Metastatic Canine Mammary Carcinomas Can Be Identified by A
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View of HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-Negative Breast Resistance to Systemic Therapy in Patients with Breast Cancer
Wen et al. Cancer Cell Int (2018) 18:128 https://doi.org/10.1186/s12935-018-0625-9 Cancer Cell International PRIMARY RESEARCH Open Access Sulbactam‑enhanced cytotoxicity of doxorubicin in breast cancer cells Shao‑hsuan Wen1†, Shey‑chiang Su2†, Bo‑huang Liou3, Cheng‑hao Lin1 and Kuan‑rong Lee1* Abstract Background: Multidrug resistance (MDR) is a major obstacle in breast cancer treatment. The predominant mecha‑ nism underlying MDR is an increase in the activity of adenosine triphosphate (ATP)-dependent drug efux trans‑ porters. Sulbactam, a β-lactamase inhibitor, is generally combined with β-lactam antibiotics for treating bacterial infections. However, sulbactam alone can be used to treat Acinetobacter baumannii infections because it inhibits the expression of ATP-binding cassette (ABC) transporter proteins. This is the frst study to report the efects of sulbactam on mammalian cells. Methods: We used the breast cancer cell lines as a model system to determine whether sulbactam afects cancer cells. The cell viabilities in the present of doxorubicin with or without sulbactam were measured by MTT assay. Protein identities and the changes in protein expression levels in the cells after sulbactam and doxorubicin treatment were determined using LC–MS/MS. Real-time reverse transcription polymerase chain reaction (real-time RT-PCR) was used to analyze the change in mRNA expression levels of ABC transporters after treatment of doxorubicin with or without sulbactam. The efux of doxorubicin was measures by the doxorubicin efux assay. Results: MTT assay revealed that sulbactam enhanced the cytotoxicity of doxorubicin in breast cancer cells. The results of proteomics showed that ABC transporter proteins and proteins associated with the process of transcription and initiation of translation were reduced. -
Crystal Structure of Human Gadd45 Reveals an Active Dimer
Protein Cell 2011, 2(10): 814–826 Protein & Cell DOI 10.1007/s13238-011-1090-6 RESEARCH ARTICLE Crystal structure of human Gadd45 reveals an active dimer 1,2,3* 1* 4 4 1 1 3 Wenzheng Zhang , Sheng Fu , Xuefeng Liu , Xuelian Zhao✉ , Wenchi Zhang✉ , Wei Peng , Congying Wu , Yuanyuan Li3, Xuemei Li1, Mark Bartlam1,2, Zong-Hao Zeng1 , Qimin Zhan4 , Zihe Rao1,2,3 1 National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China 2 Tianjin Key Laboratory of Protein Science, College of Life Sciences, Nankai University, Tianjin 300071, China 3 Laboratory of Structural Biology, Tsinghua University, Beijing 100084, China 4 State Key Laboratory of Molecular Oncology, Cancer Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China ✉ Correspondence: [email protected] (Z.-H. Zeng), [email protected] (Q. Zhan) Received August 23, 2011 Accepted September 19, 2011 ABSTRACT INTRODUCTION The human Gadd45 protein family plays critical roles in The human Gadd45 family of growth arrest and DNA DNA repair, negative growth control, genomic stability, damage-inducible proteins has three members, Gadd45α, cell cycle checkpoints and apoptosis. Here we report the -β, and -γ, grouped together on the basis of their amino acid crystal structure of human Gadd45, revealing a unique sequences and functional similarities (Vairapandi et al., 1996; dimer formed via a bundle of four parallel helices, Takekawa and Saito, 1998; Nakayama et al., 1999). The involving the most conserved residues among the genes encoding the α, β and γ isoforms of Gadd45 are Gadd45 isoforms. -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
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. -
Open Dogan Phdthesis Final.Pdf
The Pennsylvania State University The Graduate School Eberly College of Science ELUCIDATING BIOLOGICAL FUNCTION OF GENOMIC DNA WITH ROBUST SIGNALS OF BIOCHEMICAL ACTIVITY: INTEGRATIVE GENOME-WIDE STUDIES OF ENHANCERS A Dissertation in Biochemistry, Microbiology and Molecular Biology by Nergiz Dogan © 2014 Nergiz Dogan Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2014 ii The dissertation of Nergiz Dogan was reviewed and approved* by the following: Ross C. Hardison T. Ming Chu Professor of Biochemistry and Molecular Biology Dissertation Advisor Chair of Committee David S. Gilmour Professor of Molecular and Cell Biology Anton Nekrutenko Professor of Biochemistry and Molecular Biology Robert F. Paulson Professor of Veterinary and Biomedical Sciences Philip Reno Assistant Professor of Antropology Scott B. Selleck Professor and Head of the Department of Biochemistry and Molecular Biology *Signatures are on file in the Graduate School iii ABSTRACT Genome-wide measurements of epigenetic features such as histone modifications, occupancy by transcription factors and coactivators provide the opportunity to understand more globally how genes are regulated. While much effort is being put into integrating the marks from various combinations of features, the contribution of each feature to accuracy of enhancer prediction is not known. We began with predictions of 4,915 candidate erythroid enhancers based on genomic occupancy by TAL1, a key hematopoietic transcription factor that is strongly associated with gene induction in erythroid cells. Seventy of these DNA segments occupied by TAL1 (TAL1 OSs) were tested by transient transfections of cultured hematopoietic cells, and 56% of these were active as enhancers. Sixty-six TAL1 OSs were evaluated in transgenic mouse embryos, and 65% of these were active enhancers in various tissues. -
Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers
Identification and Characterization of TPRKB Dependency in TP53 Deficient Cancers. by Kelly Kennaley A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Molecular and Cellular Pathology) in the University of Michigan 2019 Doctoral Committee: Associate Professor Zaneta Nikolovska-Coleska, Co-Chair Adjunct Associate Professor Scott A. Tomlins, Co-Chair Associate Professor Eric R. Fearon Associate Professor Alexey I. Nesvizhskii Kelly R. Kennaley [email protected] ORCID iD: 0000-0003-2439-9020 © Kelly R. Kennaley 2019 Acknowledgements I have immeasurable gratitude for the unwavering support and guidance I received throughout my dissertation. First and foremost, I would like to thank my thesis advisor and mentor Dr. Scott Tomlins for entrusting me with a challenging, interesting, and impactful project. He taught me how to drive a project forward through set-backs, ask the important questions, and always consider the impact of my work. I’m truly appreciative for his commitment to ensuring that I would get the most from my graduate education. I am also grateful to the many members of the Tomlins lab that made it the supportive, collaborative, and educational environment that it was. I would like to give special thanks to those I’ve worked closely with on this project, particularly Dr. Moloy Goswami for his mentorship, Lei Lucy Wang, Dr. Sumin Han, and undergraduate students Bhavneet Singh, Travis Weiss, and Myles Barlow. I am also grateful for the support of my thesis committee, Dr. Eric Fearon, Dr. Alexey Nesvizhskii, and my co-mentor Dr. Zaneta Nikolovska-Coleska, who have offered guidance and critical evaluation since project inception. -
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. -
Evolution of the DAN Gene Family in Vertebrates
bioRxiv preprint doi: https://doi.org/10.1101/794404; this version posted June 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. RESEARCH ARTICLE Evolution of the DAN gene family in vertebrates Juan C. Opazo1,2,3, Federico G. Hoffmann4,5, Kattina Zavala1, Scott V. Edwards6 1Instituto de Ciencias Ambientales y Evolutivas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile. 2David Rockefeller Center for Latin American Studies, Harvard University, Cambridge, MA 02138, USA. 3Millennium Nucleus of Ion Channels-Associated Diseases (MiNICAD). 4 Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, 39762, USA. Cite as: Opazo JC, Hoffmann FG, 5 Zavala K, Edwards SV (2020) Institute for Genomics, Biocomputing, and Biotechnology, Mississippi State Evolution of the DAN gene family in University, Mississippi State, 39762, USA. vertebrates. bioRxiv, 794404, ver. 3 peer-reviewed and recommended by 6 PCI Evolutionary Biology. doi: Department of Organismic and Evolutionary Biology, Harvard University, 10.1101/794404 Cambridge, MA 02138, USA. This article has been peer-reviewed and recommended by Peer Community in Evolutionary Biology Posted: 29 June 2020 doi: 10.24072/pci.evolbiol.100104 ABSTRACT Recommender: Kateryna Makova The DAN gene family (DAN, Differential screening-selected gene Aberrant in Neuroblastoma) is a group of genes that is expressed during development and plays fundamental roles in limb bud formation and digitation, kidney formation and morphogenesis and left-right axis specification. -
Expression Profiling of KLF4
Expression Profiling of KLF4 AJCR0000006 Supplemental Data Figure S1. Snapshot of enriched gene sets identified by GSEA in Klf4-null MEFs. Figure S2. Snapshot of enriched gene sets identified by GSEA in wild type MEFs. 98 Am J Cancer Res 2011;1(1):85-97 Table S1: Functional Annotation Clustering of Genes Up-Regulated in Klf4 -Null MEFs ILLUMINA_ID Gene Symbol Gene Name (Description) P -value Fold-Change Cell Cycle 8.00E-03 ILMN_1217331 Mcm6 MINICHROMOSOME MAINTENANCE DEFICIENT 6 40.36 ILMN_2723931 E2f6 E2F TRANSCRIPTION FACTOR 6 26.8 ILMN_2724570 Mapk12 MITOGEN-ACTIVATED PROTEIN KINASE 12 22.19 ILMN_1218470 Cdk2 CYCLIN-DEPENDENT KINASE 2 9.32 ILMN_1234909 Tipin TIMELESS INTERACTING PROTEIN 5.3 ILMN_1212692 Mapk13 SAPK/ERK/KINASE 4 4.96 ILMN_2666690 Cul7 CULLIN 7 2.23 ILMN_2681776 Mapk6 MITOGEN ACTIVATED PROTEIN KINASE 4 2.11 ILMN_2652909 Ddit3 DNA-DAMAGE INDUCIBLE TRANSCRIPT 3 2.07 ILMN_2742152 Gadd45a GROWTH ARREST AND DNA-DAMAGE-INDUCIBLE 45 ALPHA 1.92 ILMN_1212787 Pttg1 PITUITARY TUMOR-TRANSFORMING 1 1.8 ILMN_1216721 Cdk5 CYCLIN-DEPENDENT KINASE 5 1.78 ILMN_1227009 Gas2l1 GROWTH ARREST-SPECIFIC 2 LIKE 1 1.74 ILMN_2663009 Rassf5 RAS ASSOCIATION (RALGDS/AF-6) DOMAIN FAMILY 5 1.64 ILMN_1220454 Anapc13 ANAPHASE PROMOTING COMPLEX SUBUNIT 13 1.61 ILMN_1216213 Incenp INNER CENTROMERE PROTEIN 1.56 ILMN_1256301 Rcc2 REGULATOR OF CHROMOSOME CONDENSATION 2 1.53 Extracellular Matrix 5.80E-06 ILMN_2735184 Col18a1 PROCOLLAGEN, TYPE XVIII, ALPHA 1 51.5 ILMN_1223997 Crtap CARTILAGE ASSOCIATED PROTEIN 32.74 ILMN_2753809 Mmp3 MATRIX METALLOPEPTIDASE -
Structural Insights Into Oligomerization and Mitochondrial Remodeling of Dynamin 1-Like Protein
Structural insights into oligomerization and mitochondrial remodeling of dynamin 1-like protein Dissertation zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften (Dr. rer. nat.) eingereicht im Fachbereich Biologie, Chemie, Pharmazie der Freien Universität Berlin vorgelegt von Dipl.-Ing. Chris Fröhlich aus der Lutherstadt Eisleben Berlin 2013 ii Die vorliegende Arbeit wurde im Zeitraum April 2009 bis März 2013 am Max-Delbrück-Centrum für Molekulare Medizin Berlin-Buch unter Anleitung von PROF. DR. OLIVER DAUMKE angefertigt. 1. Gutachter: Prof. Dr. Udo Heinemann 2. Gutachter: Prof. Dr. Oliver Daumke Tag der Disputation: 29.07.2013 iii … to boldly go where no man has gone before. iv Contents ___________________________________________________________________________ Contents Contents ................................................................................................................................................... v List of Figures ........................................................................................................................................... ix List of Tables ............................................................................................................................................ xi 1. Introduction ..................................................................................................................................... 1 1.1. Mitochondria .......................................................................................................................... -
DNA Modifications in Models of Alcohol Use Disorders
Alcohol 60 (2017) 19e30 Contents lists available at ScienceDirect Alcohol journal homepage: http://www.alcoholjournal.org/ DNA modifications in models of alcohol use disorders * Christopher T. Tulisiak a, R. Adron Harris a, b, Igor Ponomarev a, b, a Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, 2500 Speedway, A4800, Austin, TX 78712, USA b The College of Pharmacy, The University of Texas at Austin, 2409 University Avenue, A1900, Austin, TX 78712, USA article info abstract Article history: Chronic alcohol use and abuse result in widespread changes to gene expression, some of which Received 2 September 2016 contribute to the development of alcohol-use disorders (AUD). Gene expression is controlled, in part, by a Received in revised form group of regulatory systems often referred to as epigenetic factors, which includes, among other 3 November 2016 mechanisms, chemical marks made on the histone proteins around which genomic DNA is wound to Accepted 5 November 2016 form chromatin, and on nucleotides of the DNA itself. In particular, alcohol has been shown to perturb the epigenetic machinery, leading to changes in gene expression and cellular functions characteristic of Keywords: AUD and, ultimately, to altered behavior. DNA modifications in particular are seeing increasing research Alcohol fi Epigenetics in the context of alcohol use and abuse. To date, studies of DNA modi cations in AUD have primarily fi fi fi DNA methylation looked at global methylation pro les in human brain and blood, gene-speci c methylation pro les in DNMT animal models, methylation changes associated with prenatal ethanol exposure, and the potential DNA hydroxymethylation therapeutic abilities of DNA methyltransferase inhibitors. -
Supporting Information
Supporting Information Pouryahya et al. SI Text Table S1 presents genes with the highest absolute value of Ricci curvature. We expect these genes to have significant contribution to the network’s robustness. Notably, the top two genes are TP53 (tumor protein 53) and YWHAG gene. TP53, also known as p53, it is a well known tumor suppressor gene known as the "guardian of the genome“ given the essential role it plays in genetic stability and prevention of cancer formation (1, 2). Mutations in this gene play a role in all stages of malignant transformation including tumor initiation, promotion, aggressiveness, and metastasis (3). Mutations of this gene are present in more than 50% of human cancers, making it the most common genetic event in human cancer (4, 5). Namely, p53 mutations play roles in leukemia, breast cancer, CNS cancers, and lung cancers, among many others (6–9). The YWHAG gene encodes the 14-3-3 protein gamma, a member of the 14-3-3 family proteins which are involved in many biological processes including signal transduction regulation, cell cycle pro- gression, apoptosis, cell adhesion and migration (10, 11). Notably, increased expression of 14-3-3 family proteins, including protein gamma, have been observed in a number of human cancers including lung and colorectal cancers, among others, suggesting a potential role as tumor oncogenes (12, 13). Furthermore, there is evidence that loss Fig. S1. The histogram of scalar Ricci curvature of 8240 genes. Most of the genes have negative scalar Ricci curvature (75%). TP53 and YWHAG have notably low of p53 function may result in upregulation of 14-3-3γ in lung cancer Ricci curvatures.