Erratum: Pathway reporter genes define molecular phenotypes of human cells. [BMC Genomics. 16, (2015), (342)] DOI:10.1186/s12864-015-1532-2;Pathway reporter genes define molecular phenotypes of human cells

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Zhang, Jitao David ^a   Küng, Erich ^a   Boess, Franziska ^a   Certa, Ulrich ^a   Ebeling, Martin ^a  

^a F HOFFMANN LA ROCHE LTD   (Switzerland)

Author keywords

Gene expression; Molecular phenotype; Pathway analysis; Pathway reporter genes

Indexed keywords

DICLOFENAC; NONSTEROID ANTIINFLAMMATORY AGENT;

ALGORITHM; CARDIAC MUSCLE CELL; CELL DIFFERENTIATION; CYTOLOGY; DRUG EFFECTS; GENETICS; HUMAN; INDUCED PLURIPOTENT STEM CELL; LIVER CELL; METABOLISM; PHENOTYPE; PRINCIPAL COMPONENT ANALYSIS; REPORTER GENE; SIGNAL TRANSDUCTION;

ALGORITHMS; ANTI-INFLAMMATORY AGENTS, NON-STEROIDAL; CELL DIFFERENTIATION; DICLOFENAC; GENES, REPORTER; HEPATOCYTES; HUMANS; INDUCED PLURIPOTENT STEM CELLS; METABOLIC NETWORKS AND PATHWAYS; MYOCYTES, CARDIAC; PHENOTYPE; PRINCIPAL COMPONENT ANALYSIS; SIGNAL TRANSDUCTION;

EID: 84928567662     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-017-3730-6     Document Type: Erratum

References (59)

1. Slonim DK. From patterns to pathways: gene expression data analysis comes of age. Nat Genet. 2002;32:502-8.
2. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42(D1):D472-7.
3. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, et al. PID: the pathway interaction database. Nucleic Acids Res. 2009;37 suppl 1:D674-9.
4. Emmert-Streib F, Glazko GV. Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Comput Biol. 2011;7(5):e1002053.
5. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006;439(7074):353-7.
6. Richard A, Lyons P, Peters J, Biasci D, Flint S, Lee J, et al. Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation. BMC Genomics. 2014;15(1):649.
7. Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002;30(4):e15.
8. Hebenstreit D, Fang M, Gu M, Charoensawan V, van Oudenaarden A, Teichmann SA. RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol Syst Biol. 2011;7(1):497.
9. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nature Rev Drug Discov. 2004;3(8):711-6.
10. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9. 1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013;41(D1):D808-15.
11. QIAGEN RT-PCR arrays. [ http://www.sabiosciences.com/ArrayList.php?pline=PCRArray ]
12. BIO-RAD RT-PCR arrays. [ http://www.bio-rad.com/de-de/prime-pcr-assays/pathway/primepcr-pathways ]
13. Page L, Brin S, Motwani R, Winograd T. The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford InfoLab, 1999.
14. Roy J, Winter C, Isik Z, Schroeder M. Network information improves cancer outcome prediction. Brief Bioinform. 2014;15(4):612-25.
15. Horvát E-Á, Zhang JD, Uhlmann S, Sahin Ö, Zweig KA. A network-based method to assess the statistical significance of mild co-regulation effects. PLoS One. 2013;8(9):e73413.
16. Obayashi T, Okamura Y, Ito S, Tadaka S, Motoike IN, Kinoshita K. COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals. Nucleic Acids Res. 2013;41(D1):D1014-20.
17. Consortium GO. Gene Ontology annotations and resources. Nucleic Acids Res. 2013;41(D1):D530-5.
18. Bauer-Mehren A, Rautschka M, Sanz F, Furlong LI. DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics. 2010;26(22):2924-6.
19. The MetaBase database. [ http://thomsonreuters.com/metabase/ ]
20. Chindelevitch L, Ziemek D, Enayetallah A, Randhawa R, Sidders B, Brockel C, et al. Causal reasoning on biological networks: interpreting transcriptional changes. Bioinformatics. 2012;28(8):1114-21.
21. Boudina S, Abel ED. Diabetic cardiomyopathy revisited. Circulation. 2007;115(25):3213-23.
22. Zhang J, Schindler T, Kung E, Ebeling M, Certa U. Highly sensitive amplicon-based transcript quantification by semiconductor sequencing. BMC Genomics. 2014;15(1):565.
23. Culhane A, Perriere G, Higgins D. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics. 2003;4(1):59.
24. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14(1):7.
25. Nouspikel T, Hanawalt PC. Terminally differentiated human neurons repair transcribed genes but display attenuated global DNA repair and modulation of repair gene expression. Mol Cell Biol. 2000;20(5):1562-70.
26. Nouspikel T, Hanawalt PC. DNA repair in terminally differentiated cells. DNA Repair. 2002;1(1):59-75.
27. Xi R, Xie T. Stem cell self-renewal controlled by chromatin remodeling factors. Science. 2005;310(5753):1487-9.
28. Babiarz JE, Ravon M, Sridhar S, Ravindran P, Swanson B, Bitter H, et al. Determination of the human cardiomyocyte mRNA and miRNA differentiation network by fine-scale profiling. Stem Cells Dev. 2011;21(11):1956-65.
29. Zhang XM, Ramalho-Santos M, McMahon AP. Smoothened mutants reveal redundant roles for Shh and Ihh signaling including regulation of L/R asymmetry by the mouse node. Cell. 2001;105(6):781-92.
30. Hami D, Grimes AC, Tsai H-J, Kirby ML. Zebrafish cardiac development requires a conserved secondary heart field. Development. 2011;138(11):2389-98.
31. Kusano KF, Pola R, Murayama T, Curry C, Kawamoto A, Iwakura A, et al. Sonic hedgehog myocardial gene therapy: tissue repair through transient reconstitution of embryonic signaling. Nat Med. 2005;11(11):1197-204.
32. Cao F, Wagner RA, Wilson KD, Xie X, Fu J-D, Drukker M, et al. Transcriptional and Functional Profiling of Human Embryonic Stem Cell-Derived Cardiomyocytes. PLoS One. 2008;3(10):e3474.
33. Zj W, Fm Z, Ls W, Yw Y, Zhao Q, Gao X. Lipopolysaccharides can protect mesenchymal stem cells (MSCs) from oxidative stress-induced apoptosis and enhance proliferation of MSCs via Toll-like receptor (TLR)-4 and PI3K/Akt. Cell Biol Int. 2009;33(6):665-74.
34. Durbin JE, Hackenmiller R, Simon MC, Levy DE. Targeted disruption of the mouse Stat1 gene results in compromised innate immunity to viral disease. Cell. 1996;84(3):443-50.
35. Snyder M, Huang X-Y, Zhang JJ. Stat3 directly controls the expression of Tbx5, Nkx2. 5, and GATA4 and is essential for cardiomyocyte differentiation of P19CL6 cells. J Biol Chem. 2010;285(31):23639-46.
36. Harvey RP. Patterning the vertebrate heart. Nature Rev Genet. 2002;3(7):544-56.
37. Picard F, Brehm M, Fassbach M, Pelzer B, Scheuring S, Küry P, et al. Increased cardiac mRNA expression of matrix metalloproteinase-1 (MMP-1) and its inhibitor (TIMP-1) in DCM patients. Clin Res Cardiol. 2006;95(5):261-9.
38. Bersell K, Arab S, Haring B, Kühn B. Neuregulin1/ErbB4 signaling induces cardiomyocyte proliferation and repair of heart injury. Cell. 2009;138(2):257-70.
39. Woodman SE, Park DS, Cohen AW, Cheung MW-C, Chandra M, Shirani J, et al. Caveolin-3 knock-out mice develop a progressive cardiomyopathy and show hyperactivation of the p42/44 MAPK Cascade. J Biol Chem. 2002;277(41):38988-97.
40. Simmons SO, Fan C-Y, Ramabhadran R. Cellular stress response pathway system as a sentinel ensemble in toxicological screening. Toxicol Sci. 2009;111(2):202-25.
41. Andersen ME, Krewski D. Toxicity testing in the 21st century: bringing the vision to life. Toxicol Sci. 2009;107(2):324-30.
42. Zhang J, Berntenis N, Roth A, Ebeling M. Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity. Pharmacogenomics J. 2014;14(3):208-16.
43. Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 2015 Jan; 43(Database issue):D921-7.
44. Yamashita K, Imaizumi T, Hatakeyama M, Tamo W, Kimura D, Kumagai M, et al. Effect of hypoxia on the expression of fractalkine in human endothelial cells. Tohoku J Exp Med. 2003;200(4):187-94.
45. Vorrink SU, Severson PL, Kulak MV, Futscher BW, Domann FE. Hypoxia perturbs aryl hydrocarbon receptor signaling and CYP1A1 expression induced by PCB 126 in human skin and liver-derived cell lines. Toxicol Appl Pharmacol. 2014;274(3):408-16.
46. Ock J, Jeong J, Choi WS, Lee WH, Kim SH, Kim IK, et al. Regulation of Toll-like receptor 4 expression and its signaling by hypoxia in cultured microglia. J Neurosci Res. 2007;85(9):1989-95.
47. Lehnardt S, Massillon L, Follett P, Jensen FE, Ratan R, Rosenberg PA, et al. Activation of innate immunity in the CNS triggers neurodegeneration through a Toll-like receptor 4-dependent pathway. Proc Natl Acad Sci. 2003;100(14):8514-9.
48. Naldini A, Pucci A, Carraro F. Hypoxia induces the expression and release of interleukin 1 receptor antagonist in mitogen-activated mononuclear cells. Cytokine. 2001;13(6):334-41.
49. Uhlmann S, Mannsperger H, Zhang JD, Horvat E-Á, Schmidt C, Küblbeck M, et al. Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer. Mol Syst Biol. 2012;8(1):570.
50. Moisan A, Lee Y-KL, Zhang JD, Hudak CS, Meyer CAM, Prummer M, et al. White-to-brown metabolic conversion of human adipocytes by JAK inhibition. Nature Cell Biol 2014:In print.
51. Kholodenko B, Yaffe MB, Kolch W. Computational approaches for analyzing information flow in biological networks. Sci Signal. 2012;5(220):re1.
52. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375.
53. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics. 2010;26(12):i237-45.
54. Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim J-s, et al. A novel signaling pathway impact analysis. Bioinformatics. 2009;25(1):75-82.
55. Peck D, Crawford ED, Ross KN, Stegmaier K, Golub TR, Lamb J. A method for high-throughput gene expression signature analysis. Genome Biol. 2006;7(7):R61.
56. Li H, Zhou H, Wang D, Qiu J, Zhou Y, Li X, et al. Versatile pathway-centric approach based on high-throughput sequencing to anticancer drug discovery. Proc Natl Acad Sci. 2012;109(12):4609-14.
57. Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Syst 2006, 1695.
58. Robinson MD, McCarthy DJ, Smyth GK, Edge R. a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-40.
59. Smyth GK. Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor. Springer; 2005: 397-420.