Transcript Profiling Identifies Early Response Genes Against FMDV
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Pancreatic Fibroblast Heterogeneity: from Development to Cancer
cells Review Pancreatic Fibroblast Heterogeneity: From Development to Cancer 1, 2, 2,3 Paloma E. Garcia y, Michael K. Scales y, Benjamin L. Allen and Marina Pasca di Magliano 2,3,4,* 1 Program in Molecular and Cellular Pathology, University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48105, USA; [email protected] 2 Department of Cell and Developmental Biology, University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109, USA; [email protected] (M.K.S.); [email protected] (B.L.A.) 3 Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA 4 Department of Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA * Correspondence: [email protected]; Tel.: +1-734-276-7104 Authors contributed equally to this work. y Received: 22 October 2020; Accepted: 10 November 2020; Published: 12 November 2020 Abstract: Pancreatic ductal adenocarcinoma (PDA) is characterized by an extensive fibroinflammatory microenvironment that accumulates from the onset of disease progression. Cancer-associated fibroblasts (CAFs) are a prominent cellular component of the stroma, but their role during carcinogenesis remains controversial, with both tumor-supporting and tumor-restraining functions reported in different studies. One explanation for these contradictory findings is the heterogeneous nature of the fibroblast populations, and the different roles each subset might play in carcinogenesis. Here, we review the current literature on the origin and function of pancreatic fibroblasts, from the developing organ to the healthy adult pancreas, and throughout the initiation and progression of PDA. We also discuss clinical approaches to targeting fibroblasts in PDA. Keywords: fibroblasts; pancreas; cancer-associated fibroblasts; pancreas development; pancreatic cancer; tumor microenvironment; hedgehog; transforming growth factor Beta 1. -
Multifactorial Erβ and NOTCH1 Control of Squamous Differentiation and Cancer
Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks, … , Karine Lefort, G. Paolo Dotto J Clin Invest. 2014;124(5):2260-2276. https://doi.org/10.1172/JCI72718. Research Article Oncology Downmodulation or loss-of-function mutations of the gene encoding NOTCH1 are associated with dysfunctional squamous cell differentiation and development of squamous cell carcinoma (SCC) in skin and internal organs. While NOTCH1 receptor activation has been well characterized, little is known about how NOTCH1 gene transcription is regulated. Using bioinformatics and functional screening approaches, we identified several regulators of the NOTCH1 gene in keratinocytes, with the transcription factors DLX5 and EGR3 and estrogen receptor β (ERβ) directly controlling its expression in differentiation. DLX5 and ERG3 are required for RNA polymerase II (PolII) recruitment to the NOTCH1 locus, while ERβ controls NOTCH1 transcription through RNA PolII pause release. Expression of several identified NOTCH1 regulators, including ERβ, is frequently compromised in skin, head and neck, and lung SCCs and SCC-derived cell lines. Furthermore, a keratinocyte ERβ–dependent program of gene expression is subverted in SCCs from various body sites, and there are consistent differences in mutation and gene-expression signatures of head and neck and lung SCCs in female versus male patients. Experimentally increased ERβ expression or treatment with ERβ agonists inhibited proliferation of SCC cells and promoted NOTCH1 expression and squamous differentiation both in vitro and in mouse xenotransplants. Our data identify a link between transcriptional control of NOTCH1 expression and the estrogen response in keratinocytes, with implications for differentiation therapy of squamous cancer. Find the latest version: https://jci.me/72718/pdf Research article Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks,1,2 Paola Ostano,3 Seung-Hee Jo,1,2 Jun Dai,1,2 Spiro Getsios,4 Piotr Dziunycz,5 Günther F.L. -
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. -
CCAAT/Enhancer Binding Protein Epsilon) Thomas Burmeister Charite, Med
Atlas of Genetics and Cytogenetics in Oncology and Haematology OPEN ACCESS JOURNAL INIST-CNRS Gene Section Short Communication CEBPE (CCAAT/enhancer binding protein epsilon) Thomas Burmeister Charite, Med. Klinik fur Hamatologie, Onkologie und Tumorimmunologie, Hindenburgdamm 30, 12200 Berlin, Germany; [email protected] Published in Atlas Database: March 2017 Online updated version : http://AtlasGeneticsOncology.org/Genes/CEBPEID42984ch14q11.html Printable original version : http://documents.irevues.inist.fr/bitstream/handle/2042/69005/03-2017-CEBPEID42984ch14q11.pdf DOI: 10.4267/2042/69005 This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 France Licence. © 2017 Atlas of Genetics and Cytogenetics in Oncology and Haematology alternative 3-exon-organization of the human Abstract CEBPE gene (Figure 1b). However, exon 1, as described by Yamanaka et al. contains a frameshift Review on CEBPE, with data on DNA, on the according to the GRCh38.p7 NCBI assembly. protein encoded, and where the gene is implicated. Transcription Keywords CEBPE; Transcription factor; Neutrophil specific Various transcripts have been reported, resulting in granule deficiency; Acute lymphoblastic leukemia; four protein isoforms (Lekstrom-Himes 2001, Translocation. Yamanaka 1997; Figure 1c). All transcripts share a common 3' end. Identity Protein Other names: CRP1 Description HGNC (Hugo): CEBPE CEBPE is a member of the CCAAT/enhancer- Location: 14q11.2 binding protein (C/EBP) family, which also Location (base pair) includes CEBPA, CEBPB, CEBPG, CEBPD and Starts at 23117306 and ends at 23119611 bp from CEBPZ (Ramji & Foka; 2002). A common pter (according to GRCh38.p7 Annotation Release structural feature of the C/EBP proteins is the 108, May 5 2016) presence of a highly conserved 55-65 amino acid sequence at the C-terminus which encodes a basic DNA/RNA leucine zipper motif (bZIP domain) that functions as a dimerization domain. -
Supplementary Materials
Supplementary Materials: Supplemental Table 1 Abbreviations FMDV Foot and Mouth Disease Virus FMD Foot and Mouth Disease NC Non-treated Control DEGs Differentially Expressed Genes RNA-seq High-throughput Sequencing of Mrna RT-qPCR Quantitative Real-time Reverse Transcriptase PCR TCID50 50% Tissue Culture Infective Doses CPE Cytopathic Effect MOI Multiplicity of Infection DMEM Dulbecco's Modified Eagle Medium FBS Fetal Bovine Serum PBS Phosphate Buffer Saline QC Quality Control FPKM Fragments per Kilo bases per Million fragments method GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes R Pearson Correlation Coefficient NFKBIA NF-kappa-B Inhibitor alpha IL6 Interleukin 6 CCL4 C-C motif Chemokine 4 CXCL2 C-X-C motif Chemokine 2 TNF Tumor Necrosis Factor VEGFA Vascular Endothelial Growth Gactor A CCL20 C-C motif Chemokine 20 CSF2 Macrophage Colony-Stimulating Factor 2 GADD45B Growth Arrest and DNA Damage Inducible 45 beta MYC Myc proto-oncogene protein FOS Proto-oncogene c-Fos MCL1 Induced myeloid leukemia cell differentiation protein Mcl-1 MAP3K14 Mitogen-activated protein kinase kinase kinase 14 IRF1 Interferon regulatory factor 1 CCL5 C-C motif chemokine 5 ZBTB3 Zinc finger and BTB domain containing 3 OTX1 Orthodenticle homeobox 1 TXNIP Thioredoxin-interacting protein ZNF180 Znc Finger Protein 180 ZNF36 Znc Finger Protein 36 ZNF182 Zinc finger protein 182 GINS3 GINS complex subunit 3 KLF15 Kruppel-like factor 15 Supplemental Table 2 Primers for Verification of RNA-seq-detected DEGs with RT-qPCR TNF F: CGACTCAGTGCCGAGATCAA R: -
NFIL3 Mutations Alter Immune Homeostasis and Sensitise For
Ann Rheum Dis: first published as 10.1136/annrheumdis-2018-213764 on 14 December 2018. Downloaded from Basic and translational research EXTENDED REPORT NFIL3 mutations alter immune homeostasis and sensitise for arthritis pathology Susan Schlenner,1,2 Emanuela Pasciuto,1,2 Vasiliki Lagou,1,2 Oliver Burton,1,2 Teresa Prezzemolo,1,2 Steffie Junius,1,2 Carlos P Roca,1,2 Cyril Seillet,3,4 Cynthia Louis,3 James Dooley,1,2 Kylie Luong,3,4 Erika Van Nieuwenhove,1,2,5 Ian P Wicks,3,4 Gabrielle Belz,3,4 Stéphanie Humblet-Baron,1,2 Carine Wouters,1,5 Adrian Liston1,2 Handling editor Josef S ABSTRact Key messages Smolen Objectives NFIL3 is a key immunological transcription factor, with knockout mice studies identifying functional ► Additional material is Homozygous NFIL3 mutations identified in roles in multiple immune cell types. Despite the importance ► published online only. To view monozygotic twins with juvenile idiopathic please visit the journal online of NFIL3, little is known about its function in humans. arthritis. (http:// dx. doi. org/ 10. 1136/ Methods Here, we characterised a kindred of two Enhanced susceptibility to arthritis induction in annrheumdis- 2018- 213764). monozygotic twin girls with juvenile idiopathic arthritis at ► Nfil3-knockout mice. 1 the genetic and immunological level, using whole exome Department of Microbiology NFIL3 loss in patients and mice is associated sequencing, single cell sequencing and flow cytometry. ► and Immunology, KUL - with elevated production of IL-1 . University of Leuven, Leuven, Parallel studies were performed in a mouse model. β Knockdown of NFIL3 in healthy macrophages Belgium Results The patients inherited a novel p.M170I in NFIL3 ► 2VIB Center for Brain and drives IL-1β production. -
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. -
A Gene Expression Resource Generated by Genome-Wide Lacz
© 2015. Published by The Company of Biologists Ltd | Disease Models & Mechanisms (2015) 8, 1467-1478 doi:10.1242/dmm.021238 RESOURCE ARTICLE A gene expression resource generated by genome-wide lacZ profiling in the mouse Elizabeth Tuck1,**, Jeanne Estabel1,*,**, Anika Oellrich1, Anna Karin Maguire1, Hibret A. Adissu2, Luke Souter1, Emma Siragher1, Charlotte Lillistone1, Angela L. Green1, Hannah Wardle-Jones1, Damian M. Carragher1,‡, Natasha A. Karp1, Damian Smedley1, Niels C. Adams1,§, Sanger Institute Mouse Genetics Project1,‡‡, James N. Bussell1, David J. Adams1, Ramiro Ramırez-Soliś 1, Karen P. Steel1,¶, Antonella Galli1 and Jacqueline K. White1,§§ ABSTRACT composite of RNA-based expression data sets. Strong agreement was observed, indicating a high degree of specificity in our data. Knowledge of the expression profile of a gene is a critical piece of Furthermore, there were 1207 observations of expression of a information required to build an understanding of the normal and particular gene in an anatomical structure where Bgee had no essential functions of that gene and any role it may play in the data, indicating a large amount of novelty in our data set. development or progression of disease. High-throughput, large- Examples of expression data corroborating and extending scale efforts are on-going internationally to characterise reporter- genotype-phenotype associations and supporting disease gene tagged knockout mouse lines. As part of that effort, we report an candidacy are presented to demonstrate the potential of this open access adult mouse expression resource, in which the powerful resource. expression profile of 424 genes has been assessed in up to 47 different organs, tissues and sub-structures using a lacZ reporter KEY WORDS: Gene expression, lacZ reporter, Mouse, Resource gene. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Cells Using Gene Expression Profiling and Insulin Gene Methylation
RESEARCH ARTICLE Comparison of enteroendocrine cells and pancreatic β-cells using gene expression profiling and insulin gene methylation ☯ ☯ Gyeong Ryul Ryu , Esder Lee , Jong Jin Kim, Sung-Dae MoonID, Seung-Hyun Ko, Yu- Bae Ahn, Ki-Ho SongID* Division of Endocrinology & Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea ☯ These authors contributed equally to this work. a1111111111 * [email protected] a1111111111 a1111111111 a1111111111 a1111111111 Abstract Various subtypes of enteroendocrine cells (EECs) are present in the gut epithelium. EECs and pancreatic β-cells share similar pathways of differentiation during embryonic develop- ment and after birth. In this study, similarities between EECs and β-cells were evaluated in OPEN ACCESS detail. To obtain specific subtypes of EECs, cell sorting by flow cytometry was conducted Citation: Ryu GR, Lee E, Kim JJ, Moon S-D, Ko S- from STC-1 cells (a heterogenous EEC line), and each single cell was cultured and pas- H, Ahn Y-B, et al. (2018) Comparison of saged. Five EEC subtypes were established according to hormone expression, measured enteroendocrine cells and pancreatic β-cells using by quantitative RT-PCR and immunostaining: L, K, I, G and S cells expressing glucagon-like gene expression profiling and insulin gene methylation. PLoS ONE 13(10): e0206401. https:// peptide-1, glucose-dependent insulinotropic polypeptide, cholecystokinin, gastrin and doi.org/10.1371/journal.pone.0206401 secretin, respectively. Each EEC subtype was found to express not only the corresponding Editor: Wataru Nishimura, International University gut hormone but also other gut hormones. Global microarray gene expression profiles of Health and Welfare School of Medicine, JAPAN revealed a higher similarity between each EEC subtype and MIN6 cells (a β-cell line) than Received: March 29, 2018 between C2C12 cells (a myoblast cell line) and MIN6 cells, and all EEC subtypes were highly similar to each other. -
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.