IDENTIFICATION OF GENES INVOLVED IN THE PRODUCTION OF NOVEL ANTIMICROBIAL PRODUCTS CAPABLE OF INHIBITING MULTI-DRUG RESISTANT PATHOGENS

Ryan Harris

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

August 2019

Committee:

Hans Wildschutte, Advisor

Timothy Davis

Robert Huber © 2019 Ryan Harris All Rights Reserved iii

ABSTRACT

Hans Wildschutte, Advisor

The research described here focuses on the phylogenetic characterization of water-derived pseudomonads and their antagonistic activity against multi-drug resistance (MDR) P. aeruginosa and Burkholderia species. Phylogenetic work was based on the gyrB housekeeping gene. Genetic techniques have been optimized and employed to identify genes associated with antimicrobial production via transposon (Tn) mutagenesis using a triparental mating system approach with

Pseudomonas as the model organism. This study expands on theses previous studies in the lab to identify biosynthetic gene clusters (BGC) involved in production of novel antibiotics capable of inhibiting the growth of MDR pathogens. We utilize a previously optimized workflow to identify genes from environmental isolates involved in the inhibition of MDR P. aeruginosa and species within the Burkholderia cepacia complex (Bcc). We show that both MDR Bcc and P. aeruginosa pathogens were inhibit by environmental Pseudomonas strains. Out of 7,784 interactions, 210 of these were antagonistic. Superkillers (SK), defined as strains that inhibit ≥3 of MDR pathogens, were selected for optimization of Tn mutagenesis to identify gene cluster whose products inhibit these MDR pathogens. Only six out of the 24 SK’s were capable of this process. Out of these six, three were selected for large scale mutagenesis to identify loss of inhibition (LOI) mutants. Four

LOI mutations were found for strain S5F11, one of which had an insertion within a BGC predicted to produce an NRPS complex. Seven LOI mutants were found for S3E7. Although none of these insertions were identified within a BGC, genes have been identified that are observed to be heavily involved in antibiotic production. This study suggests that environmental Pseudomonas strains iv have the capacity to inhibit the growth of CF-derived MDR pathogens. Using Tn mutagenesis, we have identified novel loci that are associated with antibiotic production. v

ACKNOWLEDGMENTS

This thesis would not have been possible without the constant support of my friends, family and colleagues at BGSU. I would firstly like to thank my parents as they were always there to push me through the toughest times of my degree and provide help where ever they could. I would also like to thank my friends Nate Fry, Steven Finley and Ryon Luzier who would always provide some comic relief and a drink when needed. My lab mates Joe Basalla, Kaylee

Wilburn and Lexi Lake would always be there to help pour media, helped spread plate 600 plates during my mutant hunts and provide insights into concepts I’d never come across before. A big thank you to Dr. Hans Wildschutte, my advisor. He was always there to answer any question I had and provided insight to experimental procedures when things weren’t going my way. And lastly, I would like to thank my partner Kierra who without her, I couldn’t have gotten through the last two months. You believed in me when I didn’t believe in myself and helped me have the motivation to cross the finish line. I will forever be grateful. vi

TABLE OF CONTENTS

Page

CHAPTER I. INTRODUCTION ...... 1

1.1 Introduction to secondary metabolites ...... 1

1.2 Antibiotics and how bacteria resist their activity ...... 1

1.3 Resistance crisis ...... 3

1.4 Modern approaches to aid novel antibiotic discovery and alternatives ...... 4

1.5 Cystic fibrosis and bacterial infection ...... 5

1.5.1 Cystic fibrosis ...... 5

1.5.2 Pseudomonas aeruginosa and Burkholderia infection ...... 6

1.6 Environmental Pseudomonas...... 8

1.6.1 Genomic diversity and secondary metabolite production ...... 8

CHAPTER II. METHODS ...... 10

2.1 Isolation of environmental strains ...... 10

2.2 Multi-drug resistant CF-derived pathogens ...... 11

2.3 Phylogenetic approach ...... 11

2.4 Antagonistic assay ...... 13

2.5 Conjugation and transposon mutagenesis ...... 14

2.5.1 Growth conditions for tri-parental mating ...... 14 vii

2.5.2 Conjugation and tri-parental mating ...... 15

2.5.3 Optimization for large scale mutant hunt...... 16

2.5.4 LOI mutant screen...... 16

2.5.5 Verification of LOI mutants...... 17

2.6 Large scale mutant hunt ...... 19

2.6.1 S5F11 mutant hunt ...... 18

2.6.2 S3E7 mutant hunt ...... 18

2.6.3 LE22A5 mutant hunt...... 19

2.6.4 LE22B6 mutant hunt ...... 19

2.7 S3E7 increased inhibition mutant ...... 19

2.8 Transposon insertion site identification ...... 20

2.8.1 Genomic DNA (gDNA) isolation ...... 20

2.8.2 AP-PCR...... 21

CHAPTER III. RESULTS ...... 23

3.1 Population-level approach to discover antibiotics ...... 23

3.1.1 Phylogenetic analysis and antagonistic activity ...... 23

3.2 Ability of strains to undergo Tn mutagenesis ...... 24

3.3 Large scale mutant hunt ...... 25 viii

3.3.1 S5F11 mutant hunt ...... 25

3.3.2 S3E7 mutant hunt ...... 26

3.3.3 LE22A5 mutant hunt...... 27

3.4 S3E7 increased inhibition mutant ...... 28

CHAPTER IV. DISCUSSION...... 29

4.1 Population-level and antagonistic analysis of environmental Pseudomonas ...... 29

4.2 Biosynthetic gene cluster analysis ...... 30

4.2.1 S5F11 BGC ...... 30

4.2.2 LE22A5 BGC...... 31

4.3 Non-BGC insertions...... 32

4.3.1 S5F11 non-BGC mutants ...... 32

4.3.2 S3E7 non-BGC mutants ...... 34

4.3.3 LE22A5 non-BGC mutants...... 36

4.4 S3E7 increased inhibition mutations ...... 39

4.5 Future Direction ...... 40

REFERENCES ...... 42

APPENDIX A. FIGURES ...... 60

APPENDIX A. TABLES ...... 66 1

CHAPTER I. INTRODUCTION

1.1 Introduction to secondary metabolites

Secondary metabolites are compounds that are not essential for cell viability, growth, and development but may provide a selective advantage under specific circumstances. The production of secondary metabolites enables the bacterium to perform tasks that will aid in survival such as quorum sensing (Venturi, 2006), microbial predator deterring (Neidig et al.,

2011) and most importantly reducing competition via antibiotic production (Raaijmakers et al.,

1997) allowing for the colonization of diverse habitats across the globe. For instance, siderophores are a compounds that are used for iron acquisition and transport, especially when the environment is iron depleted. In these conditions, siderophores can sequester all available iron in the environment, leaving little behind for competitors (Tyc et al., 2017). Another example of secondary metabolites is both 2,3-butanediol and acetoin which are considered as volatile compounds. These secondary metabolites are produced by two bacterial species known to be plant growth-promoting rhizobacteria. These two compounds are involved in promoting the increase of the root system biomass within Arabidopsis thaliana. The bacterial producer is provided with nutrients by the plant which allows for this symbiotic relationship to exist (Ryu et al., 2003).

1.2 Antibiotics and how bacteria resist their activity

Antibiotics are secondary metabolites produced by microorganisms that possess the ability to kill or to inhibit the growth of other microorganisms. (Clardy et al., 2009). A selective advantage of antibiotics in nature is the ability to inhibit competition that could affect nutrient availability in the habitat they reside. Because these products are of competitive importance, a constant ‘arms race’ occurs between competing organisms that produce antibiotics and others 2 that resist these effects (Larsson, 2014). As a result, the evolution of resistance mechanisms and novel antibiotic production are an ongoing process (Alonso et al., 2001).

The evolution of drug resistance was influenced by sources of antibiotics coming from both medical and agricultural use. Although in recent years, doctors have been told to prescribe antibiotics only when necessary and new regulations on the use of in agriculture have been set

(Gilbert, 2012). Even with these restrictions, pathogenic organisms are being exposed to antibiotics. This has led to the evolution and selection of microbes that have the ability to evade these compounds. The way they resist the effects of these compounds is through the use of a number of mechanisms that render the antibiotics useless. Mechanisms of resistance include changing of the antibiotics target within the cell, lowering the affinity of the molecule to its target. An example of this can be seen with vancomycin resistance. The bacterial cell wall is normally composed of a D-Ala-D-Ala structure which vancomycin can bind to, preventing its formation. But in vancomycin resistant bacteria, the D-Ala-D-Ala termina is changed to D-Ala-

D-Lac preventing the binding of vancomycin (Bugg et al., 1991). Another example involves the use of efflux pumps, the most ubiquitously found mechanism of resistance. Efflux pumps are coded for by genes within the chromosome of most bacterial species. These membrane bound protein channels work by actively pumping out the antibiotics as soon as the compound enters the cell, preventing the antibiotic from binding to its target site (Blanco et al., 2016). This highlights only a couple of examples of how these organisms are capable of resisting the mode of action potent antibiotics. Due to a rise in use of antibiotics by humans, these mechanisms are becoming increasingly prevalent (Ventola, C. L. 2015). 3

1.3 Resistance crisis

During the golden age of antibiotic discovery from 1940-1970, the idea of resistance was explored, but due to the benefits of these drugs and their frequent discovery, the disadvantages of antibiotic overuse were ignored (Laxminarayan et al., 2013). The benefits of using antibiotics by both the medical field and agricultural sectors, contributed to the evolution of multi-drug resistant (MDR) pathogens. In addition, many drugs are easily available over the counter in developing countries which also contributes to the current resistance problem (Ayukekbong et al., 2017), and studies predict that antibiotic usage within these developing countries will double to improve health of farmed animals (Van Boeckel et al., 2015).

The over use of antibiotics and the evolution of MDR pathogens has led to a post- antibiotic era (Ventola, 2015). The problem has been described by the CDC as ‘the perfect storm’. This is due to the lack of pharmaceutical companies investing money into research and the increasing prevalence of MDR pathogens resulting in few novel antibiotics available to treat bacterial infections. After discovery, a study conducted by the London College of economics has estimated that a new antibiotic would give a net present value (NPV) of minus $50 million. In contrast, the NPV for a new arthritis drug is $1 billion (Spellberg, 2014). Thus, companies would rather place resources into other, more lucrative research. Because of this, academic research labs are leading the efforts to develop new alternatives to solve the crisis (Cooper & Shlaes,

2011).

Currently 700,000 people a year are predicted to die globally from these antibiotic resistance pathogens and the death toll is increasing (Jasovský et al., 2016). According to the

CDC, by the year 2050, 10 million people are predicted to die worldwide from untreatable 4 diseases caused by these MDR pathogens annually unless alternative therapeutic agents are discovered (CDC, 2013).

1.4 Modern approaches to aid novel antibiotic discovery and alternatives

Recent work has shown that environmental bacteria continue to be a reliable source of novel antibiotics (Chatterjee et al. 2017, Davis et al. 2017, Ling et al, 2015, Hover et al, 2018).

Even with the lack of research by large pharmaceutical companies, two potent antibiotics have been discovered over the past decade using different approaches. One strategy facilitates the growth of unculturable bacteria (Ling et al, 2015) and the other involves a metagenomics approach (Hover et al, 2018). Both teixobactin and malacidin, the names given to these new antibiotics, were shown to inhibit multiple targets that affect cell wall synthesis. Moreover, the antibiotics have shown to be affective against clinically important drug resistance pathogens such as MRSA (Methicillin-Resistance Staphylococcus aureus) and VRE (Vancomycin-Resistant E. faecium) which are desperately needed to treat infections.

Due to the evolution of resistance to most antibiotics, research is being channeled into alternative strategies. This will hopefully provide relief from the development of resistance.

Examples include bacteriophage therapy which utilizes the lytic stage of these viruses to kill bacterial cells with great specificity (Lin et al. 2017). CRISPR is another example. This system will enable antibiotic resistant bacteria to be selectively killed, using their resistance genes as a selective marker (Greene, 2018). Finally another example is using predatory bacteria to kill the evading bacteria (Dashiff et al., 2010). The research into these alternatives is promising although these novel treatments are neglecting a focus of research to natural antibiotics. As a result, the proportion of MDR pathogens found in a clinical setting has increased over the years. Therefore, 5 research into novel antibiotic discovery is necessary to keep up with the rapidly evolving resistant pathogens being found in the hospitals and the environment around the globe.

1.5 Cystic fibrosis and bacterial infection

1.5.1 Cystic fibrosis

Cystic fibrosis (CF) is a debilitating Mendelian disease caused by a gene mutation that encodes the cystic fibrosis transmembrane conductance regulator (CFTR) channel (Riordan et al., 1989). The CFTR channels primary function is to maintain liquid volume on epithelial surfaces by regulating both Cl- secretion and Na+ absorption. In a healthy individual, these ions are coordinated in and out of the epithelial cells to maintain the osmolarity within the lung tissue.

This allows for the sufficient liquid volume to remove mucus that is produced within the airway.

Mutations within the gene of this transmembrane protein can reduce the secretion of Cl- causing a reduction in liquid volume reducing the transport of mucus out of the airway within the lung.

The inability to remove this mucus also prevents the removal of bacteria upon inhalation. The bacteria can then thrive within this warm mucosal environment leading to high infection rates within the CF patients (Tarran et al., 2005; Knowles & Boucher, 2002). These infections are the leading cause of morality among CF patients leading to premature death (Lyczak et al., 2002)

Furthermore, due to these chronic infections, patients undergo a strict regime of antibiotics

(Coutinho et al., 2008). Due to this high exposure to changing cocktails of antibiotics, the microbial community within the lung undergo selective pressure. This induces the proliferation of MDR pathogens within the lung making the infections harder to treat (Walters & Ratjen,

2006) This is why research into alternatives to treat these increasingly prevalent MDR pathogens must be a priority. This research hopes to address these difficulties by identifying novel compounds capable of inhibiting the growth of these problematic MDR pathogens by producing 6 novel potent antibiotics that have no current resistance mechanism that can be found in these pathogens.

1.5.2 Pseudomonas aeruginosa and Burkholderia infections

In CF patients, pathogenic bacteria persist within the lungs, and include Achromobacter,

Burkholderia, Stenotrophomons which is found to be one of the most common infection found within the CF lung (Amin & Waters, 2016), but this environment is eventually dominated by P. aeruginosa over time (Poole, 2002 & Smith et al., 1996). All of these pathogens have been found to be intrinsically multi-drug resistant and if any of these infection are present within a CF patients lung, it can increases chances of complications (Zhang et al., 2005; Esposito et al., 2017) and are one of the predominant causes of CF mortality (Belkin et al., 2006). In part due to its

MDR resistance and high pathogenic capabilities, P. aeruginosa has become well-characterized through medical research. Within CF patients, this particular species of Pseudomonas seems to outcompete and dominate all other pathogens within the CF lung making it a priority for the discovery of new treatments to combat infections (Bodey et al., 1983; Blainey et al., 2012; Diaz-

Caballero et al., 2015). This gram-negative, rod-shaped facultative anaerobe is normally found in soil and aquatic habitats, yet, in can be a serious pathogen in immunocompromised CF patients

(Fazeli et al., 2012). MDR P. aeruginosa can also be found in nosocomial environments, meaning it has the opportunity to infect immunocompromised patients that reside in the hospital

(Micek et al., 2015). P. aeruginosa can also quickly adapt to its environment and evolve resistance to antibiotics (Winstanley. et al., 2016), in part to the high amounts of antibiotic exposure when in the CF lung (Abdulwahab et al., 2017). Also, antibiotic resistance gene regulators have been shown to control genes involved in pathogenicity. This suggests that pathogenicity and antibiotic resistance phenotypes are coordinated (Yang et al., 2011). The 7

World Health Organization has listed P. aeruginosa as a priority one pathogen and they have stated that it is critical to find new alternatives to the current drugs available today (WHO, 2017).

The genus Burkholderia is comprised of many medically relevant gram-negative pathogens that cause numerous diseases. One example of this includes Burkholderia pseudomallei which is causes an illness named melioidosis which causes flu-like symptoms and is normally acquired in tropical climates (Pitt et al., 2006). Another type of Burkholderia that causes disease is the Burkholderia cepacia complex which was once thought to be one species.

B. cepacia complex is now an umbrella term that involves 17 species of Burkholderia and are shown to be involved in bioremediation, plant-growth promotion and can be used as a biopesticide (Govan et al., 2000). Although they are also involved in a disease named ‘cepacia syndrome’ which consist of necrotizing pneumonia which leads to an almost universally fatal outcome. These infections normally occur in immunocompromised patients such as CF patients

(Gilchrist et al., 2012). Patients that contract a Burkholderia infection have higher mortality rates than those who do not (Jones et al., 2004), in part due to high antibiotic resistance rates

(Zhou et al., 2007; Abbott et al., 2016) as well as immune evasion strategies employed by the pathogen (Ganesan & Sajjan., 2012). Thus, P. aeruginosa and Burkholderia pathogens present serious challenges for CF patients.

Although these pathogens thrive in the lungs of CF patients (Coutinho et al., 2008), previous studies (Chatterjee et al. 2017, Davis et al. 2017, Deredjian et al., 2014, Colinon et al.,

2013, Coenye & Vandamme, 2003) hypothesized that P. aeruginosa (and likely other pathogens) are at a low abundance in the environment and may be out competed by other microbes in ecological non-human host settings. Here, we utilize a collection of MDR pathogens has been isolated from CF-lung sputum samples which includes both P. aeruginosa and Burkholderia 8 cepacia complex species. The goal of this research is to identify gene clusters among antagonistic strains whose products that are effective against MDR P. aeruginosa and

Burkholderia pathogens.

1.6 Environmental Pseudomonas

1.6.1 Genomic diversity and secondary metabolite production

The Pseudomonas genus is comprised of gram-negative gammaproteobacteria that contain diverse species that are found in many different environments. These environments include, but are not limited to, soil and freshwater habitats where they are integral members of ecological processes. They residing in rhizospheres and improve plant health (Schippers et al.,

1987) but can also leach nutrients from roots causing pathogenicity (Xin et al., 2018), have the ability to break down pollutants in freshwater habitats (Wasi et al., 2013), and are known to produce a diverse array of secondary metabolites in order to combat competition (Wiener, 2000).

Previous studies have shown that water-derived bacteria have the capacity to produce effective secondary metabolites including antimicrobials and antitumor compounds from marine and freshwater habitats all over the world. Freshwater Actinomycetes have been found to be a potential source of antimicrobial products (Gebreyohannes et al., 2013) Previous research also seen Lake Erie-derived Pseudomonas that are capable of inhibiting the growth of P. aeruginosa

(Chatterjee et al., 2017) Also both marine and freshwater Streptomyces have been shown to have high antimicrobial potential (Dalisay et al., 2013; Kharat et al., 2009). Thus, water-derived strains represent a source of effective compounds against pathogens.

Pseudomonas strains are observed to have a core and flexible genome. The flexible genome has been proposed to have evolved from horizontal gene transfer (HGT). (Wu et al.,

2011; Wiedenbeck & Cohan, 2011). This process has allowed the acquisition and transfer of 9 genetic material in a variety of environments allowing strains to persist under different conditions. Pseudomonas have also been found to have a core genome meaning all genes are shared among strains within this genus. With Pseudomonas genomes containing between 4,237-

6,396 protein coding genes, a core genome was discovered to be only 2,468 genes across 4 species of Pseudomonas, meaning only 40%-46% of the total genome of these isolates was found to be considered the core genome (Gross & Loper, 2009). With the flexible genome diversity comes the ability to produce many distinct compounds, including antibiotics that provide the elements needed for them to thrive in these different ecological habitats. With this in mind, this study isolate and identify environmental Pseudomonas strains that are producing novel antibiotics, capable of inhibiting the growth of multi-drug resistant pathogens isolated from cystic fibrosis patients. A workflow has been designed to identify these environmental

Pseudomonas capable of inhibiting MDR pathogens and the genetic components involved in this antagonistic activity can be located using transposon mutagenesis. 10

CHAPTER II. METHODS

2.1 Isolation of environmental strains

Environmental Pseudomonas were isolated in both freshwater and soil habitats using different methods. One soil sample was collected at Moses’ Gate Country Park, Bolton, UK

(53°33'19.2"N 2°23'23.7"W) collected on 1/4/18 and another was collected from the M60 motorway, Manchester, UK (53°25'48.2"N 2°17'58.3"W) collected on 8-Jan 2019 The freshwater samples were obtained from the Central Basin of Lake Erie from two different sites.

Plates LE23 and LE24 were collected from 41°44.890N 082°17.5’W and plate LE22 was collected from 41°47.9’N 082°17.5’W.

To isolate the Pseudomonas from the soil samples a serial dilution was performed. One gram of the collected soil was added to 7 ml of sterile 0.9% NaCl. The mixture was vortexed for

1 minute and the soil was left to settle. Once settled, 50 μl of solution was spread plated onto five cetrimide agar plates to select for Pseudomonas. Then 2 ml of the solution was placed into another 7 ml of sterile NaCl 0.9% and tis serial dilution was done with another two tubes adding

2ml of the previous solution to another 7 ml of sterile NaCl 0.9%. All dilutions were spread plated on cetrimide agar using a volume of 50 μl. Plates were then incubated at 30°C for 24 hours.

To isolate Pseudomonas from the Lake Erie samples a filtration system was used, consisting of a filter flask, vacuum hose and Supor 0.2 μm pore filter paper. The filter was first washed with sterile dH2O and 25 ml of the lake water was passed through the 0.22 μm filter and the filter was placed onto a cetrimide plate and were incubated at room temperature for 48 hours.

To isolate individual strains from the sample, subculturing was performed. This was achieved by picking isolated colonies from the cetrimide agar after incubation and were streaked 11 onto nutrient broth (NB) agar. Plates were incubated at 30°C for 48 hours. A second round of subculturing was done by streaking out the round one subculturing on new NB agar to ensure cultures were pure, incubating at 30°C for 48 hours. Isolated colonies were then picked and placed into 1.1 ml NB media within individual wells in a 96 deep well block and was incubated at room temperature for 48 hours at 250 rcf. In order to keep the strains for later use, frozen stocks were produced. Following incubation, 140 μl of each environmental strain was added to a

BD-Falcon 96 shallow well plate. Then, 60 μl of 70% glycerol was added and the solutions were mixed via gentle pipetting. Plates were then stored at -80°C. This process resulted in 288 Lake

Erie derived strains (plates LE22, LE23 & LE24), 192 British country park soil derived strains

(plates BS1 & BS2) and 192 British motorway strains (plates MW1 & MW2).

2.2 Multi-drug resistant CF-derived pathogens

Strains of Pseudomonas aeruginosa and Burkholderia spp. were obtained from the

University of Michigan from the Burkholderia cepacia Research Laboratory and Repository

(BcRLR). The strains were assigned an AU strain number (Table 6) and upon receiving them, were streaked out on NB agar media and grown at 37°C for 24 hours. NB media was then inoculated with the 28 MDR strains, grown at 37°C for 24 hours at 250 rcf. Frozen glycerol stocks were then produced using 800 μl of the culture and 200 μl of 70% glycerol. This was then placed in a -80°C freezer for later use. A total of 28 MDR pathogenic strains were cryopreserved.

2.3 Phylogenetic approach

In order to identify novel antimicrobial products, a population-level approach was used by comparing location, phylogenetic relationships, and antagonistic data. The merging of this data allowed for the elucidation of which strains are distinct and worthwhile pursuing for discovery of novel BGCs involved in antimicrobial production. Competition for nutrients is a 12 strong selective pressure. This leads to the evolution of many novel antimicrobial products that can be utilized in medicine (Hibbing M.E. et al., 2010).

To establish the phylogenetic relationship between the environmental Pseudomonas, amplification of the gyrB gene was used as a phylogenetic marker. The gyrB housekeeping gene encodes for the B subunit of DNA gyrase, essential for DNA replication (Watt & Hickson,

1994). This gene is a more diverse compared to the 16S rRNA gene which has been found to be similar among strains within a species (Ash et al., 1991). In addition, the gyrB gene has been shown to resolve different species (Yamamoto & Harayama, 1995, 1996). This phylogenetic analysis was conducted on all Lake Erie-derived strains and British country park soil. This analysis was not conducted with British motorway soil strains. First, gDNA was extracted. This was achieved by inoculating NB liquid medium with environmental strains in 96 deep well block that was then incubated at room temperature for 48 hours at 250 rcf. After 24 hours of incubation, 10 μl of Y-PER yeast protein extraction reagent was added to 10 μl of the culture.

This was then placed in a thermocycler using a program to lyse the cells (65°C for 30 seconds,

8°C for 30 seconds, 65°C for 90 seconds, 97°C for 180 seconds, 8°C for 60 seconds, 65°C for

180 seconds, 97°C for 60 seconds, 65°C for 60 seconds, 80°C for 60 seconds). The resulting block was then stored at -20°C for later use.

Once gDNA was isolated, PCR was performed using the 96 well PCR plate. The gyrB was sequenced using standard concentrations for a total reaction volume of 40 μl. The gyrB primers were used for targeted amplification (gyrB 271 forward 5′TCB GCR GCV GAR GTS

ATC ATG AC3′, and gyrB 1022 reverse 5′TTG TCY TTG GTC TGS GAG CTG AA3′) producing a product 751bp in length. The PCR plate was then placed into a thermocycler and specific reaction conditions were implemented to amplify the target gene (94°C for 5 minutes, 13

[92°C for 60 seconds, 65°C for 30 seconds, 72°C for 60 seconds] x30 cycles, 72°C for 5 minutes).

Once successful amplification was verified using gel electrophoresis, purification of PCR product was achieved. One hundred microliters of 75% cold (-20°C) isopropyl alcohol (IPA) was added to each well in the 96 well plate. Mixing was achieved via gentle pipetting and the product was incubated at room temperature for 30 minutes. The plate was then centrifuged at 2900 rcf for

30 minutes at 15°C and the IPA supernatant was removed by inverting on to paper towel. Then,

100 μl of 75% cold (-20°C) isopropyl alcohol (IPA) was then added to each well and mixed via gentle pipetting. The plate was then centrifuged at 2000 rcf for 10 minutes at 15°C and the IPA was removed by inverting on to a paper towel. A dry centrifuge spin was performed with the plate facing down to remove the remaining IPA at 700 rcf for 1 minute at room temperature. The plate was then left to dry at room temperature for 20-30 minutes and 20 μl of sterile miliQ water was added to each well to resuspend purified product.

The resulting purified DNA was sent to the University of Chicago Comprehensive

Cancer Center, DNA Sequencing and Genotyping Facility (UCCCC-DSF) along with the 217F primer to sequence. The sequences were then aligned using CLC Main Workbench with a sequence length of 673bp. A phylogenetic tree was produced and was uploaded to the Interactive

Tree of Life (iTOL) (Letunic I. & Bork P., 2016) to visualize the tree and the populations present.

2.4 Antagonistic assay

The environmental Pseudomonas strains were cultured in a 96 well block containing liquid 1.1 µL NB media. This was incubated for 48 hours at room temperature at 250 rcf. The 28

MDR pathogens include 14 P. aeruginosa strains and 14 Burkholderia (table 1) species all 14 derived from CF patient sputum samples collected at the University of Michigan. These 24 pathogens underwent a Minimum Inhibitory Concentration (MIC) test to determine their antibiotic resistance profile, all of which were considered to be multi-drug resistant, most are even resistant to some last resort antibiotics (Table 2 & 3). The four other P. aeruginosa pathogens underwent Kirby Bauer disk assay to determine drug resistance profiles (Table 4).

These pathogens were streaked out on NB agar and incubated for 48 hours at 37ºC. Liquid NB cultures with a volume of 3 ml were then inoculated with the pathogens and incubated at 37ºC for 24 hours at 250 rcf. Then, 50 µl of the pathogen culture was then spread plated onto a large

150 mm x 15 mm NB agar plates allowing the pathogen to dry. The environmental strains were then stamped onto the pathogen using a Boekel 140500 Microplate Replicator (Figure 2). The strains were incubated at 30ºC for 24 hours to allow for the environmental strains to grow. The

NB plates was then shifted to 37ºC for 24 hours to allow growth of the pathogen. After incubation, the plates were examined for zones of inhibition (ZOI) around the environmental strain colony shown to be ~1 mm around the environmental strain. This suggested the environmental strain has the ability to inhibit the growth of the pathogen. ZOI in question were verified using spot assay. Briefly, liquid overnight cultures were then prepared in liquid NB, incubating pathogenic strains at 37ºC and environmental at 30ºC, both for 24 hours. Then, 50 µl of the pathogen was spread plated on to NB media and 2 µl of the environmental strains was spotted. If a ZOI was present, it was accepted as a positive result.

2.5 Conjugation and transposon mutagenesis

2.5.1 Growth conditions for tri-parental mating

The E. coli strains were cultured in selective Lysogeny Broth (LB) liquid media. The

HB101 strain was inoculated in 5ml cultures of LB containing 4.4 µl chloramphenicol (30 15

μg/ml). The CC118 strain was inoculated in 5 ml LB containing 15 µl of kanamycin (50 μg/ml) and 25 µl of Ampicillin (150 μg/ml). These cultures were incubated at 37 ºC for 24 hours at 250 rcf. The environmental Pseudomonas were grown at 30 ºC for 24 hours at 250 rcf.

2.5.2 Conjugation and tri-parental mating

The conjugation process involves a tri-parental mating system. It utilizes an E. coli helper strain HB101 which contains the plasmid pRK600. This contains pir genes necessary for conjugation. The system also utilizes strain CC118 (Martínez-García et al., 2011), containing the pBAM1 vector which carries the mini-transposon with a kanamycin resistance gene. The other strain in the mating process is the antagonistic environmental strain. These strains were cultures as described above.

To begin, the cells were washed to remove any antibiotics present from the cultures. This was achieved by adding 500 μl of each strain to three 1.7ml Eppendorf tubes. All tubes were centrifuged at 12,000rcf for 3 minutes. The supernatant was then removed, and the pellet was resuspended in 1000 μl of 10mM MgSO4. The environmental Pseudomonas strain was then subjected to heat shock to increase conjugation efficiency. This was done using a water bath at

42ᵒC for 30 minutes. Following this, 100 μl of each strain E. coli and Pseudomonas strain was placed into a 1.7 μl Eppendorf tube and centrifuged at 12,000rcf The supernatant was then discarded and the pellet containing all three strains was resuspended in 10 μl of 10 mM MgSO4 and was spotted onto NB agar media. This was then incubated at 30ᵒC for 24 hours. After incubation, the spot was then scraped up using a bent sterile 20-200 μl pipette tip and was completely resuspended into 200 μl of 10 mM MgSO4. Following this, 50 μl of the suspension was then spread plated on to cetrimide agar containing 50 μg/ml kanamycin (C+K). To test for natural resistance to kanamycin, the wt environmental Pseudomonas alone was spread plated 16 onto C+K media. If growth was observed, then the strain was discontinued from the rest of the study.

2.5.3 Optimization for large scale mutant hunt

To identify LOI mutants, the protocol above must be optimized for each individual strain to upscale to a full mutant hunt. Two different parameters need to be optimized in order to perform a successful screen for LOI mutants. The average Pseudomonas genome is 6 Mb with a gene number of roughly 6,000 (Gross & Loper, 2009, Silby et al. 2011). To increase the chances of generating an LOI mutant, the goal is to achieve 10,000 mutants to roughly mutate every gene in the genome twice. In order to achieve this, dilutions are performed to culture 30-50 transconjugate colonies per plate with an assay using 200-300 C+K agar plates.

The other parameter that requires optimization is the need for a screen that allows for easy identification of LOI mutation. To screen for these particular mutants, all transconjugates need to be screened for a lack of zones phenotype. This is done by replica-plating the transconjugates from the C+K on to an NB agar plate spread with a pathogen that is known to be antagonized by the environmental strain. The zone of inhibition after replica plating should be around 1.5mm from the surface of the colony to prevent overcrowding of zones which can lead to many false negatives. The border should also be defined so LOI mutations can be easily observed as a more diffusive border can lead to high false positive identification. Zones can be affected by amount of pathogen spread and the size of the colonies. Incubation times and temperature also affect the outcome of the screen so care had to be taken at every step of the process to keep all parameters consistent when optimizing a strain. 17

2.5.4 LOI mutant screen

Once the Pseudomonas mutants have been selected for on C+K, a pathogen that the environmental wildtype (wt) strain has been shown to inhibit the growth of is used to identify mutants with a LOI phenotype. This is done by replica-plating the mutants onto a lawn of the sensitive pathogen. The replica-plating was done using a Bel-ArtTM replicator and 11 µm pore filter paper with a diameter of 150 mm. Fifty microliters of the sensitive strain was spread-plated onto NB plates allowing for the culture to dry. The replicator was then covered with a Kim wipe and the 11 µm pore filter paper. The transconjugates on the C+K plate were then directly stamped on to the filter ensuring most colonies from the plate have successfully been transferred to the filter. The NB plates containing the spread-plated sensitive pathogen was then stamped on top of the filter with the transconjugates to transfer the colonies onto the lawn of pathogen. These

NB plates were then incubated at 30°C for 24 hours and then at room temperature for 24 hours.

2.5.5 Verification of LOI mutants

When the potential LOI mutants were identified, they were streaked on C+K plates and incubated at 30°C for 24 hours. Liquid cultures of the potential mutants and wild type were produced using 3 ml of NB media and incubated at 30°C for 24 hours at 250 rcf. After incubation, 50 μl of sensitive pathogen was spread plated onto NB agar. Once dried, 2 μl of each mutant and wt were spotted onto the spreaded pathogen and the resulting plate was incubated at

30°C for 24 hours. The plates were then screened for confirmation of loss of killing activity in comparison to the wt. If LOI was observed, then the mutant was confirmed to have lost the ability to kill. Upon confirmation, frozen stocks of the LOI mutants were produced. 18

2.6 Large scale mutant hunt

The strains chosen for this part of the study were selected due to a number of different criteria that needed to be met. The strains below were all superkillers (SK), killing at least three

MDR pathogens. They were also capable and efficient at performing transposon mutagenesis

(Table 6). Also, a pathogen that allowed for easy identification of LOI mutants was found for all of these strains.

2.6.1 S5F11 mutant hunt

S5F11 is a soil strain isolated from Bowling Green, OH (41.370315, -83.649798). The strain was chosen to undergo large-scale transposon mutagenesis to identify genes involved in inhibition using the method described above (see 2.5.2). The conjugation spot was scraped up and placed in 200 μl of MgSO4. The resulting solution was diluted to 1:2200 and 50 μl was spread on 200 C+K agar plates. The plates were then incubated at 30°C for 24 hours then at room temperature for 24 hours. Then, 200 NB agar plates were spread plated with 50 μl of a 5 ml

NB liquid culture of pathogen AU33586 previously incubated at 37°C for 24 hours at 250 rcf.

The 200 C+K plates containing 30-40 transconjugates were replica-plated onto the NB plate containing the pathogen as described above (see 2.5.4). The NB agar plates were then incubated at 30°C for 24 hours and screened for LOI mutants. Potential mutants were then verified using protocol described above (see 2.5.5)

2.6.2 S3E7 mutant hunt

S3E7 is a soil strain isolated from Bowling Green, OH, 41° 22' 9.8652'' N, 83° 39'

5.8212. This strain was chosen and Tn mutagenesis was performed as described in 2.5.2. The resulting solution was diluted to 1:950 and 60 μl was spread on 300 C+K agar plates. The plates were then incubated at 30°C for 24 hours then at room temperature for 24 hours. Then, 300 NB 19 agar plates were spread plated with 50 μl of a 5 ml NB liquid culture of pathogen AU9276 previously incubated at 37°C for 24 hours at 250 rcf. The 300 C+K plates containing 30-40 transconjugates were replica-plated onto the NB plate containing the pathogen as described above (see 2.5.4). The NB agar plates were then incubated at 30°C for 24 hours and screened for

LOI mutants. Potential mutants were then verified using protocol described above (see 2.5.5)

2.6.3 LE22A5 mutant hunt

S3E7 was chosen and Tn mutagenesis was performed as described in 2.5.2. The resulting solution was diluted to 1:4000 and 35 μl was spread on 300 C+K agar plates. The plates were then incubated at 30°C for 24 hours then at room temperature for 24 hours. Then, 300 NB agar plates were spread plated with 60 μl of a 5 ml NB liquid culture of pathogen AU33586 previously incubated at 37°C for 24 hours at 250 rcf. The 300 C+K plates containing 30-40 transconjugates were replica-plated onto the NB plate containing the pathogen as described above (see 2.5.4). The NB agar plates were then incubated at 30°C for 24 hours and screened for

LOI mutants. Potential mutants were then verified using protocol described above (see 2.5.5)

2.6.4 LE22B6 mutant hunt

This strain was not completely optimized for a full scale mutant hunt due to time restraints. Although, the dilution was optimized to 1:100 requiring 55 μl be spread on C+K.

Many pathogens were tested including the ones found to be inhibited in the antagonistic assay.

This strain can be considered a superkiller, found in population 5 and was capable of inhibiting seven of the MDR pathogens, (Table 1). Unfortunately, the pathogens tested did not work well enough to screen for LOI mutations. Therefore, more work needs to be done to fully optimize this strain for a large scale mutant hunt. 20

2.7 S3E7 increased inhibition mutant

During the S3E7 mutant hunt screening process, transconjugates were found to have a bigger zone than the wt strain. These mutants were streaked out from the C+K plate onto a new

C+K plate and incubated at 30°C for 24 hours along with the wt strain. After incubation, liquid cultures were produced in 3 ml of NB and were incubated at 30°C for 24 hours. Frozen stocks were produced using the same protocol discussed earlier (2.1). A spot test was conducted to verify the gain of inhibition phenotype. This was done by spreading 50 μl of AU9276 onto NB agar plates and 3 μl of the potential mutants were spot on top of the pathogen. Also, 3 μl of the wt S3E7 strain was spotted on each plate as a control spot. The plates were then incubated at

30°C for 24 hours and the plates were screened.

2.8 Transposon insertion site identification

2.8.1 Genomic DNA (gDNA) isolation

The gDNA from LOI, big zone (BZ) mutants and wt strain was extracted so alignment of

Tn insertion site and wt genome can be performed to find genes associated with these phenotypes. This was done using Wizard Genomic DNA Purification Kit (Promega) following the gram-negative gDNA isolation protocol. Firstly, wt and mutant strains were cultured in NB media for 24 hours at 30°C. One ml of culture was then placed in an Eppendorf tube and centrifuged for 2 minutes at 12,000 rcf to pellet cells. The supernatant was then discarded and

600 μl of Nuclei Lysis Solution was added and the pellet was resuspended by pipet. The solution was then incubated at 80°C for 5 minutes and then cooled to room temperature. After this, 3 μl of

RNase Solution was added a mix via gentle inversion and incubated at 37°C for 45 minutes.

After cooling to room temperature, 200 μl of Protein Precipitation Solution was added and the solution was vortexed for 20 seconds. Then, the mixture was then incubated on ice for 5 minutes 21 and centrifuged at 12,000 rcf for 3 minutes. Six hundred ųl of the supernatant was then transferred to 600 μl of room temperature isopropyl and was mixed via inversion and centrifuged at 12,000rcf for 2 minutes. The supernatant was decanted and 600 μl of room temperature 70% ethanol was added and mixed via inversion. The resulting mixture was then centrifuged at

12,000rcf for 2 minutes and the ethanol was then aspirated and the pellet was air-dried for 10-15 minutes. The DNA was then rehydrated overnight using 100 μl of Rehydration Solution at 4°C.

This resulting solution was sent to the University of Delaware DNA Sequencing & Genotyping

Center to perform PacBio sequencing The genomic sequences were then ran through a program named antiSMASH which enables identification of secondary metabolite producing BGC within the genome (Medema et al., 2011). The sequence was also sent to the Joint Genome Institute

(JGI) for the entire genome to be annotated. Although, strain LE22A5 could not be sent to JGI before the end of the study. Therefore, antiSMASH was used to identify any BGC’s within the gDNA sequence.

2.8.2 AP-PCR

After extraction of gDNA from the mutants, a semi-nested arbitrarily primed PCR (Das et al., 2005) was performed. This allows for the flanking regions of the Tn insertions to be amplified enabling us to localize the mutated region of the wt genome. AP-PCR I uses 2 μl of diluted gDNA (1:100) as a template and primers 5 μm ARB6 (5’GGC ACG CGT CGA CTA

GTA CNN NNN NNN NNA CGC C3’) with 5 μm ME-O-extF (5’CGG TTT ACA AGC ATA

ACT AGT GCG GC3’) or 5 μm ME-I- extR (5’CTC GTT TCA CGC TGA ATA TGG CTC3’) were used. Standard concentrations were used with a total reaction volume of 40 μl. Reactions were then placed in a thermocycler using a specific cycle program (95°C for 5 minutes, [95°C 22 for 30 seconds, 30°C for 30 seconds, 72°C for 90 seconds] x6 cycles, [ 95°C for 30 seconds,

45°C for 30 seconds, 72°C for 90 seconds] x30 cycles, 72°C for 4 minutes).

AP-PCR II was then be performed using 2 μl of the round one product as a template and primers 5 μm ARB2 (5’GGC ACG CGT CGA CTA GTA C3’) with either 5 μm ME-I- intR

(5’CAG TTT TAT TGT TCA TGA TGA TAT A3’) or 5 μm ME-O intF (5’AGA GGA TCC

CCG GGT ACC GAG CTC G3’). Standard concentrations were used for a total reaction volume of 40 μl. Reactions were then placed in a thermocycler and a specific cycle program was used

(95°C for 60 seconds, [95°C for 30 seconds, 52°C for 30 seconds, 72°C for 90 seconds] x30 cycles, 72°C for 4 minutes).

Purification of amplicon was performed using two different methods the first method was done using the available protocols for the NucleoSpin Gel and PCR clean-up kit (Macherey-

Nagel). The other method of purification was by using ExoSAP-IT Express PCR product clean up using available protocol online (Thermo Fisher Scientific. Applied Biosystems). The purified

DNA was then sent to the University of Chicago Comprehensive Cancer Center, DNA

Sequencing and Genotyping Facility (UCCCC-DSF) where Sanger sequencing was performed.

Primers 5 μm ME-I intR or 5 μm ME-O intF were used for this process. Once sequences were received, the amplicon was aligned with the wt genome using NCBI BLAST and the annotated genome was produced by JGI allowing for predicted gene products to be determined at Tn insertion site. Although, strain LE22A5 was not annotated by JGI so the gene insertions were determined by BLASTX to determine the gene product prediction. 23

CHAPTER III. RESULTS

3.1 Population-level approach to discover antibiotics

3.1.1 Phylogenetic analysis and antagonistic activity

The gyrB gene was amplified and sequenced, and 245 out of the total 276 Lake Erie- derived Pseudomonas and two Pseudomonas soil strains (S5F11 & S3E7) were aligned and edited using CLC Main Workbench. A phylogenetic tree was produced and iTOL (Letunic I. &

Bork P., 2016) was used to visualize the tree and overlay with antagonistic data (Figure 1). Nine clades within the tree were shaded to show closely related groups that are defined as populations.

Strains from the British country park soil were also initially included in the analysis but were found to be clonal so were discontinued from the study.

All 278 environmental strains were competed against 28 of MDR pathogens. Minimal inhibitory concentrations (MIC) of 24 of the pathogens were determine by Dr. John LiPuma and results show most strains exhibit a MDR profile (Tables 2-3). The remaining pathogens

(AU12176, AU14282, AU16821, AU17108, AU17787, & AU18005) were tested for resistance using the Kirby Bauer disk assay (Table 4). A total of 7,784 interactions between environmental strains and MDR pathogens. Out of these interactions 210 antagonistic events occurred resulting in only 2.6% of these interaction being inhibitory against MDR pathogens (Figure 3).

Antagonistic data was then overlaid on top of the tree to show how antagonistic these populations are in respect to one another (Figure 1 & Table 1). These interactions were not uniformly spread across all environmental strains. A total of 194 strains did not produce any antagonistic activity against the MDR pathogens. Some strains showed high rates of antagonistic activity and strains that had the ability to inhibit the growth of ≥3 MDR pathogens were considered super-killers (SK). There was a total of 26 SKs identified within the environmental 24 strains (Table 6). These antagonistic strains represent candidates that produce effective antibiotics against MDR pathogens. Another way to view this data is to observe which MDR resistant pathogens were more susceptible to the environmental isolates (Table 5 and Figure 3).

The Burkholderia strain AU31398 was shown to be the most susceptible to the environmental strains, being inhibited by 27 of the 276 isolates. The Burkholderia strains were to be inhibited more than the P. aeruginosa strain with the Burkholderia being inhibited by 111 environmental strains and the P. aeruginosa pathogens by just 70 of the isolates.

When the antagonistic data was overlaid onto the phylogenetic tree some populations were more antagonizing than others (Figure 1). For example, population 3 had a total of 10 strains, eight of which were capable of antagonizing ≥1 MDR pathogen and four of these strains were considered SKs. Population 8 contained 23 antagonistic strains with 10 of these being SKs.

SKs within this population represent 38.4% of all the super-killer identified. These SK strains were carried forward to the next step to identify strains capable of undergoing transposon mutagenesis.

3.2 Ability of strains to undergo Tn mutagenesis

In order to identify BGCs whose products inhibit MDR pathogens, we screened for antagonistic strains that were able to undergo conjugation and transposon mutagenesis (Figure

5A and B). Particularly, we focused on SKs that were able to inhibit the growth of ≥3 MDR pathogens (Table 6). Only SKs that were efficient at these processes were pursued for genetic manipulation. Efficient strains were identified by the presence of 1000s of transconjugates when selecting on C+K agar plate medium (Figure 5B). Out of the 24 SK strains, only six were capable of Tn mutagenesis, one having low conjugation efficiency so that strains was discontinued (Table 6). Thirteen of the strains were not capable of conjugation and five strains 25 were found to be naturally resistant to kanamycin making it difficult to decipher mutants from wt on C+K agar medium.

3.3 Large scale mutant hunt

3.3.1 S5F11 mutant hunt

S5F11 was shown to have the ability of inhibiting the growth of MDR CF-derived pathogens before the start of this study (Basalla, 2018). Based on those results, we tested this strain using the antagonistic assay (Figure 2) against our panel of MDR pathogens (Table 5) to see how efficient this strain was at inhibiting multiple MDR isolates. S5F11 was capable of inhibiting the growth of 16 out of the total 28 MDR pathogens (Table 11) and can be found in population 6 of the phylogenetic tree (Figure 1). Genomic DNA was isolated and sequenced and then annotation was performed using JGI IMG (Chen et al. 2017) and NCBI. The genome statistics revealed that 97.18% of the genome was protein coding and seven BGC were identified

(Table 7) along with their predicted products (Table 8).

The large-scale mutant hunt produced four confirmed LOI mutants; S5F11-85, S5F11-

190, S5F11-145 and S5F11-142 (Table 9). AP-PCR was performed to amplify the flanking region of the transposon, enabling the identification of the Tn insertion site. After sequencing the amplicon, it was aligned with the annotated wt genome and the insertion sites were determined.

Of the four confirmed LOI mutants, one mutation (S5F11-142) (Table 8) was found to occur in one of the seven BGC predicted by JGI. This BGC was predicted to produce an NRPS complex which could potentially be the product of an antimicrobial product. The other four mutations were found to be randomly distributed throughout the S5F11 mutant (Table 9). The S5F11-85 insertion was located within a sec-independent protein translocase protein TatB. The S5F11-190 insertion was located within an htpX heat shock protein metallopeptidase MEROPS family 26

M48B gene. The S5F11-145 insertion was located within a sulfite reductase (NADPH) hemoprotein beta-component gene (Table 9).

Using the Prediction Informatics for Secondary Metabolomes PRISM (Skinnider et al., 2017), the NRPS was predicted to encode a compound with one of two structures, one cyclic, one linear

(Figure 7B and 7C). The domains within the NRPS modules were also identified, and consists of four modules that add amino acids derivatives including hydroxybutyrate, glutamine, hydroxyornithine and diamino-butyric acid. The fourth domain involved in isomerization of the molecule (Figure 7A).

3.3.2 S3E7 mutant hunt

This strain again was previously found to inhibit the growth of CF-derived pathogens

(Basalla, 2018). Antagonistic assays (Figure 2) were performed to see how many of the 28 MDR pathogens (Table 5) were shown to be inhibited by the strain. S3E7 was capable of inhibiting the growth of 15 out of the total 28 MDR pathogens (Table 11) and grouped to population 1 (Table

1). The genome statistics uncovered by the annotation revealed that 90.24% of the genome was protein coding and three BGC were identified (Table 7) and predicted to encode products listed in Table 8.

The large scale mutant hunt produced eight verified LOI mutants; S3E7-111, S3E7-223,

S3E7-176, S3E7-106, S3E7-129, S3E7-1 and S3E7-28, none of which inserted into any of the three BGC predicted by JGI. Although, three insertions (S3E7-223, S3E7-176 & S3E7-106) were found to be located within a single gene encoding for a thioredoxin reductase which is involved in the catalysis of many redox reaction (Arnér & Holmgren, 2001). The S3E7-28 insertion was characterized as a TruD family tRNA pseudouridine synthase gene which are involved in modifying uridine into pseudouridine used in tRNA (Hamma & Ferré-D'Amaré, 27

2006) The S3E7-1 insertion was an alginate O-acetyltransferase complex protein AlgI which is involved in acetylation of alginate. S3E7-111 was identified as a poly(beta-D-mannuronate) C5 epimerase only 5,514bp upstream from the S3E7-1 insertion. The S3E7-129 insertion was identified as a glucose-1-phosphate thymidylyltransferase which catalyzes the formation of dTDP-glucose, from dTTP (Blankenfeldt et al., 2000).

3.3.3 LE22A5 mutant hunt

The Antagonistic assays (Figure 2) were performed to see how many of the 28 MDR pathogens (Table 5) were shown to be inhibited by the strain. LE22A5 was capable of inhibiting the growth of 3 out of the total 28 MDR pathogens (Table 11) and was found in population 8

(Table 1). Due to the LE22A5 genome sequence being returned later than planned, it could not be sent to JGI for annotation. Instead, antiSMASH was used to identify BGC’s within the genome. The AP-PCR sequences of the mutants was aligned with the wild-type genome to determine location of the Tn insertion and the sequences were put through BLASTX to determine the product of the gene that was mutated. A total of eight LOI mutants were verified;

LE22A5-218, LE22A5-270, LE22A5-92, LE22A5-9, LE22A5-44, LE22A5-30, LE22A5-58,

LE22A5-220. LE22A5-218 (Table 9). One Tn insertion was found to be located within a BGC predicted by antiSMASH to produce an NRPS (Table 8). Due to LE22A5 and S5F11 being in close proximity on the phylogenetic tree, the genomes were aligned through BLASTn to assess similarity. The genomes are shown to have a 99.22% identity score with a 94% query coverage.

The open reading frames of the BGC’s were also aligned using BLASTn to determine if they were identical. The identity score was found to be 99.35% with a query coverage of 100%. Both sequences of the BGC were then put through PRISM to observe how similar the NRPS domains 28 were and to see if the product structures were the same. The results show that the NRPS system and the final structure are slightly different (Figure 8).

The other Tn insertions were found to be located in genes outside of BGC. LE22A5-270 was found to be located within a cysteine synthase CysM gene. LE22A5-92 and LE22A5-9 were found to have the Tn insertion within the same gene. This gene was predicted to be involved in the tol-pal system named the YbgF gene. LE22A5-44 is found to have the insertion within a

PilZ-containing protein. Another mutation not found in a BGC was in LE22A5-30. This strain had the insertion within a gene encoding a bacterioferritin. Mutant LE22A4-58 was found to have an insertion within an ATP-binding cassette domain-containing protein. Finally, mutant

LE22A5-220 was found to have an insertion within a sulfite reductase ferredoxin.

3.4 S3E7 increased inhibition mutant

Isolation of six potential BZ mutants (Table 10) was performed and a spot test was conducted (see 2.7). After incubation, the five out of the six potentials produced a bigger zones found to be bigger in diameter than the wt strain (Figure 9). This suggests that these mutations were causing an increase in antagonistic activity of the environmental strain against the pathogen. Genetic analysis was conducted to locate the gene mutated causing this phenotype.

Out of these strains, only three AP-PCR sequences were successfully aligned to the wt genome.

The AP-PCR sequence of two BZ mutants did not significantly align to the genome when using

BLAST. The three strains that did, aligned to two different genes. Mutants S3E7-157BZ and

S3E7-263BZ had insertions in the same gene which was predicted by JGI to be a methyl- accepting chemotaxis sensory transducer with Cache sensor gene. The other mutant S3E7 was found to have an insertion in a glutathione S-transferase gene (Table 9). 29

CHAPTER IV. DISCUSSION

4.1 Population-level and antagonistic analysis of environmental Pseudomonas

Previous studies have shown that environmental Pseudomonas strains isolated from the

Northwest region of Ohio have had the capacity to inhibit the growth of CF-derived P. aeruginosa (Davis et al., 2017; Chatterjee et al., 2017). This study has furthered this research by showing that environmental Pseudomonas isolated from Lake Erie can also inhibit MDR P. aeruginosa and Bcc species (Table 5). This work suggests that there are still antimicrobial compounds in the environment produced by these isolates, potent enough to inhibit some of these problematic MDR pathogens. When looking at the antagonistic activity against these pathogens, environmental strains were capable of inhibiting the Bcc pathogens (111 antagonistic events) more than the P. aeruginosa strains (70 antagonistic events; Table 5). This might be expected since P. aeruginosa being is notorious for their ability to resist antibiotics (Okamoto et al., 2001; Murray et al., 2015). When comparing the phylogenetic analysis to the antagonistic events, some populations were shown to be more antagonistic than others with population 8 being the most actively inhibitory (Figure 1). This suggests further that phylogenetic relationship based on the gyrB gene is a useful tool for phylogenetic analysis when comparing with inhibitory activity as shown previously (Chatterjee et al., 2017).

Gram-negative pathogens are difficult to treat due to the presence of an outer membrane that is absent in Gram-positive pathogens. This outer membrane is an antibiotic resistance mechanism within itself (Ghai & Ghai, 2018). Additionally, P. aeruginosa acquires nutrients through dedicated specific porins in the outer membrane. Due to this specificity, antibiotics have a tough time entering the cell (Nikaido, 2003). These mechanisms have led to scarce options when trying to combat these infections in the hospital (Thiolas et al., 2004). Furthermore, during 30 the early 20th century when antibiotic resistance was observed, more research was conducted to find alternatives for Gram-positive pathogens. This has contributed to the issues we have today with the high prevalence of MDR Gram-negative infections and a lack of antibiotic treatments available. Therefore, any novel antimicrobial capable of inhibiting the growth of gram-negative pathogens is of most importance (Martinez-Martinez & Calvo, 2010).

The fact that these Lake Erie-derived Pseudomonas have the ability to inhibit problematic MDR pathogens allows us to assume that there are novel antibiotics in the environment that are yet to be elucidated discovered. The workflow used in this study is a useful strategy to locate environmental Pseudomonas that produce antagonistic compounds, capable of inhibiting the growth of MDR pathogens. Once identified the identification of BGCs involved in antibiotic production can be achieved. This will allow for isolation of the BGC products leading to research that will access the therapeutic capabilities of these compounds. Additionally, discovery of genes outside of BGC’s involved in antibiotic production will aid in the understanding of how these molecules are produced.

4.2 Biosynthetic gene cluster analysis

4.2.1 S5F11 BGC

Out of the four LOI mutants isolated and identified, one of these mutants was showed to have a Tn insertion within a BGC (Table 8). BGC are involved in the production of many secondary metabolites including antibiotics that are being used today. With cloning and heterologous gene expression of BGC, antibiotics can be produced at high quantities, isolated and utilized as novel therapies for bacterial infection. The BGC localized in strain S5F11 was shown to produce a non-ribosomal peptide synthetase (NRPS; Figure 7).

31

NRPS systems produce small peptides that can be utilized for many different functions without the use of the standard translational machinery. They are composed of modules that are involved in adding both traditional amino acids as well as non-proteinogenic amino acids to a growing peptide chain, producing these small peptides involved the secondary metabolism.

Within these modules, domains are involved in the addition of these amino acids. The adenylation (A) domain is used to load a specific amino acid to the NRPS module via the peptidyl carrier protein (PCP) domain. The PCP domain is covalently bonded to the amino acid associated with the A domain. Another domain named the condensation (C) domain is used to catalyze the peptide bond between the amino acid on the PCP in adjacent modules allowing for an assembly line-like process. The peptide chain then reaches a thioesterase domain which is involved in hydrolysis or cyclization of the final product, releasing it from the final PCP domain.

Many antibiotics are developed using this method of protein synthesis (Strieker et al., 2010;

Fischbach & Walsh, 2006).

4.2.2 LE22A5 BGC

Due to LE22A5 and S5F11 being very closely related on the phylogenetic tree (Figure 1), an alignment was conducted using BLASTn to identify the similarities between the two BGC that were shown to be associated with antagonistic activity. The result show that these two gene clusters are 99.32% identical. This suggests that these BGC’s produce the same product although the BGC found in LE22A5 has one more gene within the cluster and is 1,000bp smaller than the

BGC found in S5F11(Table 8).

When the nucleotide sequences of the NRPS were put through PRISM (Skinnider et al.,

2017), the NRPS was predicted to be different than the NRPS system found in S5F11. In the

S5F11 NRPS, the A domain in the third module adds a hydroxyornithine amino acid to the

32 growing chain. Conversely, the LE22A5 NRPS A domain in the third module adds an arginine to the chain. This leads to the production of two different structures (Figure 8). S5F11 was found to inhibit different pathogens compared to LE22A5 (Table 11). This can be attributed to the fact that the NRPS system is slightly different. The replacement of arginine within the molecule produced by LE22A5 may have contributed to this lack of inhibition when compared to S5F11.

This result shows that even a slight different to an antagonistic molecule can affect its ability to inhibit different pathogens.

4.3 Non-BGC insertions

4.3.1 S5F11 non-BGC mutants

Although identification of BGC’s was the main goal of this study, genes not associated with these were found to be involved in the production of antimicrobial compounds. These are still of interest as they help elucidate what genes are involved in the whole process of antibiotic production within environmental bacteria. Three mutant strains of S5F11 had LOI mutations that were not identified to be located in BGC’s suggesting the involvement of these genes in the inhibition of MDR pathogens.

One mutant had an insertion within a gene predicted to produce a heat shock protein belonging to the MEROPS M48B family of metallopeptidase proteins (htpX) (S5F11-190; Table

9). Metallopeptidase is an enzyme that catalyze the hydrolysis of peptide bonds aided by metallic ions (Cerdà-Costa et al., 2014). Previous studies have shown that metallopeptidase can be involved in antagonistic activity in the form of lysostaphin (Tossavainen et al., 2018).

Lysostaphin is a bacteriocin that has a narrow host range, capable of inhibiting Staphylococcus pathogens (Bastos, 2010). This suggests that the gene may encode antimicrobial properties itself.

Even though this particular metallopeptidase htpX is a predicted to be a membrane bound

33 protease found to be involved degradation of misfolded proteins (Akiyama, 2009; Lin et al.,

2012), the LOI mutant phenotype suggests it is involved in antagonistic activity. Literature states that single knockout mutants of htpX gene affects aminoglycosides resistance in

Stenotrophomonas maltophilia, suggesting that this gene is somehow involved in intrinsic resistance to antibiotics (Huang et al., 2018). However, the function of this gene in strain S5F11 and its involvement in antibiotic production remains unknown. Further research needs to be conducted in order to fully understand the role of this gene in antibiotic production.

Another S5F11 non-BGC mutation was located in a gene predicted to produce sulfite reductase (NADPH) hemoprotein beta-component (S5F11-145; Table 9). This protein is involved in the reduction of sulfite to sulfide which is important for the biosynthesis of L- cysteine in E.coli, a Sulphur containing amino acid (Siegel & Davis, 1974). A biosynthetic gene cluster involved in production of an NRPS can be seen 86kb upstream from the Tn insertion.

Also, microcin C transport genes are observed to be located 19kb upstream from the insertion suggesting that the mutation could have influenced transport of an antimicrobial compound, potentially a microcin C. Microcin C is an inhibitor of aspartyl tRNA synthetases and uses a

‘Trojan horse-like’ mechanisms that allows the compound to enter the cell (Severinov & Nair,

2012). Other genes surrounding the insertion seem to be involved in methionine production which in previous studies has shown to induce production of cephalosporin by donating sulfate to the developing antibiotic (Drew & Demain., 1975). This may be why a Tn insertion within this area of the genome has caused the LOI phenotype.

The third non-BGC LOI mutation of the S5F11 strain was found to be located in a gene predicted to be involved in production of sec-independent protein translocase protein TatB

(S5F11-85; Table 9). This protein which is involved in the transport of large folded proteins

34 containing a twin arginine motif across the membrane. Interestingly, this gene is directly upstream from what looks like an operon that is involved in ubiquinone biosynthesis.

Ubiquinone is a membrane bound coenzyme that is involved in respiration in the electron transport chain. Although, research (Aussel et al., 2014) has also shown that ubiquinone is also involved in creating a proton motive force (PMF) enabling antibiotics to be exported outside the cell, inducing antibiotic resistance. This suggests that this particular region of the genome may be involved in export of the inhibitory compound by providing a PMF. Therefore, mutations within this region may prevent generation of this PMF ultimately preventing export of the antibiotic which has resulted in the LOI mutation observed.

4.3.2 S3E7 non-BGC mutants

Although S3E7 had three biosynthetic gene clusters (Table 8), all identified LOI mutations identified in strain S3E7 were not found to be located in these regions. One mutation was found to be located in a glucose-1-phosphate thymidylyltransferase (Table 9). This gene catalyzes the formation of dTDP-glucose (Zuccotti, et al., 2001) which is involved in biosynthesis of antibiotics. Other research has shown that this gene is involved in the production of oleandomycin, a macrolide antibiotic synthesized by Streptomyces antibioticus

(Aguirrezabalaga et al., 2000). This gene is also seen to be involved in the production of erythromycin C in E. coli (Peiru et al., 2005) and novobiocin in Streptomyces spheroides

(Steffensky et al., 2000). When using JGI IMG, general antibiotic biosynthesis and in particular streptomycin biosynthesis, were both predicted KEGG pathways that were associated with this gene. This suggests that this gene is involved in the production of the antimicrobial capable of inhibiting the growth of many MDR pathogens.

35

In three other LOI mutants, the Tn inserted in a gene predicted to encode a thioredoxin reductase gene (Table 9). Thioredoxin reductase is involved in the reducing disulphide bonds within protein that reside intracellularly using NADPH as an electron donor. This helps stabilize the proteins and prevents oxidation and formation of disulphide bonds by reactive oxygen species within the cell. Previous literature suggests mutations within thioredoxin system genes cause formation of disulphide bonds within proteins. This can change biological function of these proteins affecting cell physiology (Stewart et al., 1998). Thioredoxin reductase has also been shown to be involved in the biosynthesis of beta-lactam antibiotics. The reductase enzyme is involved in reducing disulphide bonds in a precursor in the biosynthesis of penicillin and cephalosporin, enabling production of these antibiotics. Without this reduction, the precursors can’t bind to biosynthetic enzymes, prevent production of antibiotics (Aharonowitz et al., 1993).

This may explain our results. The reason for the LOI mutation may be due to the lack of reduction of disulphide bonds within proteins involved in biosynthesis of the antibiotic capable of inhibiting the growth of the MDR pathogens.

Another LOI mutant had a Tn insertion in a gene producing a TruD family tRNA pseudouridine synthase (Table 9). This gene is normally involved in post-transcriptional modification of tRNA’s, converting uridine to pseudouridine (Kaya & Ofengand, 2003).

Although, research in 2018 has shown that a gene closely related to TruD was found to be involved in biosynthesis of a C-nucleoside antibiotic named pseudouridimycin (PUM).

Pseudouridine is converted into intermediates in the biosynthetic pathway of PUM and TruD-like protein is involved in isomerization of N- to C-nucleoside-like intermediates enabling synthesis of this novel antibiotic (Sosio et al., 2018). Although the nearest BGC (NRPS) is 417 kb away

36 from this gene, it may be involved in the production of an antibiotic similar to the pathway suggested in the Sosio et al. (2018) paper.

The final LOI mutations were found in very close proximity to one another. One mutation was found in the algG gene that encodes a poly(beta-D-mannuronate) C5 epimerase

(Douthit et al., 2005) and the other found in an alginate O-acetyltransferase complex protein algI gene (Table 9). Both of these genes seemed to be involved in the biosynthesis of alginate and three other genes involved in this biosynthetic pathway are adjacent to these two genes suggesting a BGC for alginate synthesis. Mutations in the algI gene have been shown to produce a thinner biofilm which leads to difficulties forming microcolonies which aids in survivability of the organisms in harsh environments (Tielen et al., 2005) The algG gene is involved in isomerization of alginate and mutations in this gene caused a defect in alginate production

(Morea et al., 2001). No literature can be found on alginate production affecting antibiotic activity. This may be due to research focusing more on the effect of alginate production enabling antibiotic resistance. More research is needed to elucidate why and how alginate production can affect an antibiotics killing capabilities.

4.3.3 LE22A5 non-BGC mutants

The other Tn insertions were found to be located in genes outside of BGC. LE22A5-270 was found to be located within a cysteine synthase CysM gene. Which is an enzyme involved in cysteine synthesis (Vermeij & Kertesz, 1999) This insertion may have reduced the amount of available cysteine within the cell which could lead to the genes associated with secondary metabolism to be repressed. This could explain why an insertion within this gene caused the LOI phenotype. Cysteine is require for many antimicrobial products (Couto et al., 1992; Huber et al.,

2018). What’s also interesting is the insertion within mutant LE22A5-220 was found to have an

37 insertion within a sulfite reductase ferredoxin. This enzyme is involved in sulfur assimilation converting sulfite to sulfide which is important for the production of cysteine by utilizing iron which can be used to produce proteins (Rossi et al., 2014). This suggests that cysteine production is essential for the production of this antimicrobial. Two separate mutations within genes coding for enzymes involved in the production of cysteine both caused a disruption in antagonistic activity.

LE22A5-44 is found to have the insertion within a PilZ-containing protein. Proteins containing the PilZ domain are normally receptors for the second messenger c-di-GMP

(Ryjenkov et al., 2006). This secondary messenger has been observed to be involved in alginate synthesis as well as motility control. Previous research has also shown that this signaling pathway has been directly linked to antibiotic production. Over expression of the cdgA gene, a cyclic-di-GMP diguanylate cyclase, caused a reduction in actinorhodin production within

Streptomyces coelicolor (Tran et al., 2011). This suggests that c-di-GMP could be involved in the production of the antagonistic product found in LE22A5.

Another mutation not found in a BGC was in LE22A5-30. This strain had the insertion within a gene encoding a bacterioferritin. These proteins are involved in the storage of iron within the cell by converting the Fe2+ ion to Fe3+ (Rivera, 2017). The reason why a mutation in this gene caused a LOI phenotype maybe because the antagonistic compound may be involved in iron sequestration in the form of a siderophores. As mention previously, siderophores are compounds used to sequester iron from the environment which can lead to antagonist activity by limiting availability of iron for other organisms (Tyc et al., 2017). The inability of iron to be stored due to a mutation within the bacterioferritin gene may have produced a repressive effect leading to the gene for the siderophores to be repressed. Siderophores are regulated by fur. When

38 fur is bound by Fe2+, the genes that produce siderophores are repressed (Troxell, B., & Hassan,

2013). If bacterioferritin is not present to convert Fe2+ to Fe3+, a buildup of Fe2+, will not only be toxic to the cell (Rivera, 2017) but would also lead to the repression of siderophore production

(Troxell, B., & Hassan, 2013).

Another reason why this mutation has caused an LOI mutation may be due to a lack of iron within the cell. Many enzymes used to biosynthesize secondary metabolites require iron as a cofactor to be functional. Without this iron sequestration due to a lack of bacterioferritin, the antagonistic secondary metabolite may not have been produced (Weinberg, 1990; Jarrett, 2015).

The iron that is sequestered would be used for primary metabolism. The antagonistic compound could also be a sideromycin. These compounds compose of a siderophore attached to an antibiotic. When a competing organism takes up this sideromycin, the antibiotic kills its host

(Braun et al., 2009).

Another reason why this mutation could have led to an LOI mutation may be due to iron conversion from Fe2+ to Fe3+ performed by this ferredoxin may have been required for sulfur assimilation. Mutant LE22A5-220 had an insertion in an iron dependent sulfite reductase which are involved in sulfur assimilation as discussed above leading to cysteine synthesis. This and the mutation found in the CysM gene in mutants LE22A5-270 (Table 9) suggests the heavy involvement of cysteine in the production of this antagonistic compound.

LE22A5-92 and LE22A5-9 were found to have the Tn insertion within the same gene.

This gene was predicted to be involved in the tol-pal system named the YbgF gene. The tol-pal system is found to be involved in maintaining integrity of the outer membrane and is involved in import of antimicrobial compound colicin and bacteriophage DNA (Walburger et al., 2002). The protein encoded by this gene is named cpoB and its function is involved in the coordination of

39 peptidoglycan synthesis and outer membrane constriction during cell division. Not a lot of information can be found on the tol/pal system in regards to antibiotic production.

Mutant LE22A4-58 was found to have an insertion within an ATP-binding cassette domain-containing protein. These proteins use ATP for energy to provide a certain function. The most discussed are the ABC transporters which utilize ATP to actively pump out toxin and pump in nutrients (Davidson et al., 2008). This ABC transporter has been found to be involved in export of antibiotics. Both as a resistance mechanism (Greene et al., 2018) and as a transport system for antibiotics produced by the cell (Mendez & Salas, 2001). This may be the cause of the

LOI phenotype observed when this gene was mutated. Export of the antibiotic compound could not have been achieved due to a lack of active transporters within the cell membrane.

4.4 S3E7 increased inhibition mutations

The two AP-PCR sequences from S3E7-37BZ and S3E7-77BZ (Table 10) that did not aligned with the wt genome may have been contamination. This would explain the discrepancy in the antagonistic phenotype observed in the mutant hunt and spot test. Two of these BZ mutants were found to have insertion in the same gene described by JGI as a methyl-accepting chemotaxis sensory transducer with Cache sensor (Table 10). This gene is involved in production of sensory membrane proteins that are involved in chemotaxis. Chemotaxis is a mechanism that allows the bacteria to sense its environment enabling the bacterium to avoid unsafe environments containing toxins such as antibiotics and to swim toward more favorable conditions such as nutrient-rich areas using concentration gradients of these compounds within the environment

(Sourjik & Wingreen 2012). The reason for the increased antagonistic activity may be due to the lack of chemotaxis leading to a misinterpretation of the environment that the microbe is in. This may lead to the an increase in production of antimicrobial products leading to the phenotype

40 visualized when an antagonistic event occurs such as the one conducted in the mutant hunt and spot test.

The other mutant S3E7-119BZ was found to have an insertion in a glutathione S- transferase (GST) gene (Table 10). These enzymes are normally involved in detoxification of xenobiotics and are primarily found in the periplasm of the organism (Allocati et al., 2008).

GST’s have also been found to detoxify antibiotics within the cell (Arca et al., 1988; Park,

2012). This may hint at a possible role for this gene within the wt strain. During export of the antibiotics, GST proteins may prevent export of antibiotics produced by the bacterium itself as a byproduct of the defense system that is primarily used to break down antibiotics produced by other organisms in the environment. It could be possible that the reason for this gain of killing phenotype within this particular mutant may be due to a lack of GST concentrations within the periplasm causing an increase in export of antibiotics from the cell.

4.5 Future Direction

The findings discussed here have helped elucidate which genes are involved in the production of antimicrobial compounds. Biosynthetic gene clusters are easy to identify but elucidating of the function of these BGC is difficult to achieve. The workflow described here can not only help address this issue by allowing antagonistic activity to be linked to a BGC, but it can also help in the understanding of characteristics that are found to be essential for a BGC to produce an antagonistic product. Furthermore, the BGC’s already recovered using this workflow can help directed amplification of the BGC products. Using homologues gene expression, purification of the product can be achieved and tests can be conducted to further understand how the compound works and if they have medical capacity to treat MDR infections. Further tests can be done to ensure that the BGC mutated is involved in antagonistic activity. For example the LOI

41 mutants with mutations within BGC’s could be competed against pathogens that were found to be inhibited by the wild type. This will determine if the BGC mutated within these mutants are actually involved in the wt antagonistic profile. This study has also illuminated the complexity of secondary metabolite production. Genes that have never been linked to antibiotic production before are required for the biosynthesis of these compounds based on these results. Further tests to understand why and how these genes are involved in the production of these antimicrobials can be conducted, helping to clarify the full biosynthetic pathway.

Theories as to why these genes may be involved can be individually verified using multiple different tests. A lot of the non-BGC gene mutants were found to be involved in iron sequestration and cysteine synthesis. An assay could be performed by adding high amounts of cysteine and iron to agar media and a spot test could be performed. If this addition resumes inhibitory activity, then this will explain why mutations within genes involved in cysteine synthesis may have led to an LOI mutant.

To conclude, this study has helped further research in the elucidating the mechanisms involved in the production of antagonistic products which are of high interest due to the increasing prevalence of MDR pathogens. More work will need to be done in order to understand the complete biosynthesis of these highly volatile compounds. A workflow has been successfully optimized to identify highly volatile compounds against MDR pathogen from environmental

Pseudomonas. Alternatives to the current antibiotics are desperately needed, especially against P. aeruginosa. This research has identified two potentially new antibiotics that could be effective against these problematic pathogens, helping in the fight against these ‘superbugs’.

42

REFERENCES

Abbott, F. K., Milne, K. E., Stead, D. A., & Gould, I. M. (2016). Combination antimicrobial

susceptibility testing of Burkholderia cepacia complex: Significance of species.

International Journal of Antimicrobial Agents, 48(5), 521-527.

doi:10.1016/j.ijantimicag.2016.07.020

Abdulwahab, A., Zahraldin, K., Ahmed, M. S., Jarir, S., Muneer, M., Mohamed, S., . . . Ibrahim,

E. (2017). The emergence of multidrug-resistant Pseudomonas aeruginosa in cystic

fibrosis patients on inhaled antibiotics. Lung India, 34(6), 527.

doi:10.4103/lungindia.lungindia_39_17

Aguirrezabalaga, I., Olano, C., Allende, N., Rodriguez, L., Braña, A. F., Méndez, C., & Salas, J.

A. (2000). Identification and expression of genes involved in biosynthesis of L-

oleandrose and its intermediate L-olivose in the oleandomycin producer Streptomyces

antibioticus. Antimicrobial agents and chemotherapy, 44(5), 1266–1275.

doi:10.1128/aac.44.5.1266-1275.2000

Aharonowitz, Y., Av-Gay, Y., Schreiber, R., & Cohen, G. (1993). Characterization of a broad-

range disulfide reductase from Streptomyces clavuligerus and its possible role in beta-

lactam antibiotic biosynthesis. Journal of Bacteriology, 175(3), 623-629.

doi:10.1128/jb.175.3.623-629.1993

Akiyama, Y. (2009). Quality Control of Cytoplasmic Membrane Proteins in Escherichia coli.

Journal of Biochemistry, 146(4), 449-454. doi:10.1093/jb/mvp071

Allocati, N., Federici, L., Masulli, M., & Ilio, C. D. (2008). Glutathione transferases in bacteria.

FEBS Journal, 276(1), 58-75. doi:10.1111/j.1742-4658.2008.06743.x

43

Alonso, A., Sanchez, P., & Martinez, J. L. (2001). Environmental selection of antibiotic

resistance genes. Minireview. Environmental Microbiology, 3(1), 1-9.

doi:10.1046/j.1462-2920.2001.00161.x

Amin, R., & Waters, V. (2016). Antibiotic treatment for Stenotrophomonas maltophilia in people

with cystic fibrosis. The Cochrane database of systematic reviews, 7(7), CD009249.

doi:10.1002/14651858.CD009249.pub4

Anzai, Y., Kim, H., Park, J. Y., Wakabayashi, H., & Oyaizu, H. (2000). Phylogenetic affiliation

of the pseudomonads based on 16S rRNA sequence. International Journal of Systematic

and Evolutionary Microbiology, 50(4), 1563-1589. doi: 10.1099/00207713-50-4-1563

Arca, P., Rico, M., Brana, A. F., Villar, C. J., Hardisson, C., & Suarez, J. E. (1988). Formation of

an adduct between fosfomycin and glutathione: A new mechanism of antibiotic resistance

in bacteria. Antimicrobial Agents and Chemotherapy, 32(10), 1552-1556.

doi:10.1128/aac.32.10.1552

Arnér, E. S., & Holmgren, A. (2000). Physiological functions of thioredoxin and thioredoxin

reductase. European Journal of Biochemistry, 267(20), 6102-6109. doi:10.1046/j.1432-

1327.2000.01701.x

Ash, C., Farrow, J. A., Dorsch, M., Stackebrandt, E., & Collins, M. D. (1991). Comparative

Analysis of Bacillus anthracis, Bacillus cereus, and Related Species on the Basis of

Reverse Transcriptase Sequencing of 16S rRNA. International Journal of Systematic

Bacteriology, 41(3), 343-346. doi: 10.1099/00207713-41-3-343

Aussel, L., Pierrel, F., Loiseau, L., Lombard, M., Fontecave, M., & Barras, F. (2014).

Biosynthesis and physiology of coenzyme Q in bacteria. Biochimica Et Biophysica Acta

(BBA) - Bioenergetics, 1837(7), 1004-1011. doi:10.1016/j.bbabio.2014.01.015

44

Ayukekbong, J. A., Ntemgwa, M., & Atabe, A. N. (2017). The threat of antimicrobial resistance

in developing countries: causes and control strategies. Antimicrobial resistance and

infection control, 6, 47. doi:10.1186/s13756-017-0208-x

Basalla, J. (2018). Identifying biosynthetic gene clusters whose products inhibit cystic fibrosis

derived pathogens. (Electronic Thesis or Dissertation). Retrieved from

https://etd.ohiolink.edu/

Bastos, M.C.F., Coutinho, B. G., & Coelho, M. L. (2010). Lysostaphin: A Staphylococcal

Bacteriolysin with Potential Clinical Applications. Pharmaceuticals, 3(4), 1139-1161.

doi:10.3390/ph3041139

Belkin, R. A. et al. (2006). Risk Factors for Death of Patients with Cystic Fibrosis Awaiting

Lung Transplantation. American Journal of Respiratory and Critical Care

Medicine, 173(6), 659-666.

Blanco, P., Hernando-Amado, S., Reales-Calderon, J. A., Corona, F., Lira, F., Alcalde-Rico, M.,

Bernardini, A., Sanchez, M. B., … Martinez, J. L. (2016). Bacterial Multidrug Efflux

Pumps: Much More Than Antibiotic Resistance Determinants. Microorganisms, 4(1), 14.

doi:10.3390/microorganisms4010014

Blankenfeldt, W., Asuncion, M., Lam, J. S., & Naismith, J. H. (2000). The structural basis of the

catalytic mechanism and regulation of glucose-1-phosphate thymidylyltransferase

(RmlA). The EMBO journal, 19(24), 6652–6663. doi:10.1093/emboj/19.24.6652

Braun, V., Pramanik, A., Gwinner, T., Köberle, M., & Bohn, E. (2009). Sideromycins: tools and

antibiotics. Biometals: an international journal on the role of metal ions in biology,

biochemistry, and medicine, 22(1), 3–13. doi:10.1007/s10534-008-9199-7

45

Bugg, T. D., Wright, G. D., Dutka-Malen, S., Arthur, M., Courvalin, P., & Walsh, C. T. (1991).

Molecular basis for vancomycin resistance in Enterococcus faecium BM4147:

Biosynthesis of a depsipeptide peptidoglycan precursor by vancomycin resistance

proteins VanH and VanA. Biochemistry, 30(43), 10408-10415. doi:

10.1021/bi00107a007

Cerdà-Costa, N., & Gomis-Rüth, F. X. (2014). Architecture and function of metallopeptidase

catalytic domains. Protein science: a publication of the Protein Society, 23(2), 123–144.

doi:10.1002/pro.2400

Chatterjee P., Davis E., Yu F., James S., Wildschutte J.H., Wiegmann D.D., Sherman D.H.,

McKay R.M., LiPuma J.J., Wildschutte H. (2017). Environmental pseudomonads inhibit

cystic fibrosis patient-derived Pseudomonas aeruginosa. Appl Environ Microbiol, 83(2).

Chen, I. A., Markowitz, V. M., Chu, K., Palaniappan, K., Szeto, E., Pillay, M., … Kyrpides, N.

C. (2017). IMG/M: integrated genome and metagenome comparative data analysis

system. Nucleic acids research, 45(D1), D507–D516. doi:10.1093/nar/gkw929

Chmiel, J. F., Aksamit, T. R., Chotirmall, S. H., Dasenbrook, E. C., Elborn, J. S., Lipuma, J. J., .

Ratjen, F. A. (2014). Antibiotic Management of Lung Infections in Cystic Fibrosis. I. The

Microbiome, Methicillin-Resistant Staphylococcus aureus, Gram-Negative Bacteria, and

Multiple Infections. Annals of the American Thoracic Society, 11(7), 1120-1129.

doi:10.1513/annalsats.201402-050as

Clardy, J., Fischbach, M. A., & Currie, C. R. (2009). The natural history of antibiotics. Current

Biology, 19(11). doi:10.1016/j.cub.2009.04.001

46

Coenye, T., & Vandamme, P. (2003). Diversity and significance of Burkholderia species

occupying diverse ecological niches. Environmental Microbiology, 5(9), 719-729.

doi:10.1046/j.1462-2920.2003.00471.x

Colinon, C., Deredjian, A., Hien, E., Brothier, E., Bouziri, L., Cournoyer, B., Nazaret, S. (2013).

Detection and enumeration of Pseudomonas aeruginosa in soil and manure assessed by

an ecfX qPCR assay. Journal of Applied Microbiology, 114(6), 1734-1749.

doi:10.1111/jam.12189

Cooper, M. A., & Shlaes, D. (2011). Fix the antibiotics pipeline. Nature, 472(7341), 32-32. doi:

10.1038/472032a

Coutinho, H., Falcão-Silva, V., Gonçalves, G. (2008). Pulmonary bacterial pathogens in cystic

fibrosis patients and antibiotic therapy: a tool for the health workers. International

Archives of Medicine, 1(24).

Couto, M. A., Harwig, S. S., Cullor, J. S., Hughes, J. P., & Lehrer, R. I. (1992). eNAP-2, a novel

cysteine-rich bactericidal peptide from equine leukocytes. Infection and

immunity, 60(12), 5042–5047.

Dalisay, D. S., Williams, D. E., Wang, X. L., Centko, R., Chen, J., & Andersen, R. J. (2013).

Marine Sediment-Derived Streptomyces Bacteria from British Columbia, Canada Are a

Promising Microbiota Resource for the Discovery of Antimicrobial Natural

Products. PLoS ONE, 8(10). doi:10.1371/journal.pone.0077078

Das, S., Noe, J. C., Paik, S., & Kitten, T. (2005). An improved arbitrary primed PCR method for

rapid characterization of transposon insertion sites. Journal of Microbiological Methods,

63(1), 89-94. doi:10.1016/j.mimet.2005.02.011

47

Dashiff, A., Junka, R., Libera, M., & Kadouri, D. (2010). Predation of human pathogens by the

predatory bacteria Micavibrio aeruginosavorus and Bdellovibrio bacteriovorus. Journal

of Applied Microbiology, 110(2), 431-444. doi:10.1111/j.1365-2672.2010.04900.x

Davis, E., Sloan, T., Aurelius, K., Barbour, A., Bodey, E., Clark, B., Wildschutte, H. (2017).

Antibiotic discovery throughout the Small World Initiative: A molecular strategy to

identify biosynthetic gene clusters involved in antagonistic activity. Microbiology

Open, 6(3). doi:10.1002/mbo3.435

Davidson, A. L., Dassa, E., Orelle, C., & Chen, J. (2008). Structure, function, and evolution of

bacterial ATP-binding cassette systems. Microbiology and molecular biology reviews:

MMBR, 72(2), 317–364. doi:10.1128/MMBR.00031-07

Demain, A. L., & A. F. (2000). The natural functions of secondary metabolites. Adv Biochem

Eng Biotechnol, 69, 1-39.

Douthit, S. A., Dlakic, M., Ohman, D. E., & Franklin, M. J. (2005). Epimerase Active Domain of

Pseudomonas aeruginosa AlgG, a Protein That Contains a Right-Handed -Helix. Journal

of Bacteriology, 187(13), 4573-4583. doi:10.1128/jb.187.13.4573-4583.2005

Drew, S. W., & Demain, A. L. (1975). Stimulation of cephalosporin production by methionine

peptides in a mutant blocked in reverse transsulfuration. The Journal of

Antibiotics, 28(11), 889-895. doi:10.7164/antibiotics.28.889

Esposito, A., Pompilio, A., Bettua, C., Crocetta, V., Giacobazzi, E., Fiscarelli, E., … Di

Bonaventura, G. (2017). Evolution of Stenotrophomonas maltophilia in Cystic Fibrosis

Lung over Chronic Infection: A Genomic and Phenotypic Population Study. Frontiers in

microbiology, 8, 1590. doi:10.3389/fmicb.2017.01590

48

Fischbach, M. A., & Walsh, C. T. (2006). Assembly-Line Enzymology for Polyketide and

Nonribosomal Peptide Antibiotics: Logic, Machinery, and Mechanisms. Chem Inform,

37(44), 3468-3496. doi:10.1002/chin.200644265

Ganesan, S., & Sajjan, U. S. (2012). Host Evasion by Burkholderia cenocepacia. Frontiers in

Cellular and Infection Microbiology, 1. doi:10.3389/fcimb.2011.00025

Gebreyohannes, G., Moges, F., Sahile, S., & Raja, N. (2013). Isolation and characterization of

potential antibiotic producing Actinomycetes from water and sediments of Lake Tana,

Ethiopia. Asian Pacific Journal of Tropical Biomedicine, 3(6), 426-435.

doi:10.1016/s2221-1691(13)60092-1

Ghai, I., & Ghai, S. (2018). Understanding antibiotic resistance via outer membrane

permeability. Infection and drug resistance, 11, 523–530. doi:10.2147/IDR.S156995

Gilbert, N. (2012). Rules tighten on use of antibiotics on farms. Nature, 481(7380), 125-125.

doi:10.1038/481125a

Gilchrist, F. J., Webb, A. K., Bright-Thomas, R. J., & Jones, A. M. (2012). Successful treatment

of cepacia syndrome with a combination of intravenous cyclosporin, antibiotics and oral

corticosteroids. Journal of Cystic Fibrosis, 11(5), 458-460. doi:10.1016/j.jcf.2012.04.002

Greene, A. C. (2018). CRISPR-Based Antibacterial’s: Transforming Bacterial Defense into

Offense. Trends in Biotechnology. doi:10.1016/j.tibtech.2018.01.009

Greene, N. P., Kaplan, E., Crow, A., & Koronakis, V. (2018). Antibiotic Resistance Mediated by

the MacB ABC Transporter Family: A Structural and Functional Perspective. Frontiers

in microbiology, 9, 950. doi:10.3389/fmicb.2018.00950

Gross H, Loper JE. (2009). Genomics of secondary metabolite production by Pseudomonas spp.

Natural Products Reports, 26: 1408-1446.

49

Hamma, T., & Ferré-D’Amaré, A. R. (2006). Pseudouridine Synthases. Chemistry & Biology,

13(11), 1125-1135. doi:10.1016/j.chembiol.2006.09.009

Hibbing, M. E., Fuqua, C., Parsek, M. R., & Peterson, S. B. (2010). Bacterial competition:

surviving and thriving in the microbial jungle. Nature reviews. Microbiology, 8(1), 15–

25. doi:10.1038/nrmicro2259

Hover, B. M., Kim, S. H., Katz, M., Powers, Z. C., & Owen, J. G. (2018). Culture-independent

discovery of the malacidins as calcium-dependent antibiotics with activity against

multidrug-resistant Gram-positive pathogens. Nature Microbiology, 3, 415-422.

Huang, H., Lin, Y., Chen, P., Li, L., Ning, H., & Yang, T. (2018). ClpA and HtpX Proteases Are

Involved in Intrinsic Aminoglycoside Resistance of Stenotrophomonas maltophilia and

Are Potential Aminoglycoside Adjuvant Targets. Antimicrobial Agents and

Chemotherapy, 62(8). doi:10.1128/aac.00554-18

Huber, A., Hajdu, D., Bratschun-Khan, D., Gáspári, Z., Varbanov, M., Philippot, S., … Batta, G.

(2018). New Antimicrobial Potential and Structural Properties of PAFB: A Cationic,

Cysteine-Rich Protein from Penicillium chrysogenum Q176. Scientific reports, 8(1),

1751. doi:10.1038/s41598-018-20002-2

Jarrett J. T. (2015). The biosynthesis of thiol- and thioether-containing cofactors and secondary

metabolites catalyzed by radical S-adenosylmethionine enzymes. The Journal of

biological chemistry, 290(7), 3972–3979. doi:10.1074/jbc.R114.599308

Jasovský, D., Littmann, J., Zorzet, A., & Cars, O. (2016). Antimicrobial resistance-a threat to the

world's sustainable development. Upsala journal of medical sciences, 121(3), 159–164.

doi:10.1080/03009734.2016.1195900

50

Jones, A. M. (2004). Burkholderia cenocepacia and Burkholderia multivorans: Influence on

survival in cystic fibrosis. Thorax, 59(11), 948-951. doi:10.1136/thx.2003.017210

Kaya, Y., & Ofengand, J. (2003). A novel unanticipated type of pseudouridine synthase with

homologs in bacteria, archaea, and eukarya. Rna, 9(6), 711-721.

doi:10.1261/rna.5230603

Kharat, K. R., Kharat, A., & Hardikar, B. P. (2009). Antimicrobial and cytotoxic activity of

Streptomyces sp. from Lonar Lake. African Journal of Biotechnology, 8(23), 6645-6648.

Knowles M.R., Boucher R.C. (2002). Mucus clearance as a primary innate defense mechanism

for mammalian airways. J Clin Invest. 109(5):571-7.

Larsson, D. G. (2014). Antibiotics in the environment. Ups J Med Sci., 119(2), 108-112.

doi:10.3109/03009734.2014.896438

Laxminarayan, R., et al. (2013). Antibiotic resistance—the need for global solutions. The Lancet

Infectious Diseases Commission, 13(12), 1057 – 1098.

Letunic I, Bork P. (2016). Interactive tree of life (iTOL) v3: an online tool for the display and

annotation of phylogenetic and other trees. Nucleic Acids Res, 44(W1):W242–W245.

doi:10.1093/nar/gkw290

Lin, D. M., Koskella, B., & Lin, H. C. (2017). Phage therapy: An alternative to antibiotics in the

age of multi-drug resistance. World Journal of Gastrointestinal Pharmacology and

Therapeutics, 8(3), 162. doi:10.4292/wjgpt.v8.i3.162.

Lin, T., Huang, S., & Shaw, G. (2012). Reexamining Transcriptional Regulation of the Bacillus

subtilis htpX Gene and the ykrK Gene, Encoding a Novel Type of Transcriptional

Regulator, and Redefining the YkrK Operator. Journal of Bacteriology, 194(24), 6758-

6765. doi:10.1128/jb.01258-12

51

Ling, L. L., Schneider, T., Peoples, A. J., Spoering, A. L., Engels, I., Conlon, B. P. Lewis, K.

(2015). A new antibiotic kills pathogens without detectable

resistance. Nature, 517(7535), 455-459. doi:10.1038/nature14098

Lyczak, J. B., Cannon, C. L., & Pier, G. B. (2002). Lung infections associated with cystic

fibrosis. Clinical Microbiology Reviews, 15(2), 194-222.

Martínez-Martínez, L., & Calvo, J. (2010). The growing problem of antibiotic resistance in

clinically relevant Gram-negative bacteria: Current situation. Enfermedades Infecciosas Y

Microbiología Clínica, 28, 25-31. doi: 10.1016/s0213-005x(10)70027-6

Medema, M. H., Blin, K., Cimermancic, P., de Jager, V., Zakrzewski, P., Fischbach, M. A., …

Breitling, R. (2011). antiSMASH: rapid identification, annotation and analysis of

secondary metabolite biosynthesis gene clusters in bacterial and fungal genome

sequences. Nucleic acids research, 39(Web Server issue), W339–W346.

doi:10.1093/nar/gkr466+

Méndez, C., & Salas, J. A. (2001). The role of ABC transporters in antibiotic-producing

organisms: Drug secretion and resistance mechanisms. Research in Microbiology, 152(3-

4), 341-350. doi:10.1016/s0923-2508(01)01205-0

Micek, S. T., Wunderink, R. G., Kollef, M. H., Chen, C., Rello, J., Chastre, J., . . . Menon, V.

(2015). An international multicenter retrospective study of Pseudomonas aeruginosa

nosocomial pneumonia: Impact of multidrug resistance. Critical Care, 19(1).

doi:10.1186/s13054-015-0926-5

Morea, A., Mathee, K., Franklin, M. J., Giacomini, A., Oregan, M., & Ohman, D. E. (2001).

Characterization of algG encoding C5-epimerase in the alginate biosynthetic gene cluster

52

of Pseudomonas fluorescens. Gene, 278(1-2), 107-114. doi:10.1016/s0378-

1119(01)00685-0

Murray, J. L., Kwon, T., Marcotte, E. M., & Whiteley, M. (2015). Intrinsic Antimicrobial

Resistance Determinants in the Superbug Pseudomonas aeruginosa. MBio, 6(6).

doi:10.1128/mbio.01603-15

Neidig, N., Paul, R. J., Scheu, S., & Jousset, A. (2011). Secondary Metabolites of Pseudomonas

fluorescens CHA0 Drive Complex Non-Trophic Interactions with Bacterivorous

Nematodes. Microbial Ecology, 61(4), 853-859. doi:10.1007/s00248-011-9821-z

Nikaido H. (2003). Molecular basis of bacterial outer membrane permeability

revisited. Microbiology and molecular biology reviews: MMBR, 67(4), 593–656.

doi:10.1128/mmbr.67.4.593-656.2003

Obritsch, M. D., Fish, D. N., Maclaren, R., & Jung, R. (2005). Nosocomial Infections Due to

Multidrug-Resistant Pseudomonas aeruginosa: Epidemiology and Treatment Options.

Pharmacotherapy, 25(10), 1353-1364. doi:10.1592/phco.2005.25.10.1353

Okamoto, K., Gotoh, N., & Nishino, T. (2001). Pseudomonas aeruginosa reveals high intrinsic

resistance to penem antibiotics: penem resistance mechanisms and their interplay.

Antimicrobial agents and chemotherapy, 45(7), 1964–1971.

doi:10.1128/AAC.45.7.1964-1971.2001

Park, H. (2012). Reduction of antibiotics using microorganisms containing glutathione S-

transferases under immobilized conditions. Environmental Toxicology and

Pharmacology, 34(2), 345-350. doi:10.1016/j.etap.2012.05.011

53

Peiru, S., Menzella, H. G., Rodriguez, E., Carney, J., & Gramajo, H. (2005). Production of the

Potent Antibacterial Polyketide Erythromycin C in Escherichia coli. Applied and

Environmental Microbiology, 71(5), 2539-2547. doi:10.1128/aem.71.5.2539-2547.2005

Pitt, T. L., Trakulsomboon, S., & Dance, D. A. (2006). Recurrent Melioidosis: Possible Role of

Infection with Multiple Strains of Burkholderia pseudomallei. Journal of Clinical

Microbiology, 45(2), 680-681. doi:10.1128/jcm.01974-06

Poole, K. (2002). Outer membranes and efflux: the path to multidrug resistance in Gram-

negative bacteria. Curr. Pharm. Biotechnol. 3, 77–98.

Raaijmakers JM, Weller DM, Thomashow LS. (1997). Frequency of Antibiotic-Producing

Pseudomonas spp. in Natural Environments. Appl Environ Microbiol, 63(3):881–887.

Riordan, J. R., Rommens, J. M., Kerem, B., Alon, N., Rozmahel, R., Grzelczak, Z., et al. (1989).

Identification of the cystic fibrosis gene: Cloning and characterization of

Complementary DNA. Science (New York, N.Y.), 245(4922), 1066-1073.

Rivera, M. (2017). Bacterioferritin: Structure, Dynamics, and Protein–Protein Interactions at

Play in Iron Storage and Mobilization. Accounts of Chemical Research, 50(2), 331-340.

doi:10.1021/acs.accounts.6b00514

Rossi, E., Motta, S., Mauri, P., & Landini, P. (2014). Sulfate assimilation pathway intermediate

phosphoadenosine 5-phosphosulfate acts as a signal molecule affecting production of

curli fibres in Escherichia coli. Microbiology, 160(Pt_9), 1832-1844.

doi:10.1099/mic.0.079699-0

Ryjenkov, D. A., Simm, R., Römling, U., & Gomelsky, M. (2006). The PilZ Domain Is a

Receptor for the Second Messenger c-di-GMP. Journal of Biological Chemistry, 281(41),

30310-30314. doi:10.1074/jbc.c600179200

54

Ryu, C., Farag, M. A., Hu, C., Reddy, M. S., Wei, H., Pare, P. W., & Kloepper, J. W. (2003).

Bacterial volatiles promote growth in Arabidopsis. Proceedings of the National Academy

of Sciences, 100(8), 4927-4932. doi:10.1073/pnas.0730845100

Schippers, B. (1987). Interactions of Deleterious and Beneficial Rhizosphere Microorganisms

and the Effect of Cropping Practices. Annual Review of Phytopathology, 25(1), 339-358.

doi:10.1146/annurev.phyto.25.1.339

Severinov, K., & Nair, S. K. (2012). Microcin C: Biosynthesis and mechanisms of bacterial

resistance. Future Microbiology, 7(2), 281-289. doi:10.2217/fmb.11.148

Sheehan, D., Meade, G., Foley, V. M., & Dowd, C. A. (2001). Structure, function and evolution

of glutathione transferases: Implications for classification of non-mammalian members of

an ancient enzyme superfamily. Biochemical Journal, 360(1), 1. doi: 10.1042/0264-

6021:3600001

Siegel L. M., Davis P. S. (1974) Reduced nicotinamide adenine dinucleotide phosphate-sulfite

reductase of enterobacteria. IV. The Escherichia coli hemoflavoprotein: subunit structure

and dissociation into hemoprotein and flavoprotein components. J. Biol. Chem. 249,

1587–1598

Silby M.W., Winstanley C., Godfrey S.A.C., Levy S.B., Jackson R.W. (2011). Pseudomonas

genomes: diverse and adaptable. Federation of European Microbiology Societies 35(4):

652-680.

Skinnider, M. A., Merwin, N. J., Johnston, C. W., & Magarvey, N. A. (2017). PRISM 3:

expanded prediction of natural product chemical structures from microbial genomes.

Nucleic acids research, 45(W1), W49–W54. doi:10.1093/nar/gkx320

55

Smith, J. J., Travis, S. M., Greenberg, E., & Welsh, M. J. (1996). Cystic Fibrosis Airway

Epithelia Fail to Kill Bacteria Because of Abnormal Airway Surface Fluid. Cell, 85(2),

229-236. doi:10.1016/s0092-8674(00)81099-5

Sourjik, V., & Wingreen, N. S. (2012). Responding to chemical gradients: bacterial chemotaxis.

Current opinion in cell biology, 24(2), 262–268. doi:10.1016/j.ceb.2011.11.008

Sosio, M., Gaspari, E., Iorio, M., Pessina, S., Medema, M. H., Bernasconi, A., . . . Donadio, S.

(2018). Analysis of the Pseudouridimycin Biosynthetic Pathway Provides Insights into

the Formation of C-nucleoside Antibiotics. Cell Chemical Biology, 25(5).

doi:10.1016/j.chembiol.2018.02.008

Spellberg B. (2014). The future of antibiotics. Critical care (London, England), 18(3), 228.

doi:10.1186/cc13948

Steffensky, M., Muhlenweg, A., Wang, Z., Li, S., & Heide, L. (2000). Identification of the

Novobiocin Biosynthetic Gene Cluster of Streptomyces spheroides NCIB 11891.

Antimicrobial Agents and Chemotherapy, 44(5), 1214-1222. doi:10.1128/aac.44.5.1214-

1222.2000

Stewart, E. J., Aslund F., Beckwith J. (1998). Disulfide bond formation in the Escherichia coli

cytoplasm: An in vivo role reversal for the thioredoxins. The EMBO Journal, 17(19),

5543-5550. doi:10.1093/emboj/17.19.5543

Strieker, M., Tanovic, A., & Marahiel, M. A. (2010). Peptide synthetases: Structures and

dynamics. Current Opinion in Structural Biology, 20, 234-240.

doi:10.1016/j.sbi.2010.01.009

56

Tarran R., Button B., Picher M., et al. (2005). Normal and cystic fibrosis airway surface liquid

homeostasis. The effects of phasic shear stress and viral infections. J Biol Chem. 280(42),

35751-9.

Thiolas, A., Bornet, C., Davin-Régli, A., Pagès, J., & Bollet, C. (2004). Resistance to imipenem,

cefepime, and cefpirome associated with mutation in Omp36 osmoporin of Enterobacter

aerogenes. Biochemical and Biophysical Research Communications, 317(3), 851-856.

doi:10.1016/j.bbrc.2004.03.130

Tielen, P., Strathmann, M., Jaeger, K., Flemming, H., & Wingender, J. (2005). Alginate

acetylation influences initial surface colonization by mucoid Pseudomonas aeruginosa.

Microbiological Research, 160(2), 165-176. doi:10.1016/j.micres.2004.11.003

Tossavainen, H., Raulinaitis, V., Kauppinen, L., Pentikäinen, U., Maaheimo, H., & Permi, P.

(2018). Structural and Functional Insights Into Lysostaphin-Substrate Interaction.

Frontiers in molecular biosciences, 5, 60. doi:10.3389/fmolb.2018.00060

Tran, N. T., Den Hengst, C. D., Gomez-Escribano, J. P., & Buttner, M. J. (2011). Identification

and characterization of CdgB, a diguanylate cyclase involved in developmental processes

in Streptomyces coelicolor. Journal of bacteriology, 193(12), 3100–3108.

doi:10.1128/JB.01460-10

Troxell, B., & Hassan, H. M. (2013). Transcriptional regulation by Ferric Uptake Regulator (Fur)

in pathogenic bacteria. Frontiers in cellular and infection microbiology, 3, 59.

doi:10.3389/fcimb.2013.00059

Tyc, O., Song, C., Dickschat, J. S., Vos, M., & Garbeva, P. (2017). The Ecological Role of

Volatile and Soluble Secondary Metabolites Produced by Soil Bacteria. Trends in

Microbiology, 25(4), 280-292. doi:10.1016/j.tim.2016.12.002

57

Van Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., …

Laxminarayan, R. (2015). Global trends in antimicrobial use in food animals.

Proceedings of the National Academy of Sciences of the United States of America,

112(18), 5649–5654. doi:10.1073/pnas.1503141112

Ventola, C. L. (2015). The Antibiotic Resistance Crisis: Part 1: Causes and Threats. Pharmacy

and Therapeutics, 40(4), 277-283.

Venturi, V. (2006). Regulation of quorum sensing in Pseudomonas. FEMS Microbiology

Reviews, 30(2), 274-291. doi:FMR012

Vermeij, P., & Kertesz, M. A. (1999). Pathways of assimilative sulfur metabolism in

Pseudomonas putida. Journal of bacteriology, 181(18), 5833–5837.

Walburger, A., Lazdunski, C., & Corda, Y. (2002). The Tol/Pal system function requires an

interaction between the C-terminal domain of TolA and the N-terminal domain of

TolB. Molecular Microbiology, 44(3), 695-708. doi:10.1046/j.1365-2958.2002.02895.x

Wasi, S., Tabrez, S., & Ahmad, M. (2013). Use of Pseudomonas spp. for the bioremediation of

environmental pollutants: A review. Environmental Monitoring and Assessment, 185(10),

8147-8155. doi:10.1007/s10661-013-3163-x

Waters, V., & Ratjen, F. (2006). Multidrug–resistant organisms in cystic fibrosis: Management

and infection–control issues. Expert Review of Anti-infective Therapy, 4(5), 807-819.

doi:10.1586/14787210.4.5.807

Watt PM, Hickson ID. (1994) Structure and function of type II DNA topoisomerases. Biochem

J., 303 ( Pt 3)(Pt 3):681–695. doi:10.1042/bj3030681

Weinberg, E. D. (1990). Roles of trace metals in transcriptional control of microbial secondary

metabolism. Biology of Metals, 2(4), 191-196. doi:10.1007/bf01141358

58

Welsh, M. J., & Smith, A. E. (1993). Molecular mechanisms of CFTR chloride channel

dysfunction in cystic fibrosis. Cell, 73(7), 1251-1254. doi:10.1016/0092-8674(93)90353-r

WHO publishes list of bacteria for which new antibiotics are urgently needed. (n.d.). Retrieved

from http://www.who.int/news-room/detail/27-02-2017-who-publishes-list-of-bacteria-

for-which-new-antibiotics-are-urgently-needed

Wiedenbeck, J., & Cohan, F. M. (2011). Origins of bacterial diversity through horizontal genetic

transfer and adaptation to new ecological niches. FEMS Microbiology Reviews, 35(5),

957-976. doi:10.1111/j.1574-6976.2011.00292.x

Wiener, P. (2000). Antibiotic production in a spatially structured environment. Ecology Letters,

3(2), 122-130. doi:10.1046/j.1461-0248.2000.00129.x

Winstanley, C., O’Brien, S., & Brockhurst, M. A. (2016). Pseudomonas aeruginosa

Evolutionary Adaptation and Diversification in Cystic Fibrosis Chronic Lung Infections.

Trends in Microbiology, 24(5), 327-337. doi:10.1016/j.tim.2016.01.008

Wu, X., Monchy, S., Taghavi, S., Zhu, W., Ramos, J., & Lelie, D. V. (2011). Comparative

genomics and functional analysis of niche-specific adaptation in Pseudomonas putida.

FEMS Microbiology Reviews, 35(2), 299-323. doi:10.1111/j.1574-6976.2010.00249.x

Xin, X., Kvitko, B., & He, S. Y. (2018). Pseudomonas syringae: What it takes to be a pathogen.

Nature Reviews Microbiology, 16(5), 316-328. doi:10.1038/nrmicro.2018.17

Yamamoto S, Harayama S. (1995). PCR amplification and direct sequencing of gyrB genes with

universal primers and their application to the detection and taxonomic analysis of

Pseudomonas putida strains. Appl Environ Microbiol, 61(3):1104–1109.

Yamamoto, S., & Harayama, S. (1996). Phylogenetic Analysis of Acinetobacter Strains Based on

the Nucleotide Sequences of gyrB Genes and on the Amino Acid Sequences of Their

59

Products. International Journal of Systematic Bacteriology, 46(2), 506-511.

doi:10.1099/00207713-46-2-506

Yamamoto, S., Kasai, H., Arnold, D. L., Jackson, R. W., Vivian, A. & Harayama, S. (2000)

Phylogeny of the genus Pseudomonas: intrageneric structure reconstructed from the

nucleotide sequences of gyrB and rpoD genes. Microbiology 146, 2385-2394. 2350

Yang, L., Chen, L., Shen, L., Surette, M., & Duan, K. (2011). Inactivation of MuxABC-OpmB

transporter system in Pseudomonas aeruginosa leads to increased ampicillin and

carbenicillin resistance and decreased virulence. The Journal of Microbiology, 49(1),

107-114. doi:10.1007/s12275-011-0186-2

Zhang, L. (2005). Antimicrobial Peptide Therapeutics for Cystic Fibrosis. American Society for

Microbiology, 49(7) 2921-2927.

Zhou, J., Chen, Y., Tabibi, S., Alba, L., Garber, E., & Saiman, L. (2006). Antimicrobial

Susceptibility and Synergy Studies of Burkholderia cepacia Complex Isolated from

Patients with Cystic Fibrosis. Antimicrobial Agents and Chemotherapy, 51(3), 1085-

1088. doi:10.1128/aac.00954-06

Zuccotti, S., Zanardi, D., Rosano, C., Sturla, L., Tonetti, M., & Bolognesi, M. (2001). Kinetic

and crystallographic analyses support a sequential-ordered bi bi catalytic mechanism for

Escherichia coli glucose-1-phosphate thymidylyltransferase. Journal of Molecular

Biology, 313(4), 831-843. doi:10.1006/jmbi.2001.5073

60

APPENDIX A. FIGURES

Figure 1. Population-level analysis of environmental isolates. Phylogenetic tree containing 245 gyrB sequences from environmental Pseudomonas derived from Lake Erie. A total of 9 population were shaded and labelled. The outer bars represent killing data with red arrows representing strains used for genetic analysis using transposon mutagenesis.

61

Figure 2. Antagonistic assay to determine strains that inhibit pathogens. A) A diagram of the assay prior to incubation. A MDR pathogen was spread-plated on NB agar and environmental strains were stamped onto the plate using a 96 well format. B) A picture of the assay at the end of the experiment. Blue arrows indicate zones of inhibition around environmental strain.

Figure 3. Antagonistic activity against MDR pathogens. A total of 210 antagonistic events occurred against the MDR pathogen by 278 environmental Pseudomonas strains. The 28 MDR pathogens are divided into genus. The higher the bar, the more susceptible a pathogen is to environmental isolates.

62

Figure 4. Transposon mutagenesis was used to identify genes whose encoded products inhibit pathogens. A) Triparental mating occurs between E.coli strains CC118 containing pBAM1 plasmid, helper strain HB101 and an environmental Pseudomonas strain known to be a SK. This mating enables the transfer of the pBAM1 plasmid from the CC118 strain to the Pseudomonas recipient via conjugation to facilitate the transfer of a transposon encoding Kan resistance. B) Transconjugates are selected on cetrimide + Kan media which inhibits growth of the E.coli and Pseudomonas that had failed to take up the KanR transposon. C) The transconjugates were then replica-plated on to the sensitive pathogen on NB agar and D) these plates were then screened for loss of inhibition indicated by the lack of zone of inhibition around the colony.

63

85 I .

II 145 III 190 . .

IV .

142 Figure 5. S5F11 insertion loci of Tn causing LOI mutations. Each red arrow represents Tn insertion within what gene (I-IV). Numbers above arrows represent number of mutant. Insertion 142 (IV) was found to be within a BGC predicted by JGI to be an NRPS complex All mutants and the gene corresponding to insertions can be found in table 8.

I. II. 1

106 176 223 III IV. 11 . 1

12 V. 9

2 Figure 6. S3E7 insertion loci of Tn causing LOI mutations. Each8 red arrow represents Tn insertion within what gene (I-V). Numbers above arrows represent number of mutant. All mutants and the gene corresponding to insertions can be found in table 8.

64

A )

B) C)

Figure 7. S5F11 PRISM predicted NRPS system and predicted structure of product. A) Shows the 3 ORF involved in the NRPS system. Orf_22 is the main complex containing 4 modules all involved in adding a specific amino acid to the growing peptide. B) and C) show two predicted structures one cyclic and the other non-cyclic.

A )

B C ) )

Figure 8. LE22A5 PRISM predicted NRPS system and predicted structure of product. A) Shows the 3 ORF involved in the NRPS system. Orf_20 is the main complex containing 4 modules all involved in adding a specific amino acid to the growing peptide. B) and C) show two predicted structures one cyclic and the other non-cyclic.

65

A) B)

Figure 9. S3E7 gain of killing mutant spot test results. Possible mutants were spotted on top of a lawn of AU9276 with wt strain being used as a control (top right quadrant). As you can see, mutants show a larger zone of inhibition than wt excluding 95-BZ (bottom right in figure 9A).

Figure 10. NRPS BGC. Tn insertion was found to be within a BGC predicted by antiSMASH to be an NRPS complex All mutants and the gene corresponding to insertions can be found in table 8.

66

APPENDIX B. TABLES

Table 1. Inhibition by Lake Erie-derived strains. Environmental # of antagonistic Population Strain events LE22A1 9 0 LE22A2 8 0 LE22A3 2 0 LE22A4 3 4 LE22A5* 8 3 LE22A6 8 0 LE22A7 3 0 LE22A8 - 3 LE22A9 8 2 LE22A10 4 0 LE22A11 8 0 LE22A12 4 0 LE22B1 - 1 LE22B2 - 0 LE22B3 6 0 LE22B4 2 0 LE22B5 8 1 LE22B6 5 7 LE22B7 - 3 LE22B8 - 0 LE22B9 - 2 LE22B10 - 0 LE22B11 - 0 LE22B12 8 0 LE22C1 8 1 LE22C2 8 1 LE22C3 8 0 LE22C4 8 3 LE22C5 8 2 LE22C6 8 8 LE22C7 2 1 LE22C8 1 2 LE22C9 8 0 LE22C10 8 0 LE22C11 8 0 LE22C12 8 0 LE22D1 - 0

67

LE22D2 - 1 LE22D3 - 5 LE22D4 - 0 LE22D5 - 0 LE22D6 - 0 LE22D7 - 3 LE22D8 - 0 LE22D9 8 0 LE22D10 8 0 LE22D11 8 0 LE22D12 8 0 LE22E1 6 1 LE22E2 8 0 LE22E3 3 1 LE22E4 8 0 LE22E5 - 0 LE22E6 - 0 LE22E7 8 0 LE22E8 8 1 LE22E9 8 1 LE22E10 - 1 LE22E11 - 0 LE22E12 2 0 LE22F1 3 0 LE22F2 3 3 LE22F3 8 1 LE22F4 - 0 LE22F5 8 0 LE22F6 5 1 LE22F7 9 1 LE22F8 5 2 LE22F9 9 0 LE22F10 7 0 LE22F11 7 0 LE22F12 8 0 LE22G1 8 0 LE22G2 8 1 LE22G3 8 1 LE22G4 8 0 LE22G5 8 1 LE22G6 2 1 LE22G7 3 3 LE22G8 3 1

68

LE22G9 8 0 LE22G10 8 1 LE22G11 8 1 LE22G12 8 0 LE22H1 - 1 LE22H2 8 0 LE22H3 3 2 LE22H4 8 0 LE22H5 2 0 LE22H6 8 0 LE22H7 8 1 LE22H8 9 1 LE22H9 8 0 LE22H10 8 0 LE22H11 8 0 LE22H12 8 0 LE23A1 7 0 LE23A2 9 0 LE23A3 8 1 LE23A4 9 1 LE23A5 - 0 LE23A6 9 0 LE23A7 9 0 LE23A8 9 1 LE23A9 7 0 LE23A10 9 0 LE23A11 7 0 LE23A12 7 0 LE23B1 - 3 LE23B2 8 3 LE23B3 7 0 LE23B4 8 0 LE23B5 6 0 LE23B6 6 0 LE23B7 6 0 LE23B8 7 0 LE23B9 9 0 LE23B10 9 0 LE23B11 9 0 LE23B12 9 0 LE23C1 7 0 LE23C2 9 1 LE23C3 9 0

69

LE23C4 7 0 LE23C5 9 0 LE23C6 9 0 LE23C7 9 0 LE23C8 9 1 LE23C9 9 0 LE23C10 9 0 LE23C11 9 0 LE23C12 9 0 LE23D1 9 0 LE23D2 7 0 LE23D3 9 1 LE23D4 7 0 LE23D5 - 2 LE23D6 - 0 LE23D7 7 2 LE23D8 9 2 LE23D9 9 0 LE23D10 9 0 LE23D11 - 0 LE23D12 - 0 LE23E1 7 0 LE23E2 9 0 LE23E3 9 0 LE23E4 7 0 LE23E5 9 0 LE23E6 9 0 LE23E7 7 0 LE23E8 7 0 LE23E9 - 5 LE23E10 7 0 LE23E11 - 0 LE23E12 9 0 LE23F1 9 2 LE23F2 - 3 LE23F3 - 3 LE23F4 - 2 LE23F5 9 4 LE23F6 9 0 LE23F7 9 0 LE23F8 7 0 LE23F9 8 0 LE23F10 9 0

70

LE23F11 7 0 LE23F12 7 0 LE23G1 7 0 LE23G2 - 2 LE23G3 9 2 LE23G4 9 0 LE23G5 9 0 LE23G6 8 7 LE23G7 9 1 LE23G8 9 1 LE23G9 7 0 LE23G10 9 0 LE23G11 2 0 LE23G12 7 0 LE23H1 7 0 LE23H2 - 2 LE23H3 9 6 LE23H4 9 0 LE23H5 9 0 LE23H6 9 0 LE23H7 2 0 LE23H8 9 1 LE23H9 - 0 LE23H10 9 0 LE23H11 7 0 LE23H12 7 0 LE24A1 7 0 LE24A2 - 0 LE24A3 9 0 LE24A4 7 0 LE24A5 9 0 LE24A6 9 0 LE24A7 7 0 LE24A8 9 0 LE24A9 7 0 LE24A10 7 0 LE24A11 7 0 LE24B1 7 1 LE24B2 1 0 LE24B3 9 0 LE24B4 7 0 LE24B5 7 0 LE24B6 - 1

71

LE24B7 7 0 LE24B8 9 0 LE24B9 9 0 LE24B10 8 3 LE24B11 9 0 LE24C1 9 0 LE24C2 9 0 LE24C3 7 0 LE24C4 7 0 LE24C5 7 0 LE24C6 7 0 LE24C7 7 0 LE24C8 9 0 LE24C9 9 0 LE24C10 9 0 LE24C11 9 0 LE24D1 6 0 LE24D2 7 0 LE24D3 7 0 LE24D4 7 0 LE24D5 7 0 LE24D6 9 0 LE24D7 7 1 LE24D8 6 0 LE24D9 - 5 LE24D10 7 0 LE24D11 1 3 LE24E1 9 0 LE24E2 7 0 LE24E3 - 0 LE24E4 7 0 LE24E5 9 0 LE24E6 9 0 LE24E7 9 0 LE24E8 6 0 LE24E9 7 0 LE24E10 9 0 LE24E11 7 0 LE24F1 9 0 LE24F2 7 0 LE24F3 - 0 LE24F4 7 0 LE24F5 7 0

72

LE24F6 7 0 LE24F7 7 0 LE24F8 9 0 LE24F9 9 0 LE24F10 7 2 LE24F11 7 1 LE24G1 7 0 LE24G2 3 4 LE24G3 9 1 LE24G4 - 1 LE24G5 7 0 LE24G6 7 1 LE24G7 2 0 LE24G8 9 0 LE24G9 9 0 LE24G10 9 1 LE24G11 9 0 LE24H1 9 1 LE24H2 7 1 LE24H3 7 1 LE24H4 3 1 LE24H5 7 0 LE24H6 7 1 LE24H7 9 0 LE24H8 9 0 LE24H9 7 0 LE24H10 9 0 LE24H11 8 9 S3E7* 1 15 S5F11* 8 16 Total number of antagonistic 210 events *Strains used for Tn mutagenesis

73

Table 2. Minimal inhibitory concentration (MIC) of MDR Burkholderia CF-derived pathogens (mg/L). breakpoint AU21833 AU25543 AU32676 AU34312 AU21952 AU31588 AU22104 AU30232 AU30447 AU30737 AU30376 AU31398 AU AU28891 Antibiotic MIC MIC 28056

Amik > 64 64 > 64 > 64 > 64 64 > 64 > 64 > 64 > 64 > 64 > 64 > 64 > 64 Aztre 16 ≤ 4 32 16 16 16 > 32 16 ≤ 4 > 32 16 > 32 8 ≤ 4 Ceftaz 8 4 1 4 4 2 16 4 2 16 4 8 4 4 Chlor > 32 32 ≤ 8 16 32 16 > 32 16 ≤ 8 16 32 ≤ 8 32 > 32 Cipro > 8 8 > 8 ≤ 2 ≤ 2 ≤ 2 8 ≤ 2 > 8 4 4 ≤ 2 8 ≤ 2 Dori > 8 2 8 > 8 8 8 8 2 8 8 8 8 4 2 Levo > 8 8 8 4 4 4 > 8 4 > 8 8 8 4 8 4 Mer 16 2 8 16 8 8 4 2 16 8 4 8 4 2 Mino 8 8 2 4 8 2 8 8 4 2 16 2 8 4 Pip/Taz 8 ≤ 4 32 64 16 ≤ 4 64 ≤ 4 64 > 128 16 32 ≤ 4 ≤ 4 Tige 16 8 16 ≤ 2 16 ≤ 2 16 ≤ 2 8 4 16 ≤ 2 > 16 ≤ 2 Tmp/Sxt 2 ≤ 1 ≤ 1 4 ≤ 1 ≤ 1 4 ≤ 1 ≤ 1 ≤ 1 ≤ 1 ≤ 1 ≤ 1 > 8 Tobra > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16 > 16

Table 3. Antibiotic resistance profile of MDR P. aeruginosa CF-derived pathogens

74

Table 3. Minimal Inhibitory Concentration (MIC) of MDR P. aeruginosa CF-derived pathogens (mg/L). AU10241 AU16015 AU16135 AU18132 AU20785 AU20836 AU22420 AU22505 AU22725 Midpoint AU9856 MIC MIC Antibiotic

Amik <16 64 >=32 >=32 >=32 >32 >32 16 >32 >32 >16 Amox/K Clav ------>16/8 >16/8 >16/8 - - Amp >32 >32 >=16 >=16 >=16 - >16 >16 >16 >16 - Amp/Sublac ------>16/8 >16/8 >16/8 >16/8 - Augem - - >=16/8 >=16/8 >=16/8 ------Aztre >32 >32 >=16 >=16 >=16 >16 >16 >16 >16 >16 >16 B Lac ------Cefaz >64 - >=16 >=16 >=16 - >16 >16 >16 >16 - Cefotax - - >=32 >=32 >=32 - >32 >32 >32 >32 - Cefote - - >=32 >=32 >=32 - >32 >32 >32 - - Cefox >32 >32 >=16 >=16 >=16 - >16 >16 >16 >16 - Cefpm >32 >32 >=16 >=16 >=16 >32 >32 >16 >16 - - Ceftaz >32 >32 >=16 >=16 >=16 >32 >32 >16 >16 >16 >8 Ceftiz - - >=32 >=32 >=32 - >32 >32 >32 - - Ceftri >64 >64 >=32 >=32 >=32 - >32 >32 >32 >32 - Cefur >32 - >=16 >=16 >=16 - >16 >16 >16 >16 - Ceph - - >=16 >=16 >=16 - >16 >16 >16 - - Chlor - - 16 <=8 <=8 - <=8 <=8 >16 - - Cipro 4 4 >=2 >=2 >=2 <=.5 >2 <=.5 >2 >2 >0.5 Dapto >4 >4 ------Dori ------>4 Doxy 16 16 ------Erta - - >=4 >=4 >=4 - - - - >4 - Gati - - >=4 >=4 4 ------Genta 8 >16 >=8 >=8 >=8 >8 >8 >8 8 >8 >4 Imip - - >=8 >=8 >=8 - >8 >8 >8 - >8 Levo 8 8 >=4 >=4 <=2 >4 >4 <=2 >4 >4 >1 Lzd >4 >4 ------Mer >16 >16 >=8 >=8 >=8 >8 >8 8 >8 >8 >8 Nitro - - >=64 >=64 >=64 - >64 >64 >64 >64 - Pen >16 >16 ------Pip/Taz >128/4 >128/4 >64/4 64/4 64/4 >64 >64 64 >64 >64 >16 Piper >128 >128 - - - - >64 32 >64 - >16 PolyB <2 <2 - - - <2 - - - - Rif >4 >4 ------Strep1000 <1000 <1000 ------Syn >4 >4 ------Tetra - - 8 >=8 8 - 8 >8 8 <=4 Ticar/K Clav ------>64 >64 >64 - >16

75

Timen >128 >128 >=64 >=64 >=64 - - - - - Tmp/Sxt 8/152 8/152 >=2/38 <=2/38 >=2/38 >2/38 >2/38 <=2/38 >2/38 <=2/38 NA Tobra <4 8 >=8 >=8 4 >8 >8 >8 4 >8 >4 Una >32/16 >32/16 >=16/8 >=16/8 >=16/8 ------Vanco >16 >16 ------Cefotax/ K ------>4 - Clav Ceftaz/ K ------>2 - Clav

Table 4. Kirby Bauer antibiotic resistance profiles of P. aeruginosa. aAntibiotic (ug/disk) AU CL CB C MEM IPM NN CIP CAZ Total strain 10 100 30 10 10 10 5 30 AR 12176 S R I R R S I R 4

14282 S S R R R S R S 4 16821 S I I S S R R S 2

17108 S R R R R S R I 5

17787 S R R R R S R R 6 18005 S R S R R R R R 6 S, susceptible; I, intermediate; R, resistant

76

Table 5. Sensitivity of CF-derived pathogens against environmental isolates. Antagonistic Number AU strain Species events 1 AU9856 P. aeruginosa 3 2 AU10241 P. aeruginosa 21

4 AU16135 P. aeruginosa 0 5 AU18132 P. aeruginosa 2 6 AU20785 P. aeruginosa 8 7 AU20836 P. aeruginosa 3 8 AU21833 B. multivorans 1

9 AU21952 B. dolosa 2 10 AU22104 B. cenocepacia 7 11 AU22420 P. aeruginosa 12 12 AU22505 P. aeruginosa 2

13 AU22725 P. aeruginosa 3 14 AU25543 B. multivorans 4 15 AU28056 B. vietnamiensis 1 16 AU28891 B. vietnamiensis 8 17 AU30232 B. cenocepacia 5

18 AU30376 B. cepacia 11 19 AU30447 B. cenocepacia 6 20 AU30737 B. cenocepacia 4 21 AU31398 B. cepacia 27

22 AU31588 B. dolosa 8 23 AU32676 B. multivorans 13 24 AU34312 B. multivorans 14 25 AU17108 P. aeruginosa 4 26 AU14282 P. aeruginosa 1

27 AU18005 P. aeruginosa 3 28 AU17787 P. aeruginosa 4 29 AU12176 P. aeruginosa 4

77

Table 6. Environmental superkillers (SK) mutagenesis capabilities. Strain Population Inhibition Mutagenesis LE22A4 3 4 Not capable LE22A5 8 3 Capable LE22A8 - 3 Not capable LE22B6 5 7 Capable LE22B7 - 3 Not capable LE22C4 8 3 Not capable LE22C6 8 8 Not capable LE22D3 - 5 Capable LE22D7 - 3 Not capable LE22F2 3 3 wt KanR LE22G7 3 3 Low efficiency LE23B1 - 3 Not capable LE23B2 8 3 wt KanR LE23E9 - 5 wt KanR LE23F2 - 3 wt KanR LE23F3 - 3 wt KanR LE23F5 9 4 Not capable LE23G6 8 7 Not capable LE23H3 9 6 Not capable LE24B10 8 3 Not capable LE24D9 - 5 Not capable LE24D11 1 3 Capable LE24G2 3 4 Capable LE24H11 8 9 Not capable

78 Table 7. Genome statistics of environmental isolates. S5F11 S3E7 LE22A5 Number % of total Number % of Number % of total total JGI Genome ID 2751186034 - 27478422 - - Total bases 5996650 - 5379044 - - Coding bases 5361321 89.41% 4854277 90.24% Total Genes 5598 - 5009 - - Protein coding 5440 97.18% 4862 97.07% genes rRNA genes 16 0.29% 22 0.44% tRNA genes 67 1.20% 75 1.50% Other RNA 75 1.34% 50 1.00% genes Protein coding 4462 79.71% 4054 80.93% genes: with function without function 978 17.47% 808 16.13% with COGs 3959 70.72% 3614 72.15% with Pfams 4628 82.67% 4216 84.17% Biosynthetic 9 - 3 - 9 - gene cluster (BGCs) Genes in BGCS 173 3.09% 90 1.80%

Table 8. Predicted BGCs for environmental strains and Tn insertions. Strain BGC # of genes in Putative BGC coordinates Tn Mutant number (Kb) BGC product hits S5F11 (wt) 53 37 NRPS 700096 - 752612 - - 77 37 NRPS 1122216 - 1199187 - - 11 11 Bacteriocin 2076759 - 2087634 - - 11 7 Bacteriocin 2246212 - 2256718 - - 43 32 Other 3423255 - 3466632 - - 11 10 Bacteriocin 3615232 - 3626077 - - 53 39 NRPS* 5486738 - 5539634 1 142 28 18 Betalactone† 74795 - 103143 - - 44 37 Arylpolyene† 3060219 - 3103794 - - S3E7 (wt) 24 20 Terpene 2924160 - 2947802 - - 63 32 Bacteriocin; 3295518 - 3358061 - - 53 38 NRPS 3548287 - 3601240 - - LE22A5 (wt) 51 30 NRPS† 245915 - 297066 - - 28 18 Betalactone† 892517 - 920865 - - 15 11 NAGGN† 1279746 - 1294553 - - 52 40 NRPS*† 1332101 - 1384080 1 218 9 9 Bacteriocin† 3075573 - 3084960 - -

79

42 32 NRPS-like† 3269000 - 3310703 - - 44 37 Arylpolyene† 3653426 - 3697001 - - 11 12 Bacteriocin† 4613624 - 4624499 - - 78 38 NRPS† 5488070 - 5564803 - - *Insertion within BGC caused LOI mutation †Predicted only in antismash

80

Table 9. Loss of killing (LOI) mutants. Strain Insertion coordinates JGI IMG Tn insertion of JGI and predicted Tn gene ID insertion gene S3E7 (wt)

106 2184584 – 2185078 2747927408 Thioredoxin reductase

176 2184791 - 2185089 2747927408 Thioredoxin reductase

223 2184776 - 2184941 2747927408 Thioredoxin reductase

1 3390453 – 3391110 2747928525 alginate O-acetyltransferase complex protein AlgI 28 3998917 – 3999567 2747929089 TruD family tRNA pseudouridine synthase

129 313734 - 314183 2747925635 glucose-1-phosphate thymidylyltransferase

111 3396624 - 3397125 2747928530 poly(beta-D-mannuronate) C5 epimerase

S5F11 (wt)

142* 5486738-5539634 2754355580 diaminobutyrate aminotransferase

190 1669728 - 1670253 2754351952 Heat shock protein. Metallopeptidase. MEROPS family M48B 145 1058681 - 1059373 2754351410 sulfite reductase (NADPH) hemoprotein beta-component 85 3133786 - 3134624 2754353352 sec-independent protein translocase protein TatB LE22A5 (wt)

92 1873245 - 1873346 - Tol-pal system protein YbgF 220 5628101 - 5628318 - Sulfite reductase 9 1873245 - 1873548 - Tol-pal system protein YbgF 58 4894592 - 4894949 - ATP-binding cassette domain-containing protein 270 1482463 - 1483012 - Cysteine synthase CysM 30 2460782 - 2461350 - bacterioferritin 218* 1352902 - 1353523 - NRPS 44 2460565 to 2461229 - PilZ-domain containing protein *Insertion within BGC

81

Table 10. S3E7 gain of inhibition mutants. BZ mutant Insertion co-ordinates gene product S3E7-157BZ 1832991 - 1833482 methyl-accepting chemotaxis sensory transducer with Cache sensor S3E7-263BZ 1832997 - 1833459 methyl-accepting chemotaxis sensory transducer with Cache sensor

S3E7-119BZ 187784 - 188442 glutathione S-transferase S3E7-35BZ - - S3E7-77BZ - -

Table 11. Pathogens inhibited by strains chosen for genetic analysis.

Strain Population Pathogens Inhibited S5F11 8 1;5;6;8;10;12;16;17;18;19;20;21;22;23;24;29 LE22A5 8 19;5;26 S3E7 1 1;2;4;5;6;7;11;12;15;16;18;19;21;28;29

82

Table 12. LE22A5 AP-PCR sequences. Mutant Sequence 92 TTTGTGCATGTACATCAGAGATTTTGAGACACAAGACGTCAGATGTGTATAAGAGACAGGCAACA AGGCGCGATTGAAGTTCTGCAGAATCAAGTGAACCAGCTCAAGCAAGAAGGCCTGGAGCGATACC AGGATCTTGATCGACGTATAGGAGCCGGCGTNNNNNNNNNNGTACTAGTCGACGCGTGCCA

220 GNNGCGGCCTTATTAAGATGTGTATAAGAGACAGAAGCTGTAGCGCTCGGCCAGGTCGGCAACGG CGTCCAGCTGTTTGTCAGTGATGTCGCCTGGGGCCACGCCGGTTGGCTTGAGCGACAGGGTCACGG CCACATAGCCTGGCTTCTTGTGGGCCAGGGTGTTGCGGGTACNCCAGCGGGCGAAGCCAGGATGCT GCTGGTCGAGGGCGGCGAGTTCGGCGTCCTGATCGCTCAGGGCCTTGTACTCGGCGTNNANNANNT NGTACTAGNCGACGCGTGCC

9 TNNGCGGCCTTATTAAGANGTGTATAAGAGACANCAGCTCTCAGCTTCGCACCGCTTGCGGTGTGG GCTGCGGTTCCTGTGGAAGATAGCAACTCTGGCTATAACAATAGCGGGAGCAGTTATCCGCCAGCG GGTTATGGCACGAACGGCGCCTATGCTGGGGGGGCGGCTACGTCCGCNCCCTCGGCACAGGGCGA ACTGTTCAATCAGCTGCAACGCATGCAGGATCAACTTGCGCAGCAACAAGGCGCGATTGAAGTTCT GCANAATCAAGTGAACCAGCTCAAGCAAGAAGGCCTGGAGCGATACCAGGATCTTGATCGACGTA TAGGAGCCGGCGTNNNNNNNNNNGTACTAGTCGACGCGTGCC

58 TTGGCGTGCCTTATTAANGANGNGTNTACGAGACNNCACAAGGTGGTTGACGACTTTTAANCTGTG ACATATTGAAATGAAAAAGGCGACCTCTGTAGGGTCGCCTTTTTTGTGCGCGCTTTTCAGCTCATTT TCAAGATTTTGAGCCCCAGCTTCTGTTGCTCGTCTTGCGCGTTGACCCAAACCACCTCGGTATCGGC TTCAAGGCCCTTGAGTGCTGCGTGCTCGGAGTCAATGCGCACGCTCAGCAGGTCACCGACGCTGAA TTGACGAGGCGCCTGCACCTGCATACCGGAGCTGGAAAGGTCCAGGCAAACACCTTCAATTTCCTG GCCTGCGTGGATCAAAGACACATTGGTATCCACCCGCATGCGGATGAAATCGCGTTTTTCGGCGTN NNNNNNNNNGTACTAGTCGACNCGTGCA 270 GTGCATGTACATCAGAGATTTTGAGACACAAGACGTCAGATGTGTATAAGAGACAGGCCCTGGCC ATGGCCGCCGCGATCAAGGGTTACAAAATGATCCTGATCATGCCCGACAACGGCAGCGCCGAGCG CAAGGCGGCAATGACCGCCTATGGCGCCGAGTTGGTGCTGGTGACCCAGGAAGAAGGCATGGAAG GTGCCCGCGACCTCGCCGAGCGCATGGCCGCCGCAGGCCGCGGCCTGGTGCTCGACCAGTTCGCCA ACGGGGATAACCCTGAGGCGCACTACACCAGCACCGGCCCGGAAATCTGGCGCCAGACCCAGGGC ACCATCACCCATTTCGTCAGTTCCATGGGCACCACCGGCACCATCATGGGCAACTCGCGTTACCTC AAGGAGCAGAACCCGGCGATCCAGATCGTCGGCCTGCAACCGATGGAAGGCGCGGCTATCCCCGG CATTCGCCGCTGGCCAGAGGCCTATCTGCCGAAGATCTACAACGCCTCGCGGGTCGATCGCATCAT CGACATGGCCCAGCGCGAAGCCGAAGACACCACCCGCCGCCTGGCCCGTGAAGAAGGCATCTTCT GCGGCGTTTCCTCCGGCGTANNAGTNGACGCNNNNCTNCA

30 CTGTGCATGTACATCAGAGATTTTGAGACACAAGACGTCAGATGTGTATAAGAGACAGTGGCAAT ACCGTGGTAGTGATCGAGCATAACTTGGATGTGATCAAAACGGCCGACTGGCTGGTGGACCTCGG GCCGGAAGGTGGCTCCAAGGGTGGCCAGATCATCGCAGTGGGCACGCCGGAGCAGGTCTCCGAGA TGCCCCAATCCCACACGGGTTACTACTTGAAACCTTTGCTGGCACGCGATCGGGCCTGATTTATCCG GCGCAATAAAAAGCCCCTGTCACCTTATTGGGTGACAGGGGCTTTTTCGTACCGGAAATCAGAACT GCGATTGCAGGTAGTTTTCCAGACCAATCAGCTTGATCAGACCCAGCTGTTTTTCCAACCAGTAGG TGTGATCTTCTTCAGTGTCGTTCAACTGCACACGCAGAATTTCGCGGGTGACGTAGTCGTTATGCTG TTCGCATAACTCAATGCCCTTGCAGAGCGCAGCACGAACTTTGTACTCAAGGCGAAGGTCGGCGGC GAGCATGTCANGNACCNNNNTGCCCNCNTCCAGATCATCAGGGCGCATACGTGGCGTCCCCTCGA GCATCAAGATGCGGCGCATCAACGCATCGGCGTGCNNNNCCTNGTACTAGTCGCCGCGTGCC

218* TTGCATGTACATCAGAGATTTTGAGACACAAGACGTCAGATGTGTATAAGAGACAGATCGAGCTG AGCCGTACGCCGCTGCTGGTGTGCAGCGAGGCGTGTCGCGAACAGGCCATCGCGTTGTTGGACGGT ATCGACTGCCAGTTGCTGGTATGGGACGAAGTGCCGGCCCGTGGTGAGAACCCGGGTGTCTACAGT GGCCCCGACAACCTCGCCTATATGATCTACACCTCCGGCTCCACCGGCCTGCCCAAAGGCGTGATG GTGCNNGTACTAGTCGANNCNTGCCCCCAGTTGAGCAAAGTGCCGTACCTGGACCTGACCGCAGC GGATGTGATCGCGCAAACCGCCTCCCAGAGTTTCGATATTTCCGTGTGGCAATTCCTCGCCGCGCC

83

GTTGTTTGGCGCGCGGGTGGACATCGTGCCGAACAGCATCGCCCACGACCCGCAAGGTTTGCTGGC CCATGTGCAGGCCCAGGGCATCAGCGTGCTGGAGAGCGTGCCGTCGTTGATTCAGGGCATGCTCGC CCAGGAGCGCCTGCACCTGGACGGCCTGCGCTGGATGCTGCCGACCGGCGAGGCGATGCCGCCTG AACTGGCGCACCAGTGGCTGCAACGCTACCCCGAGATCGGCCTGGTGAATGCGTACGGCCCGGCG GAATGTTCCGATGACGTGGCGTNNCNNNNNANGTACTAGTCGACNCNTGCC

44 CGTGCATGTACATCAGAGATTTTGAGACACAAGACGTCAGATGTGTATAAGAGACAGCTGCAGAC TTTGATGGATGTAGGCTTGTCGTATATCAAGCTCGGACAGTCGGCTACTACCNTGTCCGGCGGTGA GGCGCAACGGGTGAAGTTGTCTCGCNAACTATCCAAGCGCGATACCGGCAAGACTTTGTACATCCT GGACGAGCCGACCACCGGCCTGCACTTCGCCGATATCCAGCAACTGCTGGATGTGCTGCACCGACT CCGTGACCATGGCAATACCGTGGTAGTGATCGAGCATAACTTGGATGTGATCAAAACGGCCGACTG GCTGGTGGACCTCGGGCCGGAAGGTGGCTCCAAGGGTGGCCAGATCATCGCAGTGGGCACGCCGG AGCAGGTCTCCGAGATGCCCCAATCCCACACGGGTTACTACTTGAAACCTTTGCTGGCACGCGATC GGGCCTGATTTATCCGGCGCAATAAAAAGCCCCTGTCACCTTATTGGGTGACAGGGGCTTTTTCGT ACCGGAAATCAGAACTGCGATTGCAGGTAGTTTTCCAGACCAATCAGCTTGATCAGACCCAGCTGT TTTTCCAACCAGTAGGTGTGATCTTCTTCAGTGTCGTTCAACTGCACACGCAGAATTTCGCGGGTGA CGTAGTCGTTATGCTGTTCGCATAACTCAATGCCCTTGCAGAGCGCAGCACGAACTTTGTACTCAA GGCGAAGGTCGGCGGCGAGCATGTCANGNACCNNAGNGCCCNCNACCAGATCATCA *Insertion within BGC