Network-based method to infer the contributions of proteins to the etiology of drug side effects

doi:10.1007/s40484-015-0051-0

Quantitative Biology

2015, Vol. 3

Issue (3): 124-134 https://doi.org/10.1007/s40484-015-0051-0

RESEARCH ARTICLE

本期目录

Network-based method to infer the contributions of proteins to the etiology of drug side effects

Rui Li¹,Ting Chen^1,^2,^*(

),Shao Li^1,^*(

)

1. MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China
2. Program in Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA

全文: PDF(930 KB) HTML

Abstract：

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug-target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation---maximization algorithm and a diffusion kernel-based approach to estimate each protein’s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, <?A3B2 tf="Times New Roman Bold Italic (TrueType)"?>Tripterygium wilfordii Lei-Gong-Teng. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.

Key words： network pharmacology drug targets side effects triptolide

收稿日期: 2015-04-15 出版日期: 2015-11-04

Corresponding Author(s): Ting Chen,Shao Li

引用本文:

. [J]. Quantitative Biology, 2015, 3(3): 124-134.
Rui Li, Ting Chen, Shao Li. Network-based method to infer the contributions of proteins to the etiology of drug side effects. Quant. Biol., 2015, 3(3): 124-134.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-015-0051-0
https://academic.hep.com.cn/qb/CN/Y2015/V3/I3/124

Fig.1

Tab.1

Fig.2

Fig.3

1	Scheiber, J., Chen, B., Milik, M., Sukuru, S. C. K., Bender, A., Mikhailov, D., Whitebread, S., Hamon, J., Azzaoui, K., Urban, L., et al. (2009) Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. J. Chem. Inf. Model, 49, 308–317 https://doi.org/10.1021/ci800344p pmid: 19434832
2	Berger, S. I. and Iyengar, R. (2011) Role of systems pharmacology in understanding drug adverse events. Wiley Interdiscip. Rev. Syst. Biol. Med., 3, 129–135 https://doi.org/10.1002/wsbm.114 pmid: 20803507
3	Roses, A. D. (2004) Pharmacogenetics and drug development: the path to safer and more effective drugs. Nat. Rev. Genet., 5, 645–656 https://doi.org/10.1038/nrg1432 pmid: 15372086
4	Stevens, J. L. and Baker, T. K. (2009) The future of drug safety testing: expanding the view and narrowing the focus. Drug Discov. Today, 14, 162–167 https://doi.org/10.1016/j.drudis.2008.11.009 pmid: 19100337
5	Shah, R. R. (2006) Can pharmacogenetics help rescue drugs withdrawn from the market? Pharmacogenomics, 7, 889–908 https://doi.org/10.2217/14622416.7.6.889 pmid: 16981848
6	Zhang, W., Roederer, M. W., Chen, W.-Q., Fan, L. and Zhou, H.-H. (2012) Pharmacogenetics of drugs withdrawn from the market. Pharmacogenomics, 13, 223–231 https://doi.org/10.2217/pgs.11.137 pmid: 22256871
7	Liebler, D. C. and Guengerich, F. P. (2005) Elucidating mechanisms of drug-induced toxicity. Nat. Rev. Drug Discov., 4, 410–420 https://doi.org/10.1038/nrd1720 pmid: 15864270
8	Li, R., Ma, T., Gu, J., Liang, X. and Li, S. (2013) Imbalanced network biomarkers for traditional Chinese medicine Syndrome in gastritis patients. Sci. Rep., 3, 1543 https://doi.org/10.1038/srep01543 pmid: 23529020
9	Zhao, S. and Li, S. (2010) Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One, 5, e11764 https://doi.org/10.1371/journal.pone.0011764 pmid: 20668676
10	Zhang, B., Wang, X. and Li, S. (2013) An integrative platform of TCM network pharmacology and its application on an herbal formula, Qing-Luo-Yin. Evid-Based Compl. Alt. Med., 2013, 456747 https://doi.org/10.1155/2013/456747 pmid: 23653662
11	Li, S., Zhang, B., Jiang, D., Wei, Y. and Zhang, N. (2010) Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae. BMC Bioinformatics, 11, S6 https://doi.org/10.1186/1471-2105-11-S11-S6 pmid: 21172056
12	Li, S., Zhang, B. and Zhang, N. (2011) Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol, 5, S10 https://doi.org/10.1186/1752-0509-5-S1-S10 pmid: 21689469
13	Liang, X., Li, H. and Li, S. (2014) A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol. Biosyst., 10, 1014–1022 https://doi.org/10.1039/c3mb70507b pmid: 24492828
14	Scheiber, J., Jenkins, J. L., Sukuru, S. C. K., Bender, A., Mikhailov, D., Milik, M., Azzaoui, K., Whitebread, S., Hamon, J., Urban, L., et al. (2009) Mapping adverse drug reactions in chemical space. J. Med. Chem., 52, 3103–3107 https://doi.org/10.1021/jm801546k pmid: 19378990
15	Lee, S., Lee, K. H., Song, M. and Lee, D. (2011) Building the process-drug-side effect network to discover the relationship between biological processes and side effects. BMC Bioinformatics, 12, S2 https://doi.org/10.1186/1471-2105-12-S2-S2 pmid: 21489221
16	Wallach, I., Jaitly, N. and Lilien, R. (2010) A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways. PLoS One, 5, e12063 https://doi.org/10.1371/journal.pone.0012063 pmid: 20808786
17	Atias, N. and Sharan, R. (2011) An algorithmic framework for predicting side effects of drugs. J. Comput. Biol., 18, 207–218 https://doi.org/10.1089/cmb.2010.0255 pmid: 21385029
18	Huang, L. C., Wu, X. and Chen, J. Y. (2013) Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics, 13, 313–324 https://doi.org/10.1002/pmic.201200337 pmid: 23184540
19	Yamanishi, Y., Pauwels, E. and Kotera, M. (2012) Drug side-effect prediction based on the integration of chemical and biological spaces. J. Chem. Inf. Model, 52, 3284–3292 https://doi.org/10.1021/ci2005548 pmid: 23157436
20	Huang, L. C., Wu, X. and Chen, J. Y. (2011) Predicting adverse side effects of drugs. BMC Genomics, 12, S11 https://doi.org/10.1186/1471-2164-12-S5-S11 pmid: 22369493
21	Mizutani, S., Pauwels, E., Stoven, V., Goto, S. and Yamanishi, Y. (2012) Relating drug-protein interaction network with drug side effects. Bioinformatics, 28, i522–i528 https://doi.org/10.1093/bioinformatics/bts383 pmid: 22962476
22	Kuhn, M., Al Banchaabouchi, M., Campillos, M., Jensen, L. J., Gross, C., Gavin, A.-C. and Bork, P. (2013) Systematic identification of proteins that elicit drug side effects. Mol. Syst. Biol., 9, 663 https://doi.org/10.1038/msb.2013.10 pmid: 23632385
23	Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature, 486, 361–367 pmid: 22722194
24	Wallach, I., Jaitly, N. and Lilien, R. (2010) A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways. PLoS One, 5, e12063 https://doi.org/10.1371/journal.pone.0012063 pmid: 20808786
25	Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J. and Bork, P. (2010) A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol., 6, 343 https://doi.org/10.1038/msb.2009.98 pmid: 20087340
26	Vanherweghem, J. L., Depierreux, M., Tielemans, C., Abramowicz, D., Dratwa, M., Jadoul, M., Richard, C., Vandervelde, D., Verbeelen, D., Vanhaelen-Fastre, R., et al. (1993) Rapidly progressive interstitial renal fibrosis in young women: association with slimming regimen including Chinese herbs. Lancet, 341, 387–391 https://doi.org/10.1016/0140-6736(93)92984-2 pmid: 8094166
27	Allard, T., Wenner, T., Greten, H. J. and Efferth, T. (2013) Mechanisms of herb-induced nephrotoxicity. Curr. Med. Chem., 20, 2812–2819 https://doi.org/10.2174/0929867311320220006 pmid: 23597204
28	Nowack, R., et al. (2011) Herbal treatments of glomerulonephritis and chronic renal failure: Review and recommendations for research. J. Pharm. Phyt., 3, 124–136
29	Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al. (2011) DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res., 39, D1035–D1041 https://doi.org/10.1093/nar/gkq1126 pmid: 21059682
30	Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L. J. and Bork, P. (2012) STITCH 3: zooming in on protein-chemical interactions. Nucleic Acids Res., 40, D876–D880 https://doi.org/10.1093/nar/gkr1011 pmid: 22075997
31	Peri, S., Navarro, J. D., Amanchy, R., Kristiansen, T. Z., Jonnalagadda, C. K., Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T. K., Gronborg, M., et al. (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res., 13, 2363–2371 https://doi.org/10.1101/gr.1680803 pmid: 14525934
32	Bader, G. D., Donaldson, I., Wolting, C., Ouellette, B. F., Pawson, T. and Hogue, C. W. (2001) BIND — The Biomolecular Interaction Network Database. Nucleic Acids Res., 29, 242–245 https://doi.org/10.1093/nar/29.1.242 pmid: 11125103
33	Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., Duesbury, M., Dumousseau, M., Feuermann, M., Hinz, U., et al. (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res., 40, D841–D846 https://doi.org/10.1093/nar/gkr1088 pmid: 22121220
34	Licata, L., Briganti, L., Peluso, D., Perfetto, L., Iannuccelli, M., Galeota, E., Sacco, F., Palma, A., Nardozza, A. P., Santonico, E., et al. (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res., 40, D857–D861 https://doi.org/10.1093/nar/gkr930 pmid: 22096227
35	Brown, K. R. and Jurisica, I. (2005) Online predicted human interaction database. Bioinformatics, 21, 2076–2082 https://doi.org/10.1093/bioinformatics/bti273 pmid: 15657099
36	Szaszák, M., Chen, H.-D., Chen, H.-C., Baukal, A., Hunyady, L. and Catt, K. J. (2008) Identification of the invariant chain (CD74) as an angiotensin AGTR1-interacting protein. J. Endocrinol., 199, 165– 176 https://doi.org/10.1677/JOE-08-0190 pmid: 18719072
37	Basile, D. P., Liapis, H. and Hammerman, M. R. (1997) Expression of bcl-2 and bax in regenerating rat renal tubules following ischemic injury. Am. J. Physiol., 272, F640–F647 pmid: 9176375
38	Zhou, H., Miyaji, T., Kato, A., Fujigaki, Y., Sano, K. and Hishida, A. (1999) Attenuation of cisplatin-induced acute renal failure is associated with less apoptotic cell death. J. Lab. Clin. Med., 134, 649–658 https://doi.org/10.1016/S0022-2143(99)90106-3 pmid: 10595794
39	Qiu, L.-Q., Sinniah, R. and I-Hong Hsu, S. (2004) Downregulation of Bcl-2 by podocytes is associated with progressive glomerular injury and clinical indices of poor renal prognosis in human IgA nephropathy. J. Am. Soc. Nephrol., 15, 79–90 https://doi.org/10.1097/01.ASN.0000104573.54132.2E pmid: 14694160
40	Harris, R. C. (2006) COX-2 and the kidney. J. Cardiovasc. Pharmacol., 47, S37–S42 https://doi.org/10.1097/00005344-200605001-00007 pmid: 16785827
41	Fujihara, C. K., Antunes, G. R., Mattar, A. L., Andreoli, N., Malheiros, D. M., Noronha, I. L., Zatz, R. and Zatz, R. (2003) Cyclooxygenase-2 (COX-2) inhibition limits abnormal COX-2 expression and progressive injury in the remnant kidney. Kidney Int., 64, 2172–2181 https://doi.org/10.1046/j.1523-1755.2003.00319.x pmid: 14633140
42	Kondor, R. I. and Lafferty, J. D. (2002) Diffusion kernels on graphs and other discrete input spaces. Proceedings, ICML, 2, 315–322
43	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., et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102, 15545–15550 https://doi.org/10.1073/pnas.0506580102 pmid: 16199517
44	Chang, C.-C. and Lin, C.-J. (2011) LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 27 . https://doi.org/10.1145/1961189.1961199
45	Lin, C.-J. and Weng, R. C. (2004) Simple probabilistic predictions for support vector regression. Technical report, National Taiwan University, Taipei.

Viewed

Full text

Abstract

Cited

Shared

Discussed