GPCR Endocytosis Confers Uniformity in Responses to Chemically Distinct Ligands

Total Page:16

File Type:pdf, Size:1020Kb

GPCR Endocytosis Confers Uniformity in Responses to Chemically Distinct Ligands MOL #106369 Molecular Pharmacology GPCR endocytosis confers uniformity in responses to chemically distinct ligands Nikoleta G. Tsvetanova, Michelle Trester-Zedlitz, Billy W. Newton, Daniel P. Riordan, Aparna B. Sundaram, Jeffrey R. Johnson, Nevan J. Krogan and Mark von Zastrow Department of Psychiatry, University of California, San Francisco, CA, USA (N.G.T., M.T-Z., M.v.Z.), Department of Cellular & Molecular Pharmacology, University of California, San Francisco, CA, USA (M.v.Z.), California Institute for Quantitative Biosciences, QB3, University of California, San Francisco, CA, USA (B.W.N., J.R.J., N.J.K.), J. David Gladstone Institute, San Francisco, CA, USA (N.J.K.), Department of Biochemistry, Stanford University, Stanford, CA, USA (D.P.R.), Lung Biology Center, Department of Medicine, University of California, San Francisco, CA, USA (A.B.S.) Supplementary Information Supplemental Table 1. Summary of phospho-proteomic SILAC analysis. Data are summary of the two media-swap experiments for each condition. High iso = 1 μM isoproterenol; low iso = 10 nM isoproterenol; sal = 50 nM salmeterol. High Iso peptides 4101 High Iso unique proteins 1270 Low Iso peptides 3796 Low Iso unique proteins 1196 Sal peptides 4303 Sal unique proteins 1318 Supplemental Table 2. Beta2-AR regulated phosphosites- position of phosphorylated residue(s) and fold-changes. Values are shown as average Log2 (drug/no drug) from a set of medium swap experiments treated as two independent replicates. High iso = 1 μM isoproterenol; low iso = 10 nM isoproterenol; sal = 50 nM salmeterol. NA = neither of the replicates had a value for the peptide. Uniprot Protein ID Name Phospho-site(s) High Iso Low Iso Sal P49792 NUP358 Ser1509 3.66 3.04 2.31 O75396 SEC22B Ser137 3.32 0.52 1.16 P46821 MAP1B Ser561 2.85 2.68 NA P67809 YBX1 Ser313 2.79 2.86 NA Q6ZNB6 NFXL1 Ser835 2.62 1.80 0.65 Q9H2J7 SLC6A15 Ser699, Ser701 2.26 1.84 1.87 Q7Z3C6 ATG9A Ser735 2.08 1.64 0.95 P13591 NCAM1 Ser783 2.04 1.58 1.22 Q96D71 REPS1 Ser272, Ser273 2.04 2.06 0.44 Q3KQU3 PARCC1 Ser113 1.93 1.27 1.11 Q96D71 REPS1 Ser272, Ser273 1.89 1.70 2.15 Q9NWW5 CLN6 Ser31 1.88 1.28 1.23 O43847 NRD1 Ser94 1.86 1.77 1.43 O43318 MAP3K7 Ser389 1.83 NA 0.89 Q4KMP7 FP2461 Ser687 1.75 1.38 0.43 O43847 NRD1 Ser94 1.75 1.79 1.56 O43847 NRD1 Ser96 1.74 1.70 1.19 P16949 STMN1 Ser63 1.70 1.22 0.78 Q92614 MYO18A Ser1067, Ser1069 1.57 1.48 0.44 P53602 MPD Ser96 1.55 1.12 0.64 Q92685 ALG3 Ser13 1.54 1.39 1.31 P62753 RPS6 Ser235, Ser236 1.54 1.01 0.82 P13591 NCAM1 Ser779 1.43 1.15 NA P35222 CTNNB1 Thr551 1.35 0.90 1.01 P16403 H1F2 Ser36 1.27 0.86 0.78 1 P62753 RPS6 Ser235, Ser 236, Ser 240 1.27 1.02 1.06 P35222 CTNNB1 Ser552 1.26 1.00 0.99 P35222 CTNNB1 Thr556 1.24 1.43 NA Q9UK61 C3orf63 Ser694, Ser699 1.11 0.55 0.99 Q96TC7 FAM82A2 Ser46 1.09 0.91 0.94 Q09161 CBP80 Ser22 1.05 0.62 0.19 Q14671 PUM1 Ser709 1.05 0.30 0.05 Q96TC7 FAM82A2 Ser44 1.04 1.00 0.83 O75152 ZC3H11A Ser290 1.04 0.06 0.32 P53396 ACLY Thr453 1.01 0.85 0.44 Q09161 CBP80 Thr21 1.00 0.47 0.18 P53396 ACLY Thr453 0.68 1.13 0.54 P53396 ACLY Ser455 0.61 0.77 0.47 Q9C0C9 UBE2O Thr834 -0.32 -0.40 -1.11 P35659 DEK Ser306, Ser307 -0.84 -1.44 -0.27 O75475 PSIP1 Ser271 -0.96 -0.90 -0.89 P17096 HMGA1 Ser103 -0.99 -1.00 -0.87 P35659 DEK Ser303, Ser306, Ser307 -1.00 -0.87 -0.89 P08240 SRPR Ser296 -1.01 -0.64 -0.29 O75448 TRAP100 Ser862 -1.06 NA -1.54 Q9H1E3 NUCKS1 Ser144 -1.13 -0.89 -0.72 Q7Z4V5 HDGF2 Ser366, Ser369 -1.14 -1.05 -0.48 Q7Z4V5 HDGF2 Ser366, Ser369 -1.18 -1.01 -0.10 Q7Z4V5 HDGF2 Ser366, Ser369 -1.25 -1.00 NA P19338 NCL Ser145 -1.25 -0.92 -0.46 Q14978 NOLC1 Ser264 -1.74 -2.44 -0.57 Q14978 NOLC1 Ser362 -2.62 NA -0.60 P35659 DEK Ser301, Ser303, Ser306 NA -1.06 NA Q14978 NOLC1 Ser361 NA -1.66 -0.78 2 Supplemental Table 3. Beta2-AR regulated phosphosites- modified sequence and predicted kinases targeting the peptide. Position(s) of phosphorylated residues are indicated in parentheses. Kinases were assigned using the NetPhorest1 algorithm. Uniprot Protein Modified Previously Predicted ID Name Sequence described Kinase1 P67809 YBX1 _AADPPAENS(ph)SAPEAEQGGA TGFbR2 E_ P13591 NCAM1 _AAFSKDES(ph)KEPIVEVR_ TGFbR2 Q96TC7 FAM82A2 _S(ph)QSLPNSLDYTQTSDPGR_ Gunaratne et TGFbR2 al. (2010)2 Q14978 NOLC1 _AAES(ph)SSDSSDSDSSEDDEA TGFbR2 PSKPAGTTK_ Q14978 NOLC1 _S(ph)SSSEDSSSDEEEEQKKPM TGFbR2 K_ Q14978 NOLC1 _AAESS(ph)SDSSDSDSSEDDEA TGFbR2 PSKPAGTTK_ O43318 MAP3K7 _RMS(ph)ADMSEIEAR_ PKCtheta Q92685 ALG3 _SGS(ph)AAQAEGLCK_ PKBgam ma P53602 MPD _RNS(ph)RDGDPLPSSLSCK_ PKBgam ma O75396 SEC22B _NLGS(ph)INTELQDVQR_ Gunaratne et PKBbeta al. (2010)2 P35222 CTNNB1 _RTS(ph)MGGTQQQFVEGVR_ Gunaratne et PKAbeta al. (2010)2 P46821 MAP1B _KES(ph)KEETPEVTK_ PKAalpha Q6ZNB6 NFXL1 _KAS(ph)EIKEAEAK_ PKAalpha P49792 NUP358 _KQS(ph)LPATSIPTPASFK_ PKAalpha Q3KQU3 PARCC1 _RSS(ph)QPSPTAVPASDSPPTK PKAalpha QEVK_ Q7Z3C6 ATG9A _RES(ph)DESGESAPDEGGEGAR PKAalpha _ O43847 NRD1 _RGS(ph)LSNAGDPEIVK_ PKAalpha O43847 NRD1 _LGADESEEEGRRGS(ph)LSNAG PKAalpha DPEIVK_ P16949 STMN1 _RKS(ph)HEAEVLK_ Yip et al. PKAalpha (2014)4 Q14671 PUM1 _RDS(ph)LTGSSDLYKR_ Gunaratne et PKAalpha al. (2010)2 Q09161 CBP80 _KTS(ph)DANETEDHLESLICK_ Pim3 O43847 NRD1 _LGADESEEEGRRGSLS(ph)NAG PAK1 DPEIVK_ P35222 CTNNB1 _RT(ph)SMGGTQQQFVEGVR_ Gunaratne et MRCKa al. (2010)2 Q9C0C9 UBE2O _NMTVEQLLTGSPT(ph)SPTVEPE MAPK3 KPTR_ 3 P53396 ACLY _T(ph)ASFSESRADEVAPAKK_ ICK O75152 ZC3H11A _KFS(ph)AGGDSDPPLKR_ ICK P53396 ACLY _T(ph)ASFSESRADEVAPAK_ ICK P53396 ACLY _TAS(ph)FSESRADEVAPAKK_ Berwick et al. ICK (2002)5 O75448 TRAP100 _LLS(ph)SNEDDANILSSPTDR_ DMPK2 P62753 RPS6 _RLS(ph)S(ph)LRASTSK_ Lundby et al. CLK4; (2013)3 PKCalpha P62753 RPS6 _RLS(ph)S(ph)LRAS(ph)TSK_ Lundby et al. CLK4; (2013)3 PKCalpha Q96D71 REPS1 _RQS(ph)S(ph)SYDDPWKITDEQ CLK1; ICK R_ Q96D71 REPS1 _RQS(ph)S(ph)SYDDPWK_ CLK1; ICK Q9UK61 C3orf63 _KKS(ph)VGGDS(ph)DTEDMR_ CLK1; CK2a2 Q9H2J7 SLC6A15 _KQS(ph)GS(ph)PTLDTAPNGR_ CLK1 Q9NWW5 CLN6 _HGS(ph)VSADEAAR_ CLK1 Q4KMP7 FP2461 _RAS(ph)AGPAPGPVVTAEGLHP CLK1 SLPSPTGNSTPLGSSK_ Q92614 MYO18A _RVS(ph)SS(ph)SELDLPSGDHCE CLK1 AGLLQLDVPLLR_ P16403 H1F2 _KAS(ph)GPPVSELITK_ CLK1 Q96TC7 FAM82A2 _SQS(ph)LPNSLDYTQTSDPGR_ Gunaratne et CLK1 al. (2010)2 Q09161 CBP80 _KT(ph)SDANETEDHLESLICK_ CLK1 P13591 NCAM1 _AAFS(ph)KDESKEPIVEVR_ CK2alpha P35659 DEK _KESES(ph)EDS(ph)S(ph)DDEPLI CK2alpha KK_ P17096 HMGA1 _KLEKEEEEGISQESS(ph)EEEQ_ CK2alpha P08240 SRPR _GTGSGGQLQDLDCS(ph)SSDDE CK2alpha GAAQNSTKPSATK_ P35659 DEK _KES(ph)ES(ph)EDS(ph)SDDEPLI CK2alpha KK_ P19338 NCL _KEDS(ph)DEEEDDDSEEDEEDD CK2alpha EDEDEDEDEIEPAAMK_ P35659 DEK _KESESEDS(ph)S(ph)DDEPLIKK_ CK2alpha O75475 PSIP1 _TGVTS(ph)TSDSEEEGDDQEGE CK2a2 KKR_ Q7Z4V5 HDGF2 _GS(ph)GGS(ph)SGDELREDDEP CK2a2 VKK_ Q9H1E3 NUCKS1 _DSGSDEDFLMEDDDDS(ph)DYG CK2a2 SSK_ Q7Z4V5 HDGF2 _GSGGS(ph)S(ph)GDELREDDEP CK2a2 VKK_ Q7Z4V5 HDGF2 _GS(ph)GGS(ph)SGDELREDDEP CK2a2 VK_ P35222 CTNNB1 _RTSMGGT(ph)QQQFVEGVR_ ATR 4 Supplemental Table 4. Statistical analysis of the abundance of target phosphopeptides in isoproterenol and salmeterol. Summarized are p-values from a two-sided comparison corrected for multiple hypothesis testing based on parameters from regression analysis of the β2-AR phosphotargets. High iso = 1 μM isoproterenol; low iso = 10 nM isoproterenol; sal = 50 nM salmeterol. Condition used for null distribution simulation High Iso Low Iso Sal Sal <0.0001 0.0036 N.S. Low Iso 0.014 N.S. <0.0001 High Iso N.S. N.S. <0.0001 Supplemental Table 5. B2-AR-dependent transcriptional targets from DNA microarray experiments. Averaged Log2(Drug/No Drug) values for each condition (10 nM isoproterenol or 50 nM salmeterol) from n=3 are shown. Previously known CREB targets are indicated. Gene Description Iso Sal CREB Target C6orf176 Chromosome 6 open reading frame 176 5.36 5.70 Yes CGA Glycoprotein hormones, alpha polypeptide 3.88 4.29 Yes PCK1 Phosphoenolpyruvate carboxykinase 1 3.76 4.18 Yes NR4A3 Nuclear receptor subfamily 4, group A, 3.18 3.58 Yes member 3 NR4A1 Nuclear receptor subfamily 4, group A, 2.83 3.17 Yes member 1 NR4A2 Nuclear receptor subfamily 4, group A, 2.17 2.35 Yes member 2 PDE4B Phosphodiesterase 4B, cAMP-specific 2.08 1.48 Yes CCK Cholecystokinin 1.94 2.18 Yes DUSP1 Dual specificity phosphatase 1 1.75 1.73 Yes CCL8 Chemokine (C-C motif) ligand 8 1.65 2.59 No HIST1H4C Histone cluster 1, H4c 1.64 0.97 Yes LOC387763 Hypothetical LOC387763 1.61 1.73 No mtRNA_COX-2 Mitochondrially encoded cytochrome c 1.59 1.51 No oxidase 2 PDE4D Phosphodiesterase 4D, cAMP-specific 1.52 1.12 Yes EST_AA48139 AA481397_Exon1_331 1.41 1.07 No 7 ENO1 Enolase 1 1.36 1.31 No FOSB FBJ murine osteosarcoma viral oncogene 1.36 2.02 Yes homolog B 5 NME2 Non-metastatic cells 2, protein 1.35 1.41 No SOD1 Superoxide dismutase 1, soluble 1.30 1.24 No GNB2L1 Guanine nucleotide binding protein (G 1.28 1.30 No protein), beta polypeptide 2-like 1 EEF1G Eukaryotic translation elongation factor 1 1.27 1.22 No gamma RPS9 Ribosomal protein S9 1.27 1.30 Yes KRTAP19-5 Keratin associated protein 19-5 1.27 1.15 No RPRM Eeprimo, TP53 dependent G2 arrest 1.26 1.21 No mediator candidate mtRNA_ND4 Mitochondrially encoded NADH 1.25 1.17 No dehydrogenase subunit 4 AVPI1 Arginine vasopressin-induced 1 1.24 1.34 No PRMT1 Protein arginine methyltransferase 1 1.22 1.07 No DYNLT1 Dynein, light chain, Tctex-type 1 1.22 1.06 No RPS15 Ribosomal protein S15 1.20 1.17 No H2AFX H2A histone family, member X 1.16 1.15 No AARS Alanyl-tRNA synthetase 1.16 0.99 No mtRNA_CYTB Mitochondrially encoded cytochrome b 1.16 0.91 No mtRNA_RNR2 Mitochondrially encoded 16S ribosomal 1.14 1.09 No RNA MYL6 Myosin, light polypeptide 6 1.14 1.14 Yes ARPC2 Actin related protein 2/3 complex, subunit
Recommended publications
  • Analysis of Gene Expression Data for Gene Ontology
    ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.
    [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]
  • MBNL1 Regulates Essential Alternative RNA Splicing Patterns in MLL-Rearranged Leukemia
    ARTICLE https://doi.org/10.1038/s41467-020-15733-8 OPEN MBNL1 regulates essential alternative RNA splicing patterns in MLL-rearranged leukemia Svetlana S. Itskovich1,9, Arun Gurunathan 2,9, Jason Clark 1, Matthew Burwinkel1, Mark Wunderlich3, Mikaela R. Berger4, Aishwarya Kulkarni5,6, Kashish Chetal6, Meenakshi Venkatasubramanian5,6, ✉ Nathan Salomonis 6,7, Ashish R. Kumar 1,7 & Lynn H. Lee 7,8 Despite growing awareness of the biologic features underlying MLL-rearranged leukemia, 1234567890():,; targeted therapies for this leukemia have remained elusive and clinical outcomes remain dismal. MBNL1, a protein involved in alternative splicing, is consistently overexpressed in MLL-rearranged leukemias. We found that MBNL1 loss significantly impairs propagation of murine and human MLL-rearranged leukemia in vitro and in vivo. Through transcriptomic profiling of our experimental systems, we show that in leukemic cells, MBNL1 regulates alternative splicing (predominantly intron exclusion) of several genes including those essential for MLL-rearranged leukemogenesis, such as DOT1L and SETD1A.Wefinally show that selective leukemic cell death is achievable with a small molecule inhibitor of MBNL1. These findings provide the basis for a new therapeutic target in MLL-rearranged leukemia and act as further validation of a burgeoning paradigm in targeted therapy, namely the disruption of cancer-specific splicing programs through the targeting of selectively essential RNA binding proteins. 1 Division of Bone Marrow Transplantation and Immune Deficiency, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA. 2 Cancer and Blood Diseases Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA. 3 Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA.
    [Show full text]
  • A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast
    A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast By Jessica M. Vera B.S. University of Wisconsin-Madison, 2007 A thesis submitted to the Faculty of the Graduate School in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Molecular, Cellular, and Developmental Biology 2015 This thesis entitled: A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast written by Jessica M. Vera has been approved for the Department of Molecular, Cellular, and Developmental Biology Tom Blumenthal Robin Dowell Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline iii Vera, Jessica M. (Ph.D., Molecular, Cellular and Developmental Biology) A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast Thesis Directed by Robin Dowell Cryptic unstable transcripts (CUTs) are a largely unexplored class of nuclear exosome degraded, non-coding RNAs in budding yeast. It is highly debated whether CUT transcription has a functional role in the cell or whether CUTs represent noise in the yeast transcriptome. I sought to ascertain the extent of conserved CUT expression across a variety of Saccharomyces yeast strains to further understand and characterize the nature of CUT expression. To this end I designed a Hidden Markov Model (HMM) to analyze strand-specific RNA sequencing data from nuclear exosome rrp6Δ mutants to identify and compare CUTs in four different yeast strains: S288c, Σ1278b, JAY291 (S.cerevisiae) and N17 (S.paradoxus).
    [Show full text]
  • 1 Supporting Information for a Microrna Network Regulates
    Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia.
    [Show full text]
  • PAR-CLIP Data Indicate That Nrd1-Nab3
    Webb et al. Genome Biology 2014, 15:R8 http://genomebiology.com/2014/15/1/R8 RESEARCH Open Access PAR-CLIP data indicate that Nrd1-Nab3-dependent transcription termination regulates expression of hundreds of protein coding genes in yeast Shaun Webb2, Ralph D Hector1, Grzegorz Kudla3 and Sander Granneman1,2* Abstract Background: Nrd1 and Nab3 are essential sequence-specific yeast RNA binding proteins that function as a heterodimer in the processing and degradation of diverse classes of RNAs. These proteins also regulate several mRNA coding genes; however, it remains unclear exactly what percentage of the mRNA component of the transcriptome these proteins control. To address this question, we used the pyCRAC software package developed in our laboratory to analyze CRAC and PAR-CLIP data for Nrd1-Nab3-RNA interactions. Results: We generated high-resolution maps of Nrd1-Nab3-RNA interactions, from which we have uncovered hundreds of new Nrd1-Nab3 mRNA targets, representing between 20 and 30% of protein-coding transcripts. Although Nrd1 and Nab3 showed a preference for binding near 5′ ends of relatively short transcripts, they bound transcripts throughout coding sequences and 3′ UTRs. Moreover, our data for Nrd1-Nab3 binding to 3′ UTRs was consistent with a role for these proteins in the termination of transcription. Our data also support a tight integration of Nrd1-Nab3 with the nutrient response pathway. Finally, we provide experimental evidence for some of our predictions, using northern blot and RT-PCR assays. Conclusions: Collectively, our data support the notion that Nrd1 and Nab3 function is tightly integrated with the nutrient response and indicate a role for these proteins in the regulation of many mRNA coding genes.
    [Show full text]
  • RNA Polymerase II CTD Phosphatase Rtr1 Fine-Tunes Transcription Termination
    PLOS GENETICS RESEARCH ARTICLE RNA Polymerase II CTD phosphatase Rtr1 fine-tunes transcription termination 1☯ 1☯ 1 Jose F. VictorinoID , Melanie J. FoxID , Whitney R. Smith-KinnamanID , Sarah A. Peck 1 1 1 1 1 JusticeID , Katlyn H. BurrissID , Asha K. Boyd , Megan A. Zimmerly , Rachel R. Chan , 1 2,3 1,3¤ Gerald O. Hunter , Yunlong LiuID , Amber L. MosleyID * 1 Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 2 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 3 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America a1111111111 a1111111111 ☯ These authors contributed equally to this work. a1111111111 ¤ Current address: Amber L. Mosley, Department of Biochemistry and Molecular Biology, Indiana University a1111111111 School of Medicine, Indianapolis, Indiana, United States of America a1111111111 * [email protected] Abstract OPEN ACCESS RNA Polymerase II (RNAPII) transcription termination is regulated by the phosphorylation Citation: Victorino JF, Fox MJ, Smith-Kinnaman status of the C-terminal domain (CTD). The phosphatase Rtr1 has been shown to regulate WR, Peck Justice SA, Burriss KH, Boyd AK, et al. serine 5 phosphorylation on the CTD; however, its role in the regulation of RNAPII termina- (2020) RNA Polymerase II CTD phosphatase Rtr1 tion has not been explored. As a consequence of RTR1 deletion, interactions within the ter- fine-tunes transcription termination. PLoS Genet mination machinery and between the termination machinery and RNAPII were altered as 16(3): e1008317. https://doi.org/10.1371/journal.
    [Show full text]
  • Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
    Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement.
    [Show full text]
  • A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
    Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context.
    [Show full text]
  • Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts
    Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing
    [Show full text]
  • Measuring Gene Expression Part 3 Key Steps in Microarray Analysis
    Measuring Gene Expression Part 3 David Wishart Bioinformatics 301 [email protected] Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (peak picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Comet Tailing • Often caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution. Uneven Spotting/Blotting • Problems with print tips or with overly viscous solution • Problems with humidity in spottiing chamber High Background • Insufficient Blocking • Precipitation of labelled probe Gridding Errors Spotting errors Uneven hybridization Gridding errors Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (spot picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Microarray Scanning PMT Pinhole Detector lens Laser Beam-splitter Objective Lens Dye Glass Slide Microarray Principles Laser 1 Laser 2 Green channel Red channel Scan and detect with overlay images Image process confocal laser system and normalize and analyze Microarray Images • Resolution – standard 10µm [currently, max 5µm] – 100µm spot on chip = 10 pixels in diameter • Image format – TIFF (tagged image file format) 16 bit (64K grey levels) – 1cm x 1cm image at 16 bit = 2Mb (uncompressed) – other formats exist i.e. SCN (Stanford University) • Separate image for each fluorescent sample – channel 1, channel 2, etc. Image
    [Show full text]
  • 1471-2105-8-217.Pdf
    BMC Bioinformatics BioMed Central Software Open Access GenMAPP 2: new features and resources for pathway analysis Nathan Salomonis1,2, Kristina Hanspers1, Alexander C Zambon1, Karen Vranizan1,3, Steven C Lawlor1, Kam D Dahlquist4, Scott W Doniger5, Josh Stuart6, Bruce R Conklin1,2,7,8 and Alexander R Pico*1 Address: 1Gladstone Institute of Cardiovascular Disease, 1650 Owens Street, San Francisco, CA 94158 USA, 2Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, 513 Parnassus Avenue, San Francisco, CA 94143, USA, 3Functional Genomics Laboratory, University of California, Berkeley, CA 94720 USA, 4Department of Biology, Loyola Marymount University, 1 LMU Drive, MS 8220, Los Angeles, CA 90045 USA, 5Computational Biology Graduate Program, Washington University School of Medicine, St. Louis, MO 63108 USA, 6Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064 USA, 7Department of Medicine, University of California, San Francisco, CA 94143 USA and 8Department of Molecular and Cellular Pharmacology, University of California, San Francisco, CA 94143 USA Email: Nathan Salomonis - [email protected]; Kristina Hanspers - [email protected]; Alexander C Zambon - [email protected]; Karen Vranizan - [email protected]; Steven C Lawlor - [email protected]; Kam D Dahlquist - [email protected]; Scott W Doniger - [email protected]; Josh Stuart - [email protected]; Bruce R Conklin - [email protected]; Alexander R Pico* - [email protected] * Corresponding author Published: 24 June 2007 Received: 16 November 2006 Accepted: 24 June 2007 BMC Bioinformatics 2007, 8:217 doi:10.1186/1471-2105-8-217 This article is available from: http://www.biomedcentral.com/1471-2105/8/217 © 2007 Salomonis et al; licensee BioMed Central Ltd.
    [Show full text]