DOCSLIB.ORG

KEGGID Term Gene 4612 Antigen Processing and HLA-DRB1, HLA

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
KEGGID Term Gene 4612 Antigen Processing and HLA-DRB1, HLA KEGGID Term Gene 4612 Antigen processingHLA-DRB1, and presentation HLA-DRB5, HSPA4, KLRC3 4150 mTOR signalingINS, pathway RPS6KA2, ULK3 270 Cysteine and methionineMAT1A, MPST, metabolism SMS, TST 4940 Type I diabetesHLA-DRB1, mellitus HLA-DRB5, INS 330 Arginine and prolineASS1, SMSmetabolism 4122 Sulfur relay systemMPST, TST 4140 Regulation of autophagyINS, ULK3 5150 StaphylococcusHLA-DRB1, aureus infection HLA-DRB5 5310 Asthma HLA-DRB1, HLA-DRB5 5330 Allograft rejectionHLA-DRB1, HLA-DRB5 5332 Graft-versus-hostHLA-DRB1, disease HLA-DRB5 symbol refSeq chrom strandtxStart-txEndentrezId AADACL4 NM_001013630chr1 + 12627152-12649684343066 ABCA13 NM_152701 chr7 + 48208388-48657637154664 ABCA17P NR_003574 chr16 + 2330923-2416701650655 ABCA3 NM_001089 chr16 - 2265879-233074821 ABCC11 NM_145186 chr16 - 46758322-4682658985320 ABHD11 NR_026912 chr7 - 72788362-7279112683451 ABHD4 NM_022060 chr14 + 22136986-2215110563874 ACIN1 NM_001164814,chr14 NM_014977, NM_001164817, NM_001164815- 22597613-22634663,22985 22597613-22610619 ACP1 NR_024080 chr2 + 254868-26828252 ACSF3 NR_023316, NM_174917,chr16 NM_001127214, NM_001243279+ 87687717-87749672197322 ADAMTS8 NM_007037 chr11 - 129780027-12980374911095 ADAMTSL5 NM_213604 chr19 - 1456016-1464188339366 ADD1 NM_176801, NM_014189,chr4 NM_014190, NM_001119 + 2815381-2901600118 ADRBK1 NM_001619 chr11 + 66790480-66810605156 AHRR NM_020731, NM_001242412chr5 + 357290-49140557491 AKNA NM_030767 chr9 - 116136254-11619650680709 AMFR NM_001144 chr16 - 54952864-55016945267 AMOTL2 NM_016201 chr3 - 135556879-13557609651421 AMZ1 NM_133463 chr7 + 2685688-2721595155185 ANKRD2 NM_001129981,chr10 NM_020349 + 99322245-9933363126287 ANXA9 NM_003568 chr1 + 149221122-1492347388416 AP3D1 NM_003938 chr19 - 2051993-21025568943 ARHGAP8 NM_181335 chr22 + 43527101-4363732823779 ARHGEF10L NM_018125, NM_001011722chr1 + 17738916-17896957,55160 17779634-17896957 ARHGEF3 NM_001128615chr3 - 56736485-5708837650650 ARL3 NM_004311 chr10 - 104423473-104464180403 ASB2 NM_016150, NM_001202429chr14 - 93470251-93493520,51676 93470251-93512829 ASS1 NM_000050, NM_054012chr9 + 132309914-132366482445 ATG16L2 NM_033388 chr11 + 72203098-7221832889849 ATG4D NM_032885 chr19 + 10515646-1052509484971 ATP6AP2 NM_005765 chrX + 40325159-4035083210159 ATP6V1G1 NM_004888 chr9 + 116389814-1164009739550 BASP1 NM_006317 chr5 + 17270749-1732994310409 BATF NM_006399 chr14 + 75058536-7508308710538 BDKRB1 NM_000710 chr14 + 95792299-95800853623 BET3L NM_001139444chr6 - 116924343-1169734661E+08 BFAR NM_016561 chr16 + 14634168-1467059451283 BIRC7 NM_022161 chr20 + 61337720-6134229979444 BLVRB NM_000713 chr19 - 45645530-45663565645 BRAT1 NM_152743 chr7 - 2543969-2561918221927 BRI3BP NM_080626 chr12 + 124044146-124076302140707 BSG NM_198591, NM_001728chr19 + 522324-534493,682 523536-534493 C14orf129 NM_016472 chr14 + 95915774-9592331651527 C14orf159 NM_001102366,chr14 NM_024952, NM_001102369, NM_001102368,+ 90650109-90761456,80017 NM_001102367 90650731-90761456 C19orf33 NM_033520 chr19 + 43486643-4348748664073 C1orf106 NM_018265 chr1 + 199127249-19915148755765 C1orf151-NBL1NM_001204089,chr1 NM_001204088 + 19796057-198575361E+08 C1QTNF6 NM_182486 chr22 - 35906151-35914276114904 C2CD2 NM_015500 chr21 - 42178287-4224706825966 C3orf19 NM_016474 chr3 + 14668256-1468917051244 C3orf20 NM_001184958chr3 + 14691609-1478954784077 C3orf45 NM_153215 chr3 + 50291521-50300549132228 C4orf10 NR_015453 chr4 + 2907075-2922592317648 C4orf42 NR_033339 chr4 + 1233227-123679592070 C6orf81 NM_145028 chr6 + 35812836-35824663221481 C7orf65 NM_001123065chr7 + 47661366-47667771401335 C8orf31 NM_173687 chr8 + 144192053-144207095286122 CACNA1D NM_001128839,chr3 NM_001128840, NM_000720 + 53504070-53821532776 CACNA2D3 NM_018398 chr3 + 54131732-5508362455799 CAP2 NM_006366 chr6 + 17501714-1766600210486 CARS NM_001751, NM_001194997,chr11 NR_036542, NM_001014437,- 2978727-3035257 833NM_139273 CASP7 NM_033340 chr10 + 115428924-115480654840 CCDC151 NM_145045 chr19 - 11392271-11406980115948 CCDC159 NM_001080503chr19 + 11318180-11326620126075 CCDC40 NM_017950, NM_001243342chr17 + 75625025-75689007,55036 75625025-75679215 CCDC72 NM_015933 chr3 + 48456689-4846054151372 CCDC88C NM_001080414chr14 - 90807419-90953941440193 CCR6 NM_004367, NM_031409chr6 + 167445284-167472619,1235 167456230-167472619 CDH26 NM_177980, NM_021810chr20 + 57966865-58021563,60437 58004811-58021563 CEACAM16 NM_001039213chr19 + 49894260-49905826388551 CELSR2 NM_001408 chr1 + 109594163-1096199011952 CEP135 NM_025009 chr4 + 56509793-565942849662 CERS2 NM_181746 chr1 - 149204272-14921410329956 CHDH NM_018397 chr3 - 53825363-5385546055349 CHPT1 NM_020244 chr12 + 100615547-10064697756994 CIB2 NM_006383 chr15 - 76184045-7621093310518 CLASP2 NM_015097, NM_001207044chr3 - 33512741-33734709,23122 33512741-33675937 CLRN2 NM_001079827chr4 + 17125885-17137825645104 CLTB NM_007097 chr5 - 175752061-1757761461212 CNGA3 NM_001298 chr2 + 98329049-983814961261 CNTD2 NM_024877 chr19 - 45419954-4542443779935 COL18A1 NM_130445 chr21 + 45649524-4575806280781 COL18A1-AS1NR_027498 chr21 - 45664058-45669413378832 CORO2A NM_052820 chr9 - 99923077-999947777464 COX7A2L NM_004718 chr2 - 42431147-424418609167 CPM NM_001874 chr12 - 67531222-676432871368 CRAT NM_000755, NR_028048chr9 - 130896893-1309128911384 CRKL NM_005207 chr22 + 19601713-196380371399 CSRP1 NM_004078, NM_001193572,chr1 NM_001193571, NM_001193570- 199719280-199743010,1465 199719280-199732324, 199719280-199742590 CTBP1 NM_001328, NM_001012614chr4 - 1195227-12329081487 CXCL12 NM_199168 chr10 - 44192515-442005516387 CXXC5 NM_016463 chr5 + 139008484-13904286451523 CYP1B1 NM_000104 chr2 - 38148249-381568271545 CYP1B1-AS1 NR_027252 chr2 + 38211750-38262497285154 CYP2B7P1 NR_001278 chr19 + 46122009-461484051556 CYP4F11 NM_001128932chr19 - 15884179-1590667657834 DAAM1 NM_014992 chr14 + 58725151-5890622423002 DAP3 NM_001199851,chr1 NM_001199850, NM_001199849, NM_004632,+ 153925505-153975424,7818 NM_033657 153925508-153975424 DCAF4 NM_181341 chr14 + 72462792-7249611026094 DCUN1D3 NM_173475 chr16 - 20776896-20819062123879 DHRS3 NM_004753 chr1 - 12550525-126004079249 DHX34 NM_014681 chr19 + 52544377-525777959704 DLC1 NM_006094, NM_182643chr8 - 12985242-13035180,10395 12985242-13416800 DLG5 NM_004747 chr10 - 79220554-793563549231 DOK7 NM_173660 chr4 + 3434830-3466007285489 DPYSL4 NM_006426 chr10 + 133850403-13386927010570 DRAM1 NM_018370 chr12 + 100795235-10084153255332 DSCAM NM_001389 chr21 - 40306212-411409091826 DSCAM-AS1 NR_038896, NR_038898,chr21 NR_038900, NR_038899 + 40676879-40679155,1E+08 40676880-40679155 DSCR3 NM_006052 chr21 - 37517595-3756170310311 DUPD1 NM_001003892chr10 - 76467599-76488278338599 EDN1 NM_001955 chr6 + 12398514-124054131906 ERN1 NM_001433 chr17 - 59474121-595612342081 ESRP2 NM_024939 chr16 - 66819950-6682763780004 ESYT2 NM_020728 chr7 - 158216449-15831508057488 ETNK2 NM_018208 chr1 - 202366812-20238793055224 EVL NM_016337 chr14 + 99601503-9968032651466 EXOC3 NM_007277 chr5 + 496333-52040911336 FABP6 NM_001130958chr5 + 159546951-1595983072172 FAM110A NM_001042353chr20 + 762355-77492283541 FAM178B NM_016490 chr2 - 96905345-9692759751252 FAM26D NM_153036 chr6 + 116956887-116986724221301 FAM46A NM_017633 chr6 - 82512165-8251914755603 FAM63A NM_001040217chr1 - 149235924-14924600955793 FAM70B NM_182614 chr13 - 113599043-113651873348013 FHL2 NM_201555, NM_201557chr2 - 105343714-105382113,2274 105343714-105421662 FMN1 NM_001103184chr15 - 30845038-31147377342184 FMO9P NR_002925 chr1 + 164839776-164861097116123 FOS NM_005252 chr14 + 74815233-748186902353 FRAT2 NM_012083 chr10 - 99082243-9908444823401 FREM2 NM_207361 chr13 + 38159172-38359267341640 FSIP1 NM_152597 chr15 - 37679523-37862331161835 FUBP3 NM_003934 chr9 + 132444780-1325035608939 FXYD3 NM_001136007chr19 + 40298571-403070685349 FXYD6 NM_001164831chr11 - 117212900-11725341153826 G6PD NM_000402, NM_001042351chrX - 153412799-153428427,2539 153412799-153428981 GATSL3 NM_001037666chr22 - 29011106-29015616652968 GCSH NR_033249 chr16 - 79673052-796874812653 GHRH NM_021081 chr20 - 35312903-353187132691 GNAL NM_182978 chr18 + 11679135-118731442774 GPR77 NM_018485 chr19 + 52532210-5253711227202 GREB1 NM_014668, NM_148903,chr2 NM_033090 + 11591692-11700363,9687 11597530-11645806, 11600301-11649723 HAPLN2 NM_021817 chr1 + 154855709-15486214160484 HDAC11 NM_024827 chr3 + 13496714-1352292479885 HIPK1 NM_198268, NM_198269,chr1 NM_181358 + 114273518-114322014,204851 114295289-114322014, 114298021-114322014 HK1 NM_033498, NM_033500,chr10 NM_033497, NM_033496, NM_000188+ 70699761-70831643,3098 70745615-70831643, 70748608-70831643 HLA-DRB5 NM_002125 chr6 - 32593131-326059843127 HLA-DRB6 NR_001298 chr6, chr6_qbl_hap2 - 32628467-32635757,3128 3714099-3715251 HOGA1 NM_138413 chr10 + 99334091-99362545112817 HOOK3 NM_032410 chr8 + 42871189-4300483984376 HOXC10 NM_017409 chr12 + 52665212-526703293226 HPN NM_182983, NM_002151chr19 + 40223249-402493173249 HRAS NM_005343 chr11 - 522241-5255503265 HSPA4 NM_002154 chr5 + 132415560-1324686083308 IKBKG NM_001099856chrX + 153423652-1534464558517 IL17RB NM_018725 chr3 + 53855616-5387486755540 IL17RD NM_017563 chr3 - 57099049-5717444354756 INS NM_001185097chr11 - 2137584-21390153630 IQCD NM_138451 chr12 - 112117628-112143263115811 IRF2BP2 NM_182972 chr1 - 232806637-232811894359948 JAKMIP3 NM_001105521chr10 + 133768302-133848303282973 JARID2 NM_004973 chr6 + 15354505-156302323720 KCNK6 NM_004823 chr19 + 43502323-435114899424 KDM2A NR_027473, NM_012308chr11 + 66643315-6678212622992 KEAP1 NM_203500, NM_012289chr19 - 10457795-10475054,9817 10457795-10474481 KIAA1467 NM_020853 chr12 + 13088581-1312765057613 KLC1
Load more

Recommended publications
  • Supplementary Materials
    DEPs in osteosarcoma cells comparing to osteoblastic cells Biological Process Protein Percentage of Hits metabolic process (GO:0008152) 29.3 29.3% cellular process (GO:0009987) 20.2 20.2% localization (GO:0051179) 9.4 9.4% biological regulation (GO:0065007) 8 8.0% developmental process (GO:0032502) 7.8 7.8% response to stimulus (GO:0050896) 5.6 5.6% cellular component organization (GO:0071840) 5.6 5.6% multicellular organismal process (GO:0032501) 4.4 4.4% immune system process (GO:0002376) 4.2 4.2% biological adhesion (GO:0022610) 2.7 2.7% apoptotic process (GO:0006915) 1.6 1.6% reproduction (GO:0000003) 0.8 0.8% locomotion (GO:0040011) 0.4 0.4% cell killing (GO:0001906) 0.1 0.1% 100.1% Genes 2179Hits 3870 biological adhesion apoptotic process … reproduction (GO:0000003) , 0.8% (GO:0022610) , 2.7% locomotion (GO:0040011) ,… immune system process cell killing (GO:0001906) , 0.1% (GO:0002376) , 4.2% multicellular organismal process (GO:0032501) , metabolic process 4.4% (GO:0008152) , 29.3% cellular component organization (GO:0071840) , 5.6% response to stimulus (GO:0050896), 5.6% developmental process (GO:0032502) , 7.8% biological regulation (GO:0065007) , 8.0% cellular process (GO:0009987) , 20.2% localization (GO:0051179) , 9.
    [Show full text]
  • Supplementary Information Material and Methods
    MCT-11-0474 BKM120: a potent and specific pan-PI3K inhibitor Supplementary Information Material and methods Chemicals The EGFR inhibitor NVP-AEE788 (Novartis), the Jak inhibitor I (Merck Calbiochem, #420099) and anisomycin (Alomone labs, # A-520) were prepared as 50 mM stock solutions in 100% DMSO. Doxorubicin (Adriablastin, Pfizer), EGF (Sigma Ref: E9644), PDGF (Sigma, Ref: P4306) and IL-4 (Sigma, Ref: I-4269) stock solutions were prepared as recommended by the manufacturer. For in vivo administration: Temodal (20 mg Temozolomide capsules, Essex Chemie AG, Luzern) was dissolved in 4 mL KZI/glucose (20/80, vol/vol); Taxotere was bought as 40 mg/mL solution (Sanofi Aventis, France), and prepared in KZI/glucose. Antibodies The primary antibodies used were as follows: anti-S473P-Akt (#9271), anti-T308P-Akt (#9276,), anti-S9P-GSK3β (#9336), anti-T389P-p70S6K (#9205), anti-YP/TP-Erk1/2 (#9101), anti-YP/TP-p38 (#9215), anti-YP/TP-JNK1/2 (#9101), anti-Y751P-PDGFR (#3161), anti- p21Cip1/Waf1 (#2946), anti-p27Kip1 (#2552) and anti-Ser15-p53 (#9284) antibodies were from Cell Signaling Technologies; anti-Akt (#05-591), anti-T32P-FKHRL1 (#06-952) and anti- PDGFR (#06-495) antibodies were from Upstate; anti-IGF-1R (#SC-713) and anti-EGFR (#SC-03) antibodies were from Santa Cruz; anti-GSK3α/β (#44610), anti-Y641P-Stat6 (#611566), anti-S1981P-ATM (#200-301), anti-T2609 DNA-PKcs (#GTX24194) and anti- 1 MCT-11-0474 BKM120: a potent and specific pan-PI3K inhibitor Y1316P-IGF-1R were from Bio-Source International, Becton-Dickinson, Rockland, GenTex and internal production, respectively. The 4G10 antibody was from Millipore (#05-321MG).
    [Show full text]
  • 1 Supplementary Table 1. Mir-10B Is Predicted to Target Key Molecules
    Supplementary Table 1. miR-10b is predicted to target key molecules and pathways involved in carcinogenesis. Pathway Target gene name ECM-receptor interaction SDC1, COL24A1, COL4A4 NF-kappa B signaling pathway TAB1, CSNK2A2, UBE2I, IRAK4, MAP3K7 Toll-like receptor signaling pathway TAB1, IRAK4, MAP3K7 Glioma E2F3, CAMK2B NOD-like receptor signaling pathway TAB1, MAP3K7 Ubiquitin mediated proteolysis RNF7, UBE2I, ERCC8 Apoptosis DFFB, TP53, FASLG, BCL2L1, CAPN2, PRKX, ATM, IRAK4, PRKACG, PRKAR2A, TNFRSF10B, TNFRSF10D, BCL2, IL1RAP, CASP8, PIK3CA, PRKACA, APAF1, CHP, PIK3R3 Chronic myeloid leukemia E2F3, BCR, GRB2, TGFBR1, CBL, TP53, CDK6, BCL2L1, GAB2, PIK3CA, MDM2, SHC1, PIK3R3, CRK Regulation of cell proliferation E2F3, FOSL2, CDX2, PDGFB, OSMR, E2F7, ARNT2, RBM5, STRN, PTEN, S1PR2, CUL3, BDNF, SERPINE1, SHC1, ASPH, ITCH, SPN, CCDC88A, FOXJ1, RXRA, TP53, CDK6, IRS1, VASH2, RBBP9, VASH1, ADRB2, PDGFRA, MDM2, ADAMTS1, EIF2AK2, EIF5A2, ICOSLG, ING5, FGFR3, NDN, ST8SIA1, BCL2L1, CDH5, ARNT, LIF, VDR, HOXA3, AGGF1, TSPAN31, BCL2, BCL11B, NKX3-1, BCL6, CD28, NACC1, FLT1, NF2, JARID2, TBX5, TGFBR1, NF1, KLF11, SMAD2, IGF2, TAX1BP3, BTLA, HDAC4, LEPRE1, CNTF, NUP62, TSC1, ETS1, ID4, NR5A2, KLF4, KCTD11, NFIB Melanoma E2F3, PDGFB, PDGFRA, FGF11, TP53, FGF23, MDM2, PIK3CA, CDK6, CDH1, PIK3R3, PTEN MAPK signaling pathway FGFR3, PDGFB, GRB2, FGF11, FASLG, GNG12, SRF, PRKX, MAP3K7, PRKACG, BDNF, RAC3, MAP3K2, PRKACA, CHP, RAPGEF2, TGFBR1, NF1, TP53, FGF23, STK4, DUSP5, MAP4K4, RPS6KA2, MAPK14, PDGFRA, PLA2G3, CACNA1C, CRK, PLA2G2F Colorectal cancer
    [Show full text]
  • Swine in Biomedical Research Conference 2005
    Swine in Biomedical Research Conference 2005 www.swinegenomics.com Organizing Committee: Lawrence B. Schook, Ph.D. (Chair) Dept. Animal Sciences, Veterinary Pathobiology & Nutritional Sciences, Institute for Genomic Biology, University of Illinois Christopher Counter, Ph.D. Dept. of Oncology and Cancer Biology, Duke University Medical Center Eric Forsberg, Ph.D Vice President of Business Development, Infigen Merete Fredholm, D.V.M., Ph.D., Dr. Vet. Sci., Royal Veterinary and Agricultural Univ.Inst. Animal Science & Animal Health Medicine Thalachallour Mohanakumar, Ph.D. Dept. of Surgery, Pathology and Immunology, Washington University School of Medicine Randall Prather, Ph.D. Department of Reproductive Biotechnology, University of Missouri Steven Niemi, D.V.M. Center for Comparative Medicine, Massachusetts General Hospital Hiroshi Yasue, Ph.D. National Institute of Agrobiological Sciences, Sukuba, Japan University of Illinois Hosting Committee: Jonathan Beever, Animal Sciences Sharon Donovan, Food Science & Human Nutrition H. Rex Gaskins, Animal Sciences Russell Jamison, Materials Science & Engineering Lawrence Schook, Animal Sciences Kelley Tappenden, Food Science & Human Nutrition Michael Tumbleson, Agricultural Engineering (honorary chair) Matthew Wheeler, Animal Sciences Federico Zuckermann, Veterinary Pathobiology Poster Session Moderators: Bioengineering, Russ Jamison, UIUC Immunology & Infectious Diseases, Federico Zuckermann, UIUC Transplantation (allo & xeno), Doug Smith, Baylor & Mark Rutherford, U.Minn. Nutrition (Obesity and Diabetes), Sharon Donovan, UIUC Genomics and Cloning, Jon Beever, UIUC & Max Rothschild, ISU Cardiovascular, Rex Gaskins, UIUC Physiology, Jack Odle, NCSU Cancer, Craig Beattie, UNR Clinical Models, Steve Niemi, Harvard Sponsored by: Institute for Genomic Biology, College of Agricultural, Consumer, and Environmental Services, College of Liberal Arts & Sciences, College of Veterinary Medicine, and Office of the Vice-Chancellor of Research at the University of Illinois at Urbana-Champaign National Institutes for Health (Grant no.
    [Show full text]
  • ADVISORY COMMISSION on CHILDHOOD VACCINES TABLE of CONTENTS December 8, 2017
    ADVISORY COMMISSION ON CHILDHOOD VACCINES TABLE OF CONTENTS December 8, 2017 TAB • ACCV Agenda 1 • ACCV Charter • ACCV Roster • 2017 Meeting Dates • Meeting Minutes 2 o Draft Minutes – September 8, 2017 • Vaccine Injury Compensation Trust Fund Statement 3 o Vaccine Injury Compensation Trust Fund Summary Sheet for the Period of 10/1/2016 – 9/30/2017 • VICP Data and Statistics 4 • Meeting Presentations & Updates 5 o Report from the Division of Injury Compensation Programs 5.1 o Report from the Department of Justice 5.2 o Petitions to Add Injuries to the Vaccine Injury Table Introduction 5.3 o Petition to Add Tics as an Injury to the Vaccine Injury Table 5.4 o Petition to Add Asthma as an Injury to the Vaccine Injury Table 5.5 5.6 o Petition to Add Pediatric Autoimmune Neuropsychiatric Syndrome (PANS), Pediatric Infection-Triggered Autoimmune Neuropsychiatric Disorders (PITANDS), and Pediatric Autoimmune Neuropsychiatric Disorders (PANDAS) as Injuries to the Vaccine Injury Table o Petition to Add Experimental Autoimmune Encephalomyelitis (EAE) and/or 5.7 Acute Demyelinating Encephalomyelitis (ADEM) as injuries to the Vaccine Injury Table 5.8 o Update on the Immunization Safety Office Vaccine Activities (CDC) o Update on the National Institute of Allergy and Infectious Diseases Vaccine 5.9 Activities (NIH) o Update on the Center for Biologics, Evaluation and Research Vaccine 5.10 Activities (FDA) 5.11 o Update from the National Vaccine Program Office • Program Related Articles 6 6.1 o Popular Science, “Why Are We So Bad At Producing The Right
    [Show full text]
  • The P90 RSK Family Members: Common Functions and Isoform Specificity
    Published OnlineFirst August 22, 2013; DOI: 10.1158/0008-5472.CAN-12-4448 Cancer Review Research The p90 RSK Family Members: Common Functions and Isoform Specificity Romain Lara, Michael J. Seckl, and Olivier E. Pardo Abstract The p90 ribosomal S6 kinases (RSK) are implicated in various cellular processes, including cell proliferation, survival, migration, and invasion. In cancer, RSKs modulate cell transformation, tumorigenesis, and metastasis. Indeed, changes in the expression of RSK isoforms have been reported in several malignancies, including breast, prostate, and lung cancers. Four RSK isoforms have been identified in humans on the basis of their high degree of sequence homology. Although this similarity suggests some functional redundancy between these proteins, an increasing body of evidence supports the existence of isoform-based specificity among RSKs in mediating particular cellular processes. This review briefly presents the similarities between RSK family members before focusing on the specific function of each of the isoforms and their involvement in cancer progression. Cancer Res; 73(17); 1–8. Ó2013 AACR. Introduction subsequently cloned throughout the Metazoan kingdom (2). The extracellular signal–regulated kinase (ERK)1/2 pathway The genomic analysis of several cancer types suggests that fi is involved in key cellular processes, including cell prolifera- these genes are not frequently ampli ed or mutated, with some tion, differentiation, survival, metabolism, and migration. notable exceptions (e.g., in the case of hepatocellular carcino- More than 30% of all human cancers harbor mutations within ma; ref. 6). Table 1 summarizes reported genetic changes in this pathway, mostly resulting in gain of function and conse- RSK genes.
    [Show full text]
  • Regulates Target Gene Transcription Via Single-Stranded DNA Response Elements
    TLS/FUS (translocated in liposarcoma/fused in sarcoma) regulates target gene transcription via single-stranded DNA response elements Adelene Y. Tan1, Todd R. Riley, Tristan Coady, Harmen J. Bussemaker, and James L. Manley2 Department of Biological Sciences, Columbia University, New York, NY 10027 Contributed by James L. Manley, February 29, 2012 (sent for review December 9, 2011) TLS/FUS (TLS) is a multifunctional protein implicated in a wide range TLS has also been linked to splicing. It contains an RNP-type of cellular processes, including transcription and mRNA processing, RNA-binding domain and associates directly with SR protein as well as in both cancer and neurological disease. However, little is splicing factors (11). TET proteins have been detected in spliceo- currently known about TLS target genes and how they are recog- somes (12), and TLS was found associated with RNAP II and nized. Here, we used ChIP and promoter microarrays to identify snRNPs in a transcription and splicing complex in vitro (13). It is genes potentially regulated by TLS. Among these genes, we detected unclear whether and how TLS recruits splicing factors to sites of a number that correlate with previously known functions of TLS, active transcription, but one possibility is through its interaction and confirmed TLS occupancy at several of them by ChIP. We also with TBP and the TFIID complex. detected changes in mRNA levels of these target genes in cells where Here we provide insight into TLS regulation of RNAP II-tran- scribed genes. We used ChIP followed by promoter microarray TLS levels were altered, indicative of both activation and repression.
    [Show full text]
  • CPTC-MAPK3-1 (CAB079934) Immunohistochemistry
    CPTC-MAPK3-1 (CAB079934) Uniprot ID: P27361 Protein name: MK03_HUMAN Full name: Mitogen-activated protein kinase 3 Function: Serine/threonine kinase which acts as an essential component of the MAP kinase signal transduction pathway. MAPK1/ERK2 and MAPK3/ERK1 are the 2 MAPKs which play an important role in the MAPK/ERK cascade. They participate also in a signaling cascade initiated by activated KIT and KITLG/SCF. Depending on the cellular context, the MAPK/ERK cascade mediates diverse biological functions such as cell growth, adhesion, survival and differentiation through the regulation of transcription, translation, cytoskeletal rearrangements. The MAPK/ERK cascade plays also a role in initiation and regulation of meiosis, mitosis, and postmitotic functions in differentiated cells by phosphorylating a number of transcription factors. About 160 substrates have already been discovered for ERKs. Many of these substrates are localized in the nucleus, and seem to participate in the regulation of transcription upon stimulation. However, other substrates are found in the cytosol as well as in other cellular organelles, and those are responsible for processes such as translation, mitosis and apoptosis. Moreover, the MAPK/ERK cascade is also involved in the regulation of the endosomal dynamics, including lysosome processing and endosome cycling through the perinuclear recycling compartment (PNRC); as well as in the fragmentation of the Golgi apparatus during mitosis. The substrates include transcription factors (such as ATF2, BCL6, ELK1, ERF, FOS, HSF4 or SPZ1), cytoskeletal elements (such as CANX, CTTN, GJA1, MAP2, MAPT, PXN, SORBS3 or STMN1), regulators of apoptosis (such as BAD, BTG2, CASP9, DAPK1, IER3, MCL1 or PPARG), regulators of translation (such as EIF4EBP1) and a variety of other signaling-related molecules (like ARHGEF2, FRS2 or GRB10).
    [Show full text]
  • Functional Analysis of Structural Variation in the 2D and 3D Human Genome
    FUNCTIONAL ANALYSIS OF STRUCTURAL VARIATION IN THE 2D AND 3D HUMAN GENOME by Conor Mitchell Liam Nodzak A dissertation submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics and Computational Biology Charlotte 2019 Approved by: Dr. Xinghua Mindy Shi Dr. Rebekah Rogers Dr. Jun-tao Guo Dr. Adam Reitzel ii c 2019 Conor Mitchell Liam Nodzak ALL RIGHTS RESERVED iii ABSTRACT CONOR MITCHELL LIAM NODZAK. Functional analysis of structural variation in the 2D and 3D human genome. (Under the direction of DR. XINGHUA MINDY SHI) The human genome consists of over 3 billion nucleotides that have an average distance of 3.4 Angstroms between each base, which equates to over two meters of DNA contained within the 125 µm3 volume diploid cell nuclei. The dense compaction of chromatin by the supercoiling of DNA forms distinct architectural modules called topologically associated domains (TADs), which keep protein-coding genes, noncoding RNAs and epigenetic regulatory elements in close nuclear space. It has recently been shown that these conserved chromatin structures may contribute to tissue-specific gene expression through the encapsulation of genes and cis-regulatory elements, and mutations that affect TADs can lead to developmental disorders and some forms of cancer. At the population-level, genomic structural variation contributes more to cumulative genetic difference than any other class of mutation, yet much remains to be studied as to how structural variation affects TADs. Here, we study the func- tional effects of structural variants (SVs) through the analysis of chromatin topology and gene activity for three trio families sampled from genetically diverse popula- tions from the Human Genome Structural Variation Consortium.
    [Show full text]
  • Open Data for Differential Network Analysis in Glioma
    International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes.
    [Show full text]
  • ERBB2 Influences the Subcellular Localization of the Estrogen
    Endocrine-Related Cancer (2008) 15 985–1002 ERBB2 influences the subcellular localization of the estrogen receptor in tamoxifen-resistant MCF-7 cells leading to the activation of AKT and RPS6KA2 Sunil Pancholi, Anne E Lykkesfeldt1, Caroline Hilmi, Susana Banerjee, Alexandra Leary, Suzanne Drury, Stephen Johnston 2,MitchDowsett and Lesley-Ann Martin Institute of Cancer Research, The Breakthrough Breast Cancer Research Centre, 237 Fulham Road, London SW3 6JB, UK 1Institute of Cancer Biology, Danish Cancer Society, Copenhagen, Denmark 2Department of Medicine, Royal Marsden Hospital, Fulham Road, London, UK (Correspondence should be addressed to L-A Martin; Email: [email protected]) Abstract Acquired resistance to endocrine therapies remains a major clinical obstacle in hormone-sensitive breast tumors. We used an MCF-7 breast tumor cell line (TamR-1) resistant to tamoxifen to investigate this mechanism. We demonstrate that TamR-1 express elevated levels of phosphorylated AKT and MAPK3/1-activated RPS6KA2 compared with the parental MCF-7 cell line (MCF-7). There was no change in the level of total ESR between the two cell lines; however, the TamR-1 cells had increased phosphorylation of ESR1 ser167. SiRNA blockade of AKT or MAPK3/1 had little effect on ESR1 ser167 phosphorylation, but a combination of the two siRNAs abrogated this. Co-localization studies revealed an association between ERBB2 and ESR1 in the TamR-1 but not MCF-7 cells. ESR1 was redistributed to extranuclear sites in TamR-1 and was less transcriptionally competent compared with MCF-7 suggesting that nuclear ESR1 activity was suppressed in TamR-1. Tamoxifen resistance in the TamR-1 cells could be partially overcome by the ERBB2 inhibitor AG825 in combination with tamoxifen, and this was associated with re-localization of ESR1 to the nucleus.
    [Show full text]
  • Genome-Wide DNA Methylation Dynamics During Epigenetic
    Gómez‑Redondo et al. Clin Epigenet (2021) 13:27 https://doi.org/10.1186/s13148‑021‑01003‑x RESEARCH Open Access Genome‑wide DNA methylation dynamics during epigenetic reprogramming in the porcine germline Isabel Gómez‑Redondo1*† , Benjamín Planells1†, Sebastián Cánovas2,3, Elena Ivanova4, Gavin Kelsey4,5 and Alfonso Gutiérrez‑Adán1 Abstract Background: Prior work in mice has shown that some retrotransposed elements remain substantially methylated during DNA methylation reprogramming of germ cells. In the pig, however, information about this process is scarce. The present study was designed to examine the methylation profles of porcine germ cells during the time course of epigenetic reprogramming. Results: Sows were artifcially inseminated, and their fetuses were collected 28, 32, 36, 39, and 42 days later. At each time point, genital ridges were dissected from the mesonephros and germ cells were isolated through magnetic‑ activated cell sorting using an anti‑SSEA‑1 antibody, and recovered germ cells were subjected to whole‑genome bisulphite sequencing. Methylation levels were quantifed using SeqMonk software by performing an unbiased analysis, and persistently methylated regions (PMRs) in each sex were determined to extract those regions showing 50% or more methylation. Most genomic elements underwent a dramatic loss of methylation from day 28 to day 36, when the lowest levels were shown. By day 42, there was evidence for the initiation of genomic re‑methylation. We identifed a total of 1456 and 1122 PMRs in male and female germ cells, respectively, and large numbers of transpos‑ able elements (SINEs, LINEs, and LTRs) were found to be located within these PMRs. Twenty‑one percent of the introns located in these PMRs were found to be the frst introns of a gene, suggesting their regulatory role in the expression of these genes.
    [Show full text]