Dynamic Regulation of Nanog and Stem Cell-Signaling Pathways by Hoxa1 During Early Neuro-Ectodermal Differentiation of ES Cells
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Ascl1a/Dkk/Β-Catenin Signaling Pathway Is Necessary and Glycogen Synthase Kinase-3Β Inhibition Is Sufficient for Zebrafish Retina Regeneration
Ascl1a/Dkk/β-catenin signaling pathway is necessary and glycogen synthase kinase-3β inhibition is sufficient for zebrafish retina regeneration Rajesh Ramachandran, Xiao-Feng Zhao, and Daniel Goldman1 Molecular and Behavioral Neuroscience Institute and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109 Edited by David R. Hyde, University of Notre Dame, Notre Dame, IN, and accepted by the Editorial Board August 19, 2011 (received for review May 5, 2011) Key to successful retina regeneration in zebrafish are Müller glia necessary for proliferation of dedifferentiated MG in the injured (MG) that respond to retinal injury by dedifferentiating into a cy- retina and that glycogen synthase kinase-3β (GSK-3β) inhibition cling population of retinal progenitors. Although recent studies was sufficient to stimulate MG dedifferentiation into a pop- have identified several genes involved in retina regeneration, ulation of cycling multipotent progenitors in the uninjured ret- the signaling mechanisms underlying injury-dependent MG prolif- ina. Interestingly, Ascl1a knockdown limited the production of eration have remained elusive. Here we report that canonical Wnt neurons by progenitors in the GSK-3β inhibitor-treated retina. signaling controls the proliferation of MG-derived retinal progen- itors. We found that injury-dependent induction of Ascl1a sup- Results pressed expression of the Wnt signaling inhibitor, Dkk, and Ascl1a-Dependent Suppression of dkk Gene Expression in Injured induced expression of the Wnt ligand, Wnt4a. Genetic and phar- Retina. Wnt signaling is a conserved pathway that affects many macological inhibition of Wnt signaling suppressed injury-depen- fundamental developmental processes (18). Deregulated Wnt dent proliferation of MG-derived progenitors. -
Dynamical Gene Regulatory Networks Are Tuned by Transcriptional Autoregulation with Microrna Feedback Thomas G
www.nature.com/scientificreports OPEN Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback Thomas G. Minchington1, Sam Grifths‑Jones2* & Nancy Papalopulu1* Concepts from dynamical systems theory, including multi‑stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, infammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRNAs, is unknown. We integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identifed autoregulatory motifs involve 56% of transcription factors (TFs) studied. TFs that autoregulate have more interactions with microRNAs than non‑autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRNA. Both autoregulatory and dual feedback motifs were enriched in the network. TFs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRNAs were also conserved between tissues, but less so, and TFs regulate diferent combinations of microRNAs in a tissue‑dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identifcation of TFs which regulate the dynamical properties of human gene expression. Cell fate changes are a key feature of development, regeneration and cancer, and are ofen thought of as a “land- scape” that cells move through1,2. Cell fate changes are driven by changes in gene expression: turning genes on or of, or changing their levels above or below a threshold where a cell fate change occurs. -
KLF2 Induced
UvA-DARE (Digital Academic Repository) The transcription factor KLF2 in vascular biology Boon, R.A. Publication date 2008 Link to publication Citation for published version (APA): Boon, R. A. (2008). The transcription factor KLF2 in vascular biology. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:23 Sep 2021 Supplementary data: Genes induced by KLF2 Dekker et al. LocusLink Accession Gene Sequence Description Fold p-value ID number symbol change (FDR) 6654 AK022099 SOS1 cDNA FLJ12037 fis, clone HEMBB1001921. 100.00 5.9E-09 56999 AF086069 ADAMTS9 full length insert cDNA clone YZ35C05. 100.00 1.2E-09 6672 AF085934 SP100 full length insert cDNA clone YR57D07. 100.00 6.7E-13 9031 AF132602 BAZ1B Williams Syndrome critical region WS25 mRNA, partial sequence. -
Systematic Comparison of Sea Urchin and Sea Star Developmental Gene Regulatory Networks Explains How Novelty Is Incorporated in Early Development
ARTICLE https://doi.org/10.1038/s41467-020-20023-4 OPEN Systematic comparison of sea urchin and sea star developmental gene regulatory networks explains how novelty is incorporated in early development Gregory A. Cary 1,3,5, Brenna S. McCauley1,4,5, Olga Zueva1, Joseph Pattinato1, William Longabaugh2 & ✉ Veronica F. Hinman 1 1234567890():,; The extensive array of morphological diversity among animal taxa represents the product of millions of years of evolution. Morphology is the output of development, therefore phenotypic evolution arises from changes to the topology of the gene regulatory networks (GRNs) that control the highly coordinated process of embryogenesis. A particular challenge in under- standing the origins of animal diversity lies in determining how GRNs incorporate novelty while preserving the overall stability of the network, and hence, embryonic viability. Here we assemble a comprehensive GRN for endomesoderm specification in the sea star from zygote through gastrulation that corresponds to the GRN for sea urchin development of equivalent territories and stages. Comparison of the GRNs identifies how novelty is incorporated in early development. We show how the GRN is resilient to the introduction of a transcription factor, pmar1, the inclusion of which leads to a switch between two stable modes of Delta-Notch signaling. Signaling pathways can function in multiple modes and we propose that GRN changes that lead to switches between modes may be a common evolutionary mechanism for changes in embryogenesis. Our data additionally proposes a model in which evolutionarily conserved network motifs, or kernels, may function throughout development to stabilize these signaling transitions. 1 Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA. -
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 -
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. -
An Automatic Design Framework of Swarm Pattern Formation Based on Multi-Objective Genetic Programming
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming Zhun Fan, Senior Member, IEEE, Zhaojun Wang, Xiaomin Zhu, Bingliang Hu, Anmin Zou, and Dongwei Bao Abstract—Most existing swarm pattern formation methods predetermined trajectory and maintain a specific swarm pattern depend on a predefined gene regulatory network (GRN) structure in the execution of tasks. For example, Jin [11] proposed a that requires designers’ priori knowledge, which is difficult to hierarchical gene regulatory network for adaptive multi-robot adapt to complex and changeable environments. To dynamically adapt to the complex and changeable environments, we propose pattern formation. In this work, the swarm pattern is designed an automatic design framework of swarm pattern formation as a band of circle, which encircles targets in a dynamic based on multi-objective genetic programming. The proposed environments. In the latter case of using adaptive formation, framework does not need to define the structure of the GRN- swarm robots follow an adaptive pattern to encircle targets in based model in advance, and it applies some basic network motifs the environment. For example, Oh et al. [12] have introduced to automatically structure the GRN-based model. In addition, a multi-objective genetic programming (MOGP) combines with an evolving hierarchical gene regulatory network for morpho- NSGA-II, namely MOGP-NSGA-II, to balance the complexity genetic pattern formation in order to generate adaptive patterns and accuracy of the GRN-based model. In evolutionary process, which are adaptable to dynamic environments. an MOGP-NSGA-II and differential evolution (DE) are applied to In addition, swarm pattern formation has been widely ex- optimize the structures and parameters of the GRN-based model plored in recent years, which can be divided into four cate- in parallel. -
Hoxb1 Controls Anteroposterior Identity of Vestibular Projection Neurons
Hoxb1 Controls Anteroposterior Identity of Vestibular Projection Neurons Yiju Chen1, Masumi Takano-Maruyama1, Bernd Fritzsch2, Gary O. Gaufo1* 1 Department of Biology, University of Texas at San Antonio, San Antonio, Texas, United States of America, 2 Department of Biology, University of Iowa, Iowa City, Iowa, United States of America Abstract The vestibular nuclear complex (VNC) consists of a collection of sensory relay nuclei that integrates and relays information essential for coordination of eye movements, balance, and posture. Spanning the majority of the hindbrain alar plate, the rhombomere (r) origin and projection pattern of the VNC have been characterized in descriptive works using neuroanatomical tracing. However, neither the molecular identity nor developmental regulation of individual nucleus of the VNC has been determined. To begin to address this issue, we found that Hoxb1 is required for the anterior-posterior (AP) identity of precursors that contribute to the lateral vestibular nucleus (LVN). Using a gene-targeted Hoxb1-GFP reporter in the mouse, we show that the LVN precursors originate exclusively from r4 and project to the spinal cord in the stereotypic pattern of the lateral vestibulospinal tract that provides input into spinal motoneurons driving extensor muscles of the limb. The r4-derived LVN precursors express the transcription factors Phox2a and Lbx1, and the glutamatergic marker Vglut2, which together defines them as dB2 neurons. Loss of Hoxb1 function does not alter the glutamatergic phenotype of dB2 neurons, but alters their stereotyped spinal cord projection. Moreover, at the expense of Phox2a, the glutamatergic determinants Lmx1b and Tlx3 were ectopically expressed by dB2 neurons. Our study suggests that the Hox genes determine the AP identity and diversity of vestibular precursors, including their output target, by coordinating the expression of neurotransmitter determinant and target selection properties along the AP axis. -
Homeobox Gene Expression Profile in Human Hematopoietic Multipotent
Leukemia (2003) 17, 1157–1163 & 2003 Nature Publishing Group All rights reserved 0887-6924/03 $25.00 www.nature.com/leu Homeobox gene expression profile in human hematopoietic multipotent stem cells and T-cell progenitors: implications for human T-cell development T Taghon1, K Thys1, M De Smedt1, F Weerkamp2, FJT Staal2, J Plum1 and G Leclercq1 1Department of Clinical Chemistry, Microbiology and Immunology, Ghent University Hospital, Ghent, Belgium; and 2Department of Immunology, Erasmus Medical Center, Rotterdam, The Netherlands Class I homeobox (HOX) genes comprise a large family of implicated in this transformation proces.14 The HOX-C locus transcription factors that have been implicated in normal and has been primarily implicated in lymphomas.15 malignant hematopoiesis. However, data on their expression or function during T-cell development is limited. Using degener- Hematopoietic cells are derived from stem cells that reside in ated RT-PCR and Affymetrix microarray analysis, we analyzed fetal liver (FL) in the embryo and in the adult bone marrow the expression pattern of this gene family in human multipotent (ABM), which have the unique ability to self-renew and thereby stem cells from fetal liver (FL) and adult bone marrow (ABM), provide a life-long supply of blood cells. T lymphocytes are a and in T-cell progenitors from child thymus. We show that FL specific type of hematopoietic cells that play a major role in the and ABM stem cells are similar in terms of HOX gene immune system. They develop through a well-defined order of expression, but significant differences were observed between differentiation steps in the thymus.16 Several transcription these two cell types and child thymocytes. -
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. -
Dedifferentiation-Associated Changes in Morphology and Gene Expression in Primary Human Articular Chondrocytes in Cell Culture M
Osteoarthritis and Cartilage (2002) 10, 62–70 © 2002 OsteoArthritis Research Society International 1063–4584/02/010062+09 $35.00/0 doi:10.1053/joca.2001.0482, available online at http://www.idealibrary.com on Dedifferentiation-associated changes in morphology and gene expression in primary human articular chondrocytes in cell culture M. Schnabel*, S. Marlovits†, G. Eckhoff*, I. Fichtel*, L. Gotzen*, V. Ve´csei† and J. Schlegel‡ *Department of Traumatology, Philipps-University of Marburg, Germany †Department of Traumatology, University of Vienna, Austria; Ludwig Boltzmann Institute of Biomechanics and Cell Biology, Vienna, Austria ‡Institute of Pathology, Munich Technical University, Germany Summary Objective: The aim of the present study was the investigation of differential gene expression in primary human articular chondrocytes (HACs) and in cultivated cells derived from HACs. Design: Primary human articular chondrocytes (HACs) isolated from non-arthritic human articular cartilage and monolayer cultures of HACs were investigated by immunohistochemistry, Northern analysis, RT-PCR and cDNA arrays. Results: By immunohistochemistry we detected expression of collagen II, protein S-100, chondroitin-4-sulphate and vimentin in freshly isolated HACs. Cultivated HACs, however, showed only collagen I and vimentin expression. These data were corroborated by the results of Northern analysis using specifc cDNA probes for collagens I, II and III and chondromodulin, respectively, demonstrating collagen II and chondromodulin expression in primary HACs but not in cultivated cells. Hybridization of mRNA from primary HACs and cultivated cells to cDNA arrays revealed additional transcriptional changes associated with dedifferentiation during propagation of chondrocytes in vitro.We found a more complex hybridization pattern for primary HACs than for cultivated cells. -
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.