ALK2 Inhibitors Display Beneficial Effects in Preclinical Models
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An Autocrine Activinb Mechanism Drives TGF /Activin Signaling In
An autocrine ActivinB mechanism drives TGF β/Activin signaling in Group 3 medulloblastoma Morgane Morabito, Magalie Larcher, Florence M G Cavalli, Chloé Foray, Antoine Forget, Liliana Mirabal-ortega, Mamy Andrianteranagna, Sabine Druillennec, Alexandra Garancher, Julien Masliah-planchon, et al. To cite this version: Morgane Morabito, Magalie Larcher, Florence M G Cavalli, Chloé Foray, Antoine Forget, et al.. An autocrine ActivinB mechanism drives TGF β/Activin signaling in Group 3 medulloblastoma. EMBO Molecular Medicine, Wiley Open Access, 2019, 6, pp.e9830. 10.15252/emmm.201809830. hal-02347105 HAL Id: hal-02347105 https://hal.archives-ouvertes.fr/hal-02347105 Submitted on 5 Nov 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Article An autocrine ActivinB mechanism drives TGFb/ Activin signaling in Group 3 medulloblastoma Morgane Morabito1,2,3,4,5, Magalie Larcher1,2,3,4,5, Florence MG Cavalli6,7, Chloé Foray1,2,3,4,5, Antoine Forget1,2,3,4,5, Liliana Mirabal-Ortega1,2,3,4,5, Mamy Andrianteranagna5,8,9,10,11,12,13, Sabine Druillennec1,2,3,4,5, -
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 Collagen-Remodeling Gene Signature Regulated by TGF-B Signaling Is Associated with Metastasis and Poor Survival in Serous Ovarian Cancer
Published OnlineFirst November 11, 2013; DOI: 10.1158/1078-0432.CCR-13-1256 Clinical Cancer Imaging, Diagnosis, Prognosis Research A Collagen-Remodeling Gene Signature Regulated by TGF-b Signaling Is Associated with Metastasis and Poor Survival in Serous Ovarian Cancer Dong-Joo Cheon1, Yunguang Tong2, Myung-Shin Sim7, Judy Dering6, Dror Berel3, Xiaojiang Cui1, Jenny Lester1, Jessica A. Beach1,5, Mourad Tighiouart3, Ann E. Walts4, Beth Y. Karlan1,6, and Sandra Orsulic1,6 Abstract Purpose: To elucidate molecular pathways contributing to metastatic cancer progression and poor clinical outcome in serous ovarian cancer. Experimental Design: Poor survival signatures from three different serous ovarian cancer datasets were compared and a common set of genes was identified. The predictive value of this gene signature was validated in independent datasets. The expression of the signature genes was evaluated in primary, metastatic, and/or recurrent cancers using quantitative PCR and in situ hybridization. Alterations in gene expression by TGF-b1 and functional consequences of loss of COL11A1 were evaluated using pharmacologic and knockdown approaches, respectively. Results: We identified and validated a 10-gene signature (AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN) that is associated with poor overall survival (OS) in patients with high-grade serous ovarian cancer. The signature genes encode extracellular matrix proteins involved in collagen remodeling. Expression of the signature genes is regulated by TGF-b1 signaling and is enriched in metastases in comparison with primary ovarian tumors. We demonstrate that levels of COL11A1, one of the signature genes, continuously increase during ovarian cancer disease progression, with the highest expression in recurrent metastases. -
HIST2H3C(27Ac) Antibody Purified Mouse Monoclonal Antibody Catalog # Ao2159a
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 HIST2H3C(27Ac) Antibody Purified Mouse Monoclonal Antibody Catalog # AO2159a Specification HIST2H3C(27Ac) Antibody - Product Information Application E, WB, FC, IHC Primary Accession Q71DI3 Reactivity Human Host Mouse Clonality Monoclonal Isotype IgG1 Calculated MW 15.4kDa KDa Description Histones are basic nuclear proteins that are responsible for the nucleosome structure of the chromosomal fiber in eukaryotes. This structure consists of approximately 146 bp of DNA wrapped around a nucleosome, an octamer composed of pairs of each of the four core histones (H2A, H2B, H3, and H4). The chromatin fiber is further compacted through the interaction of a linker histone, H1, with the DNA between the nucleosomes to form higher order chromatin structures. This gene is intronless and encodes a member of the histone H3 family. Transcripts from this gene lack polyA tails; instead, they contain a palindromic termination element. This gene is found in a histone cluster on chromosome 1. This gene is one of four histone genes in the cluster that are duplicated; this record represents the telomeric copy. Immunogen Synthesized peptide of human HIST2H3C (AA: ATKAARK(Ac)SAPATGGV). Formulation Purified antibody in PBS with 0.05% sodium azide HIST2H3C(27Ac) Antibody - Additional Information Gene ID 126961;333932;653604 Dilution E~~1/10000 Page 1/2 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 WB~~1/500 - 1/2000 FC~~1/200 - 1/400 IHC~~1/200 - 1/1000 Storage Maintain refrigerated at 2-8°C for up to 6 months. -
A Meta-Analysis of the Inhibin Network Reveals Prognostic Value in Multiple Solid Tumors
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.25.171942; this version posted June 27, 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-ND 4.0 International license. 1 A meta-analysis of the inhibin network reveals prognostic value in multiple solid tumors Eduardo Listik1†, Ben Horst1,2†, Alex Seok Choi1, Nam. Y. Lee3, Balázs Győrffy4 and Karthikeyan Mythreye1 Affiliations: 1Department of Pathology, University of Alabama at Birmingham, Birmingham AL, USA, 35294. 2Department of Chemistry and Biochemistry, University of South Carolina, Columbia SC, USA, 29208. 3 Division of Pharmacology, Chemistry and Biochemistry, College of Medicine, University of Arizona, Tucson, AZ, 85721, USA. 4 TTK Cancer Biomarker Research Group, Institute of Enzymology, and Semmelweis University Department of Bioinformatics and 2nd Department of Pediatrics, Budapest, Hungary. Corresponding author: Karthikeyan Mythreye, Ph.D. Division of Molecular and Cellular Pathology, Department of Pathology, The University of Alabama at Birmingham. WTI 320B, 1824 Sixth Avenue South, Birmingham, AL, USA, 35294. Phone : +1 205.934.2746 E-mail : [email protected] †The authors contributed equally to this work. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.25.171942; this version posted June 27, 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-ND 4.0 International license. -
Expression Analysis of Progesterone‑Regulated Mirnas in Cells Derived from Human Glioblastoma
MOLECULAR MEDICINE REPORTS 23: 475, 2021 Expression analysis of progesterone‑regulated miRNAs in cells derived from human glioblastoma DIANA ELISA VELÁZQUEZ‑VÁZQUEZ1, AYLIN DEL MORAL‑MORALES1, JENIE MARIAN CRUZ‑BURGOS2, EDUARDO MARTÍNEZ‑MARTÍNEZ3, MAURICIO RODRÍGUEZ‑DORANTES2 and IGNACIO CAMACHO‑ARROYO1 1Unidad de Investigación en Reproducción Humana, Instituto Nacional de Perinatología‑Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510; 2Oncogenomics Laboratory, The National Institute of Genomic Medicine; 3Laboratory of Cell Communication and Extracellular Vesicles, The National Institute of Genomic Medicine, Mexico City 14610, Mexico Received August 16, 2020; Accepted February 2, 2021 DOI: 10.3892/mmr.2021.12114 Abstract. Glioblastomas (GBMs) are the most frequent and is characterized by being highly infiltrative, angiogenic and malignant type of brain tumor. It has been reported that resistant to chemotherapy and radiotherapy. The medical progesterone (P4) regulates the progression of GBMs by modi‑ history of patients with GBM is short as few of them survive fying the expression of genes that promote cell proliferation, more than one year (1‑3). GBM is mainly diagnosed in adults migration and invasion; however, it is not fully understood >50 years old, but it can occur at any age and the incidence is how these processes are regulated. It is possible that P4 medi‑ higher in men than in women (3:2) (4). ates some of these effects through changes in the microRNA Studies have focused on the identification of new biomarkers (miRNA) expression profile in GBM cells. The present study and therapeutic agents in GBM. Of particular interest are the investigated the effects of P4 on miRNAs expression profile microRNAs (miRNAs), which are single‑stranded, short, in U‑251MG cells derived from a human GBM. -
Table SI. Characteristics of 9 Patients Chosen from 60 Patients with Hepatocellular Carcinoma
Table SI. Characteristics of 9 patients chosen from 60 patients with hepatocellular carcinoma. Characteristic No. of patients (%) Age, years <50 3 (33.3) ≥50 6 (66.7) Sex Female 4 (44.4) Male 5 (55.6) Albumin, g/l ≥35 8 (88.9) <35 1 (11.1) AFP, ng/ml ≤20 2 (22.2) >20 7 (77.8) Tumor diameter, cm ≤5 3 (33.3) >5 6 (66.7) Portal vein invasion Without 3 (33.3) With 6 (66.7) BCLC stage A 2 (22.2) B/C 7 (77.8) AFP, α-fetoprotein; BCLC, Barcelona Clinic Liver Cancer. Table SII. List of the gene symbol and relative expression of six significant clusters. Gene symbol SPOT Profile Day 0 Day 3 Day 7 Day 14 Day 21 ALCAM ID_10 16 0 -356.5 378.5 0.1 307.8 ANGPT2 ID_13 16 0 -45.7 197.2 96 60.2 CD80 ID_24 16 0 -177 362 -29 187 CTNNB1 ID_29 16 0 -58 294 83 49 BMP4 ID_37 16 0 -107 189 0 53 CCL28 ID_49 16 0 -307 856 491 347 CCR1 ID_50 16 0 -189 391 236 112 CCR4 ID_53 16 0 -101 361 89 240 CCR7 ID_56 16 0 -98 361 103 66 CLC ID_70 16 0 -147 274 0 188 CRIM1 ID_74 16 0 -642 229 -48 -112 CXCL14 ID_83 16 0 -434 1,154.00 778 376 CXCL16 ID_84 16 0 -128 800 477 180 DKK3 ID_97 16 0 -199 610 107 3 FGFBP1 ID_129 16 0 -224 484 -155 37 FGF20 ID_147 16 0 -113 264 -32 -4 WFIKKN1 ID_163 16 0 -195 544 262 41 GFRA1 ID_175 16 0 -536 443 29 280 GFRA4 ID_178 16 0 -211 208 -47 116 TNFSF18 ID_180 16 0 -14 246 89 68 GPC5 ID_187 16 0 -180 1,345.00 298 509 GH1 ID_194 16 0 -596 206 10 -9 CCHCR1 ID_198 16 0 -108 322 50 238 NRG1 ID_200 16 0 -81 378.5 170.5 187 ICAM1 ID_207 16 0 -136 98 -17 63 IFNB1 ID_213 16 0 -87 233 143 29 IGFBP3 ID_218 16 0 -1,311.00 609 -368 -178 IGFBP4 ID_219 16 0 -
Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases
Journal of Clinical Medicine Review Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases Anna Wajda 1 , Larysa Sivitskaya 2,* and Agnieszka Paradowska-Gorycka 1 1 Department of Molecular Biology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland; [email protected] (A.W.); [email protected] (A.P.-G.) 2 Institute of Genetics and Cytology, National Academy of Sciences of Belarus, 220072 Minsk, Belarus * Correspondence: [email protected] Abstract: NGS technologies have transformed clinical diagnostics and broadly used from neonatal emergencies to adult conditions where the diagnosis cannot be made based on clinical symptoms. Autoimmune diseases reveal complicate molecular background and traditional methods could not fully capture them. Certainly, NGS technologies meet the needs of modern exploratory research, diagnostic and pharmacotherapy. Therefore, the main purpose of this review was to briefly present the application of NGS technology used in recent years in the understanding of autoimmune diseases paying particular attention to autoimmune connective tissue diseases. The main issues are presented in four parts: (a) panels, whole-genome and -exome sequencing (WGS and WES) in diagnostic, (b) Human leukocyte antigens (HLA) as a diagnostic tool, (c) RNAseq, (d) microRNA and (f) microbiome. Although all these areas of research are extensive, it seems that epigenetic impact on the development of systemic autoimmune diseases will set trends for future studies on this area. Citation: Wajda, A.; Sivitskaya, L.; Keywords: next-generation sequencing; autoimmune diseases; autoimmune connective tissue dis- Paradowska-Gorycka, A. Application eases; HLA; microRNA; microbiome of NGS Technology in Understanding the Pathology of Autoimmune Diseases. J. Clin. -
FOP) Can Be Rescued by the Drug Candidate Saracatinib
Stem Cell Reviews and Reports https://doi.org/10.1007/s12015-020-10103-9 ActivinA Induced SMAD1/5 Signaling in an iPSC Derived EC Model of Fibrodysplasia Ossificans Progressiva (FOP) Can Be Rescued by the Drug Candidate Saracatinib Susanne Hildebrandt1,2,3 & Branka Kampfrath1 & Kristin Fischer3,4 & Laura Hildebrand2,3 & Julia Haupt1 & Harald Stachelscheid3,4 & Petra Knaus 1,2 Accepted: 1 December 2020 # The Author(s) 2021 Abstract Balanced signal transduction is crucial in tissue patterning, particularly in the vasculature. Heterotopic ossification (HO) is tightly linked to vascularization with increased vessel number in hereditary forms of HO, such as Fibrodysplasia ossificans progressiva (FOP). FOP is caused by mutations in the BMP type I receptor ACVR1 leading to aberrant SMAD1/5 signaling in response to ActivinA. Whether observed vascular phenotype in human FOP lesions is connected to aberrant ActivinA signaling is unknown. Blocking of ActivinA prevents HO in FOP mice indicating a central role of the ligand in FOP. Here, we established a new FOP endothelial cell model generated from induced pluripotent stem cells (iECs) to study ActivinA signaling. FOP iECs recapitulate pathogenic ActivinA/SMAD1/5 signaling. Whole transcriptome analysis identified ActivinA mediated activation of the BMP/ NOTCH pathway exclusively in FOP iECs, which was rescued to WT transcriptional levels by the drug candidate Saracatinib. We propose that ActivinA causes transcriptional pre-patterning of the FOP endothelium, which might contribute to differential vascularity in FOP lesions compared to non-hereditary HO. Keywords FOP . BMP-receptor . Activin . iPSCs . Human endothelial cells . HO . Saracatinib Introduction pathological conditions such as cancer and chronic inflamma- tion [2]. -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................ -
The Expression of Genes Contributing to Pancreatic Adenocarcinoma Progression Is Influenced by the Respective Environment – Sagini Et Al
The expression of genes contributing to pancreatic adenocarcinoma progression is influenced by the respective environment – Sagini et al Supplementary Figure 1: Target genes regulated by TGM2. Figure represents 24 genes regulated by TGM2, which were obtained from Ingenuity Pathway Analysis. As indicated, 9 genes (marked red) are down-regulated by TGM2. On the contrary, 15 genes (marked red) are up-regulated by TGM2. Supplementary Table 1: Functional annotations of genes from Suit2-007 cells growing in pancreatic environment Categoriesa Diseases or p-Valuec Predicted Activation Number of genesf Functions activationd Z-scoree Annotationb Cell movement Cell movement 1,56E-11 increased 2,199 LAMB3, CEACAM6, CCL20, AGR2, MUC1, CXCL1, LAMA3, LCN2, COL17A1, CXCL8, AIF1, MMP7, CEMIP, JUP, SOD2, S100A4, PDGFA, NDRG1, SGK1, IGFBP3, DDR1, IL1A, CDKN1A, NREP, SEMA3E SERPINA3, SDC4, ALPP, CX3CL1, NFKBIA, ANXA3, CDH1, CDCP1, CRYAB, TUBB2B, FOXQ1, SLPI, F3, GRINA, ITGA2, ARPIN/C15orf38- AP3S2, SPTLC1, IL10, TSC22D3, LAMC2, TCAF1, CDH3, MX1, LEP, ZC3H12A, PMP22, IL32, FAM83H, EFNA1, PATJ, CEBPB, SERPINA5, PTK6, EPHB6, JUND, TNFSF14, ERBB3, TNFRSF25, FCAR, CXCL16, HLA-A, CEACAM1, FAT1, AHR, CSF2RA, CLDN7, MAPK13, FERMT1, TCAF2, MST1R, CD99, PTP4A2, PHLDA1, DEFB1, RHOB, TNFSF15, CD44, CSF2, SERPINB5, TGM2, SRC, ITGA6, TNC, HNRNPA2B1, RHOD, SKI, KISS1, TACSTD2, GNAI2, CXCL2, NFKB2, TAGLN2, TNF, CD74, PTPRK, STAT3, ARHGAP21, VEGFA, MYH9, SAA1, F11R, PDCD4, IQGAP1, DCN, MAPK8IP3, STC1, ADAM15, LTBP2, HOOK1, CST3, EPHA1, TIMP2, LPAR2, CORO1A, CLDN3, MYO1C, -
(RABBIT) Antibody - 600-401-M75
Anti-Histone H3 [methyl Lys27] (RABBIT) Antibody - 600-401-M75 Code: 600-401-M75 Size: 50 µg Product Description: Anti-Histone H3 [methyl Lys27] (RABBIT) Antibody - 600-401-M75 Concentration: 0.83 by UV absorbance at 280 nm PhysicalState: Liquid (sterile filtered) Label Unconjugated Host Rabbit Gene Name HIST2H3C Species Reactivity Human, mouse, rat, C. elegans Buffer 0.02 M Potassium Phosphate, 0.15 M Sodium Chloride, pH 7.2 Stabilizer None Preservative 0.01% (w/v) Sodium Azide Storage Condition Store vial at -20° C prior to opening. Aliquot contents and freeze at -20° C or below for extended storage. Avoid cycles of freezing and thawing. Centrifuge product if not completely clear after standing at room temperature. This product is stable for several weeks at 4° C as an undiluted liquid. Dilute only prior to immediate use. Synonyms H3.3B, H3 histone, family 3A, H3.3AH3F3H3F3B, histone H3.3, MGC87783, MGC87782, H3R2me1, H3R2me2, H3R2me3 Application Note Anti-Histone H3 K27 methyl Antibody is useful for ELISA and Western Blot. Specific conditions for reactivity should be optimized by the end user. Expect a band approximately ~15.4kDa corresponding to the appropriate cell lysate or extract. Background The nucleosome is comprised of 146 bp of DNA wrapped around a series of histone proteins arranged as an octamer consisting of 2 copies of histone H2A, H2B, H3 and H4. Within the nucleosome core the histone proteins are covalent modified at specific residues predominantly within the N-terminal tail including lysine (acetylation, methylation, SUMOylation, and ubiquitinylation), arginine methylation and citrullination, serine and threonine phosphorylation, as well as proline isomerization.