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A Computational Tool for Pathway-Based Rational Drug Repositioning Supplementary Information gene2drug: a Computational Tool for Pathway-based Rational Drug Repositioning Supplementary Information Francesco Napolitano1, Diego Carrella1, Barbara Mandriani1, Sandra Pisonero1, Diego Medina1, Nicola Brunetti-Pierri1,2, and Diego di Bernardo1,3 1Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli (NA), 80078, Italy. 2Department of Translational Medicine, Federico II University, 80131 Naples, Italy 3Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy. List of Figures S1 Relation between PPI network-based and pathway based approaches. Given a therapeutic gene and the set of pathways it is annotated to according to the different databases, the other genes in the same pathways are topologically closer to it in the PPI network as compared to randomly chosen genes. The average shortest path from the selected gene to other pathway members is reported on the y axis for each database. 3 S2 Size of intersection between existent evidence scores in the STITCH database (subset matched against the Cmap database) as a percentage of the total number of evidences. Numbers on the diagonal represent how many times a drug-target evidence exists divided by the total number of reported pairs. A \combined score" always exist if one of the other evidences exist, thus \combined score" covers 100% of the pairs. Conversely, an \experimental" score is only present for 14% of the pairs. The \Text mining" evidence strongly drives the \combined score" covering 61% of all the known protein-target interactions in STITCH. 4 S3 Relative luminescence units (RLU) in Hepa1-6 cells transfected with a plasmid ex- pressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of fulvestrant. 5 S4 Relative luminescence units (RLU) in Huh7 cells transfected with a plasmid ex- pressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of fulvestrant. 5 S5 Relative luminescence units (RLU) in Hepa1-6 cells transfected with a plasmid ex- pressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of tomatidine. 6 S6 Relative luminescence units (RLU) in Huh7 cells transfected with a plasmid ex- pressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of tomatidine. 6 S7 Relative luminescence units (RLU) in Hepa1-6 cells transfected with a plasmid ex- pressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of nifuroxazide. 7 1 S8 Relative luminescence units (RLU) in Huh7 cells transfected with a plasmid ex- pressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of nifuroxazide. 7 List of Tables S1 Drugs UP-regulating the GPT gene according to Gene2Drug. The database used was Reactome, using all the pathways that GPT is annotated to, i.e.: positive reg- ulation of transcription, DNA-dependent; embryonic placenta development; lyso- some organization; positive regulation of autophagy; autophagy; humoral immune response. ........................................ 8 S2 Drugs UP-regulating the GPT gene according to its fold-change as obtained through microarray analysis (Single Gene Expression method). For each drug, its rank according to how much GPT is UP-regulated is reported in the second column. Moreover, the rank of the gene in the profile of each drug is reported in column 2. Tomatidine, which was shown to be strongly effective (see Main Text), is ranked only 247 by the Single Gene Expression method. 8 S3 Drugs UP-regulating the TFEB gene according to Gene2Drug. The database used was GO-BP, using all the pathways that GPT is annotated to, i.e.: metabolism of amino acids and derivatives; amino acid synthesis and interconversion transam- ination. ......................................... 15 S4 Drugs UP-regulating the TFEB gene according to its fold-change as obtained through microarray analysis (Single Gene Expression method). For each drug, its rank according to how much TFEB is UP-regulated is reported in the second column. Moreover, the rank of the gene in the profile of each drug is reported in column 2. Loperamide, which was shown to be effective at low concentration (see Main Text), is ranked only 242 by the Single Gene Expression method. 15 2 1 Figures Drug targets are topologically close to pathway comembers 8 ● ● 6 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Average shortest path from target Average 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● BP MF CC CP CGP TFT KEGG MIPS Random BIOCARTA REACTOME Fig. S1: Relation between PPI network-based and pathway based approaches. Given a therapeu- tic gene and the set of pathways it is annotated to according to the different databases, the other genes in the same pathways are topologically closer to it in the PPI network as compared to randomly chosen genes. The average shortest path from the selected gene to other pathway members is reported on the y axis for each database. 3 Overlaps between golden standards combined 0.14 0.02 0.33 0.61 1 score textmining 0.03 0 0.06 0.61 Intersection % 1.00 0.75 database 0.02 0 0.33 0.50 0.25 prediction 0.01 0.02 experimental 0.14 score prediction database textmining combined experimental Fig. S2: Size of intersection between existent evidence scores in the STITCH database (subset matched against the Cmap database) as a percentage of the total number of evidences. Numbers on the diagonal represent how many times a drug-target evidence exists di- vided by the total number of reported pairs. A \combined score" always exist if one of the other evidences exist, thus \combined score" covers 100% of the pairs. Conversely, an \experimental" score is only present for 14% of the pairs. The \Text mining" evi- dence strongly drives the \combined score" covering 61% of all the known protein-target interactions in STITCH. 4 Fig. S3: Relative luminescence units (RLU) in Hepa1-6 cells transfected with a plasmid express- ing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of fulvestrant. Fig. S4: Relative luminescence units (RLU) in Huh7 cells transfected with a plasmid expressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of fulvestrant. 5 Fig. S5: Relative luminescence units (RLU) in Hepa1-6 cells transfected with a plasmid express- ing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of tomatidine. Fig. S6: Relative luminescence units (RLU) in Huh7 cells transfected with a plasmid expressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of tomatidine. 6 Fig. S7: Relative luminescence units (RLU) in Hepa1-6 cells transfected with a plasmid express- ing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of nifuroxazide. Fig. S8: Relative luminescence units (RLU) in Huh7 cells transfected with a plasmid expressing the luciferase gene under the control of the GPT promoter and incubated with various concentrations of nifuroxazide. 7 2 Tables Tab. S1: Drugs UP-regulating the GPT gene according to Gene2Drug. The database used was Reactome, using all the pathways that GPT is annotated to, i.e.: positive regulation of transcription, DNA-dependent; embryonic placenta development; lysosome organi- zation; positive regulation of autophagy; autophagy; humoral immune response. Drug Rank ES p fulvestrant 1 0.979 0.001 tomatidine 2 0.972 0.002 nifuroxazide 3 0.967 0.002 terguride 4 0.963 0.003 monastrol 5 0.958 0.004 triflusal 6 0.957 0.004 5707885 7 0.955 0.004 enilconazole 8 0.954 0.005 5666823 9 0.951 0.005 SC-19220 10 0.949 0.006 mycophenolic acid 11 0.949 0.006 ciclosporin 12 0.948 0.006 cefotiam 13 0.945 0.007 erastin 14 0.945 0.007 epitiostanol 15 0.940 0.008 benperidol 16 0.937 0.008 cefapirin 17 0.933 0.010 betulinic acid 18 0.931 0.010 nitrofural 19 0.931 0.010 phenoxybenzamine 20 0.931 0.010 apomorphine 21 0.925 0.012 riluzole 22 0.922 0.013 vanoxerine 23 0.919 0.014 oxybenzone 24 0.918 0.014 fasudil 25 0.913 0.016 16-phenyltetranorprostaglandin E2 26 0.910 0.017 oxybuprocaine 27 0.909 0.017 spaglumic acid 28 0.909 0.017 AG-013608 29 0.907 0.018 glycopyrronium bromide 30 0.907 0.018 Tab. S2: Drugs UP-regulating the GPT gene according to its fold-change as obtained through microarray analysis (Single Gene Expression method). For each drug, its rank according to how much GPT is UP-regulated is reported in the second column. Moreover, the rank of the gene in the profile of each drug is reported in column 2. Tomatidine, which was shown to be strongly effective (see Main Text), is ranked only 247 by the Single Gene Expression method. Drug Drug Rank for GPT GPT Rank for Drug PF-00562151-00 1 273 hydroxyzine 2 323 amiloride 3 378 8 piperine 4 384 theophylline 5 407 chlorphenesin 6 416 etidronic acid 7 468 ambroxol 8 483 ginkgolide A 9 518 debrisoquine 10 529 tolazoline 11 532 ascorbic acid 12 554 lumicolchicine 13 562 metanephrine 14 570 cefotiam 15 578 oxetacaine 16 594 conessine 17 605 serotonin 18 608 fenoprofen 19 622 delsoline 20 632 colforsin 21 639 quinethazone 22 652 colecalciferol 23 653 tiabendazole 24 658 betamethasone 25 670 orlistat 26 674 tetracaine 27 679 ergocalciferol 28 695
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