(DHFR) As a Modulator of B- Catenin/GSK3 Signaling
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View of HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-Negative Breast Resistance to Systemic Therapy in Patients with Breast Cancer
Wen et al. Cancer Cell Int (2018) 18:128 https://doi.org/10.1186/s12935-018-0625-9 Cancer Cell International PRIMARY RESEARCH Open Access Sulbactam‑enhanced cytotoxicity of doxorubicin in breast cancer cells Shao‑hsuan Wen1†, Shey‑chiang Su2†, Bo‑huang Liou3, Cheng‑hao Lin1 and Kuan‑rong Lee1* Abstract Background: Multidrug resistance (MDR) is a major obstacle in breast cancer treatment. The predominant mecha‑ nism underlying MDR is an increase in the activity of adenosine triphosphate (ATP)-dependent drug efux trans‑ porters. Sulbactam, a β-lactamase inhibitor, is generally combined with β-lactam antibiotics for treating bacterial infections. However, sulbactam alone can be used to treat Acinetobacter baumannii infections because it inhibits the expression of ATP-binding cassette (ABC) transporter proteins. This is the frst study to report the efects of sulbactam on mammalian cells. Methods: We used the breast cancer cell lines as a model system to determine whether sulbactam afects cancer cells. The cell viabilities in the present of doxorubicin with or without sulbactam were measured by MTT assay. Protein identities and the changes in protein expression levels in the cells after sulbactam and doxorubicin treatment were determined using LC–MS/MS. Real-time reverse transcription polymerase chain reaction (real-time RT-PCR) was used to analyze the change in mRNA expression levels of ABC transporters after treatment of doxorubicin with or without sulbactam. The efux of doxorubicin was measures by the doxorubicin efux assay. Results: MTT assay revealed that sulbactam enhanced the cytotoxicity of doxorubicin in breast cancer cells. The results of proteomics showed that ABC transporter proteins and proteins associated with the process of transcription and initiation of translation were reduced. -
SUPPLEMENTARY TABLES and FIGURE LEGENDS Supplementary
SUPPLEMENTARY TABLES AND FIGURE LEGENDS Supplementary Figure 1. Quantitation of MYC levels in vivo and in vitro. a) MYC levels in cell lines 6814, 6816, 5720, 966, and 6780 (corresponding to first half of Figure 1a in main text). MYC is normalized to tubulin. b) MYC quantitations (normalized to tubulin) for cell lines Daudi, Raji, Jujoye, KRA, KRB, GM, and 6780 corresponding to second half of Figure 1a. c) In vivo MYC quantitations, for mice treated with 0-0.5 ug/ml doxycycline in their drinking water. MYC is normalized to tubulin. d) Quantitation of changing MYC levels during in vitro titration, normalized to tubulin. e) Levels of Odc (normalized to tubulin) follow MYC levels in titration series. Supplementary Figure 2. Evaluation of doxycycline concentration in the plasma of mice treated with doxycycline in their drinking water. Luciferase expressing CHO cells (Tet- off) (Clonethech Inc) that is responsive to doxycycline by turning off luciferase expression was treated with different concentrations of doxycycline in culture. A standard curve (blue line) correlating luciferase activity (y-axis) with treatment of doxycycline (x- axis) was generated for the CHO cell in culture. Plasma from mice treated with different concentrations of doxycycline in their drinking water was separated and added to the media of the CHO cells. Luciferase activity was measured and plotted on the standard curve (see legend box). The actual concentration of doxycycline in the plasma was extrapolated for the luciferase activity measured. The doxycycline concentration 0.2 ng/ml measured in the plasma of mice correlates with 0.05 μg/ml doxycycline treatment in the drinking water of mice, the in vivo threshold for tumor regression. -
Intratumoral Injection of SYNB1891, a Synthetic Biotic Medicine Designed
Intratumoral injection of SYNB1891 A Synthetic Biotic medicine designed to activate the innate immune system. Therapy demonstrates target engagement in humans including intratumoral STING activation. Janku F, MD Anderson Cancer Center; Luke JJ, UPMC Hillman Cancer Center; Brennan AM, Synlogic; Riese RJ, Synlogic; Varterasian M, Pharmaceutical Consultant; Kuhn K, Synlogic; Sokolovska A, Synlogic; Strauss J, Mary Crowley Cancer Research Presented by Filip Janku, MD, PhD Study supported by Synlogic, Inc American Association for Cancer Research (AACR) April 2021 Introduction and Methods SYNB1891 Strain Phase 1 First-in-Human Clinical Trial • Live, modified strain of the probiotic E. coli • Enrolling patients with refractory advanced solid Nissle engineered to produce cyclic tumors or lymphoma dinucleotides (CDN) under hypoxia leading to stimulator of interferon genes (STING)- • Intratumoral (IT) injection of SYNB1891 on Days activation 1, 8 and 15 of the first 21-day cycle and then on Day 1 of each subsequent cycle. • Preferentially taken up by phagocytic antigen- presenting cells in tumors, activating • Dose escalation planned across 7 cohorts (1x106 complementary innate immune pathways – 1x109 live cells) with Arm 1 consisting of (direct CDN STING activation; cGAS-mediated SYNB1891 as monotherapy, and Arm 2 in STING activation and TLR4/MyD88 activation by combination with atezolizumab the bacterial chassis) SYNB1891 was safe and well-tolerated in heterogenous population Nov 2020: Interim Analysis IA Updated through 15 Mar 2021 15 Mar 2021: -
The Role of Dubs in the Post-Translational Control of Cell Migration
Essays in Biochemistry (2019) 63 579–594 https://doi.org/10.1042/EBC20190022 Review Article The role of DUBs in the post-translational control of cell migration Guillem Lambies1,2, Antonio Garc´ıade Herreros1,2 and V´ıctor M. D´ıaz1,2,3 1Programa de Recerca en Cancer,` Institut Hospital del Mar d’Investigacions Mediques` (IMIM), Unidad Asociada CSIC, Barcelona, Spain; 2Departament de Ciencies` Experimentals i de la Salut, Universitat Pompeu Fabra (UPF), Barcelona, Spain; 3Faculty of Medicine and Health Sciences, International University of Catalonia, Sant Cugat del Valles,` Barcelona, Spain Downloaded from https://portlandpress.com/essaysbiochem/article-pdf/63/5/579/859061/ebc-2019-0022c.pdf by guest on 05 November 2019 Correspondence: V.M. Dıaz´ ([email protected])orA.Garcıa´ de Herreros ([email protected]) Cell migration is a multifactorial/multistep process that requires the concerted action of growth and transcriptional factors, motor proteins, extracellular matrix remodeling and proteases. In this review, we focus on the role of transcription factors modulat- ing Epithelial-to-Mesenchymal Transition (EMT-TFs), a fundamental process supporting both physiological and pathological cell migration. These EMT-TFs (Snail1/2, Twist1/2 and Zeb1/2) are labile proteins which should be stabilized to initiate EMT and provide full mi- gratory and invasive properties. We present here a family of enzymes, the deubiquitinases (DUBs) which have a crucial role in counteracting polyubiquitination and proteasomal degra- dation of EMT-TFs after their induction by TGFβ, inflammatory cytokines and hypoxia. We also describe the DUBs promoting the stabilization of Smads, TGFβ receptors and other key proteins involved in transduction pathways controlling EMT. -
Functional Gene Clusters in Global Pathogenesis of Clear Cell Carcinoma of the Ovary Discovered by Integrated Analysis of Transcriptomes
International Journal of Environmental Research and Public Health Article Functional Gene Clusters in Global Pathogenesis of Clear Cell Carcinoma of the Ovary Discovered by Integrated Analysis of Transcriptomes Yueh-Han Hsu 1,2, Peng-Hui Wang 1,2,3,4,5 and Chia-Ming Chang 1,2,* 1 Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan; [email protected] (Y.-H.H.); [email protected] (P.-H.W.) 2 School of Medicine, National Yang-Ming University, Taipei 112, Taiwan 3 Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan 4 Department of Medical Research, China Medical University Hospital, Taichung 440, Taiwan 5 Female Cancer Foundation, Taipei 104, Taiwan * Correspondence: [email protected]; Tel.: +886-2-2875-7826; Fax: +886-2-5570-2788 Received: 27 April 2020; Accepted: 31 May 2020; Published: 2 June 2020 Abstract: Clear cell carcinoma of the ovary (ovarian clear cell carcinoma (OCCC)) is one epithelial ovarian carcinoma that is known to have a poor prognosis and a tendency for being refractory to treatment due to unclear pathogenesis. Published investigations of OCCC have mainly focused only on individual genes and lack of systematic integrated research to analyze the pathogenesis of OCCC in a genome-wide perspective. Thus, we conducted an integrated analysis using transcriptome datasets from a public domain database to determine genes that may be implicated in the pathogenesis involved in OCCC carcinogenesis. We used the data obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) DataSets. We found six interactive functional gene clusters in the pathogenesis network of OCCC, including ribosomal protein, eukaryotic translation initiation factors, lactate, prostaglandin, proteasome, and insulin-like growth factor. -
Correction1 4784..4785
Correction Correction: PCI-24781 Induces Caspase and Reactive Oxygen Species-Dependent Apoptosis In the article on PCI-24781 induces caspase and reactive oxygen species-dependent apoptosis published in the May 15, 2009 issue of Clinical Cancer Research, there was an error in Table 1. Down-regulated genes were incorrectly labeled as up-regulated genes. The correct table appears here. Bhalla S, Balasubramanian S, David K, et al. PCI-24781 induces caspase and reactive oxygen species-dependent apoptosis through NF-nB mechanisms and is synergistic with bortezomib in lymphoma cells. Clin Cancer Res 2009;15:3354–65. Table 1. Selected genes from expression analysis following 24-h treatment with PCI-24781, bortezomib, or the combination (in Ramos cells) Accn # Down-regulated genes 0.25 Mmol/L PCI/3 nmol/L Bor Name PCI-24781 Bortezomib Combination* Cell cycle-related NM_000075 Cyclin-dependent kinase 4 (CDK4) 0.49 0.83 0.37 NM_001237 Cyclin A2 (CCNA2) 0.43 0.87 0.37 NM_001950 E2F transcription factor 4, p107/p130-binding (E2F4) 0.48 0.79 0.40 NM_001951 E2F transcription factor 5, p130-binding (E2F5) 0.46 0.98 0.43 NM_003903 CDC16 cell division cycle 16 homolog (S cerevisiae) (CDC16) 0.61 0.78 0.43 NM_031966 Cyclin B1 (CCNB1) 0.55 0.90 0.43 NM_001760 Cyclin D3 (CCND3) 0.48 1.02 0.46 NM_001255 CDC20 cell division cycle 20 homolog (S cerevisiae; CDC20) 0.61 0.82 0.46 NM_001262 Cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4; CDKN2C) 0.61 1.15 0.56 NM_001238 Cyclin E1 (CCNE1) 0.56 1.05 0.60 NM_001239 Cyclin H (CCNH) 0.74 0.90 0.64 NM_004701 -
Genome-Wide Transcript and Protein Analysis Reveals Distinct Features of Aging in the Mouse Heart
bioRxiv preprint doi: https://doi.org/10.1101/2020.08.28.272260; this version posted April 21, 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-NC-ND 4.0 International license. Genome-wide transcript and protein analysis reveals distinct features of aging in the mouse heart Isabela Gerdes Gyuricza1, Joel M. Chick2, Gregory R. Keele1, Andrew G. Deighan1, Steven C. Munger1, Ron Korstanje1, Steven P. Gygi3, Gary A. Churchill1 1The Jackson Laboratory, Bar Harbor, Maine 04609 USA; 2Vividion Therapeutics, San Diego, California 92121, USA; 3Harvard Medical School, Boston, Massachusetts 02115, USA Corresponding author: [email protected] Key words for online indexing: Heart Aging Transcriptomics Proteomics eQTL pQTL Stoichiometry ABSTRACT Investigation of the molecular mechanisms of aging in the human heart is challenging due to confounding factors, such as diet and medications, as well limited access to tissues. The laboratory mouse provides an ideal model to study aging in healthy individuals in a controlled environment. However, previous mouse studies have examined only a narrow range of the genetic variation that shapes individual differences during aging. Here, we analyzed transcriptome and proteome data from hearts of genetically diverse mice at ages 6, 12 and 18 months to characterize molecular changes that occur in the aging heart. Transcripts and proteins reveal distinct biological processes that are altered through the course of natural aging. Transcriptome analysis reveals a scenario of cardiac hypertrophy, fibrosis, and reemergence of fetal gene expression patterns. -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................ -
I FOUR JOINTED BOX ONE, a NOVEL PRO-ANGIOGENIC PROTEIN IN
FOUR JOINTED BOX ONE, A NOVEL PRO-ANGIOGENIC PROTEIN IN COLORECTAL CARCINOMA. BY Nicole Theresa Al-Greene Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY In Cell and Developmental Biology. December, 2013 Nashville Tennessee Approved: R. Daniel Beauchamp Susan Wente James Goldenring Albert Reynolds i DEDICATION To my parents, Karen and John, who have helped me in every way possible, every single day. ii ACKNOWLEDGMENTS. Funding for this work was supported by grants DK052334, CA069457, The GI Cancer SPORE, GM088822, the VICC, the Clinical and Translational Science Award (NCRR/NIH UL1RR024975), the DDRC (P30DK058404), and the Cooperative Human Tissue Network (UO1CA094664) and U01CA094664. I was lucky enough to be allowed to perform research as an undergraduate in the lab of Ken Belanger. I will be forever grateful for that opportunity that sparked my love of research. Equally important was my time as a technician in Len Zon’s lab where I confirmed the fact that I needed to go graduate school and earn my degree. During my time at Vanderbilt I have been helped by so many individuals, and the collaborative nature of everyone I have met with is truly an amazing aspect of the research community here. I am especially thankful for all the technical help and insightful conversations I have had with Natasha Deane, Anna Means, Claudia Andl, Tanner Freeman, Connie Weaver, Keeli Lewis, Jalal Hamaamen, Jenny Zi, John Neff, Christian Kis, Andries Zjistra, Trennis Palmer, Joseph Roland, and Lynn LaPierre. -
Differential Network As an Indicator of Osteoporosis with Network Entropy
328 EXPERIMENTAL AND THERAPEUTIC MEDICINE 16: 328-332, 2018 Differential network as an indicator of osteoporosis with network entropy LILI MA, HONGMEI DU and GUANGDONG CHEN Department of Orthopaedics, Hebei Cangzhou Central Hospital, Cangzhou, Hebei 061001, P.R. China Received October 20, 2017; Accepted May 10, 2018 DOI: 10.3892/etm.2018.6169 Abstract. Osteoporosis is a common skeletal disorder charac- of osteoporosis (2,3). It is regarded that an increase of PBM terized by a decrease in bone mass and density. The peak bone by one standard deviation would reduce the fracture risk by mass (PBM) is a significant determinant of osteoporosis. To 50% (4). gain insights into the indicating effect of PBM to osteoporosis, Peripheral blood monocytes can serve as early precursors of this study focused on characterizing the PBM networks and osteoclasts (5-7). A growing body of literature has explored that identifying key genes. One biological data set with 12 mono- blood monocytes deliver many kinds of factors for bone metab- cyte low PBM samples and 11 high PBM samples was derived olism, such as interleukin-1 and tumor necrosis factor-α (8). to construct protein-protein interaction networks (PPINs). Osteoclasts in peripheral skeleton (9) and the central skeleton Based on clique-merging, module-identification algorithm was come from circulating monocytes (10). Substantial research used to identify modules from PPINs. The systematic calcu- has focused on the effect of circulating monocytes on patho- lation and comparison were performed to test whether the genesis of osteoporosis in young and middle aged adults. network entropy can discriminate the low PBM network from Research in systems biology has shown that variety in the high PBM network. -
Stem Cells Ap7609a Protein Synthesis 17 % Protein Synthesis Protein Folding 4 % F
Protein Survey STEM2PhD, J.Mountzouris 1PhD,CELL T.Gilliam 1 & C.Wu 1PhD www.abcepta.com (1) Abcepta Inc., 10320 Camino Santa Fe, Ste G, San Diego, CA 92121 (2) Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037 Distribution total transcription factors Apoptosis chromatin regulators 1 % Selected Abgent Products Surface antigens cell surface receptors 1 % Figure Target Tissue/Cell line Cat# signaling ligands DNA repair ECM and adhesion 2 % A. NANOG Human testis AP1486c A.A. BB.. cytoskeleton Protein folding SOX9 Human prostate carcinoma AP1409a RNA binding 2 % B. intracellular signaling Cell cycle C. ALDH1A1, RalDH1 Human hepatocarcinoma AP1465c DNA repair 2 % RNA binding proteins cell cycle 3 % D. PIWIL1, HIWI Human testis AP2731a Chromatin regulators apoptosis 3 % E. Eph4A, SEK, HEK8 Human stem cells AP7609a protein synthesis 17 % Protein synthesis protein folding 4 % F. Glypican-3 ,GPC3 Human liver carcinoma (HepG2) AP6337a protein processing Ligands CC.. DD.. 4 % G. KITLG, cKit ligand Human kidney (293) AP1484b metabolism Cyoskeletal proteins transport 4 % H. DRAGON, RGMB Human bone marrow (K562) AP1421b 0% 20% 40% 60% 80% 100% Protein processing TERT, Telomerase Human T cell leukemia (Jurkat) AP1410c I. HSC-LT HSC HSC-ST HSC/MPP 14 % J. SOX9 Human liver carcinoma (HepG2) AP1409c Distribution of gene products between HSC subtypes (Ref. 11,12) K. LMX1alpha HL60 AP1426c E.E. FF.. 7 % L. LIN28B Human bone marrow (K562) AP1485c Lin– Lin– Lin– M. SAE1, UBLE1A Mouse lung tissue lysate AP2011b SCA1 + SCA1 + SCA1 + KIT+ KIT+ KIT+ N. KIT, CD117, SCFR Mouse pancreas lysate AP7656b Thy1.1low Thy1.1low Thy1.1– FLT3– FLT3– FLT3+ Long-term HSC-LT HSC-ST MPP self-renewal potential Intracellular signaling + SP+ CD34 CD34 + low N-cad+ CD11b CD11b low Transcription factors TIE2+ CD48 + 13 % CD38 + MYC hi Metabolism CD150 + Rhohi 10 % Endoglin+ CD4 low Cell surface receptors MYC low Transporters 7 % Rholow ECM/cell adhesion 7 % G. -
Co-Expression Network of Neural-Differentiation Genes Shows
Maschietto et al. BMC Medical Genomics (2015) 8:23 DOI 10.1186/s12920-015-0098-9 RESEARCH ARTICLE Open Access Co-expression network of neural-differentiation genes shows specific pattern in schizophrenia Mariana Maschietto1,2†, Ana C Tahira1,2†, Renato Puga3, Leandro Lima4, Daniel Mariani4, Bruna da Silveira Paulsen5, Paulo Belmonte-de-Abreu6, Henrique Vieira4, Ana CV Krepischi7, Dirce M Carraro8, Joana A Palha9,10, Stevens Rehen5,11 and Helena Brentani1,2,12,13* Abstract Background: Schizophrenia is a neurodevelopmental disorder with genetic and environmental factors contributing to its pathogenesis, although the mechanism is unknown due to the difficulties in accessing diseased tissue during human neurodevelopment. The aim of this study was to find neuronal differentiation genes disrupted in schizophrenia and to evaluate those genes in post-mortem brain tissues from schizophrenia cases and controls. Methods: We analyzed differentially expressed genes (DEG), copy number variation (CNV) and differential methylation in human induced pluripotent stem cells (hiPSC) derived from fibroblasts from one control and one schizophrenia patient and further differentiated into neuron (NPC). Expression of the DEG were analyzed with microarrays of post-mortem brain tissue (frontal cortex) cohort of 29 schizophrenia cases and 30 controls. A Weighted Gene Co-expression Network Analysis (WGCNA) using the DEG was used to detect clusters of co-expressed genes that werenon-conserved between adult cases and controls brain samples. Results: We identified methylation alterations potentially involved with neuronal differentiation in schizophrenia, which displayed an over-representation of genes related to chromatin remodeling complex (adjP = 0.04). We found 228 DEG associated with neuronal differentiation.