Emerging Role of Tumor Cell Plasticity in Modifying Therapeutic Response
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Mir-338-3P Is Regulated by Estrogens Through GPER in Breast Cancer Cells and Cancer-Associated Fibroblasts (Cafs)
cells Article miR-338-3p Is Regulated by Estrogens through GPER in Breast Cancer Cells and Cancer-Associated Fibroblasts (CAFs) Adele Vivacqua 1,*, Anna Sebastiani 1, Anna Maria Miglietta 2, Damiano Cosimo Rigiracciolo 1, Francesca Cirillo 1, Giulia Raffaella Galli 1, Marianna Talia 1, Maria Francesca Santolla 1, Rosamaria Lappano 1, Francesca Giordano 1, Maria Luisa Panno 1 and Marcello Maggiolini 1,* 1 Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy; [email protected] (A.S.); [email protected] (D.C.R.); [email protected] (F.C.); [email protected] (G.R.G.); [email protected] (M.T.); [email protected] (M.F.S.); [email protected] (R.L.); [email protected] (F.G.); [email protected] (M.L.P.) 2 Regional HospitalCosenza, 87100 Cosenza, Italy; [email protected] * Correspondence: [email protected] (A.V.); [email protected] (M.M.); Tel.: +39-0984-493-048 (A.V.); +39-0984-493-076 (M.M.) Received: 12 October 2018; Accepted: 7 November 2018; Published: 9 November 2018 Abstract: Estrogens acting through the classic estrogen receptors (ERs) and the G protein estrogen receptor (GPER) regulate the expression of diverse miRNAs, small sequences of non-coding RNA involved in several pathophysiological conditions, including breast cancer. In order to provide novel insights on miRNAs regulation by estrogens in breast tumor, we evaluated the expression of 754 miRNAs by TaqMan Array in ER-negative and GPER-positive SkBr3 breast cancer cells and cancer-associated fibroblasts (CAFs) upon 17β-estradiol (E2) treatment. Various miRNAs were regulated by E2 in a peculiar manner in SkBr3 cancer cells and CAFs, while miR-338-3p displayed a similar regulation in both cell types. -
KDM5B Promotes Drug Resistance by Regulating Melanoma-Propagating Cell Subpopulations Xiaoni Liu1,2, Shang-Min Zhang1, Meaghan K
Published OnlineFirst December 6, 2018; DOI: 10.1158/1535-7163.MCT-18-0395 Cancer Biology and Translational Studies Molecular Cancer Therapeutics KDM5B Promotes Drug Resistance by Regulating Melanoma-Propagating Cell Subpopulations Xiaoni Liu1,2, Shang-Min Zhang1, Meaghan K. McGeary1,2, Irina Krykbaeva1,2, Ling Lai3, Daniel J. Jansen4, Stephen C. Kales4, Anton Simeonov4, Matthew D. Hall4, Daniel P. Kelly3, Marcus W. Bosenberg1,2,5, and Qin Yan1 Abstract Tumor heterogeneity is a major challenge for cancer elevated KDM5B expression, melanoma cells shift toward a À treatment, especially due to the presence of various sub- more drug-tolerant, CD34 state upon exposure to BRAF populations with stem cell or progenitor cell properties. inhibitor or combined BRAF inhibitor and MEK inhibitor þ À þ In mouse melanomas, both CD34 p75 (CD34 )and treatment. KDM5B loss or inhibition shifts melanoma cells À À À þ CD34 p75 (CD34 ) tumor subpopulations were charac- to the more BRAF inhibitor–sensitive CD34 state. These terized as melanoma-propagating cells (MPC) that exhibit results support that KDM5B is a critical epigenetic regulator some of those key features. However, these two subpopula- that governs the transition of key MPC subpopulations tions differ from each other in tumorigenic potential, with distinct drug sensitivity. This study also emphasizes ability to recapitulate heterogeneity, and chemoresistance. the importance of continuing to advance our understand- þ À In this study, we demonstrate that CD34 and CD34 ing of intratumor heterogeneity and ultimately develop V600E subpopulations carrying the BRAF mutation confer novel therapeutics by altering the heterogeneous character- differential sensitivity to targeted BRAF inhibition. -
Small Molecule Inhibitors of KDM5 Histone Demethylases Increase the Radiosensitivity of Breast Cancer Cells Overexpressing JARID1B
molecules Article Small Molecule Inhibitors of KDM5 Histone Demethylases Increase the Radiosensitivity of Breast Cancer Cells Overexpressing JARID1B Simone Pippa 1, Cecilia Mannironi 2, Valerio Licursi 1,3, Luca Bombardi 1, Gianni Colotti 2, Enrico Cundari 2, Adriano Mollica 4, Antonio Coluccia 5 , Valentina Naccarato 5, Giuseppe La Regina 5 , Romano Silvestri 5 and Rodolfo Negri 1,2,* 1 Department of Biology and Biotechnology “C. Darwin”, Sapienza University of Rome, 00185 Rome, Italy; [email protected] (S.P.); [email protected] (V.L.); [email protected] (L.B.) 2 Institute of Molecular Biology and Pathology, Italian National Research Council, 00185 Rome, Italy; [email protected] (C.M.); [email protected] (G.C.); [email protected] (E.C.) 3 Institute for Systems Analysis and Computer Science “A. Ruberti”, Italian National Research Council, 00185 Rome, Italy 4 Department of Pharmacy, University “G. d’ Annunzio” of Chieti, Via dei Vestini 31, 66100 Chieti, Italy; [email protected] 5 Department of Drug Chemistry and Technologies, Sapienza University of Rome, Laboratory affiliated to Istituto Pasteur Italia Cenci Bolognetti Foundation, Sapienza University of Rome, 00185 Rome, Italy; [email protected] (A.C.); [email protected] (V.N.); [email protected] (G.L.R.); [email protected] (R.S.) * Correspondence: [email protected]; Tel.: +39-06-4991-7790 Academic Editors: Sergio Valente and Diego Muñoz-Torrero Received: 12 October 2018; Accepted: 1 May 2019; Published: 4 May 2019 Abstract: Background: KDM5 enzymes are H3K4 specific histone demethylases involved in transcriptional regulation and DNA repair. These proteins are overexpressed in different kinds of cancer, including breast, prostate and bladder carcinomas, with positive effects on cancer proliferation and chemoresistance. -
Predictive QSAR Tools to Aid in Early Process Development of Monoclonal Antibodies
Predictive QSAR tools to aid in early process development of monoclonal antibodies John Micael Andreas Karlberg Published work submitted to Newcastle University for the degree of Doctor of Philosophy in the School of Engineering November 2019 Abstract Monoclonal antibodies (mAbs) have become one of the fastest growing markets for diagnostic and therapeutic treatments over the last 30 years with a global sales revenue around $89 billion reported in 2017. A popular framework widely used in pharmaceutical industries for designing manufacturing processes for mAbs is Quality by Design (QbD) due to providing a structured and systematic approach in investigation and screening process parameters that might influence the product quality. However, due to the large number of product quality attributes (CQAs) and process parameters that exist in an mAb process platform, extensive investigation is needed to characterise their impact on the product quality which makes the process development costly and time consuming. There is thus an urgent need for methods and tools that can be used for early risk-based selection of critical product properties and process factors to reduce the number of potential factors that have to be investigated, thereby aiding in speeding up the process development and reduce costs. In this study, a framework for predictive model development based on Quantitative Structure- Activity Relationship (QSAR) modelling was developed to link structural features and properties of mAbs to Hydrophobic Interaction Chromatography (HIC) retention times and expressed mAb yield from HEK cells. Model development was based on a structured approach for incremental model refinement and evaluation that aided in increasing model performance until becoming acceptable in accordance to the OECD guidelines for QSAR models. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Profiling G Protein-Coupled Receptors of Fasciola Hepatica Identifies Orphan Rhodopsins Unique to Phylum Platyhelminthes
bioRxiv preprint doi: https://doi.org/10.1101/207316; this version posted October 23, 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. 1 Profiling G protein-coupled receptors of Fasciola hepatica 2 identifies orphan rhodopsins unique to phylum 3 Platyhelminthes 4 5 Short title: Profiling G protein-coupled receptors (GPCRs) in Fasciola hepatica 6 7 Paul McVeigh1*, Erin McCammick1, Paul McCusker1, Duncan Wells1, Jane 8 Hodgkinson2, Steve Paterson3, Angela Mousley1, Nikki J. Marks1, Aaron G. Maule1 9 10 11 1Parasitology & Pathogen Biology, The Institute for Global Food Security, School of 12 Biological Sciences, Queen’s University Belfast, Medical Biology Centre, 97 Lisburn 13 Road, Belfast, BT9 7BL, UK 14 15 2 Institute of Infection and Global Health, University of Liverpool, Liverpool, UK 16 17 3 Institute of Integrative Biology, University of Liverpool, Liverpool, UK 18 19 * Corresponding author 20 Email: [email protected] 21 1 bioRxiv preprint doi: https://doi.org/10.1101/207316; this version posted October 23, 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. 22 Abstract 23 G protein-coupled receptors (GPCRs) are established drug targets. Despite their 24 considerable appeal as targets for next-generation anthelmintics, poor understanding 25 of their diversity and function in parasitic helminths has thwarted progress towards 26 GPCR-targeted anti-parasite drugs. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Classification Decisions Taken by the Harmonized System Committee from the 47Th to 60Th Sessions (2011
CLASSIFICATION DECISIONS TAKEN BY THE HARMONIZED SYSTEM COMMITTEE FROM THE 47TH TO 60TH SESSIONS (2011 - 2018) WORLD CUSTOMS ORGANIZATION Rue du Marché 30 B-1210 Brussels Belgium November 2011 Copyright © 2011 World Customs Organization. All rights reserved. Requests and inquiries concerning translation, reproduction and adaptation rights should be addressed to [email protected]. D/2011/0448/25 The following list contains the classification decisions (other than those subject to a reservation) taken by the Harmonized System Committee ( 47th Session – March 2011) on specific products, together with their related Harmonized System code numbers and, in certain cases, the classification rationale. Advice Parties seeking to import or export merchandise covered by a decision are advised to verify the implementation of the decision by the importing or exporting country, as the case may be. HS codes Classification No Product description Classification considered rationale 1. Preparation, in the form of a powder, consisting of 92 % sugar, 6 % 2106.90 GRIs 1 and 6 black currant powder, anticaking agent, citric acid and black currant flavouring, put up for retail sale in 32-gram sachets, intended to be consumed as a beverage after mixing with hot water. 2. Vanutide cridificar (INN List 100). 3002.20 3. Certain INN products. Chapters 28, 29 (See “INN List 101” at the end of this publication.) and 30 4. Certain INN products. Chapters 13, 29 (See “INN List 102” at the end of this publication.) and 30 5. Certain INN products. Chapters 28, 29, (See “INN List 103” at the end of this publication.) 30, 35 and 39 6. Re-classification of INN products. -
Tanibirumab (CUI C3490677) Add to Cart
5/17/2018 NCI Metathesaurus Contains Exact Match Begins With Name Code Property Relationship Source ALL Advanced Search NCIm Version: 201706 Version 2.8 (using LexEVS 6.5) Home | NCIt Hierarchy | Sources | Help Suggest changes to this concept Tanibirumab (CUI C3490677) Add to Cart Table of Contents Terms & Properties Synonym Details Relationships By Source Terms & Properties Concept Unique Identifier (CUI): C3490677 NCI Thesaurus Code: C102877 (see NCI Thesaurus info) Semantic Type: Immunologic Factor Semantic Type: Amino Acid, Peptide, or Protein Semantic Type: Pharmacologic Substance NCIt Definition: A fully human monoclonal antibody targeting the vascular endothelial growth factor receptor 2 (VEGFR2), with potential antiangiogenic activity. Upon administration, tanibirumab specifically binds to VEGFR2, thereby preventing the binding of its ligand VEGF. This may result in the inhibition of tumor angiogenesis and a decrease in tumor nutrient supply. VEGFR2 is a pro-angiogenic growth factor receptor tyrosine kinase expressed by endothelial cells, while VEGF is overexpressed in many tumors and is correlated to tumor progression. PDQ Definition: A fully human monoclonal antibody targeting the vascular endothelial growth factor receptor 2 (VEGFR2), with potential antiangiogenic activity. Upon administration, tanibirumab specifically binds to VEGFR2, thereby preventing the binding of its ligand VEGF. This may result in the inhibition of tumor angiogenesis and a decrease in tumor nutrient supply. VEGFR2 is a pro-angiogenic growth factor receptor -
Distinctive Gene Expression Signatures in Rheumatoid Arthritis Synovial Tissue Fibroblast Cells: Correlates with Disease Activity
Genes and Immunity (2007) 8, 480–491 & 2007 Nature Publishing Group All rights reserved 1466-4879/07 $30.00 www.nature.com/gene ORIGINAL ARTICLE Distinctive gene expression signatures in rheumatoid arthritis synovial tissue fibroblast cells: correlates with disease activity CL Galligan1,2, E Baig1,2, V Bykerk3,4, EC Keystone3,4 and EN Fish1,2 1Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada; 2Department of Immunology, University of Toronto, Toronto, Ontario, Canada; 3The Rebecca MacDonald Centre for Arthritis and Autoimmune Diseases, Toronto, Ontario, Canada and 4Department of Medicine, University of Toronto, Toronto, Ontario, Canada Gene expression profiling of rheumatoid arthritis (RA) and osteoarthritis (OA) joint tissue samples provides a unique insight into the gene signatures involved in disease development and progression. Fibroblast-like synovial (FLS) cells were obtained from RA, OA and control trauma joint tissues (non-RA, non-OA) and RNA was analyzed by Affymetrix microarray. Thirty-four genes specific to RA and OA FLS cells were identified (Po0.05). HOXD10, HOXD11, HOXD13, CCL8 and LIM homeobox 2 were highly and exclusively expressed in RA and CLU, sarcoglycan-g, GPR64, POU3F3, peroxisome proliferative activated receptor-g and tripartite motif-containing 2 were expressed only in OA. The data also revealed expression heterogeneity for patients with the same disease. To address disease heterogeneity in RA FLS cells, we examined the effects of clinical disease parameters (Health Assessment Questionnaire (HAQ) score, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), rheumatoid factor (RF)) and drug therapies (methotrexate/prednisone) on RA FLS cell gene expression. Eight specific and unique correlations were identified: human leukocyte antigen (HLA)-DQA2 with HAQ score; Clec12A with RF; MAB21L2, SIAT7E, HAPLN1 and BAIAP2L1 with CRP level; RGMB and OSAP with ESR. -
Smoothened Variants Explain the Majority of Drug Resistance in Basal Cell Carcinoma
Article Smoothened Variants Explain the Majority of Drug Resistance in Basal Cell Carcinoma Graphical Abstract Authors Scott X. Atwood, Kavita Y. Sarin, ..., Anthony E. Oro, Jean Y. Tang Correspondence [email protected] (A.E.O.), [email protected] (J.Y.T.) In Brief Atwood et al. identify key SMO mutations that confer resistance to SMO inhibitors in basal cell carcinomas (BCC) and show that these mutants respond to aPKC-i/l or GLI2 inhibitors, providing potential approaches for treating BCCs resistant to SMO inhibitors. Highlights Accession Numbers d Functional SMO mutations are detected in the majority of GSE58377 SMO inhibitor-resistant BCCs d Resistance occurs by suppressing drug responsiveness and SMO autoinhibition d SMO mutants explain both intrinsic and acquired tumor resistance d Inhibition of aPKC-i/l or GLI2 bypasses SMO variants to suppress Hedgehog signaling Atwood et al., 2015, Cancer Cell 27, 342–353 March 9, 2015 ª2015 Elsevier Inc. http://dx.doi.org/10.1016/j.ccell.2015.02.002 Cancer Cell Article Smoothened Variants Explain the Majority of Drug Resistance in Basal Cell Carcinoma Scott X. Atwood,1,2 Kavita Y. Sarin,1,2 Ramon J. Whitson,1 Jiang R. Li,1 Geurim Kim,1 Melika Rezaee,1 Mina S. Ally,1 Jinah Kim,1 Catherine Yao,1 Anne Lynn S. Chang,1,3 Anthony E. Oro,1,3,* and Jean Y. Tang1,3,* 1Program in Epithelial Biology and Department of Dermatology, Stanford University School of Medicine, Stanford, CA 94305, USA 2Co-first author 3Co-senior author *Correspondence: [email protected] (A.E.O.), [email protected] (J.Y.T.) http://dx.doi.org/10.1016/j.ccell.2015.02.002 SUMMARY Advanced basal cell carcinomas (BCCs) frequently acquire resistance to Smoothened (SMO) inhibitors through unknown mechanisms. -
System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation
International Journal of Molecular Sciences Article System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation Nicolas Borisov 1,† , Yaroslav Ilnytskyy 2,3,†, Boseon Byeon 2,3,4,†, Olga Kovalchuk 2,3 and Igor Kovalchuk 2,3,* 1 Moscow Institute of Physics and Technology, 9 Institutsky lane, Dolgoprudny, Moscow Region 141701, Russia; [email protected] 2 Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; [email protected] (Y.I.); [email protected] (B.B.); [email protected] (O.K.) 3 Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada 4 Biomedical and Health Informatics, Computer Science Department, State University of New York, 2 S Clinton St, Syracuse, NY 13202, USA * Correspondence: [email protected] † First three authors contributed equally to this research. Abstract: There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis.