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Computational Genomics and Proteomics Lab Discovery of drug mode of action and drug repositioning from transcriptional responses Francesco Iorioa,b, Roberta.

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Presentation on theme: "Computational Genomics and Proteomics Lab Discovery of drug mode of action and drug repositioning from transcriptional responses Francesco Iorioa,b, Roberta."— Presentation transcript:

1 Computational Genomics and Proteomics Lab 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, 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 Computational Genomics and Proteomics La b Structure Background Method & Result Conclusion

3 Computational Genomics and Proteomics La b Background Goal & Key point Drug Mode of Action New drug therapeutic effects /known Drug reposition Drug Mode of Action New drug therapeutic effects /known Drug reposition Drug Signature Extraction Drug Signature Extraction Drug Mode of Action Construction Drug Mode of Action Construction Drug Distance Assessment

4 Computational Genomics and Proteomics La b Background Data:Connectivity Map

5 Computational Genomics and Proteomics La b Background cMap Data Data size: 22277*6836 Drug treated sample Gene Log fold change: Log2(drug treated/normal) 1,267 compounds several dosages 5 cell lines: HL60, PC3, SKMEL5, and MCF7/ssMCF7

6 Computational Genomics and Proteomics La b Method & Result Overview

7 Computational Genomics and Proteomics La b Method & Result Drug Signature Extraction 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: D 2 → N: the Spearman’s Footrule distance associating to each pair of ranked lists in X, a natural number quantifying the similarity between them; B: D 2 → 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; Notation Initialization

8 Computational Genomics and Proteomics La b Method & Result Drug 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 Computational Genomics and Proteomics La b Method & Result Drug 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 Computational Genomics and Proteomics La b Method & Result Drug 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 Computational Genomics and Proteomics La b Method & Result Drug Distance Assessment Core distance algorithm: Gene Set Enrichment Analysis(GSEA)

12 Computational Genomics and Proteomics La b Method & Result Drug Mode of Action Construction Distance threshold

13 Computational Genomics and Proteomics La b Method & Result Drug Mode of Action Construction A community is defined as a group of nodes densely interconnected with each other and with fewer connections to nodes outside the group Community Identification Affinity propagation algorithm 106 community 1309 nodes 41047 edges (856086 edges total)

14 Computational Genomics and Proteomics La b Method & Result Drug Mode of Action Construction

15 Computational Genomics and Proteomics La b Method & Result Drug Mode of Action Construction Anatomical Therapeutic Chemical (ATC) code --- 49/92 assessable communities significantly enrichment GO enrichment analysis MoA-Community assessment Community-Mode of Action relationship assessment

16 Computational Genomics and Proteomics La b Method & Result Drug 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 Computational Genomics and Proteomics La b Method & Result Drug Net (DN) n.28 is closest, composed by the HSP90 in cMap data n.40 n.63 Na+∕K+- ATPaproteasome inhibitors n.104 NF-kB inhibitors HSP90 inhibitors test

18 Computational Genomics and Proteomics La b Method & Result Drug 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 Computational Genomics and Proteomics La b Method & Result Drug 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 Computational Genomics and Proteomics La b 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 Computational Genomics and Proteomics La b

22 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. Curr Bioinform 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 Chem Biol 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 Syst Biol 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.

23 Computational Genomics and Proteomics La b The End Thank you!Question?


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