1 GATA4 Is a Direct Transcriptional Activator of Cyclin D2 and Cdk4 and Is Required For
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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. -
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. -
Centromere RNA Is a Key Component for the Assembly of Nucleoproteins at the Nucleolus and Centromere
Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Centromere RNA is a key component for the assembly of nucleoproteins at the nucleolus and centromere Lee H. Wong,1,3 Kate H. Brettingham-Moore,1 Lyn Chan,1 Julie M. Quach,1 Melisssa A. Anderson,1 Emma L. Northrop,1 Ross Hannan,2 Richard Saffery,1 Margaret L. Shaw,1 Evan Williams,1 and K.H. Andy Choo1 1Chromosome and Chromatin Research Laboratory, Murdoch Childrens Research Institute & Department of Paediatrics, University of Melbourne, Royal Children’s Hospital, Parkville 3052, Victoria, Australia; 2Peter MacCallum Research Institute, St. Andrew’s Place, East Melbourne, Victoria 3002, Australia The centromere is a complex structure, the components and assembly pathway of which remain inadequately defined. Here, we demonstrate that centromeric ␣-satellite RNA and proteins CENPC1 and INCENP accumulate in the human interphase nucleolus in an RNA polymerase I–dependent manner. The nucleolar targeting of CENPC1 and INCENP requires ␣-satellite RNA, as evident from the delocalization of both proteins from the nucleolus in RNase-treated cells, and the nucleolar relocalization of these proteins following ␣-satellite RNA replenishment in these cells. Using protein truncation and in vitro mutagenesis, we have identified the nucleolar localization sequences on CENPC1 and INCENP. We present evidence that CENPC1 is an RNA-associating protein that binds ␣-satellite RNA by an in vitro binding assay. Using chromatin immunoprecipitation, RNase treatment, and “RNA replenishment” experiments, we show that ␣-satellite RNA is a key component in the assembly of CENPC1, INCENP, and survivin (an INCENP-interacting protein) at the metaphase centromere. -
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 -
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. -
XBP1 Negatively Regulates CENPF Expression Via Recruiting Atf6α to the Promoter During ER Stress Tao Shen1* , Yan Li2,3, Shuang Liang4 and Zhiguang Chen1
Shen et al. Cancer Cell Int (2020) 20:459 https://doi.org/10.1186/s12935-020-01553-9 Cancer Cell International PRIMARY RESEARCH Open Access XBP1 negatively regulates CENPF expression via recruiting ATF6α to the promoter during ER stress Tao Shen1* , Yan Li2,3, Shuang Liang4 and Zhiguang Chen1 Abstract Background: Centromere protein F (CENPF) is a key component of the kinetochore complex involved in mitosis, cell diferentiation and cellular response to stresses. However, the alteration of CENPF in response to endoplasmic reticulum (ER) stress has not been well described. In the present study, we investigate CENPF regulation in response to ER stress. Methods: Quantitative real-time polymerase chain reaction and western blotting were used to determine CENPF expression under ER stress. Luciferase activity analysis was performed to investigate the promoter regions contribut- ing to CENPF transcription in response to TG. Chromatin immunoprecipitation (ChIP) and ChIP Re-IP assays were used to determine if X-box binding protein 1 (XBP1) and/or activating transcription factor 6α (ATF6α) bind in the CENPF promoter region. Cell apoptosis and proliferation were analyzed using TUNEL, cell growth and clonogenic assays. Results: CENPF expression is dramatically reduced under ER stress induced by thapsigargin (TG), brefeldin A (BFA), or tunicamycin (TM) and this downregulation of CENPF expression was dependent on XBP1 and ATF6α. Luciferase activity analysis of the truncated CENPF promoter indicates that regions from bases 679 to 488 and from 241 to 78 in the CENPF promoter were sensitive to TG treatment. Additionally, ChIP and− ChIP Re-IP− assays reveal −that XBP1− and ATF6α were assembled on the same regions of CENPF promoter. -
Explorative Bioinformatic Analysis of Cardiomyocytes in 2D &3D in Vitro Culture System
EXPLORATIVE BIOINFORMATIC ANALYSIS OF CARDIOMYOCYTES IN 2D &3D IN VITRO CULTURE SYSTEM VERSION 2 Master Degree Project in Bioscience One years Level, 60 ECTS Sruthy Janardanan [email protected] Supervisor: Jane Synnergren [email protected] Examiner: Sanja Jurcevic [email protected] Abstract The in vitro cell culture models of human pluripotent stem cells (hPSC)-derived cardiomyocytes (CMs) have gained a predominant value in the field of drug discovery and is considered an attractive tool for cardiovascular disease modellings. However, despite several reports of different protocols for the hPSC-differentiation into CMs, the development of an efficient, controlled and reproducible 3D differentiation remains challenging. The main aim of this research study was to understand the changes in the gene expression as an impact of spatial orientation of hPSC-derived CMs in 2D(two-dimensional) and 3D(three-dimensional) culture conditions and to identify the topologically important Hub and Hub-Bottleneck proteins using centrality measures to gain new knowledge for standardizing the pre-clinical models for the regeneration of CMs. The above-mentioned aim was achieved through an extensive bioinformatic analysis on the list of differentially expressed genes (DEGs) identified from RNA-sequencing (RNA-Seq). Functional annotation analysis of the DEGs from both 2D and 3D was performed using Cytoscape plug-in ClueGO. Followed by the topological analysis of the protein-protein interaction network (PPIN) using two centrality parameters; Degree and Betweeness in Cytoscape plug-in CenTiScaPe. The results obtained revealed that compared to 2D, DEGs in 3D are primarily associated with cell signalling suggesting the interaction between cells as an impact of the 3D microenvironment and topological analysis revealed 32 and 39 proteins as Hub and Hub-Bottleneck proteins, respectively in 3D indicating the possibility of utilizing those identified genes and their corresponding proteins as cardiac disease biomarkers in future by further research. -
How Does SUMO Participate in Spindle Organization?
cells Review How Does SUMO Participate in Spindle Organization? Ariane Abrieu * and Dimitris Liakopoulos * CRBM, CNRS UMR5237, Université de Montpellier, 1919 route de Mende, 34090 Montpellier, France * Correspondence: [email protected] (A.A.); [email protected] (D.L.) Received: 5 July 2019; Accepted: 30 July 2019; Published: 31 July 2019 Abstract: The ubiquitin-like protein SUMO is a regulator involved in most cellular mechanisms. Recent studies have discovered new modes of function for this protein. Of particular interest is the ability of SUMO to organize proteins in larger assemblies, as well as the role of SUMO-dependent ubiquitylation in their disassembly. These mechanisms have been largely described in the context of DNA repair, transcriptional regulation, or signaling, while much less is known on how SUMO facilitates organization of microtubule-dependent processes during mitosis. Remarkably however, SUMO has been known for a long time to modify kinetochore proteins, while more recently, extensive proteomic screens have identified a large number of microtubule- and spindle-associated proteins that are SUMOylated. The aim of this review is to focus on the possible role of SUMOylation in organization of the spindle and kinetochore complexes. We summarize mitotic and microtubule/spindle-associated proteins that have been identified as SUMO conjugates and present examples regarding their regulation by SUMO. Moreover, we discuss the possible contribution of SUMOylation in organization of larger protein assemblies on the spindle, as well as the role of SUMO-targeted ubiquitylation in control of kinetochore assembly and function. Finally, we propose future directions regarding the study of SUMOylation in regulation of spindle organization and examine the potential of SUMO and SUMO-mediated degradation as target for antimitotic-based therapies. -
The Genetic Program of Pancreatic Beta-Cell Replication in Vivo
Page 1 of 65 Diabetes The genetic program of pancreatic beta-cell replication in vivo Agnes Klochendler1, Inbal Caspi2, Noa Corem1, Maya Moran3, Oriel Friedlich1, Sharona Elgavish4, Yuval Nevo4, Aharon Helman1, Benjamin Glaser5, Amir Eden3, Shalev Itzkovitz2, Yuval Dor1,* 1Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel 2Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. 3Department of Cell and Developmental Biology, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel 4Info-CORE, Bioinformatics Unit of the I-CORE Computation Center, The Hebrew University and Hadassah, The Institute for Medical Research Israel- Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel 5Endocrinology and Metabolism Service, Department of Internal Medicine, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Israel *Correspondence: [email protected] Running title: The genetic program of pancreatic β-cell replication 1 Diabetes Publish Ahead of Print, published online March 18, 2016 Diabetes Page 2 of 65 Abstract The molecular program underlying infrequent replication of pancreatic beta- cells remains largely inaccessible. Using transgenic mice expressing GFP in cycling cells we sorted live, replicating beta-cells and determined their transcriptome. Replicating beta-cells upregulate hundreds of proliferation- related genes, along with many novel putative cell cycle components. Strikingly, genes involved in beta-cell functions, namely glucose sensing and insulin secretion were repressed. Further studies using single molecule RNA in situ hybridization revealed that in fact, replicating beta-cells double the amount of RNA for most genes, but this upregulation excludes genes involved in beta-cell function. -
Genome-Wide Screening Identifies Genes and Biological Processes
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 10-12-2018 Genome-Wide Screening Identifies Genes and Biological Processes Implicated in Chemoresistance and Oncogene-Induced Apoptosis Tengyu Ko Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Cancer Biology Commons, Cell Biology Commons, and the Genomics Commons Recommended Citation Ko, Tengyu, "Genome-Wide Screening Identifies Genes and Biological Processes Implicated in Chemoresistance and Oncogene- Induced Apoptosis" (2018). LSU Doctoral Dissertations. 4715. https://digitalcommons.lsu.edu/gradschool_dissertations/4715 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. GENOME-WIDE SCREENING IDENTIFIES GENES AND BIOLOGICAL PROCESSES IMPLICATED IN CHEMORESISTANCE AND ONCOGENE- INDUCED APOPTOSIS A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical and Veterinary Medical Sciences through the Department of Comparative Biomedical Sciences by Tengyu Ko B.S., University of California, Santa Barbara 2010 December 2018 ACKNOWLEDGEMENTS I would like to express my sincerest gratitude to my major supervisor Dr. Shisheng Li for giving me the opportunity to join his team and the freedom to pursue projects. I appreciate all of his thoughts and efforts. Truly, none of these findings would be possible without his supervisions, supports, insightful discussions, and patience. -
CENPO Expression Regulates Gastric Cancer Cell Proliferation and Is Associated with Poor Patient Prognosis
MOLECULAR MEDICINE REPORTS 20: 3661-3670, 2019 CENPO expression regulates gastric cancer cell proliferation and is associated with poor patient prognosis YI CAO1, JIANBO XIONG1, ZHENGRONG LI1, GUOYANG ZHANG1, YI TU2, LIZHEN WANG2 and ZHIGANG JIE1 Departments of 1Gastrointestinal Surgery and 2Pathology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi 330006, P.R. China Received January 21, 2019; Accepted July 17, 2019 DOI: 10.3892/mmr.2019.10624 Abstract. Gastric cancer (GC) is one of the most common death worldwide (1-3). Due to the progression of GC and high malignancies worldwide; however, understanding of its devel- recurrence rates following surgical resection, GC remains a opment and carcinogenesis is currently limited. Centromere major public health issue. Therefore, elucidating the molecular protein O (CENPO), is a newly discovered constitutive mechanisms underlying GC tumorigenesis is required in order centromeric protein, associated with cell death. The expres- to identify prognostic markers and novel effective treatments. sion of CENPO in human cancers, including GC, is currently Previous studies have demonstrated that the normal expres- unknown. The aim of the present study was to investigate the sion of centromere proteins is essential for mitosis (4) and clinical association between CENPO and GC, and to eluci- abnormal core centromere proteins may increase the incidence date the potential mechanisms of CENPO in the process of of chromosomal instability, aneuploidy, and lead to oncogen- GC progression. The results demonstrated that CENPO was esis (5,6). In a previous study, >40 proteins were identified expressed at high levels in GC and was correlated with p-TNM in the interphase centromere complex (ICEN) by proteomic stage. -
Supplementary Materials: Molecular Signature of Subtypes of Non- Small Cell Lung Cancer by Large-Scale Transcriptional Profiling
Cancers 2020 S1 of S18 Supplementary Materials: Molecular Signature of Subtypes of Non- Small Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co- Expression Network Analysis (WGCNA) Magdalena Niemira, Francois Collin, Anna Szalkowska, Agnieszka Bielska, Karolina Chwialkowska, Joanna Reszec, Jacek Niklinski, Miroslaw Kwasniewski and Adam Kretowski Cancers 2020 S2 of S18 A B Figure S1. The top-ranked enriched canonical pathway identified in (A) SCC and (B) ADC using IPA: Eicosanoid signalling pathway. Cancers 2020 S3 of S18 A Cancers 2020 S4 of S18 Figure S2. The second-ranked enriched canonical pathway identified in (A) SCC and (B) ADC using IPA: Agranulocyte adhesion and diapedesis. Cancers 2020 S5 of S18 Figure S3. The top-ranked enriched canonical pathway identified only in lung ADC: MIF regulation of innate immunity. A B Figure S4. Cluster dendograms of the gene clusters of (A) LUAD and (B) LUSC subset from TCGA database. Cancers 2020 S6 of S18 A B C D Figure S5. Protein-protein interaction (PPI) network of genes in the red (A), lightcyan (B), darkorange (C), yellow (D) modules in ADC. The networks were constructed using Cytoscape v. 3.7.2. software. Cancers 2020 S7 of S18 A B Figure S6. Protein-protein interaction (PPI) network of genes in the blue (A) and (B) modules in SCC. The networks were constructed using Cytoscape v. 3.7.2. software. Cancers 2020 S8 of S18 Table S1. Upstream regulator analysis of DEGs in lung SCC predicted by IPA. Upstream Prediction Target