Computational Genomics and Proteomics Lab Discovery of drug mode of action and drug repositioning...
-
Upload
alexzander-lawlis -
Category
Documents
-
view
220 -
download
1
Transcript of Computational Genomics and Proteomics Lab Discovery of drug mode of action and drug repositioning...
Computational Genomics and Proteomics Lab
Discovery of drug mode of action and drugrepositioning 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
Computational Genomics and Proteomics Lab
Structure
• Background
• Method & Result
• Conclusion
Computational Genomics and Proteomics Lab
BackgroundGoal & Key point
Drug Mode of ActionNew drug therapeutic effects
/known Drug reposition
Drug SignatureExtraction
Drug Mode of Action
Construction
Drug Distance
Assessment
Computational Genomics and Proteomics Lab
BackgroundData:Connectivity Map
Computational Genomics and Proteomics Lab
BackgroundcMap Data
Data size: 22277*6836Drug treated sample
Gene
Log fold change:Log2(drug treated/normal)
• 1,267 compounds • several dosages• 5 cell lines: HL60, PC3,
SKMEL5, and MCF7/ssMCF7
Computational Genomics and Proteomics Lab
Method & ResultOverview
Computational Genomics and Proteomics Lab
Method & ResultDrug 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: 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;
Notation Initialization
Computational Genomics and Proteomics Lab
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
Computational Genomics and Proteomics Lab
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
Computational Genomics and Proteomics Lab
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.
Computational Genomics and Proteomics Lab
Method & ResultDrug Distance Assessment
Core distance algorithm: Gene Set Enrichment Analysis(GSEA)
Computational Genomics and Proteomics Lab
Method & ResultDrug Mode of Action Construction
Distance threshold
Computational Genomics and Proteomics Lab
Method & ResultDrug 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 IdentificationAffinity propagation algorithm
106 community1309 nodes41047 edges(856086 edges total)
Computational Genomics and Proteomics Lab
Method & ResultDrug Mode of Action Construction
Computational Genomics and Proteomics Lab
Method & ResultDrug 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
Computational Genomics and Proteomics Lab
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
Computational Genomics and Proteomics Lab
Method & ResultDrug 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
Computational Genomics and Proteomics Lab
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
Computational Genomics and Proteomics Lab
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
Computational Genomics and Proteomics Lab
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
Computational Genomics and Proteomics Lab
Computational Genomics and Proteomics Lab
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.
Computational Genomics and Proteomics Lab
The End
Thank you! Question?