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Discovery of drug mode of action and drug repositioning from transcriptional responses

Discovery of drug mode of action and drug repositioning from transcriptional responses. Francesco Iorioa,b , Roberta Bosottic , Emanuela Scacheric , Vincenzo Belcastroa , Pratibha Mithbaokara , Rosa Ferrieroa ,

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Discovery of drug mode of action and drug repositioning from transcriptional responses

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  1. Discovery of drug mode of action and drugrepositioning from transcriptional responses Francesco Iorioa,b, Roberta Bosottic, EmanuelaScacheric, VincenzoBelcastroa, PratibhaMithbaokara, Rosa Ferrieroa, Loredana Murinob, Roberto Tagliaferrib, Nicola Brunetti-Pierria,d, Antonella Isacchic,1, and Diego di Bernardoa,e,1 aTeleThon Institute of Genetics and Medicine, Naples, Italy; cDepartment of Biotechnology, Nerviano Medical Sciences, Milan, Italy; eDepartment of Systems and Computer Science, “Federico II” University of Naples, Naples, Italy; dDepartment of Pediatrics, “Federico II” University of Naples, Naples, Italy; and bDepartment of Mathematics and Computer Science, University of Salerno, Salerno, Italy Presenter: Chifeng Ma

  2. Structure • Background • Method & Result • Conclusion

  3. BackgroundGoal & Key point Drug Mode of Action New drug therapeutic effects /known Drug reposition Drug Signature Extraction Drug Distance Assessment Drug Mode of Action Construction

  4. BackgroundData:Connectivity Map

  5. BackgroundcMap Data • 1,267 compounds • several dosages • 5 cell lines: HL60, PC3, SKMEL5, and MCF7/ssMCF7 Data size: 22277*6836 Drug treated sample Gene Log fold change: Log2(drug treated/normal)

  6. Method & ResultOverview

  7. Method & ResultDrug Signature Extraction Notation Initialization • D: the set of all the possible permutations of microarray probe-set identifiers (MPI); • X: a set of ranked lists of probe-set identifiers computed by sorting, in decreasing order, the genome-wide differential expression profiles obtained by treating cell lines with the same drug; • δ: D2 → N: the Spearman’s Footrule distance associating to each pair of ranked lists in X, a natural number quantifying the similarity between them; • B: D2 → D: the Borda Merging Function associating to each pair of ranked lists in X a new ranked list obtained by merging them with the Borda Merging Method;

  8. Method & ResultDrug Signature Extraction Spearman’s Footrule Spearman’s Footrule between two samples x and y Number of genes in the sample here m=22283 The rank list place of the ith gene

  9. Method & ResultDrug Signature Extraction Borda Merging Function A new ranked list of probes z is obtained by sorting them according to their values in P in increasing order

  10. Method & ResultDrug Signature Extraction Prototype Ranked List Generation Once a PRL had been obtained, a signature {p,q} was extracted as the top 250 and bottom 250 as the signature.

  11. Method & ResultDrug Distance Assessment Core distance algorithm: Gene Set Enrichment Analysis(GSEA)

  12. Method & ResultDrug Mode of Action Construction Distance threshold

  13. Method & ResultDrug Mode of Action Construction Community Identification Affinity propagation algorithm • A community is defined as a group of nodes densely interconnected with each other and with fewer connections to nodes outside the group 106 community 1309 nodes 41047 edges (856086 edges total)

  14. Method & ResultDrug Mode of Action Construction

  15. Method & ResultDrug Mode of Action Construction Community-Mode of Action relationship assessment • Anatomical Therapeutic Chemical (ATC) code --- 49/92 assessable communities significantly enrichment • GO enrichment analysis • MoA-Community assessment

  16. Method & ResultDrug Distance Assessment Drug to Community distance Distance between Drug d and drug x Number of drugs in C which has a significant edges with drug d

  17. Method & ResultDrug Net (DN) HSP90 inhibitors test • n.28 is closest, composed by the HSP90 in cMap data • n.40 n.63 Na+∕K+-ATPaproteasome inhibitors • n.104 NF-kB inhibitors

  18. Method & ResultDrug Net (DN) Test of cycin-dependent kinases(CDKs) inhibitors and Topoisomerase inhibitors Biology experiment was conduct to confirm that TDK inhibitors and Topo inhibitors share the universal inhibitor p21

  19. Method & ResultDrug Net (DN) • Search DN for drugs similar to 2-deoxy-D-glucose(2DOG) ---n.1---induce autophagy • Closest Drug--- Fasudil--- never been previously linked to autophagy • Biology experiment to confirm that

  20. Conclusion • Developed a general procedure to predict the molecular effects and MoA of new compounds, and to find previously unrecognized applications of well-known drugs • Analyzed the resulting network to identify communities of drugs with similar MoA and to determine the biological pathways perturbed by these compounds. • In addition, experimentally verified a prediction • A website tool was implemented at http://mantra.tigem.it

  21. Reference • 1. Terstappen GC, Schlupen C, Raggiaschi R, Gaviraghi G (2007) Target deconvolutionstrategies in drug discovery. Nat Rev Drug Discov 6:891–903. • 2. di Bernardo D, et al. (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol 23:377–383. • 3. Ambesi-Impiombato A, di Bernardo D (2006) Computational biology and drug discovery: From single-tTarget to network drugs. CurrBioinform 1:3–13. • 4. Berger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25:2466–2472. • 5. Hopkins AL (2008) Network pharmacology: The next paradigm in drug discovery. Nat ChemBiol 4:682–690. • 6. Mani KM, et al. (2008) A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol SystBiol 4:169. • 7. Gardner TS, di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301:102–105. • 8. Hu G, Agarwal P (2009) Human disease-drug network based on genomic expression profiles. PloS One 4(8):e6536. • 9. Hughes TR, et al. (2000) Functional discovery via a compendium of expression profiles.Cell 102(1):109–126. • 10. Kohanski MA, Dwyer DJ, Wierzbowski J, Cottarel G, Collins JJ (2008) Mistranslation of membrane proteins and two-component system activation trigger antibioticmediated cell death. Cell 135(4):679–690.

  22. The End Thank you! Question?

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