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: 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 Med6 Cdk8 CEM 1 (46 datasets) Ccnc Med10 Med23 0610009D07Rik Symbol Num ofCEMGenes:7.Predicted989.SelectedDatasets:46.Strength:0.2 CEM 1,Geneset"[C]NATcomplex",Page1 Smarcad1 Smarca5 Smchd1 Trmt10c Magohb Prpf40a Nudcd2 Ncapg2 Rbmxl1 Ccdc58 Cep192 Trmt11 Rbm12 Dcaf13 Dnajc9 Psma1 Magoh Prpf4b Polr2d Med23 Med10 Med21 Med14 Srsf10 Nup54 Cks1b Caap1 Ddx21 Haus3 Pbdc1 Rad17 Abce1 Pfdn4 Asf1a Tfdp1 Cse1l Txnl1 Med6 Wdr3 Rpa3 Pno1 Ccnc Cdk8 Tipin Orc6 Hat1 Dis3 Ltv1 Ssb 0.0 1.0 GSE20954 [14] GSE15155 [12] GSE7275 [8] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE48935 [12] GSE47694 [10] GSE49237 [8] GSE50813 [24] GSE21836 [8] GSE42601 [6] GSE12993 [6] GSE24243 [6] GSE38831 [7] GSE29458 [23] GSE18135 [18] GSE44175 [18] GSE58368 [15] GSE14406 [54] GSE12454 [13] GSE27708 [9] GSE25286 [10] GSE27114 [6] GSE11201 [18] GSE9533 [35] GSE13235 [9] GSE19512 [6] GSE38693 [8] GSE37546 [20] GSE7863 [16] GSE6837 [8] GSE57797 [23] GSE13874 [14] GSE11356 [9] GSE21711 [6] GSE15330 [27] GSE51628 [15] GSE31028 [6] GSE11258 [24] GSE15794 [6] GSE26616 [6] GSE32277 [33] GSE45820 [6] GSE16874 [12] GSE5861 [6] GSE8322 [12] GSE6998 [32] GSE36513 [8] GSE27605 [8] GSE43373 [130] GSE10210 [16] GSE15325 [23] GSE36415 [14] GSE13106 [10] GSE29485 [12] GSE10644 [18] GSE24276 [6] GSE19204 [6] GSE4230 [8] GSE38257 [14] GSE12498 [12] GSE11862 [6] GSE4308 [16] GSE9913 [9] GSE36665 [6] GSE35219 [6] GSE2557 [6] GSE13693 [9] GSE7430 [12] GSE14478 [7] GSE28093 [6] GSE27261 [8] GSE10776 [15] GSE21278 [48] GSE51804 [10] GSE35366 [78] GSE27516 [17] GSE4734 [61] GSE33942 [12] GSE7424 [8] GSE30176 [12] GSE40368 [10] GSE9297 [27] GSE49346 [6] GSE38048 [20] GSE59672 [12] GSE13611 [8] GSE7764 [10] GSE24489 [14] GSE27848 [16] GSE27720 [6] GSE48204 [6] GSE28389 [20] GSE27195 [6] GSE23002 [8] GSE43825 [31] GSE23895 [18] GSE27309 [10] GSE26461 [6] GSE27378 [8] GSE44084 [8] GSE41084 [6] GSE8025 [21] GSE7225 [9] GSE6196 [9] GSE37975 [8] GSE19657 [21] GSE20944 [18] GSE49283 [12] GSE10813 [12] GSE30498 [12] GSE49248 [12] GSE6223 [13] GSE52022 [8] GSE7759 [112] GSE28664 [17] GSE30083 [12] GSE13129 [12] GSE21224 [16] GSE5333 [16] GSE8684 [10] GSE35044 [9] GSE30868 [8] GSE13553 [10] GSE46091 [8] GSE13692 [8] GSE17383 [6] GSE33471 [12] GSE45619 [6] GSE24203 [8] GSE6065 [100] GSE55809 [8] GSE10904 [6] GSE12465 [14] GSE17112 [8] GSE14415 [31] GSE30160 [6] GSE41925 [8] GSE35435 [6] GSE11982 [6] GSE14753 [6] GSE13707 [20] GSE26076 [12] GSE40296 [6] GSE51385 [8] GSE11382 [10] GSE39233 [40] GSE4749 [6] GSE26476 [6] GSE46606 [30] GSE12518 [6] GSE36810 [16] GSE18567 [24] GSE18925 [6] GSE20152 [8] GSE16992 [48] CEM+ CEM GSE16110 [16] GSE18326 [8] GSE11990 [20] GSE15433 [9] GSE15871 [18] GSE13149 [25] 0.0 GSE38574 [32] GSE7810 [9] GSE16751 [6] Scale ofaveragePearsoncorrelations GSE18745 [6] GSE4786 [9] GSE31702 [10] GSE44162 [6] GSE44101 [6] GSE31406 [12] 0.2 GSE22291 [16] GSE48004 [6] GSE37000 [47] GSE27159 [8] GSE28593 [9] GSE9146 [27] GSE43381 [26] GSE12810 [6] GSE18148 [6] 0.4 GSE7683 [12] GSE18587 [9] GSE15772 [8] GSE21063 [24] GSE10113 [12] GSE46723 [6] GSE57469 [6] GSE4193 [8] GSE28408 [6] 0.6 GSE36826 [12] GSE18993 [13] GSE17728 [12] GSE10290 [24] GSE28333 [6] GSE18669 [12] GSE19369 [8] GSE30980 [6] GSE6526 [16] 0.8 GSE7762 [36] GSE4928 [8] GSE37676 [6] GSE6482 [9] Score 34.05 34.07 34.22 34.22 34.28 34.42 34.66 34.85 35.37 35.42 35.43 35.54 35.84 36.11 36.13 36.56 36.78 36.86 37.00 37.16 37.20 37.83 38.37 38.62 38.84 38.89 39.37 39.44 39.55 39.63 39.90 40.00 40.08 40.22 40.23 40.29 40.62 40.64 40.85 41.45 42.29 43.30 48.42 1.0 Notes Symbol Num ofCEMGenes:7.Predicted989.SelectedDatasets:46.Strength:0.2 CEM 1,Geneset"[C]NATcomplex",Page2 Mphosph6 Casp8ap2 Smndc1 Armc10 Prps1l3 Anp32e Nup160 Topbp1 Exosc9 Tsen15 Dnttip2 Pgam5 Psma4 Psma2 Gtf2h2 Mak16 Nop58 Cenpk Rnpc3 Ccne2 Naa15 Tex30 Snrpe Gspt1 Mcm4 Sarnp Mis12 Atad2 Trmt6 Nol11 Lin54 Pwp1 Srsf1 Pcna Exo5 Leo1 Ints2 Dbr1 Rrs1 Gnl3 Utp6 Anln Rnf4 Nip7 Ect2 Tsr1 Taf2 Eri1 Itpa Set 0.0 1.0 GSE20954 [14] GSE15155 [12] GSE7275 [8] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE48935 [12] GSE47694 [10] GSE49237 [8] GSE50813 [24] GSE21836 [8] GSE42601 [6] GSE12993 [6] GSE24243 [6] GSE38831 [7] GSE29458 [23] GSE18135 [18] GSE44175 [18] GSE58368 [15] GSE14406 [54] GSE12454 [13] GSE27708 [9] GSE25286 [10] GSE27114 [6] GSE11201 [18] GSE9533 [35] GSE13235 [9] GSE19512 [6] GSE38693 [8] GSE37546 [20] GSE7863 [16] GSE6837 [8] GSE57797 [23] GSE13874 [14] GSE11356 [9] GSE21711 [6] GSE15330 [27] GSE51628 [15] GSE31028 [6] GSE11258 [24] GSE15794 [6] GSE26616 [6] GSE32277 [33] GSE45820 [6] GSE16874 [12] GSE5861 [6] GSE8322 [12] GSE6998 [32] GSE36513 [8] GSE27605 [8] GSE43373 [130] GSE10210 [16] GSE15325 [23] GSE36415 [14] GSE13106 [10] GSE29485 [12] GSE10644 [18] GSE24276 [6] GSE19204 [6] GSE4230 [8] GSE38257 [14] GSE12498 [12] GSE11862 [6] GSE4308 [16] GSE9913 [9] GSE36665 [6] GSE35219 [6] GSE2557 [6] GSE13693 [9] GSE7430 [12] GSE14478 [7] GSE28093 [6] GSE27261 [8] GSE10776 [15] GSE21278 [48] GSE51804 [10] GSE35366 [78] GSE27516 [17] GSE4734 [61] GSE33942 [12] GSE7424 [8] GSE30176 [12] GSE40368 [10] GSE9297 [27] GSE49346 [6] GSE38048 [20] GSE59672 [12] GSE13611 [8] GSE7764 [10] GSE24489 [14] GSE27848 [16] GSE27720 [6] GSE48204 [6] GSE28389 [20] GSE27195 [6] GSE23002 [8] GSE43825 [31] GSE23895 [18] GSE27309 [10] GSE26461 [6] GSE27378 [8] GSE44084 [8] GSE41084 [6] GSE8025 [21] GSE7225 [9] GSE6196 [9] GSE37975 [8] GSE19657 [21] GSE20944 [18] GSE49283 [12] GSE10813 [12] GSE30498 [12] GSE49248 [12] GSE6223 [13] GSE52022 [8] GSE7759 [112] GSE28664 [17] GSE30083 [12] GSE13129 [12] GSE21224 [16] GSE5333 [16] GSE8684 [10] GSE35044 [9] GSE30868 [8] GSE13553 [10] GSE46091 [8] GSE13692 [8] GSE17383 [6] GSE33471 [12] GSE45619 [6] GSE24203 [8] GSE6065 [100] GSE55809 [8] GSE10904 [6] GSE12465 [14] GSE17112 [8] GSE14415 [31] GSE30160 [6] GSE41925 [8] GSE35435 [6] GSE11982 [6] GSE14753 [6] GSE13707 [20] GSE26076 [12] GSE40296 [6] GSE51385 [8] GSE11382 [10] GSE39233 [40] GSE4749 [6] GSE26476 [6] GSE46606 [30] GSE12518 [6] GSE36810 [16] GSE18567 [24] GSE18925 [6] GSE20152 [8] GSE16992 [48] CEM+ CEM GSE16110 [16] GSE18326 [8] GSE11990 [20] GSE15433 [9] GSE15871 [18] GSE13149 [25] 0.0 GSE38574 [32] GSE7810 [9] GSE16751 [6] Scale ofaveragePearsoncorrelations GSE18745 [6] GSE4786 [9] GSE31702 [10] GSE44162 [6] GSE44101 [6] GSE31406 [12] 0.2 GSE22291 [16] GSE48004 [6] GSE37000 [47] GSE27159 [8] GSE28593 [9] GSE9146 [27] GSE43381 [26] GSE12810 [6] GSE18148 [6] 0.4 GSE7683 [12] GSE18587 [9] GSE15772 [8] GSE21063 [24] GSE10113 [12] GSE46723 [6] GSE57469 [6] GSE4193 [8] GSE28408 [6] 0.6 GSE36826 [12] GSE18993 [13] GSE17728 [12] GSE10290 [24] GSE28333 [6] GSE18669 [12] GSE19369 [8] GSE30980 [6] GSE6526 [16] 0.8 GSE7762 [36] GSE4928 [8] GSE37676 [6] GSE6482 [9] Score 29.68 29.70 29.72 29.75 29.91 29.99 30.00 30.21 30.25 30.28 30.31 30.38 30.53 30.59 30.60 30.73 30.80 30.82 31.09 31.11 31.26 31.30 31.31 31.35 31.43 31.48 31.56 31.57 31.58 31.62 31.64 31.64 31.98 32.05 32.19 32.34 32.38 32.44 32.46 32.59 32.69 33.16 33.16 33.23 33.35 33.59 33.74 33.83 33.87 33.96 1.0 Notes Symbol Num ofCEMGenes:7.Predicted989.SelectedDatasets:46.Strength:0.2 CEM 1,Geneset"[C]NATcomplex",Page3 Arhgap11a Commd2 Cenpc1 Skiv2l2 Zc3h15 Isg20l2 Rbm34 Rbmx2 Rsl1d1 Tcerg1 Eef1e1 Zfp131 Med17 Gtf2e2 Gtf2e1 Kpnb1 Lrrc40 Wdr77 Ncbp2 Actl6a Dhx15 Rad51 Kif18a Ckap2 Cdc73 Mcm6 Ccar1 Parp2 Yars2 Spdl1 Hbs1l Sf3a3 Snrpf Msh6 Bub1 G2e3 Xpo5 Fmr1 Uba2 Skp2 Hells Tfam Fen1 Rars Sfpq Npat Rfc4 Ctcf Eed Ttf2 0.0 1.0 GSE20954 [14] GSE15155 [12] GSE7275 [8] Only showingfirst200datasets-Seetxtoutputforfulldetails. GSE48935 [12] GSE47694 [10] GSE49237 [8] GSE50813 [24] GSE21836 [8] GSE42601 [6] GSE12993 [6] GSE24243 [6] GSE38831 [7] GSE29458 [23] GSE18135 [18] GSE44175 [18] GSE58368 [15] GSE14406 [54] GSE12454 [13] GSE27708 [9] GSE25286 [10] GSE27114 [6] GSE11201 [18] GSE9533 [35] GSE13235 [9] GSE19512 [6] GSE38693 [8] GSE37546 [20] GSE7863 [16] GSE6837 [8] GSE57797 [23] GSE13874 [14] GSE11356 [9] GSE21711 [6] GSE15330 [27] GSE51628 [15] GSE31028 [6] GSE11258 [24]
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]
  • KIAA0556 Is a Novel Ciliary Basal Body Component Mutated in Joubert Syndrome Anna A
    Sanders et al. Genome Biology (2015) 16:293 DOI 10.1186/s13059-015-0858-z RESEARCH Open Access KIAA0556 is a novel ciliary basal body component mutated in Joubert syndrome Anna A. W. M. Sanders1†, Erik de Vrieze2,3†, Anas M. Alazami4†, Fatema Alzahrani4, Erik B. Malarkey5, Nasrin Sorusch6, Lars Tebbe6, Stefanie Kuhns1, Teunis J. P. van Dam7, Amal Alhashem8, Brahim Tabarki8, Qianhao Lu9,10, Nils J. Lambacher1, Julie E. Kennedy1, Rachel V. Bowie1, Lisette Hetterschijt2,3, Sylvia van Beersum3,11, Jeroen van Reeuwijk3,11, Karsten Boldt12, Hannie Kremer2,3,11, Robert A. Kesterson13, Dorota Monies4, Mohamed Abouelhoda4, Ronald Roepman3,11, Martijn H. Huynen7, Marius Ueffing12, Rob B. Russell9,10, Uwe Wolfrum6, Bradley K. Yoder5, Erwin van Wijk2,3*, Fowzan S. Alkuraya4,14* and Oliver E. Blacque1* Abstract Background: Joubert syndrome (JBTS) and related disorders are defined by cerebellar malformation (molar tooth sign), together with neurological symptoms of variable expressivity. The ciliary basis of Joubert syndrome related disorders frequently extends the phenotype to tissues such as the eye, kidney, skeleton and craniofacial structures. Results: Using autozygome and exome analyses, we identified a null mutation in KIAA0556 in a multiplex consanguineous family with hallmark features of mild Joubert syndrome. Patient-derived fibroblasts displayed reduced ciliogenesis potential and abnormally elongated cilia. Investigation of disease pathophysiology revealed that Kiaa0556-/- null mice possess a Joubert syndrome-associated brain-restricted phenotype. Functional studies in Caenorhabditis elegans nematodes and cultured human cells support a conserved ciliary role for KIAA0556 linked to microtubule regulation. First, nematode KIAA0556 is expressed almost exclusively in ciliated cells, and the worm and human KIAA0556 proteins are enriched at the ciliary base.
    [Show full text]
  • Characterization of Genomic Copy Number Variation in Mus Musculus Associated with the Germline of Inbred and Wild Mouse Populations, Normal Development, and Cancer
    Western University Scholarship@Western Electronic Thesis and Dissertation Repository 4-18-2019 2:00 PM Characterization of genomic copy number variation in Mus musculus associated with the germline of inbred and wild mouse populations, normal development, and cancer Maja Milojevic The University of Western Ontario Supervisor Hill, Kathleen A. The University of Western Ontario Graduate Program in Biology A thesis submitted in partial fulfillment of the equirr ements for the degree in Doctor of Philosophy © Maja Milojevic 2019 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Genetics and Genomics Commons Recommended Citation Milojevic, Maja, "Characterization of genomic copy number variation in Mus musculus associated with the germline of inbred and wild mouse populations, normal development, and cancer" (2019). Electronic Thesis and Dissertation Repository. 6146. https://ir.lib.uwo.ca/etd/6146 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. Abstract Mus musculus is a human commensal species and an important model of human development and disease with a need for approaches to determine the contribution of copy number variants (CNVs) to genetic variation in laboratory and wild mice, and arising with normal mouse development and disease. Here, the Mouse Diversity Genotyping array (MDGA)-approach to CNV detection is developed to characterize CNV differences between laboratory and wild mice, between multiple normal tissues of the same mouse, and between primary mammary gland tumours and metastatic lung tissue.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • 1 Canonical BAF Complex in Regulatory T Cells 2 3 Chin
    bioRxiv preprint doi: https://doi.org/10.1101/2020.02.26.964981; this version posted February 27, 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. 1 A genome-wide CRISPR screen reveals a role for the BRD9-containing non- 2 canonical BAF complex in regulatory T cells 3 4 Chin-San Loo1,3,#, Jovylyn Gatchalian2,#, Yuqiong Liang1, Mathias Leblanc1, Mingjun 5 Xie1, Josephine Ho2, Bhargav Venkatraghavan1, Diana C. Hargreaves2*, and Ye 6 Zheng1* 7 8 1. NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for 9 Biological Studies 10 2. Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies 11 3. Division of Biological Sciences, University of California, San Diego 12 # Co-first authors 13 * Co-corresponding authors 14 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.26.964981; this version posted February 27, 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. 15 Summary 16 Regulatory T cells (Tregs) play a pivotal role in suppressing auto-reactive T cells 17 and maintaining immune homeostasis. Treg development and function are 18 dependent on the transcription factor Foxp3. Here we performed a genome-wide 19 CRISPR/Cas9 knockout screen to identify the regulators of Foxp3 in mouse 20 primary Tregs. The results showed that Foxp3 regulators are highly enriched in 21 genes encoding SWI/SNF and SAGA complex subunits. Among the three 22 SWI/SNF-related complexes, the non-canonical or ncBAF (also called GBAF or 23 BRD9-containing BAF) complex promoted the expression of Foxp3, whereas the 24 PBAF complex repressed its expression.
    [Show full text]
  • Plenary and Platform Abstracts
    American Society of Human Genetics 68th Annual Meeting PLENARY AND PLATFORM ABSTRACTS Abstract #'s Tuesday, October 16, 5:30-6:50 pm: 4. Featured Plenary Abstract Session I Hall C #1-#4 Wednesday, October 17, 9:00-10:00 am, Concurrent Platform Session A: 6. Variant Insights from Large Population Datasets Ballroom 20A #5-#8 7. GWAS in Combined Cancer Phenotypes Ballroom 20BC #9-#12 8. Genome-wide Epigenomics and Non-coding Variants Ballroom 20D #13-#16 9. Clonal Mosaicism in Cancer, Alzheimer's Disease, and Healthy Room 6A #17-#20 Tissue 10. Genetics of Behavioral Traits and Diseases Room 6B #21-#24 11. New Frontiers in Computational Genomics Room 6C #25-#28 12. Bone and Muscle: Identifying Causal Genes Room 6D #29-#32 13. Precision Medicine Initiatives: Outcomes and Lessons Learned Room 6E #33-#36 14. Environmental Exposures in Human Traits Room 6F #37-#40 Wednesday, October 17, 4:15-5:45 pm, Concurrent Platform Session B: 24. Variant Interpretation Practices and Resources Ballroom 20A #41-#46 25. Integrated Variant Analysis in Cancer Genomics Ballroom 20BC #47-#52 26. Gene Discovery and Functional Models of Neurological Disorders Ballroom 20D #53-#58 27. Whole Exome and Whole Genome Associations Room 6A #59-#64 28. Sequencing-based Diagnostics for Newborns and Infants Room 6B #65-#70 29. Omics Studies in Alzheimer's Disease Room 6C #71-#76 30. Cardiac, Valvular, and Vascular Disorders Room 6D #77-#82 31. Natural Selection and Human Phenotypes Room 6E #83-#88 32. Genetics of Cardiometabolic Traits Room 6F #89-#94 Wednesday, October 17, 6:00-7:00 pm, Concurrent Platform Session C: 33.
    [Show full text]
  • Integrated Bioinformatics Analysis of Aberrantly-Methylated
    Shen et al. BMC Ophthalmology (2020) 20:119 https://doi.org/10.1186/s12886-020-01392-2 RESEARCH ARTICLE Open Access Integrated bioinformatics analysis of aberrantly-methylated differentially- expressed genes and pathways in age- related macular degeneration Yinchen Shen1,2†,MoLi3†, Kun Liu1,2, Xiaoyin Xu1,2, Shaopin Zhu1,2, Ning Wang1,2, Wenke Guo4, Qianqian Zhao4, Ping Lu4, Fudong Yu4 and Xun Xu1,2* Abstract Background: Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly-methylated, differentially-expressed genes (MDEGs) in AMD and explore the involved pathways via integrated bioinformatics analysis. Methods: Data from expression profile GSE29801 and methylation profile GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially-methylated genes and differentially-expressed genes using R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using the R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape. Results: In total, 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. Based on overlap, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs (CKB, PPP3CA, TGFB2, SOCS2) overlapped with AMD risk genes in the Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated that Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism.
    [Show full text]
  • Noelia Díaz Blanco
    Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr.
    [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]
  • The Complex SNP and CNV Genetic Architecture of the Increased Risk of Congenital Heart Defects in Down Syndrome
    Downloaded from genome.cshlp.org on September 24, 2021 - Published by Cold Spring Harbor Laboratory Press Research The complex SNP and CNV genetic architecture of the increased risk of congenital heart defects in Down syndrome M. Reza Sailani,1,2 Periklis Makrythanasis,1 Armand Valsesia,3,4,5 Federico A. Santoni,1 Samuel Deutsch,1 Konstantin Popadin,1 Christelle Borel,1 Eugenia Migliavacca,1 Andrew J. Sharp,1,20 Genevieve Duriaux Sail,1 Emilie Falconnet,1 Kelly Rabionet,6,7,8 Clara Serra-Juhe´,7,9 Stefano Vicari,10 Daniela Laux,11 Yann Grattau,12 Guy Dembour,13 Andre Megarbane,12,14 Renaud Touraine,15 Samantha Stora,12 Sofia Kitsiou,16 Helena Fryssira,16 Chariklia Chatzisevastou-Loukidou,16 Emmanouel Kanavakis,16 Giuseppe Merla,17 Damien Bonnet,11 Luis A. Pe´rez-Jurado,7,9 Xavier Estivill,6,7,8 Jean M. Delabar,18 and Stylianos E. Antonarakis1,2,19,21 1–19[Author affiliations appear at the end of the paper.] Congenital heart defect (CHD) occurs in 40% of Down syndrome (DS) cases. While carrying three copies of chromosome 21 increases the risk for CHD, trisomy 21 itself is not sufficient to cause CHD. Thus, additional genetic variation and/or environmental factors could contribute to the CHD risk. Here we report genomic variations that in concert with trisomy 21, determine the risk for CHD in DS. This case-control GWAS includes 187 DS with CHD (AVSD = 69, ASD = 53, VSD = 65) as cases, and 151 DS without CHD as controls. Chromosome 21–specific association studies revealed rs2832616 and rs1943950 as CHD risk alleles (adjusted genotypic P-values <0.05).
    [Show full text]
  • Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors
    ARTICLE DOI: 10.1038/s41467-018-06366-z OPEN Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors Sudheer Kumar Gara1, Justin Lack2, Lisa Zhang1, Emerson Harris1, Margaret Cam2 & Electron Kebebew1,3 Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is 1234567890():,; unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37–57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs. 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2 Center for Cancer Research, Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3 Department of Surgery and Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Correspondence and requests for materials should be addressed to E.K. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4172 | DOI: 10.1038/s41467-018-06366-z | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06366-z drenocortical carcinoma (ACC) is a rare malignancy with types including primary ACC from the TCGA to understand our A0.7–2 cases per million per year1,2.
    [Show full text]
  • Molecular Evolutionary Analysis of Plastid Genomes in Nonphotosynthetic Angiosperms and Cancer Cell Lines
    The Pennsylvania State University The Graduate School Department or Biology MOLECULAR EVOLUTIONARY ANALYSIS OF PLASTID GENOMES IN NONPHOTOSYNTHETIC ANGIOSPERMS AND CANCER CELL LINES A Dissertation in Biology by Yan Zhang 2012 Yan Zhang Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Dec 2012 The Dissertation of Yan Zhang was reviewed and approved* by the following: Schaeffer, Stephen W. Professor of Biology Chair of Committee Ma, Hong Professor of Biology Altman, Naomi Professor of Statistics dePamphilis, Claude W Professor of Biology Dissertation Adviser Douglas Cavener Professor of Biology Head of Department of Biology *Signatures are on file in the Graduate School iii ABSTRACT This thesis explores the application of evolutionary theory and methods in understanding the plastid genome of nonphotosynthetic parasitic plants and role of mutations in tumor proliferations. We explore plastid genome evolution in parasitic angiosperms lineages that have given up the primary function of plastid genome – photosynthesis. Genome structure, gene contents, and evolutionary dynamics were analyzed and compared in both independent and related parasitic plant lineages. Our studies revealed striking similarities in changes of gene content and evolutionary dynamics with the loss of photosynthetic ability in independent nonphotosynthetic plant lineages. Evolutionary analysis suggests accelerated evolution in the plastid genome of the nonphotosynthetic plants. This thesis also explores the application of phylogenetic and evolutionary analysis in cancer biology. Although cancer has often been likened to Darwinian process, very little application of molecular evolutionary analysis has been seen in cancer biology research. In our study, phylogenetic approaches were used to explore the relationship of several hundred established cancer cell lines based on multiple sequence alignments constructed with variant codons and residues across 494 and 523 genes.
    [Show full text]