Deep Neural Networks Identify Context-Specific Determinants of Transcription Factor Binding Affinity
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Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 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/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.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 © 2016 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 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
2017.08.28 Anne Barry-Reidy Thesis Final.Pdf
REGULATION OF BOVINE β-DEFENSIN EXPRESSION THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF DUBLIN FOR THE DEGREE OF DOCTOR OF PHILOSOPHY 2017 ANNE BARRY-REIDY SCHOOL OF BIOCHEMISTRY & IMMUNOLOGY TRINITY COLLEGE DUBLIN SUPERVISORS: PROF. CLIONA O’FARRELLY & DR. KIERAN MEADE TABLE OF CONTENTS DECLARATION ................................................................................................................................. vii ACKNOWLEDGEMENTS ................................................................................................................... viii ABBREVIATIONS ................................................................................................................................ix LIST OF FIGURES............................................................................................................................. xiii LIST OF TABLES .............................................................................................................................. xvii ABSTRACT ........................................................................................................................................xix Chapter 1 Introduction ........................................................................................................ 1 1.1 Antimicrobial/Host-defence peptides ..................................................................... 1 1.2 Defensins................................................................................................................. 1 1.3 β-defensins ............................................................................................................. -
Chicken CCDC152 Shares an NFYB-Regulated Bidirectional Promoter with a Growth Hormone Receptor Antisense Transcript and Inhibits Cells Proliferation and Migration
www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 48), pp: 84039-84053 Research Paper Chicken CCDC152 shares an NFYB-regulated bidirectional promoter with a growth hormone receptor antisense transcript and inhibits cells proliferation and migration Shudai Lin1, Wei Luo1, Mingya Jiang1, Wen Luo1, Bahareldin Ali Abdalla1, Qinghua Nie1, Li Zhang2 and Xiquan Zhang1 1Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou 510642, P.R. China 2Agricultural College, Guangdong Ocean University, Zhanjiang 524088, P.R. China Correspondence to: Xiquan Zhang, email: [email protected] Li Zhang, email: [email protected] Keywords: bidirectional promoter, GHR antisense transcript, coiled-coil domain containing 152, NFYB, cell cycle Received: December 15, 2016 Accepted: September 04, 2017 Published: September 20, 2017 Copyright: Lin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT The chicken coiled-coil domain-containing protein 152 (CCDC152) recently has been identified as a novel one implicated in cell cycle regulation, cellular proliferation and migration by us. Here we demonstrate that CCDC152 is oriented in a head-to- head configuration with the antisense transcript of growth hormone receptor (GHR) gene. Through serial luciferase reporter assays, we firstly identified a minimal 102 bp intergenic region as a core bidirectional promoter to drive basal transcription in divergent orientations. -
Mediator of DNA Damage Checkpoint 1 (MDC1) Is a Novel Estrogen Receptor Co-Regulator in Invasive 6 Lobular Carcinoma of the Breast 7 8 Evelyn K
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423142; this version posted December 16, 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. 1 Running Title: MDC1 co-regulates ER in ILC 2 3 Research article 4 5 Mediator of DNA damage checkpoint 1 (MDC1) is a novel estrogen receptor co-regulator in invasive 6 lobular carcinoma of the breast 7 8 Evelyn K. Bordeaux1+, Joseph L. Sottnik1+, Sanjana Mehrotra1, Sarah E. Ferrara2, Andrew E. Goodspeed2,3, James 9 C. Costello2,3, Matthew J. Sikora1 10 11 +EKB and JLS contributed equally to this project. 12 13 Affiliations 14 1Dept. of Pathology, University of Colorado Anschutz Medical Campus 15 2Biostatistics and Bioinformatics Shared Resource, University of Colorado Comprehensive Cancer Center 16 3Dept. of Pharmacology, University of Colorado Anschutz Medical Campus 17 18 Corresponding author 19 Matthew J. Sikora, PhD.; Mail Stop 8104, Research Complex 1 South, Room 5117, 12801 E. 17th Ave.; Aurora, 20 CO 80045. Tel: (303)724-4301; Fax: (303)724-3712; email: [email protected]. Twitter: 21 @mjsikora 22 23 Authors' contributions 24 MJS conceived of the project. MJS, EKB, and JLS designed and performed experiments. JLS developed models 25 for the project. EKB, JLS, SM, and AEG contributed to data analysis and interpretation. SEF, AEG, and JCC 26 developed and performed informatics analyses. MJS wrote the draft manuscript; all authors read and revised the 27 manuscript and have read and approved of this version of the manuscript. -
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. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Cell, Vol, 80, 777-786, March 10, 1995, Copyright © 1995 by Cell Press An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3 Nicole Schreiber-Agus,*t Lynda Chin,*tt Ken Chen,t et al., 1990), and a carboxy-terminal a-helical domain re- Richard Torres, t Govinda Rao,§ Peter Guida,t quired for dimerization with another basic region-helix- Arthur h Skoultchi,§ and Ronald A. DePinhot Ioop-helix-leucine zipper (bHLH-LZ) protein, Max (Black- "rDepartments of Microbiology and Immunology wood and Eisenman, 1991; Prendergast et al., 1991). and of Medicine Many of the biochemical and biological activities of Myc §Department of Cell Biology appear to be highly dependent upon its association with ~Division of Dermatology Max (Blackwood and Eisenman, 1991 ; Prendergast et al., Albert Einstein College of Medicine 1991; Kretzner et al., 1992; Amati et al., 1993a, 1993b). Bronx, New York 10461 In addition to its key role as an obligate partner in transacti- vation-competent Myc-Max complexes, Max may also re- press Myc-responsive genes through the formation of Summary transactivation-inert complexes that are capable of bind- ing the Myc-Max recognition sequence (Blackwood et al., Documented interactions among members of the Myc 1992; Kato et al., 1992; Kretzner et al., 1992; Makela et superfamily support a yin-yang model for the regula- al., 1992; Mukherjee et al., 1992; Prendergast et al., 1992; tion of Myc-responsive genes in which t ransactivation- Ayer et al., 1993; Zervos et al., 1993). -
Transcriptional Control of Tissue-Resident Memory T Cell Generation
Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Filip Cvetkovski All rights reserved ABSTRACT Transcriptional control of tissue-resident memory T cell generation Filip Cvetkovski Tissue-resident memory T cells (TRM) are a non-circulating subset of memory that are maintained at sites of pathogen entry and mediate optimal protection against reinfection. Lung TRM can be generated in response to respiratory infection or vaccination, however, the molecular pathways involved in CD4+TRM establishment have not been defined. Here, we performed transcriptional profiling of influenza-specific lung CD4+TRM following influenza infection to identify pathways implicated in CD4+TRM generation and homeostasis. Lung CD4+TRM displayed a unique transcriptional profile distinct from spleen memory, including up-regulation of a gene network induced by the transcription factor IRF4, a known regulator of effector T cell differentiation. In addition, the gene expression profile of lung CD4+TRM was enriched in gene sets previously described in tissue-resident regulatory T cells. Up-regulation of immunomodulatory molecules such as CTLA-4, PD-1, and ICOS, suggested a potential regulatory role for CD4+TRM in tissues. Using loss-of-function genetic experiments in mice, we demonstrate that IRF4 is required for the generation of lung-localized pathogen-specific effector CD4+T cells during acute influenza infection. Influenza-specific IRF4−/− T cells failed to fully express CD44, and maintained high levels of CD62L compared to wild type, suggesting a defect in complete differentiation into lung-tropic effector T cells. -
Anti-NFYC Antibody (ARG66549)
Product datasheet [email protected] ARG66549 Package: 100 μl anti-NFYC antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes NFYC Tested Reactivity Hu, Ms Tested Application IHC-P, WB Host Rabbit Clonality Polyclonal Isotype IgG Target Name NFYC Antigen Species Human Immunogen KLH-conjugated synthetic peptide encompassing a sequence within the N-term region of Human NFYC. Conjugation Un-conjugated Alternate Names HAP5; NF-YC; Nuclear transcription factor Y subunit gamma; Nuclear transcription factor Y subunit C; CBF-C; HSM; CBFC; Transactivator HSM-1/2; CAAT box DNA-binding protein subunit C; H1TF2A Application Instructions Application table Application Dilution IHC-P 1:100 - 1:200 WB 1:500 - 1:1000 Application Note IHC-P: Antigen Retrieval: Heat mediation was performed in Sodium citrate buffer (pH 6.0). * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 50 kDa Observed Size 50 kDa Properties Form Liquid Purification Affinity purification with immunogen. Buffer 0.42% Potassium phosphate (pH 7.3), 0.87% NaCl, 0.01% Sodium azide and 30% Glycerol. Preservative 0.01% Sodium azide Stabilizer 30% Glycerol Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. www.arigobio.com 1/2 Note For laboratory research only, not for drug, diagnostic or other use. -
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. -
Systems Biology Lecture Outline (Systems Biology) Michael K
Spring 2019 – Epigenetics and Systems Biology Lecture Outline (Systems Biology) Michael K. Skinner – Biol 476/576 CUE 418, 10:35-11:50 am, Tuesdays & Thursdays January 22 & 29, 2019 Weeks 3 and 4 Systems Biology (Components & Technology) Components (DNA, Expression, Cellular, Organ, Physiology, Organism, Differentiation, Development, Phenotype, Evolution) Technology (Genomics, Transcriptomes, Proteomics) (Interaction, Signaling, Metabolism) Omics (Data Processing and Resources) Required Reading ENCODE (2012) ENCODE Explained. Nature 489:52-55. Tavassoly I, Goldfarb J, Iyengar R. (2018) Essays Biochem. 62(4):487-500. Literature Sundaram V, Wang T. Transposable Element Mediated Innovation in Gene Regulatory Landscapes of Cells: Re-Visiting the "Gene-Battery" Model. Bioessays. 2018 40(1). doi: 10.1002/bies.201700155. Abil Z, Ellefson JW, Gollihar JD, Watkins E, Ellington AD. Compartmentalized partnered replication for the directed evolution of genetic parts and circuits. Nat Protoc. 2017 Dec;12(12):2493- 2512. Filipp FV. Crosstalk between epigenetics and metabolism-Yin and Yang of histone demethylases and methyltransferases in cancer. Brief Funct Genomics. 2017 Nov 1;16(6):320-325. Chen Z, Li S, Subramaniam S, Shyy JY, Chien S. Epigenetic Regulation: A New Frontier for Biomedical Engineers. Annu Rev Biomed Eng. 2017 Jun 21;19:195-219. Hollick JB. Paramutation and related phenomena in diverse species. Nat Rev Genet. 2017 Jan;18(1):5-23. Macovei A, Pagano A, Leonetti P, Carbonera D, Balestrazzi A, Araújo SS. Systems biology and genome-wide approaches to unveil the molecular players involved in the pre-germinative metabolism: implications on seed technology traits. Plant Cell Rep. 2017 May;36(5):669-688. Bagchi DN, Iyer VR. -
The Human Proteome Co-Regulation Map Reveals Functional Relationships Between Proteins
bioRxiv preprint doi: https://doi.org/10.1101/582247; this version posted March 19, 2019. The copyright holder for this preprint (which was 19/03/2019not certified by peer review) is the author/funder, who hasNBT_manuscript_revised granted bioRxiv a license - toGoogle display Docs the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 The human proteome co-regulation map reveals functional relationships 2 between proteins 3 Georg Kustatscher1 *, Piotr Grabowski2 *, Tina A. Schrader3 , Josiah B. Passmore3 , Michael 4 Schrader3 , Juri Rappsilber1,2# 5 1 Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK 6 2 Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, 7 Germany 8 3 Biosciences, University of Exeter, Exeter EX4 4QD, UK 9 * Equal contribution 10 # Communicating author: [email protected] 11 Submission as Resource article. 12 The annotation of protein function is a longstanding challenge of cell biology that 13 suffers from the sheer magnitude of the task. Here we present ProteomeHD, which 14 documents the response of 10,323 human proteins to 294 biological perturbations, 15 measured by isotope-labelling mass spectrometry. Using this data matrix and robust 16 machine learning we create a co-regulation map of the cell that reflects functional 17 associations between human proteins. The map identifies a functional context for 18 many uncharacterized proteins, including microproteins that are difficult to study with 19 traditional methods. Co-regulation also captures relationships between proteins 20 which do not physically interact or co-localize. For example, co-regulation of the 21 peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us 22 to discover a novel organelle interface between peroxisomes and mitochondria in 23 mammalian cells.