功能基因组学和化学信息学协同的药物研发数据挖掘方法
|
摘要
药物研发是一个需要涉及到数学、化学、生物学、计算机科学等各个专业领域的综合过程。新药研发中的关键一环是找到药物的作用靶点和作用机理，人类基因组草图绘制完成后，人们普遍认为，利用大规模高通量的组学技术和生物信息学技术，药物机理的发现会呈现一个几何级的增长。但事实上药物研发的速度这些年来并没发生太大的改善，究其原因，主要在于对高通量数据分析方法的滞后。传统的分析方法只是在药物实验组的基因表达数据和对照组表达数据比较，选出其中表达量水平差别比较大的差异基因，然后在里面挑选基因来一个个验证，这种做法无异于大海捞针。
为了解决这个问题，本论文首先设计了一种大规模的跨物种跨平台整合药物转录组数据来挖掘药物机理、副作用或老药新用以及分析组合用药的方法CGEMining，通过将各种不同实验中的动物疾病模型的转录组数据和药物干扰的转录组数据关联起来，并且关联到细节的功能模块，然后从这些关联中挖掘出已知功能的小分子的未知的能治疗其他疾病的功能，以及这些小分子的一些未知的副作用，并且深入探讨其中的机理。其中使用到的主要方法是直系同源转换、GO富集分析以及基于KS检验的比较方法。而对于一些未知机理的药物小分子，也可以利用CGEMining方法通过比较分析，挖掘出其潜在的作用靶点，然后利用化学信息学中的分子对接方法来进一步确认CGEMining挖掘的结果，本文中使用了一组针对糖尿病的新的药物分子作为例子来说明此过程，并且发现PPARgamma是其潜在的作用靶点。同样对包含多种成分的中药复方，CGEMining方法也能挖掘出其复杂的作用机理，而且能够发现其新的功能。本文中挖掘出了治疗肝纤维化的复方扶正化瘀胶囊的降血糖等的新作用。除了CGEMining方法之外，本论文中还开发一种新的基于蛋白序列数据和化学信息学协同的药物数据挖掘的方法VIScreen，通过利用化学信息学的结构信息来建立病毒蛋白序列和药物小分子之间的联系，以达到快速挖掘病毒药物与靶点结合的信息以及辅助新药研发的效果。最后，本论文还介绍了一种单独使用基因转录组数据的药物小分子机理挖掘方法，是以转录因子为核心进行差异共表达分析，构建共表达的网络模块，通过比较不同网络模块之间的差异来分析两种药物在网络药理学上的不同。
Drug development is an integrated process involving mathematics,chemistry, biology, computer science and so on. The key points in thedrug development process is to find drug targets and the mechanism ofaction. After the completion of the Human Genome Project, peoplegenerally believed that along with the large-scale high-throughputgenomics and bio-informatics technology, the discovery of new drugtarget will presents a geometric growth. But in fact, the speed of drugdevelopment all these years did not have much improvement. Thereason mainly lies in the lag of high-throughput data analysis means.The traditional method for the analysis of gene expression data is toseek drug targets within a lot of difference expressed genes. Thispractice is tantamount to fish for a needle in the ocean.
To solve this problem, a large-scale cross-species data miningmethod, CGEMining, is developed in this paper to dig information ofdrug mechanism, drug repositioning, side effects, and combination ofmedication. By combining various transcriptome data of disease ordrug experiments on animals, CGEMining could dig out drugrepositioning, side effects and drug mechanisms from the drug-diseaseor drug-drug relationships. For some small molecule drugs withunknown mechanism, CGEMining methods could help find thepotential targets by comparative analysis, and then docking will be used to confirm the results. This process is illustrated by a case ofanalyzing the target of diabetes drugs. For some herbal compound thatcontains a variety of ingredients, CGEMining method could not onlydig out its complex mechanism, but also discover its new features. Inaddition to the CGEMining method, another method, VIScreen, willalso be introduced in this paper, which combines viral protein sequencedata and chemical informatics. VIScreen takes chemoinformaticsstructure information as an intermediary for creating linkages betweenviral protein sequences and drug small molecules and then mine thepotential combination of viral drugs and protein targets. Finally, thispaper also introduced a causal co-expression network analysis methodto study the difference between two drugs in network pharmacology.
为了解决这个问题，本论文首先设计了一种大规模的跨物种跨平台整合药物转录组数据来挖掘药物机理、副作用或老药新用以及分析组合用药的方法CGEMining，通过将各种不同实验中的动物疾病模型的转录组数据和药物干扰的转录组数据关联起来，并且关联到细节的功能模块，然后从这些关联中挖掘出已知功能的小分子的未知的能治疗其他疾病的功能，以及这些小分子的一些未知的副作用，并且深入探讨其中的机理。其中使用到的主要方法是直系同源转换、GO富集分析以及基于KS检验的比较方法。而对于一些未知机理的药物小分子，也可以利用CGEMining方法通过比较分析，挖掘出其潜在的作用靶点，然后利用化学信息学中的分子对接方法来进一步确认CGEMining挖掘的结果，本文中使用了一组针对糖尿病的新的药物分子作为例子来说明此过程，并且发现PPARgamma是其潜在的作用靶点。同样对包含多种成分的中药复方，CGEMining方法也能挖掘出其复杂的作用机理，而且能够发现其新的功能。本文中挖掘出了治疗肝纤维化的复方扶正化瘀胶囊的降血糖等的新作用。除了CGEMining方法之外，本论文中还开发一种新的基于蛋白序列数据和化学信息学协同的药物数据挖掘的方法VIScreen，通过利用化学信息学的结构信息来建立病毒蛋白序列和药物小分子之间的联系，以达到快速挖掘病毒药物与靶点结合的信息以及辅助新药研发的效果。最后，本论文还介绍了一种单独使用基因转录组数据的药物小分子机理挖掘方法，是以转录因子为核心进行差异共表达分析，构建共表达的网络模块，通过比较不同网络模块之间的差异来分析两种药物在网络药理学上的不同。
Drug development is an integrated process involving mathematics,chemistry, biology, computer science and so on. The key points in thedrug development process is to find drug targets and the mechanism ofaction. After the completion of the Human Genome Project, peoplegenerally believed that along with the large-scale high-throughputgenomics and bio-informatics technology, the discovery of new drugtarget will presents a geometric growth. But in fact, the speed of drugdevelopment all these years did not have much improvement. Thereason mainly lies in the lag of high-throughput data analysis means.The traditional method for the analysis of gene expression data is toseek drug targets within a lot of difference expressed genes. Thispractice is tantamount to fish for a needle in the ocean.
To solve this problem, a large-scale cross-species data miningmethod, CGEMining, is developed in this paper to dig information ofdrug mechanism, drug repositioning, side effects, and combination ofmedication. By combining various transcriptome data of disease ordrug experiments on animals, CGEMining could dig out drugrepositioning, side effects and drug mechanisms from the drug-diseaseor drug-drug relationships. For some small molecule drugs withunknown mechanism, CGEMining methods could help find thepotential targets by comparative analysis, and then docking will be used to confirm the results. This process is illustrated by a case ofanalyzing the target of diabetes drugs. For some herbal compound thatcontains a variety of ingredients, CGEMining method could not onlydig out its complex mechanism, but also discover its new features. Inaddition to the CGEMining method, another method, VIScreen, willalso be introduced in this paper, which combines viral protein sequencedata and chemical informatics. VIScreen takes chemoinformaticsstructure information as an intermediary for creating linkages betweenviral protein sequences and drug small molecules and then mine thepotential combination of viral drugs and protein targets. Finally, thispaper also introduced a causal co-expression network analysis methodto study the difference between two drugs in network pharmacology.
引文
1.蔡钦生,冯美卿,黄海,周珮:生物芯片、基因组学和蛋白质组学在药物研发中的应用.药学学报2003,38(10):795-800.
2. Rask-Andersen M, Almen MS, Schioth HB: Trends in the exploitation of novel drug targets.Nature reviews2011,10(8):579-590.
3. Hopkins AL: Network pharmacology. Nat Biotechnol2007,25(10):1110-1111.
4.刘艾林,杜冠华:网络药理学:药物发现的新思想.药学学报2010,45(12):1472-1477.
5. Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, Li X, Zhou W, Wang W, Wang Y: A systematicprediction of multiple drug-target interactions from chemical, genomic, and pharmacologicaldata. PLoS One2012,7(5):e37608.
6. Gonzalez-Diaz H, Cruz-Monteagudo M, Molina R, Tenorio E, Uriarte E: Predicting multipledrugs side effects with a general drug-target interaction thermodynamic Markov model.Bioorg Med Chem2005,13(4):1119-1129.
7. Yamamura T:[Molecular target drug development for curing multiple sclerosis]. BrainNerve2009,61(8):923-928.
8. Stroup J, Stephens J: Combination drug products: an indication for medicationreconciliation and pharmacist counseling. J Am Pharm Assoc (2003)2008,48(4):541-543.
9. Yuan S, Wang F, Chen G, Zhang H, Feng L, Wang L, Colman H, Keating MJ, Li X, Xu RH etal: Effective elimination of cancer stem cells by a novel drug combination strategy. Stem Cells2013,31(1):23-34.
10. Gopalan A, Yu W, Sanders BG, Kline K: Eliminating drug resistant breast cancer stem-likecells with combination of simvastatin and gamma-tocotrienol. Cancer Lett2013,328(2):285-296.
11. Lee JH, Nan A: Combination drug delivery approaches in metastatic breast cancer. JDrug Deliv2012,2012:915375.
12. Hopkins AL: Network pharmacology: the next paradigm in drug discovery. Nat ChemBiol2008,4(11):682-690.
13.王娟,李学军:网络药理学和药物发现研究进展.生理科学进展2011,42(4):241-245.
14. Alian OM, Shah M, Mohammad RM: Network Pharmacology: Reining in Drug Attrition?Curr Drug Discov Technol2012.
15. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB,Klein TE: Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther2012,92(4):414-417.
16. Rossi S, Calin GA: Bioinformatics, Non-coding RNAs and Its Possible Application inPersonalized Medicine. Adv Exp Med Biol2013,774:21-37.
17. Workman P, Clarke PA, Al-Lazikani B: Personalized medicine: patient-predictive panelpower. Cancer Cell2012,21(4):455-458.
18. Yu S, Zheng L, Li Y, Li C, Ma C, Yu Y, Li X, Hao P: Causal co-expression method withmodule analysis to screen drugs with specific target. Gene2012.
19. van de Waterbeemd H, Gifford E: ADMET in silico modelling: towards prediction paradise?Nat Rev Drug Discov2003,2(3):192-204.
20. Modi S: Positioning ADMET in silico tools in drug discovery. Drug Discov Today2004,9(1):14-15.
21. Banerjee C, Archin N, Michaels D, Belkina AC, Denis GV, Bradner J, Sebastiani P, MargolisDM, Montano M: BET bromodomain inhibition as a novel strategy for reactivation of HIV-1. JLeukoc Biol2012,92(6):1147-1154.
22. Lv J, Du C, Wei W, Wu Z, Zhao G, Li Z, Xie X: The antiepileptic drug valproic acidrestores T cell homeostasis and ameliorates pathogenesis of experimental autoimmuneencephalomyelitis. J Biol Chem2012,287(34):28656-28665.
23. Gilden D: When HAART is not enough. GMHC Treat Issues1996,10(10):1-6.
24. Alarcon-Aguilara FJ, Roman-Ramos R, Perez-Gutierrez S, Aguilar-Contreras A,Contreras-Weber CC, Flores-Saenz JL: Study of the anti-hyperglycemic effect of plants used asantidiabetics. J Ethnopharmacol1998,61(2):101-110.
25. Hu X, Sato J, Oshida Y, Xu M, Bajotto G, Sato Y: Effect of Gosha-jinki-gan (Chinese herbalmedicine: Niu-Che-Sen-Qi-Wan) on insulin resistance in streptozotocin-induced diabetic rats.Diabetes Res Clin Pract2003,59(2):103-111.
26. Foster H, Coley HM, Goumenou A, Pados G, Harvey A, Karteris E: Differential expressionof mTOR signalling components in drug resistance in ovarian cancer. Anticancer Res2010,30(9):3529-3534.
27. Cho SB, Kim J, Kim JH: Identifying set-wise differential co-expression in gene expressionmicroarray data. BMC Bioinformatics2009,10:109.
28. Blake JA, Harris MA: The Gene Ontology (GO) project: structured vocabularies formolecular biology and their application to genome and expression analysis. Curr ProtocBioinformatics2002, Chapter7:Unit72.
29. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K,Dwight SS, Eppig JT et al: Gene ontology: tool for the unification of biology. The GeneOntology Consortium. Nat Genet2000,25(1):25-29.
30. Aoki KF, Kanehisa M: Using the KEGG database resource. Curr Protoc Bioinformatics2005, Chapter1:Unit112.
31. Langfelder P, Horvath S: WGCNA: an R package for weighted correlation networkanalysis. BMC Bioinformatics2008,9:559.
32. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP,Subramanian A, Ross KN et al: The Connectivity Map: using gene-expression signatures toconnect small molecules, genes, and disease. Science2006,313(5795):1929-1935.
33. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. JMol Biol1990,215(3):403-410.
34. Higgins DG, Thompson JD, Gibson TJ: Using CLUSTAL for multiple sequence alignments.Methods Enzymol1996,266:383-402.
35. Mistry J, Finn R: Pfam: a domain-centric method for analyzing proteins and proteomes.Methods Mol Biol2007,396:43-58.
36. Liu X, Jiang H, Li H: SHAFTS: a hybrid approach for3D molecular similarity calculation.1. Method and assessment of virtual screening. J Chem Inf Model2011,51(9):2372-2385.
37. Lu W, Liu X, Cao X, Xue M, Liu K, Zhao Z, Shen X, Jiang H, Xu Y, Huang J et al: SHAFTS:a hybrid approach for3D molecular similarity calculation.2. Prospective case study in thediscovery of diverse p90ribosomal S6protein kinase2inhibitors to suppress cell migration. JMed Chem2011,54(10):3564-3574.
38. Nagahara AH, Merrill DA, Coppola G, Tsukada S, Schroeder BE, Shaked GM, Wang L,Blesch A, Kim A, Conner JM et al: Neuroprotective effects of brain-derived neurotrophic factorin rodent and primate models of Alzheimer's disease. Nat Med2009,15(3):331-337.
39. Chin J: Selecting a mouse model of Alzheimer's disease. Methods Mol Biol2011,670:169-189.
40. Miller JA, Horvath S, Geschwind DH: Divergence of human and mouse braintranscriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A2010,107(28):12698-12703.
41. Haldar M, Hancock JD, Coffin CM, Lessnick SL, Capecchi MR: A conditional mouse modelof synovial sarcoma: insights into a myogenic origin. Cancer Cell2007,11(4):375-388.
42. Hung KE, Maricevich MA, Richard LG, Chen WY, Richardson MP, Kunin A, Bronson RT,Mahmood U, Kucherlapati R: Development of a mouse model for sporadic and metastatic colontumors and its use in assessing drug treatment. Proceedings of the National Academy of Sciences2010.
43. Raab M, Kappel S, Kramer A, Sanhaji M, Matthess Y, Kurunci-Csacsko E, Calzada-Wack J,Rathkolb B, Rozman J, Adler T et al: Toxicity modelling of Plk1-targeted therapies ingenetically engineered mice and cultured primary mammalian cells. Nat Commun2011,2:395.
44. Chakraborty C, Hsu CH, Wen ZH, Lin CS, Agoramoorthy G: Zebrafish: a complete animalmodel for in vivo drug discovery and development. Curr Drug Metab2009,10(2):116-124.
45. Zon LI, Peterson RT: In vivo drug discovery in the zebrafish. Nat Rev Drug Discov2005,4(1):35-44.
46. Kari G, Rodeck U, Dicker AP: Zebrafish: an emerging model system for human diseaseand drug discovery. Clin Pharmacol Ther2007,82(1):70-80.
47. Singh M, Johnson L: Using Genetically Engineered Mouse Models of Cancer to Aid DrugDevelopment: An Industry Perspective. Clinical Cancer Research2006,12(18):5312-5328.
48. Sharpless NE, Depinho RA: The mighty mouse: genetically engineered mouse models incancer drug development. Nat Rev Drug Discov2006,5(9):741-754.
49. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J, Sipes G,Bracken W et al: Concordance of the toxicity of pharmaceuticals in humans and in animals.Regul Toxicol Pharmacol2000,32(1):56-67.
50. Strand AD, Aragaki AK, Baquet ZC, Hodges A, Cunningham P, Holmans P, Jones KR, Jones L,Kooperberg C, Olson JM: Conservation of regional gene expression in mouse and human brain.PLoS Genet2007,3(4):e59.
51. Chan ET, Quon GT, Chua G, Babak T, Trochesset M, Zirngibl RA, Aubin J, Ratcliffe MJ,Wilde A, Brudno M et al: Conservation of core gene expression in vertebrate tissues. J Biol2009,8(3):33.
52. Zheng-Bradley X, Rung J, Parkinson H, Brazma A: Large scale comparison of global geneexpression patterns in human and mouse. Genome Biol2010,11(12):R124.
53. Deluca TF, Wu IH, Pu J, Monaghan T, Peshkin L, Singh S, Wall DP: Roundup: amulti-genome repository of orthologs and evolutionary distances. Bioinformatics2006,22(16):2044-2046.
54. Li Y, Hao P, Zheng S, Tu K, Fan H, Zhu R, Ding G, Dong C, Wang C, Li X et al: Geneexpression module-based chemical function similarity search. Nucleic Acids Res2008,36(20):e137.
55. Laifenfeld D, Gilchrist A, Drubin D, Jorge M, Eddy SF, Frushour BP, Ladd B, Obert LA,Gosink MM, Cook JC et al: The role of hypoxia in2-butoxyethanol-induced hemangiosarcoma.Toxicol Sci2010,113(1):254-266.
56. Wall DP, Fraser HB, Hirsh AE: Detecting putative orthologs. Bioinformatics2003,19(13):1710-1711.
57. Ogata H, Goto S, Fujibuchi W, Kanehisa M: Computation with the KEGG pathwaydatabase. Biosystems1998,47(1-2):119-128.
58. Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ: Discovering statisticallysignificant pathways in expression profiling studies. Proc Natl Acad Sci U S A2005,102(38):13544-13549.
59. Fury W, Batliwalla F, Gregersen PK, Li W: Overlapping probabilities of top ranking genelists, hypergeometric distribution, and stringency of gene selection criterion. Conf Proc IEEEEng Med Biol Soc2006,1:5531-5534.
60. Blevins L, McDonald CJ: Fisher's Exact Test: an easy-to-use statistical test for comparingoutcomes. MD Comput1985,2(1):15-19,68.
61. Falcon S, Gentleman R: Using GOstats to test gene lists for GO term association.Bioinformatics2007,23(2):257-258.
62. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A,Pomeroy SL, Golub TR, Lander ES et al: Gene set enrichment analysis: a knowledge-basedapproach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A2005,102(43):15545-15550.
63. Dudley JT, Tibshirani R, Deshpande T, Butte AJ: Disease signatures are robust acrosstissues and experiments. Mol Syst Biol2009,5:307.
64. Yu S, Zheng L, Li Y, Li C, Ma C, Li X, Hao P: A cross-species analysis method to analyzeanimal models' similarity to human's disease state. BMC Syst Biol2012,6Suppl3:S18.
65. Dowdell KC, Pesnicak L, Hoffmann V, Steadman K, Remaley AT, Cohen JI, Straus SE, RaoVK: Valproic acid (VPA), a histone deacetylase (HDAC) inhibitor, diminisheslymphoproliferation in the Fas-deficient MRL/lpr(-/-) murine model of autoimmunelymphoproliferative syndrome (ALPS). Exp Hematol2009,37(4):487-494.
66. Hrzenjak A, Moinfar F, Kremser ML, Strohmeier B, Petru E, Zatloukal K, Denk H: Histonedeacetylase inhibitor vorinostat suppresses the growth of uterine sarcomas in vitro and in vivo.Mol Cancer2010,9:49.
67. Baidyaroy D, Brosch G, Graessle S, Trojer P, Walton JD: Characterization ofinhibitor-resistant histone deacetylase activity in plant-pathogenic fungi. Eukaryot Cell2002,1(4):538-547.
68. Zhang Q, Tang X, Lu QY, Zhang ZF, Brown J, Le AD: Resveratrol inhibits hypoxia-inducedaccumulation of hypoxia-inducible factor-1alpha and VEGF expression in human tonguesquamous cell carcinoma and hepatoma cells. Mol Cancer Ther2005,4(10):1465-1474.
69. Li Y, Zhang JF, Zhang YM, Ma XB: The protective effect of genistein postconditioning onhypoxia/reoxygenation-induced injury in human gastric epithelial cells. Acta Pharmacol Sin2009,30(5):576-581.
70. Sohaskey CD: Nitrate enhances the survival of Mycobacterium tuberculosis duringinhibition of respiration. J Bacteriol2008,190(8):2981-2986.
71. Vengellur A, Phillips JM, Hogenesch JB, LaPres JJ: Gene expression profiling of hypoxiasignaling in human hepatocellular carcinoma cells. Physiol Genomics2005,22(3):308-318.
72. Yamamoto S, Tanaka E, Higashi H: Mediation by intracellular calcium-dependent signalsof hypoxic hyperpolarization in rat hippocampal CA1neurons in vitro. J Neurophysiol1997,77(1):386-392.
73. Edelstein CL, Yaqoob MM, Alkhunaizi AM, Gengaro PE, Nemenoff RA, Wang KK, SchrierRW: Modulation of hypoxia-induced calpain activity in rat renal proximal tubules. Kidney Int1996,50(4):1150-1157.
74. Carver DJ, Gaston B, Deronde K, Palmer LA: Akt-mediated activation of HIF-1inpulmonary vascular endothelial cells by S-nitrosoglutathione. Am J Respir Cell Mol Biol2007,37(3):255-263.
75. Hudson CC, Liu M, Chiang GG, Otterness DM, Loomis DC, Kaper F, Giaccia AJ, AbrahamRT: Regulation of hypoxia-inducible factor1alpha expression and function by the mammaliantarget of rapamycin. Mol Cell Biol2002,22(20):7004-7014.
76. Kambe T, Tada-Kambe J, Kuge Y, Yamaguchi-Iwai Y, Nagao M, Sasaki R: Retinoic acidstimulates erythropoietin gene transcription in embryonal carcinoma cells through the directrepeat of a steroid/thyroid hormone receptor response element half-site in thehypoxia-response enhancer. Blood2000,96(9):3265-3271.
77. Tuller ER, Beavers CT, Lou JR, Ihnat MA, Benbrook DM, Ding WQ: Docosahexaenoic acidinhibits superoxide dismutase1gene transcription in human cancer cells: the involvement ofperoxisome proliferator-activated receptor alpha and hypoxia-inducible factor-2alphasignaling. Mol Pharmacol2009,76(3):588-595.
78. Schlenker EH: In hamsters dopamine D2receptors affect ventilation during and followingintermittent hypoxia. Comp Biochem Physiol A Mol Integr Physiol2007,148(3):674-680.
79. Mottet D, Michel G, Renard P, Ninane N, Raes M, Michiels C: Role of ERK and calcium inthe hypoxia-induced activation of HIF-1. J Cell Physiol2003,194(1):30-44.
80. Beitner-Johnson D, Rust RT, Hsieh TC, Millhorn DE: Hypoxia activates Akt and inducesphosphorylation of GSK-3in PC12cells. Cell Signal2001,13(1):23-27.
81. Shudo K, Fukasawa H, Nakagomi M, Yamagata N: Towards retinoid therapy forAlzheimer's disease. Curr Alzheimer Res2009,6(3):302-311.
82. Valles SL, Dolz-Gaiton P, Gambini J, Borras C, Lloret A, Pallardo FV, Vina J: Estradiol orgenistein prevent Alzheimer's disease-associated inflammation correlating with an increasePPAR gamma expression in cultured astrocytes. Brain Res2010,1312:138-144.
83. Luo W, Rodina A, Chiosis G: Heat shock protein90: translation from cancer toAlzheimer's disease treatment? BMC Neurosci2008,9Suppl2:S7.
84. Shineman DW, Dain AS, Kim ML, Lee VM: Constitutively active Akt inhibits traffickingof amyloid precursor protein and amyloid precursor protein metabolites through feedbackinhibition of phosphoinositide3-kinase. Biochemistry2009,48(17):3787-3794.
85. Wang LY, Shofer JB, Rohde K, Hart KL, Hoff DJ, McFall YH, Raskind MA, Peskind ER:Prazosin for the treatment of behavioral symptoms in patients with Alzheimer disease withagitation and aggression. Am J Geriatr Psychiatry2009,17(9):744-751.
86. Han G, Ma H, Chintala R, Fulton DJ, Barman SA, White RE: Essential role of the90-kilodalton heat shock protein in mediating nongenomic estrogen signaling in coronaryartery smooth muscle. J Pharmacol Exp Ther2009,329(3):850-855.
87. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R,Brunetti-Pierri N, Isacchi A et al: Discovery of drug mode of action and drug repositioning fromtranscriptional responses. Proc Natl Acad Sci U S A2010,107(33):14621-14626.
88. Fliss AE, Benzeno S, Rao J, Caplan AJ: Control of estrogen receptor ligand binding byHsp90. J Steroid Biochem Mol Biol2000,72(5):223-230.
89. Greenfield JP, Leung LW, Cai D, Kaasik K, Gross RS, Rodriguez-Boulan E, Greengard P, XuH: Estrogen lowers Alzheimer beta-amyloid generation by stimulating trans-Golgi networkvesicle biogenesis. J Biol Chem2002,277(14):12128-12136.
90. Wild S, Roglic G, Green A, Sicree R, King H: Global prevalence of diabetes: estimates forthe year2000and projections for2030. Diabetes Care2004,27(5):1047-1053.
91. Muruganandan S, Srinivasan K, Gupta S, Gupta PK, Lal J: Effect of mangiferin onhyperglycemia and atherogenicity in streptozotocin diabetic rats. J Ethnopharmacol2005,97(3):497-501.
92. Li X, Cui X, Sun X, Zhu Q, Li W: Mangiferin prevents diabetic nephropathy progressionin streptozotocin-induced diabetic rats. Phytother Res2010,24(6):893-899.
93. Miura T, Iwamoto N, Kato M, Ichiki H, Kubo M, Komatsu Y, Ishida T, Okada M, Tanigawa K:The suppressive effect of mangiferin with exercise on blood lipids in type2diabetes. BiolPharm Bull2001,24(9):1091-1092.
94. Sellamuthu PS, Muniappan BP, Perumal SM, Kandasamy M: Antihyperglycemic effect ofMangiferin in streptozotocin induced diabetic rats. Journal of Health Science2009,55(2):206-214.
95. Engelman JA, Berg AH, Lewis RY, Lisanti MP, Scherer PE: Tumor necrosis factoralpha-mediated insulin resistance, but not dedifferentiation, is abrogated by MEK1/2inhibitors in3T3-L1adipocytes. Mol Endocrinol2000,14(10):1557-1569.
96. Momany FA, Willett JL: Computational studies on carbohydrates: in vacuo studies usinga revised AMBER force field, AMB99C, designed for alpha-(1-->4) linkages. Carbohydr Res2000,326(3):194-209.
97. Do GM, Jung UJ, Park HJ, Kwon EY, Jeon SM, McGregor RA, Choi MS: Resveratrolameliorates diabetes-related metabolic changes via activation of AMP-activated proteinkinase and its downstream targets in db/db mice. Mol Nutr Food Res2012,56(8):1282-1291.
98. Thirunavukkarasu M, Penumathsa SV, Koneru S, Juhasz B, Zhan L, Otani H, Bagchi D, DasDK, Maulik N: Resveratrol alleviates cardiac dysfunction in streptozotocin-induced diabetes:Role of nitric oxide, thioredoxin, and heme oxygenase. Free Radic Biol Med2007,43(5):720-729.
99. Ramadori G, Gautron L, Fujikawa T, Vianna CR, Elmquist JK, Coppari R: Centraladministration of resveratrol improves diet-induced diabetes. Endocrinology2009,150(12):5326-5333.
100. Zheng W, Feng X, Qiu L, Pan Z, Wang R, Lin S, Hou D, Jin L, Li Y: Identification of theantibiotic ionomycin as an unexpected peroxisome proliferator-activated receptor gamma(PPARgamma) ligand with a unique binding mode and effective glucose-lowering activity in amouse model of diabetes. Diabetologia2013,56(2):401-411.
101. Higai K, Tsukada M, Moriya Y, Azuma Y, Matsumoto K: Prolonged high glucose suppressesphorbol12-myristate13-acetate and ionomycin-induced interleukin-2mRNA expression inJurkat cells. Biochim Biophys Acta2009,1790(1):8-15.
102. Cushman TT, Kim N, Hoyt R, Traish AM: Estradiol ameliorates diabetes-induced changesin vaginal structure of db/db mouse model. J Sex Med2009,6(9):2467-2479.
103. Cushman T, Kim N, Hoyt R, Traish AM: Estradiol restores diabetes-induced reductions insex steroid receptor expression and distribution in the vagina of db/db mouse model. J SteroidBiochem Mol Biol2009,114(3-5):186-194.
104. Raikwar HP, Muthian G, Rajasingh J, Johnson CN, Bright JJ: PPARgamma antagonistsreverse the inhibition of neural antigen-specific Th1response and experimental allergicencephalomyelitis by Ciglitazone and15-deoxy-Delta12,14-prostaglandin J2. J Neuroimmunol2006,178(1-2):76-86.
105. Jawerbaum A, Capobianco E, Pustovrh C, White V, Baier M, Salzberg S, Pesaresi M,Gonzalez E: Influence of peroxisome proliferator-activated receptor gamma activation by itsendogenous ligand15-deoxy Delta12,14prostaglandin J2on nitric oxide production in termplacental tissues from diabetic women. Mol Hum Reprod2004,10(9):671-676.
106. Cui W, Li B, Bai Y, Miao X, Chen Q, Sun W, Tan Y, Luo P, Zhang C, Zheng S et al: Potentialrole for Nrf2activation in the therapeutic effect of MG132on diabetic nephropathy in OVE26diabetic mice. Am J Physiol Endocrinol Metab2013,304(1):E87-99.
107. Kindmark H, Koohler M, Larsson O, Khan A, Berggren PO: Dissociation between exocytosisand Ca(2+)-channel activity in mouse pancreatic beta-cells stimulated with calmidazolium(compound R24571). FEBS Lett1995,369(2-3):315-320.
108. Kindmark H, Kohler M, Gerwins P, Larsson O, Khan A, Wahl MA, Berggren PO: Theimidazoline derivative calmidazolium inhibits voltage-gated Ca(2+)-channels and insulinrelease but has no effect on the phospholipase C system in insulin producing RINm5F-cells.Biosci Rep1994,14(3):145-158.
109. Jeong GS, Lee DS, Song MY, Park BH, Kang DG, Lee HS, Kwon KB, Kim YC: Butein fromRhus verniciflua protects pancreatic beta cells against cytokine-induced toxicity mediated byinhibition of nitric oxide formation. Biol Pharm Bull2011,34(1):97-102.
110. Behloul N, Wu G: Genistein: a promising therapeutic agent for obesity and diabetestreatment. Eur J Pharmacol2013,698(1-3):31-38.
111. Fu Z, Gilbert ER, Pfeiffer L, Zhang Y, Fu Y, Liu D: Genistein ameliorates hyperglycemia ina mouse model of nongenetic type2diabetes. Appl Physiol Nutr Metab2012,37(3):480-488.
112. Suh Y, Afaq F, Johnson JJ, Mukhtar H: A plant flavonoid fisetin induces apoptosis in coloncancer cells by inhibition of COX2and Wnt/EGFR/NF-kappaB-signaling pathways.Carcinogenesis2009,30(2):300-307.
113. Zheng LT, Ock J, Kwon BM, Suk K: Suppressive effects of flavonoid fisetin onlipopolysaccharide-induced microglial activation and neurotoxicity. Int Immunopharmacol2008,8(3):484-494.
114. Rose PW, Beran B, Bi C, Bluhm WF, Dimitropoulos D, Goodsell DS, Prlic A, Quesada M,Quinn GB, Westbrook JD et al: The RCSB Protein Data Bank: redesigned web site and webservices. Nucleic Acids Res2011,39(Database issue):D392-401.
115. Lu AP, Chen KJ: Integrative medicine in clinical practice: from pattern differentiation intraditional Chinese medicine to disease treatment. Chin J Integr Med2009,15(2):152.
116. Lu C, Xiao C, Chen G, Jiang M, Zha Q, Yan X, Kong W, Lu A: Cold and heat pattern ofrheumatoid arthritis in traditional Chinese medicine: distinct molecular signaturesindentified by microarray expression profiles in CD4-positive T cell. Rheumatol Int2010.
117.肖诚,赵林华,吕诚等:类风湿关节炎类风湿因子阴性和阳性寒热证候患者外周血CD4+T细胞基因差异表达研究.中国中西医结合杂志2006,26(8):689-693.
118.吴素芬,孙静汾,郭述真:复方更年康对老年雌鼠性激素及骨雌激素受体表达的影响.中国药物与临床2005,11(3):205-208.
119. Zuo F, Zhou ZM, Yan MZ, Liu ML, Xiong YL, Zhang Q, Song HY, Ye WH: Metabolism ofconstituents in Huangqin-Tang, a prescription in traditional Chinese medicine, by humanintestinal flora. Biol Pharm Bull2002,25(5):558-563.
120. Cao Y, Xia QH, Meng H, Zhong AP: Pharmacological effects of serum containing chinesemedicine Bushen Huayu Jiedu Compound Recipe in lung cancer drug-resistance cells. Chin JIntegr Med2008,14(1):46-50.
121. Wang YT, Tan QR, Sun LL, Cao J, Dou KF, Xia B, Wang W: Possible therapeutic effect of aTraditional Chinese Medicine, Sinisan, on chronic restraint stress related disorders. NeurosciLett2009,449(3):215-219.
122. Tanaka K, Sawamura S: Therapeutic effect of a traditional Chinese medicine,ren-shen-yang-rong-tang (Japanese name: Ninjin'yoeito) on nitric oxide-mediated lung injuryin a mouse infected with murine cytomegalovirus. Int Immunopharmacol2006,6(4):678-685.
123. Li S, Zhang B, Jiang D, Wei Y, Zhang N: Herb network construction and co-moduleanalysis for uncovering the combination rule of traditional Chinese herbal formulae. BMCBioinformatics2010,11Suppl11:S6.
124. Li S, Zhang B, Zhang N: Network target for screening synergistic drug combinations withapplication to traditional Chinese medicine. BMC Syst Biol2011,5Suppl1:S10.
125. Li S, Zhang ZQ, Wu LJ, Zhang XG, Li YD, Wang YY: Understanding ZHENG intraditional Chinese medicine in the context of neuro-endocrine-immune network. IET Syst Biol2007,1(1):51-60.
126. Yu S, Guo Z, Guan Y, Lu YY, Hao P, Li Y, Su SB: Combining ZHENG Theory andHigh-Throughput Expression Data to Predict New Effects of Chinese Herbal Formulae. EvidBased Complement Alternat Med2012,2012:986427.
127.苗桂珍,梁秀珍,王亚红:补气活血法治疗糖尿病周围神经病变的临床观察.中国中西医结合杂志2003,23(11):826-828.
128.阙华发,朱元颖,王云飞等:益气化瘀中药对糖尿病皮肤溃疡大鼠缺氧诱导因子-1α和血管内皮细胞生长因子的影响.中西医结合学报2007,5(2):165-169.
129.徐列明,胡义扬:脂肪肝的中药治疗.中西医结合学报2003,1(2):138-141.
130.段学忠,杨丁友,孙西庆等:益脉降压流浸膏对老年气虚血瘀证高血压病患者血浆神经肽Y含量的影响.中国中西医结合杂志2000,20(10):750-752.
131.张琴,刘平,章浩伟等:900例肝炎后肝硬化中医证候判别模式的研究.中国中西医结合杂志2006,26(8):694-697.
132. Liu C, Hu Y, Xu L, Liu P: Effect of Fuzheng Huayu formula and its actions against liverfibrosis. Chin Med2009,4:12.
133. Larsen FS, Vainer B, Eefsen M, Bjerring PN, Adel Hansen B: Low-dose tacrolimusameliorates liver inflammation and fibrosis in steroid refractory autoimmune hepatitis. WorldJ Gastroenterol2007,13(23):3232-3236.
134. Patsenker E, Schneider V, Ledermann M, Saegesser H, Dorn C, Hellerbrand C, Stickel F:Potent antifibrotic activity of mTOR inhibitors sirolimus and everolimus but not ofcyclosporine A and tacrolimus in experimental liver fibrosis. J Hepatol2011,55(2):388-398.
135. Bergman R, Parkes M: Systematic review: the use of mesalazine in inflammatory boweldisease. Aliment Pharmacol Ther2006,23(7):841-855.
136. el-Andere W, Lerario AC, Wajchenberg BL: Effect of short-and long-term chlorpropamidetherapy on oral glucose tolerance and erythrocyte insulin receptors in non-obese non-insulindependent diabetes mellitus. Horm Metab Res1987,19(6):257-263.
137. Fidan E, Onder Ersoz H, Yilmaz M, Yilmaz H, Kocak M, Karahan C, Erem C: The effects ofrosiglitazone and metformin on inflammation and endothelial dysfunction in patients withtype2diabetes mellitus. Acta Diabetol2011.
138. Bergson P, Lipkind G, Lee SP, Duban ME, Hanck DA: Verapamil block of T-type calciumchannels. Mol Pharmacol2011,79(3):411-419.
139. Santilli AA, Scotese AC, Tomarelli RM: A potent antihypercholesterolemic agent:(4-chloro-6-(2,3-xylidino)-2-pyrimidinylthio) acetic acid (Wy-14643). Experientia1974,30(10):1110-1111.
140.陈文东,安文林,楚晋等:茯苓水提液对新生大鼠神经细胞内钙离子浓度的影响.中国中西医结合杂志1998,18(5):293-295.
141.富杭育,贺玉琢,李晓芹等:桂枝汤对汗腺分泌作用的实验研究.中国中西医结合杂志1991,11(1):34-36,36.
142. Lee JS: Effects of soy protein and genistein on blood glucose, antioxidant enzymeactivities, and lipid profile in streptozotocin-induced diabetic rats. Life Sci2006,79(16):1578-1584.
143. Nogowski L, Mackowiak P, Kandulska K, Szkudelski T, Nowak KW: Genistein-inducedchanges in lipid metabolism of ovariectomized rats. Ann Nutr Metab1998,42(6):360-366.
144. Si H, Liu D: Genistein, a soy phytoestrogen, upregulates the expression of humanendothelial nitric oxide synthase and lowers blood pressure in spontaneously hypertensiverats. J Nutr2008,138(2):297-304.
145. Ji G, Yang Q, Hao J, Guo L, Chen X, Hu J, Leng L, Jiang Z: Anti-inflammatory effect ofgenistein on non-alcoholic steatohepatitis rats induced by high fat diet and its potentialmechanisms. Int Immunopharmacol2011,11(6):762-768.
146.赵长青,顾宏图,成扬等:扶正化瘀肝糖综合治疗方案治疗乙型肝炎后肝硬化合并糖代谢异常的临床研究.中国中西医结合杂志2008,28(1):24-27.
147. Holguin A, Erazo K, Escobar G, de Mulder M, Yebra G, Martin L, Jovel LE, Castaneda L,Perez E: Drug resistance prevalence in human immunodeficiency virus type1infectedpediatric populations in Honduras and El Salvador during1989-2009. Pediatr Infect Dis J2011,30(5):e82-87.
148. Ito K:[Bioinformatics technologies for the analysis of antigenic evolution of influenzaviruses]. Uirusu2011,61(1):3-13.
149. Lai MM: SARS virus: the beginning of the unraveling of a new coronavirus. J Biomed Sci2003,10(6Pt2):664-675.
150. Stadler K, Masignani V, Eickmann M, Becker S, Abrignani S, Klenk HD, Rappuoli R:SARS--beginning to understand a new virus. Nat Rev Microbiol2003,1(3):209-218.
151. Kuljic-Kapulica N:[SARS corona virus--a new dilemma]. Med Pregl2005,58(1-2):43-46.
152. Chan JF, Li KS, To KK, Cheng VC, Chen H, Yuen KY: Is the discovery of the novel humanbetacoronavirus2c EMC/2012(HCoV-EMC) the beginning of another SARS-like pandemic?J Infect2012,65(6):477-489.
153. Rosenbloom DI, Hill AL, Rabi SA, Siliciano RF, Nowak MA: Antiretroviral dynamicsdetermines HIV evolution and predicts therapy outcome. Nat Med2012,18(9):1378-1385.
154. Gifford RJ, Liu TF, Rhee SY, Kiuchi M, Hue S, Pillay D, Shafer RW: The calibratedpopulation resistance tool: standardized genotypic estimation of transmitted HIV-1drugresistance. Bioinformatics2009,25(9):1197-1198.
155. Ewing TJ, Makino S, Skillman AG, Kuntz ID: DOCK4.0: search strategies for automatedmolecular docking of flexible molecule databases. J Comput Aided Mol Des2001,15(5):411-428.
156. Trott O, Olson AJ: AutoDock Vina: improving the speed and accuracy of docking with anew scoring function, efficient optimization, and multithreading. J Comput Chem2010,31(2):455-461.
157. Hendlich M, Rippmann F, Barnickel G: LIGSITE: automatic and efficient detection ofpotential small molecule-binding sites in proteins. J Mol Graph Model1997,15(6):359-363,389.
158. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J: CASTp: computed atlas ofsurface topography of proteins with structural and topographical mapping of functionallyannotated residues. Nucleic Acids Res2006,34(Web Server issue):W116-118.
159. An J, Totrov M, Abagyan R: Pocketome via comprehensive identification and classificationof ligand binding envelopes. Mol Cell Proteomics2005,4(6):752-761.
160. Halgren T: New method for fast and accurate binding-site identification and analysis.Chem Biol Drug Des2007,69(2):146-148.
161. Yuan Y, Pei J, Lai L: Binding Site Detection and Druggability Prediction of ProteinTargets for Structure-Based Drug Design. Curr Pharm Des2012.
162. Kouranov A, Xie L, de la Cruz J, Chen L, Westbrook J, Bourne PE, Berman HM: The RCSBPDB information portal for structural genomics. Nucleic Acids Res2006,34(Databaseissue):D302-305.
163. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V et al:DrugBank3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res2011,39(Database issue):D1035-1041.
164. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK: BindingDB: a web-accessible database ofexperimentally determined protein-ligand binding affinities. Nucleic Acids Res2007,35(Database issue):D198-201.
165. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR: Open Babel:An open chemical toolbox. J Cheminform2011,3:33.
166. Yuan Y, Pei J, Lai L: LigBuilder2: A Practical de Novo Drug Design Approach. J Chem InfModel2011.
167. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR,Sonnhammer EL et al: The Pfam protein families database. Nucleic Acids Res2008,36(Databaseissue):D281-288.
168. Yang S, Chen SJ, Hsu MF, Wu JD, Tseng CT, Liu YF, Chen HC, Kuo CW, Wu CS, Chang LWet al: Synthesis, crystal structure, structure-activity relationships, and antiviral activity of apotent SARS coronavirus3CL protease inhibitor. J Med Chem2006,49(16):4971-4980.
169. Wu CJ, Jan JT, Chen CM, Hsieh HP, Hwang DR, Liu HW, Liu CY, Huang HW, Chen SC,Hong CF et al: Inhibition of severe acute respiratory syndrome coronavirus replication byniclosamide. Antimicrob Agents Chemother2004,48(7):2693-2696.
170. Shie JJ, Fang JM, Kuo CJ, Kuo TH, Liang PH, Huang HJ, Yang WB, Lin CH, Chen JL, WuYT et al: Discovery of potent anilide inhibitors against the severe acute respiratory syndrome3CL protease. J Med Chem2005,48(13):4469-4473.
171. Chou KC, Wei DQ, Du QS, Sirois S, Zhong WZ: Progress in computational approach todrug development against SARS. Curr Med Chem2006,13(27):3263-3270.
172. Ghosh AK, Takayama J, Aubin Y, Ratia K, Chaudhuri R, Baez Y, Sleeman K, Coughlin M,Nichols DB, Mulhearn DC et al: Structure-based design, synthesis, and biological evaluation ofa series of novel and reversible inhibitors for the severe acute respiratorysyndrome-coronavirus papain-like protease. J Med Chem2009,52(16):5228-5240.
173. Skalova T, Dohnalek J, Duskova J, Petrokova H, Hradilek M, Soucek M, Konvalinka J, HasekJ: HIV-1protease mutations and inhibitor modifications monitored on a series of complexes.Structural basis for the effect of the A71V mutation on the active site. J Med Chem2006,49(19):5777-5784.
174. Meher BR, Wang Y: Interaction of I50V mutant and I50L/A71V double mutantHIV-protease with inhibitor TMC114(darunavir): molecular dynamics simulation andbinding free energy studies. J Phys Chem B2012,116(6):1884-1900.
175. Lansdon EB, Liu Q, Leavitt SA, Balakrishnan M, Perry JK, Lancaster-Moyer C, Kutty N, LiuX, Squires NH, Watkins WJ et al: Structural and binding analysis of pyrimidinol carboxylicacid and N-hydroxy quinazolinedione HIV-1RNase H inhibitors. Antimicrob Agents Chemother2011,55(6):2905-2915.
176. Chung S, Himmel DM, Jiang JK, Wojtak K, Bauman JD, Rausch JW, Wilson JA, Beutler JA,Thomas CJ, Arnold E et al: Synthesis, activity, and structural analysis of novelalpha-hydroxytropolone inhibitors of human immunodeficiency virus reversetranscriptase-associated ribonuclease H. J Med Chem2011,54(13):4462-4473.
177. Fauce SR, Jamieson BD, Chin AC, Mitsuyasu RT, Parish ST, Ng HL, Kitchen CM, Yang OO,Harley CB, Effros RB: Telomerase-based pharmacologic enhancement of antiviral function ofhuman CD8+T lymphocytes. J Immunol2008,181(10):7400-7406.
178. Yildirim MA, Goh K-I, Cusick ME, Barabasi A-L, Vidal M: Drug[mdash]target network.Nat Biotech2007,25(10):1119-1126.
179. Ravasi T, Suzuki H, Cannistraci CV, Katayama S, Bajic VB, Tan K, Akalin A, Schmeier S,Kanamori-Katayama M, Bertin N et al: An atlas of combinatorial transcriptional regulation inmouse and man. Cell2010,140(5):744-752.
180. Hobert O: Gene Regulation by Transcription Factors and MicroRNAs. In., vol.319;2008:1785-1786.
181. Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM: A census of humantranscription factors: function, expression and evolution. Nat Rev Genet2009,10(4):252-263.
182. Hudson NJ, Reverter A, Dalrymple BP: A differential wiring analysis of expression datacorrectly identifies the gene containing the causal mutation. PLoS computational biology2009,5(5):e1000382.
183. Yu S, Zheng L, Li Y, Li C, Ma C, Yu Y, Li X, Hao P: Causal co-expression method withmodule analysis to screen drugs with specific target. Gene2013,518(1):145-151.
184. Nickeleit I, Zender S, Sasse F, Geffers R, Brandes G, Soensen I, Steinmetz H, Kubicka S,Carlomagno T, Menche D et al: Argyrin A Reveals a Critical Role for the Tumor SuppressorProtein p27kip1in Mediating Antitumor Activities in Response to Proteasome Inhibition.Cancer cell2008,14(1):23-35.
185. Watson M: CoXpress: differential co-expression in gene expression data. BMCBioinformatics2006,7:509.
186. Mentzen WI, Floris M, de la Fuente A: Dissecting the dynamics of dysregulation of cellularprocesses in mouse mammary gland tumor. BMC Genomics2009,10:601.
2. Rask-Andersen M, Almen MS, Schioth HB: Trends in the exploitation of novel drug targets.Nature reviews2011,10(8):579-590.
3. Hopkins AL: Network pharmacology. Nat Biotechnol2007,25(10):1110-1111.
4.刘艾林,杜冠华:网络药理学:药物发现的新思想.药学学报2010,45(12):1472-1477.
5. Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, Li X, Zhou W, Wang W, Wang Y: A systematicprediction of multiple drug-target interactions from chemical, genomic, and pharmacologicaldata. PLoS One2012,7(5):e37608.
6. Gonzalez-Diaz H, Cruz-Monteagudo M, Molina R, Tenorio E, Uriarte E: Predicting multipledrugs side effects with a general drug-target interaction thermodynamic Markov model.Bioorg Med Chem2005,13(4):1119-1129.
7. Yamamura T:[Molecular target drug development for curing multiple sclerosis]. BrainNerve2009,61(8):923-928.
8. Stroup J, Stephens J: Combination drug products: an indication for medicationreconciliation and pharmacist counseling. J Am Pharm Assoc (2003)2008,48(4):541-543.
9. Yuan S, Wang F, Chen G, Zhang H, Feng L, Wang L, Colman H, Keating MJ, Li X, Xu RH etal: Effective elimination of cancer stem cells by a novel drug combination strategy. Stem Cells2013,31(1):23-34.
10. Gopalan A, Yu W, Sanders BG, Kline K: Eliminating drug resistant breast cancer stem-likecells with combination of simvastatin and gamma-tocotrienol. Cancer Lett2013,328(2):285-296.
11. Lee JH, Nan A: Combination drug delivery approaches in metastatic breast cancer. JDrug Deliv2012,2012:915375.
12. Hopkins AL: Network pharmacology: the next paradigm in drug discovery. Nat ChemBiol2008,4(11):682-690.
13.王娟,李学军:网络药理学和药物发现研究进展.生理科学进展2011,42(4):241-245.
14. Alian OM, Shah M, Mohammad RM: Network Pharmacology: Reining in Drug Attrition?Curr Drug Discov Technol2012.
15. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB,Klein TE: Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther2012,92(4):414-417.
16. Rossi S, Calin GA: Bioinformatics, Non-coding RNAs and Its Possible Application inPersonalized Medicine. Adv Exp Med Biol2013,774:21-37.
17. Workman P, Clarke PA, Al-Lazikani B: Personalized medicine: patient-predictive panelpower. Cancer Cell2012,21(4):455-458.
18. Yu S, Zheng L, Li Y, Li C, Ma C, Yu Y, Li X, Hao P: Causal co-expression method withmodule analysis to screen drugs with specific target. Gene2012.
19. van de Waterbeemd H, Gifford E: ADMET in silico modelling: towards prediction paradise?Nat Rev Drug Discov2003,2(3):192-204.
20. Modi S: Positioning ADMET in silico tools in drug discovery. Drug Discov Today2004,9(1):14-15.
21. Banerjee C, Archin N, Michaels D, Belkina AC, Denis GV, Bradner J, Sebastiani P, MargolisDM, Montano M: BET bromodomain inhibition as a novel strategy for reactivation of HIV-1. JLeukoc Biol2012,92(6):1147-1154.
22. Lv J, Du C, Wei W, Wu Z, Zhao G, Li Z, Xie X: The antiepileptic drug valproic acidrestores T cell homeostasis and ameliorates pathogenesis of experimental autoimmuneencephalomyelitis. J Biol Chem2012,287(34):28656-28665.
23. Gilden D: When HAART is not enough. GMHC Treat Issues1996,10(10):1-6.
24. Alarcon-Aguilara FJ, Roman-Ramos R, Perez-Gutierrez S, Aguilar-Contreras A,Contreras-Weber CC, Flores-Saenz JL: Study of the anti-hyperglycemic effect of plants used asantidiabetics. J Ethnopharmacol1998,61(2):101-110.
25. Hu X, Sato J, Oshida Y, Xu M, Bajotto G, Sato Y: Effect of Gosha-jinki-gan (Chinese herbalmedicine: Niu-Che-Sen-Qi-Wan) on insulin resistance in streptozotocin-induced diabetic rats.Diabetes Res Clin Pract2003,59(2):103-111.
26. Foster H, Coley HM, Goumenou A, Pados G, Harvey A, Karteris E: Differential expressionof mTOR signalling components in drug resistance in ovarian cancer. Anticancer Res2010,30(9):3529-3534.
27. Cho SB, Kim J, Kim JH: Identifying set-wise differential co-expression in gene expressionmicroarray data. BMC Bioinformatics2009,10:109.
28. Blake JA, Harris MA: The Gene Ontology (GO) project: structured vocabularies formolecular biology and their application to genome and expression analysis. Curr ProtocBioinformatics2002, Chapter7:Unit72.
29. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K,Dwight SS, Eppig JT et al: Gene ontology: tool for the unification of biology. The GeneOntology Consortium. Nat Genet2000,25(1):25-29.
30. Aoki KF, Kanehisa M: Using the KEGG database resource. Curr Protoc Bioinformatics2005, Chapter1:Unit112.
31. Langfelder P, Horvath S: WGCNA: an R package for weighted correlation networkanalysis. BMC Bioinformatics2008,9:559.
32. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP,Subramanian A, Ross KN et al: The Connectivity Map: using gene-expression signatures toconnect small molecules, genes, and disease. Science2006,313(5795):1929-1935.
33. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. JMol Biol1990,215(3):403-410.
34. Higgins DG, Thompson JD, Gibson TJ: Using CLUSTAL for multiple sequence alignments.Methods Enzymol1996,266:383-402.
35. Mistry J, Finn R: Pfam: a domain-centric method for analyzing proteins and proteomes.Methods Mol Biol2007,396:43-58.
36. Liu X, Jiang H, Li H: SHAFTS: a hybrid approach for3D molecular similarity calculation.1. Method and assessment of virtual screening. J Chem Inf Model2011,51(9):2372-2385.
37. Lu W, Liu X, Cao X, Xue M, Liu K, Zhao Z, Shen X, Jiang H, Xu Y, Huang J et al: SHAFTS:a hybrid approach for3D molecular similarity calculation.2. Prospective case study in thediscovery of diverse p90ribosomal S6protein kinase2inhibitors to suppress cell migration. JMed Chem2011,54(10):3564-3574.
38. Nagahara AH, Merrill DA, Coppola G, Tsukada S, Schroeder BE, Shaked GM, Wang L,Blesch A, Kim A, Conner JM et al: Neuroprotective effects of brain-derived neurotrophic factorin rodent and primate models of Alzheimer's disease. Nat Med2009,15(3):331-337.
39. Chin J: Selecting a mouse model of Alzheimer's disease. Methods Mol Biol2011,670:169-189.
40. Miller JA, Horvath S, Geschwind DH: Divergence of human and mouse braintranscriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A2010,107(28):12698-12703.
41. Haldar M, Hancock JD, Coffin CM, Lessnick SL, Capecchi MR: A conditional mouse modelof synovial sarcoma: insights into a myogenic origin. Cancer Cell2007,11(4):375-388.
42. Hung KE, Maricevich MA, Richard LG, Chen WY, Richardson MP, Kunin A, Bronson RT,Mahmood U, Kucherlapati R: Development of a mouse model for sporadic and metastatic colontumors and its use in assessing drug treatment. Proceedings of the National Academy of Sciences2010.
43. Raab M, Kappel S, Kramer A, Sanhaji M, Matthess Y, Kurunci-Csacsko E, Calzada-Wack J,Rathkolb B, Rozman J, Adler T et al: Toxicity modelling of Plk1-targeted therapies ingenetically engineered mice and cultured primary mammalian cells. Nat Commun2011,2:395.
44. Chakraborty C, Hsu CH, Wen ZH, Lin CS, Agoramoorthy G: Zebrafish: a complete animalmodel for in vivo drug discovery and development. Curr Drug Metab2009,10(2):116-124.
45. Zon LI, Peterson RT: In vivo drug discovery in the zebrafish. Nat Rev Drug Discov2005,4(1):35-44.
46. Kari G, Rodeck U, Dicker AP: Zebrafish: an emerging model system for human diseaseand drug discovery. Clin Pharmacol Ther2007,82(1):70-80.
47. Singh M, Johnson L: Using Genetically Engineered Mouse Models of Cancer to Aid DrugDevelopment: An Industry Perspective. Clinical Cancer Research2006,12(18):5312-5328.
48. Sharpless NE, Depinho RA: The mighty mouse: genetically engineered mouse models incancer drug development. Nat Rev Drug Discov2006,5(9):741-754.
49. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J, Sipes G,Bracken W et al: Concordance of the toxicity of pharmaceuticals in humans and in animals.Regul Toxicol Pharmacol2000,32(1):56-67.
50. Strand AD, Aragaki AK, Baquet ZC, Hodges A, Cunningham P, Holmans P, Jones KR, Jones L,Kooperberg C, Olson JM: Conservation of regional gene expression in mouse and human brain.PLoS Genet2007,3(4):e59.
51. Chan ET, Quon GT, Chua G, Babak T, Trochesset M, Zirngibl RA, Aubin J, Ratcliffe MJ,Wilde A, Brudno M et al: Conservation of core gene expression in vertebrate tissues. J Biol2009,8(3):33.
52. Zheng-Bradley X, Rung J, Parkinson H, Brazma A: Large scale comparison of global geneexpression patterns in human and mouse. Genome Biol2010,11(12):R124.
53. Deluca TF, Wu IH, Pu J, Monaghan T, Peshkin L, Singh S, Wall DP: Roundup: amulti-genome repository of orthologs and evolutionary distances. Bioinformatics2006,22(16):2044-2046.
54. Li Y, Hao P, Zheng S, Tu K, Fan H, Zhu R, Ding G, Dong C, Wang C, Li X et al: Geneexpression module-based chemical function similarity search. Nucleic Acids Res2008,36(20):e137.
55. Laifenfeld D, Gilchrist A, Drubin D, Jorge M, Eddy SF, Frushour BP, Ladd B, Obert LA,Gosink MM, Cook JC et al: The role of hypoxia in2-butoxyethanol-induced hemangiosarcoma.Toxicol Sci2010,113(1):254-266.
56. Wall DP, Fraser HB, Hirsh AE: Detecting putative orthologs. Bioinformatics2003,19(13):1710-1711.
57. Ogata H, Goto S, Fujibuchi W, Kanehisa M: Computation with the KEGG pathwaydatabase. Biosystems1998,47(1-2):119-128.
58. Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ: Discovering statisticallysignificant pathways in expression profiling studies. Proc Natl Acad Sci U S A2005,102(38):13544-13549.
59. Fury W, Batliwalla F, Gregersen PK, Li W: Overlapping probabilities of top ranking genelists, hypergeometric distribution, and stringency of gene selection criterion. Conf Proc IEEEEng Med Biol Soc2006,1:5531-5534.
60. Blevins L, McDonald CJ: Fisher's Exact Test: an easy-to-use statistical test for comparingoutcomes. MD Comput1985,2(1):15-19,68.
61. Falcon S, Gentleman R: Using GOstats to test gene lists for GO term association.Bioinformatics2007,23(2):257-258.
62. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A,Pomeroy SL, Golub TR, Lander ES et al: Gene set enrichment analysis: a knowledge-basedapproach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A2005,102(43):15545-15550.
63. Dudley JT, Tibshirani R, Deshpande T, Butte AJ: Disease signatures are robust acrosstissues and experiments. Mol Syst Biol2009,5:307.
64. Yu S, Zheng L, Li Y, Li C, Ma C, Li X, Hao P: A cross-species analysis method to analyzeanimal models' similarity to human's disease state. BMC Syst Biol2012,6Suppl3:S18.
65. Dowdell KC, Pesnicak L, Hoffmann V, Steadman K, Remaley AT, Cohen JI, Straus SE, RaoVK: Valproic acid (VPA), a histone deacetylase (HDAC) inhibitor, diminisheslymphoproliferation in the Fas-deficient MRL/lpr(-/-) murine model of autoimmunelymphoproliferative syndrome (ALPS). Exp Hematol2009,37(4):487-494.
66. Hrzenjak A, Moinfar F, Kremser ML, Strohmeier B, Petru E, Zatloukal K, Denk H: Histonedeacetylase inhibitor vorinostat suppresses the growth of uterine sarcomas in vitro and in vivo.Mol Cancer2010,9:49.
67. Baidyaroy D, Brosch G, Graessle S, Trojer P, Walton JD: Characterization ofinhibitor-resistant histone deacetylase activity in plant-pathogenic fungi. Eukaryot Cell2002,1(4):538-547.
68. Zhang Q, Tang X, Lu QY, Zhang ZF, Brown J, Le AD: Resveratrol inhibits hypoxia-inducedaccumulation of hypoxia-inducible factor-1alpha and VEGF expression in human tonguesquamous cell carcinoma and hepatoma cells. Mol Cancer Ther2005,4(10):1465-1474.
69. Li Y, Zhang JF, Zhang YM, Ma XB: The protective effect of genistein postconditioning onhypoxia/reoxygenation-induced injury in human gastric epithelial cells. Acta Pharmacol Sin2009,30(5):576-581.
70. Sohaskey CD: Nitrate enhances the survival of Mycobacterium tuberculosis duringinhibition of respiration. J Bacteriol2008,190(8):2981-2986.
71. Vengellur A, Phillips JM, Hogenesch JB, LaPres JJ: Gene expression profiling of hypoxiasignaling in human hepatocellular carcinoma cells. Physiol Genomics2005,22(3):308-318.
72. Yamamoto S, Tanaka E, Higashi H: Mediation by intracellular calcium-dependent signalsof hypoxic hyperpolarization in rat hippocampal CA1neurons in vitro. J Neurophysiol1997,77(1):386-392.
73. Edelstein CL, Yaqoob MM, Alkhunaizi AM, Gengaro PE, Nemenoff RA, Wang KK, SchrierRW: Modulation of hypoxia-induced calpain activity in rat renal proximal tubules. Kidney Int1996,50(4):1150-1157.
74. Carver DJ, Gaston B, Deronde K, Palmer LA: Akt-mediated activation of HIF-1inpulmonary vascular endothelial cells by S-nitrosoglutathione. Am J Respir Cell Mol Biol2007,37(3):255-263.
75. Hudson CC, Liu M, Chiang GG, Otterness DM, Loomis DC, Kaper F, Giaccia AJ, AbrahamRT: Regulation of hypoxia-inducible factor1alpha expression and function by the mammaliantarget of rapamycin. Mol Cell Biol2002,22(20):7004-7014.
76. Kambe T, Tada-Kambe J, Kuge Y, Yamaguchi-Iwai Y, Nagao M, Sasaki R: Retinoic acidstimulates erythropoietin gene transcription in embryonal carcinoma cells through the directrepeat of a steroid/thyroid hormone receptor response element half-site in thehypoxia-response enhancer. Blood2000,96(9):3265-3271.
77. Tuller ER, Beavers CT, Lou JR, Ihnat MA, Benbrook DM, Ding WQ: Docosahexaenoic acidinhibits superoxide dismutase1gene transcription in human cancer cells: the involvement ofperoxisome proliferator-activated receptor alpha and hypoxia-inducible factor-2alphasignaling. Mol Pharmacol2009,76(3):588-595.
78. Schlenker EH: In hamsters dopamine D2receptors affect ventilation during and followingintermittent hypoxia. Comp Biochem Physiol A Mol Integr Physiol2007,148(3):674-680.
79. Mottet D, Michel G, Renard P, Ninane N, Raes M, Michiels C: Role of ERK and calcium inthe hypoxia-induced activation of HIF-1. J Cell Physiol2003,194(1):30-44.
80. Beitner-Johnson D, Rust RT, Hsieh TC, Millhorn DE: Hypoxia activates Akt and inducesphosphorylation of GSK-3in PC12cells. Cell Signal2001,13(1):23-27.
81. Shudo K, Fukasawa H, Nakagomi M, Yamagata N: Towards retinoid therapy forAlzheimer's disease. Curr Alzheimer Res2009,6(3):302-311.
82. Valles SL, Dolz-Gaiton P, Gambini J, Borras C, Lloret A, Pallardo FV, Vina J: Estradiol orgenistein prevent Alzheimer's disease-associated inflammation correlating with an increasePPAR gamma expression in cultured astrocytes. Brain Res2010,1312:138-144.
83. Luo W, Rodina A, Chiosis G: Heat shock protein90: translation from cancer toAlzheimer's disease treatment? BMC Neurosci2008,9Suppl2:S7.
84. Shineman DW, Dain AS, Kim ML, Lee VM: Constitutively active Akt inhibits traffickingof amyloid precursor protein and amyloid precursor protein metabolites through feedbackinhibition of phosphoinositide3-kinase. Biochemistry2009,48(17):3787-3794.
85. Wang LY, Shofer JB, Rohde K, Hart KL, Hoff DJ, McFall YH, Raskind MA, Peskind ER:Prazosin for the treatment of behavioral symptoms in patients with Alzheimer disease withagitation and aggression. Am J Geriatr Psychiatry2009,17(9):744-751.
86. Han G, Ma H, Chintala R, Fulton DJ, Barman SA, White RE: Essential role of the90-kilodalton heat shock protein in mediating nongenomic estrogen signaling in coronaryartery smooth muscle. J Pharmacol Exp Ther2009,329(3):850-855.
87. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R,Brunetti-Pierri N, Isacchi A et al: Discovery of drug mode of action and drug repositioning fromtranscriptional responses. Proc Natl Acad Sci U S A2010,107(33):14621-14626.
88. Fliss AE, Benzeno S, Rao J, Caplan AJ: Control of estrogen receptor ligand binding byHsp90. J Steroid Biochem Mol Biol2000,72(5):223-230.
89. Greenfield JP, Leung LW, Cai D, Kaasik K, Gross RS, Rodriguez-Boulan E, Greengard P, XuH: Estrogen lowers Alzheimer beta-amyloid generation by stimulating trans-Golgi networkvesicle biogenesis. J Biol Chem2002,277(14):12128-12136.
90. Wild S, Roglic G, Green A, Sicree R, King H: Global prevalence of diabetes: estimates forthe year2000and projections for2030. Diabetes Care2004,27(5):1047-1053.
91. Muruganandan S, Srinivasan K, Gupta S, Gupta PK, Lal J: Effect of mangiferin onhyperglycemia and atherogenicity in streptozotocin diabetic rats. J Ethnopharmacol2005,97(3):497-501.
92. Li X, Cui X, Sun X, Zhu Q, Li W: Mangiferin prevents diabetic nephropathy progressionin streptozotocin-induced diabetic rats. Phytother Res2010,24(6):893-899.
93. Miura T, Iwamoto N, Kato M, Ichiki H, Kubo M, Komatsu Y, Ishida T, Okada M, Tanigawa K:The suppressive effect of mangiferin with exercise on blood lipids in type2diabetes. BiolPharm Bull2001,24(9):1091-1092.
94. Sellamuthu PS, Muniappan BP, Perumal SM, Kandasamy M: Antihyperglycemic effect ofMangiferin in streptozotocin induced diabetic rats. Journal of Health Science2009,55(2):206-214.
95. Engelman JA, Berg AH, Lewis RY, Lisanti MP, Scherer PE: Tumor necrosis factoralpha-mediated insulin resistance, but not dedifferentiation, is abrogated by MEK1/2inhibitors in3T3-L1adipocytes. Mol Endocrinol2000,14(10):1557-1569.
96. Momany FA, Willett JL: Computational studies on carbohydrates: in vacuo studies usinga revised AMBER force field, AMB99C, designed for alpha-(1-->4) linkages. Carbohydr Res2000,326(3):194-209.
97. Do GM, Jung UJ, Park HJ, Kwon EY, Jeon SM, McGregor RA, Choi MS: Resveratrolameliorates diabetes-related metabolic changes via activation of AMP-activated proteinkinase and its downstream targets in db/db mice. Mol Nutr Food Res2012,56(8):1282-1291.
98. Thirunavukkarasu M, Penumathsa SV, Koneru S, Juhasz B, Zhan L, Otani H, Bagchi D, DasDK, Maulik N: Resveratrol alleviates cardiac dysfunction in streptozotocin-induced diabetes:Role of nitric oxide, thioredoxin, and heme oxygenase. Free Radic Biol Med2007,43(5):720-729.
99. Ramadori G, Gautron L, Fujikawa T, Vianna CR, Elmquist JK, Coppari R: Centraladministration of resveratrol improves diet-induced diabetes. Endocrinology2009,150(12):5326-5333.
100. Zheng W, Feng X, Qiu L, Pan Z, Wang R, Lin S, Hou D, Jin L, Li Y: Identification of theantibiotic ionomycin as an unexpected peroxisome proliferator-activated receptor gamma(PPARgamma) ligand with a unique binding mode and effective glucose-lowering activity in amouse model of diabetes. Diabetologia2013,56(2):401-411.
101. Higai K, Tsukada M, Moriya Y, Azuma Y, Matsumoto K: Prolonged high glucose suppressesphorbol12-myristate13-acetate and ionomycin-induced interleukin-2mRNA expression inJurkat cells. Biochim Biophys Acta2009,1790(1):8-15.
102. Cushman TT, Kim N, Hoyt R, Traish AM: Estradiol ameliorates diabetes-induced changesin vaginal structure of db/db mouse model. J Sex Med2009,6(9):2467-2479.
103. Cushman T, Kim N, Hoyt R, Traish AM: Estradiol restores diabetes-induced reductions insex steroid receptor expression and distribution in the vagina of db/db mouse model. J SteroidBiochem Mol Biol2009,114(3-5):186-194.
104. Raikwar HP, Muthian G, Rajasingh J, Johnson CN, Bright JJ: PPARgamma antagonistsreverse the inhibition of neural antigen-specific Th1response and experimental allergicencephalomyelitis by Ciglitazone and15-deoxy-Delta12,14-prostaglandin J2. J Neuroimmunol2006,178(1-2):76-86.
105. Jawerbaum A, Capobianco E, Pustovrh C, White V, Baier M, Salzberg S, Pesaresi M,Gonzalez E: Influence of peroxisome proliferator-activated receptor gamma activation by itsendogenous ligand15-deoxy Delta12,14prostaglandin J2on nitric oxide production in termplacental tissues from diabetic women. Mol Hum Reprod2004,10(9):671-676.
106. Cui W, Li B, Bai Y, Miao X, Chen Q, Sun W, Tan Y, Luo P, Zhang C, Zheng S et al: Potentialrole for Nrf2activation in the therapeutic effect of MG132on diabetic nephropathy in OVE26diabetic mice. Am J Physiol Endocrinol Metab2013,304(1):E87-99.
107. Kindmark H, Koohler M, Larsson O, Khan A, Berggren PO: Dissociation between exocytosisand Ca(2+)-channel activity in mouse pancreatic beta-cells stimulated with calmidazolium(compound R24571). FEBS Lett1995,369(2-3):315-320.
108. Kindmark H, Kohler M, Gerwins P, Larsson O, Khan A, Wahl MA, Berggren PO: Theimidazoline derivative calmidazolium inhibits voltage-gated Ca(2+)-channels and insulinrelease but has no effect on the phospholipase C system in insulin producing RINm5F-cells.Biosci Rep1994,14(3):145-158.
109. Jeong GS, Lee DS, Song MY, Park BH, Kang DG, Lee HS, Kwon KB, Kim YC: Butein fromRhus verniciflua protects pancreatic beta cells against cytokine-induced toxicity mediated byinhibition of nitric oxide formation. Biol Pharm Bull2011,34(1):97-102.
110. Behloul N, Wu G: Genistein: a promising therapeutic agent for obesity and diabetestreatment. Eur J Pharmacol2013,698(1-3):31-38.
111. Fu Z, Gilbert ER, Pfeiffer L, Zhang Y, Fu Y, Liu D: Genistein ameliorates hyperglycemia ina mouse model of nongenetic type2diabetes. Appl Physiol Nutr Metab2012,37(3):480-488.
112. Suh Y, Afaq F, Johnson JJ, Mukhtar H: A plant flavonoid fisetin induces apoptosis in coloncancer cells by inhibition of COX2and Wnt/EGFR/NF-kappaB-signaling pathways.Carcinogenesis2009,30(2):300-307.
113. Zheng LT, Ock J, Kwon BM, Suk K: Suppressive effects of flavonoid fisetin onlipopolysaccharide-induced microglial activation and neurotoxicity. Int Immunopharmacol2008,8(3):484-494.
114. Rose PW, Beran B, Bi C, Bluhm WF, Dimitropoulos D, Goodsell DS, Prlic A, Quesada M,Quinn GB, Westbrook JD et al: The RCSB Protein Data Bank: redesigned web site and webservices. Nucleic Acids Res2011,39(Database issue):D392-401.
115. Lu AP, Chen KJ: Integrative medicine in clinical practice: from pattern differentiation intraditional Chinese medicine to disease treatment. Chin J Integr Med2009,15(2):152.
116. Lu C, Xiao C, Chen G, Jiang M, Zha Q, Yan X, Kong W, Lu A: Cold and heat pattern ofrheumatoid arthritis in traditional Chinese medicine: distinct molecular signaturesindentified by microarray expression profiles in CD4-positive T cell. Rheumatol Int2010.
117.肖诚,赵林华,吕诚等:类风湿关节炎类风湿因子阴性和阳性寒热证候患者外周血CD4+T细胞基因差异表达研究.中国中西医结合杂志2006,26(8):689-693.
118.吴素芬,孙静汾,郭述真:复方更年康对老年雌鼠性激素及骨雌激素受体表达的影响.中国药物与临床2005,11(3):205-208.
119. Zuo F, Zhou ZM, Yan MZ, Liu ML, Xiong YL, Zhang Q, Song HY, Ye WH: Metabolism ofconstituents in Huangqin-Tang, a prescription in traditional Chinese medicine, by humanintestinal flora. Biol Pharm Bull2002,25(5):558-563.
120. Cao Y, Xia QH, Meng H, Zhong AP: Pharmacological effects of serum containing chinesemedicine Bushen Huayu Jiedu Compound Recipe in lung cancer drug-resistance cells. Chin JIntegr Med2008,14(1):46-50.
121. Wang YT, Tan QR, Sun LL, Cao J, Dou KF, Xia B, Wang W: Possible therapeutic effect of aTraditional Chinese Medicine, Sinisan, on chronic restraint stress related disorders. NeurosciLett2009,449(3):215-219.
122. Tanaka K, Sawamura S: Therapeutic effect of a traditional Chinese medicine,ren-shen-yang-rong-tang (Japanese name: Ninjin'yoeito) on nitric oxide-mediated lung injuryin a mouse infected with murine cytomegalovirus. Int Immunopharmacol2006,6(4):678-685.
123. Li S, Zhang B, Jiang D, Wei Y, Zhang N: Herb network construction and co-moduleanalysis for uncovering the combination rule of traditional Chinese herbal formulae. BMCBioinformatics2010,11Suppl11:S6.
124. Li S, Zhang B, Zhang N: Network target for screening synergistic drug combinations withapplication to traditional Chinese medicine. BMC Syst Biol2011,5Suppl1:S10.
125. Li S, Zhang ZQ, Wu LJ, Zhang XG, Li YD, Wang YY: Understanding ZHENG intraditional Chinese medicine in the context of neuro-endocrine-immune network. IET Syst Biol2007,1(1):51-60.
126. Yu S, Guo Z, Guan Y, Lu YY, Hao P, Li Y, Su SB: Combining ZHENG Theory andHigh-Throughput Expression Data to Predict New Effects of Chinese Herbal Formulae. EvidBased Complement Alternat Med2012,2012:986427.
127.苗桂珍,梁秀珍,王亚红:补气活血法治疗糖尿病周围神经病变的临床观察.中国中西医结合杂志2003,23(11):826-828.
128.阙华发,朱元颖,王云飞等:益气化瘀中药对糖尿病皮肤溃疡大鼠缺氧诱导因子-1α和血管内皮细胞生长因子的影响.中西医结合学报2007,5(2):165-169.
129.徐列明,胡义扬:脂肪肝的中药治疗.中西医结合学报2003,1(2):138-141.
130.段学忠,杨丁友,孙西庆等:益脉降压流浸膏对老年气虚血瘀证高血压病患者血浆神经肽Y含量的影响.中国中西医结合杂志2000,20(10):750-752.
131.张琴,刘平,章浩伟等:900例肝炎后肝硬化中医证候判别模式的研究.中国中西医结合杂志2006,26(8):694-697.
132. Liu C, Hu Y, Xu L, Liu P: Effect of Fuzheng Huayu formula and its actions against liverfibrosis. Chin Med2009,4:12.
133. Larsen FS, Vainer B, Eefsen M, Bjerring PN, Adel Hansen B: Low-dose tacrolimusameliorates liver inflammation and fibrosis in steroid refractory autoimmune hepatitis. WorldJ Gastroenterol2007,13(23):3232-3236.
134. Patsenker E, Schneider V, Ledermann M, Saegesser H, Dorn C, Hellerbrand C, Stickel F:Potent antifibrotic activity of mTOR inhibitors sirolimus and everolimus but not ofcyclosporine A and tacrolimus in experimental liver fibrosis. J Hepatol2011,55(2):388-398.
135. Bergman R, Parkes M: Systematic review: the use of mesalazine in inflammatory boweldisease. Aliment Pharmacol Ther2006,23(7):841-855.
136. el-Andere W, Lerario AC, Wajchenberg BL: Effect of short-and long-term chlorpropamidetherapy on oral glucose tolerance and erythrocyte insulin receptors in non-obese non-insulindependent diabetes mellitus. Horm Metab Res1987,19(6):257-263.
137. Fidan E, Onder Ersoz H, Yilmaz M, Yilmaz H, Kocak M, Karahan C, Erem C: The effects ofrosiglitazone and metformin on inflammation and endothelial dysfunction in patients withtype2diabetes mellitus. Acta Diabetol2011.
138. Bergson P, Lipkind G, Lee SP, Duban ME, Hanck DA: Verapamil block of T-type calciumchannels. Mol Pharmacol2011,79(3):411-419.
139. Santilli AA, Scotese AC, Tomarelli RM: A potent antihypercholesterolemic agent:(4-chloro-6-(2,3-xylidino)-2-pyrimidinylthio) acetic acid (Wy-14643). Experientia1974,30(10):1110-1111.
140.陈文东,安文林,楚晋等:茯苓水提液对新生大鼠神经细胞内钙离子浓度的影响.中国中西医结合杂志1998,18(5):293-295.
141.富杭育,贺玉琢,李晓芹等:桂枝汤对汗腺分泌作用的实验研究.中国中西医结合杂志1991,11(1):34-36,36.
142. Lee JS: Effects of soy protein and genistein on blood glucose, antioxidant enzymeactivities, and lipid profile in streptozotocin-induced diabetic rats. Life Sci2006,79(16):1578-1584.
143. Nogowski L, Mackowiak P, Kandulska K, Szkudelski T, Nowak KW: Genistein-inducedchanges in lipid metabolism of ovariectomized rats. Ann Nutr Metab1998,42(6):360-366.
144. Si H, Liu D: Genistein, a soy phytoestrogen, upregulates the expression of humanendothelial nitric oxide synthase and lowers blood pressure in spontaneously hypertensiverats. J Nutr2008,138(2):297-304.
145. Ji G, Yang Q, Hao J, Guo L, Chen X, Hu J, Leng L, Jiang Z: Anti-inflammatory effect ofgenistein on non-alcoholic steatohepatitis rats induced by high fat diet and its potentialmechanisms. Int Immunopharmacol2011,11(6):762-768.
146.赵长青,顾宏图,成扬等:扶正化瘀肝糖综合治疗方案治疗乙型肝炎后肝硬化合并糖代谢异常的临床研究.中国中西医结合杂志2008,28(1):24-27.
147. Holguin A, Erazo K, Escobar G, de Mulder M, Yebra G, Martin L, Jovel LE, Castaneda L,Perez E: Drug resistance prevalence in human immunodeficiency virus type1infectedpediatric populations in Honduras and El Salvador during1989-2009. Pediatr Infect Dis J2011,30(5):e82-87.
148. Ito K:[Bioinformatics technologies for the analysis of antigenic evolution of influenzaviruses]. Uirusu2011,61(1):3-13.
149. Lai MM: SARS virus: the beginning of the unraveling of a new coronavirus. J Biomed Sci2003,10(6Pt2):664-675.
150. Stadler K, Masignani V, Eickmann M, Becker S, Abrignani S, Klenk HD, Rappuoli R:SARS--beginning to understand a new virus. Nat Rev Microbiol2003,1(3):209-218.
151. Kuljic-Kapulica N:[SARS corona virus--a new dilemma]. Med Pregl2005,58(1-2):43-46.
152. Chan JF, Li KS, To KK, Cheng VC, Chen H, Yuen KY: Is the discovery of the novel humanbetacoronavirus2c EMC/2012(HCoV-EMC) the beginning of another SARS-like pandemic?J Infect2012,65(6):477-489.
153. Rosenbloom DI, Hill AL, Rabi SA, Siliciano RF, Nowak MA: Antiretroviral dynamicsdetermines HIV evolution and predicts therapy outcome. Nat Med2012,18(9):1378-1385.
154. Gifford RJ, Liu TF, Rhee SY, Kiuchi M, Hue S, Pillay D, Shafer RW: The calibratedpopulation resistance tool: standardized genotypic estimation of transmitted HIV-1drugresistance. Bioinformatics2009,25(9):1197-1198.
155. Ewing TJ, Makino S, Skillman AG, Kuntz ID: DOCK4.0: search strategies for automatedmolecular docking of flexible molecule databases. J Comput Aided Mol Des2001,15(5):411-428.
156. Trott O, Olson AJ: AutoDock Vina: improving the speed and accuracy of docking with anew scoring function, efficient optimization, and multithreading. J Comput Chem2010,31(2):455-461.
157. Hendlich M, Rippmann F, Barnickel G: LIGSITE: automatic and efficient detection ofpotential small molecule-binding sites in proteins. J Mol Graph Model1997,15(6):359-363,389.
158. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J: CASTp: computed atlas ofsurface topography of proteins with structural and topographical mapping of functionallyannotated residues. Nucleic Acids Res2006,34(Web Server issue):W116-118.
159. An J, Totrov M, Abagyan R: Pocketome via comprehensive identification and classificationof ligand binding envelopes. Mol Cell Proteomics2005,4(6):752-761.
160. Halgren T: New method for fast and accurate binding-site identification and analysis.Chem Biol Drug Des2007,69(2):146-148.
161. Yuan Y, Pei J, Lai L: Binding Site Detection and Druggability Prediction of ProteinTargets for Structure-Based Drug Design. Curr Pharm Des2012.
162. Kouranov A, Xie L, de la Cruz J, Chen L, Westbrook J, Bourne PE, Berman HM: The RCSBPDB information portal for structural genomics. Nucleic Acids Res2006,34(Databaseissue):D302-305.
163. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V et al:DrugBank3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res2011,39(Database issue):D1035-1041.
164. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK: BindingDB: a web-accessible database ofexperimentally determined protein-ligand binding affinities. Nucleic Acids Res2007,35(Database issue):D198-201.
165. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR: Open Babel:An open chemical toolbox. J Cheminform2011,3:33.
166. Yuan Y, Pei J, Lai L: LigBuilder2: A Practical de Novo Drug Design Approach. J Chem InfModel2011.
167. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz HR, Ceric G, Forslund K, Eddy SR,Sonnhammer EL et al: The Pfam protein families database. Nucleic Acids Res2008,36(Databaseissue):D281-288.
168. Yang S, Chen SJ, Hsu MF, Wu JD, Tseng CT, Liu YF, Chen HC, Kuo CW, Wu CS, Chang LWet al: Synthesis, crystal structure, structure-activity relationships, and antiviral activity of apotent SARS coronavirus3CL protease inhibitor. J Med Chem2006,49(16):4971-4980.
169. Wu CJ, Jan JT, Chen CM, Hsieh HP, Hwang DR, Liu HW, Liu CY, Huang HW, Chen SC,Hong CF et al: Inhibition of severe acute respiratory syndrome coronavirus replication byniclosamide. Antimicrob Agents Chemother2004,48(7):2693-2696.
170. Shie JJ, Fang JM, Kuo CJ, Kuo TH, Liang PH, Huang HJ, Yang WB, Lin CH, Chen JL, WuYT et al: Discovery of potent anilide inhibitors against the severe acute respiratory syndrome3CL protease. J Med Chem2005,48(13):4469-4473.
171. Chou KC, Wei DQ, Du QS, Sirois S, Zhong WZ: Progress in computational approach todrug development against SARS. Curr Med Chem2006,13(27):3263-3270.
172. Ghosh AK, Takayama J, Aubin Y, Ratia K, Chaudhuri R, Baez Y, Sleeman K, Coughlin M,Nichols DB, Mulhearn DC et al: Structure-based design, synthesis, and biological evaluation ofa series of novel and reversible inhibitors for the severe acute respiratorysyndrome-coronavirus papain-like protease. J Med Chem2009,52(16):5228-5240.
173. Skalova T, Dohnalek J, Duskova J, Petrokova H, Hradilek M, Soucek M, Konvalinka J, HasekJ: HIV-1protease mutations and inhibitor modifications monitored on a series of complexes.Structural basis for the effect of the A71V mutation on the active site. J Med Chem2006,49(19):5777-5784.
174. Meher BR, Wang Y: Interaction of I50V mutant and I50L/A71V double mutantHIV-protease with inhibitor TMC114(darunavir): molecular dynamics simulation andbinding free energy studies. J Phys Chem B2012,116(6):1884-1900.
175. Lansdon EB, Liu Q, Leavitt SA, Balakrishnan M, Perry JK, Lancaster-Moyer C, Kutty N, LiuX, Squires NH, Watkins WJ et al: Structural and binding analysis of pyrimidinol carboxylicacid and N-hydroxy quinazolinedione HIV-1RNase H inhibitors. Antimicrob Agents Chemother2011,55(6):2905-2915.
176. Chung S, Himmel DM, Jiang JK, Wojtak K, Bauman JD, Rausch JW, Wilson JA, Beutler JA,Thomas CJ, Arnold E et al: Synthesis, activity, and structural analysis of novelalpha-hydroxytropolone inhibitors of human immunodeficiency virus reversetranscriptase-associated ribonuclease H. J Med Chem2011,54(13):4462-4473.
177. Fauce SR, Jamieson BD, Chin AC, Mitsuyasu RT, Parish ST, Ng HL, Kitchen CM, Yang OO,Harley CB, Effros RB: Telomerase-based pharmacologic enhancement of antiviral function ofhuman CD8+T lymphocytes. J Immunol2008,181(10):7400-7406.
178. Yildirim MA, Goh K-I, Cusick ME, Barabasi A-L, Vidal M: Drug[mdash]target network.Nat Biotech2007,25(10):1119-1126.
179. Ravasi T, Suzuki H, Cannistraci CV, Katayama S, Bajic VB, Tan K, Akalin A, Schmeier S,Kanamori-Katayama M, Bertin N et al: An atlas of combinatorial transcriptional regulation inmouse and man. Cell2010,140(5):744-752.
180. Hobert O: Gene Regulation by Transcription Factors and MicroRNAs. In., vol.319;2008:1785-1786.
181. Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM: A census of humantranscription factors: function, expression and evolution. Nat Rev Genet2009,10(4):252-263.
182. Hudson NJ, Reverter A, Dalrymple BP: A differential wiring analysis of expression datacorrectly identifies the gene containing the causal mutation. PLoS computational biology2009,5(5):e1000382.
183. Yu S, Zheng L, Li Y, Li C, Ma C, Yu Y, Li X, Hao P: Causal co-expression method withmodule analysis to screen drugs with specific target. Gene2013,518(1):145-151.
184. Nickeleit I, Zender S, Sasse F, Geffers R, Brandes G, Soensen I, Steinmetz H, Kubicka S,Carlomagno T, Menche D et al: Argyrin A Reveals a Critical Role for the Tumor SuppressorProtein p27kip1in Mediating Antitumor Activities in Response to Proteasome Inhibition.Cancer cell2008,14(1):23-35.
185. Watson M: CoXpress: differential co-expression in gene expression data. BMCBioinformatics2006,7:509.
186. Mentzen WI, Floris M, de la Fuente A: Dissecting the dynamics of dysregulation of cellularprocesses in mouse mammary gland tumor. BMC Genomics2009,10:601.