Targeting the Splice Factor Kinase CLK1 in Prostate Cancer
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An Extensive Program of Periodic Alternative Splicing Linked to Cell
RESEARCH ARTICLE An extensive program of periodic alternative splicing linked to cell cycle progression Daniel Dominguez1,2, Yi-Hsuan Tsai1,3, Robert Weatheritt4, Yang Wang1,2, Benjamin J Blencowe4*, Zefeng Wang1,5* 1Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, United States; 2Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, United States; 3Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States; 4Donnelly Centre and Department of Molecular Genetics, University of Toronto, Toronto, Canada; 5Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Science, Shanghai, China Abstract Progression through the mitotic cell cycle requires periodic regulation of gene function at the levels of transcription, translation, protein-protein interactions, post-translational modification and degradation. However, the role of alternative splicing (AS) in the temporal control of cell cycle is not well understood. By sequencing the human transcriptome through two continuous cell cycles, we identify ~ 1300 genes with cell cycle-dependent AS changes. These genes are significantly enriched in functions linked to cell cycle control, yet they do not significantly overlap genes subject to periodic changes in steady-state transcript levels. Many of the periodically spliced genes are controlled by the SR protein kinase CLK1, whose level undergoes cell cycle- dependent fluctuations via an auto-inhibitory circuit. Disruption of CLK1 causes pleiotropic cell cycle defects and loss of proliferation, whereas CLK1 over-expression is associated with various *For correspondence: cancers. These results thus reveal a large program of CLK1-regulated periodic AS intimately [email protected] (BJB); associated with cell cycle control. -
Network-Based Prediction of Drug Combinations
Corrected: Publisher correction ARTICLE https://doi.org/10.1038/s41467-019-09186-x OPEN Network-based prediction of drug combinations Feixiong Cheng1,2,3,4,5, Istvań A. Kovacś1,2 & Albert-Laszló ́Barabasí1,2,6,7 Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By 1234567890():,; quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development. 1 Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA 02115, USA. 2 Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. 3 Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA. 4 Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA. 5 Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA. -
Metabolism, Ubiquinone Synthesis, and Longevity
Downloaded from genesdev.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press PERSPECTIVE Metabolism, ubiquinone synthesis, and longevity Hugo Aguilaniu,1 Jenni Durieux,1 and Andrew Dillin2 Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA “You have made your way from worm to man, but much gram of tissue and lived longer than animals with higher within you is still a worm.” metabolic rates. Exceptions have been discovered since —Nieztsche Pearl’s initial observations, such as the high metabolic rates of some long-lived birds (Holmes et al. 2001); how- Until recently, the mechanisms that control the aging ever, Pearl’s initial observation led to the formulation of process were thought to be immensely complex and the “rate of living theory of aging” (Pearl 1928). The nearly impossible to dissect at the molecular level. How- theory suggests that reduced metabolic rates in an ani- ever, genetic analysis, primarily in model organisms mal should result in an increased life span. such as yeast, worms, and flies, is dramatically changing Several years later, the “rate of living theory of aging” this view. It seems that at least three distinct genetic became more refined with the formulation of the “oxy- networks control the aging process. These networks in- gen radical theory of aging” proposed by Denham Har- clude the insulin/IGF-1 signaling pathway, signaling man (Harman 1956). Harman reasoned that reactions us- that arises from the mitochondrial electron transport • ing molecular oxygen create, on occasion, toxic O − radi- chain, and finally, mechanisms that govern the response 2 cals. -
Dual-Specificity, Tyrosine Phosphorylation-Regulated Kinases
International Journal of Molecular Sciences Review Dual-Specificity, Tyrosine Phosphorylation-Regulated Kinases (DYRKs) and cdc2-Like Kinases (CLKs) in Human Disease, an Overview Mattias F. Lindberg and Laurent Meijer * Perha Pharmaceuticals, Perharidy Peninsula, 29680 Roscoff, France; [email protected] * Correspondence: [email protected] Abstract: Dual-specificity tyrosine phosphorylation-regulated kinases (DYRK1A, 1B, 2-4) and cdc2- like kinases (CLK1-4) belong to the CMGC group of serine/threonine kinases. These protein ki- nases are involved in multiple cellular functions, including intracellular signaling, mRNA splicing, chromatin transcription, DNA damage repair, cell survival, cell cycle control, differentiation, ho- mocysteine/methionine/folate regulation, body temperature regulation, endocytosis, neuronal development, synaptic plasticity, etc. Abnormal expression and/or activity of some of these kinases, DYRK1A in particular, is seen in many human nervous system diseases, such as cognitive deficits associated with Down syndrome, Alzheimer’s disease and related diseases, tauopathies, demen- tia, Pick’s disease, Parkinson’s disease and other neurodegenerative diseases, Phelan-McDermid syndrome, autism, and CDKL5 deficiency disorder. DYRKs and CLKs are also involved in dia- betes, abnormal folate/methionine metabolism, osteoarthritis, several solid cancers (glioblastoma, breast, and pancreatic cancers) and leukemias (acute lymphoblastic leukemia, acute megakaryoblas- Citation: Lindberg, M.F.; Meijer, L. tic leukemia), viral infections (influenza, HIV-1, HCMV, HCV, CMV, HPV), as well as infections Dual-Specificity, Tyrosine caused by unicellular parasites (Leishmania, Trypanosoma, Plasmodium). This variety of pathological Phosphorylation-Regulated Kinases implications calls for (1) a better understanding of the regulations and substrates of DYRKs and (DYRKs) and cdc2-Like Kinases CLKs and (2) the development of potent and selective inhibitors of these kinases and their evaluation (CLKs) in Human Disease, an as therapeutic drugs. -
Abstracts [PDF]
Beyond the Identification of Transcribed Sequences 2002 Workshop Beyond the Identification of Transcribed Sequences: Functional, Evolutionary and Expression Analysis 12th International Workshop October 25-28, 2002 Washington, DC Sponsored by the U.S. Department of Energy Home * List of Abstracts * Speakers Meeting Objective Program Planning Committee: The 12th workshop in this series was held in Tom Freeman, MRC Human Genome Washington D.C., Friday evening, October 25 through Programme, Hinxton, UK Monday afternoon, October 28, 2002. Interested Katheleen Gardiner, Eleanor Roosevelt Institute, investigators actively engaged in any aspect of the Denver, CO, USA functional, expression or evolutionary analysis of Bernhard Korn, German Cancer Research Center, transcribed sequences were invited to send an abstract. Heidelberg, Germany Blair Hedges, Penn State University, University Topics discussed include but are not limited to: Park, PA, USA mammalian gene and genome organization as Sherman Weissman, Yale University, New determined from the construction of transcriptional Haven, CT, USA maps and genomic sequence analysis; expression Thomas Werner, Institute for Saeugertiergenetik, analysis of novel mammalian genes; analysis of Oberschleissheim, Germany genomic sequence, including gene and regulatory sequence prediction and verification, and annotation for For Questions or Additional Information, public databases; expression and mutation analysis, and contact: comparative mapping and genomic sequence analysis in model organisms (e.g. yeast, -
Downloaded from Phosphositeplus
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.22.215897; this version posted July 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. July 21, 2020 Phosphoproteomic Identification of Vasopressin-Regulated Protein Kinases in Collecting Duct Cells Arnab Datta1,2, Chin-Rang Yang1, Karim Salhadar1, Chung-Lin Chou1, Viswanathan Raghuram1, and Mark A. Knepper1 1Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 2Yenepoya Research Center, Yenepoya (Deemed to be University), University Road, Deralakatte, Mangalore 575018, Karnataka, India Running title: Vasopressin-regulated Kinases in Renal Collecting Duct Correspondence to Mark Knepper, MD, PhD, Division of Intramural Research, National Heart, Lung and Blood Institute, NIH, Bethesda, MD 20892 Phone: 301-496-3064; Email: [email protected] Keywords: mpkCCD, GPCR signaling, V2 receptor signaling, Desmopressin bioRxiv preprint doi: https://doi.org/10.1101/2020.07.22.215897; this version posted July 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. ABSTRACT Background and Purpose: The peptide hormone vasopressin regulates water transport in the renal collecting duct largely via the V2 receptor, which triggers a cAMP-mediated activation of a protein kinase A (PKA)-dependent signaling network. -
Autocrine IFN Signaling Inducing Profibrotic Fibroblast Responses By
Downloaded from http://www.jimmunol.org/ by guest on September 23, 2021 Inducing is online at: average * The Journal of Immunology , 11 of which you can access for free at: 2013; 191:2956-2966; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication August 2013; doi: 10.4049/jimmunol.1300376 http://www.jimmunol.org/content/191/6/2956 A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Autocrine IFN Signaling Feng Fang, Kohtaro Ooka, Xiaoyong Sun, Ruchi Shah, Swati Bhattacharyya, Jun Wei and John Varga J Immunol cites 49 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2013/08/20/jimmunol.130037 6.DC1 This article http://www.jimmunol.org/content/191/6/2956.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 23, 2021. The Journal of Immunology A Synthetic TLR3 Ligand Mitigates Profibrotic Fibroblast Responses by Inducing Autocrine IFN Signaling Feng Fang,* Kohtaro Ooka,* Xiaoyong Sun,† Ruchi Shah,* Swati Bhattacharyya,* Jun Wei,* and John Varga* Activation of TLR3 by exogenous microbial ligands or endogenous injury-associated ligands leads to production of type I IFN. -
Whole Exome Sequencing Identifies APCDD1 and HDAC5 Genes As Potentially Cancer Predisposing in Familial Colorectal Cancer
International Journal of Molecular Sciences Article Whole Exome Sequencing Identifies APCDD1 and HDAC5 Genes as Potentially Cancer Predisposing in Familial Colorectal Cancer Diamanto Skopelitou 1,2,3,4, Beiping Miao 1,2,3, Aayushi Srivastava 1,2,3,4, Abhishek Kumar 1,5,6, Magdalena Ku´swik 7, Dagmara Dymerska 7, Nagarajan Paramasivam 8, Matthias Schlesner 9 , Jan Lubinski 7, Kari Hemminki 1,10,11, Asta Försti 1,2,3 and Obul Reddy Bandapalli 1,2,3,4,* 1 Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; [email protected] (D.S.); [email protected] (B.M.); [email protected] (A.S.); [email protected] (A.K.); [email protected] (K.H.); [email protected] (A.F.) 2 Hopp Children’s Cancer Center (KiTZ), 69120 Heidelberg, Germany 3 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), 69120 Heidelberg, Germany 4 Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany 5 Institute of Bioinformatics, International Technology Park, Bangalore 560066, India 6 Manipal Academy of Higher Education (MAHE), Manipal 576104, India 7 Department of Genetics and Pathology, Pomeranian Medical University, 71252 Szczecin, Poland; [email protected] (M.K.); [email protected] (D.D.); [email protected] (J.L.) 8 Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; [email protected] 9 Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Citation: Skopelitou, D.; Miao, B.; [email protected] 10 Srivastava, A.; Kumar, A.; Ku´swik, Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany 11 Biomedical Center, Faculty of Medicine in Pilsen, Charles University in Prague, 30605 Pilsen, Czech Republic M.; Dymerska, D.; Paramasivam, N.; * Correspondence: [email protected]; Tel.: +49-6221-421809 Schlesner, M.; Lubinski, J.; Hemminki, K.; et al. -
Supplementary Data
SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial. -
Supp Material.Pdf
Supplemental Information Supplemental Figure S1: Molecules per cell estimation of a high-coverage intron by qRT-PCR; bioinformatic pipeline for detecting statistically significant intron detention levels. (A) Left, qPCR of plasmid DNA dilutions containing the cloned region spanning exons 32-34 of the mouse Fn1 gene. Inset shows slope and amplification efficiency of each primer set. Right, estimation of molecules per cell. Total RNA was extracted from counted mESCs and the per cell RNA mass calculated to ~13 pg per cell. Using the known input amount cross-referenced to the amplification of known quantities from the data in the left panel, the qRT-PCR signal (graphed as mean N=3, +/- SEM) was converted to estimated molecules per cell. (B) Schematic of computational pipeline for determining statistically significant intron detention. The gray box indicates the process by which raw RNA-seq data are mapped and processed. The brown box shows the process for extracting a non-overlapping set of introns for quantification. Below, in the yellow box, are the steps using reads mapped to introns for identifying statistically-significant detained introns. Supplemental Data S1&2: Genomic coordinates of human and mouse detained introns For each detained intron, chromosome, start, end, name, and strand are indicated in the .bed format. Human intron names come from the Aceview annotation corresponding to the intron, while mouse intron names were determined by overlap of sequenced junctions with known Ensembl transcriptional start and end coordinates. Field 5 in the human .bed file is a place holder, while in the mouse .bed, the alternative splice type and whether the intron shows increased or decreased splicing or no change (More, Less, or NC respectively) in response to 2 hours of CB19 treatment is indicated, separated from splice class by and underscore ª_º, (e.g. -
Supporting Information
Supporting Information Miller et al. 10.1073/pnas.0914257107 SI Materials and Methods files with 20 to 40 samples and 5,629 to 9,731 genes each and 20 Data Set Acquisition and Filtering. All data sets were downloaded mouse expression files with 18 and 44 samples and 5,176 to 6,157 from the GEO database, and consisted of experiments run on genes each. All preprocessed data files (as well as the resulting either mouse or human brain tissue (Fig. 1A). We filtered out all network data and some associated code and support files) are but a core collection of data sets that were similar enough for available at the WGCNA group Web site (www.genetics.ucla.edu/ useful bioinformatic comparison. First, we removed all data sets labs/horvath/CoexpressionNetwork/MouseHumanBrain). that were not run on an Affymetrix platform, leaving three From these preprocessed expression files we created a human platforms in human (HG-U95A, HG-U133A, HG-U133 Plus 2) and a mouse consensus network (method modified from ref. 4). and two in mouse (MG-U74A, MG-U430A). Second, we ex- For each consensus network we first created correlation matrices cluded all samples in each data set that were not taken from from each data set (obtained by calculating the Pearson correla- brain tissue (for example, in one expression atlas study, more tions between all variable probe sets across all subjects in each than 80% of the samples were excluded). Third, to make the data set), and then weighted them based on the number of sam- correlations between genes more comparable across studies, we ples used in that data set. -
S41598-021-86908-6.Pdf
www.nature.com/scientificreports OPEN CDC2‑like (CLK) protein kinase inhibition as a novel targeted therapeutic strategy in prostate cancer Simon Uzor1,5, Sean R. Porazinski1,6, Ling Li2, Bethany Clark1, Masahiko Ajiro3, Kei Iida3, Masatoshi Hagiwara3, Abdullah A. Alqasem4, Claire M. Perks4, Ian D. Wilson 1, Sebastian Oltean 2,7* & Michael R. Ladomery1,7* Dysregulation of alternative splicing is a feature of cancer, both in aetiology and progression. It occurs because of mutations in splice sites or sites that regulate splicing, or because of the altered expression and activity of splice factors and of splice factor kinases that regulate splice factor activity. Recently the CDC2‑like kinases (CLKs) have attracted attention due to their increasing involvement in cancer. We measured the efect of the CLK inhibitor, the benzothiazole TG003, on two prostate cancer cell lines. TG003 reduced cell proliferation and increased apoptosis in PC3 and DU145 cells. Conversely, the overexpression of CLK1 in PC3 cells prevented TG003 from reducing cell proliferation. TG003 slowed scratch closure and reduced cell migration and invasion in a transwell assay. TG003 decisively inhibited the growth of a PC3 cell line xenograft in nude mice. We performed a transcriptomic analysis of cells treated with TG003. We report widespread and consistent changes in alternative splicing of cancer‑ associated genes including CENPE, ESCO2, CKAP2, MELK, ASPH and CD164 in both HeLa and PC3 cells. Together these fndings suggest that targeting CLKs will provide novel therapeutic opportunities in prostate cancer. Te vast majority of human genes are alternatively spliced (> 94%), expressing splice isoforms that ofen exhibit antagonistic properties1,2.