The SON RNA Splicing Factor Is Required for Intracellular Trafficking That Promotes Centriole Assembly
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Title a New Centrosomal Protein Regulates Neurogenesis By
Title A new centrosomal protein regulates neurogenesis by microtubule organization Authors: Germán Camargo Ortega1-3†, Sven Falk1,2†, Pia A. Johansson1,2†, Elise Peyre4, Sanjeeb Kumar Sahu5, Loïc Broic4, Camino De Juan Romero6, Kalina Draganova1,2, Stanislav Vinopal7, Kaviya Chinnappa1‡, Anna Gavranovic1, Tugay Karakaya1, Juliane Merl-Pham8, Arie Geerlof9, Regina Feederle10,11, Wei Shao12,13, Song-Hai Shi12,13, Stefanie M. Hauck8, Frank Bradke7, Victor Borrell6, Vijay K. Tiwari§, Wieland B. Huttner14, Michaela Wilsch- Bräuninger14, Laurent Nguyen4 and Magdalena Götz1,2,11* Affiliations: 1. Institute of Stem Cell Research, Helmholtz Center Munich, German Research Center for Environmental Health, Munich, Germany. 2. Physiological Genomics, Biomedical Center, Ludwig-Maximilian University Munich, Germany. 3. Graduate School of Systemic Neurosciences, Biocenter, Ludwig-Maximilian University Munich, Germany. 4. GIGA-Neurosciences, Molecular regulation of neurogenesis, University of Liège, Belgium 5. Institute of Molecular Biology (IMB), Mainz, Germany. 6. Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández, Sant Joan d’Alacant, Spain. 7. Laboratory for Axon Growth and Regeneration, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. 8. Research Unit Protein Science, Helmholtz Centre Munich, German Research Center for Environmental Health, Munich, Germany. 9. Protein Expression and Purification Facility, Institute of Structural Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Munich, Germany. 10. Institute for Diabetes and Obesity, Monoclonal Antibody Core Facility, Helmholtz Center Munich, German Research Center for Environmental Health, Munich, Germany. 11. SYNERGY, Excellence Cluster of Systems Neurology, Biomedical Center, Ludwig- Maximilian University Munich, Germany. 12. Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, USA 13. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
University of Leicester, Msc Medical Statistics, Thesis, Wilmar
Thesis MSc in Medical Statistics Department of Health Sciences University of Leicester, United Kingdom Application of Bayesian hierarchical generalized linear models using weakly informative prior distributions to identify rare genetic variant effects on blood pressure Wilmar Igl March 2015 Summary Background Currently rare genetic variants are discussed as a source of \missing heritability" of complex traits. Bayesian hierarchical models were proposed as an efficient method for the estimation and aggregation of conditional effects of rare variants. Here, such models are applied to identify rare variant effects on blood pressure. Methods Empirical data provided by the Genetic Analysis Workshop 19 (2014) included 1,851 Mexican-American individuals with diastolic blood pressure (DBP), systolic blood pressure (SBP), hypertension (HTN) and 461,868 variants from whole- exome sequencing of odd-numbered chromosomes. Bayesian hierarchical generalized linear models using weakly informative prior distributions were applied. Results Associations of rare variants chr1:204214013 (estimate = 39.6, Credible In- terval (CrI) 95% = [25.3, 53.9], Bayesian p = 6:8 × 10−8) in the PLEKHA6 gene and chr11:118518698 (estimate = 32.2, CrI95% = [20.6, 43.9], Bayesian p = 7:0 × 10−8) in the PHLEDB1 gene were identified. Joint effects of grouped rare variants on DBP in 23 genes (Bayesian p = [8:8 × 10−14, 9:3 × 10−8]) and on SBP in 21 genes (Bayesian p = [8:6 × 10−12, 7:8 × 10−8]) in pathways related to hemostasis, sodi- um/calcium transport, ciliary activity, and others were found. No association with hypertension was detected. Conclusions Bayesian hierarchical generalized linear models with weakly informa- tive priors can successfully be applied in exome-wide genetic association analyses of rare variants. -
Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Ruiz De Porras Et Al. Taxane-Induced Attenuation of the CXCR2/BCL-2
Ruiz de Porras et al. Taxane-induced attenuation of the CXCR2/BCL-2 axis and sensitizes prostate cancer to platinum based treatments Extended materials and methods Supplementary Figures: Supplementary Figure 1. Pathway analysis on Docetaxel exposed human mCRPC patients. (Related to Figure 1) Supplementary Figure 2. CXCR2, CXCR1 and CXCL-chemokine expression in docetaxel- sensitive and resistant cell lines (Related to Figure 2) Supplementary Tables Supplementary Table 1. Differentially expressed genes in taxane-exposed vs. naïve PC patients in the SU2C dataset Supplementary Table 2. Pathway enrichment analysis Supplementary Table 3. CXCR2 and BCL-2 correlation to Hallmark pathway enrichment Supplementary Table 4. List of antibodies and primers used in this study (see Extended materials and methods) Extended materials and methods: Computational analysis of human prostate cancer data: Human mCRPC transcriptome data was downloaded directly from Cbioportal [11]. Differential gene expression between taxane exposed (N=22) and taxane naïve (N=82) patients was estimated using a two-sample two-tail Student’s t-test (Supplementary table 1) and Pathway enrichment using Gene Set Enrichment Analysis (GSEA) on the Hallmark Pathways dataset from the MSigDB (Supplementary table 2). An unsupervised cluster analysis was done based on the leading edge genes of the Hallmarks apoptosis pathway. Samples annotation data was retrieved directly from SU2C clinical data. Univariated analyses were performed using Spearman rank correlation tests. For bivariate analysis comparing the interaction effects between gene expression and taxane status over different scores, linear regressions were performed (Supplementary table 3). For each analysis, predicted data was normalized using bestNormalize method at homonymous R package. -
Supplemental Information Proximity Interactions Among Centrosome
Current Biology, Volume 24 Supplemental Information Proximity Interactions among Centrosome Components Identify Regulators of Centriole Duplication Elif Nur Firat-Karalar, Navin Rauniyar, John R. Yates III, and Tim Stearns Figure S1 A Myc Streptavidin -tubulin Merge Myc Streptavidin -tubulin Merge BirA*-PLK4 BirA*-CEP63 BirA*- CEP192 BirA*- CEP152 - BirA*-CCDC67 BirA* CEP152 CPAP BirA*- B C Streptavidin PCM1 Merge Myc-BirA* -CEP63 PCM1 -tubulin Merge BirA*- CEP63 DMSO - BirA* CEP63 nocodazole BirA*- CCDC67 Figure S2 A GFP – + – + GFP-CEP152 + – + – Myc-CDK5RAP2 + + + + (225 kDa) Myc-CDK5RAP2 (216 kDa) GFP-CEP152 (27 kDa) GFP Input (5%) IP: GFP B GFP-CEP152 truncation proteins Inputs (5%) IP: GFP kDa 1-7481-10441-1290218-1654749-16541045-16541-7481-10441-1290218-1654749-16541045-1654 250- Myc-CDK5RAP2 150- 150- 100- 75- GFP-CEP152 Figure S3 A B CEP63 – – + – – + GFP CCDC14 KIAA0753 Centrosome + – – + – – GFP-CCDC14 CEP152 binding binding binding targeting – + – – + – GFP-KIAA0753 GFP-KIAA0753 (140 kDa) 1-496 N M C 150- 100- GFP-CCDC14 (115 kDa) 1-424 N M – 136-496 M C – 50- CEP63 (63 kDa) 1-135 N – 37- GFP (27 kDa) 136-424 M – kDa 425-496 C – – Inputs (2%) IP: GFP C GFP-CEP63 truncation proteins D GFP-CEP63 truncation proteins Inputs (5%) IP: GFP Inputs (5%) IP: GFP kDa kDa 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl 1-135136-424425-4961-424136-496FL Ctl Myc- 150- Myc- 100- CCDC14 KIAA0753 100- 100- 75- 75- GFP- GFP- 50- CEP63 50- CEP63 37- 37- Figure S4 A siCtl -
Title: Therapeutic Potential of HSP90 Inhibition for Neurofibromatosis Type 2
Author Manuscript Published OnlineFirst on May 28, 2013; DOI: 10.1158/1078-0432.CCR-12-3167 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Title: Therapeutic Potential of HSP90 Inhibition for Neurofibromatosis type 2 Karo Tanaka1, Ascia Eskin3, Fabrice Chareyre1, Walter J. Jessen4, Jan Manent5, Michiko Niwa-Kawakita6, Ruihong Chen7, Cory H. White2, Jeremie Vitte1, Zahara M. Jaffer1, Stanley F. Nelson3, Allan E. Rubenstein8, Marco Giovannini1,9§. Authors’ affiliations: House Research Institute, 1Center for Neural Tumor Research and 2Section on Genetics of Hereditary Ear Disorders, Los Angeles, CA; 3Department of Human Genetics, University of California, Los Angeles, CA; 4Informatics, Covance Inc., Princeton, NJ; 5Peter MacCallum Cancer Institute, Melbourne, Australia; 6Inserm U944, CNRS U7212, Université Paris, Institut Universitaire d'Hématologie, Paris, France; 7NexGenix Pharmaceuticals, Burlingame, CA; and 8New York University Langone Medical Center, New York, NY; and Department of Cell and Neurobiology, University of Southern California, Keck School of Medicine, Los Angeles, CA Running title: HSP90 Inhibition for NF2 Keywords: NF2, HSP90 inhibitors, Transcriptome Financial support: This work was supported by a Drug Discovery Initiative Award, Children’s Tumor Foundation, to M.G., and by the House Research Institute. Corresponding author: Marco Giovannini, House Research Institute, Center for Neural Tumor Research, 2100 West 3rd street, Los Angeles, CA90057. Phone: +1-213-989-6708; Fax: +1-213-989-6778; E-mail: [email protected] 1 Downloaded from clincancerres.aacrjournals.org on September 30, 2021. © 2013 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 28, 2013; DOI: 10.1158/1078-0432.CCR-12-3167 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. -
SNX17 Recruits USP9X to Antagonize MIB1-Mediated Ubiquitination and Degradation of PCM1 During Serum-Starvation-Induced Ciliogenesis
cells Article SNX17 Recruits USP9X to Antagonize MIB1-Mediated Ubiquitination and Degradation of PCM1 during Serum-Starvation-Induced Ciliogenesis Pengtao Wang 1,2,3,4, Jianhong Xia 2,3, Leilei Zhang 2,3, Shaoyang Zhao 2,3, Shengbiao Li 2,3, Haiyun Wang 2,3, Shan Cheng 4, Heying Li 2,3, Wenguang Yin 2,3, Duanqing Pei 2,3,5,* and Xiaodong Shu 2,3,4,5,6,* 1 School of Life Sciences, University of Science and Technology of China, Hefei 230027, China; [email protected] 2 CAS Key Laboratory of Regenerative Biology, South China Institutes for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; [email protected] (J.X.); [email protected] (L.Z.); [email protected] (S.Z.); [email protected] (S.L.); [email protected] (H.W.); [email protected] (H.L.); [email protected] (W.Y.) 3 Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou 510530, China 4 Hefei Institute of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; [email protected] 5 Guangzhou Regenerative Medicine and Health Guangdong Laboratory (GRMH-GDL), Guangzhou 510005, China 6 Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 511436, China * Correspondence: [email protected] (D.P.); [email protected] (X.S.); Tel.: +86-20-3201-5310 (X.S.) Academic Editor: Tomer Avidor-Reiss Received: 13 September 2019; Accepted: 27 October 2019; Published: 29 October 2019 Abstract: Centriolar satellites are non-membrane cytoplasmic granules that deliver proteins to centrosome during centrosome biogenesis and ciliogenesis. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
USP9X Regulates Centrosome Duplication and Promotes Breast Carcinogenesis
ARTICLE Received 21 Feb 2016 | Accepted 31 Jan 2017 | Published 31 Mar 2017 DOI: 10.1038/ncomms14866 OPEN USP9X regulates centrosome duplication and promotes breast carcinogenesis Xin Li1,*, Nan Song1,*, Ling Liu1, Xinhua Liu1, Xiang Ding2, Xin Song3, Shangda Yang1, Lin Shan1, Xing Zhou1, Dongxue Su1, Yue Wang1, Qi Zhang1, Cheng Cao1, Shuai Ma1,NaYu1, Fuquan Yang2, Yan Wang1, Zhi Yao4, Yongfeng Shang1,5 & Lei Shi1,4 Defective centrosome duplication is implicated in microcephaly and primordial dwarfism as well as various ciliopathies and cancers. Yet, how the centrosome biogenesis is regulated remains poorly understood. Here we report that the X-linked deubiquitinase USP9X is physically associated with centriolar satellite protein CEP131, thereby stabilizing CEP131 through its deubiquitinase activity. We demonstrate that USP9X is an integral component of centrosome and is required for centrosome biogenesis. Loss-of-function of USP9X impairs centrosome duplication and gain-of-function of USP9X promotes centrosome amplification and chromosome instability. Significantly, USP9X is overexpressed in breast carcinomas, and its level of expression is correlated with that of CEP131 and higher histologic grades of breast cancer. Indeed, USP9X, through regulation of CEP131 abundance, promotes breast carcino- genesis. Our experiments identify USP9X as an important regulator of centrosome biogenesis and uncover a critical role for USP9X/CEP131 in breast carcinogenesis, supporting the pursuit of USP9X/CEP131 as potential targets for breast cancer intervention. 1 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China. 2 Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. -
The Transformation of the Centrosome Into the Basal Body: Similarities and Dissimilarities Between Somatic and Male Germ Cells and Their Relevance for Male Fertility
cells Review The Transformation of the Centrosome into the Basal Body: Similarities and Dissimilarities between Somatic and Male Germ Cells and Their Relevance for Male Fertility Constanza Tapia Contreras and Sigrid Hoyer-Fender * Göttingen Center of Molecular Biosciences, Johann-Friedrich-Blumenbach Institute for Zoology and Anthropology-Developmental Biology, Faculty of Biology and Psychology, Georg-August University of Göttingen, 37077 Göttingen, Germany; [email protected] * Correspondence: [email protected] Abstract: The sperm flagellum is essential for the transport of the genetic material toward the oocyte and thus the transmission of the genetic information to the next generation. During the haploid phase of spermatogenesis, i.e., spermiogenesis, a morphological and molecular restructuring of the male germ cell, the round spermatid, takes place that includes the silencing and compaction of the nucleus, the formation of the acrosomal vesicle from the Golgi apparatus, the formation of the sperm tail, and, finally, the shedding of excessive cytoplasm. Sperm tail formation starts in the round spermatid stage when the pair of centrioles moves toward the posterior pole of the nucleus. The sperm tail, eventually, becomes located opposed to the acrosomal vesicle, which develops at the anterior pole of the nucleus. The centriole pair tightly attaches to the nucleus, forming a nuclear membrane indentation. An Citation: Tapia Contreras, C.; articular structure is formed around the centriole pair known as the connecting piece, situated in the Hoyer-Fender, S. The Transformation neck region and linking the sperm head to the tail, also named the head-to-tail coupling apparatus or, of the Centrosome into the Basal in short, HTCA.