Dysfunction of the Ubiquitin Proteasome and Ubiquitin-Like Systems in Schizophrenia
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ONCOGENOMICS Through the Use of Chemotherapeutic Agents
Oncogene (2002) 21, 7749 – 7763 ª 2002 Nature Publishing Group All rights reserved 0950 – 9232/02 $25.00 www.nature.com/onc Expression profiling of primary non-small cell lung cancer for target identification Jim Heighway*,1, Teresa Knapp1, Lenetta Boyce1, Shelley Brennand1, John K Field1, Daniel C Betticher2, Daniel Ratschiller2, Mathias Gugger2, Michael Donovan3, Amy Lasek3 and Paula Rickert*,3 1Target Identification Group, Roy Castle International Centre for Lung Cancer Research, University of Liverpool, 200 London Road, Liverpool L3 9TA, UK; 2Institute of Medical Oncology, University of Bern, 3010 Bern, Switzerland; 3Incyte Genomics Inc., 3160 Porter Drive, Palo Alto, California, CA 94304, USA Using a panel of cDNA microarrays comprising 47 650 Introduction transcript elements, we have carried out a dual-channel analysis of gene expression in 39 resected primary The lack of generally effective therapies for lung cancer human non-small cell lung tumours versus normal lung leads to the high mortality rate associated with this tissue. Whilst *11 000 elements were scored as common disease. Recorded 5-year survival figures vary, differentially expressed at least twofold in at least one to some extent perhaps reflecting differences in sample, 96 transcripts were scored as over-represented ascertainment methods, but across Europe are approxi- fourfold or more in at least seven out of 39 tumours mately 10% (Bray et al., 2002). Whilst surgery is an and 30 sequences 16-fold in at least two out of 39 effective strategy for the treatment of localized lesions, tumours, including 24 transcripts in common. Tran- most patients present with overtly or covertly dissemi- scripts (178) were found under-represented fourfold in nated disease. -
Isoform-Specific Monobody Inhibitors of Small Ubiquitin-Related Modifiers Engineered Using Structure-Guided Library Design
Isoform-specific monobody inhibitors of small ubiquitin-related modifiers engineered using structure-guided library design Ryan N. Gilbretha, Khue Truongb, Ikenna Madub, Akiko Koidea, John B. Wojcika, Nan-Sheng Lia, Joseph A. Piccirillia,c, Yuan Chenb, and Shohei Koidea,1 aDepartment of Biochemistry and Molecular Biology, and cDepartment of Chemistry, University of Chicago, 929 East 57th Street, Chicago, IL 60637; and bDepartment of Molecular Medicine, Beckman Research Institute of the City of Hope, 1450 East Duarte Road, Duarte, CA 91010 Edited by David Baker, University of Washington, Seattle, WA, and approved March 16, 2011 (received for review February 10, 2011) Discriminating closely related molecules remains a major challenge which SUMOylation alters protein function appears to be in the engineering of binding proteins and inhibitors. Here we through SUMO-mediated interactions with other proteins con- report the development of highly selective inhibitors of small ubi- taining a short peptide motif known as a SUMO-interacting motif quitin-related modifier (SUMO) family proteins. SUMOylation is (SIM) (4, 7, 8). involved in the regulation of diverse cellular processes. Functional There are few inhibitors of SUMO/SIM interactions, a defi- differences between two major SUMO isoforms in humans, SUMO1 ciency that limits our ability to finely dissect SUMO biology. In and SUMO2∕3, are thought to arise from distinct interactions the only reported example of such an inhibitor, a SIM-containing mediated by each isoform with other proteins containing SUMO- linear peptide was used to inhibit SUMO/SIM interactions, estab- interacting motifs (SIMs). However, the roles of such isoform- lishing their importance in coordinating DNA repair by nonho- specific interactions are largely uncharacterized due in part to the mologous end joining (9). -
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. -
Roles of Ubiquitination and Sumoylation in the Regulation of Angiogenesis
Curr. Issues Mol. Biol. (2020) 35: 109-126. Roles of Ubiquitination and SUMOylation in the Regulation of Angiogenesis Andrea Rabellino1*, Cristina Andreani2 and Pier Paolo Scaglioni2 1QIMR Berghofer Medical Research Institute, Brisbane City, Queensland, Australia. 2Department of Internal Medicine, Hematology and Oncology; University of Cincinnati, Cincinnati, OH, USA. *Correspondence: [email protected] htps://doi.org/10.21775/cimb.035.109 Abstract is tumorigenesis-induced angiogenesis, during Te generation of new blood vessels from the which hypoxic and starved cancer cells activate existing vasculature is a dynamic and complex the molecular pathways involved in the formation mechanism known as angiogenesis. Angiogenesis of novel blood vessels, in order to supply nutri- occurs during the entire lifespan of vertebrates and ents and oxygen required for the tumour growth. participates in many physiological processes. Fur- Additionally, more than 70 diferent disorders have thermore, angiogenesis is also actively involved been associated to de novo angiogenesis including in many human diseases and disorders, including obesity, bacterial infections and AIDS (Carmeliet, cancer, obesity and infections. Several inter-con- 2003). nected molecular pathways regulate angiogenesis, At the molecular level, angiogenesis relays on and post-translational modifcations, such as phos- several pathways that cooperate in order to regulate phorylation, ubiquitination and SUMOylation, in a precise spatial and temporal order the process. tightly regulate these mechanisms and play a key In this context, post-translational modifcations role in the control of the process. Here, we describe (PTMs) play a central role in the regulation of these in detail the roles of ubiquitination and SUMOyla- events, infuencing the activation and stability of tion in the regulation of angiogenesis. -
BC-Box Protein Domain-Related Mechanism for VHL Protein Degradation
BC-box protein domain-related mechanism for VHL protein degradation Maria Elena Pozzebona,1,2, Archana Varadaraja,1, Domenico Mattoscioa, Ellis G. Jaffrayb, Claudia Miccoloa, Viviana Galimbertic, Massimo Tommasinod, Ronald T. Hayb, and Susanna Chioccaa,3 aDepartment of Experimental Oncology, European Institute of Oncology, 20139 Milan, Italy; cSenology Division, European Institute of Oncology, 20141 Milan, Italy; dInternational Agency for Research on Cancer, World Health Organization, 69372 Lyon, France; and bCentre for Gene Regulation and Expression, University of Dundee, Dundee DD1 5EH, United Kingdom Edited by William G. Kaelin, Jr., Harvard Medical School, Boston, MA, and approved September 23, 2013 (received for review June 18, 2013) The tumor suppressor VHL (von Hippel–Lindau) protein is a sub- effects of the wild-type Gam1 protein (18, 20, 21), supporting the strate receptor for Ubiquitin Cullin Ring Ligase complexes (CRLs), idea that these effects may depend on Gam1 ability to act as containing a BC-box domain that associates to the adaptor Elongin substrate-receptor protein. B/C. VHL targets hypoxia-inducible factor 1α to proteasome- VHL (von Hippel–Lindau) protein is a cellular BC box-con- dependent degradation. Gam1 is an adenoviral protein, which also taining substrate receptor and associates with Cullin2-based E3 possesses a BC-box domain that interacts with the host Elongin B/C, ligases (22–24). VHL is a tumor suppressor, and its loss leads to – thereby acting as a viral substrate receptor. Gam1 associates with the von Hippel Lindau syndrome that often develops into renal both Cullin2 and Cullin5 to form CRL complexes targeting the host clear-cell carcinoma and other highly vascularized tumors (25, 26). -
Supporting Information
Supporting Information Figure S1. The functionality of the tagged Arp6 and Swr1 was confirmed by monitoring cell growth and sensitivity to hydeoxyurea (HU). Five-fold serial dilutions of each strain were plated on YPD with or without 50 mM HU and incubated at 30°C or 37°C for 3 days. Figure S2. Localization of Arp6 and Swr1 on chromosome 3. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position of Tel 3L, Tel 3R, CEN3, and the RP gene are shown under the panels. Figure S3. Localization of Arp6 and Swr1 on chromosome 4. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) in the whole chromosome region are compared. The position of Tel 4L, Tel 4R, CEN4, SWR1, and RP genes are shown under the panels. Figure S4. Localization of Arp6 and Swr1 on the region including the SWR1 gene of chromosome 4. The binding of Arp6- FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position and orientation of the SWR1 gene is shown. Figure S5. Localization of Arp6 and Swr1 on chromosome 5. The binding of Arp6-FLAG (top), Swr1-FLAG (middle), and Arp6-FLAG in swr1 cells (bottom) are compared. The position of Tel 5L, Tel 5R, CEN5, and the RP genes are shown under the panels. Figure S6. Preferential localization of Arp6 and Swr1 in the 5′ end of genes. Vertical bars represent the binding ratio of proteins in each locus. -
Supplementary Tables
Supplementary Tables Supplementary Table S1: Preselected miRNAs used in feature selection Univariate Cox proportional hazards regression analysis of the endpoint freedom from recurrence in the training set (DKTK-ROG sample) allowed the pre-selection of 524 miRNAs (P< 0.5), which were used in the feature selection. P-value was derived from log-rank test. miRNA p-value miRNA p-value miRNA p-value miRNA p-value hsa-let-7g-3p 0.0001520 hsa-miR-1304-3p 0.0490161 hsa-miR-7108-5p 0.1263245 hsa-miR-6865-5p 0.2073121 hsa-miR-6825-3p 0.0004257 hsa-miR-4298 0.0506194 hsa-miR-4453 0.1270967 hsa-miR-6893-5p 0.2120664 hsa-miR-668-3p 0.0005188 hsa-miR-484 0.0518625 hsa-miR-200a-5p 0.1276345 hsa-miR-25-3p 0.2123829 hsa-miR-3622b-3p 0.0005885 hsa-miR-6851-3p 0.0531446 hsa-miR-6090 0.1278692 hsa-miR-3189-5p 0.2136060 hsa-miR-6885-3p 0.0006452 hsa-miR-1276 0.0557418 hsa-miR-148b-3p 0.1279811 hsa-miR-6073 0.2139702 hsa-miR-6875-3p 0.0008188 hsa-miR-3173-3p 0.0559962 hsa-miR-4425 0.1288330 hsa-miR-765 0.2141536 hsa-miR-487b-5p 0.0011381 hsa-miR-650 0.0564616 hsa-miR-6798-3p 0.1293342 hsa-miR-338-5p 0.2153079 hsa-miR-210-5p 0.0012316 hsa-miR-6133 0.0571407 hsa-miR-4472 0.1300006 hsa-miR-6806-5p 0.2173515 hsa-miR-1470 0.0012822 hsa-miR-4701-5p 0.0571720 hsa-miR-4465 0.1304841 hsa-miR-98-5p 0.2184947 hsa-miR-6890-3p 0.0016539 hsa-miR-202-3p 0.0575741 hsa-miR-514b-5p 0.1308790 hsa-miR-500a-3p 0.2185577 hsa-miR-6511b-3p 0.0017165 hsa-miR-4733-5p 0.0616138 hsa-miR-378c 0.1317442 hsa-miR-4515 0.2187539 hsa-miR-7109-3p 0.0021381 hsa-miR-595 0.0629350 hsa-miR-3121-3p -
Uncovering Ubiquitin and Ubiquitin-Like Signaling Networks Alfred C
REVIEW pubs.acs.org/CR Uncovering Ubiquitin and Ubiquitin-like Signaling Networks Alfred C. O. Vertegaal* Department of Molecular Cell Biology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands CONTENTS 8. Crosstalk between Post-Translational Modifications 7934 1. Introduction 7923 8.1. Crosstalk between Phosphorylation and 1.1. Ubiquitin and Ubiquitin-like Proteins 7924 Ubiquitylation 7934 1.2. Quantitative Proteomics 7924 8.2. Phosphorylation-Dependent SUMOylation 7935 8.3. Competition between Different Lysine 1.3. Setting the Scenery: Mass Spectrometry Modifications 7935 Based Investigation of Phosphorylation 8.4. Crosstalk between SUMOylation and the and Acetylation 7925 UbiquitinÀProteasome System 7935 2. Ubiquitin and Ubiquitin-like Protein Purification 9. Conclusions and Future Perspectives 7935 Approaches 7925 Author Information 7935 2.1. Epitope-Tagged Ubiquitin and Ubiquitin-like Biography 7935 Proteins 7925 Acknowledgment 7936 2.2. Traps Based on Ubiquitin- and Ubiquitin-like References 7936 Binding Domains 7926 2.3. Antibody-Based Purification of Ubiquitin and Ubiquitin-like Proteins 7926 1. INTRODUCTION 2.4. Challenges and Pitfalls 7926 Proteomes are significantly more complex than genomes 2.5. Summary 7926 and transcriptomes due to protein processing and extensive 3. Ubiquitin Proteomics 7927 post-translational modification (PTM) of proteins. Hundreds ff fi 3.1. Proteomic Studies Employing Tagged of di erent modi cations exist. Release 66 of the RESID database1 (http://www.ebi.ac.uk/RESID/) contains 559 dif- Ubiquitin 7927 ferent modifications, including small chemical modifications 3.2. Ubiquitin Binding Domains 7927 such as phosphorylation, acetylation, and methylation and mod- 3.3. Anti-Ubiquitin Antibodies 7927 ification by small proteins, including ubiquitin and ubiquitin- 3.4. -
(Ubl-Ptms): Small Peptides with Huge Impact in Liver Fibrosis
cells Review Ubiquitin-Like Post-Translational Modifications (Ubl-PTMs): Small Peptides with Huge Impact in Liver Fibrosis 1 1, 1, Sofia Lachiondo-Ortega , Maria Mercado-Gómez y, Marina Serrano-Maciá y , Fernando Lopitz-Otsoa 2, Tanya B Salas-Villalobos 3, Marta Varela-Rey 1, Teresa C. Delgado 1,* and María Luz Martínez-Chantar 1 1 Liver Disease Lab, CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 48160 Derio, Spain; [email protected] (S.L.-O.); [email protected] (M.M.-G.); [email protected] (M.S.-M.); [email protected] (M.V.-R.); [email protected] (M.L.M.-C.) 2 Liver Metabolism Lab, CIC bioGUNE, 48160 Derio, Spain; fl[email protected] 3 Department of Biochemistry and Molecular Medicine, School of Medicine, Autonomous University of Nuevo León, Monterrey, Nuevo León 66450, Mexico; [email protected] * Correspondence: [email protected]; Tel.: +34-944-061318; Fax: +34-944-061301 These authors contributed equally to this work. y Received: 6 November 2019; Accepted: 1 December 2019; Published: 4 December 2019 Abstract: Liver fibrosis is characterized by the excessive deposition of extracellular matrix proteins including collagen that occurs in most types of chronic liver disease. Even though our knowledge of the cellular and molecular mechanisms of liver fibrosis has deeply improved in the last years, therapeutic approaches for liver fibrosis remain limited. Profiling and characterization of the post-translational modifications (PTMs) of proteins, and more specifically NEDDylation and SUMOylation ubiquitin-like (Ubls) modifications, can provide a better understanding of the liver fibrosis pathology as well as novel and more effective therapeutic approaches. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
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.