PDF Output of CLIC (Clustering by Inferred Co-Expression)

Total Page:16

File Type:pdf, Size:1020Kb

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: 6 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: 6. Num of CEMs: 1. 0.0 0.2 0.4 0.6 0.8 1.0 Rfc5 Rfc4 Rfc3 Pcna Rfc2 Chtf18 Rfc5 Rfc4 Rfc3 CEM 1 (641 datasets) Pcna Rfc2 Chtf18 Symbol Num ofCEMGenes:6.Predicted424.SelectedDatasets:641.Strength:10.9 CEM 1,Geneset"[C]PCNA-CHL12-RFC2-5complex",Page1 Rad51ap1 Ncapd2 Chaf1b Cenph Chtf18 Cenpk Ncaph Cdc20 Cks1b Kif20a Cdca8 Cdca5 Cdc45 Rad51 Spc24 Fignl1 Aurkb Gmnn Mcm4 Mcm2 Mcm6 Mcm7 Mcm5 Gins1 Prim1 Prim2 Gins2 Asf1b Uhrf1 Rrm1 Birc5 Bub1 Kif22 Cdc6 Rpa2 Cdk1 Exo1 Pcna Tipin Fen1 Melk Hat1 Rfc2 Rfc3 Rfc4 Rfc5 Pole Lig1 Pbk Tk1 0.0 1.0 GSE38031 [8] GSE40513 [6] GSE31028 [6] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE54215 [13] GSE20954 [14] GSE23200 [6] GSE18135 [18] GSE27605 [8] GSE15741 [6] GSE4694 [6] GSE12498 [12] GSE39449 [6] GSE20100 [15] GSE6998 [32] GSE51883 [30] GSE12454 [13] GSE21393 [6] GSE21379 [10] GSE12465 [14] GSE13302 [30] GSE48204 [6] GSE17794 [44] GSE57543 [6] GSE51628 [15] GSE21900 [12] GSE28093 [6] GSE38257 [14] GSE28457 [24] GSE13225 [6] GSE48397 [10] GSE13693 [9] GSE16073 [6] GSE31313 [22] GSE33308 [10] GSE51243 [7] GSE51075 [12] GSE23833 [12] GSE32386 [13] GSE46797 [6] GSE54490 [12] GSE19004 [9] GSE24628 [16] GSE17796 [39] GSE44260 [10] GSE14004 [9] GSE13873 [27] GSE50813 [24] GSE17266 [59] GSE31359 [8] GSE22180 [60] GSE51385 [8] GSE21309 [9] GSE20696 [8] GSE54653 [6] GSE38693 [8] GSE6875 [8] GSE13874 [14] GSE46209 [21] GSE30160 [6] GSE46090 [12] GSE16925 [15] GSE29241 [6] GSE12993 [6] GSE5041 [8] GSE44101 [6] GSE11220 [44] GSE45895 [27] GSE51608 [6] GSE17316 [12] GSE18660 [10] GSE51804 [10] GSE10176 [6] GSE6689 [12] GSE15155 [12] GSE53951 [10] GSE13692 [8] GSE24512 [29] GSE25257 [6] GSE9763 [20] GSE28621 [21] GSE7503 [6] GSE46091 [8] GSE15872 [18] GSE5891 [6] GSE9287 [8] GSE6065 [100] GSE48203 [9] GSE39034 [9] GSE17462 [8] GSE16675 [72] GSE46942 [7] GSE13563 [6] GSE9247 [15] GSE11358 [8] GSE39621 [51] GSE52474 [154] GSE21278 [48] GSE11222 [42] GSE7694 [12] GSE40655 [6] GSE51213 [16] GSE30192 [6] GSE19436 [8] GSE15541 [12] GSE1435 [27] GSE41342 [26] GSE46970 [15] GSE28389 [20] GSE20426 [35] GSE21299 [12] GSE23845 [15] GSE12464 [23] GSE16110 [16] GSE32598 [11] GSE4768 [18] GSE19403 [12] GSE6837 [8] GSE48004 [6] GSE51365 [28] GSE16679 [8] GSE22005 [23] GSE26355 [6] GSE38277 [18] GSE42047 [24] GSE7020 [8] GSE19925 [6] GSE10895 [8] GSE7875 [16] GSE24210 [16] GSE29632 [42] GSE21224 [16] GSE20620 [22] GSE27901 [23] GSE10525 [18] GSE14012 [24] GSE16874 [12] GSE30962 [16] GSE18042 [18] GSE18326 [8] GSE44175 [18] GSE39984 [18] GSE28593 [9] GSE14308 [12] GSE40230 [15] GSE51483 [45] GSE4260 [6] GSE30485 [15] GSE13493 [6] GSE47967 [18] GSE11775 [12] GSE33156 [18] GSE14753 [6] GSE14769 [24] GSE10192 [24] CEM+ CEM GSE5332 [12] GSE39766 [6] GSE30488 [52] GSE15267 [8] GSE15580 [14] GSE25423 [10] 0.0 GSE34729 [6] GSE19517 [6] GSE45465 [39] Scale ofaveragePearsoncorrelations GSE13044 [59] GSE23925 [6] GSE36814 [20] GSE55705 [10] GSE27114 [6] GSE54207 [9] 0.2 GSE55855 [6] GSE39897 [36] GSE17513 [12] GSE30138 [51] GSE5245 [16] GSE8678 [6] GSE4518 [12] GSE45143 [6] GSE58307 [20] 0.4 GSE11759 [6] GSE43419 [20] GSE19885 [9] GSE18534 [15] GSE5976 [12] GSE14406 [54] GSE42049 [8] GSE16992 [48] GSE23496 [9] 0.6 GSE23502 [8] GSE13259 [10] GSE21606 [6] GSE56777 [8] GSE29318 [9] GSE31598 [12] GSE20500 [6] GSE16684 [6] GSE32277 [33] 0.8 GSE18136 [12] GSE46185 [6] GSE21568 [12] GSE46242 [12] Score 1122.54 1128.94 1129.73 1131.22 1134.96 1145.55 1153.29 1161.31 1169.33 1173.82 1179.42 1182.92 1186.72 1188.05 1193.93 1198.91 1198.94 1209.80 1211.97 1214.64 1225.42 1227.00 1234.16 1259.79 1267.00 1272.46 1274.57 1279.29 1292.98 1311.22 1316.52 1319.16 1337.66 1338.09 1343.70 1349.66 1351.20 1374.93 1409.46 1429.05 1472.49 1479.41 1504.07 1531.32 1.0 Notes Symbol Num ofCEMGenes:6.Predicted424.SelectedDatasets:641.Strength:10.9 CEM 1,Geneset"[C]PCNA-CHL12-RFC2-5complex",Page2 BC055324 Racgap1 Mms22l Shcbp1 Topbp1 Wdhd1 Dnajc9 Cenpw Incenp Zwilch Cenpu Cenpp Cenpn Dnmt1 Apitd1 Bub1b Dtymk Nup85 Ccnb2 Ncapg Ercc6l Spag5 Ndc80 Trip13 Ube2c Ccna2 Fbxo5 Hirip3 Aurka Brca1 Ube2t Cenpi Kntc1 Spdl1 Sgol1 Espl1 Eme1 Pola1 Pole2 Rrm2 Kif11 Ppil1 Hells Tpx2 Orc6 Dbf4 Nuf2 Cdt1 Plk4 Plk1 0.0 1.0 GSE38031 [8] GSE40513 [6] GSE31028 [6] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE54215 [13] GSE20954 [14] GSE23200 [6] GSE18135 [18] GSE27605 [8] GSE15741 [6] GSE4694 [6] GSE12498 [12] GSE39449 [6] GSE20100 [15] GSE6998 [32] GSE51883 [30] GSE12454 [13] GSE21393 [6] GSE21379 [10] GSE12465 [14] GSE13302 [30] GSE48204 [6] GSE17794 [44] GSE57543 [6] GSE51628 [15] GSE21900 [12] GSE28093 [6] GSE38257 [14] GSE28457 [24] GSE13225 [6] GSE48397 [10] GSE13693 [9] GSE16073 [6] GSE31313 [22] GSE33308 [10] GSE51243 [7] GSE51075 [12] GSE23833 [12] GSE32386 [13] GSE46797 [6] GSE54490 [12] GSE19004 [9] GSE24628 [16] GSE17796 [39] GSE44260 [10] GSE14004 [9] GSE13873 [27] GSE50813 [24] GSE17266 [59] GSE31359 [8] GSE22180 [60] GSE51385 [8] GSE21309 [9] GSE20696 [8] GSE54653 [6] GSE38693 [8] GSE6875 [8] GSE13874 [14] GSE46209 [21] GSE30160 [6] GSE46090 [12] GSE16925 [15] GSE29241 [6] GSE12993 [6] GSE5041 [8] GSE44101 [6] GSE11220 [44] GSE45895 [27] GSE51608 [6] GSE17316 [12] GSE18660 [10] GSE51804 [10] GSE10176 [6] GSE6689 [12] GSE15155 [12] GSE53951 [10] GSE13692 [8] GSE24512 [29] GSE25257 [6] GSE9763 [20] GSE28621 [21] GSE7503 [6] GSE46091 [8] GSE15872 [18] GSE5891 [6] GSE9287 [8] GSE6065 [100] GSE48203 [9] GSE39034 [9] GSE17462 [8] GSE16675 [72] GSE46942 [7] GSE13563 [6] GSE9247 [15] GSE11358 [8] GSE39621 [51] GSE52474 [154] GSE21278 [48] GSE11222 [42] GSE7694 [12] GSE40655 [6] GSE51213 [16] GSE30192 [6] GSE19436 [8] GSE15541 [12] GSE1435 [27] GSE41342 [26] GSE46970 [15] GSE28389 [20] GSE20426 [35] GSE21299 [12] GSE23845 [15] GSE12464 [23] GSE16110 [16] GSE32598 [11] GSE4768 [18] GSE19403 [12] GSE6837 [8] GSE48004 [6] GSE51365 [28] GSE16679 [8] GSE22005 [23] GSE26355 [6] GSE38277 [18] GSE42047 [24] GSE7020 [8] GSE19925 [6] GSE10895 [8] GSE7875 [16] GSE24210 [16] GSE29632 [42] GSE21224 [16] GSE20620 [22] GSE27901 [23] GSE10525 [18] GSE14012 [24] GSE16874 [12] GSE30962 [16] GSE18042 [18] GSE18326 [8] GSE44175 [18] GSE39984 [18] GSE28593 [9] GSE14308 [12] GSE40230 [15] GSE51483 [45] GSE4260 [6] GSE30485 [15] GSE13493 [6] GSE47967 [18] GSE11775 [12] GSE33156 [18] GSE14753 [6] GSE14769 [24] GSE10192 [24] CEM+ CEM GSE5332 [12] GSE39766 [6] GSE30488 [52] GSE15267 [8] GSE15580 [14] GSE25423 [10] 0.0 GSE34729 [6] GSE19517 [6] GSE45465 [39] Scale ofaveragePearsoncorrelations GSE13044 [59] GSE23925 [6] GSE36814 [20] GSE55705 [10] GSE27114 [6] GSE54207 [9] 0.2 GSE55855 [6] GSE39897 [36] GSE17513 [12] GSE30138 [51] GSE5245 [16] GSE8678 [6] GSE4518 [12] GSE45143 [6] GSE58307 [20] 0.4 GSE11759 [6] GSE43419 [20] GSE19885 [9] GSE18534 [15] GSE5976 [12] GSE14406 [54] GSE42049 [8] GSE16992 [48] GSE23496 [9] 0.6 GSE23502 [8] GSE13259 [10] GSE21606 [6] GSE56777 [8] GSE29318 [9] GSE31598 [12] GSE20500 [6] GSE16684 [6] GSE32277 [33] 0.8 GSE18136 [12] GSE46185 [6] GSE21568 [12] GSE46242 [12] Score 906.01 907.52 913.88 916.92 919.95 922.71 936.32 939.71 942.30 949.93 950.67 952.89 954.42 957.95 962.01 967.04 972.23 980.34 983.71 987.12 991.52 992.76 995.93 1019.15 1020.81 1025.69 1029.59 1031.45 1031.94 1032.11 1034.87 1040.91 1041.29 1045.19 1053.91 1055.68 1065.19 1072.81 1086.83 1089.16 1089.36 1094.65 1095.16 1096.99 1102.37 1103.51 1105.27 1110.72 1112.74 1115.09 1.0 Notes Symbol Num ofCEMGenes:6.Predicted424.SelectedDatasets:641.Strength:10.9 CEM 1,Geneset"[C]PCNA-CHL12-RFC2-5complex",Page3 Rnaseh2b Mis18bp1 Timeless Suv39h1 Ranbp1 Nup133 Nup107 Ncapg2 Nusap1 Rad54b Cdc25c Cenpm Dnaaf2 Cenpe Kif18b Kpna2 Haus5 Ckap2 Cdca2 Haus1 Poc1a Esco2 Mybl2 Cenpf Mki67 Gins4 Atad5 Atad2 Pold2 Pold1 H2afx Tfdp1 Lsm2 Tcf19 Lsm3 Gsg2 Rpa3 Cks2 Nek2 Ska3 Ezh2 Orc1 Oip5 Ccnf Prc1 Ect2 Dhfr Blm Kif4 Ttf2 0.0 1.0 GSE38031 [8] GSE40513 [6] GSE31028 [6] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE54215 [13] GSE20954 [14] GSE23200 [6] GSE18135 [18] GSE27605 [8] GSE15741 [6] GSE4694 [6] GSE12498 [12] GSE39449 [6] GSE20100 [15] GSE6998 [32] GSE51883 [30] GSE12454 [13] GSE21393 [6] GSE21379 [10] GSE12465 [14] GSE13302 [30]
Recommended publications
  • Regulation and Dysregulation of Chromosome Structure in Cancer
    Regulation and Dysregulation of Chromosome Structure in Cancer The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hnisz, Denes et al. “Regulation and Dysregulation of Chromosome Structure in Cancer.” Annual Review of Cancer Biology 2, 1 (March 2018): 21–40 © 2018 Annual Reviews As Published https://doi.org/10.1146/annurev-cancerbio-030617-050134 Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/117286 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ Regulation and dysregulation of chromosome structure in cancer Denes Hnisz1*, Jurian Schuijers1, Charles H. Li1,2, Richard A. Young1,2* 1 Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA 02142, USA 2 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA * Corresponding authors Corresponding Authors: Denes Hnisz Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-7181 Fax: (617) 258-0376 [email protected] Richard A. Young Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-5218 Fax: (617) 258-0376 [email protected] 1 Summary Cancer arises from genetic alterations that produce dysregulated gene expression programs. Normal gene regulation occurs in the context of chromosome loop structures called insulated neighborhoods, and recent studies have shown that these structures are altered and can contribute to oncogene dysregulation in various cancer cells. We review here the types of genetic and epigenetic alterations that influence neighborhood structures and contribute to gene dysregulation in cancer, present models for insulated neighborhoods associated with the most prominent human oncogenes, and discuss how such models may lead to further advances in cancer diagnosis and therapy.
    [Show full text]
  • RFC2 Antibody
    Product Datasheet RFC2 Antibody Catalog No: #43122 Orders: [email protected] Description Support: [email protected] Product Name RFC2 Antibody Host Species Rabbit Clonality Polyclonal Purification Antigen affinity purification. Applications WB Species Reactivity Hu Specificity The antibody detects endogenous levels of total RFC2 protein. Immunogen Type peptide Immunogen Description Synthetic peptide of human RFC2 Target Name RFC2 Other Names RFC40 Accession No. Swiss-Prot#: P35250Gene ID: 5982 Calculated MW 39kd Concentration 3.5mg/ml Formulation Rabbit IgG in pH7.4 PBS, 0.05% NaN3, 40% Glycerol. Storage Store at -20°C Application Details Western blotting: 1:200-1:1000 Immunohistochemistry: 1:30-1:150 Images Gel: 10%SDS-PAGE Lysate: 40 µg Lane: Human liver cancer tissue Primary antibody: 1/500 dilution Secondary antibody: Goat anti rabbit IgG at 1/8000 dilution Exposure time: 20 seconds Background This gene encodes a member of the activator 1 small subunits family. The elongation of primed DNA templates by DNA polymerase delta and epsilon requires the action of the accessory proteins, proliferating cell nuclear antigen (PCNA) and replication factor C (RFC). Replication factor C, also called activator 1, is a protein complex consisting of five distinct subunits. This gene encodes the 40 kD subunit, which has been shown to be responsible for binding ATP and may help promote cell survival. Disruption of this gene is associated with Williams syndrome. Alternatively spliced transcript variants Address: 8400 Baltimore Ave., Suite 302, College Park, MD 20740, USA http://www.sabbiotech.com 1 encoding distinct isoforms have been described. A pseudogene of this gene has been defined on chromosome 2.
    [Show full text]
  • DNA Replication Stress Response Involving PLK1, CDC6, POLQ
    DNA replication stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients C. Allera-Moreau, I. Rouquette, B. Lepage, N. Oumouhou, M. Walschaerts, E. Leconte, V. Schilling, K. Gordien, L. Brouchet, Mb Delisle, et al. To cite this version: C. Allera-Moreau, I. Rouquette, B. Lepage, N. Oumouhou, M. Walschaerts, et al.. DNA replica- tion stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients. Oncogenesis, Nature Publishing Group: Open Access Journals - Option C, 2012, 1, pp.e30. 10.1038/oncsis.2012.29. hal-00817701 HAL Id: hal-00817701 https://hal.archives-ouvertes.fr/hal-00817701 Submitted on 9 Jun 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives| 4.0 International License Citation: Oncogenesis (2012) 1, e30; doi:10.1038/oncsis.2012.29 & 2012 Macmillan Publishers Limited All rights reserved 2157-9024/12 www.nature.com/oncsis ORIGINAL ARTICLE DNA replication stress response involving PLK1, CDC6, POLQ, RAD51 and CLASPIN upregulation prognoses the outcome of early/mid-stage non-small cell lung cancer patients C Allera-Moreau1,2,7, I Rouquette2,7, B Lepage3, N Oumouhou3, M Walschaerts4, E Leconte5, V Schilling1, K Gordien2, L Brouchet2, MB Delisle1,2, J Mazieres1,2, JS Hoffmann1, P Pasero6 and C Cazaux1 Lung cancer is the leading cause of cancer deaths worldwide.
    [Show full text]
  • Supplemental Information
    Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig.
    [Show full text]
  • Structure of the Human Clamp Loader Reveals an Autoinhibited Conformation of a Substrate-Bound AAA+ Switch
    Structure of the human clamp loader reveals an autoinhibited conformation of a substrate-bound AAA+ switch Christl Gaubitza,1, Xingchen Liua,b,1, Joseph Magrinoa,b, Nicholas P. Stonea, Jacob Landecka,b, Mark Hedglinc, and Brian A. Kelcha,2 aDepartment of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester MA 01605; bGraduate School of Biomedical Sciences, University of Massachusetts Medical School, Worcester MA 01605; and cDepartment of Chemistry, The Pennsylvania State University, University Park, PA 16802 Edited by Michael E. O’Donnell, HHMI and Rockefeller University, New York, NY, and approved July 27, 2020 (received for review April 20, 2020) DNA replication requires the sliding clamp, a ring-shaped protein areflexia syndrome (15), Hutchinson–Gilford progeria syn- complex that encircles DNA, where it acts as an essential cofactor drome (16), and in the replication of some viruses (17–19). It for DNA polymerases and other proteins. The sliding clamp needs is unknown whether loading by RFC contributes to PARD to be opened and installed onto DNA by a clamp loader ATPase of disease. the AAA+ family. The human clamp loader replication factor C Clamp loaders are members of the AAA+ family of ATPases (RFC) and sliding clamp proliferating cell nuclear antigen (PCNA) (ATPases associated with various cellular activities), a large are both essential and play critical roles in several diseases. De- protein family that uses the chemical energy of adenosine 5′- spite decades of study, no structure of human RFC has been re- triphosphate (ATP) to generate mechanical force (20). Most solved. Here, we report the structure of human RFC bound to AAA+ proteins form hexameric motors that use an undulating PCNA by cryogenic electron microscopy to an overall resolution ∼ spiral staircase mechanism to processively translocate a substrate of 3.4 Å.
    [Show full text]
  • Identification and Characterization of a Novel Non-Homologous End Joining Factor MRI
    Washington University in St. Louis Washington University Open Scholarship Arts & Sciences Electronic Theses and Dissertations Arts & Sciences Spring 5-15-2020 Identification and Characterization of a Novel Non-homologous End Joining Factor MRI Putzer Joseph Hung Washington University in St. Louis Follow this and additional works at: https://openscholarship.wustl.edu/art_sci_etds Part of the Allergy and Immunology Commons, Immunology and Infectious Disease Commons, and the Medical Immunology Commons Recommended Citation Hung, Putzer Joseph, "Identification and Characterization of a Novel Non-homologous End Joining Factor MRI" (2020). Arts & Sciences Electronic Theses and Dissertations. 2201. https://openscholarship.wustl.edu/art_sci_etds/2201 This Dissertation is brought to you for free and open access by the Arts & Sciences at Washington University Open Scholarship. It has been accepted for inclusion in Arts & Sciences Electronic Theses and Dissertations by an authorized administrator of Washington University Open Scholarship. For more information, please contact [email protected]. WASHINGTON UNIVERSITY IN ST. LOUIS Division of Biology and Biomedical Sciences Immunology Dissertation Examination Committee: Barry Sleckman, Chair Gaya Amarasinghe Brian Edelson Takeshi Egawa Nima Mosammaparast Kenneth Murphy Sheila Stewart Identification and Characterization of a Novel Non-homologous End Joining Factor MRI by Putzer Joseph Hung A dissertation presented to The Graduate School of Washington University in partial fulfillment of the requirements
    [Show full text]
  • Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.
    [Show full text]
  • Supplementary Table S1. Correlation Between the Mutant P53-Interacting Partners and PTTG3P, PTTG1 and PTTG2, Based on Data from Starbase V3.0 Database
    Supplementary Table S1. Correlation between the mutant p53-interacting partners and PTTG3P, PTTG1 and PTTG2, based on data from StarBase v3.0 database. PTTG3P PTTG1 PTTG2 Gene ID Coefficient-R p-value Coefficient-R p-value Coefficient-R p-value NF-YA ENSG00000001167 −0.077 8.59e-2 −0.210 2.09e-6 −0.122 6.23e-3 NF-YB ENSG00000120837 0.176 7.12e-5 0.227 2.82e-7 0.094 3.59e-2 NF-YC ENSG00000066136 0.124 5.45e-3 0.124 5.40e-3 0.051 2.51e-1 Sp1 ENSG00000185591 −0.014 7.50e-1 −0.201 5.82e-6 −0.072 1.07e-1 Ets-1 ENSG00000134954 −0.096 3.14e-2 −0.257 4.83e-9 0.034 4.46e-1 VDR ENSG00000111424 −0.091 4.10e-2 −0.216 1.03e-6 0.014 7.48e-1 SREBP-2 ENSG00000198911 −0.064 1.53e-1 −0.147 9.27e-4 −0.073 1.01e-1 TopBP1 ENSG00000163781 0.067 1.36e-1 0.051 2.57e-1 −0.020 6.57e-1 Pin1 ENSG00000127445 0.250 1.40e-8 0.571 9.56e-45 0.187 2.52e-5 MRE11 ENSG00000020922 0.063 1.56e-1 −0.007 8.81e-1 −0.024 5.93e-1 PML ENSG00000140464 0.072 1.05e-1 0.217 9.36e-7 0.166 1.85e-4 p63 ENSG00000073282 −0.120 7.04e-3 −0.283 1.08e-10 −0.198 7.71e-6 p73 ENSG00000078900 0.104 2.03e-2 0.258 4.67e-9 0.097 3.02e-2 Supplementary Table S2.
    [Show full text]
  • Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer
    Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer Shuying Sun ( [email protected] ) Texas State University San Marcos https://orcid.org/0000-0003-3974-6996 Austin Zane Texas A&M University College Station Carolyn Fulton Schreiner University Jasmine Philipoom Case Western Reserve University Research article Keywords: Methylation, Hemimethylation, Lung Cancer, Bioinformatics, Epigenetics Posted Date: October 12th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-17794/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on March 12th, 2021. See the published version at https://doi.org/10.1186/s12885-021-07990-7. Page 1/29 Abstract Background: DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non- small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its signicant impact. Therefore, it is important to identify cancer hemimethylation patterns. Methods: In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites.
    [Show full text]
  • Disruption of Chtf18 Causes Defective Meiotic Recombination in Male Mice
    Disruption of Chtf18 Causes Defective Meiotic Recombination in Male Mice Karen M. Berkowitz1,2*, Aislinn R. Sowash1,2, Lydia R. Koenig1,2, Dawnette Urcuyo1,2, Fahmida Khan1,2, Fang Yang3, P. Jeremy Wang3, Thomas A. Jongens4, Klaus H. Kaestner4 1 Department of Obstetrics and Gynecology, Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America, 2 Department of Biochemistry and Molecular Biology, Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America, 3 Department of Animal Biology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, 4 Department of Genetics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America Abstract CHTF18 (chromosome transmission fidelity factor 18) is an evolutionarily conserved subunit of the Replication Factor C-like complex, CTF18-RLC. CHTF18 is necessary for the faithful passage of chromosomes from one daughter cell to the next during mitosis in yeast, and it is crucial for germline development in the fruitfly. Previously, we showed that mouse Chtf18 is expressed throughout the germline, suggesting a role for CHTF18 in mammalian gametogenesis. To determine the role of CHTF18 in mammalian germ cell development, we derived mice carrying null and conditional mutations in the Chtf18 gene. Chtf18-null males exhibit 5-fold decreased sperm concentrations compared to wild-type controls, resulting in subfertility. Loss of Chtf18 results in impaired spermatogenesis; spermatogenic cells display abnormal morphology, and the stereotypical arrangement of cells within seminiferous tubules is perturbed. Meiotic recombination is defective and homologous chromosomes separate prematurely during prophase I. Repair of DNA double-strand breaks is delayed and incomplete; both RAD51 and cH2AX persist in prophase I.
    [Show full text]
  • Datasheet Blank Template
    SAN TA C RUZ BI OTEC HNOL OG Y, INC . RFC4 (E-12): sc-28300 BACKGROUND RECOMMENDED SUPPORT REAGENTS Replication factor C (RFC) is an essential DNA polymerase accessory protein To ensure optimal results, the following support reagents are recommended: that is required for numerous aspects of DNA metabolism including DNA 1) Western Blotting: use m-IgG κ BP-HRP: sc-516102 or m-IgG κ BP-HRP replication, DNA repair and telomere metabolism. RFC is a heteropentameric (Cruz Marker): sc-516102-CM (dilution range: 1:1000-1:10000), Cruz Marker™ complex that recognizes a primer on a template DNA, binds to a primer termi - Molecular Weight Standards: sc-2035, TBS Blotto A Blocking Reagent: nus, and loads proliferating cell nuclear antigen (PCNA) onto DNA at primer- sc-2333 and Western Blotting Luminol Reagent: sc-2048. 2) Immunoprecip- template junctions in an ATP-dependent reaction. All five of the RFC subunits itation: use Protein A/G PLUS-Agarose: sc-2003 (0.5 ml agarose/2.0 ml). share a set of related sequences (RFC boxes) that include nucleotide-binding 3) Immunofluorescence: use m-IgG κ BP-FITC: sc-516140 or m-IgG κ BP-PE: consensus sequences. Four of the five RFC genes (RFC1, RFC2, RFC3 and RFC4) sc-516141 (dilution range: 1:50-1:200) with UltraCruz ® Mounting Medium: have consensus ATP-binding motifs. The small RFC proteins, RFC2, RFC3, RFC4 sc-24941 or UltraCruz ® Hard-set Mounting Medium: sc-359850. and RFC5, interact with Rad24, whereas the RFC1 subunit does not. Speci- fically, RFC4 plays a role in checkpoint regulation.
    [Show full text]
  • Cryptic Splicing Events in the Iron Transporter ABCB7 and Other Key Target Genes in SF3B1-Mutant Myelodysplastic Syndromes
    OPEN Leukemia (2016) 30, 2322–2331 www.nature.com/leu ORIGINAL ARTICLE Cryptic splicing events in the iron transporter ABCB7 and other key target genes in SF3B1-mutant myelodysplastic syndromes H Dolatshad1,2,8, A Pellagatti1,2,8, FG Liberante3, M Llorian4, E Repapi5, V Steeples1,2, S Roy1,2, L Scifo1,2, RN Armstrong1,2, J Shaw1,2, BH Yip1,2, S Killick6,RKušec7, S Taylor5, KI Mills3, KI Savage3, CWJ Smith4 and J Boultwood1,2 The splicing factor SF3B1 is the most frequently mutated gene in myelodysplastic syndromes (MDS), and is strongly associated with the presence of ring sideroblasts (RS). We have performed a systematic analysis of cryptic splicing abnormalities from RNA sequencing data on hematopoietic stem cells (HSCs) of SF3B1-mutant MDS cases with RS. Aberrant splicing events in many downstream target genes were identified and cryptic 3′ splice site usage was a frequent event in SF3B1-mutant MDS. The iron transporter ABCB7 is a well-recognized candidate gene showing marked downregulation in MDS with RS. Our analysis unveiled aberrant ABCB7 splicing, due to usage of an alternative 3′ splice site in MDS patient samples, giving rise to a premature termination codon in the ABCB7 mRNA. Treatment of cultured SF3B1-mutant MDS erythroblasts and a CRISPR/Cas9-generated SF3B1-mutant cell line with the nonsense-mediated decay (NMD) inhibitor cycloheximide showed that the aberrantly spliced ABCB7 transcript is targeted by NMD. We describe cryptic splicing events in the HSCs of SF3B1-mutant MDS, and our data support a model in which NMD-induced downregulation of the iron exporter ABCB7 mRNA transcript resulting from aberrant splicing caused by mutant SF3B1 underlies the increased mitochondrial iron accumulation found in MDS patients with RS.
    [Show full text]