Evidence for a Functional Role of Epigenetically Regulated Midcluster HOXB Genes in the Development of Barrett Esophagus
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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. -
Detailed Review Paper on Retinoid Pathway Signalling
1 1 Detailed Review Paper on Retinoid Pathway Signalling 2 December 2020 3 2 4 Foreword 5 1. Project 4.97 to develop a Detailed Review Paper (DRP) on the Retinoid System 6 was added to the Test Guidelines Programme work plan in 2015. The project was 7 originally proposed by Sweden and the European Commission later joined the project as 8 a co-lead. In 2019, the OECD Secretariat was added to coordinate input from expert 9 consultants. The initial objectives of the project were to: 10 draft a review of the biology of retinoid signalling pathway, 11 describe retinoid-mediated effects on various organ systems, 12 identify relevant retinoid in vitro and ex vivo assays that measure mechanistic 13 effects of chemicals for development, and 14 Identify in vivo endpoints that could be added to existing test guidelines to 15 identify chemical effects on retinoid pathway signalling. 16 2. This DRP is intended to expand the recommendations for the retinoid pathway 17 included in the OECD Detailed Review Paper on the State of the Science on Novel In 18 vitro and In vivo Screening and Testing Methods and Endpoints for Evaluating 19 Endocrine Disruptors (DRP No 178). The retinoid signalling pathway was one of seven 20 endocrine pathways considered to be susceptible to environmental endocrine disruption 21 and for which relevant endpoints could be measured in new or existing OECD Test 22 Guidelines for evaluating endocrine disruption. Due to the complexity of retinoid 23 signalling across multiple organ systems, this effort was foreseen as a multi-step process. -
Genetic Variability in the Italian Heavy Draught Horse from Pedigree Data and Genomic Information
Supplementary material for manuscript: Genetic variability in the Italian Heavy Draught Horse from pedigree data and genomic information. Enrico Mancin†, Michela Ablondi†, Roberto Mantovani*, Giuseppe Pigozzi, Alberto Sabbioni and Cristina Sartori ** Correspondence: [email protected] † These two Authors equally contributed to the work Supplementary Figure S1. Mares and foal of Italian Heavy Draught Horse (IHDH; courtesy of Cinzia Stoppa) Supplementary Figure S2. Number of Equivalent Generations (EqGen; above) and pedigree completeness (PC; below) over years in Italian Heavy Draught Horse population. Supplementary Table S1. Descriptive statistics of homozygosity (observed: Ho_obs; expected: Ho_exp; total: Ho_tot) in 267 genotyped individuals of Italian Heavy Draught Horse based on the number of homozygous genotypes. Parameter Mean SD Min Max Ho_obs 35,630.3 500.7 34,291 38,013 Ho_exp 35,707.8 64.0 35,010 35,740 Ho_tot 50,674.5 93.8 49,638 50,714 1 Definitions of the methods for inbreeding are in the text. Supplementary Figure S3. Values of BIC obtained by analyzing values of K from 1 to 10, corresponding on the same amount of clusters defining the proportion of ancestry in the 267 genotyped individuals. Supplementary Table S2. Estimation of genomic effective population size (Ne) traced back to 18 generations ago (Gen. ago). The linkage disequilibrium estimation, adjusted for sampling bias was also included (LD_r2), as well as the relative standard deviation (SD(LD_r2)). Gen. ago Ne LD_r2 SD(LD_r2) 1 100 0.009 0.014 2 108 0.011 0.018 3 118 0.015 0.024 4 126 0.017 0.028 5 134 0.019 0.031 6 143 0.021 0.034 7 156 0.023 0.038 9 173 0.026 0.041 11 189 0.029 0.046 14 213 0.032 0.052 18 241 0.036 0.058 Supplementary Table S3. -
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. -
Pdf Sub-Classification of Patients with a Molecular Alteration Provides Better Response [57]
Theranostics 2021, Vol. 11, Issue 12 5759 Ivyspring International Publisher Theranostics 2021; 11(12): 5759-5777. doi: 10.7150/thno.57659 Research Paper Homeobox B5 promotes metastasis and poor prognosis in Hepatocellular Carcinoma, via FGFR4 and CXCL1 upregulation Qin He1, Wenjie Huang2, Danfei Liu1, Tongyue Zhang1, Yijun Wang1, Xiaoyu Ji1, Meng Xie1, Mengyu Sun1, Dean Tian1, Mei Liu1, Limin Xia1 1. Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China. 2. Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases; Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; Clinical Medicine Research Center for Hepatic Surgery of Hubei Province; Key Laboratory of Organ Transplantation, Ministry of Education and Ministry of Public Health, Wuhan, Hubei, 430030, China. Corresponding author: Dr. Limin Xia, Department of Gastroenterology, Institute of Liver and Gastrointestinal Diseases, Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China; Phone: 86 27 6937 8507; Fax: 86 27 8366 2832; E-mail: [email protected]. © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. Received: 2020.12.29; Accepted: 2021.03.17; Published: 2021.03.31 Abstract Background: Since metastasis remains the main reason for HCC-associated death, a better understanding of molecular mechanism underlying HCC metastasis is urgently needed. -
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. -
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. -
Ten Commandments for a Good Scientist
Unravelling the mechanism of differential biological responses induced by food-borne xeno- and phyto-estrogenic compounds Ana María Sotoca Covaleda Wageningen 2010 Thesis committee Thesis supervisors Prof. dr. ir. Ivonne M.C.M. Rietjens Professor of Toxicology Wageningen University Prof. dr. Albertinka J. Murk Personal chair at the sub-department of Toxicology Wageningen University Thesis co-supervisor Dr. ir. Jacques J.M. Vervoort Associate professor at the Laboratory of Biochemistry Wageningen University Other members Prof. dr. Michael R. Muller, Wageningen University Prof. dr. ir. Huub F.J. Savelkoul, Wageningen University Prof. dr. Everardus J. van Zoelen, Radboud University Nijmegen Dr. ir. Toine F.H. Bovee, RIKILT, Wageningen This research was conducted under the auspices of the Graduate School VLAG Unravelling the mechanism of differential biological responses induced by food-borne xeno- and phyto-estrogenic compounds Ana María Sotoca Covaleda Thesis submitted in fulfillment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus Prof. dr. M.J. Kropff, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Tuesday 14 September 2010 at 4 p.m. in the Aula Unravelling the mechanism of differential biological responses induced by food-borne xeno- and phyto-estrogenic compounds. Ana María Sotoca Covaleda Thesis Wageningen University, Wageningen, The Netherlands, 2010, With references, and with summary in Dutch. ISBN: 978-90-8585-707-5 “Caminante no hay camino, se hace camino al andar. Al andar se hace camino, y al volver la vista atrás se ve la senda que nunca se ha de volver a pisar” - Antonio Machado – A mi madre. -
SUPPLEMENTARY MATERIAL Bone Morphogenetic Protein 4 Promotes
www.intjdevbiol.com doi: 10.1387/ijdb.160040mk SUPPLEMENTARY MATERIAL corresponding to: Bone morphogenetic protein 4 promotes craniofacial neural crest induction from human pluripotent stem cells SUMIYO MIMURA, MIKA SUGA, KAORI OKADA, MASAKI KINEHARA, HIROKI NIKAWA and MIHO K. FURUE* *Address correspondence to: Miho Kusuda Furue. Laboratory of Stem Cell Cultures, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka 567-0085, Japan. Tel: 81-72-641-9819. Fax: 81-72-641-9812. E-mail: [email protected] Full text for this paper is available at: http://dx.doi.org/10.1387/ijdb.160040mk TABLE S1 PRIMER LIST FOR QRT-PCR Gene forward reverse AP2α AATTTCTCAACCGACAACATT ATCTGTTTTGTAGCCAGGAGC CDX2 CTGGAGCTGGAGAAGGAGTTTC ATTTTAACCTGCCTCTCAGAGAGC DLX1 AGTTTGCAGTTGCAGGCTTT CCCTGCTTCATCAGCTTCTT FOXD3 CAGCGGTTCGGCGGGAGG TGAGTGAGAGGTTGTGGCGGATG GAPDH CAAAGTTGTCATGGATGACC CCATGGAGAAGGCTGGGG MSX1 GGATCAGACTTCGGAGAGTGAACT GCCTTCCCTTTAACCCTCACA NANOG TGAACCTCAGCTACAAACAG TGGTGGTAGGAAGAGTAAAG OCT4 GACAGGGGGAGGGGAGGAGCTAGG CTTCCCTCCAACCAGTTGCCCCAAA PAX3 TTGCAATGGCCTCTCAC AGGGGAGAGCGCGTAATC PAX6 GTCCATCTTTGCTTGGGAAA TAGCCAGGTTGCGAAGAACT p75 TCATCCCTGTCTATTGCTCCA TGTTCTGCTTGCAGCTGTTC SOX9 AATGGAGCAGCGAAATCAAC CAGAGAGATTTAGCACACTGATC SOX10 GACCAGTACCCGCACCTG CGCTTGTCACTTTCGTTCAG Suppl. Fig. S1. Comparison of the gene expression profiles of the ES cells and the cells induced by NC and NC-B condition. Scatter plots compares the normalized expression of every gene on the array (refer to Table S3). The central line -
HOXB6 Homeo Box B6 HOXB5 Homeo Box B5 WNT5A Wingless-Type
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