An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15
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Supplementary Information Method CLEAR-CLIP. Mouse Keratinocytes
Supplementary Information Method CLEAR-CLIP. Mouse keratinocytes of the designated genotype were maintained in E-low calcium medium. Inducible cells were treated with 3 ug/ml final concentration doxycycline for 24 hours before performing CLEAR-CLIP. One 15cm dish of confluent cells was used per sample. Cells were washed once with cold PBS. 10mls of cold PBS was then added and cells were irradiated with 300mJ/cm2 UVC (254nM wavelength). Cells were then scraped from the plates in cold PBS and pelleted by centrifugation at 1,000g for 2 minutes. Pellets were frozen at -80oC until needed. Cells were then lysed on ice with occasional vortexing in 1ml of lysis buffer (50mM Tris-HCl pH 7.4, 100mM NaCl, 1mM MgCl2, 0.1 mM CaCl2, 1% NP-40, 0.5% Sodium Deoxycholate, 0.1% SDS) containing 1X protease inhibitors (Roche #88665) and RNaseOUT (Invitrogen #10777019) at 4ul/ml final concentration. Next, TurboDNase (Invitrogen #AM2238, 10U), RNase A (0.13ug) and RNase T1 (0.13U) were added and samples were incubated at 37oC for 5 minutes with occasional mixing. Samples were immediately placed on ice and then centrifuged at 16,160g at 4oC for 20 minutes to clear lysate. 25ul of Protein-G Dynabeads (Invitrogen #10004D) were used per IP. Dynabeads were pre-washed with lysis buffer and pre- incubated with 3ul of Wako Anti-Mouse-Ago2 (2D4) antibody. The dynabead/antibody mixture was added to the lysate and rocked for 2 hours at 4oC. All steps after the IP were done on bead until samples were loaded into the polyacrylamide gel. -
Under Serratia Marcescens Treatment Kai Feng1,2, Xiaoyu Lu1,2, Jian Luo1,2 & Fang Tang1,2*
www.nature.com/scientificreports OPEN SMRT sequencing of the full‑length transcriptome of Odontotermes formosanus (Shiraki) under Serratia marcescens treatment Kai Feng1,2, Xiaoyu Lu1,2, Jian Luo1,2 & Fang Tang1,2* Odontotermes formosanus (Shiraki) is an important pest in the world. Serratia marcescens have a high lethal efect on O. formosanus, but the specifc insecticidal mechanisms of S. marcescens on O. formosanus are unclear, and the immune responses of O. formosanus to S. marcescens have not been clarifed. At present, genetic database resources of O. formosanus are extremely scarce. Therefore, using O. formosanus workers infected by S. marcescens and the control as experimental materials, a full-length transcriptome was sequenced using the PacBio Sequel sequencing platform. A total of 10,364 isoforms were obtained as the fnal transcriptome. The unigenes were further annotated with the Nr, Swiss-Prot, EuKaryotic Orthologous Groups (KOG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog public databases. In a comparison between the control group and a Serratia marcescens-infected group, a total of 259 diferentially expressed genes (DEGs) were identifed, including 132 upregulated and 127 downregulated genes. Pathway enrichment analysis indicated that the expression of the mitogen-activated protein kinase (MAPK) pathway, oxidative stress genes and the AMP-activated protein kinase (AMPK) pathway in O. formosanus may be associated with S. marcescens treatment. This research intensively studied O. formosanus at the high-throughput full-length transcriptome level, laying a foundation for further development of molecular markers and mining of target genes in this species and thereby promoting the biological control of O. -
Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School July 2017 Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Role of Amylase in Ovarian Cancer by Mai Mohamed A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D. Date of Approval: June 29, 2017 Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion Copyright © 2017, Mai Mohamed Dedication This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed. -
Supporting Information
Supporting Information Pouryahya et al. SI Text Table S1 presents genes with the highest absolute value of Ricci curvature. We expect these genes to have significant contribution to the network’s robustness. Notably, the top two genes are TP53 (tumor protein 53) and YWHAG gene. TP53, also known as p53, it is a well known tumor suppressor gene known as the "guardian of the genome“ given the essential role it plays in genetic stability and prevention of cancer formation (1, 2). Mutations in this gene play a role in all stages of malignant transformation including tumor initiation, promotion, aggressiveness, and metastasis (3). Mutations of this gene are present in more than 50% of human cancers, making it the most common genetic event in human cancer (4, 5). Namely, p53 mutations play roles in leukemia, breast cancer, CNS cancers, and lung cancers, among many others (6–9). The YWHAG gene encodes the 14-3-3 protein gamma, a member of the 14-3-3 family proteins which are involved in many biological processes including signal transduction regulation, cell cycle pro- gression, apoptosis, cell adhesion and migration (10, 11). Notably, increased expression of 14-3-3 family proteins, including protein gamma, have been observed in a number of human cancers including lung and colorectal cancers, among others, suggesting a potential role as tumor oncogenes (12, 13). Furthermore, there is evidence that loss Fig. S1. The histogram of scalar Ricci curvature of 8240 genes. Most of the genes have negative scalar Ricci curvature (75%). TP53 and YWHAG have notably low of p53 function may result in upregulation of 14-3-3γ in lung cancer Ricci curvatures. -
A Workbench for Interactive Exploratory Data Analysis of Large Expression Datasets
bioRxiv preprint doi: https://doi.org/10.1101/698969; this version posted July 14, 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. MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets Urminder Singh1,2, Manhoi Hur2, Karin Dorman1,2,3, and Eve Wurtele1,2* 1Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA 2Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA 50011, USA 3Department of Statistics, Iowa State University, Ames, IA 50011, USA The diverse and growing omics data in public domains provide can be attained with bigger datasets and the wide variety researchers with a tremendous opportunity to extract hidden of biological conditions can reveal the diverse transcription knowledge. However, the challenge of providing domain ex- spectrum of genes. Yet, despite the availability of such vast perts with easy access to these big data has resulted in the vast data resources, most bioinformatic studies use only a limited majority of archived data remaining unused. Here, we present amount of the available data. MetaOmGraph (MOG), a free, open-source, standalone soft- A common goal of analyzing omics data is to infer func- ware for exploratory data analysis of massive datasets by sci- entific researchers. Using MOG, a researcher can interactively tional roles of particular features by investigating differen- visualize and statistically analyze the data, in the context of its tial expression and coexpression patterns. A wide variety metadata. Researchers can interactively hone-in on groups of of R-platform tools can provide specific analyses (9–16). -
Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma
G C A T T A C G G C A T genes Article Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma Dumitru A. Iacobas Personalized Genomics Laboratory, CRI Center for Computational Systems Biology, Roy G Perry College of Engineering, Prairie View A&M University, Prairie View, TX 77446, USA; [email protected]; Tel.: +1-936-261-9926 Received: 1 August 2020; Accepted: 1 September 2020; Published: 2 September 2020 Abstract: Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B. -
Mathematical Modeling Reveals That G2/M Checkpoint Override Creates a Therapeutic Vulnerability in Tsc2-Null Tumors
The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 5-2017 MATHEMATICAL MODELING REVEALS THAT G2/M CHECKPOINT OVERRIDE CREATES A THERAPEUTIC VULNERABILITY IN TSC2-NULL TUMORS Hui Ju Hsieh Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Medicine and Health Sciences Commons Recommended Citation Hsieh, Hui Ju, "MATHEMATICAL MODELING REVEALS THAT G2/M CHECKPOINT OVERRIDE CREATES A THERAPEUTIC VULNERABILITY IN TSC2-NULL TUMORS" (2017). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 808. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/808 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. MATHEMATICAL MODELING REVEALS THAT G2/M CHECKPOINT OVERRIDE CREATES A THERAPEUTIC VULNERABILITY IN TSC2-NULL TUMORS by Hui-Ju Hsieh, -
Evidence for the Role of Ywha in Mouse Oocyte Maturation
EVIDENCE FOR THE ROLE OF YWHA IN MOUSE OOCYTE MATURATION A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Science By Ariana Claire Detwiler August, 2015 © Copyright All rights reserved Except for previously published materials Thesis written by Ariana Claire Detwiler B.S., Pennsylvania State University, 2012 M.S., Kent State University, 2015 Approved by ___________________________________________________________ Douglas W. Kline, Professor, Ph.D., Department of Biological Sciences, Masters Advisor ___________________________________________________________ Laura G. Leff, Professor, PhD., Chair, Department of Biological Sciences ___________________________________________________________ James L. Blank, Professor, Dean, College of Arts and Sciences i TABLE OF CONTENTS List of Figures ……………………………………………………………………………………v List of Tables ……………………………………………………………………………………vii Acknowledgements …………………………………………………………………………….viii Abstract ……………………………………………………………………………………….....1 Chapter I Introduction…………………………………………………………………………………..2 1.1 Introduction …………………………………………………………………………..2 1.2 Ovarian Function ……………………………………………………………………..2 1.3 Oogenesis and Folliculogenesis ………………………………………………………3 1.4 Oocyte Maturation ……………………………………………………………………5 1.5 Maternal to Embryonic Messenger RNA Transition …………………………………8 1.6 Meiotic Spindle Formation …………………………………………………………...9 1.7 YWHA Isoforms and Oocyte Maturation …………………………………………...10 Aim…………………………………………………………………………………………..15 Chapter II Methods……………………………………………………………………………………...16 -
Autism and Cancer Share Risk Genes, Pathways, and Drug Targets
TIGS 1255 No. of Pages 8 Forum Table 1 summarizes the characteristics of unclear whether this is related to its signal- Autism and Cancer risk genes for ASD that are also risk genes ing function or a consequence of a second for cancers, extending the original finding independent PTEN activity, but this dual Share Risk Genes, that the PI3K-Akt-mTOR signaling axis function may provide the rationale for the (involving PTEN, FMR1, NF1, TSC1, and dominant role of PTEN in cancer and Pathways, and Drug TSC2) was associated with inherited risk autism. Other genes encoding common Targets for both cancer and ASD [6–9]. Recent tumor signaling pathways include MET8[1_TD$IF],[2_TD$IF] genome-wide exome-sequencing studies PTK7, and HRAS, while p53, AKT, mTOR, Jacqueline N. Crawley,1,2,* of de novo variants in ASD and cancer WNT, NOTCH, and MAPK are compo- Wolf-Dietrich Heyer,3,4 and have begun to uncover considerable addi- nents of signaling pathways regulating Janine M. LaSalle1,4,5 tional overlap. What is surprising about the the nuclear factors described above. genes in Table 1 is not necessarily the Autism is a neurodevelopmental number of risk genes found in both autism Autism is comorbid with several mono- and cancer, but the shared functions of genic neurodevelopmental disorders, disorder, diagnosed behaviorally genes in chromatin remodeling and including Fragile X (FMR1), Rett syndrome by social and communication genome maintenance, transcription fac- (MECP2), Phelan-McDermid (SHANK3), fi de cits, repetitive behaviors, tors, and signal transduction pathways 15q duplication syndrome (UBE3A), and restricted interests. Recent leading to nuclear changes [7,8]. -
An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15
International Journal of Molecular Sciences Article An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15 1, 1, 1 1 1 2 Yanjing Wang y , Xiangeng Wang y , Yi Xiong , Cheng-Dong Li , Qin Xu , Lu Shen , Aman Chandra Kaushik 3,* and Dong-Qing Wei 1,4,* 1 State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; [email protected] (Y.W.); [email protected] (X.W.); [email protected] (Y.X.); [email protected] (C.-D.L.); [email protected] (Q.X.) 2 Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200030, China; [email protected] 3 Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China 4 Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen 518055, China * Correspondence: [email protected] (A.C.K.); [email protected] (D.-Q.W.) These authors contributed equally to this work. y Received: 21 July 2019; Accepted: 4 December 2019; Published: 10 December 2019 Abstract: G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. -
Xo PANEL DNA GENE LIST
xO PANEL DNA GENE LIST ~1700 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes (at 400 -500x average coverage on tumor) Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 -
Integrin Αvβ6-EGFR Crosstalk Regulates Bidirectional Force Transmission and Controls Breast Cancer Invasion
bioRxiv preprint doi: https://doi.org/10.1101/407908; this version posted September 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. Integrin αVβ6-EGFR crosstalk regulates bidirectional force transmission and controls breast cancer invasion Joanna R. Thomas1#, Kate M. Moore2#, Caroline Sproat2, Horacio J. Maldonado-Lorca1, Stephanie Mo1, Syed Haider3, Dean Hammond1, Gareth J. Thomas5, Ian A. Prior1, Pedro R. Cutillas2, Louise J. Jones2, John F. Marshall2†, Mark R. Morgan1† 1 Institute of Translational Medicine, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK. 2 Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK. 3 The Weatherall Institute of Molecular Medicine, Department of Oncology, University of Oxford, Oxford OX3 9DS, UK. 4 Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventative Medicine, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK. 5 Cancer Sciences Division, Somers Building, Southampton General Hospital, Southampton, SO16 6YA, UK. # Denotes equal contribution † Corresponding author Correspondence to: Dr Mark R. Morgan, PhD, Cellular & Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Crown Street, Liverpool, L69 3BX,