Table 1A SIRT1 Differential Binding Gene List Down
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MDR-1, Bcl-Xl, H. Pylori, and Wnt&Sol;Β-Catenin Signalling in the Adult Stomach
Laboratory Investigation (2012) 92, 1670–1673 & 2012 USCAP, Inc All rights reserved 0023-6837/12 $32.00 EDITORIAL MDR-1, Bcl-xL, H. pylori, and Wnt/b-catenin signalling in the adult stomach: how much is too much? R John MacLeod Laboratory Investigation (2012) 92, 1670–1673; doi:10.1038/labinvest.2012.151 ultiple drug resistance (MDR) is a interaction between the antiapoptotic protein major cause of failure of che- Bcl-xL and MDR-1. Knockdown of MDR-1 motherapy in cancer treatment. increases the apoptotic index of these cells exposed The membrane transporter P-gly- to oxidative stress consistent with a role for MDR- coprotein (MDR-1, Pgp) encoded 1 in apoptosis. Several questions emerge from Mby the adenosine triphosphate-binding cassette, these findings. The first is why is MDR-1 increased subfamily B, member 1 is the main mechanism for in some HP-positive mucosa but in 100% of the decreased intracellular drug accumulation in MDR intestinal metaplasia samples? A likely causative cancer.1 Increases in Mdr-1 expression prevent effector of the increase in MDR-1 is the activation tumor cells from a variety of induced apoptosis, of canonical or Wnt/b-catenin signaling. It has but how this occurs is poorly understood. It is been known for a dozen years that the MDR-1 essential to understand how this occurs to be able gene may be stimulated by Tcf4.4 Yamada et al4 to design effective therapeutic interventions. The clearly demonstrated the presence of Tcf4 sites on study by Rocco et al2 (this issue) clearly shows that the MDR-1 promoter and showed that MDR-1 in the mitochondria of gastric cancer cell lines protein had substantially increased in adenomas MDR1 physically interacts with Bcl-xL, a well- and colon cancer. -
Lysophosphatidic Acid and Its Receptors: Pharmacology and Therapeutic Potential in Atherosclerosis and Vascular Disease
JPT-107404; No of Pages 13 Pharmacology & Therapeutics xxx (2019) xxx Contents lists available at ScienceDirect Pharmacology & Therapeutics journal homepage: www.elsevier.com/locate/pharmthera Lysophosphatidic acid and its receptors: pharmacology and therapeutic potential in atherosclerosis and vascular disease Ying Zhou a, Peter J. Little a,b, Hang T. Ta a,c, Suowen Xu d, Danielle Kamato a,b,⁎ a School of Pharmacy, University of Queensland, Pharmacy Australia Centre of Excellence, Woolloongabba, QLD 4102, Australia b Department of Pharmacy, Xinhua College of Sun Yat-sen University, Tianhe District, Guangzhou 510520, China c Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, St Lucia, QLD 4072, Australia d Aab Cardiovascular Research Institute, Department of Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA article info abstract Available online xxxx Lysophosphatidic acid (LPA) is a collective name for a set of bioactive lipid species. Via six widely distributed G protein-coupled receptors (GPCRs), LPA elicits a plethora of biological responses, contributing to inflammation, Keywords: thrombosis and atherosclerosis. There have recently been considerable advances in GPCR signaling especially Lysophosphatidic acid recognition of the extended role for GPCR transactivation of tyrosine and serine/threonine kinase growth factor G-protein coupled receptors receptors. This review covers LPA signaling pathways in the light of new information. The use of transgenic and Atherosclerosis gene knockout animals, gene manipulated cells, pharmacological LPA receptor agonists and antagonists have Gproteins fi β-arrestins provided many insights into the biological signi cance of LPA and individual LPA receptors in the progression Transactivation of atherosclerosis and vascular diseases. -
Functional and Molecular Heterogeneity Of
ARTICLE https://doi.org/10.1038/s41467-020-15716-9 OPEN Functional and molecular heterogeneity of D2R neurons along dorsal ventral axis in the striatum ✉ Emma Puighermanal1,2 , Laia Castell1, Anna Esteve-Codina 3, Su Melser4,5, Konstantin Kaganovsky 6, Charleine Zussy1, Jihane Boubaker-Vitre1, Marta Gut3,7, Stephanie Rialle1, Christoph Kellendonk8,9, Elisenda Sanz2, Albert Quintana 2, Giovanni Marsicano4,5, Miquel Martin1, Marcelo Rubinstein10,11,12, ✉ Jean-Antoine Girault 13,14,15, Jun B. Ding6 & Emmanuel Valjent 1 1234567890():,; Action control is a key brain function determining the survival of animals in their environ- ment. In mammals, neurons expressing dopamine D2 receptors (D2R) in the dorsal striatum (DS) and the nucleus accumbens (Acb) jointly but differentially contribute to the fine reg- ulation of movement. However, their region-specific molecular features are presently unknown. By combining RNAseq of striatal D2R neurons and histological analyses, we identified hundreds of novel region-specific molecular markers, which may serve as tools to target selective subpopulations. As a proof of concept, we characterized the molecular identity of a subcircuit defined by WFS1 neurons and evaluated multiple behavioral tasks after its temporally-controlled deletion of D2R. Consequently, conditional D2R knockout mice displayed a significant reduction in digging behavior and an exacerbated hyperlocomotor response to amphetamine. Thus, targeted molecular analyses reveal an unforeseen hetero- geneity in D2R-expressing striatal neuronal populations, underlying specific D2R’s functional features in the control of specific motor behaviors. 1 IGF, CNRS, INSERM, Université Montpellier, Montpellier, France. 2 Neuroscience Institute, Department of Cell Biology, Physiology and Immunology, Autonomous University of Barcelona, Bellaterra, Spain. -
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 -
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. -
Targeting the Tryptophan Hydroxylase 2 Gene for Functional Analysis in Mice and Serotonergic Differentiation of Embryonic Stem Cells
TARGETING THE TRYPTOPHAN HYDROXYLASE 2 GENE FOR FUNCTIONAL ANALYSIS IN MICE AND SEROTONERGIC DIFFERENTIATION OF EMBRYONIC STEM CELLS Inaugural-Dissertation to obtain the academic degree Doctor rerum naturalium (Dr. rer. nat.) submitted to the Department of Biology, Chemistry and Pharmacy of Freie Universität Berlin by Dana Kikic, M.Sc. in Molecular biology and Physiology from Nis June, 2009 The doctorate studies were performed in the research group of Prof. Michael Bader Molecular Biology of Peptide Hormones at Max-Delbrück-Center for Molecular Medicine in Berlin, Buch Mai 2005 - September 2008. 1st Reviewer: Prof. Michael Bader 2nd Reviewer: Prof. Udo Heinemann date of defence: 13. August 2009 ACKNOWLEDGMENTS Herewith, I would like to acknowledge the persons who made this thesis possible and without whom my initiation in the world of basic science research would not have the spin it has now, neither would my scientific illiteracy get the chance to eradicate. I am expressing my very personal gratitude and recognition to: Prof. Michael Bader, for an inexhaustible guidance in all the matters arising during the course of scientific work, for an instinct in defining and following the intellectual challenge and for letting me following my own, for necessary financial support, for defining the borders of reasonable and unreasonable, for an invaluable time and patience, and an amazing efficiency in supporting, motivating, reading, correcting and shaping my scientific language during the last four years. Prof. Harald Saumweber and Prof. Udo Heinemann, for taking over the academic supervision of the thesis, and for breathing in it a life outside the laboratory walls and their personal signature. -
See Also Figure 1
Figure S1. Box-and-whisker plots depicting the range of expression values per developmental stage, with DESeq normalization (A) or quantile normalization (B). See also Figure 1. Figure S2. Lv-Setmar expression has low variation over developmental time. A. A plot of Lv-setmar versus Lv-ubiquitin expression over time demonstrates that Lv-setmar exhibits less temporal variation than Lv-ubiquitin. B. A representative gel showing Lv-setmar qPCR products amplified from cDNAs representing each sequenced stage in this study, demonstrating comparable product levels and an absence of spurious amplification products. See also Figure 1E. Figure S3. LvEDGE database. Screen shots showing the home page (A), the search window (B), an example search with a temporal expression plot (C), and the numerical data reflected in the plot (D) for the LvEDGE public database, which hosts the data described herein. stage 1 2 3 4 5 6 7 8 9 10 11 Category Subcategory 2-cell 60-cell EB HB TVP MB EG MG LG EP LP meiotic Cell Division Cytokinesis Mitosis checkpoint cell division recombination cell cycle stem cell left-right cell left-right Development maintenance asymmetry morphogenesis asymmetry regulation of multicellular organismal process cell soma cell soma Gene Expression chromatin SWI/SNF Control Chromatin modification chromatin binding complex methylated histone Binding negative sequence- sequence- sequence- regulation of sequence- specific DNA specific DNA specific DNA transcription specific DNA sequence-specific DNA binding binding binding binding factor activity -
Supplementary Table S5. Differentially Expressed Gene Lists of PD-1High CD39+ CD8 Tils According to 4-1BB Expression Compared to PD-1+ CD39- CD8 Tils
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Immunother Cancer Supplementary Table S5. Differentially expressed gene lists of PD-1high CD39+ CD8 TILs according to 4-1BB expression compared to PD-1+ CD39- CD8 TILs Up- or down- regulated genes in Up- or down- regulated genes Up- or down- regulated genes only PD-1high CD39+ CD8 TILs only in 4-1BBneg PD-1high CD39+ in 4-1BBpos PD-1high CD39+ CD8 compared to PD-1+ CD39- CD8 CD8 TILs compared to PD-1+ TILs compared to PD-1+ CD39- TILs CD39- CD8 TILs CD8 TILs IL7R KLRG1 TNFSF4 ENTPD1 DHRS3 LEF1 ITGA5 MKI67 PZP KLF3 RYR2 SIK1B ANK3 LYST PPP1R3B ETV1 ADAM28 H2AC13 CCR7 GFOD1 RASGRP2 ITGAX MAST4 RAD51AP1 MYO1E CLCF1 NEBL S1PR5 VCL MPP7 MS4A6A PHLDB1 GFPT2 TNF RPL3 SPRY4 VCAM1 B4GALT5 TIPARP TNS3 PDCD1 POLQ AKAP5 IL6ST LY9 PLXND1 PLEKHA1 NEU1 DGKH SPRY2 PLEKHG3 IKZF4 MTX3 PARK7 ATP8B4 SYT11 PTGER4 SORL1 RAB11FIP5 BRCA1 MAP4K3 NCR1 CCR4 S1PR1 PDE8A IFIT2 EPHA4 ARHGEF12 PAICS PELI2 LAT2 GPRASP1 TTN RPLP0 IL4I1 AUTS2 RPS3 CDCA3 NHS LONRF2 CDC42EP3 SLCO3A1 RRM2 ADAMTSL4 INPP5F ARHGAP31 ESCO2 ADRB2 CSF1 WDHD1 GOLIM4 CDK5RAP1 CD69 GLUL HJURP SHC4 GNLY TTC9 HELLS DPP4 IL23A PITPNC1 TOX ARHGEF9 EXO1 SLC4A4 CKAP4 CARMIL3 NHSL2 DZIP3 GINS1 FUT8 UBASH3B CDCA5 PDE7B SOGA1 CDC45 NR3C2 TRIB1 KIF14 TRAF5 LIMS1 PPP1R2C TNFRSF9 KLRC2 POLA1 CD80 ATP10D CDCA8 SETD7 IER2 PATL2 CCDC141 CD84 HSPA6 CYB561 MPHOSPH9 CLSPN KLRC1 PTMS SCML4 ZBTB10 CCL3 CA5B PIP5K1B WNT9A CCNH GEM IL18RAP GGH SARDH B3GNT7 C13orf46 SBF2 IKZF3 ZMAT1 TCF7 NECTIN1 H3C7 FOS PAG1 HECA SLC4A10 SLC35G2 PER1 P2RY1 NFKBIA WDR76 PLAUR KDM1A H1-5 TSHZ2 FAM102B HMMR GPR132 CCRL2 PARP8 A2M ST8SIA1 NUF2 IL5RA RBPMS UBE2T USP53 EEF1A1 PLAC8 LGR6 TMEM123 NEK2 SNAP47 PTGIS SH2B3 P2RY8 S100PBP PLEKHA7 CLNK CRIM1 MGAT5 YBX3 TP53INP1 DTL CFH FEZ1 MYB FRMD4B TSPAN5 STIL ITGA2 GOLGA6L10 MYBL2 AHI1 CAND2 GZMB RBPJ PELI1 HSPA1B KCNK5 GOLGA6L9 TICRR TPRG1 UBE2C AURKA Leem G, et al. -
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. -
Associations Among Dietary Non-Fiber Carbohydrate, Ruminal Microbiota
Shen et al. Microbiome (2017) 5:123 DOI 10.1186/s40168-017-0341-z RESEARCH Open Access Associations among dietary non-fiber carbohydrate, ruminal microbiota and epithelium G-protein-coupled receptor, and histone deacetylase regulations in goats Hong Shen1,2†, Zhongyan Lu3*†, Zhihui Xu1,2, Zhan Chen1,2 and Zanming Shen3 Abstract Background: Diet-derived short-chain fatty acids (SCFAs) in the rumen have broad effects on the health and growth of ruminants. The microbe-G-protein-coupled receptor (GPR) and microbe-histone deacetylase (HDAC) axes might be the major pathway mediating these effects. Here, an integrated approach of transcriptome sequencing and 16S rRNA gene sequencing was applied to investigate the synergetic responses of rumen epithelium and rumen microbiota to the increased intake of dietary non-fiber carbohydrate (NFC) from 15 to 30% in the goat model. In addition to the analysis of the microbial composition and identification of the genes and signaling pathways related to the differentially expressed GPRs and HDACs, the combined data including the expression of HDACs and GPRs, the relative abundance of the bacteria, and the molar proportions of the individual SCFAs were used to identify the significant co-variation of the SCFAs, clades, and transcripts. Results: The major bacterial clades promoted by the 30% NFC diet were related to lactate metabolism and cellulose degradation in the rumen. The predominant functions of the GPR and HDAC regulation network, under the 30% NFC diet, were related to the maintenance of epithelium integrity and the promotion of animal growth. In addition, the molar proportion of butyrate was inversely correlated with the expression of HDAC1, and the relative abundance of the bacteria belonging to Clostridum_IV was positively correlated with the expression of GPR1. -
Identification of Key Genes and Pathways Involved in Response To
Deng et al. Biol Res (2018) 51:25 https://doi.org/10.1186/s40659-018-0174-7 Biological Research RESEARCH ARTICLE Open Access Identifcation of key genes and pathways involved in response to pain in goat and sheep by transcriptome sequencing Xiuling Deng1,2†, Dong Wang3†, Shenyuan Wang1, Haisheng Wang2 and Huanmin Zhou1* Abstract Purpose: This aim of this study was to investigate the key genes and pathways involved in the response to pain in goat and sheep by transcriptome sequencing. Methods: Chronic pain was induced with the injection of the complete Freund’s adjuvant (CFA) in sheep and goats. The animals were divided into four groups: CFA-treated sheep, control sheep, CFA-treated goat, and control goat groups (n 3 in each group). The dorsal root ganglions of these animals were isolated and used for the construction of a cDNA= library and transcriptome sequencing. Diferentially expressed genes (DEGs) were identifed in CFA-induced sheep and goats and gene ontology (GO) enrichment analysis was performed. Results: In total, 1748 and 2441 DEGs were identifed in CFA-treated goat and sheep, respectively. The DEGs identi- fed in CFA-treated goats, such as C-C motif chemokine ligand 27 (CCL27), glutamate receptor 2 (GRIA2), and sodium voltage-gated channel alpha subunit 3 (SCN3A), were mainly enriched in GO functions associated with N-methyl- D-aspartate (NMDA) receptor, infammatory response, and immune response. The DEGs identifed in CFA-treated sheep, such as gamma-aminobutyric acid (GABA)-related DEGs (gamma-aminobutyric acid type A receptor gamma 3 subunit [GABRG3], GABRB2, and GABRB1), SCN9A, and transient receptor potential cation channel subfamily V member 1 (TRPV1), were mainly enriched in GO functions related to neuroactive ligand-receptor interaction, NMDA receptor, and defense response. -
Genetic Evidence That Celsr3 and Celsr2, Together with Fzd3, Regulate Forebrain Wiring in a Vangl-Independent Manner
Genetic evidence that Celsr3 and Celsr2, together with Fzd3, regulate forebrain wiring in a Vangl-independent manner Yibo Qua, Yuhua Huanga, Jia Fenga, Gonzalo Alvarez-Boladob, Elizabeth A. Grovec, Yingzi Yangd, Fadel Tissire, Libing Zhoua,f,1,2, and Andre M. Goffinete,1,2 aGuangdong–Hong Kong–Macau Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China; bDepartment of Neuroanatomy, Heidelberg University, D-69120 Heidelberg, Germany; cNeuroscience, The University of Chicago, Chicago, IL 60637; dNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892; eWELBIO - Walloon Excellence in Life Sciences and Biotechnology and Institute of Neuroscience, University of Louvain, B1200 Brussels, Belgium; and fState Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong Edited* by Jeremy Nathans, The Johns Hopkins University, Baltimore, MD, and approved June 18, 2014 (received for review February 3, 2014) Celsr3 and Fzd3, members of “core planar cell polarity” (PCP) AC contains commissural axons from the anterior olfactory nu- genes, were shown previously to control forebrain axon guidance clei and from the temporal cortex, which cross the midline at and wiring by acting in axons and/or guidepost cells. Here, we embryonic day 13.5 (E13.5) to E14.5 (11–14). The IC contains show that Celsr2 acts redundantly with Celsr3, and that their com- three main axonal components. Thalamocortical axons (TCA) bined mutation mimics that of Fzd3. The phenotypes generated emerge from the thalamus—formerly called “dorsal” thalamus upon inactivation of Fzd3 in different forebrain compartments are (15)—at E12.5. They run through the prethalamus (former similar to those in conditional Celsr2-3 mutants, indicating that “ventral” thalamus), turn and cross the diencephalon–telen- Fzd3 and Celsr2-3 act in the same population of cells.