Establishing Neuronal Diversity in the Spinal Cord: a Time and a Place Andreas Sagner* and James Briscoe*
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Methylation of the Homeobox Gene, HOPX, Is Frequently Detected in Poorly Differentiated Colorectal Cancer
ANTICANCER RESEARCH 31: 2889-2892 (2011) Methylation of the Homeobox Gene, HOPX, Is Frequently Detected in Poorly Differentiated Colorectal Cancer YOSHIKUNI HARADA, KAZUHIRO KIJIMA, KAZUKI SHINMURA, MAKIKO SAKATA, KAZUMA SAKURABA, KAZUAKI YOKOMIZO, YOUHEI KITAMURA, ATSUSHI SHIRAHATA, TETSUHIRO GOTO, HIROKI MIZUKAMI, MITSUO SAITO, GAKU KIGAWA, HIROSHI NEMOTO and KENJI HIBI Gastroenterological Surgery, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-ku, Yokohama 227-8501, Japan Abstract. Background: Homeodomein only protein x pathogenesis of colorectal cancer (4-8). It is also known that (HOPX) gene methylation has frequently been detected in gene promoter hypermethylation is involved in the cancer tissues. The methylation status of the HOPX gene in development and progression of cancer (9). An investigation colorectal cancer was examined and compared to the of genetic changes is important in order to clarify the clinocopathological findings. Materials and Methods: tumorigenic pathway of colorectal cancer (10). Eighty-nine tumor samples and corresponding normal tissues The homeodomain only protein x (HOPX) gene, also were obtained from colorectal cancer patients who known as NECC1 (not expressed in choriocarcinoma clone underwent surgery at our hospital. The methylation status of 1), LAGY (lung cancer-associated gene Y), and OB1 (odd the HOPX gene in these samples was examined by homeobox 1 protein), was initially identified as an essential quantitative methylation-specific PCR (qMSP). Subsequently, gene for the modulation of cardiac growth and development the clinicopathological findings were correlated with the (11). Three spliced transcript variants, HOPX-α, HOPX-β methylation status of the HOPX gene. Results: HOPX gene and HOPX-γ, encode the same protein, which contains a methylation was found in 46 (52%) out of the 89 colorectal putative homeodomain motif that acts as an adapter protein carcinomas, suggesting that it was frequently observed in to mediate transcriptional repression (12). -
Core Transcriptional Regulatory Circuitries in Cancer
Oncogene (2020) 39:6633–6646 https://doi.org/10.1038/s41388-020-01459-w REVIEW ARTICLE Core transcriptional regulatory circuitries in cancer 1 1,2,3 1 2 1,4,5 Ye Chen ● Liang Xu ● Ruby Yu-Tong Lin ● Markus Müschen ● H. Phillip Koeffler Received: 14 June 2020 / Revised: 30 August 2020 / Accepted: 4 September 2020 / Published online: 17 September 2020 © The Author(s) 2020. This article is published with open access Abstract Transcription factors (TFs) coordinate the on-and-off states of gene expression typically in a combinatorial fashion. Studies from embryonic stem cells and other cell types have revealed that a clique of self-regulated core TFs control cell identity and cell state. These core TFs form interconnected feed-forward transcriptional loops to establish and reinforce the cell-type- specific gene-expression program; the ensemble of core TFs and their regulatory loops constitutes core transcriptional regulatory circuitry (CRC). Here, we summarize recent progress in computational reconstitution and biologic exploration of CRCs across various human malignancies, and consolidate the strategy and methodology for CRC discovery. We also discuss the genetic basis and therapeutic vulnerability of CRC, and highlight new frontiers and future efforts for the study of CRC in cancer. Knowledge of CRC in cancer is fundamental to understanding cancer-specific transcriptional addiction, and should provide important insight to both pathobiology and therapeutics. 1234567890();,: 1234567890();,: Introduction genes. Till now, one critical goal in biology remains to understand the composition and hierarchy of transcriptional Transcriptional regulation is one of the fundamental mole- regulatory network in each specified cell type/lineage. -
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. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Identification of NFIB As Recurrent Translocation Partner Gene of HMGIC in Pleomorphic Adenomas
Oncogene (1998) 16, 865 ± 872 1998 Stockton Press All rights reserved 0950 ± 9232/98 $12.00 Identi®cation of NFIB as recurrent translocation partner gene of HMGIC in pleomorphic adenomas Jan MW Geurts1, Eric FPM Schoenmakers1, Eva RoÈ ijer2, Anna-Karin AstroÈ m2,GoÈran Stenman2 and Wim JM van de Ven1 1Laboratory for Molecular Oncology, Center for Human Genetics, University of Leuven & Flanders Interuniversity Institute for Biotechnology, B-3000 Leuven, Belgium; 2Laboratory of Cancer Genetics, Department of Pathology, GoÈteborg University, Sahlgrenska University Hospital, S-41345 GoÈteborg, Sweden Approximately 12% of all pleomorphic adenomas of the guished, i.e. tumors with rearrangements of 8q12 or salivary glands are characterized by chromosome 12q13 ± 15. Whereas the t(3;8)(p21;q12) is the most aberrations involving the chromosome segment 12q13 ± frequently observed translocation, many dierent 15. Several chromosomes have been found as transloca- chromosome segments can act as translocation tion partners of chromosome 12, and some of these partners of both 8q12 and 12q13 ± 15 (Sandros et al., recurrently. Recently, the HMGIC gene was identi®ed as 1990; Bullerdiek et al., 1993). The short arm of the target gene aected by the 12q13 ± 15 aberrations. chromosome 9, with breakpoints in the segment Here, we report the identi®cation and characterization of 9p12 ± 24, is the preferential translocation partner of a new translocation partner gene of HMGIC in chromosome 12q13 ± 15 (Stenman et al., 1994). The pleomorphic adenomas. 3'-RACE analysis of a primary reason for the variability of the breakpoints in 9p is adenoma with an apparently normal karyotype revealed likely to be due to the limited resolution of the banding an HMGIC fusion transcript containing ectopic se- pattern in 9p12 ± 24 and 12q13 ± 15, making it almost quences derived from the human NFIB gene, previously impossible to determine the exact locations of the mapped to chromosome band 9p24.1. -
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 -
2016 Joint Meeting Program
April 15 – 17, 2016 Fairmont Chicago Millennium Park • Chicago, Illinois The AAP/ASCI/APSA conference is jointly provided by Boston University School of Medicine and AAP/ASCI/APSA. Meeting Program and Abstracts www.jointmeeting.org www.jointmeeting.org Special Events at the 2016 AAP/ASCI/APSA Joint Meeting Friday, April 15 Saturday, April 16 ASCI President’s Reception ASCI Food and Science Evening 6:15 – 7:15 p.m. 6:30 – 9:00 p.m. Gold Room The Mid-America Club, Aon Center ASCI Dinner & New Member AAP Member Banquet Induction Ceremony (Ticketed guests only) (Ticketed guests only) 7:00 – 10:00 p.m. 7:30 – 9:45 p.m. Imperial Ballroom, Level B2 Rouge, Lobby Level How to Solve a Scientific Puzzle: Speaker: Clara D. Bloomfield, MD Clues from Stockholm and Broadway The Ohio State University Comprehensive Cancer Center Speaker: Joe Goldstein, MD APSA Welcome Reception & University of Texas Southwestern Medical Center at Dallas Presidential Address APSA Dinner (Ticketed guests only) 9:00 p.m. – Midnight Signature Room, 360 Chicago, 7:30 – 9:00 p.m. John Hancock Center (off-site) Rouge, Lobby Level Speaker: Daniel DelloStritto, APSA President Finding One’s Scientific Niche: Musings from a Clinical Neuroscientist Speaker: Helen Mayberg, MD, Emory University Dessert Reception (open to all attendees) 10:00 p.m. – Midnight Imperial Foyer, Level B2 Sunday, April 17 APSA Future of Medicine and www.jointmeeting.org Residency Luncheon Noon – 2:00 p.m. Rouge, Lobby Level 2 www.jointmeeting.org Program Contents General Program Information 4 Continuing Medical Education Information 5 Faculty and Speaker Disclosures 7 Scientific Program Schedule 9 Speaker Biographies 16 Call for Nominations: 2017 Harrington Prize for Innovation in Medicine 26 AAP/ASCI/APSA Joint Meeting Faculty 27 Award Recipients 29 Call for Nominations: 2017 Harrington Scholar-Innovator Award 31 Call for Nominations: George M. -
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. -
Co-Occupancy by Multiple Cardiac Transcription Factors Identifies
Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart Aibin Hea,b,1, Sek Won Konga,b,c,1, Qing Maa,b, and William T. Pua,b,2 aDepartment of Cardiology and cChildren’s Hospital Informatics Program, Children’s Hospital Boston, Boston, MA 02115; and bHarvard Stem Cell Institute, Harvard University, Cambridge, MA 02138 Edited by Eric N. Olson, University of Texas Southwestern, Dallas, TX, and approved February 23, 2011 (received for review November 12, 2010) Identification of genomic regions that control tissue-specific gene study of a handful of model genes (e.g., refs. 7–10), it has not been expression is currently problematic. ChIP and high-throughput se- evaluated using unbiased, genome-wide approaches. quencing (ChIP-seq) of enhancer-associated proteins such as p300 In this study, we used a modified ChIP-seq approach to define identifies some but not all enhancers active in a tissue. Here we genome wide the binding sites of these cardiac TFs (1). We show that co-occupancy of a chromatin region by multiple tran- provide unbiased support for collaborative TF interactions in scription factors (TFs) identifies a distinct set of enhancers. GATA- driving cardiac gene expression and use this principle to show that chromatin co-occupancy by multiple TFs identifies enhancers binding protein 4 (GATA4), NK2 transcription factor-related, lo- with cardiac activity in vivo. The majority of these multiple TF- cus 5 (NKX2-5), T-box 5 (TBX5), serum response factor (SRF), and “ binding loci (MTL) enhancers were distinct from p300-bound myocyte-enhancer factor 2A (MEF2A), here referred to as cardiac enhancers in location and functional properties. -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 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 The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
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