Phenotype MicroArrays: A Platform for Phenotypic Characterization of Cells and Species Description

Transcription

1 Stacy O. Montgomery, Ph.D. ICCC12 Florianopolis Brazil 30 Sept Phenotype MicroArrays: A Platform for Phenotypic Characterization of Cells and Species Description

2 Agenda Microbial Identification (MI) Fundamental Technology Phenotype MicroArray (PM) Technology PM Technology for Gene Function PM Technology for Drug Discovery PM Technology for Strain Characterization Fungal Applications

3 Primary Markets Industrial QC Clinical Food & Water Biolog Fundamental Technology Veterinary Environmental Agriculture Microbial Ecology Research & Education

4 Chemistry Platform: Carbon Utilization Patterns begin developing in as little as 4 hours

5 Species are DEFINED by Patterns of Utilization

6 Universal Reporter System - Respiration N Carbon P S Transport Catabolism Small molecule biosynthesis Macromolecular synthesis Synthesis of cellular structures Growth NADH Electron transport in membrane or mitochondria TV ox (colorless) TV red (purple)

7 Biolog GEN III System No pre-categorization: no gram stain, oxidase, catalase No additional follow-on tests One test panel for both GN and GP One minute set up One color change Over 1300 taxa Provides biologically relevant information

8 Phenotype MicroArrays Scanning 2000 Pathways of E. coli

9 Why Measure Cellular Phenotypes? DNA RNA PROTEIN PHENOTYPE Affymetrix, 1993 O Farrell, 1975 Biolog, 2001 Molecular Analyses Cellular Analysis

10 Measure 2000 Phenotypes Carbon Pathways N P S Biosynthetic Pathways Nitrogen Pathways Osmotic & Ion Effects ph Effects Sensitivity to Chemicals

11 Comparing Two Cell Lines Add cell A Add cell B OmniLog PM System PM Kinetic Result PM Pattern

12 PM Platform Comparing Two Cell Lines OmniLog PM System PM Kinetic Result PM Pattern 1 hr Automatic hr

13 Metabolic Curves Compared Control Experiment Metabolism Metabolism Time Time Metabolism Comparison Phenotypic differences: (Lag, Slope, Max) Metabolic differences Time

14 Cellular Pathways Phenotypes

15 Inhibitors Knockout Various Pathways X X X X X X X X

16 Phenotype MicroArray Applications Testing Cell Lines with Genetic Differences Determining Gene Function Testing Cell Lines Exposed to Drugs / Chemicals Evaluating New Drug Candidates Direct Testing of Cell Lines Strain Description Strain Characterization Optimizing Growth Conditions / Production Characteristics Testing Cells for Phenotypic Stability QA / QC of cell lines

17 Phenotype MicroArray Testing Genetic Effects on Cells

18 Assaying Genetic Changes Genotype Phenotype MicroArrays Phenotype Knock out a gene Which phenotypes change? Compare Mutant to Wild Type to Determine Gene Function

19 E. coli malf::tn10 vs MG1655 Dextrin Maltose Maltotriose tetracyclines tetracyclines Red = Phenotypes Lost Green = Phenotypes Gained

20 aminoglycosides E. coli oxyr::kan vs MG1655

21 Phenotype MicroArrays Direct Testing of Cell Lines

22 sorbitol Comparison of Strains Pathogenic (0157) and non-pathogenic (MG1655) E. coli tellurite

23 Culturing an Obligate Intracellular Pathogen

24 Annotation of Transporter Genes in P. aeruginosa Ian Paulsen and coworkers (PLoS Genetics, Sept. 2008) examined phenotypes of knockouts of transporter genes and compared them with functional annotations based on DNA homology. Only 12/27 (44%) precisely matched predicted annotation In 10/27 (37%) a more precise annotation was obtained In 5/27 (18%) a significant reannotation was enabled Novel transporters were identified for L-glutamate, N-acetyl-L-glutamate, hydroxy-l-proline, and histamine

25 Phenotype MicroArrays Testing the Effects of Chemicals

26 Testing & Evaluating Drugs Add cells Add cells + drug OmniLog PM System PM Pattern Patterns based on effect of drug under 2000 different growth conditions Bioinformatics Analysis

27 Drug Interactions 1 MIC Antagonism [Cpd B] Indifference 1/2 MIC Synergy 0 MIC 0 MIC 1/2 MIC [Cpd A] 1 MIC

28 Isoblograms: Indifference 1 Classical 1 Gradient No Growth [Cpd B] / MIC Growth [Cpd B] / MIC Growth Gradient [Cpd A] / MIC [Cpd A] / MIC

29 Isobolograms of Antibiotics Tested in S. aureus Time at Maximal Growth Rate (Hr) 1.5 Synergy 1.5 Indifference 1.5 Antagonism [Em] / MIC [Tet] / MIC [Em] / MIC [Nor] / MIC Em = Erythromycin Tet = Tetracycline Nor = Norfloxacin

30 Cluster Inhibitors into Groups Additivity Antagonism Synergy Oxolinic Acid Ofloxacin Norfloxacin Nalidixic acid Phleomycin Tetracycline_2 Tetracycline_3 Tetracycline_1 Doxycycline Chlormaphenicol Erythromycin Oleandomycin Puromycin Cefotaxime Cefazolin Cefamandole Naftate Streptomycin Amikacin Topoisomerase IV inhibitors DNA Gyrase /DNA Nicking Tetracyclines Macrolides Cephalosporins Aminoglycosides Similar Mechanisms of Action

31 Fungal Applications

32 Metabolic Profiling of Closely Related Fungi

33 Metabolic Profiling of Closely Related Fungi Range of substrate utilization Growth Antimicrobial properties Secondary metabolite production Dereplication of closely related strains

34 M. Singh J. Micro. Methods (2009) 77:102

35 Substrate Utilization

36 Anti-microbial Activity

37 Target Compound Levels

38 Phomopsis spp. Dereplication

39 Induction of Toxin Synthesis in Fusarium

40 Insertion of TRI5 GFP at the TRI5 locus

41 Culture Conditions Inducing Toxin Synthesis Culture conditions inducing synthesis of a trichothecene mycotoxin in the wheat pathogen, Fusarium graminearum. Induction was highest with arginine, putrescine, agmatine, and guanine as nitrogen sources. D. Gardiner et al Fungal Gen & Biol (2009)

42 Growth Independent Induction

43 In vitro vs. In planta

44 Carbon Source Dependent Sporulation

45 Carbon Source Dependent Sporulation Hypocrea atroviridis is frequently used as a photomorphogenetic model due to its ability to conidiate upon exposure to light. Light is thereby believed to be the primary trigger for spore formation. In contrast, we show here that conidiation is primarily carbon source dependent and that illumination plays a catalytic role;

46 Carbon Source Dependent Sporulation

47 Carbon Source Dependent Sporulation Of a total of 95 tested carbon sources, only a small set of carbohydrates, polyols, and sugar acids allowed conidiation in darkness, and on most of them, conidiation was significantly more strongly expressed in light. In addition, there are also a number of carbon sources on which H. atroviridis conidiates in darkness, but light does not further stimulate the process. Yet on another small set of carbon sources (L-sorbitol, D-fucose, D- and L-arabinose, and erythritol), H. atroviridis shows better sporulation in darkness than in light. No sporulation was observed on organic acids and amino acids.

48 Advantage of PM uses in Various Applications Advantages: Robust and straightforward technology Automated incubation and data collection Complementary to genomic and proteomic technologies Bacterial, fungal and mammalian Cells Applications: Quality assurance of stock or reference cultures Understanding metabolism in cells for basic research Functional genomics Inferring MOA of new drug compounds Pathogen host interactions Optimal conditions for growth, selection of specialized cell lines, development of selective assays

49 OmniLog PM Data Analysis

50 OmniLog PM Software

51 OmniLog PM Software

52 OmniLog PM Software

53 OmniLog PM Software

54 OmniLog PM Software

55 OmniLog PM Software

56 OmniLog PM Software