Table S2 A375P Vs SK-MEL-2

Total Page:16

File Type:pdf, Size:1020Kb

Table S2 A375P Vs SK-MEL-2 Supplemental Data: Table S2 tracking_id A375P SK-MEL2 Fold change des ZEB2-AS1 1.35E-286 0.315166 2.3284E+285 ZEB2 antisense RNA 1 PCDHGC5 5.45E-242 0.284138 5.2132E+240 protocadherin gamma subfamily C, 5 APOBEC3B-AS1 3.98E-240 0.130813 3.2873E+238 APOBEC3B antisense RNA 1 C6orf201 1.14E-227 0.15186 1.3345E+226 chromosome 6 open reading frame 201 LRRC24 1.93E-168 0.459062 2.3783E+167 leucine rich repeat containing 24 ZACN 3.22E-158 0.0670639 2.0857E+156 zinc activated ligand-gated ion channel LOC100506071 1.62E-144 3.33E-38 2.0564E+106 uncharacterized LOC100506071 EGFR-AS1 4.29E-101 0.0771076 1.7966E+99 EGFR antisense RNA 1 PRR5-ARHGAP8 3.44E-103 3.89E-10 1.13143E+93 PRR5-ARHGAP8 readthrough LOC100129148 8.77E-284 2.60E-214 2.96023E+69 uncharacterized LOC100129148 VLDLR-AS1 3.00E-65 0.0959249 3.20259E+63 VLDLR antisense RNA 1 ZASP 4.04E-72 4.00E-16 9.91857E+55 ZO-2 associated speckle protein SYNE1-AS1 7.86E-57 0.166307 2.11625E+55 SYNE1 antisense RNA 1 MED4-AS1 8.51E-47 0.193771 2.27656E+45 MED4 antisense RNA 1 MSTO2P 2.80E-42 0.111398 3.97942E+40 misato family member 2, pseudogene CABP7 2.99E-36 0.0263724 8.82596E+33 calcium binding protein 7 KCTD14 1.36E-151 3.05E-121 2.23303E+30 potassium channel tetramerization domain containing 14 RAET1E-AS1 1.26E-31 0.215175 1.71406E+30 RAET1E antisense RNA 1 ST7-OT3 2.06E-30 0.0817968 3.98019E+28 ST7 overlapping transcript 3 PKLR 3.86E-28 0.0268357 6.9509E+25 pyruvate kinase, liver and RBC LY75-CD302 2.64E-26 0.184984 7.01824E+24 LY75-CD302 readthrough TMEM189-UBE2V1 7.87E-23 0.322541 4.09933E+21 TMEM189-UBE2V1 readthrough PGBD3 2.12E-28 4.44E-07 2.0928E+21 piggyBac transposable element derived 3 XAGE1E 3.68E-18 0.122103 3.32106E+16 X antigen family, member 1E HSPE1-MOB4 2.23E-17 0.157601 7.06262E+15 HSPE1-MOB4 readthrough FIGF 2.43E-16 0.429744 1.77085E+15 c-fos induced growth factor (vascular endothelial growth factor D) TBC1D3 2.47E-42 1.02E-29 4.12291E+12 TBC1 domain family, member 3 CT45A1 8.67E-18 9.94E-07 1.14715E+11 cancer/testis antigen family 45, member A1 MRPL42P5 1.23E-12 0.138235 1.12105E+11 mitochondrial ribosomal protein L42 pseudogene 5 ADORA2A 1.89E-25 1.69E-15 8945107222 adenosine A2a receptor LOC100133286 7.26E-11 0.0851365 1171951033 uncharacterized LOC100133286 KLF3-AS1 1.67E-09 0.253589 151799706.7 KLF3 antisense RNA 1 TP53TG3 1.10E-09 0.162432 147676194.6 LOC101927051 4.03E-10 0.0397308 98679661.42 uncharacterized LOC101927051 LOC100288866 8.54E-09 0.594396 69611023.54 uncharacterized LOC100288866 RNF157-AS1 3.09E-09 0.0342455 11088032.74 RNF157 antisense RNA 1 TP53TG3B 1.41E-12 8.99E-06 6360086.599 TP53 target 3B LOC285819 1.06E-83 2.13E-77 2002227.465 uncharacterized LOC285819 DPY19L2P3 1.94E-07 0.169621 875779.6365 DPY19L2 pseudogene 3 SAA2-SAA4 1.95E-07 0.134017 686355.0464 SAA2-SAA4 readthrough LOC103171574 3.03E-07 0.179603 591876.0113 uncharacterized LOC103171574 SAPCD1 1.93E-06 0.661436 343177.0425 suppressor APC domain containing 1 SPTY2D1-AS1 2.72E-07 0.0929461 342060.6939 SPTY2D1 antisense RNA 1 RGPD2 1.40E-07 0.02554 182007.3544 RANBP2-like and GRIP domain containing 2 TNNI3K 3.08E-12 1.28E-07 41393.05032 TNNI3 interacting kinase SYNJ2BP-COX16 3.52E-06 0.0966859 27471.33132 SYNJ2BP-COX16 readthrough MMP23B 2.47E-06 0.0638591 25906.11841 matrix metallopeptidase 23B RNF103-CHMP3 5.15E-06 0.129952 25241.68125 RNF103-CHMP3 readthrough DNAJC25-GNG10 1.58E-05 0.312706 19831.05559 DNAJC25-GNG10 readthrough BOLA2B 1.43E-05 0.243764 17079.75701 bolA family member 2B URGCP-MRPS24 1.10E-07 0.00121781 11090.45871 URGCP-MRPS24 readthrough H3F3A 0.00141218 6.86913 4864.202864 H3 histone, family 3A DCT 0.0624829 284.921 4559.983612 dopachrome tautomerase LOC541473 2.32E-05 0.081158 3505.487718 FK506 binding protein 6, 36kDa pseudogene ZNF625-ZNF20 0.000123639 0.329237 2662.889541 ZNF625-ZNF20 readthrough (NMD candidate) PMEL 0.426334 1072.75 2516.219678 premelanosome protein RPS4Y1 0.0919449 180.389 1961.925022 ribosomal protein S4, Y-linked 1 MAGEA10-MAGEA5 2.31E-05 0.0400526 1731.622431 RGPD6 2.50E-05 0.0226537 904.3682034 RANBP2-like and GRIP domain containing 6 CELF2 0.0168651 15.1924 900.8188508 CUGBP, Elav-like family member 2 MYEOV 0.188445 162.359 861.5723421 myeloma overexpressed LCP1 0.183462 134.08 730.832543 lymphocyte cytosolic protein 1 (L-plastin) TMC5 0.0188893 13.4806 713.6632909 transmembrane channel-like 5 ARMCX1 0.0338789 23.3065 687.9355587 armadillo repeat containing, X-linked 1 ITIH5 0.173853 113.085 650.4633225 inter-alpha-trypsin inhibitor heavy chain family, member 5 LINC00518 0.0233296 13.8745 594.7165832 long intergenic non-protein coding RNA 518 SSX1 0.118177 68.7288 581.575095 synovial sarcoma, X breakpoint 1 ALDH1L1 0.0235821 12.9787 550.3623511 aldehyde dehydrogenase 1 family, member L1 PHKA2-AS1 0.0620347 33.9326 546.9938599 PHKA2 antisense RNA 1 LOC146481 0.080657 42.666 528.9807456 SMIM10 0.0487491 23.9358 490.9998338 small integral membrane protein 10 CAPN3 0.504442 219.645 435.4217135 calpain 3, (p94) LOC100507065 0.0974265 40.9329 420.1413373 uncharacterized LOC100507065 KCNN2 0.103505 43.4419 419.7082267 potassium channel, calcium activated intermediate/small conductance subfamily N alpha, member 2 GALM 0.0287394 11.9635 416.275218 galactose mutarotase (aldose 1-epimerase) BIRC7 0.0582455 23.5514 404.3471169 baculoviral IAP repeat containing 7 TBC1D3C 6.66E-10 2.53E-07 379.5507207 TBC1 domain family, member 3C BMX 0.169632 63.9133 376.7761979 BMX non-receptor tyrosine kinase SLC24A5 0.055791 20.6956 370.9487193 solute carrier family 24 (sodium/potassium/calcium exchanger), member 5 PCNA-AS1 2.65E-07 9.56E-05 360.5274772 PCNA antisense RNA 1 INO80B-WBP1 0.00457204 1.62935 356.3726477 INO80B-WBP1 readthrough (NMD candidate) SLC15A1 0.0224679 7.7178 343.5033982 solute carrier family 15 (oligopeptide transporter), member 1 MLANA 0.49183 168.94 343.4926702 melan-A RFPL4B 0.0384573 12.222 317.8070223 ret finger protein-like 4B LOC101927040 0.0341406 10.7807 315.7735951 uncharacterized LOC101927040 GPR128 0.0223693 6.92704 309.6672672 STXBP2 0.155013 42.6336 275.0324166 syntaxin binding protein 2 MEIOB 0.0430797 11.6798 271.1207367 meiosis specific with OB domains TSPAN7 0.0406748 10.9249 268.5913637 tetraspanin 7 LOC400644 0.0391152 10.2656 262.4452898 RGS1 0.0540326 14.0118 259.3212246 regulator of G-protein signaling 1 CNDP1 0.0655798 16.7417 255.2874513 carnosine dipeptidase 1 (metallopeptidase M20 family) GPM6B 0.109051 26.7367 245.1761103 glycoprotein M6B MB21D1 0.0407749 9.99031 245.0112692 Mab-21 domain containing 1 TRIM48 0.104489 25.5973 244.9760262 tripartite motif containing 48 ATP6V0D2 0.151097 36.9015 244.2239091 ATPase, H+ transporting, lysosomal 38kDa, V0 subunit d2 STK32A 0.0279351 6.67584 238.9767712 serine/threonine kinase 32A FLJ16779 0.00888566 2.08682 234.8525602 uncharacterized LOC100192386 ALDH3B2 0.0572018 13.373 233.7863494 aldehyde dehydrogenase 3 family, member B2 FILIP1 0.0294972 6.77665 229.7387549 filamin A interacting protein 1 PRUNE2 0.01603 3.39768 211.9575795 prune homolog 2 (Drosophila) CLDN14 0.0420915 8.89957 211.4339 claudin 14 CHL1 0.034005 7.09565 208.6649022 cell adhesion molecule L1-like OCIAD2 0.260965 53.1906 203.8227348 OCIA domain containing 2 APOD 1.8068 368.038 203.6960372 apolipoprotein D LOC100128682 0.0211425 4.16248 196.8773797 FAM167B 1.44485 262.289 181.5337232 family with sequence similarity 167, member B SCN9A 0.0207549 3.76748 181.5224357 sodium channel, voltage gated, type IX alpha subunit MAGEA1 0.345986 62.0685 179.3959871 melanoma antigen family A1 PHACTR1 0.13181 22.6246 171.6455504 phosphatase and actin regulator 1 DPPA4 0.025105 4.09289 163.0308703 developmental pluripotency associated 4 SCARNA13 4.74E-06 0.00075542 159.2668102 small Cajal body-specific RNA 13 MYCN 0.054592 8.50811 155.8490255 v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog ECHDC3 0.0448575 6.89751 153.7649223 enoyl CoA hydratase domain containing 3 CA8 0.15771 23.7692 150.7146028 carbonic anhydrase VIII MATN2 0.0505877 7.45796 147.4263507 matrilin 2 LDB3 0.0415073 5.76998 139.0112101 LIM domain binding 3 DNAH6 0.0367808 5.03326 136.8447668 dynein, axonemal, heavy chain 6 PROM1 0.0179822 2.3955 133.2150682 prominin 1 XKR8 0.0669032 8.29965 124.0546043 XK, Kell blood group complex subunit-related family, member 8 CPED1 0.249591 30.8613 123.6474873 cadherin-like and PC-esterase domain containing 1 TMX2-CTNND1 6.16E-06 0.000758202 123.1087225 TMX2-CTNND1 readthrough (NMD candidate) FUT4 0.0225832 2.54757 112.8081937 fucosyltransferase 4 (alpha (1,3) fucosyltransferase, myeloid-specific) ADAMTS2 0.0403372 4.46274 110.6358399 ADAM metallopeptidase with thrombospondin type 1 motif, 2 WNK3 0.0120559 1.30982 108.6455594 WNK lysine deficient protein kinase 3 TMEM232 0.0219837 2.3669 107.6661345 transmembrane protein 232 LOC100133445 0.282585 30.1585 106.7236407 TLR2 0.0205527 2.17072 105.6172668 toll-like receptor 2 CA14 0.042364 4.47316 105.5887074 carbonic anhydrase XIV LINC00634 0.0498486 5.26295 105.5786923 long intergenic non-protein coding RNA 634 TDRP 0.0204198 2.13342 104.4780066 testis development related protein NSG1 0.17743 18.4474 103.9700163 neuron specific gene family member 1 SLC24A2 0.00623271 0.639807 102.6530995 solute carrier family 24 (sodium/potassium/calcium exchanger), member 2 P2RX7 0.0603769 5.91636 97.99045662 purinergic receptor P2X, ligand gated ion channel, 7 LOC157273 0.020301 1.9776 97.4139205 uncharacterized LOC157273 FAT2 0.0369389 3.5896 97.17668907 FAT atypical cadherin 2 TPTEP1 0.105739 10.1879 96.34950208 transmembrane phosphatase with tensin
Recommended publications
  • MLR) Model and Filtration
    SUPPLEMENT Contents 1. Extended description of the Data sets. 2. Extended description of the multiple linear regression (MLR) model and filtration. 3. Extended description of the random-gene classifiers. 4. Extended description of the comparison with Dakhova et al. (1)and Richardson et al. (2) 5. Extended description of preparation of RNA and the XP_PCR protocol. 6. Table S1. Comparison of 131-probe set Diagnostic Classifier to classifiers generated with ‘random’ genes. 7. Table S2. Concordance of 38 overlapping genes/probe sets of the 339 probe sets ( basis) of the Diagnostic Classifier with the sign of differential change of Dakhova et al. (1). 8. Table S3. Function enrichment analysis. 9. Table S4. PCR validation of preferential expression in stroma by representative genes of the Diagnostic Classifier. 10. Figure S1. The incidence numbers of 339 probe sets obtained by 105-fold permutation procedure for gene selection. 11. Figure S2. Heatmap using the Diagnostic Classifier to categorize all training cases. 12. Figure S3. Heatmap of all 364 test samples used in this study as categorized by the 131 probe set Diagnostic Classifier. 13. Figure S4. Cluster diagram of the cases of Dakhova et al. (1) using only the 38 overlapping genes. 14. References for the Supplement. 1 1. Extended description of the Data Sets. Datasets 1 and 2 (Table 1) are based on post-prostatectomy frozen tissue samples obtained by informed consent using IRB-approved and HIPPA-compliant protocols. All tissues, except where noted, were collected at surgery and escorted to pathology for expedited review, dissection, and snap freezing in liquid nitrogen.
    [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]
  • 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).
    [Show full text]
  • Structural and Functional Diversity of Caspase Homologues in Non-Metazoan Organisms
    Protoplasma DOI 10.1007/s00709-017-1145-5 REVIEW ARTICLE Structural and functional diversity of caspase homologues in non-metazoan organisms Marina Klemenčič1,2 & Christiane Funk1 Received: 1 June 2017 /Accepted: 5 July 2017 # The Author(s) 2017. This article is an open access publication Abstract Caspases, the proteases involved in initiation and supports the role of metacaspases and orthocaspases as im- execution of metazoan programmed cell death, are only pres- portant contributors to cell homeostasis during normal physi- ent in animals, while their structural homologues can be ological conditions or cell differentiation and ageing. found in all domains of life, spanning from simple prokary- otes (orthocaspases) to yeast and plants (metacaspases). All members of this wide protease family contain the p20 do- Keywords Algae . Cyanobacteria . Cell death . Cysteine main, which harbours the catalytic dyad formed by the two protease . Metacaspase . Orthocaspase amino acid residues, histidine and cysteine. Despite the high structural similarity of the p20 domain, metacaspases and orthocaspases were found to exhibit different substrate speci- ficities than caspases. While the former cleave their substrates Introduction after basic amino acid residues, the latter accommodate sub- strates with negative charge. This observation is crucial for BOut of life’s school of war: What does not destroy me, the re-evaluation of non-metazoan caspase homologues being makes me stronger.^ wrote the German philosopher involved in processes of programmed cell death. In this re- Friedrich Nietzsche in his book Twilight of the Idols or view, we analyse the structural diversity of enzymes contain- how to philosophize with a hammer. Even though ing the p20 domain, with focus on the orthocaspases, and reformatted to more common use, this phrase has been summarise recent advances in research of orthocaspases and used to describe the dual nature of caspase homologues metacaspases of cyanobacteria, algae and higher plants.
    [Show full text]
  • Investigation of Candidate Genes and Mechanisms Underlying Obesity
    Prashanth et al. BMC Endocrine Disorders (2021) 21:80 https://doi.org/10.1186/s12902-021-00718-5 RESEARCH ARTICLE Open Access Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules G. Prashanth1 , Basavaraj Vastrad2 , Anandkumar Tengli3 , Chanabasayya Vastrad4* and Iranna Kotturshetti5 Abstract Background: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. Methods: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. Results: A total of 820 DEGs were identified between
    [Show full text]
  • Cell Reprogramming Technologies for Treatment And
    CELL REPROGRAMMING TECHNOLOGIES FOR TREATMENT AND UNDERSTANDING OF GENETIC DISORDERS OF MYELIN by ANGELA MARIE LAGER Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Thesis advisor: Paul J Tesar, PhD Department of Genetics and Genome Sciences CASE WESTERN RESERVE UNIVERSITY May 2015 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Angela Marie Lager Candidate for the Doctor of Philosophy degree*. (signed) Ronald A Conlon, PhD (Committee Chair) Paul J Tesar, PhD (Advisor) Craig A Hodges, PhD Warren J Alilain, PhD (date) 31 March 2015 *We also certify that written approval has been obtained from any proprietary material contained therein. TABLE OF CONTENTS Table of Contents……………………………………………………………………….1 List of Figures……………………………………………………………………………4 Acknowledgements……………………………………………………………………..7 Abstract…………………………………………………………………………………..8 Chapter 1: Introduction and Background………………………………………..11 1.1 Overview of mammalian oligodendrocyte development in the spinal cord and myelination of the central nervous system…………………..11 1.1.1 Introduction……………………………………………………..11 1.1.2 The establishment of the neuroectoderm and ventral formation of the neural tube…………………………………..12 1.1.3 Ventral patterning of the neural tube and specification of the pMN domain in the spinal cord……………………………….15 1.1.4 Oligodendrocyte progenitor cell production through the process of gliogenesis ………………………………………..16 1.1.5 Oligodendrocyte progenitor cell to oligodendrocyte differentiation…………………………………………………...22
    [Show full text]
  • Serine Proteases with Altered Sensitivity to Activity-Modulating
    (19) & (11) EP 2 045 321 A2 (12) EUROPEAN PATENT APPLICATION (43) Date of publication: (51) Int Cl.: 08.04.2009 Bulletin 2009/15 C12N 9/00 (2006.01) C12N 15/00 (2006.01) C12Q 1/37 (2006.01) (21) Application number: 09150549.5 (22) Date of filing: 26.05.2006 (84) Designated Contracting States: • Haupts, Ulrich AT BE BG CH CY CZ DE DK EE ES FI FR GB GR 51519 Odenthal (DE) HU IE IS IT LI LT LU LV MC NL PL PT RO SE SI • Coco, Wayne SK TR 50737 Köln (DE) •Tebbe, Jan (30) Priority: 27.05.2005 EP 05104543 50733 Köln (DE) • Votsmeier, Christian (62) Document number(s) of the earlier application(s) in 50259 Pulheim (DE) accordance with Art. 76 EPC: • Scheidig, Andreas 06763303.2 / 1 883 696 50823 Köln (DE) (71) Applicant: Direvo Biotech AG (74) Representative: von Kreisler Selting Werner 50829 Köln (DE) Patentanwälte P.O. Box 10 22 41 (72) Inventors: 50462 Köln (DE) • Koltermann, André 82057 Icking (DE) Remarks: • Kettling, Ulrich This application was filed on 14-01-2009 as a 81477 München (DE) divisional application to the application mentioned under INID code 62. (54) Serine proteases with altered sensitivity to activity-modulating substances (57) The present invention provides variants of ser- screening of the library in the presence of one or several ine proteases of the S1 class with altered sensitivity to activity-modulating substances, selection of variants with one or more activity-modulating substances. A method altered sensitivity to one or several activity-modulating for the generation of such proteases is disclosed, com- substances and isolation of those polynucleotide se- prising the provision of a protease library encoding poly- quences that encode for the selected variants.
    [Show full text]
  • UNIVERSITY of CALIFORNIA RIVERSIDE Investigations Into The
    UNIVERSITY OF CALIFORNIA RIVERSIDE Investigations into the Role of TAF1-mediated Phosphorylation in Gene Regulation A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Cell, Molecular and Developmental Biology by Brian James Gadd December 2012 Dissertation Committee: Dr. Xuan Liu, Chairperson Dr. Frank Sauer Dr. Frances M. Sladek Copyright by Brian James Gadd 2012 The Dissertation of Brian James Gadd is approved Committee Chairperson University of California, Riverside Acknowledgments I am thankful to Dr. Liu for her patience and support over the last eight years. I am deeply indebted to my committee members, Dr. Frank Sauer and Dr. Frances Sladek for the insightful comments on my research and this dissertation. Thanks goes out to CMDB, especially Dr. Bachant, Dr. Springer and Kathy Redd for their support. Thanks to all the members of the Liu lab both past and present. A very special thanks to the members of the Sauer lab, including Silvia, Stephane, David, Matt, Stephen, Ninuo, Toby, Josh, Alice, Alex and Flora. You have made all the years here fly by and made them so enjoyable. From the Sladek lab I want to thank Eugene, John, Linh and Karthi. Special thanks go out to all the friends I’ve made over the years here. Chris, Amber, Stephane and David, thank you so much for feeding me, encouraging me and keeping me sane. Thanks to the brothers for all your encouragement and prayers. To any I haven’t mentioned by name, I promise I haven’t forgotten all you’ve done for me during my graduate years.
    [Show full text]
  • RSC Advances
    RSC Advances This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. This Accepted Manuscript will be replaced by the edited, formatted and paginated article as soon as this is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. www.rsc.org/advances Page 1 of 8 RSC Advances The Effects of Solvent Composition on the Affinity of a Peptide towards Hair Keratin: Experimental and Molecular Dynamics Data. Egipto Antunes a, Célia F. Cruz a, Nuno G. Azoia a, Artur Cavaco-Paulo a* a CEB – Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal Table of Contents Manuscript Accepted Molecular dynamics simulations with a developed hair protofibril model demonstrated capability to improve a peptide uptake by hair shafts. Advances RSC RSC Advances Page 2 of 8 RSC Advances RSCPublishing ARTICLE The Effects of Solvent Composition on the Affinity of a Peptide towards Hair Keratin: Experimental and Cite this: DOI: 10.1039/x0xx00000x Molecular Dynamics Data.
    [Show full text]
  • TITLE PAGE Oxidative Stress and Response to Thymidylate Synthase
    Downloaded from molpharm.aspetjournals.org at ASPET Journals on October 2, 2021 -Targeted -Targeted 1 , University of of , University SC K.W.B., South Columbia, (U.O., Carolina, This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • Gene Expression Profiles Complement the Analysis of Genomic Modifiers of the Clinical Onset of Huntington Disease
    bioRxiv preprint doi: https://doi.org/10.1101/699033; this version posted July 11, 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. 1 Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease Galen E.B. Wright1,2,3; Nicholas S. Caron1,2,3; Bernard Ng1,2,4; Lorenzo Casal1,2,3; Xiaohong Xu5; Jolene Ooi5; Mahmoud A. Pouladi5,6,7; Sara Mostafavi1,2,4; Colin J.D. Ross3,7 and Michael R. Hayden1,2,3* 1Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada; 2Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada; 3BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada; 4Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada; 5Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR), Singapore; 6Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 7Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 8Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; *Corresponding author ABSTRACT Huntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in the HTT gene. In an attempt to identify genomic modifiers that contribute towards the age of onset of HD, we performed a transcriptome wide association study assessing heritable differences in genetically determined expression in diverse tissues, employing genome wide data from over 4,000 patients.
    [Show full text]