DOCSLIB.ORG

PDF Download

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
PDF Download ARTICLE https://doi.org/10.1038/s41467-020-16539-4 OPEN Machine learning uncovers cell identity regulator by histone code Bo Xia1,2,3,4,7, Dongyu Zhao1,2,3,4,7, Guangyu Wang1,2,3,4,7, Min Zhang2,3,4, Jie Lv1,2,3,4, Alin S. Tomoiaga5, ✉ ✉ Yanqiang Li 1,2,3,4, Xin Wang 1,2,3,4, Shu Meng2,3,4, John P. Cooke2,3,4, Qi Cao 6 , Lili Zhang2,3,4 & ✉ Kaifu Chen 1,2,3,4 Conversion between cell types, e.g., by induced expression of master transcription factors, 1234567890():,; holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and their expression regulation. Here, we develop CEFCIG, an artificial intelligent framework to uncover CIGs and further define their master regulators. On the basis of machine learning, CEFCIG reveals unique histone codes for transcriptional regulation of reported CIGs, and utilizes these codes to predict CIGs and their master regulators with high accuracy. Applying CEFCIG to 1,005 epigenetic profiles, our analysis uncovers the landscape of regulation network for identity genes in individual cell or tissue types. Together, this work provides insights into cell identity regulation, and delivers a powerful technique to facilitate regenerative medicine. 1 Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA. 2 Center for Cardiovascular Regeneration, Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, USA. 3 Department of Cardiothoracic Surgeries, Weill Cornell Medical College, Cornell University, New York, NY, USA. 4 Institute for Academic Medicine, Houston Methodist Research Institute, Houston, TX, USA. 5 Business Analytics, CIS & Law Department, The O’Malley School of Business Accounting, Manhattan College, Riverdale, NY, USA. 6 Department of Urology, Robert H. Lurie Comprehensive Cancer Center, Chicago, IL, USA. 7These authors contributed equally: Bo Xia, Dongyu Zhao, Guangyu Wang. ✉ email: [email protected]; [email protected]; [email protected] NATURE COMMUNICATIONS | (2020) 11:2696 | https://doi.org/10.1038/s41467-020-16539-4 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16539-4 cell type is determined by the combinatorial function of transcription factors, whose depletion impaired the differentia- Aits cell identity genes (CIGs), which jointly dictate phe- tion towards a specific cell type; (3) genes required for key notypes of the cell and play critical roles in differentiation, functions or phenotypes of a cell type; (4) genes that were widely development, and disease. CIGs form an intricate regulation used as markers for a cell type. Together, we curated 247 known network, in which a set of master transcription factors governs identity genes for ten well-defined cell types (Supplementary the expression program of CIGs1. Transitions between a few cell Data 1). These genes include 42–82 identity genes in each of the types have been achieved in vitro by induced expression of master four categories (Fig. 1a) and 18 to 36 identity genes for each of the transcription factors2 and hold great promise for cellular ther- ten cell types (Fig. 1b). As was reported before, chromatin apy3. However, constrained by limited ability to uncover the immunoprecipitation sequencing (ChIP-seq) signal of activating accurate catalog of CIGs, our understanding of cell identity is epigenetic marks, such as H3K4me3, H3K4me1, and H3K27ac, incomplete. Conventional methods define cell identity by several showed a unique broad pattern of enrichment at individual marker genes, a methodology that is widely recognized to be known CIGs (Supplementary Fig. 1A, B), e.g., SOX2 in H1 suboptimal. Without comprehensive information about cell human embryonic stem cell (hESC)2, GATA2 in endothelial cells identity fidelity, clinical application of progenitor-derived cells (ECs)15, LMO2 in CD34 + hematopoietic stem cells (HPCs)16, might be encumbered by phenotypic aberration or instability4–9. and PAX6 in mid-radical glial cells (MRGs)17 (Supplementary Thus, it is of pivotal importance to uncover the complete catalog Fig. 1C). These known CIGs have a high but widely distributed of CIGs, which will provide a blueprint to facilitate basic inves- Tau index of expression specificity, with their index values ran- tigation on cell identity, and to guide clinical application of ging from ~1 to 0.6 and ranked from top to median among all engineered cells. genes (Supplementary Fig. 1d). Scientists have been defining CIGs based on genome-wide We noted that different histone modifications differ in expression profile10, which is not yet an optimal method. Many distribution pattern on chromatin and thus require different genes expressed in a cell type are not related to cell identity. parameters to analyze their ChIP-Seq data. We therefore Alternatively, scientists also define CIGs based on the expression developed GridGO, a grid-based genetic algorithm to automati- specificity analysis10, which requires comparison between a query cally optimize the detection of histone modification features for cell type and most, if not all, other cell types. It also requires CIGs (Supplementary Fig. 2a). The algorithm aggressively distinguishing between cell-type-specific and biological- searches for optimal cutoff for each of three important condition-specific genes, e.g., heat shock genes. Therefore, it is parameters in ChIP-Seq analysis, including the height cutoff for not cost-effective, if not impossible, to collect data for all cell peak calling and the upstream or downstream distance cutoffs for types under all biological conditions for the expression specificity assigning a peak to a nearby Transcription Start Site (TSS). To analysis. Further, the relationship between CIGs and cell-type- investigate additional histone modification features for CIGs, we specific genes can be complicated and uncertain. Some identity further developed algorithms in GridGO to analyze six statistical genes may be highly cell-type-specific, whereas other identity features for each ChIP-Seq enrichment peak. These features genes may be expressed in multiple related cell types and have include peak width, height, skewness, kurtosis, total signal, and less expression specificity. As such, to define CIGs solely by their coverage in a given genomic region (Supplementary Fig. 2b). expression profile may be not optimal. It becomes apparent GridGO effectively improved P-values of differences in most of recently that CIGs are different from other expressed genes in the histone modification features between CIGs and the remaining epigenetic mechanism to regulate their transcription. For exam- genes (Supplementary Fig. 2c, d). We further compared the ples, super enhancers and a unique broad pattern of H3K4me3 receiver operating characteristic (ROC) curve for recapturing the modification were found to regulate CIGs, whereas it is typical curated CIGs using each feature before and after GridGO enhancer and sharp H3K4me3 modification that regulate other optimization. Sixteen signatures, each defined as one feature of expressed genes such as housekeeping genes11–14. These dis- one histone modification, display significant improvement coveries suggest strong potential to systematically uncover CIGs (Supplementary Fig. 2e-g). Intriguingly, H3K4me3, H3K4me1, on the basis of epigenetic signature analysis. and H3K27ac showed significant difference in each of their Here we develop Computational Epigenetic Framework for features between known CIGs and random control genes (Fig. 1c). Cell Identity Gene (CEFCIG), a computational framework to However, H3K27me3, one of the most studied repressive histone uncover CIGs and further define their master regulators. On the modifications, displayed little such difference (Fig. 1c). basis of machine learning, CEFCIG reveals unique histone codes By combining gene expression profile with histone modifica- for transcriptional regulation of reported CIGs and utilizes these tion signatures, we developed a logistic regression model, codes to predict CIGs and their master regulators with high CIGdiscover, for CIGs discovery (Supplementary Fig. 3a). A accuracy. Applying CEFCIG to 1005 epigenetic profiles, our histone modification may have distinct biological implications analysis uncovers the landscape of regulation network for identity when it appears on the promoter vs. on the gene body. Therefore, genes in individual cell or tissue types. Together, this work pro- for each histone modification feature, CIGdiscover calculated a vides insights into cell identity regulation and delivers a powerful value for the associated promoter and further a value for the technique to facilitate regenerative medicine. associated gene body. These together led to 49 signatures. Considering that some peak features occasionally show consider- able correlation with each other, CIGdiscover tried the backward Results feature elimination and forward feature construction methods to Prediction of CIGs by CIGdiscover on the basis of the unique select an optimal combination of features for prediction of CIGs. histone codes. To build a solid foundation for study on CIGs, we Intriguingly, the backward method did not improve the model, performed a thorough review of literature (see “Methods”)to whereas the forward method successfully removed 40 of the develop Cell Identity Gene Data Base (CIGDB), the first database 49 signatures to achieve an optimal prediction accuracy
Load more

Recommended publications
  • Entrez Symbols Name Termid Termdesc 117553 Uba3,Ube1c
    Entrez Symbols Name TermID TermDesc 117553 Uba3,Ube1c ubiquitin-like modifier activating enzyme 3 GO:0016881 acid-amino acid ligase activity 299002 G2e3,RGD1310263 G2/M-phase specific E3 ubiquitin ligase GO:0016881 acid-amino acid ligase activity 303614 RGD1310067,Smurf2 SMAD specific E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 308669 Herc2 hect domain and RLD 2 GO:0016881 acid-amino acid ligase activity 309331 Uhrf2 ubiquitin-like with PHD and ring finger domains 2 GO:0016881 acid-amino acid ligase activity 316395 Hecw2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2 GO:0016881 acid-amino acid ligase activity 361866 Hace1 HECT domain and ankyrin repeat containing, E3 ubiquitin protein ligase 1 GO:0016881 acid-amino acid ligase activity 117029 Ccr5,Ckr5,Cmkbr5 chemokine (C-C motif) receptor 5 GO:0003779 actin binding 117538 Waspip,Wip,Wipf1 WAS/WASL interacting protein family, member 1 GO:0003779 actin binding 117557 TM30nm,Tpm3,Tpm5 tropomyosin 3, gamma GO:0003779 actin binding 24779 MGC93554,Slc4a1 solute carrier family 4 (anion exchanger), member 1 GO:0003779 actin binding 24851 Alpha-tm,Tma2,Tmsa,Tpm1 tropomyosin 1, alpha GO:0003779 actin binding 25132 Myo5b,Myr6 myosin Vb GO:0003779 actin binding 25152 Map1a,Mtap1a microtubule-associated protein 1A GO:0003779 actin binding 25230 Add3 adducin 3 (gamma) GO:0003779 actin binding 25386 AQP-2,Aqp2,MGC156502,aquaporin-2aquaporin 2 (collecting duct) GO:0003779 actin binding 25484 MYR5,Myo1e,Myr3 myosin IE GO:0003779 actin binding 25576 14-3-3e1,MGC93547,Ywhah
    [Show full text]
  • Supp Material.Pdf
    Simon et al. Supplementary information: Table of contents p.1 Supplementary material and methods p.2-4 • PoIy(I)-poly(C) Treatment • Flow Cytometry and Immunohistochemistry • Western Blotting • Quantitative RT-PCR • Fluorescence In Situ Hybridization • RNA-Seq • Exome capture • Sequencing Supplementary Figures and Tables Suppl. items Description pages Figure 1 Inactivation of Ezh2 affects normal thymocyte development 5 Figure 2 Ezh2 mouse leukemias express cell surface T cell receptor 6 Figure 3 Expression of EZH2 and Hox genes in T-ALL 7 Figure 4 Additional mutation et deletion of chromatin modifiers in T-ALL 8 Figure 5 PRC2 expression and activity in human lymphoproliferative disease 9 Figure 6 PRC2 regulatory network (String analysis) 10 Table 1 Primers and probes for detection of PRC2 genes 11 Table 2 Patient and T-ALL characteristics 12 Table 3 Statistics of RNA and DNA sequencing 13 Table 4 Mutations found in human T-ALLs (see Fig. 3D and Suppl. Fig. 4) 14 Table 5 SNP populations in analyzed human T-ALL samples 15 Table 6 List of altered genes in T-ALL for DAVID analysis 20 Table 7 List of David functional clusters 31 Table 8 List of acquired SNP tested in normal non leukemic DNA 32 1 Simon et al. Supplementary Material and Methods PoIy(I)-poly(C) Treatment. pIpC (GE Healthcare Lifesciences) was dissolved in endotoxin-free D-PBS (Gibco) at a concentration of 2 mg/ml. Mice received four consecutive injections of 150 μg pIpC every other day. The day of the last pIpC injection was designated as day 0 of experiment.
    [Show full text]
  • Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
    LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research.
    [Show full text]
  • Plasma Cells in Vitro Generation of Long-Lived Human
    Downloaded from http://www.jimmunol.org/ by guest on September 24, 2021 is online at: average * The Journal of Immunology , 32 of which you can access for free at: 2012; 189:5773-5785; Prepublished online 16 from submission to initial decision 4 weeks from acceptance to publication November 2012; doi: 10.4049/jimmunol.1103720 http://www.jimmunol.org/content/189/12/5773 In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco, Sophie Stephenson, Matthew A. Care, Darren Newton, Nicholas A. Barnes, Adam Davison, Andy Rawstron, David R. Westhead, Gina M. Doody and Reuben M. Tooze J Immunol cites 65 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription http://www.jimmunol.org/content/suppl/2012/11/16/jimmunol.110372 0.DC1 This article http://www.jimmunol.org/content/189/12/5773.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 24, 2021. The Journal of Immunology In Vitro Generation of Long-lived Human Plasma Cells Mario Cocco,*,1 Sophie Stephenson,*,1 Matthew A.
    [Show full text]
  • Investigation of Differentially Expressed Genes in Nasopharyngeal Carcinoma by Integrated Bioinformatics Analysis
    916 ONCOLOGY LETTERS 18: 916-926, 2019 Investigation of differentially expressed genes in nasopharyngeal carcinoma by integrated bioinformatics analysis ZhENNING ZOU1*, SIYUAN GAN1*, ShUGUANG LIU2, RUjIA LI1 and jIAN hUANG1 1Department of Pathology, Guangdong Medical University, Zhanjiang, Guangdong 524023; 2Department of Pathology, The Eighth Affiliated hospital of Sun Yat‑sen University, Shenzhen, Guangdong 518033, P.R. China Received October 9, 2018; Accepted April 10, 2019 DOI: 10.3892/ol.2019.10382 Abstract. Nasopharyngeal carcinoma (NPC) is a common topoisomerase 2α and TPX2 microtubule nucleation factor), malignancy of the head and neck. The aim of the present study 8 modules, and 14 TFs were identified. Modules analysis was to conduct an integrated bioinformatics analysis of differ- revealed that cyclin-dependent kinase 1 and exportin 1 were entially expressed genes (DEGs) and to explore the molecular involved in the pathway of Epstein‑Barr virus infection. In mechanisms of NPC. Two profiling datasets, GSE12452 and summary, the hub genes, key modules and TFs identified in GSE34573, were downloaded from the Gene Expression this study may promote our understanding of the pathogenesis Omnibus database and included 44 NPC specimens and of NPC and require further in-depth investigation. 13 normal nasopharyngeal tissues. R software was used to identify the DEGs between NPC and normal nasopharyngeal Introduction tissues. Distributions of DEGs in chromosomes were explored based on the annotation file and the CYTOBAND database Nasopharyngeal carcinoma (NPC) is a common malignancy of DAVID. Gene ontology (GO) and Kyoto Encyclopedia of occurring in the head and neck. It is prevalent in the eastern Genes and Genomes (KEGG) pathway enrichment analysis and southeastern parts of Asia, especially in southern China, were applied.
    [Show full text]
  • Mouse Haus1 Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Haus1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Haus1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Haus1 gene (NCBI Reference Sequence: NM_146089 ; Ensembl: ENSMUSG00000041840 ) is located on Mouse chromosome 18. 9 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 9 (Transcript: ENSMUST00000048192). Exon 3~4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Haus1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-196O5 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 3 starts from about 24.7% of the coding region. The knockout of Exon 3~4 will result in frameshift of the gene. The size of intron 2 for 5'-loxP site insertion: 2655 bp, and the size of intron 4 for 3'-loxP site insertion: 951 bp. The size of effective cKO region: ~2727 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 3 4 5 6 9 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Haus1 Homology arm cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • Identification of Differential Expression of P53 Associated Rnas at 3 Different Treatment Timepoints, and Association with Chip- Seq Identified P53 Genes
    Rochester Institute of Technology RIT Scholar Works Theses 5-19-2017 Identification of Differential Expression of p53 associated RNAs at 3 Different Treatment Timepoints, and Association with ChIP- seq Identified p53 Genes. Julia Freewoman [email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Freewoman, Julia, "Identification of Differential Expression of p53 associated RNAs at 3 Different Treatment Timepoints, and Association with ChIP-seq Identified p53 Genes." (2017). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. R.I.T. Identification of Differential Expression of p53 associated RNAs at 3 Different Treatment Timepoints, and Association with ChIP-seq Identified p53 Genes. by Julia Freewoman A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Bioinformatics. School of Thomas H. Gosnell School of Life Sciences Bioinformatics Program College of Science Rochester Institute of Technology Rochester, NY May 19, 2017 Thesis Committee Members: Feng Cui, Ph.D., thesis advisor Andre Hudson, Ph.D. Gary R. Skuse, Ph.D. ii Table of Contents Abstract ........................................................................................................................................... 1 I. Introduction
    [Show full text]
  • Identification of Genes Associated with Castration‑Resistant Prostate Cancer by Gene Expression Profile Analysis
    MOLECULAR MEDICINE REPORTS 16: 6803-6813, 2017 Identification of genes associated with castration‑resistant prostate cancer by gene expression profile analysis CHUI GUO HUANG1, FENG XI LI2, SONG PAN3, CHANG BAO XU1, JUN QIANG DAI4 and XING HUA ZHAO1 1Department of Urology, The Second Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan 450014; 2Department of Gastrointestinal Glands Surgery, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi 530000; 3Department of Urology, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan 450014; 4Department of Neurosurgery, The Second Affiliated Hospital, Lanzhou University, Lanzhou, Gansu 730030, P.R. China Received December 16, 2016; Accepted July 17, 2017 DOI: 10.3892/mmr.2017.7488 Abstract. Prostate cancer (CaP) is a serious and common western countries (1). CaP growth is driven by androgens; genital tumor. Generally, men with metastatic CaP can easily therefore, the conventional treatment option is to lower the develop castration-resistant prostate cancer (CRPC). However, levels of male sex hormones (2). Current treatment strate- the pathogenesis and tumorigenic pathways of CRPC remain to gies for CaP include surgery, external beam radiotherapy, be elucidated. The present study performed a comprehensive brachytherapy, chemotherapy and androgen-deprivation analysis on the gene expression profile of CRPC in order to therapy (ADT) (2,3). ADT often involves surgically determine the pathogenesis and tumorigenic of CRPC. The removing the testicles or using drugs to block the andro- GSE33316 microarray, which consisted of 5 non-castrated gens from affecting the body (4). Unfortunately, ADT has samples and 5 castrated samples, was downloaded from the gene a limited role in preventing majority of patients progressing expression omnibus database.
    [Show full text]
  • Supplemental Data.Pdf
    Supplementary material -Table of content Supplementary Figures (Fig 1- Fig 6) Supplementary Tables (1-13) Lists of genes belonging to distinct biological processes identified by GREAT analyses to be significantly enriched with UBTF1/2-bound genes Supplementary Table 14 List of the common UBTF1/2 bound genes within +/- 2kb of their TSSs in NIH3T3 and HMECs. Supplementary Table 15 List of gene identified by microarray expression analysis to be differentially regulated following UBTF1/2 knockdown by siRNA Supplementary Table 16 List of UBTF1/2 binding regions overlapping with histone genes in NIH3T3 cells Supplementary Table 17 List of UBTF1/2 binding regions overlapping with histone genes in HMEC Supplementary Table 18 Sequences of short interfering RNA oligonucleotides Supplementary Table 19 qPCR primer sequences for qChIP experiments Supplementary Table 20 qPCR primer sequences for reverse transcription-qPCR Supplementary Table 21 Sequences of primers used in CHART-PCR Supplementary Methods Supplementary Fig 1. (A) ChIP-seq analysis of UBTF1/2 and Pol I (POLR1A) binding across mouse rDNA. UBTF1/2 is enriched at the enhancer and promoter regions and along the entire transcribed portions of rDNA with little if any enrichment in the intergenic spacer (IGS), which separates the rDNA repeats. This enrichment coincides with the distribution of the largest subunit of Pol I (POLR1A) across the rDNA. All sequencing reads were mapped to the published complete sequence of the mouse rDNA repeat (Gene bank accession number: BK000964). The graph represents the frequency of ribosomal sequences enriched in UBTF1/2 and Pol I-ChIPed DNA expressed as fold change over those of input genomic DNA.
    [Show full text]
  • 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: 7 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: 7. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Med14 Med21 Med6 Cdk8 Ccnc Med10 Med23 Med14 Med21
    [Show full text]
  • Receptor Signaling Through Osteoclast-Associated Monocyte
    Downloaded from http://www.jimmunol.org/ by guest on September 29, 2021 is online at: average * The Journal of Immunology The Journal of Immunology , 20 of which you can access for free at: 2015; 194:3169-3179; Prepublished online 27 from submission to initial decision 4 weeks from acceptance to publication February 2015; doi: 10.4049/jimmunol.1402800 http://www.jimmunol.org/content/194/7/3169 Collagen Induces Maturation of Human Monocyte-Derived Dendritic Cells by Signaling through Osteoclast-Associated Receptor Heidi S. Schultz, Louise M. Nitze, Louise H. Zeuthen, Pernille Keller, Albrecht Gruhler, Jesper Pass, Jianhe Chen, Li Guo, Andrew J. Fleetwood, John A. Hamilton, Martin W. Berchtold and Svetlana Panina J Immunol cites 43 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Author Choice option Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Freely available online through http://www.jimmunol.org/content/suppl/2015/02/27/jimmunol.140280 0.DCSupplemental This article http://www.jimmunol.org/content/194/7/3169.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Author Choice Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2015 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606.
    [Show full text]
  • A Deep Proteomics Perspective on CRM1-Mediated Nuclear Export and Nucleocytoplasmic Partitioning
    A deep proteomics perspective on CRM1-mediated nuclear export and nucleocytoplasmic partitioning Koray Kırlı 1#, Samir Karaca1,2#, Heinz-Jürgen Dehne 1, Matthias Samwer 1,4, Kuan- Ting Pan 2, Christof Lenz 2,3, Henning Urlaub2,3* and Dirk Görlich1* 1 Department of Cellular Logistics and 2 Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany; 3 Bioanalytics, Institute for Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany. 4 Current address: Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna, Austria. # Joint first authors; *Joint corresponding authors; E-mail: [email protected]; [email protected] Abstract CRM1 is a highly conserved, RanGTPase-driven exportin that carries proteins and RNPs from the nucleus to the cytoplasm. We now explored the cargo-spectrum of CRM1 in depth and identified surprisingly large numbers, namely >700 export substrates from the yeast S. cerevisiae, ≈ 1000 from Xenopus oocytes and >1050 from human cells. In addition, we quantified the partitioning of ≈5000 unique proteins between nucleus and cytoplasm of Xenopus oocytes. The data suggest new CRM1 functions in spatial control of vesicle coat-assembly, centrosomes, autophagy, peroxisome biogenesis, cytoskeleton, ribosome maturation, translation, mRNA degradation, and more generally in precluding a potentially detrimental action of cytoplasmic pathways within the nuclear interior. There are also numerous new instances where CRM1 appears to act in regulatory circuits. Altogether, our dataset allows unprecedented insights into the nucleocytoplasmic organisation of eukaryotic cells, into the contributions of an exceedingly promiscuous exportin and it provides a new basis for NES prediction. 2 Introduction The nuclear envelope (NE) separates the cell nucleus from the cytoplasm.
    [Show full text]