The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes

Chuanbo Huang; Weili Yang; Junpei Wang; Yuan Zhou; Bin Geng; Georgios Kararigas; Jichun Yang; Qinghua Cui

doi:10.1016/j.jgg.2018.07.002

Volume 45 Issue 7

Jul. 2018

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The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes

doi: 10.1016/j.jgg.2018.07.002

a
Department of Biomedical Informatics, Department of Physiology and Pathophysiology, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Center for Non-Coding RNA Medicine, Peking University, Beijing 100191, China
b
School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, China
c
Hypertension Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Beijing 100037, China
d
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Institute of Gender in Medicine and Center for Cardiovascular Research, DZHK (German Centre for Cardiovascular Research), 10115 Berlin, Germany
e
Center of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China

More Information

Corresponding author: E-mail address: yangj@bjmu.edu.cn (Jichun Yang); E-mail address: cuiqinghua@bjmu.edu.cn (Qinghua Cui)
Received Date: 2018-01-08
Accepted Date: 2018-07-04
Rev Recd Date: 2018-05-18

Available Online: 2018-07-24

Publish Date: 2018-07-20

Abstract

Abstract

Enrichment analysis methods, e.g., gene set enrichment analysis, represent one class of important bioinformatical resources for mining patterns in biomedical datasets. However, tools for inferring patterns and rules of a list of drugs are limited. In this study, we developed a web-based tool, DrugPattern, for drug set enrichment analysis. We first collected and curated 7019 drug sets, including indications, adverse reactions, targets, pathways, etc. from public databases. For a list of interested drugs, DrugPattern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we employed it for the prediction of the effects of oxidized low-density lipoprotein (oxLDL), a factor expected to be deleterious. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by evidence in the literature. Because DrugPattern predicted the potential beneficial effects of oxLDL in type 2 diabetes (T2D), animal experiments were then performed to further verify this prediction. As a result, the experimental evidences validated the DrugPattern prediction that oxLDL indeed has beneficial effects on T2D in the case of energy restriction. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases. This study provides a tool to infer patterns and rules in biomedical datasets based on drug set enrichment analysis. DrugPattern is available at http://www.cuilab.cn/drugpattern.
- Enrichment analysis,
- Drug,
- Data mining,
- oxLDL,
- Type 2 diabetes

These authors contributed equally to this work.

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References(49)

References

[1]	Bahadoran, Z., Mirmiran, P., Hosseinpanah, F. et al. Broccoli sprouts powder could improve serum triglyceride and oxidized LDL/LDL-cholesterol ratio in type 2 diabetic patients: a randomized double-blind placebo-controlled clinical trial Diabetes Res. Clin. Pract., 96 (2012),pp. 348-354
[2]	Boezio, B., Audouze, K., Ducrot, P. et al. Network-based approaches in pharmacology Mol. Inform. (2017)
[3]	Cai, M.C., Xu, Q., Pan, Y.J. et al. ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms Nucleic Acids Res., 43 (2015),pp. D907-D913
[4]	Calara, F., Dimayuga, P., Niemann, A. et al. An animal model to study local oxidation of LDL and its biological effects in the arterial wall Arterioscler. Thromb. Vasc. Biol., 18 (1998),pp. 884-893
[5]	Chaussabel, D., Pulendran, B. A vision and a prescription for big data-enabled medicine Nat. Immunol., 16 (2015),pp. 435-439
[6]	Culver, A.L., Ockene, I.S., Balasubramanian, R. et al. Statin use and risk of diabetes mellitus in postmenopausal women in the Women's Health Initiative Arch. Intern. Med., 172 (2012),pp. 144-152
[7]	de Lemos, J.A., Rohatgi, A., Ayers, C.R. Applying a big data approach to biomarker discovery: running before we walk? Circulation, 132 (2015),pp. 2289-2292
[8]	Galland, F., Duvillard, L., Petit, J.M. et al. Effect of insulin treatment on plasma oxidized LDL/LDL-cholesterol ratio in type 2 diabetic patients Diabetes Metab., 32 (2006),pp. 625-631
[9]	Gerstein, H.C., Pare, G., McQueen, M.J. et al. Identifying novel biomarkers for cardiovascular events or death in people with dysglycemia Circulation, 132 (2015),pp. 2297-2304
[10]	Ginsberg, J., Mohebbi, M.H., Patel, R.S. et al. Detecting influenza epidemics using search engine query data Nature, 457 (2009),pp. 1012-1014
[11]	Goldstein, J.L., Brown, M.S. A century of cholesterol and coronaries: from plaques to genes to statins Cell, 161 (2015),pp. 161-172
[12]	Gradinaru, D., Borsa, C., Ionescu, C. et al. Oxidized LDL and NO synthesis–Biomarkers of endothelial dysfunction and ageing Mech. Ageing Dev., 151 (2015),pp. 101-113
[13]	Hamazaki, T., Okuyama, H., Ogushi, Y. et al. Towards a paradigm shift in cholesterol treatment. A re-examination of the cholesterol issue in Japan Ann. Nutr. Metab., 66 (2015)
[14]	Hofnagel, O., Luechtenborg, B., Weissen-Plenz, G. et al. Statins and foam cell formation: impact on LDL oxidation and uptake of oxidized lipoproteins via scavenger receptors Biochim. Biophys. Acta, 1771 (2007),pp. 1117-1124
[15]	Hoogeveen, R.C., Ballantyne, C.M., Bang, H. et al. Circulating oxidised low-density lipoprotein and intercellular adhesion molecule-1 and risk of type 2 diabetes mellitus: the atherosclerosis risk in communities study Diabetologia, 50 (2007),pp. 36-42
[16]	Huang da, W., Sherman, B.T., Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Nat. Protoc., 4 (2009),pp. 44-57
[17]	Jiang, P., Liu, X.S. Big data mining yields novel insights on cancer Nat. Genet., 47 (2015),pp. 103-104
[18]	Kankainen, M., Gopalacharyulu, P., Holm, L. et al. MPEA–metabolite pathway enrichment analysis Bioinformatics, 27 (2011),pp. 1878-1879
[19]	Lamb, J., Crawford, E.D., Peck, D. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease Science, 313 (2006),pp. 1929-1935
[20]	Law, V., Knox, C., Djoumbou, Y. et al. DrugBank 4.0: shedding new light on drug metabolism Nucleic Acids Res., 42 (2014),pp. D1091-D1097
[21]	Li, J., Chi, Y., Wang, C. et al. Pancreatic-derived factor promotes lipogenesis in the mouse liver: role of the Forkhead box 1 signaling pathway Hepatology, 53 (2011),pp. 1906-1916
[22]	Li, L., Cheng, W.Y., Glicksberg, B.S. et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity Sci. Transl. Med., 7 (2015)
[23]	Li, Z., Wang, S., Zhao, W. et al. Oxidized low-density lipoprotein upregulates microRNA-146a via JNK and NF-kappaB signaling Mol. Med. Rep., 13 (2016),pp. 1709-1716
[24]	Lotta, L.A., Sharp, S.J., Burgess, S. et al. Association between low-density pipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes: a meta-analysis J. Am. Med. Assoc., 316 (2016),pp. 1383-1391
[25]	Lu, M., Shi, B., Wang, J. et al. TAM: a method for enrichment and depletion analysis of a microRNA category in a list of microRNAs BMC Bioinf., 11 (2010),p. 419
[26]	Ma, Y., Culver, A., Rossouw, J. et al. Statin therapy and the risk for diabetes among adult women: do the benefits outweigh the risk? Ther. Adv. Cardiovasc. Dis., 7 (2013),pp. 41-44
[27]	Marin, M.T., Dasari, P.S., Tryggestad, J.B. et al. Oxidized HDL and LDL in adolescents with type 2 diabetes compared to normal weight and obese peers J. Diabet. Complicat., 29 (2015),pp. 679-685
[28]	Mayer-Schonberger, V. Big data for cardiology: novel discovery? Eur. Heart J., 37 (2016),pp. 996-1001
[29]	Mikhailidis, D.P., Athyros, V.G. Dyslipidaemia in 2013: new statin guidelines and promising novel therapeutics Nat. Rev. Cardiol., 11 (2014),pp. 72-74
[30]	Napolitano, F., Sirci, F., Carrella, D. et al. Drug-set enrichment analysis: a novel tool to investigate drug mode of action Bioinformatics, 32 (2016),pp. 235-241
[31]	Newman, T.B., Hulley, S.B. Carcinogenicity of lipid-lowering drugs J. Am. Med. Assoc., 275 (1996),pp. 55-60
[32]	Persons, J.E., Fiedorowicz, J.G. Depression and serum low-density lipoprotein: a systematic review and meta-analysis J. Affect. Disord., 206 (2016),pp. 55-67
[33]	Ravnskov, U. High cholesterol may protect against infections and atherosclerosis QJM, 96 (2003),pp. 927-934
[34]	Ravnskov, U., Diamond, D.M., Hama, R. et al. Lack of an association or an inverse association between low-density-lipoprotein cholesterol and mortality in the elderly: a systematic review BMJ Open, 6 (2016)
[35]	Ravnskov, U., McCully, K.S., Rosch, P.J. The statin-low cholesterol-cancer conundrum QJM, 105 (2012),pp. 383-388
[36]	Ridker, P.M., Pradhan, A., MacFadyen, J.G. et al. Cardiovascular benefits and diabetes risks of statin therapy in primary prevention: an analysis from the JUPITER trial Lancet, 380 (2012),pp. 565-571
[37]	Ried, J.S., Doring, A., Oexle, K. et al. PSEA: phenotype set enrichment analysis–a new method for analysis of multiple phenotypes Genet. Epidemiol., 36 (2012),pp. 244-252
[38]	Rumsfeld, J.S., Joynt, K.E., Maddox, T.M. Big data analytics to improve cardiovascular care: promise and challenges Nat. Rev. Cardiol., 13 (2016),pp. 350-359
[39]	Sleire, L., Forde-Tislevoll, H.E., Netland, I.A. et al. Drug repurposing in cancer Pharmacol. Res., 124 (2017),pp. 74-91
[40]	Steinberg, D. Lewis A. Conner memorial lecture. Oxidative modification of LDL and atherogenesis Circulation, 95 (1997),pp. 1062-1071
[41]	Subramanian, A., Tamayo, P., Mootha, V.K. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proc. Natl. Acad. Sci. U. S. A., 102 (2005),pp. 15545-15550
[42]	von Hahn, T., Lindenbach, B.D., Boullier, A. et al. Oxidized low-density lipoprotein inhibits hepatitis C virus cell entry in human hepatoma cells Hepatology, 43 (2006),pp. 932-942
[43]	Wang, C., Chen, Z., Li, S. et al. Hepatic overexpression of ATP synthase beta subunit activates PI3K/Akt pathway to ameliorate hyperglycemia of diabetic mice Diabetes, 63 (2014),pp. 947-959
[44]	Wang, C., Chi, Y., Li, J. et al. FAM3A activates PI3K p110alpha/Akt signaling to ameliorate hepatic gluconeogenesis and lipogenesis Hepatology, 59 (2014),pp. 1779-1790
[45]	Wang, J.J., Chen, X.L., Xu, C.B. et al. The ERK1/2 pathway participates in the upregulation of the expression of mesenteric artery alpha1 receptors by intravenous tail injections of mmLDL in mice Vasc. Pharmacol., 77 (2016),pp. 80-88
[46]	Westhaus, S., Bankwitz, D., Ernst, S. et al. Hepatology, 57 (2013),pp. 1716-1724
[47]	Xia, J., Wishart, D.S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data Nucleic Acids Res., 38 (2010),pp. W71-W77
[48]	Yang, Z., Wang, X., Wen, J. et al. Prevalence of non-alcoholic fatty liver disease and its relation to hypoadiponectinaemia in the middle-aged and elderly Chinese population Arch. Med. Sci., 7 (2011),pp. 665-672
[49]	Zhu, Z., Gorman, M.J., McKenzie, L.D. et al. Zika virus has oncolytic activity against glioblastoma stem cells J. Exp. Med., 214 (2017),pp. 2843-2857