DOCSLIB.ORG

Regulator Combinations Identify Systemic Sclerosis Patients with More Severe Disease

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Regulator Combinations Identify Systemic Sclerosis Patients with More Severe Disease Regulator combinations identify systemic sclerosis patients with more severe disease Yue Wang, … , Monique Hinchcliff, Michael L. Whitfield JCI Insight. 2020. https://doi.org/10.1172/jci.insight.137567. Research In-Press Preview Dermatology Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production and internal organ dysfunction. We previously identified four ‘intrinsic’ subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We find regulator activity scores can reproduce the intrinsic molecular subsets with distinct sets of regulators identified for inflammatory, fibroproliferative and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identify a subgroup of fibroproliferative/inflammatory SSc patients with more severe pathophenotypes. We further identify a subgroup of SSc patients that had higher MRSS and increased likelihood of interstitial lung disease. Using an independent cohort, we show this group was most likely to show forced vital capacity decline over a period of 36 – 54 months. Our results demonstrate an association between the activation of regulators, gene expression subsets and clinical variables that can identify SSc patients with more severe disease. Find the latest version: https://jci.me/137567/pdf 1 Regulator combinations identify systemic sclerosis patients with more severe 2 disease 3 4 Yue Wang1, Jennifer M. Franks1, Monica Yang2, Diana M. Toledo1, Tammara A. Wood1, Monique 5 Hinchcliff2 and Michael L. Whitfield1,* 6 7 1Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New 8 Hampshire 03756, USA. 2Department of Internal Medicine, Northwestern University Feinberg School of 9 Medicine, Chicago, Illinois, 60611, USA 10 11 12 Corresponding Author: 13 Michael L. Whitfield, Ph.D. 14 Department of Biomedical Data Science 15 Geisel School of Medicine at Dartmouth 16 1 Medical Center Drive 17 Lebanon, NH 03756 18 [email protected] 19 Phone: 603-650-1109, Fax: 603-650-1188 20 21 Conflict of interest statement 22 The authors have declared that no conflict of interest exists. 23 Abstract 24 Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, 25 autoantibody production and internal organ dysfunction. We previously identified four ‘intrinsic’ 26 subsets of SSc based upon skin gene expression that are found across organ systems. Gene 27 expression regulators that underlie the SSc intrinsic subsets, or are associated with clinical 28 covariates, have not been systematically characterized. Here we present a computational 29 framework to calculate the activity scores of gene expression regulators and identify their 30 associations with SSc clinical outcomes. We find regulator activity scores can reproduce the 31 intrinsic molecular subsets with distinct sets of regulators identified for inflammatory, 32 fibroproliferative, limited and normal-like samples. Regulators most highly correlated with 33 modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identify subgroups of 34 fibroproliferative and inflammatory SSc patients with more severe pathophenotypes, such as 35 higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent 36 cohort, we show the group with more severe ILD was more likely to show forced vital capacity 37 decline over a period of 36 – 54 months. Our results demonstrate an association between the 38 activation of regulators, gene expression subsets and clinical variables that can identify SSc 39 patients with more severe disease. 40 41 42 Keywords: Systemic sclerosis, Biomarkers, Pulmonary fibrosis, forced vital capacity decline, 43 Computational biology 44 Introduction 45 Systemic sclerosis (SSc) is a heterogeneous autoimmune disease that results in the production of 46 autoantibodies, skin fibrosis, and internal organ involvement (1); the pattern and severity of skin 47 and organ involvement varies across patients. Clinically, SSc is divided into two subtypes based 48 on the extent of skin involvement including limited cutaneous (lc) and diffuse cutaneous (dc) SSc 49 (2). The lungs, heart, kidney, and other organs may also be involved (1, 3, 4). We have previously 50 identified SSc ‘intrinsic’ subsets (fibroproliferative, inflammatory, limited, and normal-like) based 51 upon skin gene expression (5-7), and that may predict response to therapy (8, 9). Analysis of skin 52 gene expression across cohorts identified interactions between immune and stromal cells that may 53 act as key drivers of SSc pathogenesis in patients with a permissive genetic background (10, 11). 54 Regulators, either acting at the transcriptional or post-transcriptional level, control the 55 expression of their target gene networks, thus they contribute to different biological phenotypes 56 and can be proxies for tightly, coordinated and regulated pathways (12). However, the regulators 57 that underlie SSc and the association with clinical phenotypes have not been systematically 58 investigated. The goals of these analyses were two-fold. First, to identify regulators of gene 59 expression, such as transcription factors (TFs) and microRNAs (miRNAs), that are enriched in the 60 SSc intrinsic subsets, and second to identify regulators that could identify SSc patients with more 61 severe skin and lung disease. Our reasoning is that the regulators, and the network of genes that 62 they control, may be informative of pathological processes acting in SSc. 63 Herein, we constructed a computational framework to systematically examine the activity 64 of 836 regulators across 431 SSc skin and 35 SSc blood samples using publicly available gene 65 expression data (see Figure 1 for an overview). We characterized each regulator’s target genes and 66 their expression profile to infer the regulator activity scores for each SSc sample using the BASe 67 algorithm (13). Regulator activity scores were correlated with the modified Rodnan Skin Score 68 (MRSS), a common measure of SSc severity, to identify those with activity scores highly 69 associated with severity of skin disease, particularly in fibroproliferative and inflammatory 70 patients. We then built an interaction network using regulators that were significantly associated 71 with MRSS to give a comprehensive picture of the regulatory interactions within the SSc intrinsic 72 subsets. Then, novel subgroups within the fibroproliferative or inflammatory intrinsic subset were 73 identified by using a combination of the activity scores of two MRSS-correlated regulators. 74 Further, we found a novel group of SSc samples containing patients with higher MRSS and 75 significant decline in forced vital capacity (FVC) over 36 months of follow-up. 76 77 Results 78 Regulator activity scores reproduce the SSc intrinsic subsets 79 We analyzed the regulator activity scores in four independent, publicly available SSc datasets of 80 Milano et al., Pendergrass et al., Hinchcliff et al., and Assassi et al. (5-7, 14). For each dataset, we 81 first calculated sample-specific regulator activity scores using BASe (13) by integrating gene 82 expression profiles and regulator target gene sets (see Methods). We applied intrinsic gene analysis 83 to calculate a within-between score (15) to select regulators that showed the most consistent 84 activity scores within an SSc intrinsic subset, but had the most variable activity scores across all 85 subsets. The 270 regulators that had a false discovery rate (FDR) less than 2% in at least three 86 datasets were considered to be “intrinsic SSc regulators” and included activator proteins; 87 CCAAT/enhancer-binding proteins; members of the e2F family, eTS family, STAT family, and 88 GATA family; glucocorticoid receptors; interferon regulatory factors; RUNX1-related core 89 binding factors; B- and T-cell-related transcription factors; and numerous miRNAs 90 (Supplementary Table S1). 91 We organized the samples and regulators in each dataset by hierarchical clustering of the 92 activity scores with the 270 regulators. Broadly, we found that samples in the fibroproliferative 93 subset clustered together and displayed activation of key regulators of cell proliferation, such as 94 members of the e2F, MYC, and eTS families (Figure 2 and Supplementary Figures S1 to S4). 95 Target genes of these fibroproliferative cluster regulators are highly enriched in cell cycle and 96 DNA replication pathways (Supplementary Table S2 and Figure S5; corrected P-value < 0.05), 97 consistent with the activation of biological processes enriched in the fibroproliferative subset. 98 Immune-related proteins, such as those from the STAT family, Runx1-related core binding factors, 99 and nuclear factor of activated T cells (NFAT), are enriched in the inflammatory subset (Fig. 2 100 and Supplementary Figures. S1 to S4). Their target genes are significantly involved in immune- 101 related and signal transduction pathways such as the B-/T-cell receptor signaling pathway, Th17- 102 cell differentiation, and the TGF-beta signaling pathway (Supplementary Table S2 and Figure S6; 103 corrected
Load more

Recommended publications
  • RBP-J Signaling − Cells Through Notch Novel IRF8-Controlled
    Sca-1+Lin−CD117− Mesenchymal Stem/Stromal Cells Induce the Generation of Novel IRF8-Controlled Regulatory Dendritic Cells through Notch −RBP-J Signaling This information is current as of September 25, 2021. Xingxia Liu, Shaoda Ren, Chaozhuo Ge, Kai Cheng, Martin Zenke, Armand Keating and Robert C. H. Zhao J Immunol 2015; 194:4298-4308; Prepublished online 30 March 2015; doi: 10.4049/jimmunol.1402641 Downloaded from http://www.jimmunol.org/content/194/9/4298 Supplementary http://www.jimmunol.org/content/suppl/2015/03/28/jimmunol.140264 http://www.jimmunol.org/ Material 1.DCSupplemental References This article cites 59 articles, 19 of which you can access for free at: http://www.jimmunol.org/content/194/9/4298.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 25, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2015 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Sca-1+Lin2CD1172 Mesenchymal Stem/Stromal Cells Induce the Generation of Novel IRF8-Controlled Regulatory Dendritic Cells through Notch–RBP-J Signaling Xingxia Liu,*,1 Shaoda Ren,*,1 Chaozhuo Ge,* Kai Cheng,* Martin Zenke,† Armand Keating,‡,x and Robert C.
    [Show full text]
  • Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis.
    [Show full text]
  • Deciphering the Functions of Ets2, Pten and P53 in Stromal Fibroblasts in Multiple
    Deciphering the Functions of Ets2, Pten and p53 in Stromal Fibroblasts in Multiple Breast Cancer Models DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Julie Wallace Graduate Program in Molecular, Cellular and Developmental Biology The Ohio State University 2013 Dissertation Committee: Michael C. Ostrowski, PhD, Advisor Gustavo Leone, PhD Denis Guttridge, PhD Dawn Chandler, PhD Copyright by Julie Wallace 2013 Abstract Breast cancer is the second most common cancer in American women, and is also the second leading cause of cancer death in women. It is estimated that nearly a quarter of a million new cases of invasive breast cancer will be diagnosed in women in the United States this year, and approximately 40,000 of these women will die from breast cancer. Although death rates have been on the decline for the past decade, there is still much we need to learn about this disease to improve prevention, detection and treatment strategies. The majority of early studies have focused on the malignant tumor cells themselves, and much has been learned concerning mutations, amplifications and other genetic and epigenetic alterations of these cells. However more recent work has acknowledged the strong influence of tumor stroma on the initiation, progression and recurrence of cancer. Under normal conditions this stroma has been shown to have protective effects against tumorigenesis, however the transformation of tumor cells manipulates this surrounding environment to actually promote malignancy. Fibroblasts in particular make up a significant portion of this stroma, and have been shown to impact various aspects of tumor cell biology.
    [Show full text]
  • (12) United States Patent (10) Patent No.: US 9,109,232 B2 Schwartz Et Al
    US009 109232B2 (12) United States Patent (10) Patent No.: US 9,109,232 B2 Schwartz et al. (45) Date of Patent: Aug. 18, 2015 (54) ETS2 AND MESP1 GENERATE CARDIAC Islas et al., Transcription factors ETS2 and MESP1 transdifferentiate PROGENITORS FROM FIBROBLASTS human dermal fibroblasts into cardiac progenitors; PNAS, Published online before print Jul. 23, 2012, doi: 10.1073/pnas. 11202991.09, 2012.* (71) Applicants: University of Houston, Houston, TX Melotti et al., Ets-2 and c-Myb Act Independently in Regulating (US); Texas Heart Institute, Houston, Expression of the Hematopoietic StemCell Antigen CD34:JBC, vol. TX (US); The Texas A&M University 269, No. 41, pp. 25303-25309, 1994.* System, College Station, TX (US) Takahashi et al., Induction of pluripotent stem cells from adulthuman fibroblasts by defined factors; Cell, vol. 131, pp. 861-872, 2007.* Naldini et al. In vivo gene delivery and stable transduction of (72) Inventors: Robert J. Schwartz, Houston, TX (US); nondividing cells by a lentiviral vector; Science, vol. 272, pp. 263 Vladimir N. Potaman, Houston, TX 267, 1996.* (US); Jose Francisco Islas, Houston, TX European Patent Office; Office Action; European Application No. (US) 117114082; Jul 22, 2013. European Patent Office; Response to Office Action; European Appli (73) Assignees: University of Houston, Houston, TX cation No. 11711408.2; Aug. 30, 2013. Chinese Patent Office; Office Action; Chinese Patent Application No. (US); Texas Heart Institute, Houston, 2011800 19561.0; Sep. 18, 2013. TX (US); The Texas A&M University Chinese Patent Office; Office Action (English translation); Chinese System, College Station, TX (US) Patent Application No. 2011800 19561.0; Sep.
    [Show full text]
  • Identification of GA-Binding Protein Transcription Factor Alpha Subunit
    International Journal of Molecular Sciences Article Identification of GA-Binding Protein Transcription Factor Alpha Subunit (GABPA) as a Novel Bookmarking Factor Shunya Goto 1, Masashi Takahashi 1, Narumi Yasutsune 1, Sumiki Inayama 1, Dai Kato 2, Masashi Fukuoka 3, Shu-ichiro Kashiwaba 1 and Yasufumi Murakami 1,2,* 1 Department of Biological Science and Technology, Faculty of Industrial Science and Technology, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan; [email protected] (S.G.); [email protected] (M.T.); [email protected] (N.Y.); [email protected] (S.I.); [email protected] (S.K.) 2 Order-MadeMedical Research Inc., 208Todai-Kashiwa VP, 5-4-19 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-0882, Japan; [email protected] 3 Department of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan; [email protected] * Correspondence: [email protected]; Tel.: +81-3-5876-1717 (ext. 1919); Fax: +81-3-5876-1470 Received: 7 February 2019; Accepted: 27 February 2019; Published: 4 March 2019 Abstract: Mitotic bookmarking constitutes a mechanism for transmitting transcriptional patterns through cell division. Bookmarking factors, comprising a subset of transcription factors (TFs), and multiple histone modifications retained in mitotic chromatin facilitate reactivation of transcription in the early G1 phase. However, the specific TFs that act as bookmarking factors remain largely unknown. Previously, we identified the “early G1 genes” and screened TFs that were predicted to bind to the upstream region of these genes, then identified GA-binding protein transcription factor alpha subunit (GABPA) and Sp1 transcription factor (SP1) as candidate bookmarking factors.
    [Show full text]
  • Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
    Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • GABPA Is a Master Regulator of Luminal Identity and Restrains Aggressive Diseases in Bladder Cancer
    Cell Death & Differentiation (2020) 27:1862–1877 https://doi.org/10.1038/s41418-019-0466-7 ARTICLE GABPA is a master regulator of luminal identity and restrains aggressive diseases in bladder cancer 1,2,3 3,4 5 2,5 5 3 5 5 Yanxia Guo ● Xiaotian Yuan ● Kailin Li ● Mingkai Dai ● Lu Zhang ● Yujiao Wu ● Chao Sun ● Yuan Chen ● 5 6 3 1,2 1,2 3,7 Guanghui Cheng ● Cheng Liu ● Klas Strååt ● Feng Kong ● Shengtian Zhao ● Magnus Bjorkhölm ● Dawei Xu 3,7 Received: 3 June 2019 / Revised: 20 November 2019 / Accepted: 21 November 2019 / Published online: 4 December 2019 © The Author(s) 2019. This article is published with open access Abstract TERT promoter mutations occur in the majority of glioblastoma, bladder cancer (BC), and other malignancies while the ETS family transcription factors GABPA and its partner GABPB1 activate the mutant TERT promoter and telomerase in these tumors. GABPA depletion or the disruption of the GABPA/GABPB1 complex by knocking down GABPB1 was shown to inhibit telomerase, thereby eliminating the tumorigenic potential of glioblastoma cells. GABPA/B1 is thus suggested as a cancer therapeutic target. However, it is unclear about its role in BC. Here we unexpectedly observed that GABPA ablation 1234567890();,: 1234567890();,: inhibited TERT expression, but robustly increased proliferation, stem, and invasive phenotypes and cisplatin resistance in BC cells, while its overexpression exhibited opposite effects, and inhibited in vivo metastasizing in a xenograft transplant model. Mechanistically, GABPA directly activates the transcription of FoxA1 and GATA3, key transcription factors driving luminal differentiation of urothelial cells. Consistently, TCGA/GEO dataset analyses show that GABPA expression is correlated positively with luminal while negatively with basal signatures.
    [Show full text]
  • Distinct Contributions of DNA Methylation and Histone Acetylation to the Genomic Occupancy of Transcription Factors
    Downloaded from genome.cshlp.org on October 8, 2021 - Published by Cold Spring Harbor Laboratory Press Research Distinct contributions of DNA methylation and histone acetylation to the genomic occupancy of transcription factors Martin Cusack,1 Hamish W. King,2 Paolo Spingardi,1 Benedikt M. Kessler,3 Robert J. Klose,2 and Skirmantas Kriaucionis1 1Ludwig Institute for Cancer Research, University of Oxford, Oxford, OX3 7DQ, United Kingdom; 2Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, United Kingdom; 3Target Discovery Institute, University of Oxford, Oxford, OX3 7FZ, United Kingdom Epigenetic modifications on chromatin play important roles in regulating gene expression. Although chromatin states are often governed by multilayered structure, how individual pathways contribute to gene expression remains poorly under- stood. For example, DNA methylation is known to regulate transcription factor binding but also to recruit methyl-CpG binding proteins that affect chromatin structure through the activity of histone deacetylase complexes (HDACs). Both of these mechanisms can potentially affect gene expression, but the importance of each, and whether these activities are inte- grated to achieve appropriate gene regulation, remains largely unknown. To address this important question, we measured gene expression, chromatin accessibility, and transcription factor occupancy in wild-type or DNA methylation-deficient mouse embryonic stem cells following HDAC inhibition. We observe widespread increases in chromatin accessibility at ret- rotransposons when HDACs are inhibited, and this is magnified when cells also lack DNA methylation. A subset of these elements has elevated binding of the YY1 and GABPA transcription factors and increased expression. The pronounced ad- ditive effect of HDAC inhibition in DNA methylation–deficient cells demonstrates that DNA methylation and histone deacetylation act largely independently to suppress transcription factor binding and gene expression.
    [Show full text]
  • CDC2 Mediates Progestin Initiated Endometrial Stromal Cell Proliferation: a PR Signaling to Gene Expression Independently of Its Binding to Chromatin
    CDC2 Mediates Progestin Initiated Endometrial Stromal Cell Proliferation: A PR Signaling to Gene Expression Independently of Its Binding to Chromatin Griselda Vallejo1, Alejandro D. La Greca1., Inti C. Tarifa-Reischle1., Ana C. Mestre-Citrinovitz1, Cecilia Ballare´ 2, Miguel Beato2,3, Patricia Saragu¨ eta1* 1 Instituto de Biologı´a y Medicina Experimental, IByME-Conicet, Buenos Aires, Argentina, 2 Centre de Regulacio´ Geno`mica, (CRG), Barcelona, Spain, 3 University Pompeu Fabra (UPF), Barcelona, Spain Abstract Although non-genomic steroid receptor pathways have been studied over the past decade, little is known about the direct gene expression changes that take place as a consequence of their activation. Progesterone controls proliferation of rat endometrial stromal cells during the peri-implantation phase of pregnancy. We showed that picomolar concentration of progestin R5020 mimics this control in UIII endometrial stromal cells via ERK1-2 and AKT activation mediated by interaction of Progesterone Receptor (PR) with Estrogen Receptor beta (ERb) and without transcriptional activity of endogenous PR and ER. Here we identify early downstream targets of cytoplasmic PR signaling and their possible role in endometrial stromal cell proliferation. Microarray analysis of global gene expression changes in UIII cells treated for 45 min with progestin identified 97 up- and 341 down-regulated genes. The most over-represented molecular functions were transcription factors and regulatory factors associated with cell proliferation and cell cycle, a large fraction of which were repressors down-regulated by hormone. Further analysis verified that progestins regulate Ccnd1, JunD, Usf1, Gfi1, Cyr61, and Cdkn1b through PR- mediated activation of ligand-free ER, ERK1-2 or AKT, in the absence of genomic PR binding.
    [Show full text]
  • 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.
    [Show full text]