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A Genetic Screening Identifies a Component of the SWI/SNF Complex, Arid1b As a Senescence Regulator
A genetic screening identifies a component of the SWI/SNF complex, Arid1b as a senescence regulator Sadaf Khan A thesis submitted to Imperial College London for the degree of Doctor in Philosophy MRC Clinical Sciences Centre Imperial College London, School of Medicine July 2013 Statement of originality All experiments included in this thesis were performed by myself unless otherwise stated. Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives license. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the license terms of this work. 2 Abstract Senescence is an important tumour suppressor mechanism, which prevents the proliferation of stressed or damaged cells. The use of RNA interference to identify genes with a role in senescence is an important tool in the discovery of novel cancer genes. In this work, a protocol was established for conducting bypass of senescence screenings, using shRNA libraries together with next-generation sequencing. Using this approach, the SWI/SNF subunit Arid1b was identified as a regulator of cellular lifespan in MEFs. SWI/SNF is a large multi-subunit complex that remodels chromatin. Mutations in SWI/SNF proteins are frequently associated with cancer, suggesting that SWI/SNF components are tumour suppressors. Here the role of ARID1B during senescence was investigated. Depletion of ARID1B extends the proliferative capacity of primary mouse and human fibroblasts. -
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Identification of the Genes Up- and Down-Regulated by the High Mobility Group A1 (HMGA1) Proteins: Tissue Specificity of the HMGA1-Dependent Gene Regulation
[CANCER RESEARCH 64, 5728–5735, August 15, 2004] Identification of the Genes Up- and Down-Regulated by the High Mobility Group A1 (HMGA1) Proteins: Tissue Specificity of the HMGA1-Dependent Gene Regulation Josefina Martinez Hoyos,1 Monica Fedele,1 Sabrina Battista,1 Francesca Pentimalli,1,2 Mogens Kruhoffer,3 Claudio Arra,4 Torben F. Orntoft,3 Carlo Maria Croce,2 and Alfredo Fusco1 1Dipartimento di Biologia e Patologia Cellulare e Molecolare e/o Istituto di Endocrinologia ed Oncologia Sperimentale del CNR, Facolta` di Medicina e Chirurgia di Napoli, Universita` degli Studi di Napoli “Federico II,” Naples, Italy; 2Kimmel Cancer Center, Jefferson Medical College, Philadelphia, Pennsylvania; 3Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark; and 4Istituto Dei Tumori Di Napoli “Fondazione Pascale,” Naples, Italy. ABSTRACT To identify the differentiation pathways in which HMGA1 is in- volved and to assess the role of the HMGA1 proteins in development, High mobility group A (HMGA) proteins are chromatinic proteins that we generated embryonic stem (ES) cells in which one or both hmga1 do not have transcriptional activity per se, however, by interacting with alleles are disrupted. We reported recently that hmga1Ϫ/Ϫ ES cells the transcription machinery, they regulate, negatively or positively, the expression of several genes. We searched for genes regulated by HMGA1 generate less T-cell precursors than do wild-type ES cells after in proteins using microarray analysis in embryonic stem (ES) cells bearing vitro-specific differentiation. Indeed, they preferentially differentiate one or two disrupted hmga1 alleles. We identified 87 transcripts increased to B cells, probably consequent to decreased IL-2 expression and and 163 transcripts decreased of at least 4-fold in hmga1؊/؊ ES cells. -
1 Evidence for Gliadin Antibodies As Causative Agents in Schizophrenia
1 Evidence for gliadin antibodies as causative agents in schizophrenia. C.J.Carter PolygenicPathways, 20 Upper Maze Hill, Saint-Leonard’s on Sea, East Sussex, TN37 0LG [email protected] Tel: 0044 (0)1424 422201 I have no fax Abstract Antibodies to gliadin, a component of gluten, have frequently been reported in schizophrenia patients, and in some cases remission has been noted following the instigation of a gluten free diet. Gliadin is a highly immunogenic protein, and B cell epitopes along its entire immunogenic length are homologous to the products of numerous proteins relevant to schizophrenia (p = 0.012 to 3e-25). These include members of the DISC1 interactome, of glutamate, dopamine and neuregulin signalling networks, and of pathways involved in plasticity, dendritic growth or myelination. Antibodies to gliadin are likely to cross react with these key proteins, as has already been observed with synapsin 1 and calreticulin. Gliadin may thus be a causative agent in schizophrenia, under certain genetic and immunological conditions, producing its effects via antibody mediated knockdown of multiple proteins relevant to the disease process. Because of such homology, an autoimmune response may be sustained by the human antigens that resemble gliadin itself, a scenario supported by many reports of immune activation both in the brain and in lymphocytes in schizophrenia. Gluten free diets and removal of such antibodies may be of therapeutic benefit in certain cases of schizophrenia. 2 Introduction A number of studies from China, Norway, and the USA have reported the presence of gliadin antibodies in schizophrenia 1-5. Gliadin is a component of gluten, intolerance to which is implicated in coeliac disease 6. -
Supplementary Figure S1. Intracellular Ca2+ Levels Following Decursin Treatment in F11 Cells in the Presence of Menthol
Supplementary Figure S1. Intracellular Ca2+ levels following decursin treatment in F11 cells in the presence of menthol (A) Intracellular Ca2+ levels after treatment with decursin every 3 s. The red arrow indicates the duration of treatment with 200 μM of menthol and decursin. NC: The negative control treated with DMSO only; PC: The positive control treated with 200 μM menthol without decursin. (B) Average intracellular Ca2+ levels after treatment with decursin. The average was quantified from the normalized Δ340/380 ratio for 10 cycles after treatment with the decursin solution at the 10th cycle, as shown in Fig. 1A. The normalized Δ340/380 ratio was calculated using the following for- mula: [ratio of fluorescence intensity at 510 nm (emission) to that at 340 nm (excitation)]/[ratio of fluorescence intensity at 510 nm (emission) to that at a wavelength of 380 nm (excitation)]. Cells 2021, 10, 547. https://doi.org/10.3390/cells10030547 www.mdpi.com/journal/cells Cells 2021, 10, 547 2 of 5 Table S1. List of protein targets of decursin detected by the SwissTargetPrediction web tool Common Target Uniprot ID ChEMBL ID Target Class Probability name Poly [ADP-ribose] polymerase-1 PARP1 P09874 CHEMBL3105 Enzyme 0.104671941 N-acylsphingosine-amidohydro- NAAA Q02083 CHEMBL4349 Enzyme 0.104671941 lase Acid ceramidase ASAH1 Q13510 CHEMBL5463 Enzyme 0.104671941 Family A G protein- Neuropeptide Y receptor type 5 NPY5R Q15761 CHEMBL4561 0.104671941 coupled receptor Family A G protein- Melatonin receptor 1A MTNR1A P48039 CHEMBL1945 0.104671941 coupled -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
A Preliminary Transcriptome Analysis Suggests a Transitory Effect of Vitamin D on Mitochondrial Function in Obese Young Finnish Subjects
ID: 18-0537 8 5 E Einarsdottir et al. Effect of vitamin D on gene 8:5 559–570 expression RESEARCH A preliminary transcriptome analysis suggests a transitory effect of vitamin D on mitochondrial function in obese young Finnish subjects Elisabet Einarsdottir1,2,3,†, Minna Pekkinen1,4, Kaarel Krjutškov2,5, Shintaro Katayama3, Juha Kere1,2,3,6, Outi Mäkitie1,4,7,8 and Heli Viljakainen1,9 1Folkhälsan Institute of Genetics, University of Helsinki, Helsinki, Finland 2Molecular Neurology Research Program, University of Helsinki, Helsinki, Finland 3Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden 4Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland 5Competence Centre on Health Technologies, Tartu, Estonia 6School of Basic and Medical Biosciences, King’s College London, Guy’s Hospital, London, United Kingdom 7Department of Molecular Medicine and Surgery and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden 8Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden 9Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland Correspondence should be addressed to H Viljakainen: [email protected] †(E Einarsdottir is now at Department of Gene Technology, Science for Life Laboratory, KTH-Royal Institute of Technology, Solna, Sweden) Abstract Objective: The effect of vitamin D at the transcriptome level is poorly understood, Key Words and furthermore, it is unclear if it differs between obese and normal-weight subjects. f vitamin D The objective of the study was to explore the transcriptome effects of vitamin D f gene expression supplementation. f obesity Design and methods: We analysed peripheral blood gene expression using GlobinLock f transcriptome oligonucleotides followed by RNA sequencing in individuals participating in a 12-week f mitochondrial function randomised double-blinded placebo-controlled vitamin D intervention study. -
RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases
G C A T T A C G G C A T genes Review RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases Amber Willbanks, Shaun Wood and Jason X. Cheng * Department of Pathology, Hematopathology Section, University of Chicago, Chicago, IL 60637, USA; [email protected] (A.W.); [email protected] (S.W.) * Correspondence: [email protected] Abstract: Chromatin structure plays an essential role in eukaryotic gene expression and cell identity. Traditionally, DNA and histone modifications have been the focus of chromatin regulation; however, recent molecular and imaging studies have revealed an intimate connection between RNA epigenetics and chromatin structure. Accumulating evidence suggests that RNA serves as the interplay between chromatin and the transcription and splicing machineries within the cell. Additionally, epigenetic modifications of nascent RNAs fine-tune these interactions to regulate gene expression at the co- and post-transcriptional levels in normal cell development and human diseases. This review will provide an overview of recent advances in the emerging field of RNA epigenetics, specifically the role of RNA modifications and RNA modifying proteins in chromatin remodeling, transcription activation and RNA processing, as well as translational implications in human diseases. Keywords: 5’ cap (5’ cap); 7-methylguanosine (m7G); R-loops; N6-methyladenosine (m6A); RNA editing; A-to-I; C-to-U; 2’-O-methylation (Nm); 5-methylcytosine (m5C); NOL1/NOP2/sun domain Citation: Willbanks, A.; Wood, S.; (NSUN); MYC Cheng, J.X. RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases. Genes 2021, 12, 627. -
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. -
1 Supplementary Table S1. Primers Used for RT-Qpcr PROX1
Supplementary Table S1. Primers used for RT-qPCR PROX1 (Prospero Homeobox 1) 5’ – CCAGCTCCAATATGCTGAAGACCTA – 3’ 5’ – CATCGTTGATGGCTTGACGTG – 3‘ MMP-1 (Matrix Metallopeptidase 1) 5' –CTGTCCCTGAACAGCCCAGTACTTA– 3' 5' –CTGGCCACAACTGCCAAATG– 3' FGF2 (Fibroblast Growth Factor 2) 5′ - GGCTTCTTCCTGCGCATCCA – 3′ 5′ – GCTCTTAGCAGACATTGGAAGA – 3′ MMP-3 (Matrix Metallopeptidase 3) GAAATGAGGTACGAGCTGGATACC– 3’ 5’ –ATGGCTGCATCGATTTTCCT– 3’ NUDT6 (Nudix Hydrolase 6) 5’ –GGCGAGCTGGACAGATTC– 3’ 5’ –GCAGCAGGGGCAATAAATCG– 3’ BAIAP2 (BAI1 Associated Protein 2) 5’ –AAGTCCACAGGCAGATCCAG– 3’ 5’ –GCCTTTGCTCCTTTGCTCAG– 3’ VEGFC (Vascular Endothelial Growth 5’ –GCCACGGCTTATGCAAGCAAAGAT– 3’ Factor C) 5’ –AGTTGAGGTTGGCCTGTTCTCTGT– 3’ ANGPT1 (Angiopoietin 1) 5’ –GAAGGGAACCGAGCCTATTC– 3’ 5’ –AGCATCAAACCACCATCCTC– 3’ KDR (Kinase Insert Domain Receptor) 5’ –AGGAGAGCGTGTCTTTGTGG– 3’ 5’ –GCCTGTCTTCAGTTCCCCTC– 3’ VEGFA (Vascular Endothelial Growth 5’ –CTTGCCTTGCTGCTCTACCT– 3’ Factor A) 5’ –AAGATGTCCACCAGGGTCTC– 3’ PLAT (Plasminogen Activator, Tissue 5’ –AGGAGAGCGTGTCTTTGTGG– 3’ Type) 5’ –GCCTGTCTTCAGTTCCCCTC– 3’ MDK (Midkine) 5’ –CCTGCAACTGGAAGAAGGAG– 3’ 5’ -- CTTTCCCTTCCCTTTCTTGG– 3’ ADAMTS9 (ADAM Metallopeptidase 5’ –ACGAAAAACCTGCCGTAATG– 3’ With Thrombospondin Type 1 Motif 9) 5’ –TCAGAGTCTCCATGCACCAG– 3’ TIMP3 (TIMP Metallopeptidase Inhibitor 5’ –CTGACAGGTCGCGTCTATGA– 3’ 3) 5’ –AGTCACAAAGCAAGGCAGGT– 3’ ACTB (Beta Actin) 5’ – GCCGAGGACTTTGATTGC – 3’ 5’– CTGTGTGGACTTGGGAGAG – 3’ 1 Figure S1. Efficient silencing of PROX1 in CGTH-W-1 and FTC-133 cells. Western blotting analysis shows a decrease in PROX1 protein level by targeting with siRNAs purchased from Santa Cruz (SC) and Sigma-Aldrich (SA) in both CGTH-W-1 and FTC-133 cell line. Beta-actin was used as a loading control of protein lysates. Figure S2. The tube formation assay. The silencing of PROX1 in CGTH-W-1 and FTC-133 cells enhances the angiogenesis in vitro of endothelial cells. HUVECs were cultured in 96-well plates coated with a semi-solid Matrigel. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
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