A Subcellular Map of the Human Kinome
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Human RSK4 / RPS6KA6 Protein (GST Tag)
Human RSK4 / RPS6KA6 Protein (GST Tag) Catalog Number: 10147-H09B General Information SDS-PAGE: Gene Name Synonym: PP90RSK4; RSK4 Protein Construction: A DNA sequence encoding the full length of human RSK4 (NP_055311.1) (Met 1-Leu 745) was fused with the GST tag at the N-terminus. Source: Human Expression Host: Baculovirus-Insect Cells QC Testing Purity: > 85 % as determined by SDS-PAGE Bio Activity: Protein Description No Kinase Activity Ribosomal protein S6 kinase alpha-6, also known as Ribosomal S6 kinase Endotoxin: 4, 90 kDa ribosomal protein S6 kinase 6,RSK-4, RSK4 and RPS6KA6, is a protein which belongs to theprotein kinase superfamily, AGC Ser/Thr < 1.0 EU per μg of the protein as determined by the LAL method protein kinase family and S6 kinase subfamily. RPS6KA6 contains oneAGC-kinase C-terminal domain and twoprotein kinase domains. Stability: RPS6KA6 forms a complex with either ERK1 or ERK2 in quiescent cells. Samples are stable for up to twelve months from date of receipt at -70 ℃ RPS6KA6 shows a high level of homology to three isolated members of the human RSK family. RSK2 is involved in Coffin-Lowry syndrome and Predicted N terminal: Met nonspecific MRX. The localization of RPS6KA6 in the interval that is commonly deleted in mentally retarded males together with the high degree Molecular Mass: of amino acid identity with RSK2 suggests that RPS6KA6 plays a role in normal neuronal development. Further mutation analyses in males with X- The recombinant human RSK4/GST chimera consists of 972 amino acids and linked mental retardation must prove that the gene of RPS6KA6 is indeed a migrates as an approximately 110 kDa band as predicted in SDS-PAGE under novel MRX gene. -
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 -
Characterization of TRIB2-Mediated Resistance to Pharmacological Inhibition of MEK
VANESSA MENDES HENRIQUES Characterization of TRIB2-mediated resistance to pharmacological inhibition of MEK Oncobiology Master Thesis Faro, 2017 VANESSA MENDES HENRIQUES Characterization of TRIB2-mediated resistance to pharmacological inhibition of MEK Supervisors: Dr. Wolfgang Link Dr. Bibiana Ferreira Oncobiology Master Thesis Faro, 2017 Título: “Characterization of TRIB2-mediated resistance to pharmacological inhibition of MEK” Declaração de autoria do trabalho Declaro ser a autora deste trabalho, que é original e inédito. Autores e trabalhos consultados estão devidamente citados no texto e constam da listagem de referências incluída. Copyright Vanessa Mendes Henriques _____________________________ A Universidade do Algarve tem o direito, perpétuo e sem limites geográficos, de arquivar e publicitar este trabalho através de exemplares impressos reproduzidos em papel ou de forma digital, ou por qualquer outro meio conhecido ou que venha a ser inventado, de o divulgar através de repositórios científicos e de admitir a sua cópia e distribuição com objetivos educacionais ou de investigação, não comerciais, desde que seja dado crédito ao autor e editor. i Acknowledgements First, I would like to thank the greatest opportunity given by professor doctor Wolfgang Link to accepting me into his team, contributing to my scientific and personal progress. Thank You for all Your knowledge and help across the year. A special thanks to Bibiana Ferreira who stayed by me all year and trained me. Thank You for all you taught me, thank you for all your patience and time and all the support. Thank you for all the great times that You provided me. It was an amazing experience to learn and work with You, which made me grow as a scientist and also as a person. -
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). -
Profiling Data
Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8 -
Kinome Profiling of Clinical Cancer Specimens
Published OnlineFirst March 23, 2010; DOI: 10.1158/0008-5472.CAN-09-3989 Review Cancer Research Kinome Profiling of Clinical Cancer Specimens Kaushal Parikh and Maikel P. Peppelenbosch Abstract Over the past years novel technologies have emerged to enable the determination of the transcriptome and proteome of clinical samples. These data sets will prove to be of significant value to our elucidation of the mechanisms that govern pathophysiology and may provide biological markers for future guidance in person- alized medicine. However, an equally important goal is to define those proteins that participate in signaling pathways during the disease manifestation itself or those pathways that are made active during successful clinical treatment of the disease: the main challenge now is the generation of large-scale data sets that will allow us to define kinome profiles with predictive properties on the outcome-of-disease and to obtain insight into tissue-specific analysis of kinase activity. This review describes the current techniques available to gen- erate kinome profiles of clinical tissue samples and discusses the future strategies necessary to achieve new insights into disease mechanisms and treatment targets. Cancer Res; 70(7); 2575–8. ©2010 AACR. Background the current state of the cell or tissue as characterized by pro- teomic and metabolic measurements (3). The past five years have seen an exponential increase in In this review we describe and evaluate the various tech- technological development to explore the genome and the niques that have recently become available to study the ki- proteome to understand the molecular basis of disease. How- nomes of clinical tissue samples. -
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. -
Kinase Profiling Book
Custom and Pre-Selected Kinase Prof iling to f it your Budget and Needs! As of July 1, 2021 19.8653 mm 128 196 12 Tyrosine Serine/Threonine Lipid Kinases Kinases Kinases Carna Biosciences, Inc. 2007 Carna Biosciences, Inc. Profiling Assays available from Carna Biosciences, Inc. As of July 1, 2021 Page Kinase Name Assay Platform Page Kinase Name Assay Platform 4 ABL(ABL1) MSA 21 EGFR[T790M/C797S/L858R] MSA 4 ABL(ABL1)[E255K] MSA 21 EGFR[T790M/L858R] MSA 4 ABL(ABL1)[T315I] MSA 21 EPHA1 MSA 4 ACK(TNK2) MSA 21 EPHA2 MSA 4 AKT1 MSA 21 EPHA3 MSA 5 AKT2 MSA 22 EPHA4 MSA 5 AKT3 MSA 22 EPHA5 MSA 5 ALK MSA 22 EPHA6 MSA 5 ALK[C1156Y] MSA 22 EPHA7 MSA 5 ALK[F1174L] MSA 22 EPHA8 MSA 6 ALK[G1202R] MSA 23 EPHB1 MSA 6 ALK[G1269A] MSA 23 EPHB2 MSA 6 ALK[L1196M] MSA 23 EPHB3 MSA 6 ALK[R1275Q] MSA 23 EPHB4 MSA 6 ALK[T1151_L1152insT] MSA 23 Erk1(MAPK3) MSA 7 EML4-ALK MSA 24 Erk2(MAPK1) MSA 7 NPM1-ALK MSA 24 Erk5(MAPK7) MSA 7 AMPKα1/β1/γ1(PRKAA1/B1/G1) MSA 24 FAK(PTK2) MSA 7 AMPKα2/β1/γ1(PRKAA2/B1/G1) MSA 24 FER MSA 7 ARG(ABL2) MSA 24 FES MSA 8 AurA(AURKA) MSA 25 FGFR1 MSA 8 AurA(AURKA)/TPX2 MSA 25 FGFR1[V561M] MSA 8 AurB(AURKB)/INCENP MSA 25 FGFR2 MSA 8 AurC(AURKC) MSA 25 FGFR2[V564I] MSA 8 AXL MSA 25 FGFR3 MSA 9 BLK MSA 26 FGFR3[K650E] MSA 9 BMX MSA 26 FGFR3[K650M] MSA 9 BRK(PTK6) MSA 26 FGFR3[V555L] MSA 9 BRSK1 MSA 26 FGFR3[V555M] MSA 9 BRSK2 MSA 26 FGFR4 MSA 10 BTK MSA 27 FGFR4[N535K] MSA 10 BTK[C481S] MSA 27 FGFR4[V550E] MSA 10 BUB1/BUB3 MSA 27 FGFR4[V550L] MSA 10 CaMK1α(CAMK1) MSA 27 FGR MSA 10 CaMK1δ(CAMK1D) MSA 27 FLT1 MSA 11 CaMK2α(CAMK2A) MSA 28 -
Discovery of Driver Non-Coding Splice-Site-Creating Mutations in Cancer
ARTICLE https://doi.org/10.1038/s41467-020-19307-6 OPEN Discovery of driver non-coding splice-site-creating mutations in cancer Song Cao1,2, Daniel Cui Zhou 1,2, Clara Oh1,2, Reyka G. Jayasinghe1,2, Yanyan Zhao1, Christopher J. Yoon1,2, Matthew A. Wyczalkowski 1,2, Matthew H. Bailey 1,2, Terrence Tsou 1,2, Qingsong Gao1,2, ✉ ✉ Andrew Malone 1, Sheila Reynolds3, Ilya Shmulevich3, Michael C. Wendl2,4,5, Feng Chen1,6 & Li Ding1,2,4,6 Non-coding mutations can create splice sites, however the true extent of how such somatic 1234567890():,; non-coding mutations affect RNA splicing are largely unexplored. Here we use the MiSplice pipeline to analyze 783 cancer cases with WGS data and 9494 cases with WES data, discovering 562 non-coding mutations that lead to splicing alterations. Notably, most of these mutations create new exons. Introns associated with new exon creation are significantly larger than the genome-wide average intron size. We find that some mutation-induced splicing alterations are located in genes important in tumorigenesis (ATRX, BCOR, CDKN2B, MAP3K1, MAP3K4, MDM2, SMAD4, STK11, TP53 etc.), often leading to truncated proteins and affecting gene expression. The pattern emerging from these exon-creating mutations sug- gests that splice sites created by non-coding mutations interact with pre-existing potential splice sites that originally lacked a suitable splicing pair to induce new exon formation. Our study suggests the importance of investigating biological and clinical consequences of noncoding splice-inducing mutations that were previously neglected by conventional anno- tation pipelines. MiSplice will be useful for automatically annotating the splicing impact of coding and non-coding mutations in future large-scale analyses. -
And Rasopathies-Associated SHP2 and BRAF Mutations
Functional Characterization of Cancer- and RASopathies-associated SHP2 and BRAF Mutations Dissertation zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) im Fach Biologie/ Molekularbiologie eingereicht an der Lebenswissenschaftlichen Fakult¨at der Humboldt–Universit¨at zu Berlin von M. Sc. Paula Andrea Medina-P´erez Pr¨asident der Humboldt–Universit¨at zu Berlin Prof. Dr. Jan–Hendrik Olbertz Dekan der Lebenswissenschaftlichen Fakult¨at Prof. Dr. Richard Lucius Gutachter: 1. Prof. Dr. Reinhold Sch¨afer 2. Prof. Dr. Hanspeter Herzel 3. Prof. Dr. Holger S¨ultmann Tag der m¨undlichen Pr¨ufung: 12.03.2015 Abstract Deregulation of the Ras/MAPK signaling is implicated in a wide variety of human diseases, including developmental disorders and cancer. In the last years, a group of developmental disorders, characterized by an overlapping phenotype in patients, was clustered under the term RASopathies. These disorders result from germline mutations in genes encoding key components of the Ras/MAPK signaling cascade. Although the incidence of solid tumors in patients suffering from these disorders is rather low, reports on different forms of leukemia have considerably increased. In this work, a group of mutations in the genes SHP2/PTPN11 and BRAF, both key regulators of the MAPK signaling pathway and implicated in RASopathies and cancer, were selected for expression in well-established cell systems for a comprehensive molecular and phenotypic characterization using high-throughput approaches and functional assays. Synthetic cDNA sequences carrying the SHP2 mutations T42A, E76D, I282V (Noonan syndrome-associated), E76G, E76K, E139D (Noonan- and leukemia-associated), T468M (LEOPARD syndrome -associated) and the BRAF mutations Q257R, S467A, L485F and K499E (cardio-facio-cutaneous syndrome-associated) were shuttled into the modified lentivi- ral vector pCDH-EF1-IRES-GFP. -
Methods in and Applications of the Sequencing of Short Non-Coding Rnas" (2013)
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Methods in and Applications of the Sequencing of Short Non- Coding RNAs Paul Ryvkin University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Molecular Biology Commons Recommended Citation Ryvkin, Paul, "Methods in and Applications of the Sequencing of Short Non-Coding RNAs" (2013). Publicly Accessible Penn Dissertations. 922. https://repository.upenn.edu/edissertations/922 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/922 For more information, please contact [email protected]. Methods in and Applications of the Sequencing of Short Non-Coding RNAs Abstract Short non-coding RNAs are important for all domains of life. With the advent of modern molecular biology their applicability to medicine has become apparent in settings ranging from diagonistic biomarkers to therapeutics and fields angingr from oncology to neurology. In addition, a critical, recent technological development is high-throughput sequencing of nucleic acids. The convergence of modern biotechnology with developments in RNA biology presents opportunities in both basic research and medical settings. Here I present two novel methods for leveraging high-throughput sequencing in the study of short non- coding RNAs, as well as a study in which they are applied to Alzheimer's Disease (AD). The computational methods presented here include High-throughput Annotation of Modified Ribonucleotides (HAMR), which enables researchers to detect post-transcriptional covalent modifications ot RNAs in a high-throughput manner. In addition, I describe Classification of RNAs by Analysis of Length (CoRAL), a computational method that allows researchers to characterize the pathways responsible for short non-coding RNA biogenesis. -
Identification of Six Autophagy-Related-Lncrna Prognostic Biomarkers in Uveal Melanoma
Hindawi Disease Markers Volume 2021, Article ID 2401617, 12 pages https://doi.org/10.1155/2021/2401617 Research Article Identification of Six Autophagy-Related-lncRNA Prognostic Biomarkers in Uveal Melanoma Yao Chen , Lu Chen , Jinwei Wang , Jia Tan, and Sha Wang Hunan Key Laboratory of Ophthalmology, Eye Center of Xiangya Hospital, Central South University, China Correspondence should be addressed to Sha Wang; [email protected] Received 7 June 2021; Revised 16 July 2021; Accepted 23 July 2021; Published 13 August 2021 Academic Editor: Ting Su Copyright © 2021 Yao Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Currently, no autophagy-related long noncoding RNA (lncRNA) has been reported to predict the prognosis of uveal melanoma patients. Our study screened for autophagy-related lncRNAs in 80 samples downloaded from The Cancer Genome Atlas (TCGA) database through lncRNA-mRNA coexpression. We used univariate Cox to further filter the lncRNAs. Multivariate Cox regression and LASSO regression were applied to construct an autophagy-associated lncRNA predictive model and calculate the risk score. Clinical risk factors were validated using Cox regression to determine whether they were independent prognostic indicators. Functional enrichment was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The model was built with six predictive autophagy-associated lncRNAs and clustered uveal melanoma patients into high- and low-risk groups. The risk score of our model was a significant independent prognostic factor (hazard ratio = 1:0; p <0:001).