Original Article Prognostic Value and Interrelated Expression Profile of GINS Complex Subunits in Lung Adenocarcinoma
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Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement
School of Natural Sciences and Mathematics Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data—Supplement UT Dallas Author(s): Michael Q. Zhang Rights: CC BY 4.0 (Attribution) ©2017 The Authors Citation: Liu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-z This document is being made freely available by the Eugene McDermott Library of the University of Texas at Dallas with permission of the copyright owner. All rights are reserved under United States copyright law unless specified otherwise. File name: Supplementary Information Description: Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references. CCNE1 CCNE1 CCNE1 CCNE1 36 40 32 34 32 35 30 32 28 30 30 28 28 26 24 25 Normalized Expression Normalized Expression Normalized Expression Normalized Expression 26 G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage CCNE1 CCNE1 CCNE1 CCNE1 40 32 40 40 35 30 38 30 30 28 36 25 26 20 20 34 Normalized Expression Normalized Expression Normalized Expression 24 Normalized Expression G1 S G2/M G1 S G2/M G1 S G2/M G1 S G2/M Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Cell Cycle Stage Supplementary Figure 1 | High stochasticity of single-cell gene expression means, as demonstrated by relative expression levels of gene Ccne1 using the mESC-SMARTer data. For every panel, 20 sample cells were randomly selected for each of the three stages, followed by plotting the mean expression levels at each stage. -
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. -
Biallelic GINS2 Variant P.(Arg114leu) Causes Meier-Gorlin Syndrome With
Genotype- phenotype correlations J Med Genet: first published as 10.1136/jmedgenet-2020-107572 on 5 August 2021. Downloaded from Short report Biallelic GINS2 variant p.(Arg114Leu) causes Meier- Gorlin syndrome with craniosynostosis Maria J Nabais Sá ,1,2 Kerry A Miller,3 Mary McQuaid,4 Nils Koelling,3 Andrew O M Wilkie ,3 Hugo Wurtele,4 Arjan P M de Brouwer,1 Jorge Oliveira 5,6 ► Additional supplemental ABSTRACT complex (Cdc45/MCM2–7/GINS) is a DNA heli- material is published online Introduction Replication of the nuclear genome is an case that separates the two strands of the DNA only. To view, please visit essential step for cell division. Pathogenic variants in double helix at replication origins, subsequently the journal online (http:// dx. 8 doi. org/ 10. 1136/ jmedgenet- genes coding for highly conserved components of the enabling their replication. 2020- 107572). DNA replication machinery cause Meier- Gorlin syndrome The genotype of individuals with MGORS (MGORS). requires at least one allele of genes encoding essen- For numbered affiliations see Objective Identification of novel genes associated with tial DNA replication factors allowing for some end of article. MGORS. residual activity (hypomorphic variant).2–6 Further- more, less severe phenotypes are often associated Correspondence to Methods Exome sequencing was performed to Dr Maria J Nabais Sá, investigate the genotype of an individual presenting with with two hypomorphic variants, while more severe Department of Human Genetics prenatal and postnatal growth restriction, a craniofacial phenotypes result from a combination of a hypo- (route 836), Radboud University gestalt of MGORS and coronal craniosynostosis. The morphic and a loss- of- function variant.1 Never- Medical Center, P.O. -
Identification of Biomarkers in Colon Cancer Based on Bioinformatic Analysis
4895 Original Article Identification of biomarkers in colon cancer based on bioinformatic analysis Ying Zhu1#^, Leitao Sun2#^, Jieru Yu3, Yuying Xiang1^, Minhe Shen2^, Harpreet S. Wasan4^, Shanming Ruan2^, Shengliang Qiu2^ 1The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China; 2Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China; 3College of Basic Medical Science, Zhejiang Chinese Medical University, Hangzhou, China; 4Department of Cancer Medicine, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK Contributions: (I) Conception and design: Y Zhu, L Sun; (II) Administrative support: M Shen, S Ruan, S Qiu; (III) Provision of study materials or patients: Z Ying, L Sun; (IV) Collection and assembly of data: J Yu, Y Xiang; (V) Data analysis and interpretation: Ying Zhu, Leitao Sun, Harpreet S. Wasan, S Qiu, S Ruan; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Shengliang Qiu. Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng, Hangzhou 310006, China. Email: [email protected]; Shanming Ruan. Department of Medical Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng, Hangzhou 310006, China. Email: shanmingruan@ zcmu.edu.cn. Background: Colon cancer is one of the most common cancers in the world. Targeting biomarkers is helpful for the diagnosis and treatment of colon cancer. This study aimed to identify biomarkers in colon cancer, in addition to those that have already been reported, using microarray datasets and bioinformatics analysis. -
Node and Edge Attributes
Cytoscape User Manual Table of Contents Cytoscape User Manual ........................................................................................................ 3 Introduction ...................................................................................................................... 48 Development .............................................................................................................. 4 License ...................................................................................................................... 4 What’s New in 2.7 ....................................................................................................... 4 Please Cite Cytoscape! ................................................................................................. 7 Launching Cytoscape ........................................................................................................... 8 System requirements .................................................................................................... 8 Getting Started .......................................................................................................... 56 Quick Tour of Cytoscape ..................................................................................................... 12 The Menus ............................................................................................................... 15 Network Management ................................................................................................. 18 The -
Distal Cpg Islands Can Serve As Alternative Promoters to Transcribe Genes with Silenced Proximal Promoters
Downloaded from genome.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press Research Distal CpG islands can serve as alternative promoters to transcribe genes with silenced proximal promoters Shrutii Sarda,1 Avinash Das,1 Charles Vinson,2 and Sridhar Hannenhalli1 1Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA; 2Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA DNA methylation at the promoter of a gene is presumed to render it silent, yet a sizable fraction of genes with methylated proximal promoters exhibit elevated expression. Here, we show, through extensive analysis of the methylome and transcrip- tome in 34 tissues, that in many such cases, transcription is initiated by a distal upstream CpG island (CGI) located several kilobases away that functions as an alternative promoter. Specifically, such genes are expressed precisely when the neigh- boring CGI is unmethylated but remain silenced otherwise. Based on CAGE and Pol II localization data, we found strong evidence of transcription initiation at the upstream CGI and a lack thereof at the methylated proximal promoter itself. Consistent with their alternative promoter activity, CGI-initiated transcripts are associated with signals of stable elongation and splicing that extend into the gene body, as evidenced by tissue-specific RNA-seq and other DNA-encoded splice signals. Furthermore, based on both inter- and intra-species analyses, such CGIs were found to be under greater purifying selection relative to CGIs upstream of silenced genes. Overall, our study describes a hitherto unreported conserved mechanism of transcription of genes with methylated proximal promoters in a tissue-specific fashion. -
A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family
Rockefeller University Digital Commons @ RU Student Theses and Dissertations 2018 A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family Linda Molla Follow this and additional works at: https://digitalcommons.rockefeller.edu/ student_theses_and_dissertations Part of the Life Sciences Commons A HIGH-THROUGHPUT APPROACH TO UNCOVER NOVEL ROLES OF APOBEC2, A FUNCTIONAL ORPHAN OF THE AID/APOBEC FAMILY A Thesis Presented to the Faculty of The Rockefeller University in Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy by Linda Molla June 2018 © Copyright by Linda Molla 2018 A HIGH-THROUGHPUT APPROACH TO UNCOVER NOVEL ROLES OF APOBEC2, A FUNCTIONAL ORPHAN OF THE AID/APOBEC FAMILY Linda Molla, Ph.D. The Rockefeller University 2018 APOBEC2 is a member of the AID/APOBEC cytidine deaminase family of proteins. Unlike most of AID/APOBEC, however, APOBEC2’s function remains elusive. Previous research has implicated APOBEC2 in diverse organisms and cellular processes such as muscle biology (in Mus musculus), regeneration (in Danio rerio), and development (in Xenopus laevis). APOBEC2 has also been implicated in cancer. However the enzymatic activity, substrate or physiological target(s) of APOBEC2 are unknown. For this thesis, I have combined Next Generation Sequencing (NGS) techniques with state-of-the-art molecular biology to determine the physiological targets of APOBEC2. Using a cell culture muscle differentiation system, and RNA sequencing (RNA-Seq) by polyA capture, I demonstrated that unlike the AID/APOBEC family member APOBEC1, APOBEC2 is not an RNA editor. Using the same system combined with enhanced Reduced Representation Bisulfite Sequencing (eRRBS) analyses I showed that, unlike the AID/APOBEC family member AID, APOBEC2 does not act as a 5-methyl-C deaminase. -
Bioinformatics Analysis of Differentially Expressed Genes and Pathways in the Development of Cervical Cancer Baojie Wu* and Shuyi Xi
Wu and Xi BMC Cancer (2021) 21:733 https://doi.org/10.1186/s12885-021-08412-4 RESEARCH Open Access Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer Baojie Wu* and Shuyi Xi Abstract Background: This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods: Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results: A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. -
Microarray Bioinformatics and Its Applications to Clinical Research
Microarray Bioinformatics and Its Applications to Clinical Research A dissertation presented to the School of Electrical and Information Engineering of the University of Sydney in fulfillment of the requirements for the degree of Doctor of Philosophy i JLI ··_L - -> ...·. ...,. by Ilene Y. Chen Acknowledgment This thesis owes its existence to the mercy, support and inspiration of many people. In the first place, having suffering from adult-onset asthma, interstitial cystitis and cold agglutinin disease, I would like to express my deepest sense of appreciation and gratitude to Professors Hong Yan and David Levy for harbouring me these last three years and providing me a place at the University of Sydney to pursue a very meaningful course of research. I am also indebted to Dr. Craig Jin, who has been a source of enthusiasm and encouragement on my research over many years. In the second place, for contexts concerning biological and medical aspects covered in this thesis, I am very indebted to Dr. Ling-Hong Tseng, Dr. Shian-Sehn Shie, Dr. Wen-Hung Chung and Professor Chyi-Long Lee at Change Gung Memorial Hospital and University of Chang Gung School of Medicine (Taoyuan, Taiwan) as well as Professor Keith Lloyd at University of Alabama School of Medicine (AL, USA). All of them have contributed substantially to this work. In the third place, I would like to thank Mrs. Inge Rogers and Mr. William Ballinger for their helpful comments and suggestions for the writing of my papers and thesis. In the fourth place, I would like to thank my swim coach, Hirota Homma. -
Novel Gene Signatures for Stage Classification of the Squamous Cell
www.nature.com/scientificreports OPEN Novel gene signatures for stage classifcation of the squamous cell carcinoma of the lung Angel Juarez‑Flores, Gabriel S. Zamudio & Marco V. José* The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the frst cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages of the disease with few treatment options available. The goal of this work was to fnd some key components underlying each stage of the disease, to help in the classifcation of tumor samples, and to increase the available options for experimental assays and molecular targets that could be used in treatment development. We employed two approaches. The frst was based in the classic method of diferential gene expression analysis, network analysis, and a novel concept known as network gatekeepers. The second approach was using machine learning algorithms. From our combined approach, we identifed two sets of genes that could function as a signature to identify each stage of the cancer pathology. We also arrived at a network of 55 nodes, which according to their biological functions, they can be regarded as drivers in this cancer. Although biological experiments are necessary for their validation, we proposed that all these genes could be used for cancer development treatments. As GLOBOCAN reported in 2018, lung cancer was the frst cause of deaths and new cases by cancer worldwide 1. Squamous cell carcinoma of the lung (SCC) is one type of lung cancer which comprises approximately 30% of all lung cancer cases. -
Tissue-Specific Requirement for the GINS Complex During Zebrafish
fcell-08-00373 May 26, 2020 Time: 17:57 # 1 ORIGINAL RESEARCH published: 28 May 2020 doi: 10.3389/fcell.2020.00373 Tissue-Specific Requirement for the GINS Complex During Zebrafish Development Máté Varga1,2*, Kitti Csályi1, István Bertyák1, Dóra K. Menyhárd3, Richard J. Poole2, Kara L. Cerveny4, Dorottya Kövesdi5,6, Balázs Barátki6, Hannah Rouse2, Zsuzsa Vad1, Thomas A. Hawkins2, Heather L. Stickney2, Florencia Cavodeassi2,7, Quenten Schwarz8, Rodrigo M. Young2† and Stephen W. Wilson2* Edited by: 1 Department of Genetics, ELTE Eötvös Loránd University, Budapest, Hungary, 2 Department of Cell and Developmental Brian C. Schaefer, Biology, Division of Biosciences, University College London, London, United Kingdom, 3 HAS-ELTE Protein Modeling Uniformed Services University of the Research Group and Laboratory of Structural Chemistry and Biology, ELTE Eötvös Loránd University, Budapest, Hungary, Health Sciences, United States 4 Biology Department, Reed College, Portland, OR, United States, 5 Office of Supported Research Groups of the Hungarian 6 Reviewed by: Academy of Sciences, Budapest, Hungary, Department of Immunology, ELTE Eötvös Loránd University, Budapest, 7 Robert M. Brosh, Hungary, Institute of Medical and Biomedical Education, St. George’s University of London, London, United Kingdom, 8 National Institute on Aging, National Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA, Australia Institutes of Health (NIH), United States Efficient and accurate DNA replication is particularly critical in stem and progenitor Masato Kanemaki, National Institute of Genetics, Japan cells for successful proliferation and survival. The replisome, an amalgam of protein *Correspondence: complexes, is responsible for binding potential origins of replication, unwinding the Máté Varga double helix, and then synthesizing complimentary strands of DNA. -
Cell Cycle Arrest Through Indirect Transcriptional Repression by P53: I Have a DREAM
Cell Death and Differentiation (2018) 25, 114–132 Official journal of the Cell Death Differentiation Association OPEN www.nature.com/cdd Review Cell cycle arrest through indirect transcriptional repression by p53: I have a DREAM Kurt Engeland1 Activation of the p53 tumor suppressor can lead to cell cycle arrest. The key mechanism of p53-mediated arrest is transcriptional downregulation of many cell cycle genes. In recent years it has become evident that p53-dependent repression is controlled by the p53–p21–DREAM–E2F/CHR pathway (p53–DREAM pathway). DREAM is a transcriptional repressor that binds to E2F or CHR promoter sites. Gene regulation and deregulation by DREAM shares many mechanistic characteristics with the retinoblastoma pRB tumor suppressor that acts through E2F elements. However, because of its binding to E2F and CHR elements, DREAM regulates a larger set of target genes leading to regulatory functions distinct from pRB/E2F. The p53–DREAM pathway controls more than 250 mostly cell cycle-associated genes. The functional spectrum of these pathway targets spans from the G1 phase to the end of mitosis. Consequently, through downregulating the expression of gene products which are essential for progression through the cell cycle, the p53–DREAM pathway participates in the control of all checkpoints from DNA synthesis to cytokinesis including G1/S, G2/M and spindle assembly checkpoints. Therefore, defects in the p53–DREAM pathway contribute to a general loss of checkpoint control. Furthermore, deregulation of DREAM target genes promotes chromosomal instability and aneuploidy of cancer cells. Also, DREAM regulation is abrogated by the human papilloma virus HPV E7 protein linking the p53–DREAM pathway to carcinogenesis by HPV.Another feature of the pathway is that it downregulates many genes involved in DNA repair and telomere maintenance as well as Fanconi anemia.