613 Up-Regulated Genes in SOS Compared to Control Livers (FDR Adjusted P-Value ≤ 0.05) Fold- Chang Adjusted P-Value E (SOS P-Value Gene Symbol Name (SOS Vs

Total Page:16

File Type:pdf, Size:1020Kb

613 Up-Regulated Genes in SOS Compared to Control Livers (FDR Adjusted P-Value ≤ 0.05) Fold- Chang Adjusted P-Value E (SOS P-Value Gene Symbol Name (SOS Vs 11/29/2010 Rubbia-Brandt et al. 1 Table S1: 613 up-regulated genes in SOS compared to control livers (FDR adjusted p-value ≤ 0.05) Fold- Chang adjusted p-value e (SOS p-value Gene Symbol Name (SOS vs. vs. (SOS vs. Control) Contro Control) l) CCL20 chemokine (C-C motif) ligand 20 10.97 0.0001 0.0142 COL15A1 collagen, type XV, alpha 1 5.42 0.0000 0.0057 DLK1 delta-like 1 homolog (Drosophila) 4.30 0.0002 0.0171 CHI3L1 chitinase 3-like 1 (cartilage glycoprotein-39) 4.14 0.0001 0.0130 CRP C-reactive protein 4.01 0.0127 0.0339 GABBR1 gamma-aminobutyric acid (GABA) B receptor, 1 3.77 0.0001 0.0167 SLC38A1 solute carrier family 38, member 1 3.70 0.0001 0.0155 pleckstrin homology-like domain, family A, PHLDA2 3.66 0.0007 0.0284 member 2 RASD1 RAS, dexamethasone-induced 1 3.65 0.0001 0.0168 FABP4 fatty acid binding protein 4, adipocyte 3.64 0.0000 0.0094 SOD2 superoxide dismutase 2, mitochondrial 3.50 0.0003 0.0205 COL1A2 collagen, type I, alpha 2 3.48 0.0001 0.0157 HBB hemoglobin, beta 3.46 0.0002 0.0173 heat shock protein 90kDa alpha (cytosolic), class HSP90AB1 3.43 0.0032 0.0498 B member 1 VWF von Willebrand factor 3.07 0.0000 0.0110 serpin peptidase inhibitor, clade E (nexin, SERPINE1 3.04 0.0018 0.0390 plasminogen activator inhibitor type fatty acid binding protein 5 (psoriasis- FABP5 3.04 0.0020 0.0412 associated) COL1A1 collagen, type I, alpha 1 3.03 0.0002 0.0171 GDF15 growth differentiation factor 15 3.02 0.0030 0.0487 EGF-containing fibulin-like extracellular matrix EFEMP1 2.99 0.0000 0.0057 protein 1 methylenetetrahydrofolate dehydrogenase MTHFD2 2.99 0.0000 0.0071 (NADP+ dependent) 2, methenyltetrahydrof BIRC3 baculoviral IAP repeat-containing 3 2.99 0.0011 0.0323 CASP4 caspase 4, apoptosis-related cysteine peptidase 2.97 0.0003 0.0206 BCAT1 branched chain aminotransferase 1, cytosolic 2.93 0.0000 0.0087 COL4A1 collagen, type IV, alpha 1 2.92 0.0000 0.0057 Fc fragment of IgG, high affinity Ib, receptor FCGR1B 2.89 0.0001 0.0130 (CD64) IFI16 interferon, gamma-inducible protein 16 2.86 0.0008 0.0300 F2RL1 coagulation factor II (thrombin) receptor-like 1 2.84 0.0015 0.0365 GPNMB glycoprotein (transmembrane) nmb 2.84 0.0009 0.0306 RGS4 regulator of G-protein signaling 4 2.82 0.0000 0.0057 SLC7A1 solute carrier family 7 (cationic amino acid 2.81 0.0001 0.0159 11/29/2010 Rubbia-Brandt et al. 2 transporter, y+ system), member 1 CCL2 chemokine (C-C motif) ligand 2 2.80 0.0009 0.0304 RRM2 ribonucleotide reductase M2 2.78 0.0000 0.0057 LON peptidase N-terminal domain and ring LONRF2 2.77 0.0002 0.0197 finger 2 IL32 interleukin 32 2.72 0.0006 0.0276 NAMPT nicotinamide phosphoribosyltransferase 2.72 0.0016 0.0372 G0S2 G0/G1switch 2 2.69 0.0010 0.0319 THBS2 thrombospondin 2 2.68 0.0000 0.0057 TNC tenascin C 2.64 0.0017 0.0379 CXCR4 chemokine (C-X-C motif) receptor 4 2.63 0.0009 0.0304 tankyrase, TRF1-interacting ankyrin-related TNKS2 2.61 0.0006 0.0272 ADP-ribose polymerase 2 CTHRC1 collagen triple helix repeat containing 1 2.60 0.0001 0.0130 THBS1 thrombospondin 1 2.58 0.0017 0.0379 SGMS2 sphingomyelin synthase 2 2.57 0.0020 0.0412 FRMD6 FERM domain containing 6 2.55 0.0009 0.0304 TNFAIP8 tumor necrosis factor, alpha-induced protein 8 2.53 0.0000 0.0079 EXT1 exostoses (multiple) 1 2.53 0.0025 0.0451 HSPA6 heat shock 70kDa protein 6 (HSP70B') 2.52 0.0007 0.0288 LBP lipopolysaccharide binding protein 2.52 0.0010 0.0320 BAG3 BCL2-associated athanogene 3 2.49 0.0007 0.0277 TOP2A topoisomerase (DNA) II alpha 170kDa 2.47 0.0001 0.0130 GK glycerol kinase 2.45 0.0005 0.0240 HBA1 /// HBA2 hemoglobin, alpha 1 /// hemoglobin, alpha 2 2.41 0.0001 0.0143 STC1 stanniocalcin 1 2.41 0.0000 0.0121 CCT2 chaperonin containing TCP1, subunit 2 (beta) 2.39 0.0022 0.0430 ODC1 ornithine decarboxylase 1 2.38 0.0011 0.0330 ER degradation enhancer, mannosidase alpha- EDEM3 2.38 0.0020 0.0410 like 3 GLIS3 GLIS family zinc finger 3 2.38 0.0002 0.0187 ZNF295 zinc finger protein 295 2.37 0.0023 0.0439 aryl hydrocarbon receptor nuclear translocator- ARNTL2 2.37 0.0016 0.0376 like 2 LOXL4 lysyl oxidase-like 4 2.37 0.0022 0.0427 LOC387763 hypothetical protein LOC387763 2.36 0.0002 0.0171 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 2.36 0.0028 0.0471 methylenetetrahydrofolate dehydrogenase MTHFD1L 2.36 0.0001 0.0130 (NADP+ dependent) 1-like FCGR1A /// Fc fragment of IgG, high affinity Ia, receptor 2.35 0.0001 0.0159 FCGR1C (CD64) /// Fc fragment of IgG, hi procollagen-lysine, 2-oxoglutarate 5- PLOD2 2.34 0.0007 0.0277 dioxygenase 2 serpin peptidase inhibitor, clade B (ovalbumin), SERPINB1 2.34 0.0015 0.0365 member 1 11/29/2010 Rubbia-Brandt et al. 3 NCOA7 nuclear receptor coactivator 7 2.32 0.0004 0.0224 FNDC3B fibronectin type III domain containing 3B 2.29 0.0010 0.0314 CHSY3 chondroitin sulfate synthase 3 2.29 0.0012 0.0340 C12orf5 chromosome 12 open reading frame 5 2.28 0.0000 0.0130 CD69 CD69 molecule 2.28 0.0004 0.0230 GTP binding protein overexpressed in skeletal GEM 2.28 0.0011 0.0328 muscle CH25H cholesterol 25-hydroxylase 2.28 0.0011 0.0331 SOCS1 suppressor of cytokine signaling 1 2.27 0.0089 0.0491 PALLD palladin, cytoskeletal associated protein 2.26 0.0028 0.0469 DTL denticleless homolog (Drosophila) 2.26 0.0004 0.0226 TIMP1 TIMP metallopeptidase inhibitor 1 2.26 0.0000 0.0109 LOXL2 lysyl oxidase-like 2 2.25 0.0000 0.0057 serpin peptidase inhibitor, clade B (ovalbumin), SERPINB8 2.25 0.0012 0.0342 member 8 GPATCH4 G patch domain containing 4 2.25 0.0008 0.0300 LUM lumican 2.25 0.0004 0.0224 TOP1 topoisomerase (DNA) I 2.24 0.0019 0.0407 CTSC cathepsin C 2.24 0.0029 0.0478 potassium channel tetramerisation domain KCTD14 2.23 0.0028 0.0473 containing 14 PTGFRN prostaglandin F2 receptor negative regulator 2.23 0.0000 0.0124 VCAN versican 2.22 0.0026 0.0454 FBN1 fibrillin 1 2.22 0.0003 0.0221 MAP1B microtubule-associated protein 1B 2.22 0.0000 0.0083 ARL5B ADP-ribosylation factor-like 5B 2.22 0.0025 0.0449 COL4A2 collagen, type IV, alpha 2 2.21 0.0001 0.0141 STK17A serine/threonine kinase 17a 2.21 0.0006 0.0276 integrin, beta 1 (fibronectin receptor, beta ITGB1 2.20 0.0031 0.0492 polypeptide, antigen CD29 includes CSDA cold shock domain protein A 2.20 0.0003 0.0205 UDP-GlcNAc:betaGal beta-1,3-N- B3GNT5 2.19 0.0001 0.0159 acetylglucosaminyltransferase 5 VIM vimentin 2.18 0.0005 0.0241 pro-platelet basic protein (chemokine (C-X-C PPBP 2.17 0.0004 0.0235 motif) ligand 7) ATR ataxia telangiectasia and Rad3 related 2.17 0.0015 0.0367 IFNGR1 interferon gamma receptor 1 2.16 0.0006 0.0276 MKI67 (FHA domain) interacting nucleolar MKI67IP 2.16 0.0004 0.0236 phosphoprotein DDX21 DEAD (Asp-Glu-Ala-Asp) box polypeptide 21 2.15 0.0016 0.0372 SLC35C1 solute carrier family 35, member C1 2.15 0.0015 0.0365 GOLM1 golgi membrane protein 1 2.15 0.0004 0.0226 XAF1 XIAP associated factor 1 2.14 0.0008 0.0297 HSPA2 heat shock 70kDa protein 2 2.13 0.0003 0.0205 11/29/2010 Rubbia-Brandt et al. 4 interleukin 6 signal transducer (gp130, IL6ST 2.13 0.0120 0.0313 oncostatin M receptor) ANXA2 annexin A2 2.13 0.0001 0.0161 THBD thrombomodulin 2.13 0.0000 0.0130 apolipoprotein B mRNA editing enzyme, APOBEC3B 2.13 0.0003 0.0204 catalytic polypeptide-like 3B protein phosphatase 2, regulatory subunit B', PPP2R5E 2.13 0.0004 0.0235 epsilon isoform PBK PDZ binding kinase 2.13 0.0001 0.0141 TNFAIP3 tumor necrosis factor, alpha-induced protein 3 2.12 0.0006 0.0272 PLSCR1 phospholipid scramblase 1 2.12 0.0007 0.0287 DTNA dystrobrevin, alpha 2.12 0.0029 0.0478 NRP1 neuropilin 1 2.12 0.0014 0.0360 COL3A1 collagen, type III, alpha 1 2.12 0.0004 0.0223 LOC286434 hypothetical protein LOC286434 2.11 0.0007 0.0286 pregnancy-associated plasma protein A, PAPPA 2.11 0.0000 0.0065 pappalysin 1 DKC1 dyskeratosis congenita 1, dyskerin 2.11 0.0003 0.0221 NARG1 NMDA receptor regulated 1 2.10 0.0028 0.0471 TMEM165 transmembrane protein 165 2.09 0.0001 0.0153 CLEC7A C-type lectin domain family 7, member A 2.09 0.0032 0.0498 LOC100288985 hypothetical protein LOC100288985 2.09 0.0008 0.0292 structural maintenance of chromosomes flexible SMCHD1 2.09 0.0017 0.0384 hinge domain containing 1 karyopherin alpha 2 (RAG cohort 1, importin KPNA2 2.08 0.0001 0.0167 alpha 1) MINA MYC induced nuclear antigen 2.08 0.0016 0.0369 ETV6 ets variant 6 2.08 0.0013 0.0345 IER5 immediate early response 5 2.08 0.0002 0.0194 eukaryotic translation elongation factor 1 EEF1E1 2.07 0.0006 0.0265 epsilon 1 IGSF6 immunoglobulin superfamily, member 6 2.07 0.0021 0.0425 FAM169B family with sequence similarity 169, member B 2.06 0.0019 0.0402 LGALS3 lectin, galactoside-binding, soluble, 3 2.06 0.0007 0.0276 tumor necrosis factor receptor superfamily, TNFRSF10A 2.05 0.0007 0.0276 member 10a v-myc myelocytomatosis viral oncogene MYC 2.05 0.0025 0.0450 homolog (avian) EHBP1L1 EH domain binding protein 1-like 1 2.05 0.0001 0.0137 solute carrier family 6 (neurotransmitter SLC6A6 2.05 0.0000 0.0057 transporter, taurine), member 6 RAB27A RAB27A, member RAS oncogene family 2.05 0.0004 0.0229 GAS2L3 Growth arrest-specific 2 like 3 2.04 0.0001 0.0130 GJC1 gap junction protein, gamma 1, 45kDa 2.04 0.0000 0.0094 11/29/2010 Rubbia-Brandt et al.
Recommended publications
  • Strategies to Increase ß-Cell Mass Expansion
    This electronic thesis or dissertation has been downloaded from the King’s Research Portal at https://kclpure.kcl.ac.uk/portal/ Strategies to increase -cell mass expansion Drynda, Robert Lech Awarding institution: King's College London The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without proper acknowledgement. END USER LICENCE AGREEMENT Unless another licence is stated on the immediately following page this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ You are free to copy, distribute and transmit the work Under the following conditions: Attribution: You must attribute the work in the manner specified by the author (but not in any way that suggests that they endorse you or your use of the work). Non Commercial: You may not use this work for commercial purposes. No Derivative Works - You may not alter, transform, or build upon this work. Any of these conditions can be waived if you receive permission from the author. Your fair dealings and other rights are in no way affected by the above. Take down policy If you believe that this document breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 Strategies to increase β-cell mass expansion A thesis submitted by Robert Drynda For the degree of Doctor of Philosophy from King’s College London Diabetes Research Group Division of Diabetes & Nutritional Sciences Faculty of Life Sciences & Medicine King’s College London 2017 Table of contents Table of contents .................................................................................................
    [Show full text]
  • Hypoxia and Oxygen-Sensing Signaling in Gene Regulation and Cancer Progression
    International Journal of Molecular Sciences Review Hypoxia and Oxygen-Sensing Signaling in Gene Regulation and Cancer Progression Guang Yang, Rachel Shi and Qing Zhang * Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (G.Y.); [email protected] (R.S.) * Correspondence: [email protected]; Tel.: +1-214-645-4671 Received: 6 October 2020; Accepted: 29 October 2020; Published: 31 October 2020 Abstract: Oxygen homeostasis regulation is the most fundamental cellular process for adjusting physiological oxygen variations, and its irregularity leads to various human diseases, including cancer. Hypoxia is closely associated with cancer development, and hypoxia/oxygen-sensing signaling plays critical roles in the modulation of cancer progression. The key molecules of the hypoxia/oxygen-sensing signaling include the transcriptional regulator hypoxia-inducible factor (HIF) which widely controls oxygen responsive genes, the central members of the 2-oxoglutarate (2-OG)-dependent dioxygenases, such as prolyl hydroxylase (PHD or EglN), and an E3 ubiquitin ligase component for HIF degeneration called von Hippel–Lindau (encoding protein pVHL). In this review, we summarize the current knowledge about the canonical hypoxia signaling, HIF transcription factors, and pVHL. In addition, the role of 2-OG-dependent enzymes, such as DNA/RNA-modifying enzymes, JmjC domain-containing enzymes, and prolyl hydroxylases, in gene regulation of cancer progression, is specifically reviewed. We also discuss the therapeutic advancement of targeting hypoxia and oxygen sensing pathways in cancer. Keywords: hypoxia; PHDs; TETs; JmjCs; HIFs 1. Introduction Molecular oxygen serves as a co-factor in many biochemical processes and is fundamental for aerobic organisms to maintain intracellular ATP levels [1,2].
    [Show full text]
  • PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday, November 6 41
    American Society of Human Genetics 62nd Annual Meeting November 6–10, 2012 San Francisco, California PLATFORM ABSTRACTS Abstract Abstract Numbers Numbers Tuesday, November 6 41. Genes Underlying Neurological Disease Room 134 #196–#204 2. 4:30–6:30pm: Plenary Abstract 42. Cancer Genetics III: Common Presentations Hall D #1–#6 Variants Ballroom 104 #205–#213 43. Genetics of Craniofacial and Wednesday, November 7 Musculoskeletal Disorders Room 124 #214–#222 10:30am–12:45 pm: Concurrent Platform Session A (11–19): 44. Tools for Phenotype Analysis Room 132 #223–#231 11. Genetics of Autism Spectrum 45. Therapy of Genetic Disorders Room 130 #232–#240 Disorders Hall D #7–#15 46. Pharmacogenetics: From Discovery 12. New Methods for Big Data Ballroom 103 #16–#24 to Implementation Room 123 #241–#249 13. Cancer Genetics I: Rare Variants Room 135 #25–#33 14. Quantitation and Measurement of Friday, November 9 Regulatory Oversight by the Cell Room 134 #34–#42 8:00am–10:15am: Concurrent Platform Session D (47–55): 15. New Loci for Obesity, Diabetes, and 47. Structural and Regulatory Genomic Related Traits Ballroom 104 #43–#51 Variation Hall D #250–#258 16. Neuromuscular Disease and 48. Neuropsychiatric Disorders Ballroom 103 #259–#267 Deafness Room 124 #52–#60 49. Common Variants, Rare Variants, 17. Chromosomes and Disease Room 132 #61–#69 and Everything in-Between Room 135 #268–#276 18. Prenatal and Perinatal Genetics Room 130 #70–#78 50. Population Genetics Genome-Wide Room 134 #277–#285 19. Vascular and Congenital Heart 51. Endless Forms Most Beautiful: Disease Room 123 #79–#87 Variant Discovery in Genomic Data Ballroom 104 #286–#294 52.
    [Show full text]
  • Upregulation of Peroxisome Proliferator-Activated Receptor-Α And
    Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer Chih-Yang Wang, Ying-Jui Chao, Yi-Ling Chen, Tzu-Wen Wang, Nam Nhut Phan, Hui-Ping Hsu, Yan-Shen Shan, Ming-Derg Lai 1 Supplementary Table 1. Demographics and clinical outcomes of five patients with ampullary cancer Time of Tumor Time to Age Differentia survival/ Sex Staging size Morphology Recurrence recurrence Condition (years) tion expired (cm) (months) (months) T2N0, 51 F 211 Polypoid Unknown No -- Survived 193 stage Ib T2N0, 2.41.5 58 F Mixed Good Yes 14 Expired 17 stage Ib 0.6 T3N0, 4.53.5 68 M Polypoid Good No -- Survived 162 stage IIA 1.2 T3N0, 66 M 110.8 Ulcerative Good Yes 64 Expired 227 stage IIA T3N0, 60 M 21.81 Mixed Moderate Yes 5.6 Expired 16.7 stage IIA 2 Supplementary Table 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of an ampullary cancer microarray using the Database for Annotation, Visualization and Integrated Discovery (DAVID). This table contains only pathways with p values that ranged 0.0001~0.05. KEGG Pathway p value Genes Pentose and 1.50E-04 UGT1A6, CRYL1, UGT1A8, AKR1B1, UGT2B11, UGT2A3, glucuronate UGT2B10, UGT2B7, XYLB interconversions Drug metabolism 1.63E-04 CYP3A4, XDH, UGT1A6, CYP3A5, CES2, CYP3A7, UGT1A8, NAT2, UGT2B11, DPYD, UGT2A3, UGT2B10, UGT2B7 Maturity-onset 2.43E-04 HNF1A, HNF4A, SLC2A2, PKLR, NEUROD1, HNF4G, diabetes of the PDX1, NR5A2, NKX2-2 young Starch and sucrose 6.03E-04 GBA3, UGT1A6, G6PC, UGT1A8, ENPP3, MGAM, SI, metabolism
    [Show full text]
  • Table 2. Significant
    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
    [Show full text]
  • Mutant IDH, (R)-2-Hydroxyglutarate, and Cancer
    Downloaded from genesdev.cshlp.org on October 1, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW What a difference a hydroxyl makes: mutant IDH, (R)-2-hydroxyglutarate, and cancer Julie-Aurore Losman1 and William G. Kaelin Jr.1,2,3 1Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA; 2Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA Mutations in metabolic enzymes, including isocitrate whether altered cellular metabolism is a cause of cancer dehydrogenase 1 (IDH1) and IDH2, in cancer strongly or merely an adaptive response of cancer cells in the face implicate altered metabolism in tumorigenesis. IDH1 of accelerated cell proliferation is still a topic of some and IDH2 catalyze the interconversion of isocitrate and debate. 2-oxoglutarate (2OG). 2OG is a TCA cycle intermediate The recent identification of cancer-associated muta- and an essential cofactor for many enzymes, including tions in three metabolic enzymes suggests that altered JmjC domain-containing histone demethylases, TET cellular metabolism can indeed be a cause of some 5-methylcytosine hydroxylases, and EglN prolyl-4-hydrox- cancers (Pollard et al. 2003; King et al. 2006; Raimundo ylases. Cancer-associated IDH mutations alter the enzymes et al. 2011). Two of these enzymes, fumarate hydratase such that they reduce 2OG to the structurally similar (FH) and succinate dehydrogenase (SDH), are bone fide metabolite (R)-2-hydroxyglutarate [(R)-2HG]. Here we tumor suppressors, and loss-of-function mutations in FH review what is known about the molecular mechanisms and SDH have been identified in various cancers, in- of transformation by mutant IDH and discuss their im- cluding renal cell carcinomas and paragangliomas.
    [Show full text]
  • Subject Index
    Cambridge University Press 0521462924 - Amino Acids and Peptides G. C. Barrett and D. T. Elmore Index More information Subject index acetamidomalonate synthesis 123–4 in fossil dating 15 S-adenosyl--methionine 11, 12, 174, 181 in geological samples 15 alanine, N-acetyl 9 in Nature 1 structure 4 IR spectrometry 36 alkaloids, biosynthesis from amino acids 16–17 isolation from proteins 121 alloisoleucine 6 mass spectra 61 allosteric change 178 metabolism, products of 187 allothreonine 6 metal-binding properties 34 Alzheimer’s disease 14 NMR 41 amidation at peptide C-terminus 57, 94, 181 physicochemical properties 32 amides, cis–trans isomerism 20 protein 3 N-acylation 72 PTC derivatives 87 N-alkylation 72 quaternary ammonium salts 50 hydrolysis 57 reactions of amino group 49, 51 O-trimethylsilylation 72 reactions of carboxy group 49, 53 reduction to ␤-aminoalkanols 72 racemisation 56 amidocarbonylation in amino acid synthesis 125 routine spectrometry 35 amino acids, abbreviated names 7 Schöllkopf synthesis 127–8 acid–base properties 32 Schiff base formation 49 antimetabolites 200 sequence determination 97 et seq. as food additives 14 following selective chemical degradation 107 as neurotransmitters 17 following selective enzymic degradation 109 asymmetric synthesis 127 general strategy for 92 ␤- 17–18 identification of C-terminus 106–7 biosynthesis 121 identification of N-terminus 94, 97 biosynthesis from, of creatinine 183 by solid-phase methodology 100 of nitric oxide 186 by stepwise chemical degradation 97 of porphyrins 185 by use of dipeptidyl
    [Show full text]
  • A Few Experimental Suggestions Using Minerals to Obtain Peptides with a High Concentration of L-Amino Acids and Protein Amino Acids
    S S symmetry Hypothesis A Few Experimental Suggestions Using Minerals to Obtain Peptides with a High Concentration of L-Amino Acids and Protein Amino Acids Dimas A. M. Zaia 1,* and Cássia Thaïs B. V. Zaia 2 1 Laboratório de Química Prebiótica-LQP, Departamento de Química, Universidade Estadual de Londrina, CEP 86 057-970 Londrina, Brazil 2 Laboratório de Fisiologia Neuroendocrina e Metabolismo–LaFiNeM, Departamento de Ciências Fisiológicas, Universidade Estadual de Londrina, CEP 86 057-970 Londrina, Brazil; [email protected] * Correspondence: [email protected] Received: 9 October 2020; Accepted: 25 November 2020; Published: 10 December 2020 Abstract: The peptides/proteins of all living beings on our planet are mostly made up of 19 L-amino acids and glycine, an achiral amino acid. Arising from endogenous and exogenous sources, the seas of the prebiotic Earth could have contained a huge diversity of biomolecules (including amino acids), and precursors of biomolecules. Thus, how were these amino acids selected from the huge number of available amino acids and other molecules? What were the peptides of prebiotic Earth made up of? How were these peptides synthesized? Minerals have been considered for this task, since they can preconcentrate amino acids from dilute solutions, catalyze their polymerization, and even make the chiral selection of them. However, until now, this problem has only been studied in compartmentalized experiments. There are separate experiments showing that minerals preconcentrate amino acids by adsorption or catalyze their polymerization, or separate L-amino acids from D-amino acids. Based on the [GADV]-protein world hypothesis, as well as the relative abundance of amino acids on prebiotic Earth obtained by Zaia, several experiments are suggested.
    [Show full text]
  • Steroid-Dependent Regulation of the Oviduct: a Cross-Species Transcriptomal Analysis
    University of Kentucky UKnowledge Theses and Dissertations--Animal and Food Sciences Animal and Food Sciences 2015 Steroid-dependent regulation of the oviduct: A cross-species transcriptomal analysis Katheryn L. Cerny University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cerny, Katheryn L., "Steroid-dependent regulation of the oviduct: A cross-species transcriptomal analysis" (2015). Theses and Dissertations--Animal and Food Sciences. 49. https://uknowledge.uky.edu/animalsci_etds/49 This Doctoral Dissertation is brought to you for free and open access by the Animal and Food Sciences at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Animal and Food Sciences by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known.
    [Show full text]
  • Patient-Based Cross-Platform Comparison of Oligonucleotide Microarray Expression Profiles
    Laboratory Investigation (2005) 85, 1024–1039 & 2005 USCAP, Inc All rights reserved 0023-6837/05 $30.00 www.laboratoryinvestigation.org Patient-based cross-platform comparison of oligonucleotide microarray expression profiles Joerg Schlingemann1,*, Negusse Habtemichael2,*, Carina Ittrich3, Grischa Toedt1, Heidi Kramer1, Markus Hambek4, Rainald Knecht4, Peter Lichter1, Roland Stauber2 and Meinhard Hahn1 1Division of Molecular Genetics, Deutsches Krebsforschungszentrum, Heidelberg, Germany; 2Chemotherapeutisches Forschungsinstitut Georg-Speyer-Haus, Frankfurt am Main, Germany; 3Central Unit Biostatistics, Deutsches Krebsforschungszentrum, Heidelberg, Germany and 4Department of Otorhinolaryngology, Universita¨tsklinik, Johann-Wolfgang-Goethe-Universita¨t Frankfurt, Frankfurt, Germany The comparison of gene expression measurements obtained with different technical approaches is of substantial interest in order to clarify whether interplatform differences may conceal biologically significant information. To address this concern, we analyzed gene expression in a set of head and neck squamous cell carcinoma patients, using both spotted oligonucleotide microarrays made from a large collection of 70-mer probes and commercial arrays produced by in situ synthesis of sets of multiple 25-mer oligonucleotides per gene. Expression measurements were compared for 4425 genes represented on both platforms, which revealed strong correlations between the corresponding data sets. Of note, a global tendency towards smaller absolute ratios was observed when
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • CDH12 Cadherin 12, Type 2 N-Cadherin 2 RPL5 Ribosomal
    5 6 6 5 . 4 2 1 1 1 2 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 A A A A A A A A A A A A A A A A A A A A C C C C C C C C C C C C C C C C C C C C R R R R R R R R R R R R R R R R R R R R B , B B B B B B B B B B B B B B B B B B B , 9 , , , , 4 , , 3 0 , , , , , , , , 6 2 , , 5 , 0 8 6 4 , 7 5 7 0 2 8 9 1 3 3 3 1 1 7 5 0 4 1 4 0 7 1 0 2 0 6 7 8 0 2 5 7 8 0 3 8 5 4 9 0 1 0 8 8 3 5 6 7 4 7 9 5 2 1 1 8 2 2 1 7 9 6 2 1 7 1 1 0 4 5 3 5 8 9 1 0 0 4 2 5 0 8 1 4 1 6 9 0 0 6 3 6 9 1 0 9 0 3 8 1 3 5 6 3 6 0 4 2 6 1 0 1 2 1 9 9 7 9 5 7 1 5 8 9 8 8 2 1 9 9 1 1 1 9 6 9 8 9 7 8 4 5 8 8 6 4 8 1 1 2 8 6 2 7 9 8 3 5 4 3 2 1 7 9 5 3 1 3 2 1 2 9 5 1 1 1 1 1 1 5 9 5 3 2 6 3 4 1 3 1 1 4 1 4 1 7 1 3 4 3 2 7 6 4 2 7 2 1 2 1 5 1 6 3 5 6 1 3 6 4 7 1 6 5 1 1 4 1 6 1 7 6 4 7 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m
    [Show full text]