sciences, Department of Biochemistry and Molecular Medicine, Cancer Center and 3Department of Urology, University of California Davis, Sacramento, California 95817, USA 4Veterans Affairs Northern California Health Care System, Mather, California 95655, USA (Correspondence should be addressed to M Mudryj at Department of Medical Microbiology and Immunology, University of California, Davis; Email: [email protected]) Abstract Prostate carcinoma (CaP) is a heterogeneous multifocal disease where gene expression and regulation are altered not only with disease progression but also between metastatic lesions. The androgen receptor (AR) regulates the growth of metastatic CaPs; however, sensitivity to androgen ablation is short lived, yielding to emergence of castrate-resistant CaP (CRCaP). CRCaP prostate cancers continue to express the AR, a pivotal prostate regulator, but it is not known whether the AR targets similar or different genes in different castrate-resistant cells. In this study, we investigated AR binding and AR-dependent transcription in two related castrate-resistant cell lines derived from androgen-dependent CWR22-relapsed tumors: CWR22Rv1 (Rv1) and CWR-R1 (R1). Expression microarray analysis revealed that R1 and Rv1 cells had significantly different gene expression profiles individually and in response to androgen. -
Identification of Transcriptomic Differences Between Lower
International Journal of Molecular Sciences Article Identification of Transcriptomic Differences between Lower Extremities Arterial Disease, Abdominal Aortic Aneurysm and Chronic Venous Disease in Peripheral Blood Mononuclear Cells Specimens Daniel P. Zalewski 1,*,† , Karol P. Ruszel 2,†, Andrzej St˛epniewski 3, Dariusz Gałkowski 4, Jacek Bogucki 5 , Przemysław Kołodziej 6 , Jolanta Szyma ´nska 7 , Bartosz J. Płachno 8 , Tomasz Zubilewicz 9 , Marcin Feldo 9,‡ , Janusz Kocki 2,‡ and Anna Bogucka-Kocka 1,‡ 1 Chair and Department of Biology and Genetics, Medical University of Lublin, 4a Chod´zkiSt., 20-093 Lublin, Poland; [email protected] 2 Chair of Medical Genetics, Department of Clinical Genetics, Medical University of Lublin, 11 Radziwiłłowska St., 20-080 Lublin, Poland; [email protected] (K.P.R.); [email protected] (J.K.) 3 Ecotech Complex Analytical and Programme Centre for Advanced Environmentally Friendly Technologies, University of Marie Curie-Skłodowska, 39 Gł˛ebokaSt., 20-612 Lublin, Poland; [email protected] 4 Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, One Robert Wood Johnson Place, New Brunswick, NJ 08903-0019, USA; [email protected] 5 Chair and Department of Organic Chemistry, Medical University of Lublin, 4a Chod´zkiSt., Citation: Zalewski, D.P.; Ruszel, K.P.; 20-093 Lublin, Poland; [email protected] St˛epniewski,A.; Gałkowski, D.; 6 Laboratory of Diagnostic Parasitology, Chair and Department of Biology and Genetics, Medical University of Bogucki, J.; Kołodziej, P.; Szyma´nska, Lublin, 4a Chod´zkiSt., 20-093 Lublin, Poland; [email protected] J.; Płachno, B.J.; Zubilewicz, T.; Feldo, 7 Department of Integrated Paediatric Dentistry, Chair of Integrated Dentistry, Medical University of Lublin, M.; et al. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Neuronal Network Dysfunction in a Human Model for Kleefstra Syndrome Mediated by Enhanced NMDAR Signaling
bioRxiv preprint doi: https://doi.org/10.1101/585596; this version posted March 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Neuronal network dysfunction in a human model for Kleefstra syndrome mediated by enhanced NMDAR signaling Monica Frega1,2†, Katrin Linda1†, Jason M. Keller1, Güvem Gümüş-Akay1, Britt Mossink1, Jon- Ruben van Rhijn3, Moritz Negwer1, Teun Klein Gunnewiek4, Katharina Foreman3, Nine Kompier3, Chantal Schoenmaker1, Willem van den Akker1, Astrid Oudakker1, Huiqing Zhou1,5, Tjitske Kleefstra1, Dirk Schubert3, Hans van Bokhoven1,3, Nael Nadif Kasri1,3* 1Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, the Netherlands 2Department of Clinical neurophysiology, University of Twente, 7522 NB Enschede, the Netherlands 3Department of Cognitive Neuroscience, Radboudumc, Donders Institute for Brain, Cognition and Behaviour, 6500 HB Nijmegen, the Netherlands 4Department of Anatomy, Radboudumc, Donders Institute for Brain, Cognition and Behaviour, 6500 HB Nijmegen, the Netherlands 5Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, 6500 HB Nijmegen, the Netherlands †These authors contributed equally to the work *Corresponding Author dr. Nael Nadif Kasri Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/585596; this version posted March 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Epigenetic regulation of gene transcription plays a critical role in neural network development and in the etiology of Intellectual Disability (ID) and Autism Spectrum Disorder (ASD). -
(P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds. -
A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. -
Region Based Gene Expression Via Reanalysis of Publicly Available Microarray Data Sets
University of Louisville ThinkIR: The University of Louisville's Institutional Repository Electronic Theses and Dissertations 5-2018 Region based gene expression via reanalysis of publicly available microarray data sets. Ernur Saka University of Louisville Follow this and additional works at: https://ir.library.louisville.edu/etd Part of the Bioinformatics Commons, Computational Biology Commons, and the Other Computer Sciences Commons Recommended Citation Saka, Ernur, "Region based gene expression via reanalysis of publicly available microarray data sets." (2018). Electronic Theses and Dissertations. Paper 2902. https://doi.org/10.18297/etd/2902 This Doctoral Dissertation is brought to you for free and open access by ThinkIR: The University of Louisville's Institutional Repository. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of ThinkIR: The University of Louisville's Institutional Repository. This title appears here courtesy of the author, who has retained all other copyrights. For more information, please contact [email protected]. REGION BASED GENE EXPRESSION VIA REANALYSIS OF PUBLICLY AVAILABLE MICROARRAY DATA SETS By Ernur Saka B.S. (CEng), University of Dokuz Eylul, Turkey, 2008 M.S., University of Louisville, USA, 2011 A Dissertation Submitted To the J. B. Speed School of Engineering in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science and Engineering Department of Computer Engineering and Computer Science University of Louisville Louisville, Kentucky May 2018 Copyright 2018 by Ernur Saka All rights reserved REGION BASED GENE EXPRESSION VIA REANALYSIS OF PUBLICLY AVAILABLE MICROARRAY DATA SETS By Ernur Saka B.S. (CEng), University of Dokuz Eylul, Turkey, 2008 M.S., University of Louisville, USA, 2011 A Dissertation Approved On April 20, 2018 by the following Committee __________________________________ Dissertation Director Dr. -
Supp Table 6.Pdf
Supplementary Table 6. Processes associated to the 2037 SCL candidate target genes ID Symbol Entrez Gene Name Process NM_178114 AMIGO2 adhesion molecule with Ig-like domain 2 adhesion NM_033474 ARVCF armadillo repeat gene deletes in velocardiofacial syndrome adhesion NM_027060 BTBD9 BTB (POZ) domain containing 9 adhesion NM_001039149 CD226 CD226 molecule adhesion NM_010581 CD47 CD47 molecule adhesion NM_023370 CDH23 cadherin-like 23 adhesion NM_207298 CERCAM cerebral endothelial cell adhesion molecule adhesion NM_021719 CLDN15 claudin 15 adhesion NM_009902 CLDN3 claudin 3 adhesion NM_008779 CNTN3 contactin 3 (plasmacytoma associated) adhesion NM_015734 COL5A1 collagen, type V, alpha 1 adhesion NM_007803 CTTN cortactin adhesion NM_009142 CX3CL1 chemokine (C-X3-C motif) ligand 1 adhesion NM_031174 DSCAM Down syndrome cell adhesion molecule adhesion NM_145158 EMILIN2 elastin microfibril interfacer 2 adhesion NM_001081286 FAT1 FAT tumor suppressor homolog 1 (Drosophila) adhesion NM_001080814 FAT3 FAT tumor suppressor homolog 3 (Drosophila) adhesion NM_153795 FERMT3 fermitin family homolog 3 (Drosophila) adhesion NM_010494 ICAM2 intercellular adhesion molecule 2 adhesion NM_023892 ICAM4 (includes EG:3386) intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)adhesion NM_001001979 MEGF10 multiple EGF-like-domains 10 adhesion NM_172522 MEGF11 multiple EGF-like-domains 11 adhesion NM_010739 MUC13 mucin 13, cell surface associated adhesion NM_013610 NINJ1 ninjurin 1 adhesion NM_016718 NINJ2 ninjurin 2 adhesion NM_172932 NLGN3 neuroligin -
Methylated Spirits: Epigenetic Hypotheses of Psychiatric Disorders
Trends in Psychopharmacology Methylated Spirits: Epigenetic Hypotheses of Psychiatric Disorders Stephen M. Stahl, MD, PhD NEW TREND IN various brain circuits and creating risk for develop- PSYCHOPHARMACOLOGY ing a symptom of a mental illness. Now comes the Our spirits may be regulated by the methylation role of epigenetic actions in mental illnesses. If nor- of our genes. Methylation, acetylation, and other mal genes make normal gene products but at the biochemical processes are the molecular switches wrong time, either being epigenetically expressed for turning genes on and off. There is evidence in neurons when they should be silenced or epi- now that certain behaviors, feelings, and psychiat- genetically silenced in neurons when they should ric symptoms may be modified by turning various be expressed, particularly under the influence of genes on or off. If classical genetics is the sequence environmental factors and stress, this, too, can of DNA that is inherited, then epigenetics is a par- contribute to inefficient information processing in allel process determining whether a given gene brain circuits, increasing the chance of develop- (ie, a sequence of DNA coding for transcription) is ing symptoms of a psychiatric disorder. Here we expressed into its RNA or is silenced. Epigenetics describe the role of epigenetics and methylomics is now entering psychiatry with the hypothesis (methylating or demethylating upstream genes that normal genes as well as risk genes can both and downstream molecules) in various psychiatric contribute to a mental disorder. That is, it has long disorders, emphasizing schizophrenia, and demon- been hypothesized that when “abnormal” genes strate whether your spirits can be truly methylated. -
Fig1-13Tab1-5.Pdf
Supplementary Information Promoter hypomethylation of EpCAM-regulated bone morphogenetic protein genes in advanced endometrial cancer Ya-Ting Hsu, Fei Gu, Yi-Wen Huang, Joseph Liu, Jianhua Ruan, Rui-Lan Huang, Chiou-Miin Wang, Chun-Liang Chen, Rohit R. Jadhav, Hung-Cheng Lai, David G. Mutch, Paul J. Goodfellow, Ian M. Thompson, Nameer B. Kirma, and Tim Hui-Ming Huang Tables of contents Page Table of contents 2 Supplementary Methods 4 Supplementary Figure S1. Summarized sequencing reads and coverage of MBDCap-seq 8 Supplementary Figure S2. Reproducibility test of MBDCap-seq 10 Supplementary Figure S3. Validation of MBDCap-seq by MassARRAY analysis 11 Supplementary Figure S4. Distribution of differentially methylated regions (DMRs) in endometrial tumors relative to normal control 12 Supplementary Figure S5. Network analysis of differential methylation loci by using Steiner-tree analysis 13 Supplementary Figure S6. DNA methylation distribution in early and late stage of the TCGA endometrial cancer cohort 14 Supplementary Figure S7. Relative expression of BMP genes with EGF treatment in the presence or absence of PI3K/AKT and Raf (MAPK) inhibitors in endometrial cancer cells 15 Supplementary Figure S8. Induction of invasion by EGF in AN3CA and HEC1A cell lines 16 Supplementary Figure S9. Knockdown expression of BMP4 and BMP7 in RL95-2 cells 17 Supplementary Figure S10. Relative expression of BMPs and BMPRs in normal endometrial cell and endometrial cancer cell lines 18 Supplementary Figure S11. Microfluidics-based PCR analysis of EMT gene panel in RL95-2 cells with or without EGF treatment 19 Supplementary Figure S12. Knockdown expression of EpCAM by different shRNA sequences in RL95-2 cells 20 Supplementary Figure S13.