Expression and Prognostic Values of ARID Family Members in Breast Cancer
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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. -
Regulation of Transcription and Regulatory Networks for Muscle Growth * * * * A
Regulation Of Transcription And Regulatory Networks For Muscle Growth * * * * A. Reverter , N.J. Hudson , Q. Gu and B.P. Dalrymple Introduction The advent of microarray gene expression technology has provided animal scientists with an unprecedented ability to profile the transcriptional changes during skeletal muscle growth. With respect to meat quality, most of the effort has concentrated on the understanding of fat and energy metabolism (reviewed by Hausman et al . (2009)). Graugnard et al . (2009) explored the network among 31 genes associated with aspects of adipogenesis and energy metabolism in bovine skeletal muscle and in response to two distinct diets. Also, Freyssenet (2007) reviewed the roles that energy-sensing molecules and mitochondria have in the regulation of gene expression in muscle. However, other mechanisms such as cell cycle, glycolysis, extra-cellular matrix, ribosomal proteins and the immune system play a significant role in development, and this role can work in a tissue-specific manner. Hudson et al . (2009a) reported various functional modules underpinning the transcriptional regulation of bovine skeletal muscle. The authors integrated a total of six gene co-expression networks, each developed using the PCIT algorithm (Reverter and Chan (2008)), and proposed a Module-to-Regulator heuristic by which those transcription factors (TF) with the highest average absolute correlation co-expression with the genes present in each module are deemed to be the relevant regulators. However, this Module-to-Regulator approach failed to capture some well-known regulators of muscle fibre type composition, and the use of more sophisticated methods such as the differential wiring approach of Hudson et al . -
Watsonjn2018.Pdf (1.780Mb)
UNIVERSITY OF CENTRAL OKLAHOMA Edmond, Oklahoma Department of Biology Investigating Differential Gene Expression in vivo of Cardiac Birth Defects in an Avian Model of Maternal Phenylketonuria A THESIS SUBMITTED TO THE GRADUATE FACULTY In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN BIOLOGY By Jamie N. Watson Edmond, OK June 5, 2018 J. Watson/Dr. Nikki Seagraves ii J. Watson/Dr. Nikki Seagraves Acknowledgements It is difficult to articulate the amount of gratitude I have for the support and encouragement I have received throughout my master’s thesis. Many people have added value and support to my life during this time. I am thankful for the education, experience, and friendships I have gained at the University of Central Oklahoma. First, I would like to thank Dr. Nikki Seagraves for her mentorship and friendship. I lucked out when I met her. I have enjoyed working on this project and I am very thankful for her support. I would like thank Thomas Crane for his support and patience throughout my master’s degree. I would like to thank Dr. Shannon Conley for her continued mentorship and support. I would like to thank Liz Bullen and Dr. Eric Howard for their training and help on this project. I would like to thank Kristy Meyer for her friendship and help throughout graduate school. I would like to thank my committee members Dr. Robert Brennan and Dr. Lilian Chooback for their advisement on this project. Also, I would like to thank the biology faculty and staff. I would like to thank the Seagraves lab members: Jailene Canales, Kayley Pate, Mckayla Muse, Grace Thetford, Kody Harvey, Jordan Guffey, and Kayle Patatanian for their hard work and support. -
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. -
Loss of Fam60a, a Sin3a Subunit, Results in Embryonic Lethality and Is Associated with Aberrant Methylation at a Subset of Gene
RESEARCH ARTICLE Loss of Fam60a, a Sin3a subunit, results in embryonic lethality and is associated with aberrant methylation at a subset of gene promoters Ryo Nabeshima1,2, Osamu Nishimura3,4, Takako Maeda1, Natsumi Shimizu2, Takahiro Ide2, Kenta Yashiro1†, Yasuo Sakai1, Chikara Meno1, Mitsutaka Kadota3,4, Hidetaka Shiratori1†, Shigehiro Kuraku3,4*, Hiroshi Hamada1,2* 1Developmental Genetics Group, Graduate School of Frontier Biosciences, Osaka University, Suita, Japan; 2Laboratory for Organismal Patterning, RIKEN Center for Developmental Biology, Kobe, Japan; 3Phyloinformatics Unit, RIKEN Center for Life Science Technologies, Kobe, Japan; 4Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan Abstract We have examined the role of Fam60a, a gene highly expressed in embryonic stem cells, in mouse development. Fam60a interacts with components of the Sin3a-Hdac transcriptional corepressor complex, and most Fam60a–/– embryos manifest hypoplasia of visceral organs and die in utero. Fam60a is recruited to the promoter regions of a subset of genes, with the expression of these genes being either up- or down-regulated in Fam60a–/– embryos. The DNA methylation level of the Fam60a target gene Adhfe1 is maintained at embryonic day (E) 7.5 but markedly reduced at –/– *For correspondence: E9.5 in Fam60a embryos, suggesting that DNA demethylation is enhanced in the mutant. [email protected] (SK); Examination of genome-wide DNA methylation identified several differentially methylated regions, [email protected] (HH) which were preferentially hypomethylated, in Fam60a–/– embryos. Our data suggest that Fam60a is †These authors contributed required for proper embryogenesis, at least in part as a result of its regulation of DNA methylation equally to this work at specific gene promoters. -
CDC2 Mediates Progestin Initiated Endometrial Stromal Cell Proliferation: a PR Signaling to Gene Expression Independently of Its Binding to Chromatin
CDC2 Mediates Progestin Initiated Endometrial Stromal Cell Proliferation: A PR Signaling to Gene Expression Independently of Its Binding to Chromatin Griselda Vallejo1, Alejandro D. La Greca1., Inti C. Tarifa-Reischle1., Ana C. Mestre-Citrinovitz1, Cecilia Ballare´ 2, Miguel Beato2,3, Patricia Saragu¨ eta1* 1 Instituto de Biologı´a y Medicina Experimental, IByME-Conicet, Buenos Aires, Argentina, 2 Centre de Regulacio´ Geno`mica, (CRG), Barcelona, Spain, 3 University Pompeu Fabra (UPF), Barcelona, Spain Abstract Although non-genomic steroid receptor pathways have been studied over the past decade, little is known about the direct gene expression changes that take place as a consequence of their activation. Progesterone controls proliferation of rat endometrial stromal cells during the peri-implantation phase of pregnancy. We showed that picomolar concentration of progestin R5020 mimics this control in UIII endometrial stromal cells via ERK1-2 and AKT activation mediated by interaction of Progesterone Receptor (PR) with Estrogen Receptor beta (ERb) and without transcriptional activity of endogenous PR and ER. Here we identify early downstream targets of cytoplasmic PR signaling and their possible role in endometrial stromal cell proliferation. Microarray analysis of global gene expression changes in UIII cells treated for 45 min with progestin identified 97 up- and 341 down-regulated genes. The most over-represented molecular functions were transcription factors and regulatory factors associated with cell proliferation and cell cycle, a large fraction of which were repressors down-regulated by hormone. Further analysis verified that progestins regulate Ccnd1, JunD, Usf1, Gfi1, Cyr61, and Cdkn1b through PR- mediated activation of ligand-free ER, ERK1-2 or AKT, in the absence of genomic PR binding. -
Inherited Variation in Mir-290 Expression Suppresses Breast Cancer Progression by Targeting the Metastasis Susceptibility Gene Arid4b
Published OnlineFirst February 27, 2013; DOI: 10.1158/0008-5472.CAN-12-3513 Cancer Tumor and Stem Cell Biology Research Inherited Variation in miR-290 Expression Suppresses Breast Cancer Progression by Targeting the Metastasis Susceptibility Gene Arid4b Natalie Goldberger1, Renard C. Walker1, Chang Hee Kim2, Scott Winter1, and Kent W. Hunter1 Abstract The metastatic cascade is a complex and extremely inefficient process with many potential barriers. Understanding this process is of critical importance because the majority of cancer mortality is associated with metastatic disease. Recently, it has become increasingly clear that microRNAs (miRNA) play important roles in tumorigenesis and metastasis, yet few studies have examined how germline variations may dysregulate miRNAs, in turn affecting metastatic potential. To explore this possibility, the highly metastatic MMTV-PyMT mice were crossed with 25 AKXD (AKR/J Â DBA/2J) recombinant inbred strains to produce F1 progeny with varying metastatic indices. When mammary tumors from the F1 progeny were analyzed by miRNA microarray, miR-290 (containing miR-290-3p and miR-290-5p) was identified as a top candidate progression-associated miRNA. The microarray results were validated in vivo when miR-290 upregulation in two independent breast cancer cell lines suppressed both primary tumor and metastatic growth. Computational analysis identified breast cancer progression gene Arid4b as a top target of miR-290-3p, which was confirmed by luciferase reporter assay. Surprisingly, pathway analysis identified estrogen receptor (ER) signaling as the top canonical pathway affected by miR-290 upregulation. Further analysis showed that ER levels were elevated in miR-290–expressing tumors and positively correlated with apoptosis. -
Mir-376C Promotes Carcinogenesis and Serves As a Plasma Marker for Gastric Carcinoma
RESEARCH ARTICLE miR-376c promotes carcinogenesis and serves as a plasma marker for gastric carcinoma Pei-Shih Hung1, Chin-Yau Chen2, Wei-Ting Chen2, Chen-Yu Kuo3, Wen-Liang Fang4,5, Kuo-Hung Huang4,5, Peng-Chih Chiu5, Su-Shun Lo2,6* 1 Department of Education and Medical Research, National Yang-Ming University Hospital, Yilan, Taiwan, 2 Department of Surgery, National Yang-Ming University Hospital, Yilan, Taiwan, 3 Department of Medicine, National Yang-Ming University Hospital, Yilan, Taiwan, 4 Division of General Surgery, Veterans General Hospital±Taipei, Taipei, Taiwan, 5 Department of Dentistry, National Yang-Ming University Hospital, Yilan, Taiwan, 6 School of Medicine, National Yang-Ming University, Taipei, Taiwan a1111111111 [email protected] a1111111111 * a1111111111 a1111111111 a1111111111 Abstract Gastric carcinoma is highly prevalent throughout the world. Understanding the pathogenesis of this disease will benefit diagnosis and resolution. Studies show that miRNAs are involved in the tumorigenesis of gastric carcinoma. An initial screening followed by subsequent vali- OPEN ACCESS dation identified that miR-376c is up-regulated in gastric carcinoma tissue and the plasma Citation: Hung P-S, Chen C-Y, Chen W-T, Kuo C-Y, of patients with the disease. In addition, the urinary level of miR-376c is also significantly Fang W-L, Huang K-H, et al. (2017) miR-376c increased in gastric carcinoma patients. The plasma miR-376c level was validated as a bio- promotes carcinogenesis and serves as a plasma marker for gastric carcinoma. PLoS ONE 12(5): marker for gastric carcinoma, including early stage tumors. The induction of miR-376c was e0177346. -
HCC and Cancer Mutated Genes Summarized in the Literature Gene Symbol Gene Name References*
HCC and cancer mutated genes summarized in the literature Gene symbol Gene name References* A2M Alpha-2-macroglobulin (4) ABL1 c-abl oncogene 1, receptor tyrosine kinase (4,5,22) ACBD7 Acyl-Coenzyme A binding domain containing 7 (23) ACTL6A Actin-like 6A (4,5) ACTL6B Actin-like 6B (4) ACVR1B Activin A receptor, type IB (21,22) ACVR2A Activin A receptor, type IIA (4,21) ADAM10 ADAM metallopeptidase domain 10 (5) ADAMTS9 ADAM metallopeptidase with thrombospondin type 1 motif, 9 (4) ADCY2 Adenylate cyclase 2 (brain) (26) AJUBA Ajuba LIM protein (21) AKAP9 A kinase (PRKA) anchor protein (yotiao) 9 (4) Akt AKT serine/threonine kinase (28) AKT1 v-akt murine thymoma viral oncogene homolog 1 (5,21,22) AKT2 v-akt murine thymoma viral oncogene homolog 2 (4) ALB Albumin (4) ALK Anaplastic lymphoma receptor tyrosine kinase (22) AMPH Amphiphysin (24) ANK3 Ankyrin 3, node of Ranvier (ankyrin G) (4) ANKRD12 Ankyrin repeat domain 12 (4) ANO1 Anoctamin 1, calcium activated chloride channel (4) APC Adenomatous polyposis coli (4,5,21,22,25,28) APOB Apolipoprotein B [including Ag(x) antigen] (4) AR Androgen receptor (5,21-23) ARAP1 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1 (4) ARHGAP35 Rho GTPase activating protein 35 (21) ARID1A AT rich interactive domain 1A (SWI-like) (4,5,21,22,24,25,27,28) ARID1B AT rich interactive domain 1B (SWI1-like) (4,5,22) ARID2 AT rich interactive domain 2 (ARID, RFX-like) (4,5,22,24,25,27,28) ARID4A AT rich interactive domain 4A (RBP1-like) (28) ARID5B AT rich interactive domain 5B (MRF1-like) (21) ASPM Asp (abnormal -
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 -
Core Circadian Clock Transcription Factor BMAL1 Regulates Mammary Epithelial Cell
bioRxiv preprint doi: https://doi.org/10.1101/2021.02.23.432439; this version posted February 23, 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. 1 Title: Core circadian clock transcription factor BMAL1 regulates mammary epithelial cell 2 growth, differentiation, and milk component synthesis. 3 Authors: Theresa Casey1ǂ, Aridany Suarez-Trujillo1, Shelby Cummings1, Katelyn Huff1, 4 Jennifer Crodian1, Ketaki Bhide2, Clare Aduwari1, Kelsey Teeple1, Avi Shamay3, Sameer J. 5 Mabjeesh4, Phillip San Miguel5, Jyothi Thimmapuram2, and Karen Plaut1 6 Affiliations: 1. Department of Animal Science, Purdue University, West Lafayette, IN, USA; 2. 7 Bioinformatics Core, Purdue University; 3. Animal Science Institute, Agriculture Research 8 Origination, The Volcani Center, Rishon Letsiyon, Israel. 4. Department of Animal Sciences, 9 The Robert H. Smith Faculty of Agriculture, Food, and Environment, The Hebrew University of 10 Jerusalem, Rehovot, Israel. 5. Genomics Core, Purdue University 11 Grant support: Binational Agricultural Research Development (BARD) Research Project US- 12 4715-14; Photoperiod effects on milk production in goats: Are they mediated by the molecular 13 clock in the mammary gland? 14 ǂAddress for correspondence. 15 Theresa M. Casey 16 BCHM Room 326 17 175 South University St. 18 West Lafayette, IN 47907 19 Email: [email protected] 20 Phone: 802-373-1319 21 22 bioRxiv preprint doi: https://doi.org/10.1101/2021.02.23.432439; this version posted February 23, 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. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7,