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Patterns in expression profiles point to mode of action in drug discovery © 2001,Pharmacia, Inc. - All Rights Reserved. Antifungal therapy: Opportunistic systemic infections: candidiasis and aspergillosis Candida albicans is 4th most-frequently infectious hospital isolate Nosocomial fungal infections affect > 2 million patient / year Available therapies: Polyenes (Amphotericin B): effective but with side effects Azoles (Fluconazole, Itraconazole): safe but less effective Candins (Cancidas): just approved Model organism: Saccharomyces cerevisiae, aka baker’s yeast Drug discovery objectives: Characterization of novel agents Have we seen this type of agent before? What biological processes does it impact? Are improvements making it better or different? How can we measure activity? Topics: Transcript profiles with Affymetrix microarrays Microarray profiles within an experimental class Relationships between experiments Identification of functional patterns Relationships among responsive genes Transcript profiles: Transcript profile = snapshot of all mRNA species in sample Yeast: Stress: Profile =>unstressed, normal growth Profile => response to agent ? target pathway ? “secondary” response ? surrogate expression marker Affymetrix Gene Chip Hybridization: Result: intensity value for each mRNA represented on chip Measures of [mRNA] agree: fluconazole 9 Taqman ~ 1 / [mRNA] (PCR cycle number) voriconazole terbinafine 8 PCR cycle number GeneChip ~ [mRNA] log (Chip Intensity) itraconazole amorolfine ketoconazole clotrimazole PNU-144248E untreated fluconazole terbinafine voriconazole clotrimazole untreated ketoconazole 7 6 5 4 3 PNU-144248E amorolfine itraconazole 2 2 2.5 3 3.5 log(Chip Intensity) 4 Experimental design: Agents X Exposure 1 3 5 7 = Treatment Treatment profiles reveal similarity in response: Treatments genes 1 Aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 Signal from chip 2 3 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 Treatment profiles: 4 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 Measure of similarity between biological response to different treatments Identify common biological response: Distinguish a compound with distinct effect: Correlation: Pairwise Pearson correlation coefficient between each pair of treatments Correlations between experiments: Biological effects, proof of concept: farnesyl pyrophosphate ERG9 8 chemical agents: allylamine ERG1 ERG7 ERG11 5 azoles ERG24 3 genetic morpholine ERG25 changes: Novel imidizole ERG26 PNU-144248E DERG6 ERG6 DERG2 ERG2 Define method to identify ERG3 DERG5 responsive transcripts ERG5 ERG24 ergosterol Responsive genes: Expressed above background Significantly changed from untreated XX X Changed in multiple related treatments Response to ergosterol perturbation: AcylCoA-> -> ERG19 -> farnesyl pyrophosphate ERG9 Responsive genes in blue ERG1 ERG7 ERG11, NCP1 ERG24 112 transcripts related to ergosterol ERG25/ERG26 59 genes of unknown function ERG6 52 “other” changed transcripts ERG2 ERG3 ERG5 ERG4 ergosterol Facets of response: 20 stressresponse 29 lipid, fatty-acid sterol associated 36 mito ergosterol plasma membrane inner mito membrane Erg11p contains heme 12 hypoxic 5 13 hemeresponsive 16 membrane -assoc. 5 vesicular transport 5 cell wall Signal transduction Stress responses Mitochondrial: metabolism energy production translation DNA synthesis / repair / recombination Structure RNA synthesis and processing Major Facilitator Superfamily Cell wall biosynthesis Amino acid metabolism Transporters Lipid, sterol and fatty acid, biosynthesis Carbohydrate metabolism Protein Translation Nucleoside, etc. metabolism processing Global patterns of responsive genes: Each row is histogram of responsive genes in given treatment Second dimension -- gene profiles: Treatment profiles / gene profiles treatments genes 1 Aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa aaaa 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 2 3 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 4 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 11111 Gene profiles: Treatment profiles: => gene families co-regulated in response to treatment => biological similarity Treatment profiles / gene profiles Heat Map Genes profiles A1 D1 B1 C1 B3 A3 D3 C3 A5 B5 A7 B7 Treatments D7 C5 C7 Gene correlations Heat Map Pairwise Pearson correlation coefficients for gene profiles Rows 1 Rows 12 Rows 23 Rows 34 Rows 45 Rows 56 Rows 67 Rows 78 Rows 89 Rows 100 Rows 111 Rows 122 Conclusions: Expression profiles: identify responsive genes find significant pathway(s) in response find unanticipated responsive pathways identify surrogate expression markers identify agents eliciting similar responses distinguish biological response to apparently similar agents Acknowledgements: Pharmacia: Gary Bammert, ID Genomics Chad Storer, ID Genomics Mark Johnson, Computer-Aided Drug Discovery Tom Vidmar, Biostatistics Affymetrix: Mike Lelivelt Proteome: Everyone supporting YPD Spotfire: Bill Ladd Shawn Kenner
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