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Inhibition of Breast and Brain Cancer Cell Growth by Bccipα, An
Oncogene (2001) 20, 336 ± 345 ã 2001 Nature Publishing Group All rights reserved 0950 ± 9232/01 $15.00 www.nature.com/onc Inhibition of breast and brain cancer cell growth by BCCIPa,an evolutionarily conserved nuclear protein that interacts with BRCA2 Jingmei Liu1, Yuan Yuan1,2, Juan Huan2 and Zhiyuan Shen*,1 1Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center; 915 Camino de Salud, NE. Albuquerque, New Mexico, NM 87131, USA; 2Graduate Program of Molecular Genetics, College of Medicine, University of Illinois at Chicago, 900 S. Ashland Ave. Chicago, Illinois, IL 60607, USA BRCA2 is a tumor suppressor gene involved in mammary mouse BRCA2. It is expected that important functions tumorigenesis. Although important functions have been of BRCA2 reside in these conserved domains. Based on assigned to a few conserved domains of BRCA2, little is the functional analysis of the conserved BRCA2 known about the longest internal conserved domain domains, several models have been proposed for the encoded by exons 14 ± 24. We identi®ed a novel protein, role of BRCA2 in tumor suppression. designated BCCIPa, that interacts with part of the An N-terminus conserved domain in exon 3 (amino internal conserved region of human BRCA2. Human acids 48 ± 105) has been implicated in transcriptional BCCIP represents a family of proteins that are regulation of gene expression (Milner et al., 1997; evolutionarily conserved, and contain three distinct Nordling et al., 1998). Deletion of this region has been domains: an N-terminus acidic domain (NAD) of 30 ± identi®ed in breast cancers (Nordling et al., 1998). -
Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
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. -
Bioinformatics Identi Cation of Prognostic Factors Associated With
Bioinformatics Identication of Prognostic Factors Associated with Breast Cancer Ying Wei Sichuan University https://orcid.org/0000-0001-8178-4705 Shipeng Zhang College of Pharmacy, North Sichuan Medical College Li Xiao West China School of Basic Medical Sciences and Forensic Medicine Jing Zou West China School of Basic Medical Sciences and Forensic Medicine Yingqing Fu West China School of Basic Medical Sciences and Forensic Medicine Yi Ye West China School of Basic Medical Sciences and Forensic Medicine Linchuan Liao ( [email protected] ) West China School of Basic Medical Sciences and Forensic Medicine https://orcid.org/0000-0003-3700-8471 Research Keywords: Breast cancer, Differentially expressed genes, miRNAs, Transcription factors, Bioinformatic analysis Posted Date: December 2nd, 2020 DOI: https://doi.org/10.21203/rs.3.rs-117477/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/23 Abstract Background: Breast cancer (BRCA) remains one of the most common forms of cancer and is the most prominent driver of cancer-related death among women. The mechanistic basis for BRCA, however, remains incompletely understood. In particular, the relationships between driver mutations and signaling pathways in BRCA are poorly characterized, making it dicult to identify reliable clinical biomarkers that can be employed in diagnostic, therapeutic, or prognostic contexts. Methods: First, we downloaded publically available BRCA datasets (GSE45827, GSE42568, and GSE61304) from the Gene Expression Omnibus (GEO) database. We then compared gene expression proles between tumor and control tissues in these datasets using Venn diagrams and the GEO2R analytical tool. We further explore the functional relevance of BRCA-associated differentially expressed genes (DEGs) via functional and pathway enrichment analyses using the DAVID tool, and we then constructed a protein-protein interaction network incorporating DEGs of interest using the Search Tool for the Retrieval of Interacting Genes (STRING) database. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
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. -
Ankrd9 Is a Metabolically-Controlled Regulator of Impdh2 Abundance and Macro-Assembly
ANKRD9 IS A METABOLICALLY-CONTROLLED REGULATOR OF IMPDH2 ABUNDANCE AND MACRO-ASSEMBLY by Dawn Hayward A dissertation submitted to The Johns Hopkins University in conformity with the requirements of the degree of Doctor of Philosophy Baltimore, Maryland April 2019 ABSTRACT Members of a large family of Ankyrin Repeat Domains proteins (ANKRD) regulate numerous cellular processes by binding and changing properties of specific protein targets. We show that interactions with a target protein and the functional outcomes can be markedly altered by cells’ metabolic state. ANKRD9 facilitates degradation of inosine monophosphate dehydrogenase 2 (IMPDH2), the rate-limiting enzyme in GTP biosynthesis. Under basal conditions ANKRD9 is largely segregated from the cytosolic IMPDH2 by binding to vesicles. Upon nutrient limitation, ANKRD9 loses association with vesicles and assembles with IMPDH2 into rod-like structures, in which IMPDH2 is stable. Inhibition of IMPDH2 with Ribavirin favors ANKRD9 binding to rods. The IMPDH2/ANKRD9 assembly is reversed by guanosine, which restores association of ANKRD9 with vesicles. The conserved Cys109Cys110 motif in ANKRD9 is required for the vesicles-to-rods transition as well as binding and regulation of IMPDH2. ANKRD9 knockdown increases IMPDH2 levels and prevents formation of IMPDH2 rods upon nutrient limitation. Thus, the status of guanosine pools affects the mode of ANKRD9 action towards IMPDH2. Advisor: Dr. Svetlana Lutsenko, Department of Physiology, Johns Hopkins University School of Medicine Second reader: -
EGFR Phosphorylation of DCBLD2 Recruits TRAF6 and Stimulates AKT-Promoted Tumorigenesis
The Journal of Clinical Investigation RESEARCH ARTICLE EGFR phosphorylation of DCBLD2 recruits TRAF6 and stimulates AKT-promoted tumorigenesis Haizhong Feng,1,2 Giselle Y. Lopez,3 Chung Kwon Kim,2 Angel Alvarez,2 Christopher G. Duncan,3 Ryo Nishikawa,4 Motoo Nagane,5 An-Jey A. Su,6 Philip E. Auron,6 Matthew L. Hedberg,7 Lin Wang,7 Jeffery J. Raizer,2 John A. Kessler,2 Andrew T. Parsa,8 Wei-Qiang Gao,1 Sung-Hak Kim,9 Mutsuko Minata,9 Ichiro Nakano,9 Jennifer R. Grandis,7 Roger E. McLendon,3 Darell D. Bigner,3 Hui-Kuan Lin,10 Frank B. Furnari,11 Webster K. Cavenee,11 Bo Hu,2 Hai Yan,3 and Shi-Yuan Cheng1,2 1State Key Laboratory of Oncogenes and Related Genes, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 2Department of Neurology and Northwestern Brain Tumor Institute, Center for Genetic Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 3Pediatric Brain Tumor Foundation Institute at Duke, The Preston Robert Tisch Brain Tumor Center, and Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA. 4Department of Neuro-Oncology/Neurosurgery, International Medical Center, Saitama Medical University, Saitama, Japan. 5Department of Neurosurgery, Kyorin University, Tokyo, Japan. 6Department of Biological Sciences, Duquesne University, Pittsburgh, Pennsylvania, USA. 7Departments of Otolaryngology and Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. 8Department of Neurological Surgery and Northwestern Brain Tumor Institute, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. -
An Amplified Fatty Acid-Binding Protein Gene Cluster In
cancers Review An Amplified Fatty Acid-Binding Protein Gene Cluster in Prostate Cancer: Emerging Roles in Lipid Metabolism and Metastasis Rong-Zong Liu and Roseline Godbout * Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB T6G 1Z2, Canada; [email protected] * Correspondence: [email protected]; Tel.: +1-780-432-8901 Received: 6 November 2020; Accepted: 16 December 2020; Published: 18 December 2020 Simple Summary: Prostate cancer is the second most common cancer in men. In many cases, prostate cancer grows very slowly and remains confined to the prostate. These localized cancers can usually be cured. However, prostate cancer can also metastasize to other organs of the body, which often results in death of the patient. We found that a cluster of genes involved in accumulation and utilization of fats exists in multiple copies and is expressed at much higher levels in metastatic prostate cancer compared to localized disease. These genes, called fatty acid-binding protein (or FABP) genes, individually and collectively, promote properties associated with prostate cancer metastasis. We propose that levels of these FABP genes may serve as an indicator of prostate cancer aggressiveness, and that inhibiting the action of FABP genes may provide a new approach to prevent and/or treat metastatic prostate cancer. Abstract: Treatment for early stage and localized prostate cancer (PCa) is highly effective. Patient survival, however, drops dramatically upon metastasis due to drug resistance and cancer recurrence. The molecular mechanisms underlying PCa metastasis are complex and remain unclear. It is therefore crucial to decipher the key genetic alterations and relevant molecular pathways driving PCa metastatic progression so that predictive biomarkers and precise therapeutic targets can be developed. -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics
GBE Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics Marisa C.W. Lim1,*, Christopher C. Witt2, Catherine H. Graham1,3,andLilianaM.Davalos 1,4 1Department of Ecology and Evolution, Stony Brook University 2 Museum of Southwestern Biology and Department of Biology, University of New Mexico Downloaded from https://academic.oup.com/gbe/article-abstract/11/6/1552/5494706 by guest on 08 June 2019 3Swiss Federal Research Institute (WSL), Birmensdorf, Switzerland 4Consortium for Inter-Disciplinary Environmental Research, Stony Brook University *Corresponding author: E-mail: [email protected]. Accepted: May 12, 2019 Data deposition: The raw read data have been deposited in the NCBI Sequence Read Archive under BioProject: PRJNA543673, BioSample: SAMN11774663-SAMN11774674, SRA Study: SRP198856. All scripts used for analyses are available on Dryad: doi:10.5061/dryad.v961mb4. Abstract High-elevation organisms experience shared environmental challenges that include low oxygen availability, cold temperatures, and intense ultraviolet radiation. Consequently, repeated evolution of the same genetic mechanisms may occur across high-elevation taxa. To test this prediction, we investigated the extent to which the same biochemical pathways, genes, or sites were subject to parallel molecular evolution for 12 Andean hummingbird species (family: Trochilidae) representing several independent transitions to high elevation across the phylogeny. Across high-elevation species, we discovered parallel evolution for several pathways and genes with evidence of positive selection. In particular, positively selected genes were frequently part of cellular respiration, metabolism, or cell death pathways. To further examine the role of elevation in our analyses, we compared results for low- and high-elevation species and tested different thresholds for defining elevation categories. -
Par6c Is at the Mother Centriole and Controls Centrosomal Protein
860 Research Article Par6c is at the mother centriole and controls centrosomal protein composition through a Par6a-dependent pathway Vale´rian Dormoy, Kati Tormanen and Christine Su¨ tterlin* Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697-2300, USA *Author for correspondence ([email protected]) Accepted 3 December 2012 Journal of Cell Science 126, 860–870 ß 2013. Published by The Company of Biologists Ltd doi: 10.1242/jcs.121186 Summary The centrosome contains two centrioles that differ in age, protein composition and function. This non-membrane bound organelle is known to regulate microtubule organization in dividing cells and ciliogenesis in quiescent cells. These specific roles depend on protein appendages at the older, or mother, centriole. In this study, we identified the polarity protein partitioning defective 6 homolog gamma (Par6c) as a novel component of the mother centriole. This specific localization required the Par6c C-terminus, but was independent of intact microtubules, the dynein/dynactin complex and the components of the PAR polarity complex. Par6c depletion resulted in altered centrosomal protein composition, with the loss of a large number of proteins, including Par6a and p150Glued, from the centrosome. As a consequence, there were defects in ciliogenesis, microtubule organization and centrosome reorientation during migration. Par6c interacted with Par3 and aPKC, but these proteins were not required for the regulation of centrosomal protein composition. Par6c also associated with Par6a, which controls protein recruitment to the centrosome through p150Glued. Our study is the first to identify Par6c as a component of the mother centriole and to report a role of a mother centriole protein in the regulation of centrosomal protein composition.