GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity

Total Page:16

File Type:pdf, Size:1020Kb

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity Article GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity Graphical Abstract Authors Sharon M. Louie, Elizabeth A. Grossman, Lisa A. Crawford, ..., Andrei Goga, Eranthie Weerapana, Daniel K. Nomura Correspondence [email protected] In Brief Using a reactivity-based chemoproteomic platform, Louie et al. have identified GSTP1 as a triple-negative breast cancer target that, when inhibited, impairs breast cancer pathogenicity through inhibiting GAPDH activity and downstream metabolism and signaling pathways. Highlights d We used chemoproteomics to profile metabolic drivers of breast cancer d GSTP1 is a novel triple-negative breast cancer-specific target d GSTP1 inhibition impairs triple-negative breast cancer pathogenicity d GSTP1 inhibition impairs GAPDH activity to affect metabolism and signaling Louie et al., 2016, Cell Chemical Biology 23, 1–12 May 19, 2016 ª 2016 Elsevier Ltd. http://dx.doi.org/10.1016/j.chembiol.2016.03.017 Please cite this article in press as: Louie et al., GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.03.017 Cell Chemical Biology Article GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity Sharon M. Louie,1 Elizabeth A. Grossman,1 Lisa A. Crawford,2 Lucky Ding,1 Roman Camarda,3 Tucker R. Huffman,1 David K. Miyamoto,1 Andrei Goga,3 Eranthie Weerapana,2 and Daniel K. Nomura1,* 1Departments of Chemistry and Nutritional Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA 2Department of Chemistry, Boston College, Chestnut Hill, MA 02467, USA 3Department of Cell and Tissue Biology and Medicine, University of California, San Francisco, San Francisco, CA 94143, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.chembiol.2016.03.017 SUMMARY subtypes that are correlated with heightened malignancy and poor prognosis remain poorly understood. Breast cancers possess fundamentally altered meta- Mortality from breast cancer is almost always attributed to bolism that fuels their pathogenicity. While many metastatic spread of the disease to other organs, thus pre- metabolic drivers of breast cancers have been iden- cluding resection as a treatment method. Unfortunately, con- tified, the metabolic pathways that mediate breast ventional chemotherapy fails to eradicate most human cancers, cancer malignancy and poor prognosis are less including aggressive breast cancers. Studies over the past well understood. Here, we used a reactivity-based decade have uncovered certain breast cancer types and cell types that are associated with poor prognosis, such as chemoproteomic platform to profile metabolic en- estrogen/progesterone/HER2 receptor-negative (triple-nega- zymes that are enriched in breast cancer cell types tive) breast cancers (TNBCs) or cancer stem/precursor cells linked to poor prognosis, including triple-negative that possess self-renewing and tumor-initiating capabilities, breast cancer (TNBC) cells and breast cancer cells epithelial-to-mesenchymal transition (EMT), poor prognosis, that have undergone an epithelial-mesenchymal and chemotherapy resistance within breast tumors (Dawson transition-like state of heightened malignancy. We et al., 2009; Dietze et al., 2015; Polyak and Weinberg, 2009). identified glutathione S-transferase Pi 1 (GSTP1) as While eliminating these breast cancer types is critical in combat- a novel TNBC target that controls cancer pathoge- ting breast cancer, there are currently few to no therapies that nicity by regulating glycolytic and lipid metabolism, target this malignant population of breast cancer cells. energetics, and oncogenic signaling pathways In this study, we used a reactivity-based chemoproteomic plat- through a protein interaction that activates glyceral- form to identify metabolic enzymes that are heightened in TNBC cells or upon induction of an EMT-like programming of height- dehyde-3-phosphate dehydrogenase activity. We ened malignancy in breast cancer cells. Through this profiling show that genetic or pharmacological inactivation effort, we identified glutathione S-transferase Pi 1 (GSTP1) as a of GSTP1 impairs cell survival and tumorigenesis critical metabolic driver that is heightened specifically in TNBCs in TNBC cells. We put forth GSTP1 inhibitors as a to control multiple critical nodes in cancer metabolism and novel therapeutic strategy for combatting TNBCs signaling pathways to drive breast cancer pathogenicity. through impairing key cancer metabolism and signaling pathways. RESULTS Profiling Dysregulated Metabolic Enzymes in TNBC INTRODUCTION Cells and CDH1 Knockdown Breast Cancer Cells To identify metabolic drivers of breast cancer pathogenicity in Breast cancers possess fundamentally altered metabolism that aggressive breast cancer cell types associated with malignancy drives their pathogenic features. Since Otto Warburg’s seminal and poor prognosis, we used a reactivity-based chemical prote- discovery in the 1920s that cancer cells have heightened glucose omic strategy to map cysteine and lysine reactivity in TNBC cells uptake and aerobic glycolysis, recent studies have identified and breast cancer cells with EMT-like features (Figure 1; Table many other biochemical alterations in cancer cells, including S1). Both TNBC cells and breast cancer cells that have under- heightened glutamine-dependent anaplerosis and de novo lipid gone EMT have been linked to heightened aggressiveness and biosynthesis, which serve as metabolic platforms for breast poor prognosis. Specifically, we wanted to (1) identify TNBC- cancer cells to generate biomass for cell division and metabo- specific metabolic enzyme targets by comparing a panel of lites that modulate cancer cell signaling, epigenetics, and path- four non-TNBC and five TNBC cell lines, and (2) identify upregu- ogenicity (Benjamin et al., 2012; Cantor and Sabatini, 2012; lated enzyme targets in MCF7 breast cancer cells upon knock- Pavlova and Thompson, 2016). While targeting dysregulated down of CDH1, a critical mediator of EMT and cell-cell adhesion. metabolism is a promising strategy for breast cancer treatment, We knocked down CDH1 in MCF7 cells with short-hairpin (sh) the metabolic pathways that drive pathogenicity in breast cancer oligonucleotides (shCDH1 cells) to induce an EMT-like state. Cell Chemical Biology 23, 1–12, May 19, 2016 ª 2016 Elsevier Ltd. 1 Please cite this article in press as: Louie et al., GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.03.017 ABClysine reactivity in non-TNBC cysteine reactivity significantly upregulated metabolic enzyme and TNBC cells in MCF7 shCDH1 cells targets in TNBC cells 100 metabolic enzymes metabolic enzymes * s non-TNBC 80 non-TNBC TNBC TNBC 60 al count r 40 * ct pe shControl shCDH1 s ZR751 HCC70 231MFP MCF10A MCF7 T47D MDA-MB-361 HCC1143 HCC38 MDA-MB-468 20 * GSTP1 ALDH1A3 PSAT1 LDHB ASS1 ENO3 AGPS PFKM ACAA2 CTH *** SOD2 ACOT7 AGPS SAMHD1 PYGL ALDH1L2 MTHFD2 DLAT PLOD1 0 GNE NCEH1 ACSL5 PSAT1 CTPS LDLR PNPLA6 3 ACOT9 GLS ACP6 DPYD ALDH3A2 ALDH2 T1 PLOD3 PSPH PC SGPL1 ADH5 CBR1 GCAT GMDS AT2 PGM1 PLD3 ALDH1A2 AK2 ALDH1A1 CPS1 ASS1 DHCR7 TXNRD2 ENPP1 GYS1 PLAA GSTP1 TYMP GSTP1 ACSL PSA AC ABHD12 CBS GSTK1 PLCG1 OAS3 PNPT1 RDH11 ADSL PPAT BCKDK C11orf54 DPYSL2 GSTM1 CTPS LDHAL6B MGST3 PNP SOAT1 ACSS2 AGPAT9 AKR1B1 AKR1B10 ADSL AKR1C3 CA2 CPPED1 DEGS1 PFAS DHPS DHRS7 ETFDH D significantly upregulated metabolic enzyme targets FAR1 ACADSB GALE GCLM GGCX GNPTAB ACLY IDI1 LPCAT2 MCAT SCP2 ME2 MPI MTAP in MCF7 shCDH1 cells NMT1 GSPT1 80 P4HA1 P4HA2 PCYT2 PDPR ESD PIP4K2C PLCG2 UGCG MAOA PRPSAP2 GLS ACAT2 PC ALDH1B1 CKB shControl OAT ACSL4 ts * NAMPT PHGDH IDH3B PHGDH GSTO1 C21orf33 ALDH1B1 PGLS OGT 60 shCDH1 ACADM CBR1 ASNS NSDHL MTHFD1L ADK SORD PYGB ADSS SUCLG2 oun GART ALDH7A1 UGP2 PLOD2 HMGCL ACAA1 OAT ACSL3 PYGM PFKP DLAT ACAT1 ACOT8 SCP2 ECHS1 40 OXCT1 PAFAH1B1 LSS LDHB ral c ILVBL GLRX3 SDHA CMAS t SDHB ACAD9 SHMT2 PPA1 GLUD1 * HPRT1 * ENO2 PDHA1 SHMT2 DHCR24 DCI * FDPS PYCR2 MDH1 HSD17B4 MAT2A PGLS ECH1 20 SUCLG1 ENO1 NNT PCMT1 spec NDUFA9 * AGPAT1 * FLAD1 * * TALDO1 GOT2 OGDH HADHA * * * GPD2 * ACO2 PDHB * PGK1 ** DLD * * * MTHFD1 USP9X GFPT1 * * * * HK2 * GANAB ACADVL IMPDH2 LDHA ACLY 0 PDHA1 TKT MTHFD1L ALG2 GLO1 GOT2 IMPDH2 ACAT2 ACACA 2 B ALDH18A1 ACAT1 M L2HGDH CMAS PKLR PDXK CS ALDH18A1 K HK1 DH PTGES2 PCK2 MCCC1 GSTM3 GLS MDH2 C OAT GLUD2 PFKL PKM2 ALDH9A1 HIBADH DLAT HSDL2 LDHB PPATCTPS ADSLPFAS IDH3A PFKAGPS ASNS PGD PAPSS1 SORD HMGCS1 CCBL2 PSAT1 C12orf5 MDH1 ALDH GSTP1PLCG1 SOAT1 ACAT1 ESD GPX1 PHG GSTZ1 NDUFV1 ACAA2 LDH1A2 PAFAH1B3 UGGT1 PDHB ACADSB HADHB GMPS AHCY A ALDH1B1 ACSL1 ALDH7A1 GPI CKMT1A IDH1 GMPS NDUFS1 HMOX2 FH MVK ALDOA DHTKD1 MTHFD1 HADH PDXDC1 ACO1 GAPDH SDHA PGAM1 ACSF2 SACM1L GOT1 MDH2 GSTP1 lysine reactivity in TNBC cells MAT2B E NDUFS2 PFKL FAH GSTO1 LTA4H PFKM HSD17B10 OXCT1 SHMT1 ACOX1 250 ISYNA1 ts 100 IVD TKT * MPST s PCYOX1 CKMT1A ALDOC ASMTL ALDH2 NANS COMT ALDH5A1 HADHA BCKDHA NME1 IDH2 HSDL2 200 FDXR 80 FASN NQO1 SORD ACO2 ALDH9A1 G6PD ALDH16A1 coun GRHPR HSD17B10 SPTLC2 CKB DECR1 count CPT1A PRDX1 AKR1A1 150 PTPLAD1 l 60 ALDH6A1 ALDH5A1 PAPSS2 GSR UGDH GSTM3 ral PTPLAD1 a LPCAT1 COASY t BLVRA r MCCC2 TXNDC5 FBP1 t ABAT c ACOT2 100 ACOX3 GFPT1 40 AKR1C1 AKR7A2 c ALDH4A1 ALOX15B PTGES2 APRT BCKDHB BDH1 GAPDH pe BLMH CMBL COMTD1 DBT ECHS1 pe DCXR s 50 DERA DHRS2 20 DLST PGD DUT s EPHX1 FAHD1 FAHD2A ALDH7A1
Recommended publications
  • Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
    Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent
    [Show full text]
  • Figure S1. DMD Module Network. the Network Is Formed by 260 Genes from Disgenet and 1101 Interactions from STRING. Red Nodes Are the Five Seed Candidate Genes
    Figure S1. DMD module network. The network is formed by 260 genes from DisGeNET and 1101 interactions from STRING. Red nodes are the five seed candidate genes. Figure S2. DMD module network is more connected than a random module of the same size. It is shown the distribution of the largest connected component of 10.000 random modules of the same size of the DMD module network. The green line (x=260) represents the DMD largest connected component, obtaining a z-score=8.9. Figure S3. Shared genes between BMD and DMD signature. A) A meta-analysis of three microarray datasets (GSE3307, GSE13608 and GSE109178) was performed for the identification of differentially expressed genes (DEGs) in BMD muscle biopsies as compared to normal muscle biopsies. Briefly, the GSE13608 dataset included 6 samples of skeletal muscle biopsy from healthy people and 5 samples from BMD patients. Biopsies were taken from either biceps brachii, triceps brachii or deltoid. The GSE3307 dataset included 17 samples of skeletal muscle biopsy from healthy people and 10 samples from BMD patients. The GSE109178 dataset included 14 samples of controls and 11 samples from BMD patients. For both GSE3307 and GSE10917 datasets, biopsies were taken at the time of diagnosis and from the vastus lateralis. For the meta-analysis of GSE13608, GSE3307 and GSE109178, a random effects model of effect size measure was used to integrate gene expression patterns from the two datasets. Genes with an adjusted p value (FDR) < 0.05 and an │effect size│>2 were identified as DEGs and selected for further analysis. A significant number of DEGs (p<0.001) were in common with the DMD signature genes (blue nodes), as determined by a hypergeometric test assessing the significance of the overlap between the BMD DEGs and the number of DMD signature genes B) MCODE analysis of the overlapping genes between BMD DEGs and DMD signature genes.
    [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]
  • Directed Differentiation of Human Embryonic Stem Cells Into Functional Dendritic Cells Through the Myeloid Pathway
    Directed Differentiation of Human Embryonic Stem Cells into Functional Dendritic Cells through the Myeloid Pathway This information is current as Igor I. Slukvin, Maxim A. Vodyanik, James A. Thomson, of September 24, 2021. Maryna E. Gumenyuk and Kyung-Dal Choi J Immunol 2006; 176:2924-2932; ; doi: 10.4049/jimmunol.176.5.2924 http://www.jimmunol.org/content/176/5/2924 Downloaded from References This article cites 57 articles, 33 of which you can access for free at: http://www.jimmunol.org/content/176/5/2924.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication by guest on September 24, 2021 *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2006 by The American Association of Immunologists All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Directed Differentiation of Human Embryonic Stem Cells into Functional Dendritic Cells through the Myeloid Pathway1 Igor I. Slukvin,2*†‡ Maxim A. Vodyanik,† James A. Thomson,†‡§¶ Maryna E. Gumenyuk,* and Kyung-Dal Choi* We have established a system for directed differentiation of human embryonic stem (hES) cells into myeloid dendritic cells (DCs).
    [Show full text]
  • Activation of PGK1 Under Hypoxic Conditions Promotes Glycolysis and Increases Stem Cell‑Like Properties and the Epithelial‑M
    INTERNATIONAL JOURNAL OF ONCOLOGY 57: 743-755, 2020 Activation of PGK1 under hypoxic conditions promotes glycolysis and increases stem cell‑like properties and the epithelial‑mesenchymal transition in oral squamous cell carcinoma cells via the AKT signalling pathway YADONG ZHANG1,2, HONGSHI CAI1,2, YAN LIAO1,2, YUE ZHU1,2, FANG WANG1,2 and JINSONG HOU1,2 1Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Guanghua School of Stomatology; 2Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong 510055, P.R. China Received January 13, 2020; Accepted April 22, 2020 DOI: 10.3892/ijo.2020.5083 Abstract. Although it has been previously documented that a study demonstrate that hypoxia can increase PGK1 expression, hypoxic environment can promote glycolysis and the malignant resulting in the promotion of glycolysis, enhancing stem progression of oral squamous cell carcinoma (OSCC) cell-like properties and EMT by activating AKT signalling in cells, the specific underlying mechanism remains unclear. OSCC. Phosphoglycerate kinase 1 (PGK1) has been previously reported to serve an important role in tumor metabolism. Introduction The aim of the present study was to investigate the effects of hypoxia and PGK1 on glycolysis, stem cell-like properties Oral squamous cell carcinoma (OSCC) is the most prevalent and epithelial-mesenchymal transition (EMT) in OSCC cells. malignant tumour in the oral and maxillofacial region (1). The Cell Counting Kit-8 assays were performed to examine tumor high incidence of oral cancer is attributed to physical, chemical cell viability under hypoxic conditions. Sphere formation, and biological factors. According to the statistics of American immunohistochemistry, western blotting, Transwell assays and Cancer Association, there were about 48,000 newly diagnosed mouse xenograft studies were performed to assess the biological cases of OSCC in 2016, which accounted for 3% of all new effects of PGK1.
    [Show full text]
  • Structures, Functions, and Mechanisms of Filament Forming Enzymes: a Renaissance of Enzyme Filamentation
    Structures, Functions, and Mechanisms of Filament Forming Enzymes: A Renaissance of Enzyme Filamentation A Review By Chad K. Park & Nancy C. Horton Department of Molecular and Cellular Biology University of Arizona Tucson, AZ 85721 N. C. Horton ([email protected], ORCID: 0000-0003-2710-8284) C. K. Park ([email protected], ORCID: 0000-0003-1089-9091) Keywords: Enzyme, Regulation, DNA binding, Nuclease, Run-On Oligomerization, self-association 1 Abstract Filament formation by non-cytoskeletal enzymes has been known for decades, yet only relatively recently has its wide-spread role in enzyme regulation and biology come to be appreciated. This comprehensive review summarizes what is known for each enzyme confirmed to form filamentous structures in vitro, and for the many that are known only to form large self-assemblies within cells. For some enzymes, studies describing both the in vitro filamentous structures and cellular self-assembly formation are also known and described. Special attention is paid to the detailed structures of each type of enzyme filament, as well as the roles the structures play in enzyme regulation and in biology. Where it is known or hypothesized, the advantages conferred by enzyme filamentation are reviewed. Finally, the similarities, differences, and comparison to the SgrAI system are also highlighted. 2 Contents INTRODUCTION…………………………………………………………..4 STRUCTURALLY CHARACTERIZED ENZYME FILAMENTS…….5 Acetyl CoA Carboxylase (ACC)……………………………………………………………………5 Phosphofructokinase (PFK)……………………………………………………………………….6
    [Show full text]
  • Metabolic Plasticity Is an Essential Requirement of Acquired Tyrosine Kinase Inhibitor Resistance in Chronic Myeloid Leukemia
    cancers Article Metabolic Plasticity Is an Essential Requirement of Acquired Tyrosine Kinase Inhibitor Resistance in Chronic Myeloid Leukemia Miriam G. Contreras Mostazo 1,2,3, Nina Kurrle 3,4,5, Marta Casado 6,7 , Dominik Fuhrmann 8, Islam Alshamleh 4,9, Björn Häupl 3,4,5, Paloma Martín-Sanz 7,10, Bernhard Brüne 5,8,11 , 3,4,5 4,9 3,4,5, 1,2,7,12, , Hubert Serve , Harald Schwalbe , Frank Schnütgen y , Silvia Marin * y 1,2,7,12, , and Marta Cascante * y 1 Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain; [email protected] 2 Institute of Biomedicine of University of Barcelona, 08028 Barcelona, Spain 3 Department of Medicine, Hematology/Oncology, University Hospital Frankfurt, Goethe-University, 60590 Frankfurt am Main, Germany; [email protected] (N.K.); [email protected] (B.H.); [email protected] (H.S.); [email protected] (F.S.) 4 German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; [email protected] (I.A.); [email protected] (H.S.) 5 Frankfurt Cancer Institute (FCI), Goethe University, 60590 Frankfurt am Main, Germany; [email protected] 6 Biomedicine Institute of Valencia, IBV-CSIC, 46010 Valencia, Spain; [email protected] 7 CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain; [email protected] 8 Institute of Biochemistry I, Faculty of
    [Show full text]
  • Platelet Isoform of Phosphofructokinase Promotes Aerobic Glycolysis and the Progression of Non‑Small Cell Lung Cancer
    MOLECULAR MEDICINE REPORTS 23: 74, 2021 Platelet isoform of phosphofructokinase promotes aerobic glycolysis and the progression of non‑small cell lung cancer FUAN WANG1, LING LI2 and ZHEN ZHANG3 1Department of Surgical Group, Medical College of Pingdingshan University, Pingdingshan, Henan 467000; 2Department of Respiratory Medicine, First People's Hospital of Jinan, Jinan, Shandong 250000; 3Department of Neurosurgery, Shandong Provincial Hospital, Jinan, Shandong 250012, P.R. China Received April 2, 2020; Accepted October 19, 2020 DOI: 10.3892/mmr.2020.11712 Abstract. The platelet isoform of phosphofructokinase by western blotting. Glucose uptake, lactate production and (PFKP) is a rate‑limiting enzyme involved in glycolysis that the adenosine trisphosphate/adenosine diphosphate ratio serves an important role in various types of cancer. The aim were measured using the corresponding kits. The results of of the present study was to explore the specific regulatory the present study demonstrated that PFKP expression was relationship between PFKP and non‑small cell lung cancer upregulated in NSCLC tissues and cells, and PFKP expression (NSCLC) progression. PFKP expression in NSCLC tissues was related to lymph node metastasis and histological grade. and corresponding adjacent tissues was detected using In addition, overexpression of PFKP inhibited cell apoptosis, reverse transcription‑quantitative polymerase chain reac‑ and promoted proliferation, migration, invasion and glycolysis tion (RT‑qPCR) and immunohistochemical analysis. PFKP of H1299 cells, whereas knockdown of PFKP had the opposite expression in human bronchial epithelial cells (16HBE) and effects. In conclusion, PFKP inhibited cell apoptosis, and NSCLC cells (H1299, H23 and A549) was also detected using promoted proliferation, migration, invasion and glycolysis of RT‑qPCR.
    [Show full text]
  • Toxicogenomic Approach to Impact Assessment of Whole Wastewater Effluents and Development of Effluent- Title Responsive Biomarker
    Toxicogenomic Approach to Impact Assessment of Whole Wastewater Effluents and Development of Effluent- Title Responsive Biomarker Author(s) 山村, 宏江 Citation 北海道大学. 博士(工学) 甲第11137号 Issue Date 2013-09-25 DOI 10.14943/doctoral.k11137 Doc URL http://hdl.handle.net/2115/77180 Type theses (doctoral) File Information Hiroe_Yamamura.pdf Instructions for use Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP Doctoral Thesis Toxicogenomic Approach to Impact Assessment of Whole Wastewater Effluents and Development of Effluent-Responsive Biomarker Hiroe Hara-Yamamura Acknowledgement My doctoral research works presented here cannot be completed without a numerous number of tangible and intangible supports from my peers, my supervisor, instructors, friends, and family. I would first express my deepest gratitude to Prof. Satoshi OKABE for his technical advices and crisp ideas which often came to break the deadlock of my research progress, and for continued provision of another chance to me, even in the least fruitful season. Indeed, my three years in Prof. OKABE’s lab was “luxuri- ous” time in my life with a lot of supports, encouragements, and chances. In addition, I appreciate both Prof. Daisuke SANO and Dr. Satoshi ISHII for offering their insights on my experimental design and data analysis as well as giving me words of encouragements from time to time. Also, thank you to Prof. Takashi KUSUI from Toyama Prefectural University for his assistance to bioassay techniques and valuable discussion dur- ing my 1st year and 2nd year evaluation presentations. Furthermore, I would like to express my gratitude to specific efforts kindly provide by: Mr. Kenzo Kudo, Prof.
    [Show full text]
  • PFKP Antibody A
    Revision 1 C 0 2 - t PFKP Antibody a e r o t S Orders: 877-616-CELL (2355) [email protected] Support: 877-678-TECH (8324) 2 1 Web: [email protected] 4 www.cellsignal.com 5 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source: UniProt ID: Entrez-Gene Id: WB, IP H Mk Endogenous 80 Rabbit Q01813 5214 Product Usage Information Application Dilution Western Blotting 1:1000 Immunoprecipitation 1:50 Storage Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA and 50% glycerol. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity PFKP Antibody detects endogenous levels of total PFKP protein. Species Reactivity: Human, Monkey Source / Purification Polyclonal antibodies are produced by immunizing animals with a synthetic peptide corresponding to residues near the amino terminus of human PFKP protein. Antibodies are purified by protein A and peptide affinity chromatography. Background Phosphofructokinase (PFK) catalyzes the phosphorylation of fructose-6-phosphate in glycolysis (1). There are three isozymes: muscle-type, liver-type, and platelet-type (2,3). Platelet-type phosphofructokinase (PFKP) is expressed in various cell types (4,5). Research studies have shown that genetic variations in PFKP are associated with individuals born small for gestational age that are prone to obesity and diabetes later in adulthood (6). 1. Mediavilla, D. et al. (2008) J Biochem 144, 235-44. 2. Eto, K. et al. (1994) Biochem Biophys Res Commun 198, 990-8. 3. Hannemann, A. et al.
    [Show full text]
  • Supplemental Information
    Supplemental Figures Supplemental Figure 1. 1 Supplemental Figure 1. (A) Fold changes in the mRNA expression levels of genes involved in glucose metabolism, pentose phosphate pathway (PPP), lipid biosynthesis and beta cell markers, in sorted beta cells from MIP-GFP adult mice (P25) compared to beta cells from neonatal MIP-GFP mice (P4). (N=1; pool of cells sorted from 6-8 mice in each group). (B) Fold changes in the mRNA expression levels of genes involved in glucose metabolism, pentose phosphate pathway (PPP), lipid biosynthesis and beta cell markers, in quiescent MEFs compared to proliferating MEFs. (N=1). (C) Expression Glut2 (red) in representative pancreatic sections from wildtype mice at indicated ages, by immunostaining. DAPI (blue) counter-stains the nuclei. (D) Bisulfite sequencing analysis for the Ldha and AldoB loci at indicated regions comparing sorted beta cells from P4 and P25 MIP-GFP mice (representative clones from N=3 mice). Each horizontal line with dots is an independent clone and 10 clones are shown here. These regions are almost fully DNA methylated (filled circles) in beta cells from P25 mice, but largely hypomethylated (open circles) in beta cells from P4 mice. For all experiments unless indicated otherwise, N=3 independent experiments. 2 Supplemental Figure 2. 3 Supplemental Figure 2. (A) Expression profile of Dnmt3a in representative pancreatic sections from wildtyype mice at indicated ages (P1 to 6 weeks) using immunostaining for Dnmt3a (red) and insulin (Ins; green). DAPI (blue) counter-stains the nuclei. (B) Representative pancreatic sections from 2 weeks old 3aRCTom-KO and littermate control 3aRCTom-Het animals, immunostained for Dnmt3a (red) and GFP (green).
    [Show full text]
  • Long Non‑Coding RNA HOTAIRM1‑1 Silencing in Cartilage Tissue Induces Osteoarthritis Through Microrna‑125B
    EXPERIMENTAL AND THERAPEUTIC MEDICINE 22: 933, 2021 Long non‑coding RNA HOTAIRM1‑1 silencing in cartilage tissue induces osteoarthritis through microRNA‑125b WEN‑BIN LIU1*, QI‑JIN FENG2*, GUI‑SHI LI3, PENG SHEN4, YA‑NAN LI5 and FU‑JIANG ZHANG1 1Department of Joint Surgery, Tianjin Hospital, Tianjin 300211; 2Department of Orthopedics, Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300150; 3Department of Joint Surgery, Yuhuangding Hospital, Yantai, Shandong 264000; 4Department of Rheumatology and Immunology, Tianjin First Center Hospital, Tianjin 300192; 5Department of Orthopedics, Tianjin Dongli Hospital, Tianjin 300300, P.R. China Received September 4, 2020; Accepted March 11, 2021 DOI: 10.3892/etm.2021.10365 Abstract. Aberrations in long noncoding RNA (lncRNA) Introduction expression have been recognized in numerous human diseases. In the present study, the of role the long noncoding Osteoarthritis (OA) is the most common type of arthritis, and RNA HOX antisense intergenic RNA myeloid 1 variant it affects >10% of the adult population (1). Physicians consider (HOTAIRM1‑1) in regulating the pathological progression the disease to be a degenerative disease that involves all the of osteoarthritis (OA) was investigated. The aberrant expres‑ tissues of the joint. In OA, cartilage within a joint begins to sion of HOTAIRM1‑1 in OA was demonstrated, but the break down, and the underlying bone begins to change (2). OA molecular mechanisms require further analysis. The aim of can induce substantial limb pain, stiffness, disability, and even the present study was to explore the function of miR‑125b loss of whole‑body mobility. OA occurs most frequently in the in modulating chondrocyte viability and apoptosis, and to hands, hips and knees, leading to difficulties in patients' daily address the functional association between HOTAIRM1‑1 activities (3).
    [Show full text]