DOCSLIB.ORG

Conditional Deletion of ELL2 Induces Murine Prostate Intraepithelial Neoplasia

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Conditional Deletion of ELL2 Induces Murine Prostate Intraepithelial Neoplasia 235 2 L E PASCAL, K Z MASOODI, J LIU Prostate neoplasia in 235:2 123–136 Research and others ELL2-knockout mouse Conditional deletion of ELL2 induces murine prostate intraepithelial neoplasia Laura E Pascal1,*, Khalid Z Masoodi1,2,*, June Liu1,*,†, Xiaonan Qiu1,3, Qiong Song1,4, Yujuan Wang1, Yachen Zang1,5, Tiejun Yang1,6, Yao Wang1,7, Lora H Rigatti8, Uma Chandran9, Leandro M Colli10, Ricardo Z N Vencio11, Yi Lu12,13, Jian Zhang12,13 and Zhou Wang1,14,15 1Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA 2Transcriptomics Lab, Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, India 3School of Medicine, Tsinghua University, Beijing, China 4Center for Translational Medicine, Guangxi Medical University, Nanning, Guangxi, China 5Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, China 6Department of Urology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China 7Department of Urology, China-Japan Hospital of Jilin University, Changchun, Jilin, China 8Division of Laboratory Animal Resources, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA 9Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA 10Ribeirao Preto Medical School, University of São Paulo, Ribeirão Preto-SP, Brazil 11Department of Computing and Mathematics FFCLRP-USP, University of São Paulo, Ribeirão Preto, Brazil 12Key Laboratory of Longevity and Aging-related Diseases, Ministry of Education, China and Center for Translational Medicine Guangxi Medical University, Nanning, Guangxi, China Endocrinology 13Department of Biology, Southern University of Science and Technology School of Medicine, Shenzhen, Guangdong, China of 14 University of Pittsburgh Cancer Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA Correspondence 15 Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, should be addressed Pennsylvania, USA to Z Wang Journal *(L E Pascal, K Z Masoodi and J Liu contributed equally to this work) Email † (J Liu is now at Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA) [email protected] Abstract Elongation factor, RNA polymerase II, 2 (ELL2) is an RNA Pol II elongation factor with Key Words functional properties similar to ELL that can interact with the prostate tumor suppressor f ELL2 EAF2. In the prostate, ELL2 is an androgen response gene that is upregulated in benign f prostatic intraepithelial prostatic hyperplasia (BPH). We recently showed that ELL2 loss could enhance prostate neoplasia cancer cell proliferation and migration, and that ELL2 gene expression was downregulated f prostate cancer in high Gleason score prostate cancer specimens. Here, prostate-specific deletion of ELL2 in f EAF2 a mouse model revealed a potential role for ELL2 as a prostate tumor suppressor in vivo. f HIF1α Ell2-knockout mice exhibited prostatic defects including increased epithelial proliferation, vascularity and PIN lesions similar to the previously determined prostate phenotype in Eaf2-knockout mice. Microarray analysis of prostates from Ell2-knockout and wild-type mice on a C57BL/6J background at age 3 months and qPCR validation at 17 months of age revealed a number of differentially expressed genes associated with proliferation, cellular motility and epithelial and neural differentiation. OncoPrint analysis identified combined downregulation or deletion in prostate adenocarcinoma cases from the Cancer Genome Atlas (TCGA) data portal. These results suggest that ELL2 and its pathway genes likely play Journal of Endocrinology an important role in the development and progression of prostate cancer. (2017) 235, 123–136 http://joe.endocrinology-journals.org © 2017 Society for Endocrinology Published by Bioscientifica Ltd. DOI: 10.1530/JOE-17-0112 Printed in Great Britain Downloaded from Bioscientifica.com at 09/28/2021 06:24:52PM via free access 10.1530/JOE-17-0112 Research L E PASCAL, K Z MASOODI, J LIU Prostate neoplasia in 235:2 124 and others ELL2-knockout mouse Introduction ELL2 (elongation factor, RNA polymerase II, 2; previously protein (Lavau et al. 2000), MLL–EAF2 could immortalize eleven-nineteen lysine-rich leukemia 2) is an RNA Pol II hematopoietic progenitor cells in vitro. Recently, eaf-1 elongation factor with functional properties similar to and ell-1, worm orthologs of EAF1, EAF2 and ELL1 and ELL and ELL3 (Shilatifard et al. 1997, Miller et al. 2000). ELL2, were shown to have overlapping function in the ELL2 suppresses transient pausing of RNA polymerase regulation of fertility, survival and cuticle formation in II activity along the DNA strand and facilitates the C. elegans (Cai et al. 2011). In advanced prostate cancer, transcription process (Shilatifard et al. 1997). The ELL EAF2 protein was downregulated (Xiao et al. 2003, family proteins are components of the super elongation Ai et al. 2013, Pascal et al. 2013); and, overexpression of complex (SEC), which regulate HOX gene expression in EAF2 in prostate cancer cell lines induced apoptosis and MLL-based hematological malignancies by controlling inhibited the growth of xenograft tumors (Xiao et al. genes involved in early development and in immediate 2003). Eaf2-knockout mice developed high-grade murine early gene transcription (Lin et al. 2010, 2011, Smith et al. prostatic intraepithelial neoplasia (mPIN) and increased 2011, Takahashi et al. 2011). ELL was recently identified vascularity in several murine strains (Xiao et al. 2008, Pascal as a component in the little elongation complex (LEC), et al. 2013). Recently, we reported that siRNA knockdown which is involved in RNA polymerase II transcription of of ELL2 in combination with RB knockdown enhanced small nuclear RNA (snRNA) genes (Smith et al. 2011). ELL proliferation, migration and invasion in prostate cancer and EAF proteins also bind to MED26, a component of the cell lines LNCaP, C4-2 and 22RV1, while ELL2 knockdown human mediator that plays a key role in transcriptional alone enhanced migration and invasion but did not induce activation (Takahashi et al. 2011). ELL2 was also reported a statistically significant increased proliferation Qiu( et al. to direct immunoglobulin secretion in plasma cells 2017). ELL2 expression was downregulated in high by stimulating alternative RNA processing associated Gleason score prostate cancer specimens. Cumulatively, with histone methylations (Martincic et al. 2009, these studies suggest that ELL2 may play a significant role Milcarek et al. 2011). in maintaining prostate homeostasis similar to EAF2. Endocrinology ELL is frequently translocated with the MLL gene In the current study, the potential role of ELL2 in of on chromosome 11q23 in acute myeloid leukemia the prostate was explored in a murine knockout model. (Thirman et al. 1994, Mitani et al. 1995); and homozygous Conditional prostate epithelial cell-specificEll2 -knockout Journal deletion of ELL is embryonic lethal in the mouse as well mice were generated and examined for histologic defects. as in Drosophila (Mitani et al. 2000, Eissenberg et al. Microarray analysis of mouse prostates was performed 2002). ELL and ELL2 interact with ELL-associated factors to identify potential target genes of ELL2. Differentially 1 and 2 (EAF1 and EAF2) (Simone et al. 2003) resulting expressed genes identified by microarray analysis in in enhanced ELL elongation activity (Kong et al. 2005). animals at 3 months of age were validated by qPCR in ELL and ELL2 expression ratios vary in different human animals at 17–20 months of age. tissues, suggesting tissue-specific roles for these genes (Shilatifard et al. 1997). Since high expression of ELL2 has been reported in the prostate (Uhlen et al. 2005), ELL2 Materials and methods may be important for prostate homeostasis. ELL2 was In situ hybridization also found to be an androgen response gene (Nelson et al. 2002, Bolton et al. 2007) that is upregulated in response Before hybridization, murine prostate tissue cryosections to chronic prostatic inflammation in rats (Funahashi et al. (ProbeOn, Fisher Biotech, Pittsburgh, PA) were washed 2015) and was upregulated in human benign prostatic with PBS, fixed in 4% paraformaldehyde, digested hyperplasia (BPH) (O’Malley et al. 2009). The specific with proteinase K at 20 µg/mL in PBS, refixed in 4% role of ELL2 in the prostate has not been fully elucidated; paraformaldehyde, rewashed in PBS and then acetylated however, transfected ELL2 protein has been shown to in 0.25% acetic anhydride in 0.1 M triethanolamine, interact with the potential prostate tumor suppressor pH 8.0. Full-length mouse ELL2 cDNA was inserted into gene ELL-associated factor 2 (EAF2) (Simone et al. the EcoRI and XhoI site between T3 and T7 promoters 2003). In this study, Simone and coworkers showed that in pBluescript II SK plasmid vector. The plasmid was endogenous EAF2 coimmunoprecipitated with transfected purified by CsCl double banding, linearized with EcoRI ELL2 in 293 cells and that like the MLL–ELL fusion or XhoI, and proteinase K treated. Purified linear DNA http://joe.endocrinology-journals.org © 2017 Society for Endocrinology Published by Bioscientifica Ltd. DOI: 10.1530/JOE-17-0112 Printed in Great Britain Downloaded from Bioscientifica.com at 09/28/2021 06:24:52PM via free access Research L E PASCAL, K Z MASOODI, J LIU Prostate
Load more

Recommended publications
  • Supplementary Data
    Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2).
    [Show full text]
  • Genetic Analysis of Retinopathy in Type 1 Diabetes
    Genetic Analysis of Retinopathy in Type 1 Diabetes by Sayed Mohsen Hosseini A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by S. Mohsen Hosseini 2014 Genetic Analysis of Retinopathy in Type 1 Diabetes Sayed Mohsen Hosseini Doctor of Philosophy Institute of Medical Science University of Toronto 2014 Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies (GWAS). To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR, ii RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194).
    [Show full text]
  • An Ancestral Role of Pericentrin in Centriole Formation Through SAS-6 Recruitment
    bioRxiv preprint doi: https://doi.org/10.1101/313494; this version posted May 3, 2018. 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 ancestral role of pericentrin in centriole formation through SAS-6 recruitment Daisuke Ito1*, Sihem Zitouni1, Swadhin Chandra Jana1, Paulo Duarte1, Jaroslaw Surkont1, Zita Carvalho-Santos1,2, José B. Pereira-Leal1, Miguel Godinho Ferreira1,3 and Mónica Bettencourt-Dias1* 1. Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal. 2. Present address: Champalimaud Centre for the Unknown, Lisbon, Portugal 3. Institute for Research on Cancer and Aging of Nice (IRCAN), INSERM U1081 UMR7284 CNRS, 06107 Nice, France *Correspondence to: [email protected] (DI); [email protected] (MBD) Running title: Ancestral role of pericentrin-SAS-6 interaction Key words: centrosome, centriole, fission yeast, SPB, SAS-6, pericentrin, calmodulin, evolution Short summary The pericentriolar matrix (PCM) is not only important for microtubule-nucleation but also can regulate centriole biogenesis. Ito et al. reveal an ancestral interaction between the centriole protein SAS-6 and the PCM component pericentrin, which regulates centriole biogenesis and elongation. 1 bioRxiv preprint doi: https://doi.org/10.1101/313494; this version posted May 3, 2018. 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 The centrosome is composed of two centrioles surrounded by a microtubule-nucleating pericentriolar matrix (PCM). Centrioles regulate matrix assembly. Here we ask whether the matrix also regulates centriole assembly.
    [Show full text]
  • Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
    Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A.
    [Show full text]
  • A Novel Computational Algorithm for Predicting Immune Cell Types Using Single-Cell RNA Sequencing Data
    A novel computational algorithm for predicting immune cell types using single-cell RNA sequencing data By Shuo Jia A hesis submitted to the Faculty of Graduate Studies of The University of Manitoba n partial fulfillment of the requirements of the degree of MASTER OF SCIENCE Department of Biochemistry and Medical Genetics University of Manitoba Winnipeg, Manitoba, Canada Copyright © 2020 by Shuo Jia Abstract Background: Cells from our immune system detect and kill pathogens to protect our body against many diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput rate, etc. These problems stack up and hinder the deep exploration of cellular heterogeneity. Immune cells that are associated with cancer tissues play a critical role in revealing the stages of tumor development. Identifying the immune composition within tumor microenvironments in a timely manner will be helpful to improve clinical prognosis and therapeutic management for cancer. Single-cell RNA sequencing (scRNA-seq), an RNA sequencing (RNA-seq) technique that focuses on a single cell level, has provided us with the ability to conduct cell type classification. Although unsupervised clustering approaches are the major methods for analyzing scRNA-seq datasets, their results vary among studies with different input parameters and sizes. However, in supervised machine learning methods, information loss and low prediction accuracy are the key limitations. Methods and Results: Genes in the human genome align to chromosomes in a particular order. Hence, we hypothesize incorporating this information into our model will potentially improve the cell type classification performance. In order to utilize gene positional information, we introduce chromosome-based neural network, namely ChrNet, a novel chromosome-specific re-trainable supervised learning method based on a one-dimensional 1 convolutional neural network (1D-CNN).
    [Show full text]
  • 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.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Mouse Eaf2 Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Eaf2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Eaf2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Eaf2 gene (NCBI Reference Sequence: NM_001113401 ; Ensembl: ENSMUSG00000022838 ) is located on Mouse chromosome 16. 6 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 6 (Transcript: ENSMUST00000114829). Exon 4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Eaf2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP24-400K10 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a null allele exhibit premature death, enlarged heart and prostate associate with hypertrophy, and increased incidence of tumors. Exon 4 starts from about 43.13% of the coding region. The knockout of Exon 4 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 2311 bp, and the size of intron 4 for 3'-loxP site insertion: 7170 bp. The size of effective cKO region: ~646 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 4 6 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Eaf2 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • Systems Genetics of Sensation Seeking. Price E
    The Jackson Laboratory The Mouseion at the JAXlibrary Faculty Research 2019 Faculty Research 3-2019 Systems genetics of sensation seeking. Price E. Dickson Tyler A Roy Kathryn A. McNaughton Troy Wilcox Padam Kumar See next page for additional authors Follow this and additional works at: https://mouseion.jax.org/stfb2019 Part of the Life Sciences Commons, and the Medicine and Health Sciences Commons Authors Price E. Dickson, Tyler A Roy, Kathryn A. McNaughton, Troy Wilcox, Padam Kumar, and Elissa J Chesler Received: 20 May 2018 Revised: 9 September 2018 Accepted: 11 September 2018 DOI: 10.1111/gbb.12519 ORIGINAL ARTICLE Systems genetics of sensation seeking Price E. Dickson | Tyler A. Roy | Kathryn A. McNaughton | Troy D. Wilcox | Padam Kumar | Elissa J. Chesler Center for Systems Neurogenetics of Addiction, The Jackson Laboratory, Bar Sensation seeking is a multifaceted, heritable trait which predicts the development of substance Harbor, Maine use and abuse in humans; similar phenomena have been observed in rodents. Genetic correla- Correspondence tions among sensation seeking and substance use indicate shared biological mechanisms, but Price E. Dickson, The Jackson Laboratory, the genes and networks underlying these relationships remain elusive. Here, we used a systems 600 Main Street, Bar Harbor, ME 04609 Email: [email protected] genetics approach in the BXD recombinant inbred mouse panel to identify shared genetic mech- Elissa J. Chesler, The Jackson Laboratory, anisms underlying substance use and preference for sensory stimuli, an intermediate phenotype 600 Main Street, Bar Harbor, ME 04609 of sensation seeking. Using the operant sensation seeking (OSS) paradigm, we quantified prefer- Email: [email protected] ence for sensory stimuli in 120 male and 127 female mice from 62 BXD strains and the Funding information C57BL/6J and DBA/2J founder strains.
    [Show full text]
  • Table SI. Genes Upregulated ≥ 2-Fold by MIH 2.4Bl Treatment Affymetrix ID
    Table SI. Genes upregulated 2-fold by MIH 2.4Bl treatment Fold UniGene ID Description Affymetrix ID Entrez Gene Change 1558048_x_at 28.84 Hs.551290 231597_x_at 17.02 Hs.720692 238825_at 10.19 93953 Hs.135167 acidic repeat containing (ACRC) 203821_at 9.82 1839 Hs.799 heparin binding EGF like growth factor (HBEGF) 1559509_at 9.41 Hs.656636 202957_at 9.06 3059 Hs.14601 hematopoietic cell-specific Lyn substrate 1 (HCLS1) 202388_at 8.11 5997 Hs.78944 regulator of G-protein signaling 2 (RGS2) 213649_at 7.9 6432 Hs.309090 serine and arginine rich splicing factor 7 (SRSF7) 228262_at 7.83 256714 Hs.127951 MAP7 domain containing 2 (MAP7D2) 38037_at 7.75 1839 Hs.799 heparin binding EGF like growth factor (HBEGF) 224549_x_at 7.6 202672_s_at 7.53 467 Hs.460 activating transcription factor 3 (ATF3) 243581_at 6.94 Hs.659284 239203_at 6.9 286006 Hs.396189 leucine rich single-pass membrane protein 1 (LSMEM1) 210800_at 6.7 1678 translocase of inner mitochondrial membrane 8 homolog A (yeast) (TIMM8A) 238956_at 6.48 1943 Hs.741510 ephrin A2 (EFNA2) 242918_at 6.22 4678 Hs.319334 nuclear autoantigenic sperm protein (NASP) 224254_x_at 6.06 243509_at 6 236832_at 5.89 221442 Hs.374076 adenylate cyclase 10, soluble pseudogene 1 (ADCY10P1) 234562_x_at 5.89 Hs.675414 214093_s_at 5.88 8880 Hs.567380; far upstream element binding protein 1 (FUBP1) Hs.707742 223774_at 5.59 677825 Hs.632377 small nucleolar RNA, H/ACA box 44 (SNORA44) 234723_x_at 5.48 Hs.677287 226419_s_at 5.41 6426 Hs.710026; serine and arginine rich splicing factor 1 (SRSF1) Hs.744140 228967_at 5.37
    [Show full text]
  • A Bootstrap-Based Regression Method for Comprehensive Discovery of Differential Gene Expressions: an Application to the Osteoporosis Study
    A bootstrap-based regression method for comprehensive discovery of differential gene expressions: an application to the osteoporosis study Yan Lu1,2, Yao-Zhong Liu3, Peng–Yuan Liu2, Volodymyr Dvornyk4, and Hong–Wen Deng1,3 1. College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing 100044, P. R. China 2. Department of Physiology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA 3. Department of Biostatistics and Bioinformatics & Center for Bioinformatics and Genomics, Tulane University School of Public Health, New Orleans, LA 70112, USA 4. School of Biological Sciences, University of Hong Kong, Pokfulam Rd., Pokfulam, Hong Kong, P.R. China Running title: Bootstrap-based regression method for microarray analysis Key words: microarray, bootstrap, regression, osteoporosis Corresponding author: Hong–Wen Deng, Ph. D. Department of Biostatistics and Bioinformatics & Center for Bioinformatics and Genomics, Tulane University School of Public Health, 1440 Canal Street, Suite 2001, New Orleans, LA 70112 Tel: 504-988-1310 Email: [email protected] 1 Abstract A common purpose of microarray experiments is to study the variation in gene expression across the categories of an experimental factor such as tissue types and drug treatments. However, it is not uncommon that the studied experimental factor is a quantitative variable rather than categorical variable. Loss of information would occur by comparing gene-expression levels between groups that are factitiously defined according to the quantitative threshold values of an experimental factor. Additionally, lack of control for some sensitive clinical factors may bring serious false positive or negative findings. In the present study, we described a bootstrap-based regression method for analyzing gene expression data from the non-categorical microarray experiments.
    [Show full text]
  • Screening and Analysis of Pathogenic Genes During DMBA- Induced Buccal Mucosa Carcinogenesis in Golden Hamsters
    1619-1624.qxd 23/4/2010 09:56 Ì ™ÂÏ›‰·1619 ONCOLOGY REPORTS 23: 1619-1624, 2010 Screening and analysis of pathogenic genes during DMBA- induced buccal mucosa carcinogenesis in golden hamsters KAI YANG1, GUODONG ZHANG1, JIE MEI1, DAN CHEN1 and MINGJUN WU2 1Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital, Chongqing Medical University, Chongqing; 2Institute of Life Sciences of Chongqing Medical University, Chongqing 400016, P.R. China Received December 18, 2009; Accepted February 9, 2010 DOI: 10.3892/or_00000803 Abstract. We designed to screen pathogenic genes related to Introduction the occurrence and development of oral buccal mucosa cancer by whole genome microarray and analyze the mechanisms There are 274,000 new cases of oral cancer in the world every of carcinogenesis. The golden hamster model of buccal year (1). Squamous cell carcinoma accounts for 90% of mucosa cancer was established by induction with DMBA. oral cancer, and buccal carcinoma is one of the most common cRNAs labeled with Cy3 were synthesized and hybridized oral cancers (2). Although the treatment of oral cancers has with Agilent Whole Rat Genome Arrays containing 41,000 markedly improved in recent decades, the 5-year survival genes/ESTs. A Venn diagram analysis was performed to rate for buccal carcinoma and other oral cancers is only 55- screen the continuously abnormally expressed genes. Our 60% (2-4). Therefore, it is of great significance to explore results show 5,255 significantly differentially expressed the molecular mechanisms of the occurrence and develop- genes in golden hamster pouch mucosa during the ment of oral mucosa carcinomas and search for effective progression of normal buccal mucosa to squamous cell therapeutic targets.
    [Show full text]