AP2 Transcription Factor Induces Apoptosis in Retinoblastoma Cells

Total Page:16

File Type:pdf, Size:1020Kb

AP2 Transcription Factor Induces Apoptosis in Retinoblastoma Cells GENES, CHROMOSOMES & CANCER 49:819–830 (2010) AP2 Transcription Factor Induces Apoptosis in Retinoblastoma Cells Xiaodong Li, Darryl D. Glubrecht, and Roseline Godbout* Departmentof Oncology,Cross Cancer Institute,Universityof Alberta,Edmonton, Alberta,T6G1Z2 Canada The underlying cause of human retinoblastoma is complete inactivation of both copies of the RB1 gene. Other chromo- some abnormalities, with the most common being extra copies of chromosome arm 6p, are also observed in retinoblas- toma. The RB protein has previously been shown to interact with TFAP2 transcription factors. Here, we show that TFAP2A and TFAP2B, which map to chromosome arm 6p, are expressed in the amacrine and horizontal cells of human ret- ina. TFAP2A RNA can readily be detected in retinoblastoma cell lines and tumors; however, the great majority of retino- blastoma cell lines and tumors are completely devoid of TFAP2A protein and TFAP2B RNA/protein. Transfection of TFAP2A and TFAP2B expression constructs into retinoblastoma cells induces apoptosis and inhibits proliferation. Our results suggest that a consequence of loss of RB1 gene function in retinoblastoma cells is inactivation of TFAP2A and TFAP2B function. We propose that inability to differentiate along the amacrine/horizontal cell lineages may underlie retino- blastoma tumor formation. VC 2010 Wiley-Liss, Inc. INTRODUCTION The function of TFAP2 has been investigated The retina is an evolutionarily conserved mul- by generating mouse models targeting individual Tfap2 Tfap2 tilayered structure derived from the walls of the s. To date, four knock-outs have Tcfap2a, Tcfap2b Tcfap2c neural tube destined to become the forebrain. been described: , , and Tcfap2e Tcfap2a The retina consists of six major classes of neurons . knock-out mice die perinatally (ganglion, amacrine, bipolar, horizontal, cone pho- and show severe defects in cranial and body wall toreceptors, and rod photoreceptors) and one glial closure and skeletal structures (Schorle et al., cell type (Mu¨ ller) distributed in three nuclear 1996; Zhang et al., 1996; West-Mays et al., 1999). layers (outer or photoreceptor, inner, and gan- The severity of the ocular defects, often resulting glion). The appearance of the different retinal in the absence of eyes, precludes analysis of the cell types follows a specific order, with ganglion role of TFAP2A during retinal development. Tcfap2a cells appearing first, followed by amacrine cells, Conditional knock-out mice specifically cone photoreceptors, and horizontal cells and targeting the retina have no obvious retinal then rod photoreceptors, bipolar cells, and Mu¨ ller defects, leading to the hypothesis that TFAP2B cells (Prada et al., 1991; Cepko et al., 1996). and possibly other TFAP2s may compensate for TFAP2 is a family of transcription factors the loss of TFAP2A in mouse retina (Bassett Tcfap2a Tcfap2b implicated in many aspects of development. Five et al., 2007). In contrast to , TFAP2 genes have been identified in humans: knock-out mice have no defects in craniofacial TFAP2A, TFAP2B, TFAP2C, TFAP2D, and and ocular structures, but die perinatally due to TFAP2E. Three of these genes, TFAP2A, massive apoptosis of renal tubular epithelia Tcfap2bÀ/À TFAP2B, and TFAP2D, have been mapped to (Moser et al., 1997). mice express 6p24, 6p12, and 6p12.1, respectively (Gaynor reduced levels of noradrenaline and et al., 1991; Williamson et al., 1996; Cheng et al., 2002). TFAP2A and TFAP2B are expressed in Additional Supporting Information may be found in the online the amacrine cells of the developing murine and version of this article. Supported by: Canadian Institutes of Health Research and chick retina, with TFAP2B also found in the hor- Alberta Cancer Foundation. izontal cells of chick retina (Bisgrove and Godb- *Correspondence to: Roseline Godbout, Department of Oncology, Cross Cancer Institute, 11560 University Avenue, out, 1999; Bassett et al., 2007). TFAP2D is Edmonton, Alberta, T6G 1Z2 Canada. E-mail: rgodbout@ualberta. expressed in a subset of ganglion cells, whereas ca or [email protected] TFAP2C and TFAP2E have a poorly defined Received 8 December 2009; Accepted 20 April 2010 expression pattern in the retina (Bassett et al., DOI 10.1002/gcc.20790 Published online 25 May 2010 in 2007; Li et al., 2008). Wiley InterScience (www.interscience.wiley.com). VC 2010 Wiley-Liss, Inc. 820 LI ET AL. noradrenaline-synthesizing dopamine b-hydroxy- Gallie, Department of Medical Genetics, Univer- lase in the peripheral nervous system (Hong sity of Toronto, Canada (Gallie et al., 1982; Glu- et al., 2008). Tcfap2c knock-out mice die after brecht et al., 2009). RB(E)1, RB(E)2, RB(E)3, gastrulation due to defective placental develop- and RB(E)5 were established from retinoblastoma ment (Auman et al., 2002; Werling and Schorle, tumor biopsies obtained from the Royal Alexan- 2002). The main phenotype associated with dra Hospital, Edmonton, Canada. Cells were Tcfap2e knock-out mice is olfactory bulb disorga- cultured in DMEM supplemented with 10% nization (Feng et al., 2009). fetal calf serum, 100 lg/ml streptomycin, and Retinoblastoma is a childhood tumor derived 100 units/ml penicillin. For proteasome inhibitor from the retina. The underlying cause of retino- experiments, RB522A and WERI-Rb1 cells blastoma is complete inactivation of both copies were treated with 10 lM MG-132 (Sigma) for 8 or of the RB1 gene located in chromosome band 24 hr. For demethylation experiments, RB522A 13q14. Although other cancers such as breast can- and WERI-Rb1 cells were treated with 10 lM cer and prostate cancer commonly have RB1 5-azacytidine (Sigma) for 24 or 48 hr. gene defects, retinoblastoma tumors are unique in their dependence on loss of pRB function to initiate the events required for tumor formation. RT-PCR and Northern Blot Analysis To this day, the susceptibility of human retinal For RT-PCR, 1 lg of poly(A)þ RNA from dif- cells to RB1 gene inactivation remains an enigma ferent retinoblastoma cell lines or 3 lg of total and is believed to be related to unique properties RNA from human fetal retina at 10-11 weeks ges- of the cell-of-origin of retinoblastoma. However, tation were reverse transcribed using oligo d(T) there is still controversy regarding the cell-of-ori- and Superscript reverse transcriptase (Invitrogen). gin of retinoblastoma (Kyritsis et al., 1984; Chen Single-strand cDNA was PCR-amplified using et al., 2004; Dyer and Bremner, 2005; Glubrecht the following primers: 50-ACCTAGCCAGGGAC et al., 2009; Xu et al., 2009). TTTG-30 (top strand) and 50-GTCACTGCTTT A number of reports have linked pRB to the TGGCGTTGTT-30 (bottom strand) for TFAP2A; TFAP2 family of transcription factors. For exam- 50-ACGTCCAGTCAGTTGAAGATG-30 (top strand) ple, TFAP2A and TFAP2B interact with pRB in and 50-TATCCTCGAGTCATTTCCTGTGTT vitro (Wu and Lee, 1998), and transcription of TCTC-30 (bottom strand) for TFAP2B. PCR genes such as BCL2 and E-cadherin is mediated products of 395 bp (1148–1542) and 844 bp (699– by pRB interacting with TFAP2 (Batsche et al., 1542) were generated for TFAP2A and TFAP2B, 1998; Decary et al., 2002). To address a possible respectively. role for TFAP2 in retinoblastoma, we have stud- For northern blotting, poly(A)þ RNAs isolated ied the expression of TFAP2A and TFAP2B in from the indicated retinoblastoma cell lines were human retina and in retinoblastoma tumors and electrophoresed in a 6% formaldehyde-1.5% aga- cell lines. We have also expressed TFAP2A and rose gel in MOPS buffer and transferred to nitro- TFAP2B in two retinoblastoma cell lines and cellulose. The filter was hybridized to 32P-labeled have examined how TFAP2 expression affects cDNA probes specific to TFAP2A, TFAP2B, and the growth properties of these cells. Our results actin and washed under high stringency. To suggest that a consequence of RB1 gene inactiva- ensure that the same amount of RNA was loaded tion in retinoblastoma cells is inactivation of the into each lane, the filter was stripped, and rehy- TFAP2 pathway. Reinstatement of a functional bridized with 32P-labeled actin cDNA. TFAP2 pathway in these cells leads to cell death and altered differentiation potential. Western Blot Analysis Retinoblastoma cells were lysed in RIPA buffer MATERIALS AND METHODS and proteins separated in a 8% or 10% acrylam- ide–SDS gel. Proteins were transferred to a nitro- Retinoblastoma Cell Lines cellulose membrane and TFAP2A detected using Y79 and WERI-Rb1 were obtained from the mouse anti-TFAP2A antibody (1:250 dilution) American Type Culture Collection. RB522A, (3B5; Developmental Studies Hybridoma Bank), RB778, RB835, RB893, RB1021, RB1581, RB787, rabbit anti-TFAP2B (1:500 dilution) (H87; Santa RB1335, RB805, RB894, RB898, RB1210, and Cruz), mouse anti-actin antibody (1:100,000 dilu- RB1442 cell lines were established by Dr. Brenda tion) (Sigma), rabbit anti-b-catenin antibody Genes, Chromosomes & Cancer DOI 10.1002/gcc ACTIVATING PROTEIN-2 IN RETINOBLASTOMA 821 (1:1000 dilution, a gift from Dr. Manijeh Pasdar, 20 min in 10 mM citrate/0.05% Tween-20; pH 6 University of Alberta), and mouse anti-syntaxin for antigen retrieval. antibody (1:2000 dilution) (HPC-1; Sigma). For immunohistochemistry, sections were stained with mouse anti-TFAP2A (1:200 dilution) (3B5), rabbit anti-TFAP2B (1:800 dilution) (H87), In Situ Hybridization and mouse anti-Ki-67 (1:1,000 dilution) (MIB-1; Human fetal retina tissue at 10–11 weeks ges- DakoCytomation) antibodies, respectively. The tation was obtained in accordance with guidelines signal was detected using the DakoCytomation
Recommended publications
  • Reconstruction of the Global Neural Crest Gene Regulatory Network in Vivo
    Reconstruction of the global neural crest gene regulatory network in vivo Ruth M Williams1, Ivan Candido-Ferreira1, Emmanouela Repapi2, Daria Gavriouchkina1,4, Upeka Senanayake1, Jelena Telenius2,3, Stephen Taylor2, Jim Hughes2,3, and Tatjana Sauka-Spengler1,∗ Supplemental Material ∗Lead and corresponding author: Tatjana Sauka-Spengler ([email protected]) 1University of Oxford, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, Oxford, OX3 9DS, UK 2University of Oxford, MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK 3University of Oxford, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK 4Present Address: Okinawa Institute of Science and Technology, Molecular Genetics Unit, Onna, 904-0495, Japan A 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R1_5-6ss) log2(R1_5-6ss) log2(R1_8-10ss) log2(R1_8-10ss) log2(R1_non-NC) 5 5 5 5 5 0 r=0.92 0 r=0.99 0 r=0.96 0 r=0.99 0 r=0.96 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R2_non-NC) log2(R2_5-6ss) log2(R3_5-6ss) log2(R2_8-10ss) log2(R3_8-10ss) 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R1_5-6ss) log2(R2_5-6ss) log2(R1_8-10ss) log2(R2_8-10ss) log2(R1_non-NC) 5 5 5 5 5 0 r=0.94 0 r=0.96 0 r=0.95 0 r=0.96 0 r=0.95 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R3_non-NC) log2(R4_5-6ss) log2(R3_5-6ss) log2(R4_8-10ss) log2(R3_8-10ss)
    [Show full text]
  • Gene PMID WBS Locus ABR 26603386 AASDH 26603386
    Supplementary material J Med Genet Gene PMID WBS Locus ABR 26603386 AASDH 26603386 ABCA1 21304579 ABCA13 26603386 ABCA3 25501393 ABCA7 25501393 ABCC1 25501393 ABCC3 25501393 ABCG1 25501393 ABHD10 21304579 ABHD11 25501393 yes ABHD2 25501393 ABHD5 21304579 ABLIM1 21304579;26603386 ACOT12 25501393 ACSF2,CHAD 26603386 ACSL4 21304579 ACSM3 26603386 ACTA2 25501393 ACTN1 26603386 ACTN3 26603386;25501393;25501393 ACTN4 21304579 ACTR1B 21304579 ACVR2A 21304579 ACY3 19897463 ACYP1 21304579 ADA 25501393 ADAM12 21304579 ADAM19 25501393 ADAM32 26603386 ADAMTS1 25501393 ADAMTS10 25501393 ADAMTS12 26603386 ADAMTS17 26603386 ADAMTS6 21304579 ADAMTS7 25501393 ADAMTSL1 21304579 ADAMTSL4 25501393 ADAMTSL5 25501393 ADCY3 25501393 ADK 21304579 ADRBK2 25501393 AEBP1 25501393 AES 25501393 AFAP1,LOC84740 26603386 AFAP1L2 26603386 AFG3L1 21304579 AGAP1 26603386 AGAP9 21304579 Codina-Sola M, et al. J Med Genet 2019; 56:801–808. doi: 10.1136/jmedgenet-2019-106080 Supplementary material J Med Genet AGBL5 21304579 AGPAT3 19897463;25501393 AGRN 25501393 AGXT2L2 25501393 AHCY 25501393 AHDC1 26603386 AHNAK 26603386 AHRR 26603386 AIDA 25501393 AIFM2 21304579 AIG1 21304579 AIP 21304579 AK5 21304579 AKAP1 25501393 AKAP6 21304579 AKNA 21304579 AKR1E2 26603386 AKR7A2 21304579 AKR7A3 26603386 AKR7L 26603386 AKT3 21304579 ALDH18A1 25501393;25501393 ALDH1A3 21304579 ALDH1B1 21304579 ALDH6A1 21304579 ALDOC 21304579 ALG10B 26603386 ALG13 21304579 ALKBH7 25501393 ALPK2 21304579 AMPH 21304579 ANG 21304579 ANGPTL2,RALGPS1 26603386 ANGPTL6 26603386 ANK2 21304579 ANKMY1 26603386 ANKMY2
    [Show full text]
  • Supplementary Table 5. the 917 Candidate Marker Genes for the Diagnostic Model for Early HCC in the Training Set
    Supplementary Table 5. The 917 candidate marker genes for the diagnostic model for early HCC in the training set. Early HCC vs. Controls Early HCC vs. CHB/LC Gene Coefficient a AUC b Coefficient a AUC b SOX9 0.722 81.30% 0.211 63.50% EVC 0.703 76.20% 0.314 68.30% CHST9 0.398 75.50% 0.150 62.40% PDX1 0.730 76.50% 0.204 60.40% NPBWR1 0.651 73.10% 0.317 63.40% FAT1 0.335 74.10% 0.108 61.00% MEIS2 0.398 71.40% 0.188 62.40% A2M 0.761 72.40% 0.235 58.90% SERPINA10 0.479 72.00% 0.177 58.40% LBP 0.597 70.20% 0.237 61.30% PROX1 0.239 69.70% 0.133 61.40% APOB 0.286 70.50% 0.104 59.10% FMO3 0.296 69.60% 0.151 60.60% FREM2 0.288 68.50% 0.130 62.80% SDC2 0.300 69.10% 0.151 60.70% FAM20A 0.453 68.50% 0.168 60.30% GPAM 0.309 68.50% 0.172 60.30% CFH 0.277 68.00% 0.178 60.80% PAH 0.208 68.30% 0.116 60.30% NR1H4 0.233 68.40% 0.108 59.80% PTPRS -0.572 66.80% -0.473 63.00% SIAH3 -0.690 66.10% -0.573 64.10% GATA4 0.296 68.00% 0.128 60.10% SALL1 0.344 68.10% 0.184 59.80% SLC27A5 0.463 67.30% 0.204 61.40% SS18L1 0.588 67.30% 0.271 60.90% TOX3 0.190 68.40% 0.079 59.00% KCNK1 0.224 67.70% 0.120 60.10% TF 0.445 68.50% 0.202 57.90% FARP1 0.417 67.50% 0.252 59.80% GOT2 0.675 67.60% 0.278 59.50% PQLC1 0.651 67.10% 0.258 60.50% SERPINA5 0.302 67.00% 0.161 60.50% SOX13 0.508 67.80% 0.187 59.30% CDH2 0.205 66.10% 0.153 62.20% ITIH2 0.322 66.20% 0.252 62.20% ADIG 0.443 65.20% 0.399 63.80% HSD17B6 0.524 67.20% 0.237 59.60% IL21R -0.451 65.90% -0.321 61.70% A1CF 0.255 67.10% 0.139 58.70% KLB 0.507 65.90% 0.383 61.20% SLC10A1 0.574 67.10% 0.218 58.80% YAP1 0.282 67.70% 0.118
    [Show full text]
  • Article Genomic Evidence That Blind Cavefishes Are Not Wrecks of Ancient Life
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.02.446701; this version posted June 2, 2021. 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. 1 Article 2 3 Genomic evidence that blind cavefishes are not wrecks of ancient life 4 5 6 Maxime Policarpo1, Patrick Laurenti2,7, Erik García-Machado3,4, Cushla Metcalfe5, Sylvie 7 Rétaux*,6 and Didier Casane*,1,7 8 9 1 Université Paris-Saclay, CNRS, IRD, UMR Évolution, Génomes, Comportement et 10 Écologie, 91198, Gif-sur-Yvette, France. 11 2 Université de Paris, Laboratoire Interdisciplinaire des Energies de Demain, Paris, France 12 3 Department of Biology, Institut de Biologie Intégrative et des Systèmes, Université Laval, 13 1030 Avenue de la Médecine, Québec City, Québec G1V 0A6, Canada. 14 4 Centro de Investigaciones Marinas, Universidad de La Habana, Calle 16, No. 114 entre 1ra y 15 3ra, Miramar, Playa, La Habana 11300, Cuba. 16 5 Independent Researcher, PO Box 21, Nambour QLD 4560, Australia. 17 6 Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, 91190, Gif-sur- 18 Yvette, France. 19 7 Université de Paris, UFR Sciences du Vivant, F-75013 Paris, France. 20 21 * Corresponding authors: e-mails: [email protected]; [email protected] 22 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.02.446701; this version posted June 2, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved.
    [Show full text]
  • 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
    [Show full text]
  • (TTA) in Macrophage-Like Cells from Atlantic Salmon (Salmo Salar L.) Fabian Grammes1,2 and Harald Takle2,3*
    Grammes and Takle BMC Immunology 2011, 12:41 http://www.biomedcentral.com/1471-2172/12/41 RESEARCH ARTICLE Open Access Anti-inflammatory effects of tetradecylthioacetic acid (TTA) in macrophage-like cells from Atlantic salmon (Salmo salar L.) Fabian Grammes1,2 and Harald Takle2,3* Abstract Background: Commercial Atlantic salmon is fed diets with high fat levels to promote fast and cost-effective growth. To avoid negative impact of obesity, food additives that stimulate fat metabolism and immune function are of high interest. TTA, tetradecylthioacetic acid, is a synthetic fatty acid that stimulates mitochondrial b-oxidation most likely by activation of peroxysome proliferator-activated receptors (PPARs). PPARs are important transcription factors regulating multiple functions including fat metabolism and immune responses. Atlantic salmon experiments have shown that TTA supplemented diets significantly reduce mortality during natural outbreaks of viral diseases, suggesting a modulatory role of the immune system. Results: To gain new insights into TTA effects on the Atlantic salmon immune system, a factorial, high-throughput microarray experiment was conducted using a 44K oligo nucleotide salmon microarray SIQ2.0 and the Atlantic salmon macrophage-like cell line ASK. The experiment was used to determine the transcriptional effects of TTA, the effects of TTA in poly(I:C) elicited cells and the effects of pretreating the cells with TTA. The expression patterns revealed that a large proportion of genes regulated by TTA were related to lipid metabolism and increased mitochondrial b-oxidation. In addition we found that for a subset of genes TTA antagonized the transcriptional effects of poly(I:C). This, together with the results from qRT-PCR showing an increased transcription of anti- inflammatory IL10 by TTA, indicates anti-inflammatory effects.
    [Show full text]
  • Transcriptional and Post-Transcriptional Regulation of ATP-Binding Cassette Transporter Expression
    Transcriptional and Post-transcriptional Regulation of ATP-binding Cassette Transporter Expression by Aparna Chhibber DISSERTATION Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Pharmaceutical Sciences and Pbarmacogenomies in the Copyright 2014 by Aparna Chhibber ii Acknowledgements First and foremost, I would like to thank my advisor, Dr. Deanna Kroetz. More than just a research advisor, Deanna has clearly made it a priority to guide her students to become better scientists, and I am grateful for the countless hours she has spent editing papers, developing presentations, discussing research, and so much more. I would not have made it this far without her support and guidance. My thesis committee has provided valuable advice through the years. Dr. Nadav Ahituv in particular has been a source of support from my first year in the graduate program as my academic advisor, qualifying exam committee chair, and finally thesis committee member. Dr. Kathy Giacomini graciously stepped in as a member of my thesis committee in my 3rd year, and Dr. Steven Brenner provided valuable input as thesis committee member in my 2nd year. My labmates over the past five years have been incredible colleagues and friends. Dr. Svetlana Markova first welcomed me into the lab and taught me numerous laboratory techniques, and has always been willing to act as a sounding board. Michael Martin has been my partner-in-crime in the lab from the beginning, and has made my days in lab fly by. Dr. Yingmei Lui has made the lab run smoothly, and has always been willing to jump in to help me at a moment’s notice.
    [Show full text]
  • An All-To-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine
    bioRxiv preprint doi: https://doi.org/10.1101/638700; this version posted May 16, 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. An All-to-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine Guang Song1,6, Guohua Wang2,6, Ximei Luo2,3,6, Ying Cheng4, Qifeng Song1, Jun Wan3, Cedric Moore1, Hongjun Song5, Peng Jin4, Jiang Qian3,7,*, Heng Zhu1,7,8,* 1Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China 3Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA 4Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA 5Department of Neuroscience and Mahoney Institute for Neurosciences, University of Pennsylvania, Philadelphia, PA 19104, USA 6These authors contributed equally 7Senior author 8Lead Contact *Correspondence: [email protected] (H.Z.), [email protected] (J.Q.). 1 bioRxiv preprint doi: https://doi.org/10.1101/638700; this version posted May 16, 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. SUMMARY Epigenetic modifications of DNA in mammals play important roles in many biological processes. Identification of readers of these epigenetic marks is a critical step towards understanding the underlying molecular mechanisms. Here, we report the invention and application of an all-to-all approach, dubbed Digital Affinity Profiling via Proximity Ligation (DAPPL), to simultaneously profile human TF-DNA interactions using mixtures of random DNA libraries carrying four different epigenetic modifications (i.e., 5-methylcytosine, 5- hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine).
    [Show full text]
  • MOCHI Enables Discovery of Heterogeneous Interactome Modules in 3D Nucleome
    Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press MOCHI enables discovery of heterogeneous interactome modules in 3D nucleome Dechao Tian1,# , Ruochi Zhang1,# , Yang Zhang1, Xiaopeng Zhu1, and Jian Ma1,* 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA #These two authors contributed equally *Correspondence: [email protected] Contact To whom correspondence should be addressed: Jian Ma School of Computer Science Carnegie Mellon University 7705 Gates-Hillman Complex 5000 Forbes Avenue Pittsburgh, PA 15213 Phone: +1 (412) 268-2776 Email: [email protected] 1 Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract The composition of the cell nucleus is highly heterogeneous, with different constituents forming complex interactomes. However, the global patterns of these interwoven heterogeneous interactomes remain poorly understood. Here we focus on two different interactomes, chromatin interaction network and gene regulatory network, as a proof-of-principle, to identify heterogeneous interactome modules (HIMs), each of which represents a cluster of gene loci that are in spatial contact more frequently than expected and that are regulated by the same group of transcription factors. HIM integrates transcription factor binding and 3D genome structure to reflect “transcriptional niche” in the nucleus. We develop a new algorithm MOCHI to facilitate the discovery of HIMs based on network motif clustering in heterogeneous interactomes. By applying MOCHI to five different cell types, we found that HIMs have strong spatial preference within the nucleus and exhibit distinct functional properties. Through integrative analysis, this work demonstrates the utility of MOCHI to identify HIMs, which may provide new perspectives on the interplay between transcriptional regulation and 3D genome organization.
    [Show full text]
  • Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks
    bioRxiv preprint doi: https://doi.org/10.1101/219667; this version posted November 14, 2017. 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-NC-ND 4.0 International license. Article Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks Weiguang Mao 1,2, Dennis Kostka 1,2,3 and Maria Chikina 1,2* 1 Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh 2 Joint Carnegie Mellon - University of Pittsburgh Ph.D. Program in Computational Biology 3 Department of Developmental Biology, School of Medicine, University of Pittsburgh * Correspondence: [email protected] Academic Editor: name Version November 9, 2017 submitted to Cells 1 Abstract: Background: Gene regulatory sequences play critical roles in ensuring tightly controlled 2 RNA expression patterns that are essential in a large variety of biological processes. Specifically, 3 enhancer sequences drive expression of their target genes, and the availability of genome-wide 4 maps of enhancer-promoter interactions has opened up the possibility to use machine learning 5 approaches to extract and interpret features that define these interactions in different biological 6 contexts. Methods: Inspired by machine translation models we develop an attention-based neural 7 network model, EPIANN, to predict enhancer-promoter interactions based on DNA sequences. Codes 8 and data are available at https://github.com/wgmao/EPIANN. Results: Our approach accurately 9 predicts enhancer-promoter interactions across six cell lines. In addition, our method generates 10 pairwise attention scores at the sequence level, which specify how short regions in the enhancer and 11 promoter pair-up to drive the interaction prediction.
    [Show full text]
  • Upregulation of the Transcription Factor TFAP2D Is Associated With
    Fraune et al. Molecular Medicine (2020) 26:24 Molecular Medicine https://doi.org/10.1186/s10020-020-00148-4 RESEARCH ARTICLE Open Access Upregulation of the transcription factor TFAP2D is associated with aggressive tumor phenotype in prostate cancer lacking the TMPRSS2:ERG fusion Christoph Fraune1†, Luisa Harms1†, Franziska Büscheck1, Doris Höflmayer1, Maria Christina Tsourlakis1, Till S. Clauditz1, Ronald Simon1* , Katharina Möller1, Andreas M. Luebke1, Christina Möller-Koop1, Stefan Steurer1, Claudia Hube-Magg1, Guido Sauter1, Sören Weidemann1, Patrick Lebok1, David Dum1, Simon Kind1, Sarah Minner1, Jakob R. Izbicki2, Thorsten Schlomm3, Hartwig Huland4, Hans Heinzer4, Eike Burandt1, Alexander Haese4, Markus Graefen4 and Cornelia Schroeder2 Abstract Background: TFAP2D is a transcription factor important for modulating gene expression in embryogenesis. Its expression and prognostic role in prostate cancer has not been evaluated. Methods: Therefore, a tissue microarray containing 17,747 prostate cancer specimens with associated pathological, clinical, and molecular data was analyzed by immunohistochemistry to assess the role of TFAP2D. Results: TFAP2D expression was typically increased in prostate cancer as compared to adjacent non-neoplastic glands. TFAP2D staining was considered negative in 24.3% and positive in 75.7% of 13,545 interpretable cancers. TFAP2D staining was significantly linked to advanced tumor stage, high classical and quantitative Gleason grade, lymph node metastasis, and a positive surgical margin (p ≤ 0.0045). TFAP2D positivity was more common in ERG fusion positive (88.7%) than in ERG negative cancers (66.8%; p < 0.0001). Subset analyses in 3776 cancers with and 4722 cancers without TMPRSS2:ERG fusion revealed that associations with tumor phenotype and patient outcome were largely driven by the subset of ERG negative tumors.
    [Show full text]
  • Co-Regulation of Hormone Receptors, Neuropeptides, and Steroidogenic Enzymes 2 Across the Vertebrate Social Behavior Network 3 4 Brent M
    bioRxiv preprint doi: https://doi.org/10.1101/435024; this version posted October 4, 2018. 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-NC-ND 4.0 International license. 1 Co-regulation of hormone receptors, neuropeptides, and steroidogenic enzymes 2 across the vertebrate social behavior network 3 4 Brent M. Horton1, T. Brandt Ryder2, Ignacio T. Moore3, Christopher N. 5 Balakrishnan4,* 6 1Millersville University, Department of Biology 7 2Smithsonian Conservation Biology Institute, Migratory Bird Center 8 3Virginia Tech, Department of Biological Sciences 9 4East Carolina University, Department of Biology 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 bioRxiv preprint doi: https://doi.org/10.1101/435024; this version posted October 4, 2018. 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-NC-ND 4.0 International license. 1 Running Title: Gene expression in the social behavior network 2 Keywords: dominance, systems biology, songbird, territoriality, genome 3 Corresponding Author: 4 Christopher Balakrishnan 5 East Carolina University 6 Department of Biology 7 Howell Science Complex 8 Greenville, NC, USA 27858 9 [email protected] 10 2 bioRxiv preprint doi: https://doi.org/10.1101/435024; this version posted October 4, 2018. 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.
    [Show full text]