DOCSLIB.ORG

Genes That Are Preferentially Expressed in Type I Cells Figure 4A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Genes That Are Preferentially Expressed in Type I Cells Figure 4A Genes that are preferentially expressed in Type I cells Figure 4A Rank Moltid 3'ACC Gene Title #Cell_lines Variance PCC 1 GC17295 AA055114 PIK3R1 phosphoinositide-3-kinase, regulatory subunit, polypeptide 1 (p85 alpha) 22 0.023 0.774 2 GC15242 N89600 Homo sapiens cDNA: FLJ22300 fis, clone HRC04759.similar to Rabbit smooth muscle myosin light chain kinase22 0.356 0.773 3 GC17179 AA041360 RIG regulated in glioma, DKK-3 (xenopus dick kopf ortholog) 22 0.086 0.766 4 GC16514 W92322 ITGA5 integrin, alpha 5 (fibronectin receptor, alpha polypeptide) 22 0.04 0.761 5 GC11221 H16240 EST 22 0.049 0.761 6 GC17336 AA056228 ZDHHC7 zinc finger, DHHC domain containing 7 22 0.047 0.759 7 GC9826 AA047403 TPM1 tropomyosin 1 (alpha) 22 0.207 0.74 8 GC16967 AA025392 DUSP3 dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related) 22 0.046 0.739 9 GC12698 H24785 ESTs 20 0.136 0.735 10 GC12641 R54846 FGFR1 fibroblast growth factor receptor 1 (fms-related tyrosine kinase 2, Pfeiffer syndrome) 22 0.178 0.734 11 GC18963 AA040161 SNK serum-inducible kinase 22 0.319 0.732 12 GC15075 N73979 SCHIP1 schwannomin interacting protein 1 22 0.117 0.732 13 GC17213 AA046749 TPM1 tropomyosin 1 (alpha) 22 0.179 0.723 14 GC14991 N71362 TRIM3 tripartite motif-containing 3 22 0.115 0.72 15 GC14893 N72246 NIT2 Nit protein 2 22 0.029 0.719 16 GC14878 N71998 ITGA3 integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor) 22 0.226 0.719 17 GC11743 T50788 UGT2B15 UDP glycosyltransferase 2 family, polypeptide B15 20 0.347 0.713 18 GC18514 AA031453 KIAA0638 KIAA0638 protein 21 0.094 0.707 19 GC14063 N20008 Homo sapiens mRNA; cDNA DKFZp434E235 (from clone DKFZp434E235) 22 0.396 0.698 20 GC10945 R54594 ESTs 18 0.235 0.698 21 GC17207 AA046117 Homo sapiens, clone IMAGE:4798592, mRNA 22 0.06 0.695 22 GC19161 AA044574 CYR61 cysteine-rich, angiogenic inducer, 61 22 0.251 0.695 23 GC10430 AA055792 PTTG1IP pituitary tumor-transforming 1 interacting protein 22 0.047 0.695 24 GC10492 AA057701 GNAS GNAS complex locus 22 0.079 0.693 25 GC10555 T66144 FGFR1 fibroblast growth factor receptor 1 (fms-related tyrosine kinase 2, Pfeiffer syndrome) 22 0.233 0.689 26 GC13411 H95623 22 0.043 0.685 27 GC17874 W90070 TNFAIP1 tumor necrosis factor, alpha-induced protein 1 (endothelial) 22 0.034 0.684 28 GC9849 AA047271 PARVA parvin, alpha 21 0.048 0.678 29 GC17063 AA027324 Homo sapiens cDNA FLJ25134 fis, clone CBR06934. 22 0.034 0.676 30 GC18611 AA034024 RAI14 retinoic acid induced 14 22 0.327 0.675 31 GC11284 H15557 DUSP3 dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related) 22 0.048 0.67 32 GC19192 AA043215 LASP1 LIM and SH3 protein 1 22 0.03 0.669 33 GC10514 AA057721 ACTG1 actin, gamma 1 22 0.025 0.669 34 GC17723 W86749 CerCAM cerebral cell adhesion molecule 21 0.039 0.668 35 GC15402 N95201 Homo sapiens mRNA; cDNA DKFZp586D1122 (from clone DKFZp586D1122) 22 0.064 0.668 36 GC16092 W72904 KIAA0116 KIAA0116 protein 22 0.027 0.664 37 GC11908 T87071 PPP2R3A protein phosphatase 2 (formerly 2A), regulatory subunit B'', alpha 20 0.162 0.662 38 GC10609 R17708 NDST1 N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 1 22 0.051 0.662 39 GC18188 AA011635 MGC14376 hypothetical protein MGC14376 22 0.154 0.66 40 GC14225 N36415 CDA08 T-cell immunomodulatory protein 22 0.056 0.657 41 GC11926 T85295 Homo sapiens, clone IMAGE:4720228, mRNA 22 0.118 0.653 42 GC11385 H18455 LPAAT-delta lysophosphatidic acid acyltransferase-delta 22 0.085 0.651 43 GC12444 R78226 COL4A1 collagen, type IV, alpha 1 21 0.534 0.651 44 GC18538 AA031696 ENIGMA enigma (LIM domain protein) 22 0.043 0.651 45 GC9736 AA054747 TIP-1 Tax interaction protein 1 22 0.041 0.65 46 GC15013 N69529 BHMT betaine-homocysteine methyltransferase 22 0.076 0.645 47 GC14337 N63451 ESTs, Weakly similar to hypothetical protein FLJ20378 21 0.042 0.645 48 GC17927 AA002153 KIAA1029 synaptopodin 19 0.052 0.643 49 GC17221 AA047566 MYL4 myosin, light polypeptide 4, alkali; atrial, embryonic 22 0.068 0.643 50 GC18554 AA032067 ARHGAP1 Rho GTPase activating protein 1 22 0.024 0.643 51 GC10113 AA054564 COL4A1 collagen, type IV, alpha 1 21 0.259 0.642 52 GC12594 H04238 TNFRSF6 tumor necrosis factor receptor superfamily, member 6 22 0.094 0.641 53 GC10692 R41635 ZDHHC9 zinc finger, DHHC domain containing 9 22 0.046 0.639 54 GC11836 T62074 UGT2B7 UDP glycosyltransferase 2 family, polypeptide B7 20 0.505 0.639 55 GC17494 AA059205 PTPNS1 protein tyrosine phosphatase, non-receptor type substrate 1 22 0.349 0.638 56 GC12383 R68763 Homo sapiens full length insert cDNA YI37C01 22 0.183 0.637 57 GC18241 AA034018 FLJ20507 hypothetical protein FLJ20507 22 0.025 0.637 58 GC11862 T88822 ESTs 20 0.09 0.636 59 GC10308 AA053251 TMEPAI transmembrane, prostate androgen induced RNA 21 0.294 0.636 60 GC15779 W46835 FHL2 four and a half LIM domains 2 22 0.211 0.635 61 GC13123 R99596 22 0.147 0.634 62 GC18321 AA027850 DUSP3 dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related) 22 0.045 0.632 63 GC12081 R02281 CSF1 colony stimulating factor 1 (macrophage) 22 0.219 0.632 64 GC18539 AA031952 ESTs 22 0.034 0.629 65 GC14622 N71935 MPDZ multiple PDZ domain protein 21 0.069 0.629 66 GC9801 AA046642 PRKAG2 protein kinase, AMP-activated, gamma 2 non-catalytic subunit 22 0.104 0.629 67 GC15789 W49563 Homo sapiens mRNA; cDNA DKFZp434E235 (from clone DKFZp434E235) 21 0.328 0.629 68 GC18798 AA037598 PEA15 phosphoprotein enriched in astrocytes 15 22 0.082 0.628 69 GC15807 W49786 LOC152009 hypothetical protein LOC152009 22 0.136 0.626 70 GC19195 AA043228 CNN3 calponin 3, acidic 22 0.215 0.625 71 GC9761 AA053331 FDPS farnesyl diphosphate synthase (farnesyl pyrophosphate synthetase, dimethylallyltranstransferase, geranyltranstransferase)22 0.234 0.625 72 GC19034 AA044053 DNAJB6 DnaJ (Hsp40) homolog, subfamily B, member 6 22 0.033 0.625 73 GC17062 AA027319 ITM2B integral membrane protein 2B 22 0.031 0.624 74 GC10445 AA057727 ESTs, Highly similar to S03700 nonhistone chromosomal protein HMG-17 - human 21 0.176 0.624 75 GC10815 R43778 APEG1 nuclear protein, marker for differentiated aortic smooth muscle and down-regulated with vascular injury 22 0.161 0.623 76 GC19373 AA045174 Homo sapiens mRNA; cDNA DKFZp586B211 (from clone DKFZp586B211) 22 0.13 0.622 77 GC15996 W73776 PSMD2 proteasome (prosome, macropain) 26S subunit, non-ATPase, 2 22 0.031 0.622 78 GC15822 W52273 HRB2 HIV-1 rev binding protein 2 22 0.118 0.62 79 GC10681 R05666 DP1 likely ortholog of mouse deleted in polyposis 1 22 0.062 0.62 80 GC13072 H52087 22 0.383 0.619 81 GC10682 R05668 SKD3 suppressor of potassium transport defect 3 22 0.045 0.619 82 GC15973 W69170 Homo sapiens cDNA FLJ31668 fis, clone NT2RI2004916. 22 0.22 0.619 83 GC18432 AA029097 LOC146784 hypothetical protein LOC146784 22 0.122 0.619 84 GC19094 AA044414 CALD1 caldesmon 1 22 0.31 0.618 85 GC18422 AA030056 DLC1 deleted in liver cancer 1 22 0.222 0.618 86 GC19310 AA046659 SERPINE1 serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1 21 0.313 0.617 87 GC17122 AA040550 AMBP alpha-1-microglobulin/bikunin precursor 21 0.031 0.616 88 GC11882 T90749 ESTs 22 0.193 0.615 89 GC12154 R09550 SDC4 syndecan 4 (amphiglycan, ryudocan) 22 0.071 0.613 90 GC11911 T78450 ESTs 19 0.062 0.612 91 GC12004 T94848 ESTs 22 0.166 0.612 92 GC9970 AA047080 UGCG UDP-glucose ceramide glucosyltransferase 22 0.137 0.611 93 GC19319 AA046721 FLNA filamin A, alpha (actin binding protein 280) 22 0.155 0.611 94 GC15965 W69630 MYL6 myosin, light polypeptide 6, alkali, smooth muscle and non-muscle 22 0.023 0.611 95 GC11243 H11983 NAV1 neuron navigator 1 22 0.278 0.61 96 GC15506 N94578 NIT2 Nit protein 2 22 0.354 0.61 97 GC14414 N46354 GABRA2 gamma-aminobutyric acid (GABA) A receptor, alpha 2 18 0.081 0.61 98 GC14474 N47107 NEK6 NIMA (never in mitosis gene a)-related kinase 6 22 0.101 0.609 99 GC11386 H18456 ESTs 22 0.368 0.608 100 GC11896 T91987 ESTs 21 0.066 0.608 101 GC18506 AA031968 VAPB VAMP (vesicle-associated membrane protein)-associated protein B and C 22 0.094 0.607 102 GC11263 H14977 21 0.843 0.607 103 GC9719 AA045751 UBE2A ubiquitin-conjugating enzyme E2A (RAD6 homolog) 22 0.035 0.607 104 GC15795 W49494 KIAA0375 KIAA0375 gene product 21 0.072 0.607 105 GC16470 W96206 EXT1 exostoses (multiple) 1 22 0.027 0.606 106 GC12221 R22919 EST-YD1 EST-YD1 protein 22 0.049 0.606 107 GC14298 N34396 Homo sapiens cDNA FLJ13034 fis, clone NT2RP3001232. 22 0.085 0.606 108 GC12495 R81328 ESTs 22 0.034 0.604 109 GC18130 AA007361 PP1628 hypothetical protein PP1628 22 0.166 0.604 110 GC14075 N20199 MGC3178 thioredoxin related protein 22 0.093 0.602 111 GC12856 H50100 C14orf34 chromosome 14 open reading frame 34 22 0.067 0.599 112 GC9774 AA058703 Homo sapiens cDNA FLJ30550 fis, clone BRAWH2001502. 22 0.346 0.598 113 GC14889 N67688 ESTs, Weakly similar to hypothetical protein FLJ20378 22 0.143 0.598 114 GC18235 AA033790 APOD apolipoprotein D 22 0.117 0.597 115 GC15251 N79566 PRICKLE2 prickle-like 2 (Drosophila) 21 0.193 0.597 116 GC17183 AA041260 FLJ20297 hypothetical protein FLJ20297 22 0.021 0.596 117 GC18479 AA031657 LOC51159 colon carcinoma related protein 22 0.181 0.596 118 GC15808 W48562 PTX3 pentaxin-related gene, rapidly induced by IL-1 beta 19 0.083 0.594 119 GC14720 N62311 MYOZ3 myozenin 3 22 0.021 0.594 120 GC17803 W88807 Homo sapiens cDNA FLJ34757 fis, clone NT2NE2001725.
Load more

Recommended publications
  • Familial Nephropathy and Multiple Exostoses with Exostosin-1 (EXT1) Gene Mutation
    PATHOPHYSIOLOGY of the RENAL BIOPSY www.jasn.org Familial Nephropathy and Multiple Exostoses With Exostosin-1 (EXT1) Gene Mutation Ian S. D. Roberts* and Jonathan M. Gleadle† *Department of Cellular Pathology, John Radcliffe Hospital, Headley Way, Headington, Oxford, United Kingdom; and †Renal Unit, Level 6, Flinders Medical Centre, Bedford Park, South Australia, Australia ABSTRACT Glomerular deposition of fibrillar collagen is a characteristic finding of genetically mained in remission with trace protein- distinct conditions, including nail-patella syndrome and collagen type III glomeru- uria until cyclosporine was stopped 3.5 lopathy. A case of familial nephropathy in which steroid-sensitive nephrotic syn- yr later. Six months after this, she suf- drome and glomerular deposits of fibrillar collagen are associated with multiple fered another relapse of nephrotic exostoses due to mutation of the EXT1 gene is described. This gene encodes a syndrome that responded to 60 mg pred- glycosyltransferase required for synthesis of heparan sulfate glycosaminoglycans. nisolone and reintroduction of cyclo- There is deficiency of heparan sulfate and perlecan, together with accumulation of sporine. After a further relapse 18 mo collagens, in the matrix of EXT1-associated osteochondromas. Similar glomerular later and because of the development of basement membrane abnormalities could offer an explanation for both the renal adverse corticosteroid effects, she was ultrastructural changes and steroid-sensitive nephrotic syndrome. treated with a 2-mo course of cyclophos- phamide (2.5 mg/kg, orally). Ten years J Am Soc Nephrol 19: 450–453, 2008. doi: 10.1681/ASN.2007080842 after her initial presentation, she remains in full remission and off steroids. Renal function has remained normal through- A 37-yr-old woman presented with the history of renal disease and hearing im- out with a current serum creatinine of nephrotic syndrome.
    [Show full text]
  • Table 2. Functional Classification of Genes Differentially Regulated After HOXB4 Inactivation in HSC/Hpcs
    Table 2. Functional classification of genes differentially regulated after HOXB4 inactivation in HSC/HPCs Symbol Gene description Fold-change (mean ± SD) Signal transduction Adam8 A disintegrin and metalloprotease domain 8 1.91 ± 0.51 Arl4 ADP-ribosylation factor-like 4 - 1.80 ± 0.40 Dusp6 Dual specificity phosphatase 6 (Mkp3) - 2.30 ± 0.46 Ksr1 Kinase suppressor of ras 1 1.92 ± 0.42 Lyst Lysosomal trafficking regulator 1.89 ± 0.34 Mapk1ip1 Mitogen activated protein kinase 1 interacting protein 1 1.84 ± 0.22 Narf* Nuclear prelamin A recognition factor 2.12 ± 0.04 Plekha2 Pleckstrin homology domain-containing. family A. (phosphoinosite 2.15 ± 0.22 binding specific) member 2 Ptp4a2 Protein tyrosine phosphatase 4a2 - 2.04 ± 0.94 Rasa2* RAS p21 activator protein 2 - 2.80 ± 0.13 Rassf4 RAS association (RalGDS/AF-6) domain family 4 3.44 ± 2.56 Rgs18 Regulator of G-protein signaling - 1.93 ± 0.57 Rrad Ras-related associated with diabetes 1.81 ± 0.73 Sh3kbp1 SH3 domain kinase bindings protein 1 - 2.19 ± 0.53 Senp2 SUMO/sentrin specific protease 2 - 1.97 ± 0.49 Socs2 Suppressor of cytokine signaling 2 - 2.82 ± 0.85 Socs5 Suppressor of cytokine signaling 5 2.13 ± 0.08 Socs6 Suppressor of cytokine signaling 6 - 2.18 ± 0.38 Spry1 Sprouty 1 - 2.69 ± 0.19 Sos1 Son of sevenless homolog 1 (Drosophila) 2.16 ± 0.71 Ywhag 3-monooxygenase/tryptophan 5- monooxygenase activation protein. - 2.37 ± 1.42 gamma polypeptide Zfyve21 Zinc finger. FYVE domain containing 21 1.93 ± 0.57 Ligands and receptors Bambi BMP and activin membrane-bound inhibitor - 2.94 ± 0.62
    [Show full text]
  • CRISPR Screening of Porcine Sgrna Library Identifies Host Factors
    ARTICLE https://doi.org/10.1038/s41467-020-18936-1 OPEN CRISPR screening of porcine sgRNA library identifies host factors associated with Japanese encephalitis virus replication Changzhi Zhao1,5, Hailong Liu1,5, Tianhe Xiao1,5, Zichang Wang1, Xiongwei Nie1, Xinyun Li1,2, Ping Qian2,3, Liuxing Qin3, Xiaosong Han1, Jinfu Zhang1, Jinxue Ruan1, Mengjin Zhu1,2, Yi-Liang Miao 1,2, Bo Zuo1,2, ✉ ✉ Kui Yang4, Shengsong Xie 1,2 & Shuhong Zhao 1,2 1234567890():,; Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that causes ence- phalitis and reproductive disorders in mammalian species. However, the host factors critical for its entry, replication, and assembly are poorly understood. Here, we design a porcine genome-scale CRISPR/Cas9 knockout (PigGeCKO) library containing 85,674 single guide RNAs targeting 17,743 protein-coding genes, 11,053 long ncRNAs, and 551 microRNAs. Subsequently, we use the PigGeCKO library to identify key host factors facilitating JEV infection in porcine cells. Several previously unreported genes required for JEV infection are highly enriched post-JEV selection. We conduct follow-up studies to verify the dependency of JEV on these genes, and identify functional contributions for six of the many candidate JEV- related host genes, including EMC3 and CALR. Additionally, we identify that four genes associated with heparan sulfate proteoglycans (HSPGs) metabolism, specifically those responsible for HSPGs sulfurylation, facilitate JEV entry into porcine cells. Thus, beyond our development of the largest CRISPR-based functional genomic screening platform for pig research to date, this study identifies multiple potentially vulnerable targets for the devel- opment of medical and breeding technologies to treat and prevent diseases caused by JEV.
    [Show full text]
  • Cisplatin and Phenanthriplatin Modulate Long-Noncoding
    www.nature.com/scientificreports OPEN Cisplatin and phenanthriplatin modulate long‑noncoding RNA expression in A549 and IMR90 cells revealing regulation of microRNAs, Wnt/β‑catenin and TGF‑β signaling Jerry D. Monroe1,2, Satya A. Moolani2,3, Elvin N. Irihamye2,4, Katheryn E. Lett1, Michael D. Hebert1, Yann Gibert1* & Michael E. Smith2* The monofunctional platinum(II) complex, phenanthriplatin, acts by blocking transcription, but its regulatory efects on long‑noncoding RNAs (lncRNAs) have not been elucidated relative to traditional platinum‑based chemotherapeutics, e.g., cisplatin. Here, we treated A549 non‑small cell lung cancer and IMR90 lung fbroblast cells for 24 h with either cisplatin, phenanthriplatin or a solvent control, and then performed microarray analysis to identify regulated lncRNAs. RNA22 v2 microRNA software was subsequently used to identify microRNAs (miRNAs) that might be suppressed by the most regulated lncRNAs. We found that miR‑25‑5p, ‑30a‑3p, ‑138‑5p, ‑149‑3p, ‑185‑5p, ‑378j, ‑608, ‑650, ‑708‑5p, ‑1253, ‑1254, ‑4458, and ‑4516, were predicted to target the cisplatin upregulated lncRNAs, IMMP2L‑1, CBR3‑1 and ATAD2B‑5, and the phenanthriplatin downregulated lncRNAs, AGO2‑1, COX7A1‑2 and SLC26A3‑1. Then, we used qRT‑PCR to measure the expression of miR‑25‑5p, ‑378j, ‑4516 (A549) and miR‑149‑3p, ‑608, and ‑4458 (IMR90) to identify distinct signaling efects associated with cisplatin and phenanthriplatin. The signaling pathways associated with these miRNAs suggests that phenanthriplatin may modulate Wnt/β‑catenin and TGF‑β signaling through the MAPK/ ERK and PTEN/AKT pathways diferently than cisplatin. Further, as some of these miRNAs may be subject to dissimilar lncRNA targeting in A549 and IMR90 cells, the monofunctional complex may not cause toxicity in normal lung compared to cancer cells by acting through distinct lncRNA and miRNA networks.
    [Show full text]
  • A Single-Cell Transcriptional Atlas Identifies Extensive Heterogeneity in the Cellular Composition of Tendons
    bioRxiv preprint doi: https://doi.org/10.1101/801266; this version posted October 10, 2019. 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. A single-cell transcriptional atlas identifies extensive heterogeneity in the cellular composition of tendons Jacob B Swanson1, Andrea J De Micheli2, Nathaniel P Disser1, Leandro M Martinez1, Nicholas R Walker1,3, Benjamin D Cosgrove2, Christopher L Mendias1,3,* 1Hospital for Special Surgery, New York, NY, USA 2Meining School of Biomedical Engineering, Cornell University, Ithaca, NY, USA 3Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA *Corresponding Author Christopher Mendias, PhD Hospital for Special Surgery 535 E 70th St New York, NY 10021 USA +1 212-606-1785 [email protected] Keywords: tenocyte; tendon fibroblast; pericyte; single-cell RNA sequencing bioRxiv preprint doi: https://doi.org/10.1101/801266; this version posted October 10, 2019. 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 Tendon is a dense, hypocellular connective tissue that transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, but little is known about the diversity of cells that populate tendon. Our objective was to explore the heterogeneity of cells in mouse Achilles tendons using single-cell RNA sequencing. We identified 13 unique cell types in tendons, including 4 previously undescribed populations of fibroblasts.
    [Show full text]
  • Selectin Ligand Sialyl-Lewis X Antigen Drives Metastasis of Hormone-Dependent Breast Cancers
    Published OnlineFirst October 24, 2011; DOI: 10.1158/0008-5472.CAN-11-1139 Cancer Tumor and Stem Cell Biology Research Selectin Ligand Sialyl-Lewis x Antigen Drives Metastasis of Hormone-Dependent Breast Cancers Sylvain Julien1, Aleksandar Ivetic2, Anita Grigoriadis3, Ding QiZe1, Brian Burford1, Daisy Sproviero1, Gianfranco Picco1, Cheryl Gillett4, Suzanne L. Papp5, Lana Schaffer5, Andrew Tutt3, Joyce Taylor-Papadimitriou1, Sarah E. Pinder4, and Joy M. Burchell1 Abstract The glycome acts as an essential interface between cells and the surrounding microenvironment. However, changes in glycosylation occur in nearly all breast cancers, which can alter this interaction. Here, we report that profiles of glycosylation vary between ER-positive and ER-negative breast cancers. We found that genes involved in the synthesis of sialyl-Lewis x (sLex; FUT3, FUT4, and ST3GAL6) are significantly increased in estrogen receptor alpha-negative (ER-negative) tumors compared with ER-positive ones. SLex expression had no influence on the survival of patients whether they had ER-negative or ER-positive tumors. However, high expression of sLex in ER- positive tumors was correlated with metastasis to the bone where sLex receptor E-selectin is constitutively expressed. The ER-positive ZR-75-1 and the ER-negative BT20 cell lines both express sLex but only ZR-75-1 cells could adhere to activated endothelial cells under dynamic flow conditions in a sLex and E-selectin–dependent manner. Moreover, L/P-selectins bound strongly to ER-negative MDA-MB-231 and BT-20 cell lines in a heparan sulfate (HS)–dependent manner that was independent of sLex expression. Expression of glycosylation genes involved in heparan biosynthesis (EXT1 and HS3ST1) was increased in ER-negative tumors.
    [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]
  • Key Pathways Involved in Prostate Cancer Based on Gene Set Enrichment Analysis and Meta Analysis
    Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis Q.Y. Ning1, J.Z. Wu1, N. Zang2, J. Liang3, Y.L. Hu2 and Z.N. Mo4 1Department of Infection, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China 2The Medical Scientific Research Centre, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China 3Department of Biology Technology, Guilin Medical University, Guilin, Guangxi Zhuang Autonomous Region, China 4Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China Corresponding authors: Y.L. Hu / Z.N. Mo E-mail: [email protected] / [email protected] Genet. Mol. Res. 10 (4): 3856-3887 (2011) Received June 7, 2011 Accepted October 14, 2011 Published December 14, 2011 DOI http://dx.doi.org/10.4238/2011.December.14.10 ABSTRACT. Prostate cancer is one of the most common male malignant neoplasms; however, its causes are not completely understood. A few recent studies have used gene expression profiling of prostate cancer to identify differentially expressed genes and possible relevant pathways. However, few studies have examined the genetic mechanics of prostate cancer at the pathway level to search for such pathways. We used gene set enrichment analysis and a meta-analysis of six independent studies after standardized microarray preprocessing, which increased concordance between these gene datasets. Based on gene set enrichment analysis, there were 12 down- and 25 up-regulated mixing pathways in more than two tissue datasets, while there were two down- and two up-regulated mixing pathways in three cell datasets.
    [Show full text]
  • Predicting Coupling Probabilities of G-Protein Coupled Receptors Gurdeep Singh1,2,†, Asuka Inoue3,*,†, J
    Published online 30 May 2019 Nucleic Acids Research, 2019, Vol. 47, Web Server issue W395–W401 doi: 10.1093/nar/gkz392 PRECOG: PREdicting COupling probabilities of G-protein coupled receptors Gurdeep Singh1,2,†, Asuka Inoue3,*,†, J. Silvio Gutkind4, Robert B. Russell1,2,* and Francesco Raimondi1,2,* 1CellNetworks, Bioquant, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 2Biochemie Zentrum Heidelberg (BZH), Heidelberg University, Im Neuenheimer Feld 328, 69120 Heidelberg, Germany, 3Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan and 4Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA Received February 10, 2019; Revised April 13, 2019; Editorial Decision April 24, 2019; Accepted May 01, 2019 ABSTRACT great use in tinkering with signalling pathways in living sys- tems (5). G-protein coupled receptors (GPCRs) control multi- Ligand binding to GPCRs induces conformational ple physiological states by transducing a multitude changes that lead to binding and activation of G-proteins of extracellular stimuli into the cell via coupling to situated on the inner cell membrane. Most of mammalian intra-cellular heterotrimeric G-proteins. Deciphering GPCRs couple with more than one G-protein giving each which G-proteins couple to each of the hundreds receptor a distinct coupling profile (6) and thus specific of GPCRs present in a typical eukaryotic organism downstream cellular responses. Determining these coupling is therefore critical to understand signalling. Here, profiles is critical to understand GPCR biology and phar- we present PRECOG (precog.russelllab.org): a web- macology. Despite decades of research and hundreds of ob- server for predicting GPCR coupling, which allows served interactions, coupling information is still missing for users to: (i) predict coupling probabilities for GPCRs many receptors and sequence determinants of coupling- specificity are still largely unknown.
    [Show full text]
  • Identification of an FHL1 Protein Complex Containing Gamma-Actin and Non-Muscle Myosin IIB by Analysis of Protein-Protein Interactions
    Identification of an FHL1 Protein Complex Containing Gamma-Actin and Non-Muscle Myosin IIB by Analysis of Protein-Protein Interactions Lili Wang1*, Jianing Miao1, Lianyong Li2,DiWu1, Yi Zhang1, Zhaohong Peng1, Lijun Zhang2, Zhengwei Yuan1, Kailai Sun3 1 Key Laboratory of Health ~Ministry for Congenital Malformation, Shengjing Hospital, China Medical University, Shenyang, China, 2 Department of Pediatric Surgery, Shengjing Hospital, China Medical University, Shenyang, China, 3 Department of Medical Genetics, China Medical University, Shenyang, China Abstract FHL1 is multifunctional and serves as a modular protein binding interface to mediate protein-protein interactions. In skeletal muscle, FHL1 is involved in sarcomere assembly, differentiation, growth, and biomechanical stress. Muscle abnormalities may play a major role in congenital clubfoot (CCF) deformity during fetal development. Thus, identifying the interactions of FHL1 could provide important new insights into its functional role in both skeletal muscle development and CCF pathogenesis. Using proteins derived from rat L6GNR4 myoblastocytes, we detected FHL1 interacting proteins by immunoprecipitation. Samples were analyzed by liquid chromatography mass spectrometry (LC-MS). Dynamic gene expression of FHL1 was studied. Additionally, the expression of the possible interacting proteins gamma-actin and non- muscle myosin IIB, which were isolated from the lower limbs of E14, E15, E17, E18, E20 rat embryos or from adult skeletal muscle was analyzed. Potential interacting proteins isolated from E17 lower limbs were verified by immunoprecipitation, and co-localization in adult gastrocnemius muscle was visualized by fluorescence microscopy. FHL1 expression was associated with skeletal muscle differentiation. E17 was found to be the critical time-point for skeletal muscle differentiation in the lower limbs of rat embryos.
    [Show full text]
  • Product Description SALSA® MS-MLPA® Probemix ME031-C1 GNAS to Be Used with the MS-MLPA General Protocol
    Product description version C1-01; Issued 19 March 2021 Product Description SALSA® MS-MLPA® Probemix ME031-C1 GNAS To be used with the MS-MLPA General Protocol. Version C1 As compared to version B2, probemix completely revised, details are shown in complete product history see page 11. Catalogue numbers: ME031-025R: SALSA MS-MLPA Probemix ME031 GNAS, 25 reactions. ME031-050R: SALSA MS-MLPA Probemix ME031 GNAS, 50 reactions. ME031-100R: SALSA MS-MLPA Probemix ME031 GNAS, 100 reactions. To be used in combination with a SALSA MLPA reagent kit, SALSA HhaI (SMR50) and Coffalyser.Net data analysis software. MLPA reagent kits are either provided with FAM or Cy5.0 dye-labelled PCR primer, suitable for Applied Biosystems and Beckman/SCIEX capillary sequencers, respectively (see www.mrcholland.com). Certificate of Analysis Information regarding storage conditions, quality tests, and a sample electropherogram from the current sales lot is available at www.mrcholland.com. Precautions and warnings For professional use only. Always consult the most recent product description AND the MS-MLPA General Protocol before use: www.mrcholland.com. It is the responsibility of the user to be aware of the latest scientific knowledge of the application before drawing any conclusions from findings generated with this product. This SALSA MS-MLPA probemix is intended for experienced MLPA users only! The exact link between the GNAS complex locus genotype and phenotype is still being investigated. Use of this ME031 GNAS probemix will not always provide you with clear-cut answers and interpretation of results can therefore be complicated. MRC Holland can only provide limited support with interpretation of results obtained with this product, and recommends thoroughly screening any available literature.
    [Show full text]
  • Functional Characterisation of Human Synaptic Genes Expressed in the Drosophila Brain Lysimachos Zografos1,2, Joanne Tang1, Franziska Hesse3, Erich E
    © 2016. Published by The Company of Biologists Ltd | Biology Open (2016) 5, 662-667 doi:10.1242/bio.016261 METHODS & TECHNIQUES Functional characterisation of human synaptic genes expressed in the Drosophila brain Lysimachos Zografos1,2, Joanne Tang1, Franziska Hesse3, Erich E. Wanker3, Ka Wan Li4, August B. Smit4, R. Wayne Davies1,5 and J. Douglas Armstrong1,5,* ABSTRACT systems biology approaches are likely to be the best route to unlock a Drosophila melanogaster is an established and versatile model new generation of neuroscience research and CNS drug organism. Here we describe and make available a collection of development that society so urgently demands (Catalá-López transgenic Drosophila strains expressing human synaptic genes. The et al., 2013). Yet these modelling type approaches also need fast, collection can be used to study and characterise human synaptic tractable in vivo models for validation. genes and their interactions and as controls for mutant studies. It was More than 100 years after the discovery of the white gene in generated in a way that allows the easy addition of new strains, as well Drosophila melanogaster, the common fruit fly remains a key tool as their combination. In order to highlight the potential value of the for the study of neuroscience and neurobiology. The fruit fly collection for the characterisation of human synaptic genes we also genome is well annotated and there is a vast genetic manipulation use two assays, investigating any gain-of-function motor and/or toolkit available. This allows interventions such as high throughput cognitive phenotypes in the strains in this collection. Using these cloning (Bischof et al., 2013; Wang et al., 2012) and the precise assays we show that among the strains made there are both types of insertion of transgenes in the genome (Groth et al., 2004; Venken gain-of-function phenotypes investigated.
    [Show full text]