Proteomics in Analysis of Bacterial Pathogens

1. Proteomics in Analysis of Bacterial Pathogens Tina Guina University of Washington, Seattle

2. Outline Postgenomic studies of Pseudomonas in context of lung infection in patients with cystic fibrosis Study of bacterial posttranslational regulation by monitoring changes in protein subcellular localization

3. Pseudomonas aeruginosa and Cystic Fibrosis Gram-negative environmental bacterium (soil, water) Invades plants, animals; causes disease in immunocompromised humans and chronic lung disease in cystic fibrosis patients Cystic fibrosis (CF): most common genetic disease in Caucasians caused by a mutation in chloride channel CFTR Chronic Pseudomonas lung infection is a major cause of morbidity in CF patients Bacteria persist and multiply in lung (up to 109 cfu/g of sputum)

4. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Environmental P. aeruginosa

5. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Environmental P. aeruginosa Unknown Innate immune defect CFTR- PA colonization - ASYMPTOMATIC

6. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Environmental P. aeruginosa Unknown Innate immune defect Bacterial Adaptation CFTR- Innate Immune Selective Pressure PA colonization - ASYMPTOMATIC

7. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Environmental Pseudomonas Increased airway inflammation Resistance to antimicrobials Bacterial Adaptation Unique surface modifications Chronic Lung Disease Unknown Innate immune defect CFTR- Innate Immune Selective Pressure Increased bacterial burden - SYMPTOMATIC PA colonization - ASYMPTOMATIC

8. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Chronic Lung Disease Bacterial Adaptation ? Increased bacterial burden - SYMPTOMATIC PA colonization - ASYMPTOMATIC

9. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Intervention Chronic Lung Disease Bacterial Adaptation ? Increased bacterial burden - SYMPTOMATIC PA colonization - ASYMPTOMATIC

10. Questions: Can we characterize stages of bacterial adaptation to the lung ? Can we use characteristics of these stages to develop assays to predict CF patients’ clinical outcome ? Can drugs be developed that would arrest adaptation ? Can Pseudomonas “staging” be used for therapy ?

11. Approaches for Studying Pseudomonas Adaptation in CF Lung • Analysis of laboratory-adapted Pseudomonas strains grown under • conditions that promote phenotypes typical to the clinical isolates • Analysis of Pseudomonas clinical isolates from CF airway • - serial isolates from young children with CF • - isolates from patients with mild vs. severe disease symptoms • Analysis of bacterial phenotypes: morphology, surface properties, • production of secreted factors • Postgenomic analysis: whole genome sequencing, genome typing, • transcriptional profiling, protein expression profiling

12. Analysis of Pseudomonas Clinical Isolates From Young Children With CF Natural history study to determine infection and inflammation in young children, three centers in US • Early isolates from 29 children, 4 to 36 months of age, 2 to 30 isolates for each patient • Later isolates from 11/29 children enrolled into the original study, currently up to 9 years of age • Isolates from upper airway (OP) and lower airway (BAL) (Rosenfeld et al. 2001)

13. Postgenomic Analysis of Pseudomonas in CF Environmental isolates Clinical CF Isolates Genomic Analysis Microarray Analysis Proteomic Analysis Phenotypic Analysis Bioinformatics Identification of CF-unique Characteristics

14. Pseudomonas Adapt to the Cystic Fibrosis Lung Environment

15. aminoarabinose NH 2 O OH O HO O OH P O O O NH 2 O O OH O NH HO O - O O O HO O O OH NH P O O O O O O OH O O HO 3-OH C10 3-OH C12 C12 3-OH C12 C16 3-OH C12 CF Isolate-Specific Characteristics: Outer Membrane LPS Modifications 1) Increased Antimicrobial Peptide Resistance 2) Increased Proinflammatory Signaling Through Tlr4 LPS modifications are induced in: - all early isolates from infants with CF (as early as 4 months of age) - laboratory-adapted strain PAO1 during magnesium limitation and anaerobic growth (Ernst et al. 1999, Hajjar et al. 2002)

16. I. Adaptation to the CF Lung: Is Genomic Organization of Pseudomonas CF Infant and Environmental Isolates Similar? Whole genome analysis using DNA microarrays - 13 CF, 4 environmental, and 3 clinical non-CF isolates - 38 common chromosomal islands divergent or absent (N >1) when compared to PAO-1 Results: Suggest no selection of a Pseudomonas subpopulation from the environment in colonization of the CF airways. (Ernst et al. 2003)

17. < 6 mo 60 mo 96 mo II. Adaptation to the CF Lung : Is Genomic Organization of Longitudinal Pseudomonas CF Isolates Similar? Sequencing of parentally-related Pseudomonas isolates from a CF patient Isolates from 6 months to 8 years of age CF416 (6 months): 4.0 X coverage CF5296 (8 years): 4.0 X coverage Results: 40 point mutations/deletions between early and late isolate (Smith, Olson et al.)

18. Analysis of 40 Chromosomal Regions:Comparison of Longitudinal CF Isolates

19. III. Adaptation to the CF Lung : Is There a Gene Expression Pattern Unique to the Infant CF Isolates? Transcriptional (mRNA) profiling using DNA microarrays CF-activated genes PA1290: probable transcriptional regulator 5 PA5095: ABC transporter permease 5 CF-repressed genes PA1008: bacterioferritin comigratory protein 5 PA1244: hypothetical gene 5 PA1708: popB - translocator protein 5 PA1752: hypothetical gene 5 PA2461: hypothetical gene 5 # of patients (N=5) Results: Mode of regulation for 7 genes is unique to a subset of clinical isolates (Ernst et al.)

20. Genes/Proteins Total Quantified Regulated Induced Represed Microarray Analysis 5600 209 108 101 Proteomic Analysis 553 122 54 68 Cellular Protein Levels Do Not Always Correlate With Levels of the Corresponding Gene Transcripts Anaerobic regulation in PAO1: Postgenomic Analysis Regulated Genes 209 Regulated Proteins 122 Quantified Proteins 553 13 42

21. ICAT-peptide mixture IV. Adaptation to the CF Lung : Is There a Protein Expression Pattern Unique to the Infant CF Isolates? Quantitative protein profiling of differentially labeled whole cell protein Strain/Condition A Whole cell protein + ICAT mLC-MS/MS in silico analysis Combine and proteolyze [Protein X in A] [Protein X in B] Strain/Condition B

22. Pseudomonas aeruginosa Proteome Analysis: Regulation by Low Magnesium Stress Induces CF isolate- Specific Surface Modifications Laboratory-adapted Pseudomonas strain PAO-1 1 mM Mg2+ 8 mM Mg2+ CF-like phenotype Differential protein labeling MS/in silico protein identification and quantitative analysis

23. Postgenomic Analysis of Pseudomonas During Mg Limitation Transcriptional Profiling:~2250 (40%) genes expressed 650 genes regulated Qualitative proteomic analysis: 1331 proteins identified Quantitative analysis (ICAT): 546 proteins quantified 76 proteins induced 69 proteins repressed ~ 50% correlation with transcriptional profiling data

24. Selected Proteins Induced During Growth of Pseudomonas in Low Mg Fold increase Conserved low Mg stress-response proteins two-component response regulator PhoP 10.3 magnesium transport ATPase MgtA 5.8 MgtC homologue 4.0 CF-specific surface modifications, resistance to antimicrobial peptides PmrH homologue 2.8 PmrF homologue 2.3 PmrI homologue 6.1 Enzymes for synthesis of quorum sensing signal PQS PA0996, PA0997, PA0998, PA0999 1.5 - 2.0

25. Quorum Sensing: Bacterial Intercellular Communication Via Small Signaling Molecules C4-HSL C12-HSL PQS

26. Quorum Sensing: Secretion of Toxins, Virulence Factors

27. Quorum Sensing: Biofilm, Antibiotic Resistance AB AB AB AB

28. PQS b-keto-decanoic acid Butyryl-ACP C4-HSL RhlI S-adenosylmethionine (SAM) Acyl-homoserine lactones LasI C12-HSL Dodecanoyl-ACP

29. Mg2+ Conc. 1 mM 1 mM 8 mM 8 mM WT PQS - PQS Production by Laboratory Strain of Pseudomonas Is Increased During Growth in Low Mg

30. UTI CF1 CF2 CF3 CF4 CF5 PQS - blood PAO-1 High Levels of PQS Are Produced by CF Pseudomonas Isolates Grown in High Mg

31. PQS Production by Pseudomonas Isolates From Infants with Cystic Fibrosis 190 isolates from 25 children up to 3 years of age analyzed for PQS production Bacteria were grown in medium with high [Mg2+]

32. PQS Production by Isolates from Infants with CF Similar to CF-specific surface modifications, most Pseudomonas clinical isolates from young children with CF produce high PQS levels

33. Model of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis Environmental Pseudomonas Lung Disease Bacterial Adaptation • surface modifications • Increased PQS • (biofilm, virulence, • antibiotic resistance) • Alginate/mucoidy • Auxotrophy Innate Immune Selective Pressure Increased bacteria - SYMPTOMATIC PA colonization-ASYMPTOMATIC

34. Natural History Study: Infant patients isolates, 8-yr vs. early isolates Mild vs. Severe Study Genome sequencing DNA Microarray, Proteomic Analyses To Identify Additional Markers

35. Natural History Study: Infant patients isolates, 8-yr vs. early isolates Mild vs. Severe Study Genome sequencing DNA Microarray, Proteomic Analyses To Identify Additional Markers Develop tests for broad screening of large CF populations to validate markers specific for Pseudomonas adaptation

36. Natural History Study: Infant patients isolates, 8-yr vs. early isolates Mild vs. Severe Study Genome sequencing DNA Microarray, Proteomic Analyses To Identify Additional Markers Develop tests for broad screening of large CF populations to validate markers specific for Pseudomonas adaptation Correlate with the disease outcome Disease outcome prediction Vaccine/drug development

37. Bacterial Posttranslational Regulation Study: Pseudomonas Envelope Remodeling During Growth In Low Mg

38. Gram-negative Bacterial Membrane

39. Magnesium Stabilizes Gram-negative Outer Membrane O O O O O O HO P O O -O P O O O O NH O O NH OH O HO Lipid A OH O HO O O O- O O P O OH O P O Mg NH O NH O O O O O OH O OH O OH O OH OH OH Growth in low magnesium Membrane stress Membrane remodeling Growth in low magnesium Membrane stress Membrane remodeling

40. Gram-Negative Envelope Remodeling During Magnesium Limitation Alteration in outer membrane proteins Lipid A acylation Proteases PagC PagN PagP PgtE OprH OM IM PmrF PhoQ PmrB MgtA MgtC Small molecule transport Nutrient acquisition LPS modifications Environmental sensing Modulation and resistance to the host innate immune defense:

41. ICAT Analysis of Pseudomonas Membrane and Whole Cell Protein During Mg Limitation Pseudomonas strain PAO-1 8 mM Mg2+ membrane 8 mM Mg2+ whole cell 1 mM Mg2+ membrane 1 mM Mg2+ whole cell ICAT analysis ICAT analysis 163 proteins 486 proteins 106 proteins were quantified in both experiments: Compare relative protein levels in membrane vs. in whole cell

42. Pseudomonas Metabolic Enzymes and Protein Translation Machinery Concentrate at the Membrane During Growth in Low Magnesium FI* membrane/FI whole cell Energy metabolism succinate dehydrogenase (A, B subunits) 1.6 - 2.4 2-oxoglutarate dehydrogenase (E1 subunit) SucA 3.0 phosphoenolpyruvate synthase 3.1 ATP synthase subunits 1.5 – 1.8 cytochrome c5 1.6 GroEL chaperone 3.0 Translation machinery 30S ribosomal proteins (S2, S4, S13, S5) 1.5 – 1.8 elongation and ribosome recycling factor G 2.0 *FI = fold induction

43. Bacterial ribosomal fractions Cytoplasmic Membrane-associated Soluble protein synthesis Membrane and secreted protein synthesis

44. Bacterial ribosomal fractions Low Mg2+ membrane stress Cytoplasmic Membrane-associated Low Mg2+ membrane stress Soluble protein synthesis Increased membrane and secreted protein synthesis Formation of stress-induced multienzyme complexes Membrane lipid and protein remodeling Decreased membrane permeability Resistance to various antimicrobials

45. Proteomic Analysis in Studying Bacterial Pathogens: Summary • Advantages: • Useful tool for analysis of bacteria for which there are little • or no genetic tools available • Analysis of posttranscriptional regulation • Analysis of protein compartmentalization, posttranslational regulation • Disadvantages: • Still expensive, time/labor intensive • Need for “dishwasher-like technology”, for improved data analysis software

46. Acknowledgements Manhong Wu Robert Ernst Hai Nguyen Sam Miller Jane Burns Eric Smith Maynard Olson David Goodlett Sam Purvine Ruedi Aebersold Jimmy Eng CFF NIH