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Implications of Zonal Architecture on Differential Gene Expression
www.nature.com/scientificreports OPEN Implications of zonal architecture on diferential gene expression profling and altered pathway expressions in mandibular condylar cartilage Aisha M. Basudan1*, Mohammad Azhar Aziz2 & Yanqi Yang3 Mandibular condylar cartilage (MCC) is a multi-zonal heterogeneous fbrocartilage containing diferent types of cells, but the factors/mechanisms governing the phenotypic transition across the zones have not been fully understood. The reliability of molecular studies heavily rely on the procurement of pure cell populations from the heterogeneous tissue. We used a combined laser-capture microdissection and microarray analysis approach which allowed identifcation of diferential zone-specifc gene expression profling and altered pathways in the MCC of 5-week-old rats. The bioinformatics analysis demonstrated that the MCC cells clearly exhibited distinguishable phenotypes from the articular chondrocytes. Additionally, a set of genes has been determined as potential markers to identify each MCC zone individually; Crab1 gene showed the highest enrichment while Clec3a was the most downregulated gene at the superfcial layer, which consists of fbrous (FZ) and proliferative zones (PZ). Ingenuity Pathway Analysis revealed numerous altered signaling pathways; Leukocyte extravasation signaling pathway was predicted to be activated at all MCC zones, in particular mature and hypertrophic chondrocytes zones (MZ&HZ), when compared with femoral condylar cartilage (FCC). Whereas Superpathway of Cholesterol Biosynthesis showed predicted activation in both FZ and PZ as compared with deep MCC zones and FCC. Determining novel zone-specifc diferences of large group of potential genes, upstream regulators and pathways in healthy MCC would improve our understanding of molecular mechanisms on regional (zonal) basis, and provide new insights for future therapeutic strategies. -
Down-Regulation of Stem Cell Genes, Including Those in a 200-Kb Gene Cluster at 12P13.31, Is Associated with in Vivo Differentiation of Human Male Germ Cell Tumors
Research Article Down-Regulation of Stem Cell Genes, Including Those in a 200-kb Gene Cluster at 12p13.31, Is Associated with In vivo Differentiation of Human Male Germ Cell Tumors James E. Korkola,1 Jane Houldsworth,1,2 Rajendrakumar S.V. Chadalavada,1 Adam B. Olshen,3 Debbie Dobrzynski,2 Victor E. Reuter,4 George J. Bosl,2 and R.S.K. Chaganti1,2 1Cell Biology Program and Departments of 2Medicine, 3Epidemiology and Biostatistics, and 4Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York Abstract on the degree and type of differentiation (i.e., seminomas, which Adult male germ cell tumors (GCTs) comprise distinct groups: resemble undifferentiated primitive germ cells, and nonseminomas, seminomas and nonseminomas, which include pluripotent which show varying degrees of embryonic and extraembryonic embryonal carcinomas as well as other histologic subtypes patterns of differentiation; refs. 2, 3). Nonseminomatous GCTs are exhibiting various stages of differentiation. Almost all GCTs further subdivided into embryonal carcinomas, which show early show 12p gain, but the target genes have not been clearly zygotic or embryonal-like differentiation, yolk sac tumors and defined. To identify 12p target genes, we examined Affymetrix choriocarcinomas, which exhibit extraembryonal forms of differ- (Santa Clara, CA) U133A+B microarray (f83% coverage of 12p entiation, and teratomas, which show somatic differentiation along genes) expression profiles of 17 seminomas, 84 nonseminoma multiple lineages (3). Both seminomas and embryonal carcinoma GCTs, and 5 normal testis samples. Seventy-three genes on 12p are known to express stem cell markers, such as POU5F1 (4) and were significantly overexpressed, including GLUT3 and REA NANOG (5). -
Spatially Heterogeneous Choroid Plexus Transcriptomes Encode Positional Identity and Contribute to Regional CSF Production
The Journal of Neuroscience, March 25, 2015 • 35(12):4903–4916 • 4903 Development/Plasticity/Repair Spatially Heterogeneous Choroid Plexus Transcriptomes Encode Positional Identity and Contribute to Regional CSF Production Melody P. Lun,1,3 XMatthew B. Johnson,2 Kevin G. Broadbelt,1 Momoko Watanabe,4 Young-jin Kang,4 Kevin F. Chau,1 Mark W. Springel,1 Alexandra Malesz,1 Andre´ M.M. Sousa,5 XMihovil Pletikos,5 XTais Adelita,1,6 Monica L. Calicchio,1 Yong Zhang,7 Michael J. Holtzman,7 Hart G.W. Lidov,1 XNenad Sestan,5 Hanno Steen,1 XEdwin S. Monuki,4 and Maria K. Lehtinen1 1Department of Pathology, and 2Division of Genetics, Boston Children’s Hospital, Boston, Massachusetts 02115, 3Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, 4Department of Pathology and Laboratory Medicine, University of California Irvine School of Medicine, Irvine, California 92697, 5Department of Neurobiology and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut 06510, 6Department of Biochemistry, Federal University of Sa˜o Paulo, Sa˜o Paulo 04039, Brazil, and 7Pulmonary and Critical Care Medicine, Department of Medicine, Washington University, St Louis, Missouri 63110 A sheet of choroid plexus epithelial cells extends into each cerebral ventricle and secretes signaling factors into the CSF. To evaluate whether differences in the CSF proteome across ventricles arise, in part, from regional differences in choroid plexus gene expression, we defined the transcriptome of lateral ventricle (telencephalic) versus fourth ventricle (hindbrain) choroid plexus. We find that positional identitiesofmouse,macaque,andhumanchoroidplexiderivefromgeneexpressiondomainsthatparalleltheiraxialtissuesoforigin.We thenshowthatmolecularheterogeneitybetweentelencephalicandhindbrainchoroidplexicontributestoregion-specific,age-dependent protein secretion in vitro. -
Derivation of Stable Microarray Cancer-Differentiating Signatures Using Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation
Research Article Derivation of Stable Microarray Cancer-Differentiating Signatures Using Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation Zhi Qun Tang,1,2 Lian Yi Han,1,2 Hong Huang Lin,1,2 Juan Cui,1,2 Jia Jia,1,2 Boon Chuan Low,2,3 Bao Wen Li,2,4 and Yu Zong Chen1,2 1Bioinformatics and Drug Design Group, Department of Pharmacy; 2Center for Computational Science and Engineering; and Departments of 3Biological Sciences and 4Physics, National University of Singapore, Singapore, Singapore Abstract sampling methods. Only 1 to 5 of the 4 to 60 selected predictor Microarrays have been explored for deriving molecular genes in each of these sets are present in more than half of the signatures to determine disease outcomes, mechanisms, other nine sets (Table 1), and 2 to 20 of the predictor genes in each targets, and treatment strategies. Although exhibiting good set are cancer related (Table 2). Despite the use of sophisticated predictive performance, some derived signatures are unstable class differentiation and signature selection methods, the selected due to noises arising from measurement variability and signatures show few overlapping predictor genes, as in the case of biological differences. Improvements in measurement, anno- other microarray data sets including non–Hodgkin lymphoma, tation, and signature selection methods have been proposed. acute lymphocytic leukemia, breast cancer, lung adenocarcinoma, We explored a new signature selection method that incorpo- medulloblastoma, hepatocellular carcinoma, and acute myeloid rates consensus scoring of multiple random sampling and leukemia (9, 15). multistep evaluation of gene-ranking consistency for maxi- Although these signatures display high cancer differentiation mally avoiding erroneous elimination of predictor genes. -
Single Cell Transcriptomics Reveal Temporal Dynamics of Critical Regulators of Germ Cell Fate During Mouse Sex Determination
bioRxiv preprint doi: https://doi.org/10.1101/747279; this version posted November 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Single cell transcriptomics reveal temporal dynamics of critical regulators of germ 2 cell fate during mouse sex determination 3 Authors: Chloé Mayère1,2, Yasmine Neirijnck1,3, Pauline Sararols1, Chris M Rands1, 4 Isabelle Stévant1,2, Françoise Kühne1, Anne-Amandine Chassot3, Marie-Christine 5 Chaboissier3, Emmanouil T. Dermitzakis1,2, Serge Nef1,2,*. 6 Affiliations: 7 1Department of Genetic Medicine and Development, University of Geneva, 1211 Geneva, 8 Switzerland; 9 2iGE3, Institute of Genetics and Genomics of Geneva, University of Geneva, 1211 10 Geneva, Switzerland; 11 3Université Côte d'Azur, CNRS, Inserm, iBV, France; 12 Lead Contact: 13 *Corresponding Author: Serge Nef, 1 rue Michel-Servet CH-1211 Genève 4, 14 [email protected]. + 41 (0)22 379 51 93 15 Running Title: Single cell transcriptomics of germ cells 1 bioRxiv preprint doi: https://doi.org/10.1101/747279; this version posted November 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 16 Abbreviations; 17 AGC: Adrenal Germ Cell 18 GC: Germ cell 19 OGC: Ovarian Germ Cell 20 TGC: Testicular Germ Cell 21 scRNA-seq: Single-cell RNA-Sequencing 22 DEG: Differentially Expressed Gene 23 24 25 Keywords: 26 Single-cell RNA-Sequencing (scRNA-seq), sex determination, ovary, testis, gonocytes, 27 oocytes, prospermatogonia, meiosis, gene regulatory network, germ cells, development, 28 RNA splicing 29 2 bioRxiv preprint doi: https://doi.org/10.1101/747279; this version posted November 2, 2020. -
Multifactorial Erβ and NOTCH1 Control of Squamous Differentiation and Cancer
Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks, … , Karine Lefort, G. Paolo Dotto J Clin Invest. 2014;124(5):2260-2276. https://doi.org/10.1172/JCI72718. Research Article Oncology Downmodulation or loss-of-function mutations of the gene encoding NOTCH1 are associated with dysfunctional squamous cell differentiation and development of squamous cell carcinoma (SCC) in skin and internal organs. While NOTCH1 receptor activation has been well characterized, little is known about how NOTCH1 gene transcription is regulated. Using bioinformatics and functional screening approaches, we identified several regulators of the NOTCH1 gene in keratinocytes, with the transcription factors DLX5 and EGR3 and estrogen receptor β (ERβ) directly controlling its expression in differentiation. DLX5 and ERG3 are required for RNA polymerase II (PolII) recruitment to the NOTCH1 locus, while ERβ controls NOTCH1 transcription through RNA PolII pause release. Expression of several identified NOTCH1 regulators, including ERβ, is frequently compromised in skin, head and neck, and lung SCCs and SCC-derived cell lines. Furthermore, a keratinocyte ERβ–dependent program of gene expression is subverted in SCCs from various body sites, and there are consistent differences in mutation and gene-expression signatures of head and neck and lung SCCs in female versus male patients. Experimentally increased ERβ expression or treatment with ERβ agonists inhibited proliferation of SCC cells and promoted NOTCH1 expression and squamous differentiation both in vitro and in mouse xenotransplants. Our data identify a link between transcriptional control of NOTCH1 expression and the estrogen response in keratinocytes, with implications for differentiation therapy of squamous cancer. Find the latest version: https://jci.me/72718/pdf Research article Multifactorial ERβ and NOTCH1 control of squamous differentiation and cancer Yang Sui Brooks,1,2 Paola Ostano,3 Seung-Hee Jo,1,2 Jun Dai,1,2 Spiro Getsios,4 Piotr Dziunycz,5 Günther F.L. -
The Role of Dlx3 in Gene Regulation in the Mouse Placenta
The role of Dlx3 in gene regulation in the mouse placenta by Li Han This thesis/dissertation document has been electronically approved by the following individuals: Roberson,Mark Stephen (Chairperson) Wolfner,Mariana Federica (Minor Member) Cohen,Paula (Field Appointed Minor Member) Weiss,Robert S. (Field Appointed Minor Member) THE ROLE OF DLX3 IN GENE REGULATION IN THE MOUSE PLACENTA A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Li Han August 2010 © 2010 Li Han THE ROLE OF DLX3 IN GENE REGULATION IN THE MOUSE PLACENTA Li Han, Ph. D. Cornell University 2010 Distal-less 3 (Dlx3) is a homeodomain containing transcription factor that is required for the normal development of the mouse placenta. Moreover in human trophoblasts, DLX3 appears to be a necessary transcriptional regulator of the glycoprotein hormone α subunit gene, a protein subunit of placental-derived chorionic gonadotropin. The aim of my studies described here was to determine the role of Dlx3 in gene regulation within the placenta. My studies initially sought to determine if Dlx3 could interact physically with other transcriptional regulators in placental trophoblast cells and how these protein- protein interactions might influence Dlx3-dependent gene expression. Yeast two- hybrid screens provide evidence that mothers against decapentaplegic homolog 6 (SMAD6) was a binding partner for DLX3. SMAD6 was found to be expressed and nuclear localized in placental trophoblasts and interacted directly with DLX3. Structure-function analysis revealed that this interaction occurred within a DLX3 domain that overlapped the homeobox, a key domain necessary for DLX3 DNA binding. -
Supplementary Materials
Supplementary Materials: Supplemental Table 1 Abbreviations FMDV Foot and Mouth Disease Virus FMD Foot and Mouth Disease NC Non-treated Control DEGs Differentially Expressed Genes RNA-seq High-throughput Sequencing of Mrna RT-qPCR Quantitative Real-time Reverse Transcriptase PCR TCID50 50% Tissue Culture Infective Doses CPE Cytopathic Effect MOI Multiplicity of Infection DMEM Dulbecco's Modified Eagle Medium FBS Fetal Bovine Serum PBS Phosphate Buffer Saline QC Quality Control FPKM Fragments per Kilo bases per Million fragments method GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes R Pearson Correlation Coefficient NFKBIA NF-kappa-B Inhibitor alpha IL6 Interleukin 6 CCL4 C-C motif Chemokine 4 CXCL2 C-X-C motif Chemokine 2 TNF Tumor Necrosis Factor VEGFA Vascular Endothelial Growth Gactor A CCL20 C-C motif Chemokine 20 CSF2 Macrophage Colony-Stimulating Factor 2 GADD45B Growth Arrest and DNA Damage Inducible 45 beta MYC Myc proto-oncogene protein FOS Proto-oncogene c-Fos MCL1 Induced myeloid leukemia cell differentiation protein Mcl-1 MAP3K14 Mitogen-activated protein kinase kinase kinase 14 IRF1 Interferon regulatory factor 1 CCL5 C-C motif chemokine 5 ZBTB3 Zinc finger and BTB domain containing 3 OTX1 Orthodenticle homeobox 1 TXNIP Thioredoxin-interacting protein ZNF180 Znc Finger Protein 180 ZNF36 Znc Finger Protein 36 ZNF182 Zinc finger protein 182 GINS3 GINS complex subunit 3 KLF15 Kruppel-like factor 15 Supplemental Table 2 Primers for Verification of RNA-seq-detected DEGs with RT-qPCR TNF F: CGACTCAGTGCCGAGATCAA R: -
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. -
¬LACZ-REPORTER MAPPING of Dlx5/6 EXPRESSION AND
Received: 26 December 2019 Revised: 10 May 2020 Accepted: 11 May 2020 DOI: 10.1002/cne.24952 RESEARCH ARTICLE LacZ-reporter mapping of Dlx5/6 expression and genoarchitectural analysis of the postnatal mouse prethalamus Luis Puelles1 | Carmen Diaz2 | Thorsten Stühmer3 | José L. Ferran1 | Margaret Martínez-de la Torre1 | John L. R. Rubenstein3 1Department of Human Anatomy and Psychobiology and IMIB-Arrixaca Institute, Abstract University of Murcia, Murcia, Spain We present here a thorough and complete analysis of mouse P0-P140 prethalamic 2 Department of Medical Sciences, School of histogenetic subdivisions and corresponding nuclear derivatives, in the context of Medicine and Institute for Research in Neurological Disabilities, University of Castilla- local tract landmarks. The study used as fundamental material brains from a transgenic La Mancha, Albacete, Spain mouse line that expresses LacZ under the control of an intragenic enhancer of Dlx5 3Nina Ireland Laboratory of Developmental Neurobiology, Department of Psychiatry, and Dlx6 (Dlx5/6-LacZ). Subtle shadings of LacZ signal, jointly with pan-DLX immu- UCSF Medical School, San Francisco, noreaction, and several other ancillary protein or RNA markers, including Calb2 and California Nkx2.2 ISH (for the prethalamic eminence, and derivatives of the rostral zona limitans Correspondence shell domain, respectively) were mapped across the prethalamus. The resulting model Luis Puelles, Department of Human Anatomy and Psychobiology, School of Medicine, of the prethalamic region postulates tetrapartite rostrocaudal and dorsoventral subdi- University of Murcia, Murcia 30071, Spain. visions, as well as a tripartite radial stratification, each cell population showing a char- Email: [email protected] acteristic molecular profile. Some novel nuclei are proposed, and some instances of Carmen Díaz, Department of Medical potential tangential cell migration were noted. -
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