HOXB7 Is an Erα Cofactor in the Activation of HER2 and Multiple ER Target Genes Leading
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Overlap of Vitamin a and Vitamin D Target Genes with CAKUT- Related Processes [Version 1; Peer Review: 1 Approved with Reservations]
F1000Research 2021, 10:395 Last updated: 21 JUL 2021 BRIEF REPORT Overlap of vitamin A and vitamin D target genes with CAKUT- related processes [version 1; peer review: 1 approved with reservations] Ozan Ozisik1, Friederike Ehrhart 2,3, Chris T Evelo 2, Alberto Mantovani4, Anaı̈s Baudot 1,5 1Aix Marseille University, Inserm, MMG, Marseille, 13385, France 2Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6200 MD, The Netherlands 3Department of Bioinformatics, NUTRIM/MHeNs, Maastricht University, Maastricht, 6200 MD, The Netherlands 4Istituto Superiore di Sanità, Rome, 00161, Italy 5Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain v1 First published: 18 May 2021, 10:395 Open Peer Review https://doi.org/10.12688/f1000research.51018.1 Latest published: 18 May 2021, 10:395 https://doi.org/10.12688/f1000research.51018.1 Reviewer Status Invited Reviewers Abstract Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) are a 1 group of abnormalities affecting the kidneys and their outflow tracts, which include the ureters, the bladder, and the urethra. CAKUT version 1 patients display a large clinical variability as well as a complex 18 May 2021 report aetiology, as only 5% to 20% of the cases have a monogenic origin. It is thereby suspected that interactions of both genetic and 1. Elena Menegola, Università degli Studi di environmental factors contribute to the disease. Vitamins are among the environmental factors that are considered for CAKUT aetiology. In Milano, Milan, Italy this study, we collected vitamin A and vitamin D target genes and Any reports and responses or comments on the computed their overlap with CAKUT-related gene sets. -
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. -
Spatiotemporal DNA Methylome Dynamics of the Developing Mouse Fetus
Article Spatiotemporal DNA methylome dynamics of the developing mouse fetus https://doi.org/10.1038/s41586-020-2119-x Yupeng He1,2, Manoj Hariharan1, David U. Gorkin3, Diane E. Dickel4, Chongyuan Luo1, Rosa G. Castanon1, Joseph R. Nery1, Ah Young Lee3, Yuan Zhao2,3, Hui Huang3,5, Received: 9 August 2017 Brian A. Williams6, Diane Trout6, Henry Amrhein6, Rongxin Fang2,3, Huaming Chen1, Bin Li3, Accepted: 11 June 2019 Axel Visel4,7,8, Len A. Pennacchio4,7,9, Bing Ren3,10 & Joseph R. Ecker1,11 ✉ Published online: 29 July 2020 Open access Cytosine DNA methylation is essential for mammalian development but Check for updates understanding of its spatiotemporal distribution in the developing embryo remains limited1,2. Here, as part of the mouse Encyclopedia of DNA Elements (ENCODE) project, we profled 168 methylomes from 12 mouse tissues or organs at 9 developmental stages from embryogenesis to adulthood. We identifed 1,808,810 genomic regions that showed variations in CG methylation by comparing the methylomes of diferent tissues or organs from diferent developmental stages. These DNA elements predominantly lose CG methylation during fetal development, whereas the trend is reversed after birth. During late stages of fetal development, non- CG methylation accumulated within the bodies of key developmental transcription factor genes, coinciding with their transcriptional repression. Integration of genome- wide DNA methylation, histone modifcation and chromatin accessibility data enabled us to predict 461,141 putative developmental tissue-specifc enhancers, the human orthologues of which were enriched for disease-associated genetic variants. These spatiotemporal epigenome maps provide a resource for studies of gene regulation during tissue or organ progression, and a starting point for investigating regulatory elements that are involved in human developmental disorders. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
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 -
NF-Ya Activates Multiple Hematopoietic Stem Cell (HSC) Regulatory Genes and Promotes HSC Self-Renewal
NF-Ya activates multiple hematopoietic stem cell (HSC) regulatory genes and promotes HSC self-renewal Jiang Zhu, Yi Zhang, Gerard J. Joe, Richard Pompetti, and Stephen G. Emerson* Departments of Medicine and Pediatrics, and Abramson Cancer Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104 Communicated by Zhu Chen, Shanghai Second Medical University, Shanghai, People’s Republic of China, April 25, 2005 (received for review December 15, 2004) Hematopoietic stem cell (HSC) self-renewal and differentiation are chased from The Jackson Laboratory and maintained in the animal influenced through multiple pathways, including homeobox tran- facilities of the University of Pennsylvania with sterile water or with scription factors, signaling through -catenin and Notch-1, telom- the water supplemented with neomycin sulfate and polymyxin B erase, and p27. How these multiple pathways interact and are (Sigma) within 3–4 weeks after BMT. orchestrated is currently unknown. We now report that NF-Ya, the regulatory and DNA-binding subunit of the trimeric transcription NF-Ya cDNA, MigR1 Retroviral Vector, and Preparation of Retrovi- factor NF-Y, plays a central, integrating role in several of these HSC ruses. cDNA for NF-Ya was amplified from human normal bone pathways. NF-Ya is preferentially expressed in HSC-enriched bone marrow (BM) cell RNA by using Pfu DNA polymerase (Strat- marrow subpopulations, and NF-Ya mRNA rapidly declines with agene) (7). MigR1 retroviral vector was obtained from Warren HSC differentiation. Overexpression of NF-Ya in primitive hema- Pear (Department of Pathology and Laboratory Medicine, Uni- topoietic cells activates the transcription of multiple HOX4 paral- versity of Pennsylvania). -
1 HOXB7 Is an Erα Cofactor in the Activation Of
HOXB7 is an ERα cofactor in the activation of HER2 and multiple ER target genes leading to endocrine resistance Kideok Jin, Sunju Park, Wei Wen Teo, Preethi Korangath, Sean Soonweng Cho, Takahiro Yoshida, Balázs Győrffy, Chirayu Pankaj Goswami, Harikrishna Nakshatri, Leigh-Ann Cruz, Weiqiang Zhou, Hongkai Ji, Ying Su, Muhammad Ekram, Zhengsheng Wu, Tao Zhu, Kornelia Polyak, and Saraswati Sukumar Supplemental Methods Plasmids Full-length promoter region including ER and one of two different HOXB7 binding sites of CA12 was cloned into the pGL3 promoter luciferase construct. Full length miR196a cDNA was cloned into the pcDNA3.1 vector. Full-length promoter region including MYC binding region of miR-196a gene was cloned into pGL3 promoter luciferase plasmid. 3’UTR region of HOXB7 was cloned into pGL2 promoter luciferase plasmid. The full-length human HER2 plasmid was a generous gift from Daniel Leahy (Johns Hopkins University, Baltimore, MD) and the HER2 cDNA was cloned to pLPCX vector. EGFR WT, pRetrosuper Myc shRNA, Lenti-sh1368 knockdown c-myc were purchased at Addgene (1-3). siRNAs against HOXB7 were purchased at Invitrogen and shRNAs against HOXB7 was purchased at Thermo scientific. Immunohistochemistry Immunohistochemical analysis of HOXB7 and HER2 protein expression was performed using monoclonal antibodies against HOXB7 (Santa Cruz Biotechnology) and HER2 (Cell signaling). After blocking with 5% goat serum in PBST for 1 hour at room temperature, the sections were treated with the HOXB7 or HER2 antibodies overnight at 4°C, then the peroxidase conjugated streptavidin 1 complex method was performed, followed by the 3, 3' diaminobenzidine (DAB) procedure according to manufacturer’s protocols (VECTASTAIN Elite ABC Kit, Vector Lab). -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Beta Protein 1 Homeoprotein Induces Cell Growth and Estrogen-Independent Tumorigenesis by Binding to the Estrogen Receptor in Breast Cancer
Himmelfarb Health Sciences Library, The George Washington University Health Sciences Research Commons Medicine Faculty Publications Medicine 7-16-2016 Beta protein 1 homeoprotein induces cell growth and estrogen-independent tumorigenesis by binding to the estrogen receptor in breast cancer. Sidney W Fu George Washington University Saurabh P Kirolikar Erika Ginsburg Xiaohui Tan Arnold Schwartz See next page for additional authors Follow this and additional works at: http://hsrc.himmelfarb.gwu.edu/smhs_medicine_facpubs Part of the Medicine and Health Sciences Commons APA Citation Fu, S., Kirolikar, S., Ginsburg, E., Tan, X., Schwartz, A., Simmens, S., Man, Y., & Pinzone, J. (2016). Beta protein 1 homeoprotein induces cell growth and estrogen-independent tumorigenesis by binding to the estrogen receptor in breast cancer.. Oncotarget, (). http://dx.doi.org/10.18632/oncotarget.10633 This Journal Article is brought to you for free and open access by the Medicine at Health Sciences Research Commons. It has been accepted for inclusion in Medicine Faculty Publications by an authorized administrator of Health Sciences Research Commons. For more information, please contact [email protected]. Authors Sidney W Fu, Saurabh P Kirolikar, Erika Ginsburg, Xiaohui Tan, Arnold Schwartz, Samuel J Simmens, Yan- Gao Man, and Joseph J Pinzone This journal article is available at Health Sciences Research Commons: http://hsrc.himmelfarb.gwu.edu/smhs_medicine_facpubs/ 793 www.impactjournals.com/oncotarget/ Oncotarget, Advance Publications 2016 Beta protein 1 homeoprotein induces cell growth and estrogen- independent tumorigenesis by binding to the estrogen receptor in breast cancer Sidney W. Fu1,*, Saurabh P. Kirolikar2,*, Erika Ginsburg3, Xiaohui Tan1, Arnold Schwartz4, Samuel J. Simmens5, Yan-gao Man6, Joseph J. -
Genome-Wide DNA Methylation Analysis Reveals Molecular Subtypes of Pancreatic Cancer
www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 17), pp: 28990-29012 Research Paper Genome-wide DNA methylation analysis reveals molecular subtypes of pancreatic cancer Nitish Kumar Mishra1 and Chittibabu Guda1,2,3,4 1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA 2Bioinformatics and Systems Biology Core, University of Nebraska Medical Center, Omaha, NE, 68198, USA 3Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA 4Fred and Pamela Buffet Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68198, USA Correspondence to: Chittibabu Guda, email: [email protected] Keywords: TCGA, pancreatic cancer, differential methylation, integrative analysis, molecular subtypes Received: October 20, 2016 Accepted: February 12, 2017 Published: March 07, 2017 Copyright: Mishra et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Pancreatic cancer (PC) is the fourth leading cause of cancer deaths in the United States with a five-year patient survival rate of only 6%. Early detection and treatment of this disease is hampered due to lack of reliable diagnostic and prognostic markers. Recent studies have shown that dynamic changes in the global DNA methylation and gene expression patterns play key roles in the PC development; hence, provide valuable insights for better understanding the initiation and progression of PC. In the current study, we used DNA methylation, gene expression, copy number, mutational and clinical data from pancreatic patients. -
HOXA10 Controls Osteoblastogenesis by Directly Activating Bone Regulatory and Phenotypic Genes
University of Massachusetts Medical School eScholarship@UMMS Open Access Articles Open Access Publications by UMMS Authors 2007-02-28 HOXA10 controls osteoblastogenesis by directly activating bone regulatory and phenotypic genes Mohammad Q. Hassan University of Massachusetts Medical School Et al. Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/oapubs Part of the Life Sciences Commons, and the Medicine and Health Sciences Commons Repository Citation Hassan MQ, Tare RS, Lee SH, Mandeville M, Weiner B, Montecino MA, Van Wijnen AJ, Stein JL, Stein GS, Lian JB. (2007). HOXA10 controls osteoblastogenesis by directly activating bone regulatory and phenotypic genes. Open Access Articles. https://doi.org/10.1128/MCB.01544-06. Retrieved from https://escholarship.umassmed.edu/oapubs/1334 This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Open Access Articles by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. MOLECULAR AND CELLULAR BIOLOGY, May 2007, p. 3337–3352 Vol. 27, No. 9 0270-7306/07/$08.00ϩ0 doi:10.1128/MCB.01544-06 Copyright © 2007, American Society for Microbiology. All Rights Reserved. HOXA10 Controls Osteoblastogenesis by Directly Activating Bone Regulatory and Phenotypic Genesᰔ Mohammad Q. Hassan,1 Rahul Tare,1† Suk Hee Lee,1 Matthew Mandeville,1 Brian Weiner,1 Martin Montecino,2 Andre J. van Wijnen,1 Janet L. Stein,1 Gary S. Stein,1 and Jane B. Lian1* Department of Cell Biology and Cancer Center, University of Massachusetts Medical School, Worcester, Massachusetts 01655,1 and Departamento de Bioquimica y Biologia Molecular, Facultad de Ciencias Biologicas, Universidad de Concepcion, Concepcion, Chile2 Received 18 August 2006/Returned for modification 4 October 2006/Accepted 9 February 2007 HOXA10 is necessary for embryonic patterning of skeletal elements, but its function in bone formation beyond this early developmental stage is unknown.