Influenza-Specific Effector Memory B Cells Predict Long-Lived Antibody Responses to Vaccination in Humans
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Identification of Genetic Determinants of Methanol
Journal of Fungi Article The Identification of Genetic Determinants of Methanol Tolerance in Yeast Suggests Differences in Methanol and Ethanol Toxicity Mechanisms and Candidates for Improved Methanol Tolerance Engineering Marta N. Mota 1,2, Luís C. Martins 1,2 and Isabel Sá-Correia 1,2,* 1 iBB—Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; [email protected] (M.N.M.); [email protected] (L.C.M.) 2 Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal * Correspondence: [email protected] Abstract: Methanol is a promising feedstock for metabolically competent yeast strains-based biore- fineries. However, methanol toxicity can limit the productivity of these bioprocesses. Therefore, the identification of genes whose expression is required for maximum methanol tolerance is important for mechanistic insights and rational genomic manipulation to obtain more robust methylotrophic yeast strains. The present chemogenomic analysis was performed with this objective based on the screening of the Euroscarf Saccharomyces cerevisiae haploid deletion mutant collection to search for susceptibility ◦ phenotypes in YPD medium supplemented with 8% (v/v) methanol, at 35 C, compared with an equivalent ethanol concentration (5.5% (v/v)). Around 400 methanol tolerance determinants were Citation: Mota, M.N.; Martins, L.C.; identified, 81 showing a marked phenotype. The clustering of the identified tolerance genes indicates Sá-Correia, I. The Identification of an enrichment of functional categories in the methanol dataset not enriched in the ethanol dataset, Genetic Determinants of Methanol Tolerance in Yeast Suggests such as chromatin remodeling, DNA repair and fatty acid biosynthesis. -
Rabbit Anti-Human ECH1 Polyclonal Antibody (DPABH-07145) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
Rabbit Anti-Human ECH1 Polyclonal Antibody (DPABH-07145) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Immunogen Recombinant Protein, antigen sequence: ISLRLTGSSAQEEASGVALGEAPDHSYESLRVTSAQKHVLHVQLNRPNKRNAMNKVFWREMVE CFNKISRDADCRAVVISGAGKMFTAGIDLMDMASDILQPKGDDVARISWYLRDIITRYQETFNVIE RCPK Isotype IgG Source/Host Rabbit Species Reactivity Human Purification Antigen affinity purified Conjugate Unconjugated Applications IHC, WB Format Liquid Size 100 μL Buffer 40% glycerol and PBS (pH 7.2). Preservative 0.02% Sodium Azide Storage Store at +4°C for short term storage. Long time storage is recommended at -20°C. Gently mix before use. Optimal concentrations and conditions for each application should be determined by the user. BACKGROUND Introduction This gene encodes a member of the hydratase/isomerase superfamily. The gene product shows high sequence similarity to enoyl-coenzyme A (CoA) hydratases of several species, particularly within a conserved domain characteristic of these proteins. The encoded protein, which contains a C-terminal peroxisomal targeting sequence, localizes to the peroxisome. The rat ortholog, 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 1 © Creative Diagnostics All Rights Reserved which localizes to the matrix of both the peroxisome and mitochondria, can isomerize 3-trans,5- cis-dienoyl-CoA to 2-trans,4-trans-dienoyl-CoA, indicating that it is a delta3,5-delta2,4-dienoyl- CoA isomerase. This enzyme -
PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Higd1a Is a Positive Regulator of Cytochrome C Oxidase
Higd1a is a positive regulator of cytochrome c oxidase Takaharu Hayashia,b, Yoshihiro Asanoa,b,1, Yasunori Shintania, Hiroshi Aoyamac, Hidetaka Kiokab, Osamu Tsukamotoa, Masahide Hikitad, Kyoko Shinzawa-Itohd, Kazuaki Takafujie, Shuichiro Higoa,b, Hisakazu Katoa, Satoru Yamazakif, Ken Matsuokab, Atsushi Nakanog, Hiroshi Asanumah, Masanori Asakurag, Tetsuo Minaminob, Yu-ichi Gotoi, Takashi Ogurad, Masafumi Kitakazeg, Issei Komuroj, Yasushi Sakatab, Tomitake Tsukiharad,k, Shinya Yoshikawad, and Seiji Takashimaa,k,1 Departments of aMedical Biochemistry and bCardiovascular Medicine, eCenter for Research Education, and cGraduate School of Pharmaceutical Science, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan; dDepartment of Life Science, University of Hyogo, 3-2-1 Kouto, Kamigohri, Akoh, Hyogo 678-1297, Japan; kCore Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan; Departments of fCell Biology and gClinical Research and Development, National Cerebral and Cardiovascular Center Research Institute, Suita, Osaka 565-8565, Japan; hDepartment of Cardiovascular Science and Technology, Kyoto Prefectural University School of Medicine, Kamigyo-ku, Kyoto 602-8566, Japan; iDepartment of Child Neurology, National Center Hospital of Neurology and Psychiatry, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8502, Japan; and jDepartment of Cardiovascular Medicine, Graduate School of Medicine, University of Tokyo, Tokyo 113-8656, Japan Edited by Gottfried Schatz, University of Basel, Reinach, Switzerland, and approved December 16, 2014 (received for review October 15, 2014) Cytochrome c oxidase (CcO) is the only enzyme that uses oxygen as we recently revealed (8). Because CcO is the only enzyme in to produce a proton gradient for ATP production during mitochon- the body that can use oxygen for energy transduction, it has been drial oxidative phosphorylation. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Allele-Specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish
Allele-specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish by Ailu Chen A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama August 1, 2015 Keywords: catfish, interspecific hybrids, allele-specific expression, ribosomal protein Copyright 2015 by Ailu Chen Approved by Zhanjiang Liu, Chair, Professor, School of Fisheries, Aquaculture and Aquatic Sciences Nannan Liu, Professor, Entomology and Plant Pathology Eric Peatman, Associate Professor, School of Fisheries, Aquaculture and Aquatic Sciences Aaron M. Rashotte, Associate Professor, Biological Sciences Abstract Interspecific hybridization results in a vast reservoir of allelic variations, which may potentially contribute to phenotypical enhancement in the hybrids. Whether the allelic variations are related to the downstream phenotypic differences of interspecific hybrid is still an open question. The recently developed genome-wide allele-specific approaches that harness high- throughput sequencing technology allow direct quantification of allelic variations and gene expression patterns. In this work, I investigated allele-specific expression (ASE) pattern using RNA-Seq datasets generated from interspecific catfish hybrids. The objective of the study is to determine the ASE genes and pathways in which they are involved. Specifically, my study investigated ASE-SNPs, ASE-genes, parent-of-origins of ASE allele and how ASE would possibly contribute to heterosis. My data showed that ASE was operating in the interspecific catfish system. Of the 66,251 and 177,841 SNPs identified from the datasets of the liver and gill, 5,420 (8.2%) and 13,390 (7.5%) SNPs were identified as significant ASE-SNPs, respectively. -
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. -
The Genetic Architecture of Hearing Impairment in Mice: Evidence for Frequency Specific 2 Genetic Determinants
G3: Genes|Genomes|Genetics Early Online, published on September 4, 2015 as doi:10.1534/g3.115.021592 1 The genetic architecture of hearing impairment in mice: evidence for frequency specific 2 genetic determinants. 3 4 Amanda L. Crow1, Jeffrey Ohmen2, Juemei Wang3, Joel Lavinsky3, Jaana Hartiala1, Qingzhong 5 Li4, Xin Li5, Pezhman Salehide 3, Eleazar Eskin6, Calvin Pan7, Aldons J. Lusis7, Hooman 6 Allayee1, Rick A. Friedman3 7 8 1Department of Preventive Medicine and Institute for Genetic Medicine, Keck School of 9 Medicine, University of Southern California, Los Angeles, CA 90033 10 2House Ear Institute, Los Angeles, CA 90057 11 3Department of Otolaryngology and Zilkha Neurogenetic Institute, Keck School of Medicine, 12 University of Southern California, Los Angeles, CA, 90033 13 4Department of Otolaryngology - Head and Neck Surgery, Eye & ENT Hospital of Fudan 14 University, Shanghai 200031, China 15 5Clinical Laboratory Department, First Affiliated Hospital of Nanchang University, Nanchang, 16 Jiangxi Province 330006, China 17 6Department of Computer Science and Inter-Departmental Program in Bioinformatics, 18 University of California, Los Angeles, Los Angeles, CA 90095 19 7Departments of Human Genetics, Medicine, and Microbiology, Immunology, and Molecular 20 Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095 21 © The Author(s) 2013. Published by the Genetics Society of America. 1 Short Title 2 Genetics of Hearing in Mice 3 4 Keywords 5 Genome-wide association study (GWAS), Hybrid Mouse Diversity Panel (HMDP), genetics, 6 genomics, ABR, hearing, cochlear function 7 8 9 Corresponding Author: 10 Rick A. Friedman 11 USC Keck School of Medicine 12 Zilkha Neurogenetic Institute 13 1501 San Pablo Street (ZNI 231) 14 Los Angeles, CA 90033 15 Tel: (323) 442-4843 16 Fax: (323) 442-2059 17 Email: [email protected] 18 19 1 Abstract 2 Genome-wide association studies (GWAS) have been successfully applied in humans for 3 the study of many complex phenotypes. -
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. -
An Automated Pipeline for Inferring Variant-Driven Gene
www.nature.com/scientificreports OPEN MAGPEL: an autoMated pipeline for inferring vAriant‑driven Gene PanEls from the full‑length biomedical literature Nafseh Saberian1, Adib Shaf 1, Azam Peyvandipour1 & Sorin Draghici 1,2* In spite of the eforts in developing and maintaining accurate variant databases, a large number of disease‑associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an efort to extract this information is a challenging task due to: (i) the complexity of natural language processing, (ii) inconsistent use of standard recommendations for variant description, and (iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant- gene‑disease associations from the full‑length biomedical literature and designs an evidence‑based variant-driven gene panel for a given condition. We validate the identifed genes by showing their diagnostic abilities to predict the patients’ clinical outcome on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer and prostate cancer. We compare these panels with other variant‑driven gene panels obtained from Clinvar, Mastermind and others from literature, as well as with a panel identifed with a classical diferentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature. One crucial step in understanding the biological mechanism underlying a disease condition is to capture the relationship between the variants and the disease risk1. -
Supplementary Figures 1-14 and Supplementary References
SUPPORTING INFORMATION Spatial Cross-Talk Between Oxidative Stress and DNA Replication in Human Fibroblasts Marko Radulovic,1,2 Noor O Baqader,1 Kai Stoeber,3† and Jasminka Godovac-Zimmermann1* 1Division of Medicine, University College London, Center for Nephrology, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK. 2Insitute of Oncology and Radiology, Pasterova 14, 11000 Belgrade, Serbia 3Research Department of Pathology and UCL Cancer Institute, Rockefeller Building, University College London, University Street, London WC1E 6JJ, UK †Present Address: Shionogi Europe, 33 Kingsway, Holborn, London WC2B 6UF, UK TABLE OF CONTENTS 1. Supplementary Figures 1-14 and Supplementary References. Figure S-1. Network and joint spatial razor plot for 18 enzymes of glycolysis and the pentose phosphate shunt. Figure S-2. Correlation of SILAC ratios between OXS and OAC for proteins assigned to the SAME class. Figure S-3. Overlap matrix (r = 1) for groups of CORUM complexes containing 19 proteins of the 49-set. Figure S-4. Joint spatial razor plots for the Nop56p complex and FIB-associated complex involved in ribosome biogenesis. Figure S-5. Analysis of the response of emerin nuclear envelope complexes to OXS and OAC. Figure S-6. Joint spatial razor plots for the CCT protein folding complex, ATP synthase and V-Type ATPase. Figure S-7. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated by GO to nucleocytoplasmic transport (GO:0006913). Figure S-8. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated to endocytosis (GO:0006897). Figure S-9. Joint spatial razor plots for 401-set proteins annotated by GO to small GTPase mediated signal transduction (GO:0007264) and/or GTPase activity (GO:0003924). -
1 AGING Supplementary Table 2
SUPPLEMENTARY TABLES Supplementary Table 1. Details of the eight domain chains of KIAA0101. Serial IDENTITY MAX IN COMP- INTERFACE ID POSITION RESOLUTION EXPERIMENT TYPE number START STOP SCORE IDENTITY LEX WITH CAVITY A 4D2G_D 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ B 4D2G_E 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ C 6EHT_D 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ D 6EHT_E 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ E 6GWS_D 41-72 41 72 100 100 3.2Å PCNA X-RAY DIFFRACTION √ F 6GWS_E 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ G 6GWS_F 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ H 6IIW_B 2-11 2 11 100 100 1.699Å UHRF1 X-RAY DIFFRACTION √ www.aging-us.com 1 AGING Supplementary Table 2. Significantly enriched gene ontology (GO) annotations (cellular components) of KIAA0101 in lung adenocarcinoma (LinkedOmics). Leading Description FDR Leading Edge Gene EdgeNum RAD51, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, CENPW, HJURP, NDC80, CDCA5, NCAPH, BUB1, ZWILCH, CENPK, KIF2C, AURKA, CENPN, TOP2A, CENPM, PLK1, ERCC6L, CDT1, CHEK1, SPAG5, CENPH, condensed 66 0 SPC24, NUP37, BLM, CENPE, BUB3, CDK2, FANCD2, CENPO, CENPF, BRCA1, DSN1, chromosome MKI67, NCAPG2, H2AFX, HMGB2, SUV39H1, CBX3, TUBG1, KNTC1, PPP1CC, SMC2, BANF1, NCAPD2, SKA2, NUP107, BRCA2, NUP85, ITGB3BP, SYCE2, TOPBP1, DMC1, SMC4, INCENP. RAD51, OIP5, CDK1, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, ESCO2, CENPW, HJURP, TTK, NDC80, CDCA5, BUB1, ZWILCH, CENPK, KIF2C, AURKA, DSCC1, CENPN, CDCA8, CENPM, PLK1, MCM6, ERCC6L, CDT1, HELLS, CHEK1, SPAG5, CENPH, PCNA, SPC24, CENPI, NUP37, FEN1, chromosomal 94 0 CENPL, BLM, KIF18A, CENPE, MCM4, BUB3, SUV39H2, MCM2, CDK2, PIF1, DNA2, region CENPO, CENPF, CHEK2, DSN1, H2AFX, MCM7, SUV39H1, MTBP, CBX3, RECQL4, KNTC1, PPP1CC, CENPP, CENPQ, PTGES3, NCAPD2, DYNLL1, SKA2, HAT1, NUP107, MCM5, MCM3, MSH2, BRCA2, NUP85, SSB, ITGB3BP, DMC1, INCENP, THOC3, XPO1, APEX1, XRCC5, KIF22, DCLRE1A, SEH1L, XRCC3, NSMCE2, RAD21.