Implication of Bidirectional Promoters Containing Duplicated GGAA Motifs of Mitochondrial Function-Associated Genes
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Figure 2S 4 7 A - C 080125 CSCs 080418 CSCs - + IFN-a 48 h + IFN-a 48 h + IFN-a 72 h 6 + IFN-a 72 h 3 5 MRFI 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 7 B 13 080125 FBS - D 080418 FBS - + IFN-a 48 h 12 + IFN-a 48 h + IFN-a 72 h + IFN-a 72 h 6 080125 FBS 11 10 5 9 8 4 7 6 3 MRFI 5 4 2 3 2 1 1 0 0 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 MHC I MHC II MICA MICB ULBP-1 ULBP-2 ULBP-3 ULBP-4 Molecule Molecule FIGURE 4S FIGURE 5S Panel A Panel B FIGURE 6S A B C D Supplemental Results Table 1S. Modulation by IFN-α of APM in GBM CSC and FBS tumor cell lines. Molecule * Cell line IFN-α‡ HLA β2-m# HLA LMP TAP1 TAP2 class II A A HC§ 2 7 10 080125 CSCs - 1∞ (1) 3 (65) 2 (91) 1 (2) 6 (47) 2 (61) 1 (3) 1 (2) 1 (3) + 2 (81) 11 (80) 13 (99) 1 (3) 8 (88) 4 (91) 1 (2) 1 (3) 2 (68) 080125 FBS - 2 (81) 4 (63) 4 (83) 1 (3) 6 (80) 3 (67) 2 (86) 1 (3) 2 (75) + 2 (99) 14 (90) 7 (97) 5 (75) 7 (100) 6 (98) 2 (90) 1 (4) 3 (87) 080418 CSCs - 2 (51) 1 (1) 1 (3) 2 (47) 2 (83) 2 (54) 1 (4) 1 (2) 1 (3) + 2 (81) 3 (76) 5 (75) 2 (50) 2 (83) 3 (71) 1 (3) 2 (87) 1 (2) 080418 FBS - 1 (3) 3 (70) 2 (88) 1 (4) 3 (87) 2 (76) 1 (3) 1 (3) 1 (2) + 2 (78) 7 (98) 5 (99) 2 (94) 5 (100) 3 (100) 1 (4) 2 (100) 1 (2) 070104 CSCs - 1 (2) 1 (3) 1 (3) 2 (78) 1 (3) 1 (2) 1 (3) 1 (3) 1 (2) + 2 (98) 8 (100) 10 (88) 4 (89) 3 (98) 3 (94) 1 (4) 2 (86) 2 (79) * expression of APM molecules was evaluated by intracellular staining and cytofluorimetric analysis; ‡ cells were treatead or not (+/-) for 72 h with 1000 IU/ml of IFN-α; # β-2 microglobulin; § β-2 microglobulin-free HLA-A heavy chain; ∞ values are indicated as ratio between the mean of fluorescence intensity of cells stained with the selected mAb and that of the negative control; bold values indicate significant MRFI (≥ 2). -
Translational Resistance of Late Alphavirus Mrna to Eif2␣ Phosphorylation: a Strategy to Overcome the Antiviral Effect of Protein Kinase PKR
Downloaded from genesdev.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Translational resistance of late alphavirus mRNA to eIF2␣ phosphorylation: a strategy to overcome the antiviral effect of protein kinase PKR Iván Ventoso,1,3 Miguel Angel Sanz,2 Susana Molina,2 Juan José Berlanga,2 Luis Carrasco,2 and Mariano Esteban1 1Departamento de Biología Molecular y Celular, Centro Nacional de Biotecnología/CSIC, Cantoblanco, E-28049 Madrid, Spain; 2Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Facultad de Ciencias, Cantoblanco, E-28049 Madrid, Spain The double-stranded RNA-dependent protein kinase (PKR) is one of the four mammalian kinases that phosphorylates the translation initiation factor 2␣ in response to virus infection. This kinase is induced by interferon and activated by double-stranded RNA (dsRNA). Phosphorylation of eukaryotic initiation factor 2␣ (eIF2␣) blocks translation initiation of both cellular and viral mRNA, inhibiting virus replication. To counteract this effect, most viruses express inhibitors that prevent PKR activation in infected cells. Here we report that PKR is highly activated following infection with alphaviruses Sindbis (SV) and Semliki Forest virus (SFV), leading to the almost complete phosphorylation of eIF2␣. Notably, subgenomic SV 26S mRNA is translated efficiently in the presence of phosphorylated eIF2␣. This modification of eIF2 does not restrict viral replication; SV 26S mRNA initiates translation with canonical methionine in the presence of high levels of phosphorylated eIF2␣. Genetic and biochemical data showed a highly stable RNA hairpin loop located downstream of the AUG initiator codon that is necessary to provide translational resistance to eIF2␣ phosphorylation. This structure can stall the ribosomes on the correct site to initiate translation of SV 26S mRNA, thus bypassing the requirement for a functional eIF2. -
DHX38 Antibody - Middle Region Rabbit Polyclonal Antibody Catalog # AI10980
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 DHX38 antibody - middle region Rabbit Polyclonal Antibody Catalog # AI10980 Specification DHX38 antibody - middle region - Product Information Application WB Primary Accession Q92620 Other Accession NM_014003, NP_054722 Reactivity Human, Mouse, Rat, Rabbit, Zebrafish, Pig, Horse, Bovine, Dog Predicted Human, Rat, WB Suggested Anti-DHX38 Antibody Rabbit, Zebrafish, Titration: 1.0 μg/ml Pig, Chicken Positive Control: OVCAR-3 Whole Cell Host Rabbit Clonality Polyclonal Calculated MW 140kDa KDa DHX38 antibody - middle region - Additional Information Gene ID 9785 Alias Symbol DDX38, KIAA0224, PRP16, PRPF16 Other Names Pre-mRNA-splicing factor ATP-dependent RNA helicase PRP16, 3.6.4.13, ATP-dependent RNA helicase DHX38, DEAH box protein 38, DHX38, DDX38, KIAA0224, PRP16 Format Liquid. Purified antibody supplied in 1x PBS buffer with 0.09% (w/v) sodium azide and 2% sucrose. Reconstitution & Storage Add 50 ul of distilled water. Final anti-DHX38 antibody concentration is 1 mg/ml in PBS buffer with 2% sucrose. For longer periods of storage, store at 20°C. Avoid repeat freeze-thaw cycles. Precautions Page 1/2 10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 DHX38 antibody - middle region is for research use only and not for use in diagnostic or therapeutic procedures. DHX38 antibody - middle region - Protein Information Name DHX38 Synonyms DDX38, KIAA0224, PRP16 Function Probable ATP-binding RNA helicase (Probable). Involved in pre-mRNA splicing as component of the spliceosome (PubMed:<a href="http://www.uniprot.org/c itations/29301961" target="_blank">29301961</a>, PubMed:<a href="http://www.uniprot.org/ci tations/9524131" target="_blank">9524131</a>). -
Role of Mitochondrial Ribosomal Protein S18-2 in Cancerogenesis and in Regulation of Stemness and Differentiation
From THE DEPARTMENT OF MICROBIOLOGY TUMOR AND CELL BIOLOGY (MTC) Karolinska Institutet, Stockholm, Sweden ROLE OF MITOCHONDRIAL RIBOSOMAL PROTEIN S18-2 IN CANCEROGENESIS AND IN REGULATION OF STEMNESS AND DIFFERENTIATION Muhammad Mushtaq Stockholm 2017 All previously published papers were reproduced with permission from the publisher. Published by Karolinska Institutet. Printed by E-Print AB 2017 © Muhammad Mushtaq, 2017 ISBN 978-91-7676-697-2 Role of Mitochondrial Ribosomal Protein S18-2 in Cancerogenesis and in Regulation of Stemness and Differentiation THESIS FOR DOCTORAL DEGREE (Ph.D.) By Muhammad Mushtaq Principal Supervisor: Faculty Opponent: Associate Professor Elena Kashuba Professor Pramod Kumar Srivastava Karolinska Institutet University of Connecticut Department of Microbiology Tumor and Cell Center for Immunotherapy of Cancer and Biology (MTC) Infectious Diseases Co-supervisor(s): Examination Board: Professor Sonia Lain Professor Ola Söderberg Karolinska Institutet Uppsala University Department of Microbiology Tumor and Cell Department of Immunology, Genetics and Biology (MTC) Pathology (IGP) Professor George Klein Professor Boris Zhivotovsky Karolinska Institutet Karolinska Institutet Department of Microbiology Tumor and Cell Institute of Environmental Medicine (IMM) Biology (MTC) Professor Lars-Gunnar Larsson Karolinska Institutet Department of Microbiology Tumor and Cell Biology (MTC) Dedicated to my parents ABSTRACT Mitochondria carry their own ribosomes (mitoribosomes) for the translation of mRNA encoded by mitochondrial DNA. The architecture of mitoribosomes is mainly composed of mitochondrial ribosomal proteins (MRPs), which are encoded by nuclear genomic DNA. Emerging experimental evidences reveal that several MRPs are multifunctional and they exhibit important extra-mitochondrial functions, such as involvement in apoptosis, protein biosynthesis and signal transduction. Dysregulations of the MRPs are associated with severe pathological conditions, including cancer. -
Gene Knockdown of CENPA Reduces Sphere Forming Ability and Stemness of Glioblastoma Initiating Cells
Neuroepigenetics 7 (2016) 6–18 Contents lists available at ScienceDirect Neuroepigenetics journal homepage: www.elsevier.com/locate/nepig Gene knockdown of CENPA reduces sphere forming ability and stemness of glioblastoma initiating cells Jinan Behnan a,1, Zanina Grieg b,c,1, Mrinal Joel b,c, Ingunn Ramsness c, Biljana Stangeland a,b,⁎ a Department of Molecular Medicine, Institute of Basic Medical Sciences, The Medical Faculty, University of Oslo, Oslo, Norway b Norwegian Center for Stem Cell Research, Department of Immunology and Transfusion Medicine, Oslo University Hospital, Oslo, Norway c Vilhelm Magnus Laboratory for Neurosurgical Research, Institute for Surgical Research and Department of Neurosurgery, Oslo University Hospital, Oslo, Norway article info abstract Article history: CENPA is a centromere-associated variant of histone H3 implicated in numerous malignancies. However, the Received 20 May 2016 role of this protein in glioblastoma (GBM) has not been demonstrated. GBM is one of the most aggressive Received in revised form 23 July 2016 human cancers. GBM initiating cells (GICs), contained within these tumors are deemed to convey Accepted 2 August 2016 characteristics such as invasiveness and resistance to therapy. Therefore, there is a strong rationale for targeting these cells. We investigated the expression of CENPA and other centromeric proteins (CENPs) in Keywords: fi CENPA GICs, GBM and variety of other cell types and tissues. Bioinformatics analysis identi ed the gene signature: fi Centromeric proteins high_CENP(AEFNM)/low_CENP(BCTQ) whose expression correlated with signi cantly worse GBM patient Glioblastoma survival. GBM Knockdown of CENPA reduced sphere forming ability, proliferation and cell viability of GICs. We also Brain tumor detected significant reduction in the expression of stemness marker SOX2 and the proliferation marker Glioblastoma initiating cells and therapeutic Ki67. -
Supplemental Data
SUPPLEMENTAL INFORMATION Glomerular cell crosstalk influences composition and assembly of extracellular matrix Adam Byron,1,*,† Michael J. Randles,1,2,† Jonathan D. Humphries,1 Aleksandr Mironov,1 Hellyeh Hamidi,1 Shelley Harris,2 Peter W. Mathieson,3 Moin A. Saleem,3 Simon S. Satchell,3 Roy Zent,4,5 Martin J. Humphries,1 and Rachel Lennon.1,2 1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester, Manchester, UK; 2Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK; 3Academic Renal Unit, Faculty of Medicine and Dentistry, University of Bristol, Bristol, UK; 4Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; and 5Veterans Affairs Hospital, Nashville, TN, USA. *Present address: Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. †These authors contributed equally to this work. Corresponding author: Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research, Michael Smith Building, University of Manchester, Manchester M13 9PT, UK. Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082. Email: [email protected] Supplementary methods Non-glomerular cell culture HEK 293T and human foreskin fibroblasts were cultured until confluent in Dulbecco's Modified Eagle Medium supplemented with 10% foetal calf serum. Lentiviral production and transduction Podocytes stably expressing GFP were produced by lentiviral transduction. Briefly, HEK 293T cells were transfected with three plasmids obtained from Addgene (psPAX2 Addgene ID 12260, pMD2.G Addgene ID 12259 and pWPXL Addgene ID 12257) using polyethyleneimine (Sigma-Aldrich). Conditioned medium containing viruses was collected after 5 days following several media changes including an 8 hr incubation with sodium butyrate- containing media to promote virus production. -
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. -
Screening of a Clinically and Biochemically Diagnosed SOD Patient Using Exome Sequencing: a Case Report with a Mutations/Variations Analysis Approach
The Egyptian Journal of Medical Human Genetics (2016) 17, 131–136 HOSTED BY Ain Shams University The Egyptian Journal of Medical Human Genetics www.ejmhg.eg.net www.sciencedirect.com CASE REPORT Screening of a clinically and biochemically diagnosed SOD patient using exome sequencing: A case report with a mutations/variations analysis approach Mohamad-Reza Aghanoori a,b,1, Ghazaleh Mohammadzadeh Shahriary c,2, Mahdi Safarpour d,3, Ahmad Ebrahimi d,* a Department of Medical Genetics, Shiraz University of Medical Sciences, Shiraz, Iran b Research and Development Division, RoyaBioGene Co., Tehran, Iran c Department of Genetics, Shahid Chamran University of Ahvaz, Ahvaz, Iran d Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Received 12 May 2015; accepted 15 June 2015 Available online 22 July 2015 KEYWORDS Abstract Background: Sulfite oxidase deficiency (SOD) is a rare neurometabolic inherited disor- Sulfite oxidase deficiency; der causing severe delay in developmental stages and premature death. The disease follows an auto- Case report; somal recessive pattern of inheritance and causes deficiency in the activity of sulfite oxidase, an Exome sequencing enzyme that normally catalyzes conversion of sulfite to sulfate. Aim of the study: SOD is an underdiagnosed disorder and its diagnosis can be difficult in young infants as early clinical features and neuroimaging changes may imitate some common diseases. Since the prognosis of the disease is poor, using exome sequencing as a powerful and efficient strat- egy for identifying the genes underlying rare mendelian disorders can provide important knowledge about early diagnosis, disease mechanisms, biological pathways, and potential therapeutic targets. -
IPS-1 Is Crucial for DAP3-Mediated Anoikis Induction by Caspase-8 Activation
Cell Death and Differentiation (2009) 16, 1615–1621 & 2009 Macmillan Publishers Limited All rights reserved 1350-9047/09 $32.00 www.nature.com/cdd IPS-1 is crucial for DAP3-mediated anoikis induction by caspase-8 activation H-M Li1, D Fujikura1, T Harada1, J Uehara1, T Kawai2,3, S Akira2,3, JC Reed4, A Iwai1 and T Miyazaki*,1 Detachment of adherent epithelial cells from the extracellular matrix induces apoptosis, a process known as anoikis. We have shown that DAP3 is critical for anoikis induction. However, the mechanism for anoikis induction mediated by DAP3 is still unclear. Here, we show that interferon-b promoter stimulator 1 (IPS-1) binds DAP3 and induces anoikis by caspase activation. Recently, IPS-1 has been shown to be critical for antiviral immune responses, although there has been no report of its function in apoptosis induction. We show that overexpression of IPS-1 induces apoptosis by activation of caspase-3, -8, and -9. In addition, IPS-1 knockout mouse embryonic fibroblasts were shown to be resistant to anoikis. Interestingly, IPS-1 expression, recruitment of caspase-8 to IPS-1, and caspase-8 activation were induced after cell detachment. Furthermore, DAP3-mediated anoikis induction was inhibited by knockdown of IPS-1 expression. Therefore, we elucidated a novel function of IPS-1 for anoikis induction by caspase-8 activation. Cell Death and Differentiation (2009) 16, 1615–1621; doi:10.1038/cdd.2009.97; published online 31 July 2009 Apoptosis, the programmed physiological cell death, is critical induces programmed cell death, a phenomenon known as for modeling tissues and maintaining homeostasis in multi- anoikis. -
PDF Output of CLIC (Clustering by Inferred Co-Expression)
PDF Output of CLIC (clustering by inferred co-expression) Dataset: Num of genes in input gene set: 13 Total number of genes: 16493 CLIC PDF output has three sections: 1) Overview of Co-Expression Modules (CEMs) Heatmap shows pairwise correlations between all genes in the input query gene set. Red lines shows the partition of input genes into CEMs, ordered by CEM strength. Each row shows one gene, and the brightness of squares indicates its correlations with other genes. Gene symbols are shown at left side and on the top of the heatmap. 2) Details of each CEM and its expansion CEM+ Top panel shows the posterior selection probability (dataset weights) for top GEO series datasets. Bottom panel shows the CEM genes (blue rows) as well as expanded CEM+ genes (green rows). Each column is one GEO series dataset, sorted by their posterior probability of being selected. The brightness of squares indicates the gene's correlations with CEM genes in the corresponding dataset. CEM+ includes genes that co-express with CEM genes in high-weight datasets, measured by LLR score. 3) Details of each GEO series dataset and its expression profile: Top panel shows the detailed information (e.g. title, summary) for the GEO series dataset. Bottom panel shows the background distribution and the expression profile for CEM genes in this dataset. Overview of Co-Expression Modules (CEMs) with Dataset Weighting Scale of average Pearson correlations Num of Genes in Query Geneset: 13. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Cenpk Cenph Cenpp Cenpu Cenpn Cenpq Cenpl Apitd1 -
CRNKL1 Is a Highly Selective Regulator of Intron-Retaining HIV-1 and Cellular Mrnas
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.04.934927; this version posted February 11, 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. 1 CRNKL1 is a highly selective regulator of intron-retaining HIV-1 and cellular mRNAs 2 3 4 Han Xiao1, Emanuel Wyler2#, Miha Milek2#, Bastian Grewe3, Philipp Kirchner4, Arif Ekici4, Ana Beatriz 5 Oliveira Villela Silva1, Doris Jungnickl1, Markus Landthaler2,5, Armin Ensser1, and Klaus Überla1* 6 7 1 Institute of Clinical and Molecular Virology, University Hospital Erlangen, Friedrich-Alexander 8 Universität Erlangen-Nürnberg, Erlangen, Germany 9 2 Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine in the 10 Helmholtz Association, Robert-Rössle-Strasse 10, 13125, Berlin, Germany 11 3 Department of Molecular and Medical Virology, Ruhr-University, Bochum, Germany 12 4 Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen- 13 Nürnberg, Erlangen, Germany 14 5 IRI Life Sciences, Institute für Biologie, Humboldt Universität zu Berlin, Philippstraße 13, 10115, Berlin, 15 Germany 16 # these two authors contributed equally 17 18 19 *Corresponding author: 20 Prof. Dr. Klaus Überla 21 Institute of Clinical and Molecular Virology, University Hospital Erlangen 22 Friedrich-Alexander Universität Erlangen-Nürnberg 23 Schlossgarten 4, 91054 Erlangen 24 Germany 25 Tel: (+49) 9131-8523563 26 e-mail: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.04.934927; this version posted February 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
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.