Twincore - Zentrum für Experimentelle und Klinische Infektionsforschung Institut für Molekulare Bakteriologie

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Twincore - Zentrum für Experimentelle und Klinische Infektionsforschung Institut für Molekulare Bakteriologie

0 HELMHOLTZ I ZENTRUM FÜR

INFEKTIONSFORSCHUNG Technische Universität Braunschweig

Institut für Mikrobiologie

Helmholtz Zentrum für Infektionsforschung Abteilung Molekulare Bakteriologie

Analysis of antibiotic resistance determinants in Pseudomonas aeruginosa - a transcriptomic approach

Von der Fakultät für Lebenswissenschaften der Technischen Universität Carolo-Wilhelmina

zu Braunschweig

zur Erlangung des Grades einer Doktorin der Naturwissenschaften

(Dr. rer. nat.) genehmigte Dissertation

von Ariane Alwine Khaledi aus Braunschweig

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I

Table of Contents

List of Figures V

List of Tables VII

List of Abbreviations VIII

1 Introduction 1

1.1 Pseudomonas aeruginosa as a model organism 1 1.2 Pseudomonas aeruginosa as an opportunistic nosocomial pathogen 1

1.3 Antibiotics: past and present 2

1.3.1 ß-lactams - inhibitors of cell wall biosynthesis 4

1.4 Antibiotic resistance in Pseudomonas aeruginosa 5

1.4.1 Chromosomally encoded ß-lactam resistance mechanisms affecting antibiotic influx

and efflux 5

1.4.2 ß-lactamases - hydrolyzing enzymes 9

1.5 How antibiotics affect bacteria - from specific targets to global networks 13

1.6 Recent progress in next generation sequencing 15

1.7 Aimsofthethesis 17

2 Materials and Methods 18

2.1 Bacterial strains and growth conditions 18

2.1.1 Clinical strain collection 19

2.2 Plasmids and Oligomers 22

2.3 DNAtransfertechniques 24

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u 25 2.3.2 Electroporation of P. aeruginosa

2.3.3 Plasmid transfer by diparental mating (conjugation) 25

2.4 Antibiotic susceptibility testing 26

2.4.1 E-test 26

2.4.2 Broth dilution 2®

2.5 RNA-sequencing - sample preparation 26

2.5.1 Culturing conditions and harvesting 26

2.5.2 RNA-extraction and DNA removal 27

2.5.3 rRNA removal by hybridization using oligo(dT)25 beads 27

2.5.4 rRNA removal by hybridization using streptavidin tagged magnetic beads 28 2.5.5 rRNA removal by hybridization using MICROBExpress 28 2.5.6 Enzymatic rRNA removal by terminator-5'-phosphate-dependent exonuclease 28 2.5.7 rRNA removal by duplex-specific nuclease (DSN) 29 2.5.8 RNA clean up and concentration by precipitation and chloroform-phenol

purification 29

2.5.9 cDNA library construction 29

2.6 RNA-sequencing - raw data processing 31

2-7 Detection of acquired resistance enzymes 32

2.8 Phylogeny 32

2.9 Data display: the Bactome database 33

2.9.1 SNP matrix calculation 33

2.9.2 Stop matrix generation 34

2.9.3 Gene expression matrix generation 34

2.9.4 Phenotype-genotype group comparisons 34

2.10 Statistics for Overall correlation 35

2.11 Carbapenemase activity assay 35

2.11.1 Carbapenemase assay with activity rescue 36

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III

2.13 OprD antibody generation 37

2.14 Antibody purification 37

2.15 OprD protein detection by Western Blot 38

2.16 Outer membrane protein visualization 38

2.17 LC-MS/MS sample preparation 39

2.18 LC-MS/MS data acquisition, data analysis and database searching 39

2.19 Fluorescence detection by flow cytometry 40

2.20 Metabolie isotope analysis 40

2.20.1 Amino acid extraction 41

3 Results 42

3.1 An optimized protoeol for cost efficient high-throughput transcriptome

sequencing off. aeruginosa 42

3.1.1 mRNA enrichment strategies 42

3.2 Transcriptome sequencing of clinical P. aeruginosa isolates 50 3.2.1 Coverage and relative transcript abundance of clinical P. aeruginosa isolates 50 3.2.2 Phylotyping by 214 genes identified a clonal outbreak 55 3.2.3 Nature and distribution of acquired ß-lactamases 58 3.2.4 A systematic approach for comprehensive gene sequence and expression data

analysis 61

3.2.5 Identifying intrinsic ß-lactam resistance markers via unbiased phenotype-genotype

correlation studies 68

3.2.6 Penicillin binding proteins show high sequence variability, but their role in ß-lactam

resistance remains unclear 77

3.2.7 The majority of ß-lactam non-susceptible clinical isolates harbor dominant genetic

resistance determinants 79

3.3 Impact of sub-inhibitory antibiotic concentrations on bacterial cells 83 3.3.1 Sub-inhibitory antibiotic treatment enhanced ROS produetion 83 3.3.2 Transcriptional responses upon sub-inhibitory antibiotic stress 84

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IV

3.3.3 Metabolie changes induced by sub-inhibitory antibiotic stress 89

4 Discussion 93

4.1 The increasing threat of bacterial antibiotic resistance 93 4.1.1 Antibiotic treatment leads to resistance selection 93 4.1.2 Antibiotic concentration reservoirs promote adaptation 93

4.2 Limitations of culture-based diagnostics 94

4.3 Transcriptome sequencing of drug resistant clinical isolates 97 4.3.1 Inexpensive whole transcriptome sequencing provided reasonable Single nucleotide

coverage throughout the whole P. aeruginosa genome 97 4.3.2 In depth taxonomical profiling could distinguish between actual clonal outbreaks

and sequence type related strains 99

4.3.3 The acquired ß-lactam resistome 99

4.3.4 Unbiased global correlation studies as an approach to detect novel phenotype

specific genetic markers 100

4.3.5 Evaluating RNA-sequencing for direct resistance prediction 102 4.3.6 High-throughput target Screening as a future clinical outlook 103 4.4 The impact of low-level antibiotic concentration reservoirs 105 4.4.1 Common transcriptional changes cope with oxidative stress 105 4.4.2 Sub-inhibitory antibiotic exposure affects the respiratory chain and leads to

fundamental metabolic changes 106

4.4.3 A need for avoiding trace-antibiotic reservoirs and targeting bacterial stress

responses 108

5 References 110

6 Danksagung 126

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