A Review of Drug Repositioning Based Chemical-induced Cell Line Expression Data

Author(s): Fei Wang, Xiujuan Lei, Fang-Xiang Wu*

Journal Name: Current Medicinal Chemistry

Volume 27 , Issue 32 , 2020

  Journal Home
Translate in Chinese
Become EABM
Become Reviewer
Call for Editor


Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

Keywords: Drug repositioning, computational biology, bioinformatics, drug candidate, connectivity map, the library of integrated network-based cellular signatures.

Emmert-Streib, F.; Tripathi, S.; Simoes, R.D.M.; Hawwa, A.F.; Dehmer, M. The human disease network: Opportuni-ties for classification, diagnosis, and prediction of disorders and disease genes. Syst. Biomed., 2013, 1(1), 20-28.
Shameer, K.; Readhead, B.; Dudley, J.T. Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. Curr. Top. Med. Chem., 2015, 15(1), 5-20.
[http://dx.doi.org/10.2174/1568026615666150112103510] [PMID: 25579574]
Boolell, M.; Allen, M.J.; Ballard, S.A.; Gepi-Attee, S.; Muirhead, G.J.; Naylor, A.M.; Osterloh, I.H.; Gingell, C. Sildenafil: an orally active type 5 cyclic GMP-specific phosphodiesterase inhibitor for the treatment of penile erectile dysfunction. Int. J. Impot. Res., 1996, 8(2), 47-52.
[PMID: 8858389]
Li, J.; Zheng, S.; Chen, B.; Butte, A.J.; Swamidass, S.J.; Lu, Z. A survey of current trends in computational drug repositioning. Brief. Bioinform., 2016, 17(1), 2-12.
[http://dx.doi.org/10.1093/bib/bbv020] [PMID: 25832646]
Bolgár, B.; Arany, Á.; Temesi, G.; Balogh, B.; Antal, P.; Mátyus, P. Drug repositioning for treatment of movement disorders: from serendipity to rational discovery strategies. Curr. Top. Med. Chem., 2013, 13(18), 2337-2363.
[http://dx.doi.org/10.2174/15680266113136660164] [PMID: 24059461]
Chen, L.; Zou, B.; Lee, V.H.; Yan, H. Analysis of the relative movements between EGFR and drug inhibitors based on molecular dynamics simulation. Curr. Bioinform., 2018, 13(3), 299-309.
Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: therapeutic target data-base. Nucleic Acids Res., 2002, 30(1), 412-415.
[http://dx.doi.org/10.1093/nar/30.1.412] [PMID: 11752352]
Li, Y.H.; Yu, C.Y.; Li, X.X.; Zhang, P.; Tang, J.; Yang, Q.; Fu, T.; Zhang, X.; Cui, X.; Tu, G.; Zhang, Y.; Li, S.; Yang, F.; Sun, Q.; Qin, C.; Zeng, X.; Chen, Z.; Chen, Y.Z.; Zhu, F. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res., 2018, 46(D1), D1121-D1127.
[http://dx.doi.org/10.1093/nar/gkx1076] [PMID: 29140520]
Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Has-sanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research., 2006, 34(1), D668-D672.
Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res., 2018, 46(D1), D1074-D1082.
[http://dx.doi.org/10.1093/nar/gkx1037] [PMID: 29126136]
Cheng, F.; Liu, C.; Jiang, J.; Lu, W.; Li, W.; Liu, G.; Zhou, W.; Huang, J.; Tang, Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput. Biol., 2012, 8(5), e1002503.
[http://dx.doi.org/10.1371/journal.pcbi.1002503] [PMID: 22589709]
Chen, L.; Chu, C.; Zhang, Y.H.; Zheng, M.; Zhu, L.; Kong, X.; Huang, T. Identification of drug-drug interactions using chemical interactions. Curr. Bioinform., 2017, 12(6), 526-534.
Wu, H.; Huang, J.; Zhong, Y.; Huang, Q. DrugSig: A resource for computational drug repositioning utilizing gene expression signatures. PLoS One, 2017, 12(5), e0177743.
[http://dx.doi.org/10.1371/journal.pone.0177743] [PMID: 28562632]
Tang, W.; Wan, S.; Yang, Z.; Teschendorff, A.E.; Zou, Q. Tumor origin detection with tissue-specific miRNA and DNA methylation markers. Bioinformatics, 2018, 34(3), 398-406.
[http://dx.doi.org/10.1093/bioinformatics/btx622] [PMID: 29028927]
Zeng, X.; Liu, L.; Lü, L.; Zou, Q. Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics, 2018, 34(14), 2425-2432.
[http://dx.doi.org/10.1093/bioinformatics/bty112] [PMID: 29490018]
Lamb, J.; Crawford, E.D.; Peck, D.; Modell, J.W.; Blat, I.C.; Wrobel, M.J.; Lerner, J.; Brunet, J.P.; Subramanian, A.; Ross, K.N.; Reich, M.; Hieronymus, H.; Wei, G.; Armstrong, S.A.; Haggarty, S.J.; Clemons, P.A.; Wei, R.; Carr, S.A.; Lander, E.S.; Golub, T.R.; Wei, R.; Carr, S.A.; Lander, E.S.; Golub, T.R. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 2006, 313(5795), 1929-1935.
[http://dx.doi.org/10.1126/science.1132939] [PMID: 17008526]
Wen, Q.; O’Reilly, P.; Dunne, P.D.; Lawler, M.; Van Schaeybroeck, S.; Salto-Tellez, M.; Hamilton, P.; Zhang, S.D. Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies. BMC Syst. Biol., 2015, 9(Suppl. 5), S4.
[http://dx.doi.org/10.1186/1752-0509-9-S5-S4] [PMID: 26356760]
National Institutes of Health The library of integrated network-based cellular signatures. Project. Available at:, http://www.lincsproject.org (Accessed Date: 3rd March, 2017)
National center for biotechnology information search database. Available at: https://www.ncbi.nlm.nih.gov (Accessed Date: 6th April 2017)
Musa, A.; Ghoraie, L.S.; Zhang, S.D.; Glazko, G.; Yli-Harja, O.; Dehmer, M.; Haibe-Kains, B.; Emmert-Streib, F. A review of connectivity map and computational approaches in pharmacogenomics. Brief. Bioinform., 2017, 18(5), 903.
[http://dx.doi.org/10.1093/bib/bbx023] [PMID: 28334173]
Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
Zhang, S.D.; Gant, T.W. sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics, 2009, 10(1), 236.
[http://dx.doi.org/10.1186/1471-2105-10-236] [PMID: 19646231]
Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K.; Lahr, D.L.; Hirschman, J.E.; Liu, Z. Do-nahue, M.; Julian, B.; Khan, M.; Wadden, D.; Smith, I.; Lam, D.; Liberzon, A.; Toder, C.; Bagul, M.; Orzechowski, M.; Enache, O. M.; Piccioni, F.; Berger, A. H.; Shamji, A.; Brooks, A. N.; Vrcic, A.; Flynn, C.; Rosains, J.; Takeda, D.; Davison, D.; Lamb, J.; Ardlie, K.; Hogstrom, L.; Gray, N. S.; Clemons, P. A.; Silver, S.; Wu, X.; Zhao, W.; Read-Button, W.; Wu, X.; Haggarty, S. J.; Ronco, L. V.; Boehm, J. S.; Schreiber, S. L.; Doench, J. G.; Bittker, Joshua A.; Root, David E.; Wong, Bang; Golub, Todd R. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 2017, 171(6), 1437-1452.e17.
[http://dx.doi.org/10.1016/j.cell.2017.10.049] [PMID: 29195078]
Edgar, R.; Domrachev, M.; Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res., 2002, 30(1), 207-210.
[http://dx.doi.org/10.1093/nar/30.1.207] [PMID: 11752295]
Temesi, G.; Bolgár, B.; Arany, A.; Szalai, C.; Antal, P.; Mátyus, P. Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy. Future Med. Chem., 2014, 6(5), 563-575.
[http://dx.doi.org/10.4155/fmc.14.4] [PMID: 24649958]
Goss, K.L.; Gordon, D.J. Gene expression signature based screening identifies ribonucleotide reductase as a candidate therapeutic target in Ewing sarcoma. Oncotarget, 2016, 7(39), 63003-63019.
[http://dx.doi.org/10.18632/oncotarget.11416] [PMID: 27557498]
Pessetto, Z.Y.; Chen, B.; Alturkmani, H.; Hyter, S.; Flynn, C.A.; Baltezor, M.; Ma, Y.; Rosenthal, H.G.; Neville, K.A.; Weir, S.J.; Butte, A.J.; Godwin, A.K. In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma. Oncotarget, 2017, 8(3), 4079-4095.
[http://dx.doi.org/10.18632/oncotarget.13385] [PMID: 27863422]
Lee, J.; Liu, J.; Feng, X.; Salazar Hernández, M.A.; Mucka, P.; Ibi, D.; Choi, J.W.; Ozcan, U. Withaferin A is a leptin sensitizer with strong antidiabetic properties in mice. Nat. Med., 2016, 22(9), 1023-1032.
[http://dx.doi.org/10.1038/nm.4145] [PMID: 27479085]
Segura-Cabrera, A.; Tripathi, R.; Zhang, X.; Gui, L.; Chou, T.F.; Komurov, K. A structure- and chemical genomics-based approach for repositioning of drugs against VCP/p97 ATPase. Sci. Rep., 2017, 7, 44912.
[http://dx.doi.org/10.1038/srep44912] [PMID: 28322292]
Chandran, V.; Coppola, G.; Nawabi, H.; Omura, T.; Versano, R.; Huebner, E.A.; Zhang, A.; Costigan, M.; Yekkirala, A.; Barrett, L.; Blesch, A.; Michaelevski, I.; Davis-Turak, J.; Gao, F.; Langfelder, P.; Horvath, S.; He, Z.; Benowitz, L.; Fainzilber, M.; Tuszynski, M.; Woolf, C.J.; Geschwind, D.H. A systems-level analysis of the peripheral nerve intrinsic axonal growth program. Neuron, 2016, 89(5), 956-970.
[http://dx.doi.org/10.1016/j.neuron.2016.01.034] [PMID: 26898779]
Xiao, Z.X.; Chen, R.Q.; Hu, D.X.; Xie, X.Q.; Yu, S.B.; Chen, X.Q. Identification of repaglinide as a therapeutic drug for glioblastoma multiforme. Biochem. Biophys. Res. Commun., 2017, 488(1), 33-39.
[http://dx.doi.org/10.1016/j.bbrc.2017.04.157] [PMID: 28476618]
Li, L.; Greene, I.; Readhead, B.; Menon, M.C.; Kidd, B.A.; Uzilov, A.V.; Wei, C.; Philippe, N.; Schroppel, B.; He, J.C.; Chen, R.; Dudley, J.T.; Murphy, B. Novel therapeutics identification for fibrosis in renal allograft using integrative informatics approach. Sci. Rep., 2017, 7, 39487.
[http://dx.doi.org/10.1038/srep39487] [PMID: 28051114]
Won, S.J.; Yen, C.H.; Hsieh, H.W.; Chang, S.W.; Lin, C.N.; Huang, C.Y.F.; Su, C.L. Using connectivity map to identify natural lignan justicidin A as a NF-κB suppressor. J. Funct. Foods, 2017, 34, 68-76.
Iorio, F.; Saez-Rodriguez, J.; di Bernardo, D. Network based elucidation of drug response: from modulators to targets. BMC Syst. Biol., 2013, 7(1), 139.
[http://dx.doi.org/10.1186/1752-0509-7-139] [PMID: 24330611]
Chen, J.; Schlitzer, A.; Chakarov, S.; Ginhoux, F.; Poiding-er, M. Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development. Nat. Commun., 2016, 7, 11988.
[PMID: 27356503]
Trapnell, C.; Cacchiarelli, D.; Grimsby, J.; Pokharel, P.; Li, S.; Morse, M.; Lennon, N.J.; Livak, K.J.; Mikkelsen, T.S.; Rinn, J.L. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 2014, 32(4), 381-386.
[http://dx.doi.org/10.1038/nbt.2859] [PMID: 24658644]
Zickenrott, S.; Angarica, V.E.; Upadhyaya, B.B.; del Sol, A. Prediction of disease-gene-drug relationships following a differential network analysis. Cell Death Dis., 2016, 7(1), e2040.
[http://dx.doi.org/10.1038/cddis.2015.393] [PMID: 26775695]
Liu, X.; Zeng, P.; Cui, Q.; Zhou, Y. Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data. PLoS One, 2017, 12(6), e0179037.
[http://dx.doi.org/10.1371/journal.pone.0179037] [PMID: 28575118]
Grammer, A.C.; Ryals, M.M.; Heuer, S.E.; Robl, R.D.; Madamanchi, S.; Davis, L.S.; Lauwerys, B.; Catalina, M.D.; Lipsky, P.E. Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis. Lupus, 2016, 25(10), 1150-1170.
[http://dx.doi.org/10.1177/0961203316657437] [PMID: 27497259]
Siavelis, J.C.; Bourdakou, M.M.; Athanasiadis, E.I.; Spyrou, G.M.; Nikita, K.S. Bioinformatics methods in drug repurposing for Alzheimer’s disease. Brief. Bioinform., 2016, 17(2), 322-335.
[http://dx.doi.org/10.1093/bib/bbv048] [PMID: 26197808]
Sirota, M.; Dudley, J.T.; Kim, J.; Chiang, A.P.; Morgan, A.A.; Sweet-Cordero, A.; Sage, J.; Butte, A.J. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med., 2011, 3(96), 96ra77.
[http://dx.doi.org/10.1126/scitranslmed.3001318] [PMID: 21849665]
Kidd, B.A.; Wroblewska, A.; Boland, M.R.; Agudo, J.; Merad, M.; Tatonetti, N.P.; Brown, B.D.; Dudley, J.T. Mapping the effects of drugs on the immune system. Nat. Biotechnol., 2016, 34(1), 47-54.
[http://dx.doi.org/10.1038/nbt.3367] [PMID: 26619012]
Ryan, N.; Chorley, B.; Tice, R.R.; Judson, R.; Corton, J.C. Moving toward integrating gene expression profiling into high-throughput testing: a gene expression biomarker accurately predicts estrogen receptor α modulation in a microarray compendium. Toxicol. Sci., 2016, 151(1), 88-103.
[http://dx.doi.org/10.1093/toxsci/kfw026] [PMID: 26865669]
Fang, H.Y.; Zeng, H.W.; Lin, L.M.; Chen, X.; Shen, X.N.; Fu, P.; Lv, C.; Liu, Q.; Liu, R.H.; Zhang, W.D.; Zhao, J. A network-based method for mechanistic investigation of Shexiang Baoxin Pill’s treatment of cardiovascular diseases. Sci. Rep., 2017, 7, 43632.
[http://dx.doi.org/10.1038/srep43632] [PMID: 28272527]
Chen, B.; Wei, W.; Ma, L.; Yang, B.; Gill, R.M.; Chua, M.S.; Butte, A.J.; So, S. Computational Discovery of Ni- closamide Ethanolamine, a Repurposed Drug Candidate That Reduces Growth of Hepatocellular Carcinoma Cells In Vitro and in Mice by Inhibiting Cell Division Cycle 37 Signaling. Gastroenterology, 2017, 152(8), 2022-2036.
[http://dx.doi.org/10.1093/nar/gkq1126 10.1053/j.gastro.2017.02.039] [PMID: 28284560]
Zhang, X.; Li, L.; Ng, M.K.; Zhang, S. Drug-target Interac-tion Prediction by Integrating Multiview Network Data. Comput. Biol. Chem., 2017.
Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A.C.; Wishart, D.S. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic acids research., 2010, 39(1), D1035-D1041.
[http://dx.doi.org/10.1093/nar/gkq1126] [PMID: 21059682]

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Published on: 24 September, 2020
Page: [5340 - 5350]
Pages: 11
DOI: 10.2174/0929867325666181101115801
Price: $65

Article Metrics

PDF: 29