DOCSLIB.ORG

Differentially Expressed Genes Between Kit+ and Kit- Samples, FC

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Supplemental Table 1: Differentially expressed genes between Kit+ and Kit- samples, FC > 2, p-value < 0.05, difference of mean > 100 Kit- Kit+ Probeset Symbol Genename FC pvalue diff (mean) (mean) 104280139 NA NA 130 1651 -12,70 0,0000 1520,78 5670239 Ear2 eosinophil-associated, ribonuclease A family, member 2 150 1848 -12,28 0,0000 1697,94 2340358 Ifitm3 interferon induced transmembrane protein 3 126 1315 -10,41 0,0000 1189,02 130465 NA NA 134 1155 -8,60 0,0000 1020,25 2360471 Ear1 eosinophil-associated, ribonuclease A family, member 1 155 1293 -8,34 0,0000 1137,61 7040095 Kit kit oncogene 409 3364 -8,22 0,0145 2955,05 1230347 LOC545854 NA 1216 9893 -8,14 0,0000 8677,76 2810059 Fcgr3a Fc fragment of IgG, low affinity IIIa, receptor 106 816 -7,73 0,0077 710,20 2510725 NA NA 394 2969 -7,53 0,0037 2574,43 5420372 NA NA 128 942 -7,33 0,0000 813,19 101990390 Ifitm2 interferon induced transmembrane protein 2 171 1181 -6,93 0,0000 1010,43 6510075 Ifitm1 interferon induced transmembrane protein 1 152 1054 -6,92 0,0004 901,68 2370286 Slc40a1 solute carrier family 40 (iron-regulated transporter), member 1 133 916 -6,91 0,0000 783,46 5860673 NA NA 143 985 -6,89 0,0239 842,27 6370309 LOC545854 NA 1850 12680 -6,85 0,0000 10829,66 101230129 Ear10 eosinophil-associated, ribonuclease A family, member 10 142 949 -6,68 0,0000 807,11 70112 S100a8 S100 calcium binding protein A8 (calgranulin A) 162 938 -5,79 0,0063 776,16 103780671 Mpeg1 macrophage expressed gene 1 105 598 -5,69 0,0000 493,10 1690184 NA NA 579 3042 -5,26 0,0002 2463,90 2450148 AI324046 expressed sequence AI324046 117 595 -5,10 0,0384 478,21 6380500 Il7r interleukin 7 receptor 237 1189 -5,03 0,0000 952,48 7050528 S100a9 S100 calcium binding protein A9 (calgranulin B) 123 609 -4,96 0,0193 485,92 2900450 Vcam1 vascular cell adhesion molecule 1 191 934 -4,90 0,0000 743,16 2120446 Slpi secretory leukocyte peptidase inhibitor 103 483 -4,69 0,0000 380,12 5270093 Cd81 CD 81 antigen 188 855 -4,55 0,0000 666,78 6860112 Lag3 lymphocyte-activation gene 3 126 534 -4,24 0,0000 408,30 5290673 H2-Aa histocompatibility 2, class II antigen A, alpha 547 2316 -4,24 0,0158 1769,04 2340093 Ifi27 interferon, alpha-inducible protein 27 309 1305 -4,23 0,0000 996,32 2650133 Ncf2 neutrophil cytosolic factor 2 306 1263 -4,13 0,0000 957,64 3120435 Nusap1 nucleolar and spindle associated protein 1 114 467 -4,11 0,0011 353,41 5890204 Prc1 protein regulator of cytokinesis 1 144 583 -4,05 0,0019 438,72 EGF-like module containing, mucin-like, hormone receptor- 1500592 Emr1 125 502 -4,02 0,0000 377,40 like sequence 1 104280301 Ifitm3 interferon induced transmembrane protein 3 114 459 -4,01 0,0026 344,36 2100403 Ugt1a7c UDP glucuronosyltransferase 1 family, polypeptide A7C 246 980 -3,98 0,0000 733,83 100580541 9130211I03Rik RIKEN cDNA 9130211I03 gene 135 530 -3,93 0,0000 394,79 6940114 Ugt1a6b UDP glucuronosyltransferase 1 family, polypeptide A6B 366 1427 -3,90 0,0000 1060,83 103140402 Ifitm1 interferon induced transmembrane protein 1 81 316 -3,89 0,0000 234,91 105550685 NA NA 2299 8925 -3,88 0,0045 6625,82 4010739 NA NA 95 368 -3,87 0,0000 272,83 5130746 Tmem108 transmembrane protein 108 136 525 -3,86 0,0000 388,81 101660133 Hbb-b1 hemoglobin, beta adult major chain 430 1615 -3,76 0,0109 1185,29 6040164 NA NA 86 321 -3,71 0,0259 234,17 5700138 Bcl2a1d B-cell leukemia/lymphoma 2 related protein A1d 818 3029 -3,70 0,0000 2211,08 610270 Emb embigin 718 2645 -3,68 0,0000 1926,22 6590066 Pld4 phospholipase D family, member 4 102 374 -3,68 0,0001 272,39 2100619 NA NA 211 771 -3,66 0,0000 560,30 6550075 NA NA 1687 6086 -3,61 0,0249 4398,89 2900019 Plac8 placenta-specific 8 317 1141 -3,60 0,0001 824,66 60025 NA NA 121 425 -3,50 0,0004 303,40 1340239 Ear2 eosinophil-associated, ribonuclease A family, member 2 125 427 -3,42 0,0412 301,97 1770095 Axl AXL receptor tyrosine kinase 122 414 -3,38 0,0000 291,70 1740687 Hmox1 heme oxygenase (decycling) 1 145 474 -3,27 0,0281 328,98 105290301 Cd68 CD68 antigen 207 667 -3,23 0,0002 460,13 730301 Pscd3 pleckstrin homology, Sec7 and coiled-coil domains 3 233 751 -3,22 0,0000 517,78 2190563 Tubb6 tubulin, beta 6 229 736 -3,22 0,0000 507,35 1990524 Nupr1 nuclear protein 1 82 265 -3,21 0,0063 182,12 7000408 Bcl2a1a B-cell leukemia/lymphoma 2 related protein A1a 490 1569 -3,20 0,0039 1078,45 5360452 Tgm2 transglutaminase 2, C polypeptide 99 314 -3,17 0,0000 215,08 2360176 Mpo myeloperoxidase 104 330 -3,16 0,0179 225,77 2760440 Mpo myeloperoxidase 107 335 -3,14 0,0399 228,57 5860047 Casp4 caspase 4, apoptosis-related cysteine peptidase 220 683 -3,11 0,0000 463,25 3290014 Ttc3 tetratricopeptide repeat domain 3 237 738 -3,11 0,0000 500,46 2370092 Bcl2a1b B-cell leukemia/lymphoma 2 related protein A1b 1565 4864 -3,11 0,0010 3298,80 3170440 Alox5ap arachidonate 5-lipoxygenase activating protein 123 382 -3,11 0,0024 259,03 2230093 Ly86 lymphocyte antigen 86 113 351 -3,10 0,0000 237,81 1170390 Ifitm2 interferon induced transmembrane protein 2 96 294 -3,07 0,0000 198,15 1740056 Ctss cathepsin S 360 1101 -3,06 0,0474 740,92 940129 Gcnt1 glucosaminyl (N-acetyl) transferase 1, core 2 313 955 -3,05 0,0001 641,81 106940451 Pkp4 plakophilin 4 486 1475 -3,03 0,0000 988,46 1500687 Ctsz cathepsin Z 166 497 -3,00 0,0000 331,32 5270563 Rnase6 ribonuclease, RNase A family, 6 102 307 -3,00 0,0000 204,89 610398 Cxcl2 chemokine (C-X-C motif) ligand 2 132 396 -3,00 0,0453 263,73 2340110 Csf1r colony stimulating factor 1 receptor 109 326 -2,99 0,0000 217,09 4610019 Slc12a2 solute carrier family 12, member 2 122 360 -2,96 0,0000 238,77 4760168 Asb2 ankyrin repeat and SOCS box-containing protein 2 354 1044 -2,95 0,0000 690,10 5570112 Kif23 kinesin family member 23 185 547 -2,95 0,0001 361,78 1690458 Dtx3 deltex 3 homolog (Drosophila) 732 2132 -2,91 0,0000 1400,30 5220692 NA NA 262 757 -2,89 0,0016 495,47 103390167 NA NA 420 1209 -2,88 0,0041 788,93 1660593 Csf2 colony stimulating factor 2 (granulocyte-macrophage) 194 550 -2,83 0,0000 355,16 guanine nucleotide binding protein (G protein), gamma 540176 Gngt2 108 306 -2,83 0,0000 197,46 transducing activity polypeptide 2 50400 Suhw3 suppressor of hairy wing homolog 3 (Drosophila) 131 370 -2,83 0,0001 239,56 2360528 Rps17 ribosomal protein S17 3735 10546 -2,82 0,0006 6811,50 3840180 Cd274 CD274 antigen 553 1554 -2,81 0,0000 1001,89 6590537 Ctla4 cytotoxic T-lymphocyte-associated protein 4 99 276 -2,78 0,0099 176,77 6660451 Cd63 Cd63 antigen 94 260 -2,77 0,0000 166,34 6220722 Ebi2 Epstein-Barr virus induced gene 2 196 545 -2,77 0,0007 348,64 1050148 Rasgrp1 RAS guanyl releasing protein 1 1572 4321 -2,75 0,0003 2749,48 2650129 Mgst2 microsomal glutathione S-transferase 2 558 1527 -2,74 0,0001 969,35 1770551 E430002D04Rik RIKEN cDNA E430002D04 gene 88 241 -2,73 0,0000 152,48 104210204 Ear3 eosinophil-associated, ribonuclease A family, member 3 139 375 -2,70 0,0000 235,89 6110605 Ly6a lymphocyte antigen 6 complex, locus A 137 367 -2,68 0,0290 230,46 2320048 Cd96 CD96 antigen 311 831 -2,67 0,0001 520,11 4560632 Ckb creatine kinase, brain 250 666 -2,66 0,0002 415,45 103190092 Osmr oncostatin M receptor 98 257 -2,63 0,0000 159,37 2810739 Cd3d CD3 antigen, delta polypeptide 1114 2918 -2,62 0,0063 1803,35 1660176 Cyp4f18 cytochrome P450, family 4, subfamily f, polypeptide 18 118 306 -2,59 0,0000 187,90 1340324 Tnfrsf26 tumor necrosis factor receptor superfamily, member 26 195 504 -2,59 0,0000 308,83 2630075 Ugt1a6a UDP glucuronosyltransferase 1 family, polypeptide A6A 357 924 -2,59 0,0104 566,56 106100035 Sft2d2 SFT2 domain containing 2 772 2000 -2,59 0,0278 1228,01 1410056 Zc3h12d zinc finger CCCH type containing 12D 159 410 -2,57 0,0000 250,49 5670576 Hsp90b1 heat shock protein 90kDa beta (Grp94), member 1 988 2530 -2,56 0,0000 1542,27 360435 Hba-a1 hemoglobin alpha, adult chain 1 446 1134 -2,55 0,0119 688,58 5360450 Lmo2 LIM domain only 2 108 273 -2,54 0,0000 165,75 105570373 Hbb-b1 hemoglobin, beta adult major chain 493 1242 -2,52 0,0386 749,19 TPX2, microtubule-associated protein homolog (Xenopus 6420324 Tpx2 142 356 -2,51 0,0040 213,96 laevis) 5690551 2010300C02Rik RIKEN cDNA 2010300C02 gene 154 382 -2,48 0,0000 228,29 100450332 NA NA 194 480 -2,48 0,0000 285,96 6350176 Fcna ficolin A 84 209 -2,48 0,0086 124,62 4570739 Rab8b RAB8B, member RAS oncogene family 1485 3671 -2,47 0,0048 2186,36 100110411 Itgav integrin alpha V 152 375 -2,46 0,0025 222,48 4010088 Cdk2 cyclin-dependent kinase 2 312 766 -2,45 0,0000 453,79 2760019 Ifi205 interferon activated gene 205 1085 2653 -2,45 0,0000 1568,98 670358 Ear10 eosinophil-associated, ribonuclease A family, member 10 88 214 -2,43 0,0000 125,75 3390086 NA NA 608 1477 -2,43 0,0000 869,03 1660242 Uhrf1 ubiquitin-like, containing PHD and RING finger domains, 1 370 896 -2,42 0,0000 526,25 5270079 Rnf43 ring finger protein 43 377 909 -2,41 0,0090 532,15 110408 Birc5 baculoviral IAP repeat-containing 5 114 274 -2,40 0,0108 160,03 101500079 Cybb cytochrome b-245, beta polypeptide 98 235 -2,40 0,0125 136,89 5690528 Mns1 meiosis-specific nuclear structural protein 1 99 238 -2,39 0,0000 138,24 5690110 Pdcd4 programmed cell death 4 2613 6248 -2,39 0,0000 3635,70 3290541 NA NA 202 482 -2,39 0,0056 280,79 580446 Rbl2 retinoblastoma-like 2 661 1576 -2,38 0,0065 915,08 102510341 Hbb-b1 hemoglobin, beta adult major chain 423 1001 -2,37 0,0404 578,02 101990441 Fryl furry homolog-like (Drosophila) 153 363 -2,37 0,0421 210,00 7040450 Marcks myristoylated alanine rich protein kinase C substrate 120 281 -2,34 0,0000 161,33 6900112 Tgfbi transforming growth factor, beta induced 106 249 -2,34 0,0000 142,38 5670131 Cdca5 cell division cycle associated 5 119 277 -2,33 0,0000 158,12 5130315 Fcgrt Fc receptor, IgG, alpha chain transporter 137 320 -2,33 0,0000 182,60 1340132 NA NA 707 1645 -2,33 0,0000 938,07 2060102 NA NA 78 181 -2,33 0,0000 103,61 SPC24, NDC80 kinetochore complex component, homolog 4280451 Spc24 111 258 -2,33 0,0000 147,24 (S.
Recommended publications
  • Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis

    Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis

    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis.
  • Mutations in DCPS and EDC3 in Autosomal Recessive Intellectual

    Mutations in DCPS and EDC3 in Autosomal Recessive Intellectual

    Human Molecular Genetics, 2015, Vol. 24, No. 11 3172–3180 doi: 10.1093/hmg/ddv069 Advance Access Publication Date: 20 February 2015 Original Article Downloaded from ORIGINAL ARTICLE Mutations in DCPS and EDC3 in autosomal recessive intellectual disability indicate a crucial role for mRNA http://hmg.oxfordjournals.org/ decapping in neurodevelopment Iltaf Ahmed1,2,†, Rebecca Buchert3,†, Mi Zhou5,†, Xinfu Jiao5,†, Kirti Mittal1, Taimoor I. Sheikh1, Ute Scheller3, Nasim Vasli1, Muhammad Arshad Rafiq1, 6 1 7 2 M. Qasim Brohi , Anna Mikhailov , Muhammad Ayaz , Attya Bhatti , at Universitaet Erlangen-Nuernberg, Wirtschafts- und Sozialwissenschaftliche Z on August 15, 2016 Heinrich Sticht4, Tanveer Nasr8,9, Melissa T. Carter10, Steffen Uebe3, André Reis3, Muhammad Ayub7,11, Peter John2, Megerditch Kiledjian5,*, John B. Vincent1,12,13,* and Rami Abou Jamra3,* 1Molecular Neuropsychiatry and Development Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario, Canada M5T 1R8, 2Atta-ur-Rehman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan, 3Institute of Human Genetics and 4Bioinformatics, Institute of Biochemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91054, Germany, 5Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ 08854, USA, 6Sir Cowasji Jehangir Institute of Psychiatry, Hyderabad, Sindh 71000, Pakistan, 7Lahore Institute of Research and Development,
  • Aberrant Modulation of Ribosomal Protein S6 Phosphorylation Confers Acquired Resistance to MAPK Pathway Inhibitors in BRAF-Mutant Melanoma

    Aberrant Modulation of Ribosomal Protein S6 Phosphorylation Confers Acquired Resistance to MAPK Pathway Inhibitors in BRAF-Mutant Melanoma

    www.nature.com/aps ARTICLE Aberrant modulation of ribosomal protein S6 phosphorylation confers acquired resistance to MAPK pathway inhibitors in BRAF-mutant melanoma Ming-zhao Gao1,2, Hong-bin Wang1,2, Xiang-ling Chen1,2, Wen-ting Cao1,LiFu1, Yun Li1, Hai-tian Quan1,2, Cheng-ying Xie1,2 and Li-guang Lou1,2 BRAF and MEK inhibitors have shown remarkable clinical efficacy in BRAF-mutant melanoma; however, most patients develop resistance, which limits the clinical benefit of these agents. In this study, we found that the human melanoma cell clones, A375-DR and A375-TR, with acquired resistance to BRAF inhibitor dabrafenib and MEK inhibitor trametinib, were cross resistant to other MAPK pathway inhibitors. In these resistant cells, phosphorylation of ribosomal protein S6 (rpS6) but not phosphorylation of ERK or p90 ribosomal S6 kinase (RSK) were unable to be inhibited by MAPK pathway inhibitors. Notably, knockdown of rpS6 in these cells effectively downregulated G1 phase-related proteins, including RB, cyclin D1, and CDK6, induced cell cycle arrest, and inhibited proliferation, suggesting that aberrant modulation of rpS6 phosphorylation contributed to the acquired resistance. Interestingly, RSK inhibitor had little effect on rpS6 phosphorylation and cell proliferation in resistant cells, whereas P70S6K inhibitor showed stronger inhibitory effects on rpS6 phosphorylation and cell proliferation in resistant cells than in parental cells. Thus regulation of rpS6 phosphorylation, which is predominantly mediated by BRAF/MEK/ERK/RSK signaling in parental cells, was switched to mTOR/ P70S6K signaling in resistant cells. Furthermore, mTOR inhibitors alone overcame acquired resistance and rescued the sensitivity of the resistant cells when combined with BRAF/MEK inhibitors.
  • Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model

    Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model

    Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.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 © 2016 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 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A.
  • Allele-Specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish

    Allele-Specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish

    Allele-specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish by Ailu Chen A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama August 1, 2015 Keywords: catfish, interspecific hybrids, allele-specific expression, ribosomal protein Copyright 2015 by Ailu Chen Approved by Zhanjiang Liu, Chair, Professor, School of Fisheries, Aquaculture and Aquatic Sciences Nannan Liu, Professor, Entomology and Plant Pathology Eric Peatman, Associate Professor, School of Fisheries, Aquaculture and Aquatic Sciences Aaron M. Rashotte, Associate Professor, Biological Sciences Abstract Interspecific hybridization results in a vast reservoir of allelic variations, which may potentially contribute to phenotypical enhancement in the hybrids. Whether the allelic variations are related to the downstream phenotypic differences of interspecific hybrid is still an open question. The recently developed genome-wide allele-specific approaches that harness high- throughput sequencing technology allow direct quantification of allelic variations and gene expression patterns. In this work, I investigated allele-specific expression (ASE) pattern using RNA-Seq datasets generated from interspecific catfish hybrids. The objective of the study is to determine the ASE genes and pathways in which they are involved. Specifically, my study investigated ASE-SNPs, ASE-genes, parent-of-origins of ASE allele and how ASE would possibly contribute to heterosis. My data showed that ASE was operating in the interspecific catfish system. Of the 66,251 and 177,841 SNPs identified from the datasets of the liver and gill, 5,420 (8.2%) and 13,390 (7.5%) SNPs were identified as significant ASE-SNPs, respectively.
  • Seq2pathway Vignette

    Seq2pathway Vignette

    seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway.
  • Dcps Is a Transcript-Specific Modulator of RNA in Mammalian Cells

    Dcps Is a Transcript-Specific Modulator of RNA in Mammalian Cells

    Downloaded from rnajournal.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press DcpS is a transcript-specific modulator of RNA in mammalian cells MI ZHOU,1 SOPHIE BAIL,1 HEATHER L. PLASTERER,2 JAMES RUSCHE,2 and MEGERDITCH KILEDJIAN1 1Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, New Jersey 08854, USA 2Repligen Corporation, Waltham, Massachusetts 02453, USA ABSTRACT The scavenger decapping enzyme DcpS is a multifunctional protein initially identified by its property to hydrolyze the resulting cap structure following 3′ end mRNA decay. In Saccharomyces cerevisiae, the DcpS homolog Dcs1 is an obligate cofactor for the 5′-3′ exoribonuclease Xrn1 while the Caenorhabditis elegans homolog Dcs-1, facilitates Xrn1 mediated microRNA turnover. In both cases, this function is independent of the decapping activity. Whether DcpS and its decapping activity can affect mRNA steady state or stability in mammalian cells remains unknown. We sought to determine DcpS target genes in mammalian cells using a cell-permeable DcpS inhibitor compound, RG3039 initially developed for therapeutic treatment of spinal muscular atrophy. Global mRNA levels were examined following DcpS decapping inhibition with RG3039. The steady-state levels of 222 RNAs were altered upon RG3039 treatment. Of a subset selected for validation, two transcripts that appear to be long noncoding RNAs HS370762 and BC011766, were dependent on DcpS and its scavenger decapping catalytic activity and referred to as DcpS-responsive noncoding transcripts (DRNT) 1 and 2, respectively. Interestingly, only the increase in DRNT1 transcript was accompanied with an increase of its RNA stability and this increase was dependent on both DcpS and Xrn1.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • Supplementary Materials

    Supplementary Materials

    1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No.
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation

    4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation

    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
  • Conserved and Novel Properties of Clathrin-Mediated Endocytosis in Dictyostelium Discoideum" (2012)

    Conserved and Novel Properties of Clathrin-Mediated Endocytosis in Dictyostelium Discoideum" (2012)

    Rockefeller University Digital Commons @ RU Student Theses and Dissertations 2012 Conserved and Novel Properties of Clathrin- Mediated Endocytosis in Dictyostelium Discoideum Laura Macro Follow this and additional works at: http://digitalcommons.rockefeller.edu/ student_theses_and_dissertations Part of the Life Sciences Commons Recommended Citation Macro, Laura, "Conserved and Novel Properties of Clathrin-Mediated Endocytosis in Dictyostelium Discoideum" (2012). Student Theses and Dissertations. Paper 163. This Thesis is brought to you for free and open access by Digital Commons @ RU. It has been accepted for inclusion in Student Theses and Dissertations by an authorized administrator of Digital Commons @ RU. For more information, please contact [email protected]. CONSERVED AND NOVEL PROPERTIES OF CLATHRIN- MEDIATED ENDOCYTOSIS IN DICTYOSTELIUM DISCOIDEUM A Thesis Presented to the Faculty of The Rockefeller University in Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy by Laura Macro June 2012 © Copyright by Laura Macro 2012 CONSERVED AND NOVEL PROPERTIES OF CLATHRIN- MEDIATED ENDOCYTOSIS IN DICTYOSTELIUM DISCOIDEUM Laura Macro, Ph.D. The Rockefeller University 2012 The protein clathrin mediates one of the major pathways of endocytosis from the extracellular milieu and plasma membrane. Clathrin functions with a network of interacting accessory proteins, one of which is the adaptor complex AP-2, to co-ordinate vesicle formation. Disruption of genes involved in clathrin-mediated endocytosis causes embryonic lethality in multicellular animals suggesting that clathrin-mediated endocytosis is a fundamental cellular process. However, loss of clathrin-mediated endocytosis genes in single cell eukaryotes, such as S.cerevisiae (yeast), does not cause lethality, suggesting that clathrin may convey specific advantages for multicellularity.
  • Mechanisms of Synaptic Plasticity Mediated by Clathrin Adaptor-Protein Complexes 1 and 2 in Mice

    Mechanisms of Synaptic Plasticity Mediated by Clathrin Adaptor-Protein Complexes 1 and 2 in Mice

    Mechanisms of synaptic plasticity mediated by Clathrin Adaptor-protein complexes 1 and 2 in mice Dissertation for the award of the degree “Doctor rerum naturalium” at the Georg-August-University Göttingen within the doctoral program “Molecular Biology of Cells” of the Georg-August University School of Science (GAUSS) Submitted by Ratnakar Mishra Born in Birpur, Bihar, India Göttingen, Germany 2019 1 Members of the Thesis Committee Prof. Dr. Peter Schu Institute for Cellular Biochemistry, (Supervisor and first referee) University Medical Center Göttingen, Germany Dr. Hans Dieter Schmitt Neurobiology, Max Planck Institute (Second referee) for Biophysical Chemistry, Göttingen, Germany Prof. Dr. med. Thomas A. Bayer Division of Molecular Psychiatry, University Medical Center, Göttingen, Germany Additional Members of the Examination Board Prof. Dr. Silvio O. Rizzoli Department of Neuro-and Sensory Physiology, University Medical Center Göttingen, Germany Dr. Roland Dosch Institute of Developmental Biochemistry, University Medical Center Göttingen, Germany Prof. Dr. med. Martin Oppermann Institute of Cellular and Molecular Immunology, University Medical Center, Göttingen, Germany Date of oral examination: 14th may 2019 2 Table of Contents List of abbreviations ................................................................................. 5 Abstract ................................................................................................... 7 Chapter 1: Introduction ............................................................................