Targeting COPZ1 Non-Oncogene Addiction Counteracts the Viability of Thyroid Tumor Cells*
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Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Transcriptomic Analysis of the Aquaporin (AQP) Gene Family
Pancreatology 19 (2019) 436e442 Contents lists available at ScienceDirect Pancreatology journal homepage: www.elsevier.com/locate/pan Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma Dimitrios E. Magouliotis a, b, Vasiliki S. Tasiopoulou c, Konstantinos Dimas d, * Nikos Sakellaridis d, Konstantina A. Svokos e, Alexis A. Svokos f, Dimitris Zacharoulis b, a Division of Surgery and Interventional Science, Faculty of Medical Sciences, UCL, London, UK b Department of Surgery, University of Thessaly, Biopolis, Larissa, Greece c Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece d Department of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa, Greece e The Warren Alpert Medical School of Brown University, Providence, RI, USA f Riverside Regional Medical Center, Newport News, VA, USA article info abstract Article history: Background: This study aimed to assess the differential gene expression of aquaporin (AQP) gene family Received 14 October 2018 interactome in pancreatic ductal adenocarcinoma (PDAC) using data mining techniques to identify novel Received in revised form candidate genes intervening in the pathogenicity of PDAC. 29 January 2019 Method: Transcriptome data mining techniques were used in order to construct the interactome of the Accepted 9 February 2019 AQP gene family and to determine which genes members are differentially expressed in PDAC as Available online 11 February 2019 compared to controls. The same techniques were used in order to evaluate the potential prognostic role of the differentially expressed genes. Keywords: PDAC Results: Transcriptome microarray data of four GEO datasets were incorporated, including 142 primary Aquaporin tumor samples and 104 normal pancreatic tissue samples. -
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. -
WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US). -
Human Gene Copy Number Spectra Analysis in Congenital Heart Malformations Aoy Tomita-Mitchell Medical College of Wisconsin
CORE Metadata, citation and similar papers at core.ac.uk Provided by epublications@Marquette Marquette University e-Publications@Marquette Mathematics, Statistics and Computer Science Mathematics, Statistics and Computer Science, Faculty Research and Publications Department of 5-1-2012 Human gene copy number spectra analysis in congenital heart malformations Aoy Tomita-Mitchell Medical College of Wisconsin Donna K. Mahnke Medical College of Wisconsin Craig Struble Marquette University, [email protected] Maureen E. Tuffnell Marquette University Karl D. Stamm Marquette University See next page for additional authors Accepted version. Physiological Genomics, Vol. 44, No. 9 (May 2012): 518-541. DOI. © 2012 The American Physiological Society. Used with permission. Authors Aoy Tomita-Mitchell, Donna K. Mahnke, Craig Struble, Maureen E. Tuffnell, Karl D. Stamm, Mats Hidestrand, Susan Harris, Mary A. Goetsch, Pippa Simpson, David P. Bick, Ulrich Broeckel, Andrew N. Pelech, James S. Tweddell, and Michael Mitchell This article is available at e-Publications@Marquette: https://epublications.marquette.edu/mscs_fac/272 NOT THE PUBLISHED VERSION; this is the author’s final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page. Human Gene Copy Number Spectra Analysis in Congenital Heart Malformations Aoy Tomita-Mitchell Department of Surgery, Division of Cardiothoracic Surgery; Biotechnology and Bioengineering Center; Human and Molecular Genetics Center; Medical College of Wisconsin; Milwaukee, WI Donna K. Mahnke Department of Surgery, Division of Cardiothoracic Surgery; Biotechnology and Bioengineering Center; Human and Molecular Genetics Center; Medical College of Wisconsin; Milwaukee, WI Craig A. Struble Department of Mathematics, Statistics and Computer Science; Marquette University; Milwaukee, WI Maureen E. -
Molecular Genetics of Microcephaly Primary Hereditary: an Overview
brain sciences Review Molecular Genetics of Microcephaly Primary Hereditary: An Overview Nikistratos Siskos † , Electra Stylianopoulou †, Georgios Skavdis and Maria E. Grigoriou * Department of Molecular Biology & Genetics, Democritus University of Thrace, 68100 Alexandroupolis, Greece; [email protected] (N.S.); [email protected] (E.S.); [email protected] (G.S.) * Correspondence: [email protected] † Equal contribution. Abstract: MicroCephaly Primary Hereditary (MCPH) is a rare congenital neurodevelopmental disorder characterized by a significant reduction of the occipitofrontal head circumference and mild to moderate mental disability. Patients have small brains, though with overall normal architecture; therefore, studying MCPH can reveal not only the pathological mechanisms leading to this condition, but also the mechanisms operating during normal development. MCPH is genetically heterogeneous, with 27 genes listed so far in the Online Mendelian Inheritance in Man (OMIM) database. In this review, we discuss the role of MCPH proteins and delineate the molecular mechanisms and common pathways in which they participate. Keywords: microcephaly; MCPH; MCPH1–MCPH27; molecular genetics; cell cycle 1. Introduction Citation: Siskos, N.; Stylianopoulou, Microcephaly, from the Greek word µικρoκεϕαλi´α (mikrokephalia), meaning small E.; Skavdis, G.; Grigoriou, M.E. head, is a term used to describe a cranium with reduction of the occipitofrontal head circum- Molecular Genetics of Microcephaly ference equal, or more that teo standard deviations -
Table S2.Up Or Down Regulated Genes in Tcof1 Knockdown Neuroblastoma N1E-115 Cells Involved in Differentbiological Process Anal
Table S2.Up or down regulated genes in Tcof1 knockdown neuroblastoma N1E-115 cells involved in differentbiological process analysed by DAVID database Pop Pop Fold Term PValue Genes Bonferroni Benjamini FDR Hits Total Enrichment GO:0044257~cellular protein catabolic 2.77E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 537 13588 1.944851 8.64E-07 8.64E-07 5.02E-07 process ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, USP17L5, FBXO11, RAD23B, NEDD8, UBE2V2, RFFL, CDC GO:0051603~proteolysis involved in 4.52E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 534 13588 1.93519 1.41E-06 7.04E-07 8.18E-07 cellular protein catabolic process ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, USP17L5, FBXO11, RAD23B, NEDD8, UBE2V2, RFFL, CDC GO:0044265~cellular macromolecule 6.09E-10 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 609 13588 1.859332 1.90E-06 6.32E-07 1.10E-06 catabolic process ISG15, RBM8A, ATG7, LOC100046898, PSENEN, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, ASB8, DCUN1D1, PSMA6, SIAH1A, TRIM32, RNF138, GM12396, RNF20, XRN2, USP17L5, FBXO11, RAD23B, UBE2V2, NED GO:0030163~protein catabolic process 1.81E-09 MKRN1, PPP2R5C, VPRBP, MYLIP, CDC16, ERLEC1, MKRN2, CUL3, 556 13588 1.87839 5.64E-06 1.41E-06 3.27E-06 ISG15, ATG7, PSENEN, LOC100046898, CDCA3, ANAPC1, ANAPC2, ANAPC5, SOCS3, ENC1, SOCS4, -
Downloaded from the Projected by No Less Than Six of the Servers Mentioned Above
ORIGINAL RESEARCH published: 11 June 2021 doi: 10.3389/fmolb.2021.687576 A Comprehensive Analysis of the Downregulation of miRNA-1827 and Its Prognostic Significance by Targeting SPTBN2 and BCL2L1 in Ovarian Cancer Penghui Feng 1†, Zhitong Ge 2†, Zaixin Guo 1, Lin Lin 1,3 and Qi Yu 1* 1Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 2Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 3Department of Obstetrics and Gynecology, The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Beijing, China Background: Previous studies demonstrated that miRNA-1827 could repress various Edited by: cancers on proliferation, angiogenesis, and metastasis. However, little attention has been Hossain Shekhar, paid to its role in ovarian cancer as a novel biomarker or intervention target, especially its University of Dhaka, Bangladesh clinical significance and underlying regulatory network. Reviewed by: Udhaya Kumar S., Methods: A meta-analysis of six microarrays was adopted here to determine the Vellore Institute of Technology, India fi Sanjay Mishra, expression trend of miRNA-1827, and was further validated by gene expression pro le The Ohio State University, data and cellular experiments. We explored the functional annotations through enrichment United States analysis for the differentially expressed genes targeted by miRNA-1827. Subsequently, we *Correspondence: identified two hub genes, SPTBN2 and BCL2L1, based on interaction analysis using two Qi Yu [email protected] online archive tools, miRWALK (it consolidates the resources of 12 miRNA-focused †These authors have contributed servers) and Gene Expression Profiling Interactive Analysis (GEPIA). -
Ykt6 Membrane-To-Cytosol Cycling Regulates Exosomal Wnt Secretion
bioRxiv preprint doi: https://doi.org/10.1101/485565; this version posted December 3, 2018. 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. Ykt6 membrane-to-cytosol cycling regulates exosomal Wnt secretion Karen Linnemannstöns1,2, Pradhipa Karuna1,2, Leonie Witte1,2, Jeanette Kittel1,2, Adi Danieli1,2, Denise Müller1,2, Lena Nitsch1,2, Mona Honemann-Capito1,2, Ferdinand Grawe3,4, Andreas Wodarz3,4 and Julia Christina Gross1,2* Affiliations: 1Hematology and Oncology, University Medical Center Goettingen, Goettingen, Germany. 2Developmental Biochemistry, University Medical Center Goettingen, Goettingen, Germany. 3Molecular Cell Biology, Institute I for Anatomy, University of Cologne Medical School, Cologne, Germany 4Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Cologne, Germany *Correspondence: Dr. Julia Christina Gross, Hematology and Oncology/Developmental Biochemistry, University Medical Center Goettingen, Justus-von-Liebig Weg 11, 37077 Goettingen Germany Abstract Protein trafficking in the secretory pathway, for example the secretion of Wnt proteins, requires tight regulation. These ligands activate Wnt signaling pathways and are crucially involved in development and disease. Wnt is transported to the plasma membrane by its cargo receptor Evi, where Wnt/Evi complexes are endocytosed and sorted onto exosomes for long-range secretion. However, the trafficking steps within the endosomal compartment are not fully understood. The promiscuous SNARE Ykt6 folds into an auto-inhibiting conformation in the cytosol, but a portion associates with membranes by its farnesylated and palmitoylated C-terminus. Here, we demonstrate that membrane detachment of Ykt6 is essential for exosomal Wnt secretion. -
Silencing the COPB2 Gene Decreases the Proliferation, Migration and Invasion of Human Triple‑Negative Breast Cancer Cells
EXPERIMENTAL AND THERAPEUTIC MEDICINE 22: 792, 2021 Silencing the COPB2 gene decreases the proliferation, migration and invasion of human triple‑negative breast cancer cells WENCHENG WU1*, CHENYU WANG1*, FENGXIA WANG1, YAN WANG2, YANLING JIN1, JING LUO1, MIN WANG1, CHENLI ZHANG1, SHUYA WANG1, FANGFANG ZHANG1 and MIN LI1,3 1Department of Pathology, School of Basic Medical Sciences, Lanzhou University; 2Gansu Provincial Hospital; 3Gansu Provincial Key Laboratory of Preclinical Study for New Drug Development, Lanzhou University, Lanzhou, Gansu 730000, P.R. China Received November 11, 2020; Accepted April 23, 2021 DOI: 10.3892/etm.2021.10224 Abstract. Triple‑negative breast cancer (TNBC) is highly develops in young women and is phenotypically defined as invasive, has a high rate of recurrence and is associated with a the lack of receptors for estrogen (ER), progesterone (PR) poor clinical outcome when compared with non‑TNBC due to a and human epidermal growth factor receptor 2 (HER‑2) (3). lack of effective and targeted treatments. The coatomer protein Cells from TNBC are more invasive, prone to lymph node complex subunit β2 (COPB2) is upregulated in various types of metastasis and are associated with poor clinical outcomes, malignant cancer. The present study demonstrated that COPB2 when compared with cells from non‑TNBC (4‑6). TNBC cells expression levels were significantly upregulated in breast respond poorly to endocrine and anti‑HER‑2 therapeutic strate‑ carcinoma HS‑578T cells (clonal cells originating from TNBC) gies, as they lack the receptors targeted by these therapies and, when compared with non‑TNBC MCF‑7 cells. HS‑578T cells thus, surgical treatment is preferred, followed by postoperative also exhibited higher rates of proliferation, invasion and chemotherapy or radiotherapy (2). -
Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus
LABORATORY SCIENCES Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes The GLC1C Locus Frank W. Rozsa, PhD; Kathleen M. Scott, BS; Hemant Pawar, PhD; John R. Samples, MD; Mary K. Wirtz, PhD; Julia E. Richards, PhD Objectives: To develop and apply a model for priori- est because of moderate expression and changes in tization of candidate glaucoma genes. expression. Transcription factor ZBTB38 emerges as an interesting candidate gene because of the overall expres- Methods: This Affymetrix GeneChip (Affymetrix, Santa sion level, differential expression, and function. Clara, Calif) study of gene expression in primary cul- ture human trabecular meshwork cells uses a positional Conclusions: Only1geneintheGLC1C interval fits our differential expression profile model for prioritization of model for differential expression under multiple glau- candidate genes within the GLC1C genetic inclusion in- coma risk conditions. The use of multiple prioritization terval. models resulted in filtering 7 candidate genes of higher interest out of the 41 known genes in the region. Results: Sixteen genes were expressed under all condi- tions within the GLC1C interval. TMEM22 was the only Clinical Relevance: This study identified a small sub- gene within the interval with differential expression in set of genes that are most likely to harbor mutations that the same direction under both conditions tested. Two cause glaucoma linked to GLC1C. genes, ATP1B3 and COPB2, are of interest in the con- text of a protein-misfolding model for candidate selec- tion. SLC25A36, PCCB, and FNDC6 are of lesser inter- Arch Ophthalmol. 2007;125:117-127 IGH PREVALENCE AND PO- identification of additional GLC1C fami- tential for severe out- lies7,18-20 who provide optimal samples for come combine to make screening candidate genes for muta- adult-onset primary tions.7,18,20 The existence of 2 distinct open-angle glaucoma GLC1C haplotypes suggests that muta- (POAG) a significant public health prob- tions will not be limited to rare descen- H1 lem. -
Mapping Transmembrane Binding Partners for E-Cadherin Ectodomains
SUPPLEMENTARY INFORMATION TITLE: Mapping transmembrane binding partners for E-cadherin ectodomains. AUTHORS: Omer Shafraz 1, Bin Xie 2, Soichiro Yamada 1, Sanjeevi Sivasankar 1, 2, * AFFILIATION: 1 Department of Biomedical Engineering, 2 Biophysics Graduate Group, University of California, Davis, CA 95616. *CORRESPONDING AUTHOR: Sanjeevi Sivasankar, Tel: (530)-754-0840, Email: [email protected] Figure S1: Western blots a. EC-BioID, WT and Ecad-KO cell lysates stained for Ecad and tubulin. b. HRP-streptavidin staining of biotinylated proteins eluted from streptavidin coated magnetic beads incubated with cell lysates of EC-BioID with (+) and without (-) exogenous biotin. c. C-BioID, WT and Ecad-KO cell lysates stained for Ecad and tubulin. d. HRP-streptavidin staining of biotinylated proteins eluted from streptavidin coated magnetic beads incubated with cell lysates of C-BioID with (+) and without (-) exogenous biotin. (+) Biotin (-) Biotin Sample 1 Sample 2 Sample 3 Sample 4 Sample 1 Sample 2 Sample 3 Sample 4 Percent Percent Percent Percent Percent Percent Percent Percent Gene ID Coverage Coverage Coverage Coverage Coverage Coverage Coverage Coverage CDH1 29.6 31.4 41.1 36.5 10.8 6.7 28.8 29.1 DSG2 26 14.6 45 37 0.8 1.9 1.6 18.7 CXADR 30.2 26.2 32.7 27.1 0.0 0.0 0.0 6.9 EFNB1 24.3 30.6 24 30.3 0.0 0.0 0.0 0.0 ITGA2 16.5 22.2 30.1 33.4 1.1 1.1 5.2 7.2 CDH3 21.8 9.7 20.6 25.3 1.3 1.3 0.0 0.0 ITGB1 11.8 16.7 23.9 20.3 0.0 2.9 8.5 5.8 DSC3 9.7 7.5 11.5 13.3 0.0 0.0 2.6 0.0 EPHA2 23.2 31.6 31.6 30.5 0.8 0.0 0.0 5.7 ITGB4 21.8 27.8 33.1 30.7 0.0 1.2 3.9 4.4 ITGB3 23.5 22.2 26.8 24.7 0.0 0.0 5.2 9.1 CDH6 22.8 18.1 28.6 24.3 0.0 0.0 0.0 9.1 CDH17 8.8 12.4 20.7 18.4 0.0 0.0 0.0 0.0 ITGB6 12.7 10.4 14 17.1 0.0 0.0 0.0 1.7 EPHB4 11.4 8.1 14.2 16.3 0.0 0.0 0.0 0.0 ITGB8 5 10 15 17.6 0.0 0.0 0.0 0.0 ITGB5 6.2 9.5 15.2 13.8 0.0 0.0 0.0 0.0 EPHB2 8.5 4.8 9.8 12.1 0.0 0.0 0.0 0.0 CDH24 5.9 7.2 8.3 9 0.0 0.0 0.0 0.0 Table S1: EC-BioID transmembrane protein hits.