Development of Novel Antimicrobial Peptides with Improved Hemocompatibility

Through Combinatorial Library Screening and Rational Sequence Engineering

AN ABSTRACT SUBMITTED ON THE TWENTY-SIXTH DAY OF OCTOBER TWO

THOUSAND SEVENTEEN TO THE DEPARTMENT OF BIOCHEMISTRY AND

MOLECULAR BIOLOGY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

OF THE GRADUATE SCHOOL OF TULANE UNIVERSITY FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY BY

______

Charles Gannon Starr

APPROVED:

______

William C. Wimley, Ph.D. Advisor

______

Samuel J. Landry, Ph.D. Hee-Won Park, Ph.D.

______

Zachary F. Pursell, Ph.D. Lisa A. Morici, Ph.D. ABSTRACT

Development of antimicrobial peptides (AMPs) as next generation clinical antibiotics has been a pursuit of the scientific community for several decades. AMPs are attractive drug candidates because of their potent antibacterial activity and a low propensity for eliciting antibiotic resistant bacterial phenotypes. However, despite substantial efforts and myriad development approaches, AMPs have yet to make inroads in the clinic due to toxicity concerns and activity loss in vivo. We hypothesized that eukaryotic cytotoxicity and antibacterial activity loss are intricately related in that peptide-induced tissue or host cell damage corresponds to depletion of free peptide available to target bacterial cells. Using human red blood cells (RBCs) as a model eukaryotic cell, we demonstrate that a cross-section of AMPs lose appreciable antibacterial activity when preincubated with concentrated eukaryotic cells (1x109 red blood cells/mL) and that this behavior can be explained by plasma membrane binding. To approach this problem in a unique manner, we synthesized a combinatorial peptide library based on the potent AMP, ARVA, and screened the library for activity in the presence of concentrated RBCs. We isolated nine unique, but similar sequences from the screen. During the screening program, we discovered that RBC-peptide interactions lead to peptide degradation through the release of cytosolic proteases. We used this knowledge to design a consensus sequence based on the nine peptides isolated from the library screen and synthesized it using only D-isomer amino acids. The novel peptide displays excellent antimicrobial activity against several human pathogens in the presence and absence of concentrated RBCs, has reduced toxicity towards eukaryotic cells, and is not susceptible to cleavage by cellular proteases. We attempted to use this peptide,

D-NOGCON, to combat P. aeruginosa in a mouse model of acute pneumonia, but were unable to ameliorate the negative outcomes associated with infection. We ultimately suggest alternative models of bacterial infection in which the peptide may be more effective and future approaches to further refining the sequence of D-NOGCON.

Development of Novel Antimicrobial Peptides with Improved Hemocompatibility

Through Combinatorial Library Screening and Rational Sequence Engineering

A DISSERTATION SUBMITTED ON THE TWENTY-SIXTH DAY OF OCTOBER

TWO THOUSAND SEVENTEEN TO THE DEPARTMENT OF BIOCHEMISTRY

AND MOLECULAR BIOLOGY IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS OF THE GRADUATE SCHOOL OF TULANE UNIVERSITY FOR

THE DEGREE OF DOCTOR OF PHILOSOPHY BY

______

Charles Gannon Starr

APPROVED:

______

William C. Wimley, Ph.D. Advisor

______

Samuel J. Landry, Ph.D. Hee-Won Park, Ph.D.

______

Zachary F. Pursell, Ph.D. Lisa A. Morici, Ph.D.

ACKNOWLEDGMENT

First, I would like to thank my doctoral advisor, Dr. William Wimley. Your ability to provide a well-established lab with ample funding was certainly important for my success as a graduate student, but your leadership and guidance during this formative period were invaluable. You seem to have mastered the art of providing enough structure to keep students on track, while leaving substantial room for us to forge our own path and develop the skills and independence needed for success at the next level. I consistently appreciated your level-headed approach to both research and life and the depth of your passion for science, which I think is exemplified by the culture of your lab as a comfortable and open place to learn and grow as a scientist.

I would also like to express my appreciation for the companionship of my fellow lab members. One of the most important things about enjoying the day-to-day of life in the lab is doing it with people whose company you enjoy. Whether it was learning a new assay, attending conferences, or just grabbing a beer after work, you were always there with ideas, advice, and support. In no particular order, thank you Jing, Taylor, Berkeley,

Shan, Hussain, Morgan, Cameron, Yilin, Eric, Jenisha, Charles, Kesany, Ryan, and Leo.

I also thank the members of my dissertation committee: Dr. Lisa Morici, Dr. Zachary

Pursell, Dr. Samuel Landry, and Dr. Hee-Won Park. I had most of you as instructors for classes and learned a great deal through these experiences in my early years in the BMS program. I’ve also worked with many of you in your labs and appreciate the tutelage at the bench, as well. Most importantly, you’ve all shown a great deal of support and the willingness to put your time and energy toward guiding me through the process of

2 obtaining a doctorate degree. I will always be grateful for everything you’ve done for my benefit.

It would be remiss not to mention the other faculty members, students, and staff of both the Department of Biochemistry and Molecular Biology and the Biomedical

Sciences graduate program. You all help contribute to the wonderful environment at

Tulane University and have supported me in many ways over the years. I wish everyone the best and hope that you all achieve the success and happiness that we all desire for the future.

On a more personal note, I deeply thank my girlfriend, Anna Koehl, who has been with me for every step of this journey. From the day we moved here in August of 2012, to the present day, and hopefully into the future, you have been and continue to be there to support me and all of my decisions. You’ve also embarked on an impressive journey of your own, completing nursing school in just two years and starting your career as a health care professional. I truly love and admire you, and can’t wait to see what the future has in store for us.

Finally, I’d like to thank the family back home, who have supported Anna and I over the past five years. My parents, Mark and Susie, and Anna’s parents, Bob and

Luanne, have always been there with timely visits, care packages, phone calls, and even financial assistance. There is no way that we could have accomplished everything that we have since moving to New Orleans and long before without the love and support that you’ve always shown us. We are so incredibly lucky to have parents like you who are always looking out for us and doing everything you can to help us succeed. I’d also like

3 to thank our siblings, my brother, Connor, and Anna’s sister, Kristine, who are always available to provide support, supply a laugh, or provide a much-needed distraction.

TABLE OF CONTENTS

ACKNOWLEDGMENT...... ii

LIST OF TABLES...... v

LIST OF FIGURES...... vi

CHAPTER 1: Introduction to biological membranes and antimicrobial peptides...... 1

CHAPTER 2: The effect of human red blood cells on the activity of antimicrobial peptides in vitro...... 31

CHAPTER 3: The determinants of inhibition of antimicrobial peptide activity by human red blood cells...... 60

CHAPTER 4: High-throughput screening of a combinatorial peptide library for antimicrobial peptides with stable activity in the presence of erythrocytes...... 106

CHAPTER 5: Rational sequence engineering of antimicrobial peptides based on sequences isolated in high-throughput screening with RBCs...... 147

CHAPTER 6: Assessment of a novel antimicrobial peptide in a murine model of bacterial pneumonia...... 187

LIST OF REFERENCES...... 208

4 LIST OF TABLES

Table 1-1. Membrane lipid compositions of model cell types...... 29

Table 2-1. MIC of AMPs against E. coli in the presence of RBCs...... 58

Table 2-2. MIC of AMPs against S. aureus in the presence of RBCs...... 59

Table 3-1: Serum-derived antimicrobial peptide degradation products...... 102

Table 3-2: Constants used in modeling of MIC...... 103

Table 4-1. Sequence information for peptides isolated from the combinatorial library..146

Table 5-1. Sequence design, underlying hypotheses, and statistical analysis for the first set of rationally engineered peptides...... 179

Table 5-2. Sequence design, underlying hypotheses, and statistical analysis for the second set of rationally engineered peptides...... 181

Table 5-3. Broth MIC values in the absence and presence of RBCs...... 183

Table 5-4. Radial diffusion MIC values, cytotoxicity, and solubility...... 185

5 LIST OF FIGURES

Figure 1-1. Models of membrane structure...... 19

Figure 1-2. Structures of Gram-positive and Gram-negative cell membranes...... 21

Figure 1-3. Structural diversity of antimicrobial peptides...... 23

Figure 1-4. Organization of amphipathic α-helices...... 25

Figure 1-5. Proposed mechanisms of antimicrobial peptide action...... 27

Figure 2-1. Hemolysis experiments with antimicrobial peptides...... 46

Figure 2-2. Design of the broth dilution assay with RBCs...... 48

Figure 2-3. Comparing AMP inhibition by RBCs and serum in broth dilution...... 50

Figure 2-4. Schematic of the radial diffusion assay with RBCs and serum...... 52

Figure 2-5. Relative antibiotic activity in the presence of biological fluids...... 54

Figure 2-6. CFU reduction assay with L and D-stereoisomers of ARVA and indolicidin. 56

Figure 3-1. Incubation of ARVA with RBCs...... 80

Figure 3-2. Incubation of indolicidin with RBCs...... 82

Figure 3-3. Comparison of the degradation of peptides with RBCs, serum, and RBC wash supernates...... 84

Figure 3-4. Comparison of the degradation patterns of serum and RBC-associated proteases...... 86

Figure 3-5. Localization of the proteolytic activity to the cytosol of erythrocytes...... 88

Figure 3-6. Incubation of a cross-section of synthetic and naturally-occurring AMPs with

RBC cytosolic extracts...... 90

Figure 3-7. Binding of ARVA to bacterial cells...... 92

Figure 3-8. The binding of ARVA to red blood cells...... 94

6 Figure 3-9. Measurement of the surviving CFUs in mock binding experiments and number of peptides bound per cell...... 96

Figure 3-10. Simulation of MIC of D-ARVA vs. E. coli based on experimentally derived parameters...... 98

Figure 3-11. A qualitative model of the barriers to systemic activity for AMPs...... 100

Equation 3-1. Single-site binding relationship with analytical solution using the quadratic formula...... 104

Figure 4-1. Design of an ARVA-derived combinatorial library...... 126

Figure 4-2. The split and recombine scheme for combinatorial library synthesis...... 128

Figure 4-3. Pre-screening characterization of the antimicrobial peptide library...... 130

Figure 4-4. Antimicrobial peptide library screening using radial diffusion assays...... 132

Figure 4-5. AMP library screening with radial diffusion and broth dilution...... 134

Figure 4-6. Library screening using only broth dilution in the presence of RBCs...... 136

Figure 4-7. Post-screening characterization of isolated AMPs in broth dilution assays. 138

Figure 4-8. Post-screening MIC in radial diffusion of library isolates...... 140

Figure 4-9. Post-screening cytotoxicity assays of library isolates...... 142

Figure 4-10. Binomial statistical analysis of the frequency of residue appearance at each combinatorial site...... 144

Figure 5-1. Comparison of the first set of rationally engineered AMPs in the absence and presence of RBCs in broth-based MIC assays...... 165

Figure 5-2. Comparison of radial diffusion MIC values for the first set of rationally engineered peptides...... 167

7 Figure 5-3. Comparison of the first set of engineered AMPs and parent sequences in eukaryotic cytotoxicity assays...... 169

Figure 5-4. Comparison of the second set of rationally engineered AMPs in the absence and presence of RBCs in broth-based MIC assays...... 171

Figure 5-5. Comparison of radial diffusion MIC values for the second set of rationally engineered peptides...... 173

Figure 5-6. Comparison of the second set of engineered AMPs and parent sequences in eukaryotic cytotoxicity assays...... 175

Figure 5-7. Comparing D-NOGCON, the result of screening and sequence engineering, to the template sequence, D-ARVA...... 177

Figure 6-1. Safety study for aspirated D-NOGCON by C57BL/6 mice...... 202

Figure 6-2. Treatment with D-NOGCON of acute pneumonia induced by P. aeruginosa aspiration...... 204

Figure 6-3. CFU burden analysis for mice receiving two doses of D-NOGCON over 24 hours...... 206

8 1

CHAPTER 1: Introduction to biological membranes and antimicrobial peptides

1.1 The biological membrane

The most basic unit of biological life is the single cell. In metazoans, groups of cells develop and work together to form networks and organ systems, ultimately becoming the complex, multicellular organisms that define the kingdom Animalia. While complex life is at the forefront of everyday experience for most humans, the origins of life and the vast majority of natural history belong to unicellular organisms.

The differences between free-living cells and those that comprise complex multicellular organisms are many, but one of the most basic requirements for the existence of cells of any type is a versatile lipid membrane. This membrane acts as a barrier separating the extracellular environment from intracellular proteins, nucleic acids, and other important macromolecules and cofactors. In addition to functioning as a barrier, the biological membrane must also maintain selective permeability such that essential nutrients can be extracted from the environment and metabolic waste and other non-useful compounds can be excreted. It is not hyperbolic to describe the plasma membrane as defining the cell itself, especially with respect to size and shape.

Perhaps the most useful description of the plasma membrane is the fluid mosaic model, originally proposed by Singer and Nicolson in 1972 and still relevant to this day 1

(Figure 1B). Prior to the development of this model, the primary components of the plasma membrane, phospholipids and proteins, were scientifically established and there was strong evidence supporting the orientation of the phospholipids in a bilayer. Still, it was not well understood how membrane proteins were oriented relative to the lipids, with many researchers believing that they were arranged in monolayers on either side of the 2 bilayer (Figure 1A)2,3. The fluid mosaic model suggests that most membrane-associated proteins are actually embedded in the bilayer and are free to diffuse laterally in the plane of the membrane1. The implications of this structural paradigm are critical to the ability of the membrane to act as a barrier to the external environment while remaining selectively permeable to certain molecules.

The phospholipid bilayer is composed of lipids with polar head groups which form a hydrophilic interface with the aqueous environment on either side. This allows the hydrophobic fatty acid side chains to associate in the center of the bilayer and create a barrier that prevents polar compounds, including water, from entering or exiting the cell.

Selective permeability is achieved by embedded proteins which serve as channels and transporters and allow the movement of molecules by passive or active transport.

The diversity of molecules utilized to construct a biological membrane includes a wide array of lipid species and an even more variable set of proteins. Despite these differences in composition between organisms, most membranes share many biophysical characteristics. Still, a discussion of the differences in membrane structure and composition is important to understanding the goals and outcomes of the research presented herein.

Bacteria are often classified in two broad categories based on their ability to retain crystal violet, an assay known as the Gram stain. One of the most widely performed procedures in the identification of bacterial species, the Gram stain reports on the structure and composition of the bacterial membrane4. The following sections will describe the differences between these two bacterial classifications, Gram-positive and

Gram-negative, and explore how they differ from eukaryotic membranes. Special 3 attention will be paid to the features that are known to modulate the selectivity of antimicrobial peptides.

1.2 Membrane structure of Gram-positive bacteria

While the composition of membrane lipids and other structural features are unique to each bacterial species, we will use Staphylococcus aureus as the model

Gram-positive organism in this discussion. Gram-positive bacteria have a single cytoplasmic membrane (Figure 2A)5. The membrane of S. aureus is composed of nearly

100% anionic lipids (42% cardiolipin, 58% phosphatidylglycerol), a feature that is of vital importance for the activity of antimicrobial peptides (Table 1)6. The net negative charge of the extracellular face of the bilayer promotes association of cationic antimicrobial peptides with the cell surface. An interesting exception is the presence of lysyl-PG, a cationic lipid, on the cytoplasmic face of the S. aureus bilayer. It has been observed that antimicrobial peptide resistant strains of S. aureus actively translocate these cationic lipids to the extracellular face of the bilayer to neutralize the net-negative charge of the more abundant lipid species6.

A more prominent feature of the Gram-positive cell, and the reason for its retention of the Gram stain, is the cell wall. Where the average thickness of a lipid bilayer is roughly 3 nm, the thickness of the Gram-positive cell wall can vary between 30-100 nm5. The cell wall is composed of repeating layers of a heterogenous polymer known as peptidoglycan. For S. aureus, the repeating glycan chain is composed of β1-4 linked

N-acetylmuramic acid (MurNAc) and N-acetylglucosamine (GlcNAc) (Figure 2A)7. The majority of the MurNAc residues are conjugated to the short peptide Ala (L) - Glu (D) -

Lys (L) - Ala (D)7. A pentaglycine cross-linking moiety then connects Ala (D) of one 4 peptide chain to Lys (L) of another via peptide bonds7. The result is a highly interconnected polymer network that forms a rigid protective barrier around the outside of the cell and confers resistance to both mechanical and osmotic lysis. Throughout the peptidoglycan network, are anionic polymers known as techoic acids, some of which are covalently attached to the peptidoglycan (wall techoic acids) and others that are anchored to the plasma membrane by lipids (lipotechoic acids)7. Because of their anionic properties, techoic acids are especially important to the study of antimicrobial peptides. It is not well understood how these polymers facilitate the trafficking of AMPs to the cytoplasmic membrane, but it has been observed that S. aureus can develop resistance to

AMPs by esterification of D-alanine residues to the polyribitol phosphate backbone, neutralizing the negative charge8. The final component of the cell wall is a variety of surface proteins, some covalently attached and others that are loosely associated. These proteins have many roles including functioning in adhesion and acting as virulence factors7. Taken together, the components of the Gram-positive membrane and cell wall, known collectively as the cell envelope, may comprise up to 20% of the dry mass of the cell9.

1.3 Membrane structure of Gram-negative bacteria

As with Gram-positive bacteria, the cytoplasmic membrane composition of

Gram-negative bacteria is unique to each species. Here, we will explore the general characteristics of Gram-negative membranes with a focus on the model organisms

Escherichia coli and Pseudomonas aeruginosa. Like Gram-positive bacteria,

Gram-negative organisms have a cytoplasmic membrane surrounded by a layer of peptidoglycan. One of the primary differences in terms of lipid composition, is the 5 presence of zwitterionic phosphatidylethanolamine (some Gram-positive organisms have

PE, but not S. aureus)6. In fact, most Gram-negative bacteria have a smaller fraction of negatively charged lipid species present in their membranes (5/15/80, 11/21/60

CL/PG/PE for E. coli and P. aeruginosa respectively) (Table 1)6. The function of the peptidoglycan is similar in both bacterial classes: conferring resistance to lysis and defining the cell shape. An important difference and one of the reasons for not retaining the Gram stain is that Gram-negative peptidoglycan is, at most, 4 nm thick10. This relatively thin layer is much less significant for the interactions and effectiveness of antimicrobial peptides.

Perhaps the most distinctive feature of Gram-negative bacteria is a second lipid bilayer surrounding the cytoplasmic membrane and peptidoglycan (Figure 2B). Referred to as the “outer membrane” (OM), it is attached to the peptidoglycan by a small lipoprotein, Lpp5. An emergent property of this double membrane organization is the presence of a compartment between the two bilayers, known as the periplasmic space.

Gram-negative organisms utilize this space in numerous ways, including for the sequestration toxic molecules and a variety of enzymes that degrade them, resulting in antibiotic resistant phenotypes5. This primitive separation of cellular functions by a set of membranes is often considered an evolutionary forerunner to the compartmentalized organelles of eukaryotic cells11.

The defining characteristic of the outer membrane is the presence of lipopolysaccharide (LPS), more commonly known as bacterial endotoxin, in the outer leaflet (Figure 2B). The innermost, membrane embedded, domain of this large molecule is lipid A. A pyrogenic molecule with a polyglucosamine core, LPS has multiple acyl 6 chains covalently attached that extend into and form the core of the bilayer 12. It also has two phosphate moieties conjugated to glucosamine, rendering it anionic and making it electrostatically attractive to antimicrobial peptides12. Similar to the strategy employed by

S. aureus, resistance to AMPs and the closely related polymyxins has been observed in both E. coli and P. aeruginosa due to the conjugation of aminoarabinose (Ara4N) to these phosphate groups, neutralizing their charges13,14.

Attached to lipid A, is a somewhat variable structure known as the core oligosaccharide. This domain can be divided into two distinct regions, known as the inner core and the outer core. The chemical structure of the inner core is highly conserved between bacterial species. The outer core, more likely to interact with host factors and the environment at large, is more variable12. An important feature of the core oligosaccharide is phosphorylation of some of the saccharide monomers. These negatively charged moieties coordinate divalent cations, creating a molecular network between multiple LPS molecules12. This network plays a vital role in stabilizing the outer membrane. It is of particular interest in the study of antimicrobial peptides as they have been shown to competitively displace these ions, leading to membrane instability and bacterial inviability15.

The final, outermost domain of LPS is a high variable structure known as the

O-antigen or O-polysaccharide. More than 60 unique monosaccharides have been identified in different variants of the O-antigen in different species12. These monosaccharides are conjugated in myriad combinations and orders and with variable linkages. An example of this variability, E. coli is known to produce at least 170 different

O-antigens, often referred to as serotypes12. The utility of the O-antigen is not well 7 defined, mainly because it is known to be an adaptation that is dependent on the organism’s unique environment. For example, the O-antigen of pathogenic bacteria is the most visible cellular structure to the host immune system. As such, the particular

O-antigen presented is likely to have a major impact on the timeliness and voracity of the host immune response. Unlike the other components of LPS, the O-antigen does not appear to have a major impact on the activity of antimicrobial peptides. Still, because it is the first structure encountered on a journey toward the membrane, it is possible that different serotypes do have some effect on the behavior of AMPs.

1.4 The structure of eukaryotic cells

The variety of cell types and morphologies in the taxon eukaryota rivals that of the bacterial classes previously examined. The simplest eukaryotes are free-living, single-celled organisms. Within the kingdoms Plantae, Fungi, and Animalia, are complex multicellular organisms in which cells become highly specialized to serve niche roles that benefit the organism as a whole.

Because of this fantastic diversity, it is once again necessary to select a model cell type to examine. In this work, we will examine the membrane structure and composition of the human erythrocyte, commonly referred to as the red blood cell or RBC. In human physiology, the erythrocyte plays an indispensable role as the major vehicle of respiratory gas exchange. Because of its physiological role, the red blood cell is a planktonic entity and travels vast distances during its 120-day lifespan16. In fact, it is estimated that each

RBC is recirculated to the heart once every minute17. During this journey, it encounters high fluid pressure and must traverse the narrow passages of capillary beds. To navigate this mechanical challenge, the RBC has as a unique discoid shape that facilitates 8 flexibility and deformation16. It is also one of the most numerous cell types in the body and is the most abundant cell type encountered by antibiotics administered in a systemic manner (e.g. intravenously). These facts form the bases for the work presented later in this study.

One of the most prominent differences between prokaryotic and eukaryotic membranes is the presence of sterols in the bilayer. Different cells utilize different sterols, but in mammals, cholesterol is the most common. The presence of cholesterol confers stability to the membrane and also promotes the formation of lipid rafts18. The red blood cell is no exception and with nearly 50% cholesterol by weight, it composes a higher membrane fraction than in most other cell types19.

The presence of cholesterol aside, the species of phospholipids present in the

RBC membrane are substantially different from those in bacteria. The anionic lipids, cardiolipin and phosphatidylglycerol, are completely absent from erythrocyte membranes and are replaced by the zwitterionic species, phosphatidylcholine and sphingomyelin19.

The only anionic species present at a significant concentration is phosphatidylserine.

Importantly, nearly all of the PS is sequestered in the cytoplasmic leaflet of the bilayer, rendering the exoplasmic leaflet electrostatically neutral with respect to lipid composition16. This is of particular importance to the selectivity of antimicrobial peptides, whose action is often dependent on the presence of anionic lipids on the cell surface. It should be noted, however, that sialylated proteins on the erythrocyte surface do confer a small amount of negative charge20.

1.5 Antimicrobial peptides: A brief overview and history 9

The study of peptides as antimicrobials began in the 1980s with the discovery of three unique peptide families which appeared to be innate immune effectors. The first family, dubbed cecropins, was discovered in the hemolymph of Hyalophora cecropia, the largest moth native to North America21. The two peptides, cecropin A and cecropin B, are

37 and 35 residues in length, respectively, and are known to adopt an α-helical conformation21,22. The identification of a second AMP family, the magainins, followed a few years later. Also α-helical in structure, magainin 1 and magainin 2 were discovered in the skin secretions of the African clawed frog, Xenopus laevis23. The third family, called

“defensins,” was initially identified in the lysates of leukocytes of rabbits and pigs 24,25.

They were later found to be present in a wide variety of vertebrates, including humans, and have been documented in every mammal in which they were sought25,26. The characteristics of defensins are described in more detail, below. Taken together, these discoveries hinted at the possibility of using peptides as antibiotics, potentially in human medicine. As such, a massive field of research began, with the goal of discovering a sequence that could serve as the forerunner for a new generation of antimicrobial compounds.

Since these initial discoveries, thousands of peptides with antimicrobial activity have been described27. Many of these were found in the context of innate immunity in diverse animal families28. Due to their conservation across diverse organisms, one of the most notable antimicrobial peptide families is the defensins. Although there is substantial sequence variation between different defensins, they are generally characterized as cationic, β-sheet rich, and most distinctively, by six conserved cysteine residues that form disulfide bonds25. In mammals, defensins are expressed in tissues that are frequently 10 exposed to microbial challenges. They exist at high concentrations in neutrophil granules, which fuse with compartments containing phagocytosed bacteria25. They are also excreted by Paneth cells in the small intestine, where they help control harmful bacteria and gut microflora25. Finally, they are often expressed in a variety of epithelia tissues, especially in locations that are likely to encounter substantial bacterial populations (e.g. tongue and trachea)25. While the defensins are some of the most well-studied AMPs, they are not the only family of interest. In mammals, other important AMP families include the cathelicidins and the protegrins29.

The discovery and study of naturally occurring AMPs has allowed researchers to understand the sequence and structural features that confer antimicrobial activity.

Although the two original AMP families were helical in nature, it soon became clear that peptides with a variety of sequences and structural features could act as potent antibiotics

(Figure 3). More important than the precise sequence, are the biophysical characteristics of the amino acids it contains30. Most AMPs have two common biophysical features: (1) they are positively charged due to overrepresentation of arginine and lysine and (2) uncharged residues are predominantly hydrophobic in nature31. While the exact sequence is often not critical to antimicrobial activity, the organization of the cationic and hydrophobic residues into domains is important. There is more than one way to accomplish this segregation. Many α-helical AMPs are amphipathic in structure. This means that the orientation of amino acids in the sequence is such, that when the helix takes shape, the charged residues reside on one face and the hydrophobic residues are on another32. In peptides without well-defined secondary structure, segregation is accomplished by the primary sequence. 11

An in depth understanding of the common features of antimicrobial peptides has prompted the development of numerous strategies for the discovery of synthetic AMPs.

Two commonly employed approaches abroad and in the research presented here are combinatorial library screening and rational sequence engineering33. Briefly, combinatorial library screening involves assessing the antimicrobial potential of an array of related peptide sequences with strategic variability at specific positions. This approach not only leads to the discovery of novel sequence variants, but also yields information on the contribution of different amino acids to the activity profile. In contrast, rational sequence engineering involves making small changes to the peptide sequence based on discreet hypotheses and testing the effect of those changes in antimicrobial and biophysical assays. Characterization is often more in depth with rational engineering, but the amount of sequence space explored is much smaller and can lead to less successful outcomes with respect to novel peptide discovery. Both approaches will be discussed in greater detail in the coming chapters.

1.6 Mechanisms of antimicrobial peptide activity

Antimicrobial peptides most commonly exert bactericidal activity by physically disrupting the integrity of the microbial lipid membrane(s)15. The physical properties described above, cationicity and hydrophobicity, are essential to this process. The cationic residues are responsible for the initial association of AMPs with the negatively charged structural features of the prokaryotic cell envelope (cell wall, membrane lipids, and LPS)15. This electrostatically driven docking brings the hydrophobic domain of the peptide into close proximity with the hydrophobic environment of the bilayer interior. It is thought that the hydrophobic residues partition into the bilayer core by virtue of their 12 physical chemical properties30. When a critical number of peptides associate with the bilayer in this manner, the structure and packing of lipids is disrupted and membrane integrity is compromised34. This mechanism is a departure from the activity of many small molecule antibiotics, which generally act by inhibiting the activity of specific bacterial enzymes (e.g. DNA gyrase, RNA polymerase). Clinically useful antibiotics with mechanisms similar to AMPs include the polymyxin family and daptomycin35.

While the mechanism of antimicrobial peptide activity is often described in broad strokes in terms of biophysical properties, specific mechanistic details remain elusive.

One of the earliest theories was the formation of discreet molecular pores in the bilayer29.

This idea is derived from the amphipathic, helical structure of many AMPs (Figure 4). In this model, the cationic face of the peptide is thought to line the aqueous environment of the inside of the pore while the hydrophobic face interacts with bilayer lipids on the exterior. Despite the logical consistency of pore-forming models, experimental evidence for their existence is less than conclusive. Thus, a number of other theoretical models have emerged to describe AMP behavior (Figure 5)36. Several of these models involve the interruption of the electrochemical balance of the cell by allowing passive leakage or directly carrying ions across the bilayer36. Other models include the clustering of charged lipid species by AMPs and the targeting of AMPs to specific regions of the cell based on membrane curvature6,37. Perhaps the most general theory, known as the carpet model, requires the accumulation of a large amount of peptide on the cell surface. When a critical density of peptide is achieved, the membrane structure collapses as the lipids are solubilized by peptide, forming micelle-like structures36. It is certainly possible that each of these mechanisms is viable for a subset of antimicrobial peptides or that a single 13 antimicrobial peptide acts via more than one mechanism. The study of AMP mechanisms may be hindered by classifying thousands of active sequence variants into a single antibiotic class. While the biophysical characteristics are generally similar, there is substantial sequence and structural diversity, permitting the existence of different mechanisms for different peptides.

The discussion of AMP mechanism is generally focused on membrane interactions. However, a small but growing group of AMPs have been shown to act on intracellular targets. Buforin 2 is thought to exert its antimicrobial activity by non-specific interactions with nucleic acids38. The bovine neutrophil isolate, indolicidin, has potent membrane permeabilizing activity but has also been shown to interact covalently with DNA39. A group of proline rich AMPs, including the insect derived pyrrhocoricin and drosocin, interact strongly with the protein folding chaperone DnaK36.

These are only a few of the alternative mechanisms that have been identified. While

AMPs with alternative mechanisms often receive less attention than their membrane active counterparts, their continued study is an important direction for the field. It is possible that many of the issues of selectivity discussed later in this study could be avoided by more focused targeting of AMPs. Conversely, the alternative targets described above, including nucleic acids and folding chaperones, are not unique to prokaryotes.

1.7 Advantages and disadvantages of peptide antibiotics

There are numerous advantages to the development of peptide antibiotics as therapeutics. Taken together, the family of antimicrobial peptides contains members with broad spectrum activity and others that target certain classes of bacteria more specifically40. This versatility is derived from the magnitude of the available sequence 14 space, which permits nearly endless variation. Additionally, the most potent antimicrobial peptides are active in the low µM concentration range: comparable or superior to conventional antibiotics deployed in clinical practice. But, perhaps the most attractive aspect of antimicrobial peptides is derived from their mechanism of action. As previously stated, AMPs differ from most small molecule antibiotics by targeting the plasma membrane instead of specific enzymatic machinery. The membrane is often described as a more general target because A) all cells have membranes and B) the biomass of the membrane and associated structures is substantial. The implication is that modification of the plasma membrane is less likely because it is costlier than modification of small, enzymatic targets. Because of this high cost, in tandem with the vast available sequence space, the emergence of resistance to AMPs is less likely30,41. In agreement with this idea, the most similar clinical antibiotics to AMPs, polymyxins and daptomycin, have some of the lowest rates of resistance. As a result, colistin (polymyxin E) is one of the drugs of last resort for highly resistant Gram-negative infections42. In the future, AMPs could share this role.

While the advantages of AMPs are numerous, this class of antibiotics also has several problematic characteristics. Limited solubility is not uncommon among AMPs, owing to the hydrophobic nature of many sequences. A closely related issue with respect to systemic administration is activity loss due to serum protein binding43. Serum albumin, the major proteinaceous component of serum, has several hydrophobic domains that have presumably evolved for the binding and transport of fatty acids44. These sites are also attractive binding partners for the hydrophobic domains of AMPs and as such, serum substantially reduces their activity. 15

Because peptides of varying shapes and sizes are ubiquitous in biology, most organisms have developed ways to neutralize harmful activities or simply break them down to reuse the amino acids. This vulnerability can be exploited directly by bacterial targets, many of which are known to release proteases into the periplasmic or extracellular space15,45. It is also possible for endogenous proteases in the host organism to break down AMPs. Moreover, in the context of a systemically administered peptide, the kidneys are excellent at removing peptides from circulation, which may lead to a half-life measured in minutes46.

A final disadvantage and a major focus of the work herein, are the off-target effects of AMPs. Although the anionic composition of bacterial membranes results in some level of selectivity over eukaryotic membranes, this behavior is far from perfect.

Many AMPs are known to be toxic to eukaryotic cells and while the concentrations required are usually higher than for prokaryotes, they are still too low to be considered viable therapeutics47.

1.8 Successes and failures in translational AMP research

Despite the initial excitement surrounding antimicrobial peptides as therapeutics, decades of research have not yielded the anticipated results. As of today, no AMP has been approved by the FDA for use in humans. However, several clinical trials have been completed for AMPs and many others are underway. A particularly notable example is the peptide MSI-78 (also known as pexiganan), which has advanced to phase III clinical trials two different times (1999, 2015)29,48. A derivative of the magainin family peptides,

MSI-78 was deployed for the treatment of microbial infections associated with diabetic ulcers29. In both instances, the peptide was unable to outperform the placebo, in part 16 because of a high rate of spontaneous infection resolution48. Iseganan, another AMP that has advanced to multiple clinical trials, is a less encouraging story. A variant of the porcine antimicrobial peptide, protegrin, iseganan was initially formulated as a mouthwash designed to prevent microbial infections in chemotherapy patients49. When it failed to outperform the placebo, it was withdrawn from the trial and ultimately re-purposed as an aerosolized treatment and prophylactic for ventilator-acquired pneumonia. Alarmingly, the treatment caused higher rates of pneumonia and mortality and the trial was ended prior to completion29. Despite these early setbacks in clinical trials, several antimicrobial peptides have advanced to the early stages in recent years and their study is ongoing48.

Although many clinical trials have been initiated for various indications, a prominent omission is anything resembling systemic administration. The reasons for this are likely related to the disadvantages of AMPs described in previous sections. Many of those roadblocks are only encountered upon systemic administration and those that occur in other environments (i.e. proteolysis) are more severe inside the body. Still, a variety of strategies have emerged to circumvent the issues encountered by AMPs without sacrificing activity. An example would be the incorporation of D-form peptides in the synthesis of the entire peptide or at strategic positions to prevent or slow proteolysis 50.

One issue that has proven difficult to resolve, however, is the binding of AMPs to hydrophobic surfaces such as proteins or off-target lipid bilayers.

1.9 Goals of the research presented in this study

The primary focus of the research presented herein will be the interaction of antimicrobial peptides with eukaryotic cells, specifically, human red blood cells. The 17 information in the preceding introduction highlights the potential of antimicrobial peptides as a new class of clinical antibiotics, but also describes the issues that have prevented successes up to this point. It is well established that antimicrobial peptides often cause lysis of eukaryotic cells. As such, one of the primary goals of this research is to discover peptides that are more compatible with red blood cells, while not sacrificing antimicrobial potency. More than this, we want to explore an understudied question in the field: the impact of host cell interactions on the antibacterial activity of antimicrobial peptides in environments where both cell types are present.

Our initial research aim was to study the behavior of several well-characterized antimicrobial peptides in the presence of red blood cells. To accomplish this, we modified standard antimicrobial assays, broth dilution and agarose diffusion, to include the presence of red blood cells. We then tested the activities of the antimicrobial peptides in these designer assays and compared them to the small-molecule antibiotics, streptomycin and vancomycin51. All of the antimicrobial peptides tested were inhibited by the presence of red blood cells, whereas the activity of small-molecule antibiotics was unhindered.

To better understand the determinants of AMP activity loss, we performed a series of cell binding experiments with the antimicrobial peptides L-ARVA, D-ARVA, and

L-indolicidin51. The results indicated that while these AMPs are somewhat selective for bacterial cells, they bind strongly to eukaryotic cells as well. We used these data to construct a computational model of AMP activity in the presence of red blood cells and

E. coli that describes the activity loss of D-ARVA51. In addition to cell binding, we also studied the activity of proteases residing in the erythrocyte cytosol. The results suggest that even in the absence of strong cell binding, these proteases have the capacity to 18 rapidly degrade antimicrobial peptides. Together, host cell binding and proteolysis present formidable barriers to antimicrobial peptide activity.

Having established that antimicrobial peptides can be inhibited by host cell interactions, we sought to engineer novel sequence variants that would retain activity in the presence of erythrocytes. We designed and synthesized a combinatorial peptide library with 28,800 unique members based on ARVA. To identify improved AMP sequences, we screened the library for activity in the presence of red blood cells. Using a combination of broth dilution and agarose diffusion methods, we performed three unique screens against Gram-negative bacteria. This screening approach yielded nine novel sequences. Characterization of these sequences revealed that while activity in the presence of RBCs was improved in some cases, the peptides still displayed unacceptable potency loss.

Due to the identification of proteolysis as a barrier to AMP activity, we pursued a rational engineering as a strategy to fine tune the library sequences and test the efficacy of D-amino acid variants. We discovered that a D-form consensus sequence based on the peptides isolated from the library retained activity much better than the original library peptides. An additional round of rational sequence engineering yielded a peptide,

D-NOGCON, that not only retained full activity in the presence of RBCs, but was also one of the most potent AMPs studied in our work.

Having discovered a radically improved antimicrobial peptide, we sought to investigate whether improvement in our in vitro assays would translate to antimicrobial efficacy in vivo. The first system we utilized was a murine model of acute pneumonia caused by aspiration of P. aeruginosa. Treatment with peptide was also administered via 19 aspiration. Unfortunately, the results indicated a lack of efficacy in controlling the infection. We ultimately propose to test the peptide in systemic and topical models of

Gram-negative bacterial infection and suggest potential future peptide development directions.

Figure 1-1. Models of membrane structure

(A) The precursor to the fluid mosaic model of membrane structure as proposed by

Danielli and Davson in 19353. This model held that the lipid bilayer was surrounded on either side by a layer of proteins. Minor modifications to the theory were made by

Robertson, which came to be known as the unit-membrane hypothesis and was widely accepted as the model for all cell membranes for several years2. This image is attributed to Richard W. Hendler in his review of membrane ultrastructural theories52.

(B) A modern interpretation of the fluid mosaic model put forth by Singer and Nicolson1.

A departure from earlier theories, the fluid mosaic model holds that many membrane-associated proteins are actually embedded in the bilayer. These proteins expose hydrophobic domains to the bilayer interior while interacting with the aqueous environment on either side via hydrophilic residues. Further, because the lipid matrix behaves as a fluid, membrane proteins are free to diffuse in the plane of the bilayer. This theory allowed for a much more dynamic view of the plasma membrane and is the basis 20 for the modern understanding of its nature and function. This image is licensed by Laura

Martin under a Creative Commons Attribution License (by 2.0). It has not been changed.

To view a copy of this license, visit http://creativecommons.org/licenses/by/2.0/ 21 22

Figure 1-2. Structures of Gram-positive and Gram-negative cell membranes

(A) The structural features of the Gram-positive membrane. A single cytoplasmic membrane is surrounded by a thick layer of peptidoglycan. Throughout the peptidoglycan layers are techoic acids and polysaccharides. A close-up depiction of the chemical structure of peptidoglycan is also presented. This membrane representation was originally published by Tripathi et al53.

(B) The structural features of a Gram-negative membrane system. The cytoplasmic membrane is surrounded by a thin layer of peptidoglycan. The outer membrane (OM) surrounds the cytoplasmic membrane and cell wall, creating a compartment known as the periplasmic space. The extracellular leaflet of the OM contains a unique lipid component known as lipopolysaccharide (LPS). A representation of the structural features of LPS is also shown. 23 24

Figure 1-3. Structural diversity of antimicrobial peptides

Nine well-studied peptides highlight the structural diversity of the AMP family. The first row shows peptides that are primarily α-helical in structure, while the second shows peptides that are composed of β-sheets. The final row represents peptides that do not have canonical secondary structure, but still demonstrate antimicrobial activity. The image shown here was originally published in the work of Nguyen and colleagues36. 25 26

Figure 1-4. Organization of amphipathic α-helices

(A) Helical wheel diagram of the synthetic antimicrobial peptide, WLBU2. This peptide was designed as an idealized amphipathic helix with half of the face composed of hydrophobic residues (yellow and green) and the other half containing cationic residues

(lavender). Most naturally occurring amphipathic helices are not segregated as severely as WLBU2. The antimicrobial activity of WLBU2 will be examined in later chapters.

This image was generated by a script created by Don Armstrong and Raphael Zidovetzk

(http://rzlab.ucr.edu/scripts/wheel/wheel.cgi).

(B) Three-dimensional structures of the amphipathic helix formed by Pseudin-2, an anuran AMP. Cationic side chains are depicted in blue; hydrophobic side chains are red.

The first image shows a top-down view of the helix. The second image is a ribbon structure of the peptide. These images were originally published by Jeon and colleagues54. 27 28

Figure 1-5. Proposed mechanisms of antimicrobial peptide action

A cross-section of mechanisms that have been proposed to explain the antibacterial activity of AMPs. The toroidal and barrel stave pore models were some of the first proposed and are most applicable to α-helical peptides. Because of a lack of helical structure but evidence for membrane disruption, the carpet model is favored to explain the behaviors of the peptides discovered in this work. The existence of many models reflects both the diversity of AMP structure and function, as well as a lack of understanding of the specific mechanistic details. The image shown here is was originally published in the work of Nguyen and colleagues36. 29 30

Table 1-1. Membrane lipid compositions of model cell types

That data conveys the molar percentage of each membrane component. Structures accurately represent the nature of the head group; acyl chains can be variable in nature. In cases where compositions do not add up to 100%, other minor lipid species are present.

The bacterial membrane compositions are from Epand and Epand6. The red cell membrane composition is derived from Dodge and Phillips19. 31

Table 1-1

S. E. coli P. aeruginosa Red blood aureus % % cell % % Phosphatidylglycerol (PG) 58 5 11 0

Cardiolipin (CL) 42 15 21 0

Phosphatidylethanolamine (PE) 0 80 60 15

Phosphatidylcholine (PC) 0 0 0 16

Phosphatidylserine (PS) 0 0 0 8

Sphingomyelin (SM) 0 0 0 14

Cholesterol 0 0 0 45 32

CHAPTER 2: The effect of human red blood cells on the activity of antimicrobial peptides in vitro

Introduction

The inception of antibiotic use for the treatment of bacterial infection was a critical point of inflection in the history of human medicine. In the years preceding, common ailments like strep throat and wound infection were serious, potentially fatal afflictions. Antibiotics have also improved outcomes with respect to surgical procedures and have allowed them to become increasingly complex and invasive55. Indeed, the past

80 years are often referred to as the “golden age” of antibiotics, due to the exceptional number of antimicrobial compounds discovered and their contribution to transforming clinical medicine56.

Unfortunately, experts in the field believe that we may be witnessing the end of this golden age due to the emergence of widespread resistance to antibiotics that were once cornerstones in the treatment of bacterial infections56. In some sense, resistance is inevitable because microbial communities are in constant competition for limited resources and their members continuously evolve to gain advantages over each other57.

Often, this competition leads to the emergence of compounds that selectively kill other microbes; the majority of the antibiotics that are used in human medicine are directly isolated from microbes or are chemical derivatives based on these naturally occurring compounds58. However, because evolution is a dynamic process, it is not unusual for mechanisms to emerge that combat exposure to these bacterial toxins57. Therefore, resistance genes are often already present in microbial communities at the time of antibiotic discovery57. Because bacteria are able to exchange genes horizontally, it is 33 usually only a matter of time before widespread human deployment of a given compound promotes the spread of resistance genes59. Still, clinicians and humans in general have not been great stewards with respect to the responsible prescription of antibiotics60. Studies have shown that physicians are more likely to prescribe antibiotics when patients ask for them, even when no diagnostic procedures have been performed or administration is thought to be unnecessary61. Perhaps more problematic, is the widespread deployment of antibiotics in agricultural livestock62. This problem is compounded, in many cases, by the long-term use of sub-therapeutic doses, a practice that maximizes the likelihood of resistance emerging62. Regardless of the cause, antibiotic resistance needs to be urgently addressed if we are to avoid a significant regression in clinical outcomes. A key element to combatting this issue is a revitalization of the dwindling antibiotic development pipeline63.

Since their discovery, membrane-active antimicrobial peptides (AMPs) have been investigated for their potential as therapeutic compounds in clinical medicine64. This pursuit is an intuitive approach, given that many AMPs are discovered in the context of innate immunity of organisms from diverse domains of natural life28,65. The initial pace of research and development in the field was extremely promising. One of the first families of AMPs discovered, the magainins, was thoroughly studied and is the basis for numerous synthetic analogues23,66,67. A potent variant, MSI-78, advanced to human clinical trials in 1997, a mere 10 years after the discovery of the parent peptide 65. The indications were for topical treatment of impetigo and infections associated with diabetic foot ulcer65. Despite its impressive antimicrobial activity in vitro, MSI-78 (also known as pexiganan) was unable to outperform existing treatments for these infections68. 34

Unfortunately, it seems that the story of MSI-78 was a harbinger for clinical antimicrobial peptide research outcomes. Over the following two decades, several other unique AMPs advanced to clinical trials, but were unable to overcome the barriers to activity in vivo33,68.

Although they have yet to achieve the success initially envisioned, research persists and a number of antimicrobial peptides are entering or undergoing clinical trials for a variety of indications48.

In the field of antimicrobial peptide research, a common theme has emerged when it comes to translating success to the clinic: peptides with potent sterilizing activity in vitro lose some or all of their potency when introduced to complex biological environments in vivo. Perhaps too much emphasis has been placed on the development of novel sequences variants, with a lack of attention to the barriers that prevent translational success. Peptide drugs, in general, must overcome several impediments if they are to become viable therapeutics. Common concerns include solubility, degradation, filtration, bioavailability, cost of syntheses, and interaction with serum components69. Somewhat unique to antimicrobial peptides, is the issue of acute cell lysis due to toxic membrane interactions. While the determinants of AMP selectivity are well-established, it is known that many peptides also have detrimental, off-target effects on human cells47. All of these issues must be addressed if antimicrobial peptides are to fulfill their potential as next generation antibiotics.

An understudied corollary to the cytotoxicity of AMPs, is the potential for host cells to sequester these peptides, reducing their efficacy against invading microbes. This concept directly follows from the mechanism of action of AMPs: physical disruption of the plasma membrane. In theory, any amount of peptide interaction with the membrane of 35 host cells not only yields potential toxicity, but reduces the pool of peptide available to target bacteria that might also be present. Such interactions could explain why AMPs display excellent activity in environments where only bacterial cells are present, but appear to be less effective when administered in vivo.

We sought to explore this hypothesis by modifying commonly used in vitro antimicrobial assays to include the presence of host cells. More particularly, we used human red blood cells to model systemic antibiotic administration. By comparing the activity of a cross-section of well-studied AMPs to a set of antibiotics that have already been approved for clinical use, we demonstrate that host cells interactions are likely a potent barrier to the efficacy of many antimicrobial peptides in vivo51.

Materials and Methods

Peptides and Antibiotics

Peptides were obtained from Bio- Synthesis Inc. (Lewisville, TX) and had purities >95% by HPLC. Vancomycin and streptomycin were obtained from Acros Organics (Geel,

Belgium). Unless otherwise stated, all solutions were prepared by dissolving lyophilized peptide powders in 0.025% (v/v) acetic acid. Peptide sequences are as follows.

Melittin: GIGAVLKVLTTGLPALISWIKRKRQQ-NH2.

Cecropin A: KWKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK.

Indolicidin: ILPWKWPWWPWRR-NH2.

LL37: LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES.

Magainin 2: GIGKFLHSAKKFGKAFVGEIMNS.

ARVA: RRGWALRLVLAY-NH2.

VVRG: WVLVLRLGY-NH2. 36

NATT: RRGWNLALTLTYGRR-NH2.

MSI-78: GIGKFLKKAKKFGKAFVKILKK-NH2.

MelP5: GIGAVLKVLATGLPALISWIKAAQQL-NH2.

WLBU2: RRWVRRVRRWVRRVVRVVRRWVRR.

Bacterial Strains and Growth Conditions

Escherichia coli strain ATCC 25922, Staphylococcus aureus strain ATCC 25923, and P. aeruginosa strain PA01 were used in this study. Subcultures, prepared by inoculating 25 mL of fresh tryptic soy broth (TSB) with 200 μL of an overnight culture, were grown to log phase (OD600 = 0.3−0.6), after which cell counts were determined by measuring the

OD600 (1.0 = 1.5x108 CFU/mL for S. aureus, 5x108 CFU/mL for E. coli, 4x108 CFU/mL for P. aeruginosa). Bacterial cells were diluted to appropriate concentrations in either

TSB or PBS, depending on the assay.

Human Serum and Erythrocytes

Fresh human serum (OTC) and human O+ erythrocytes were obtained from Interstate

Blood Bank, Inc. (Memphis, TN). Serum was vacuum-filtered through a 0.45 μm filter to remove precipitates. RBCs were subjected to four cycles of centrifugation at 1000xg with resuspension in fresh PBS. Following the final wash step, the supernatant was clear and colorless. RBC concentration was determined using a standard hemocytometer.

Minimum Sterilizing Concentration in the Presence of RBCs

Antibiotics were prepared at 5-times the final concentration needed in 0.025% acetic acid. The antibiotics were serially diluted by a factor of 2:3 horizontally across a 96-well, conical-bottomed plate, 25 μL per well. One column was reserved for controls. RBCs at

0, 2.5x109, 2.5x108, and 2.5x107 cells/mL were added in 50 μL aliquots to the appropriate 37 wells. Following a 30-minute incubation, 50 μL of TSB, inoculated with 5x105 CFU/mL, was added to all wells, and plates were incubated overnight at 37 °C. To assess bacterial growth, a second inoculation was performed with 10 μL of solution from the original plate added to 100 μL of sterile TSB. Following overnight incubation at 37 °C, the

OD600 was measured (values of less than 0.1 were considered sterilized).

Radial Diffusion in the Presence of RBCs

Underlay agarose was prepared by adding 5 g of low EEO agarose and 0.03 g of TSB to

500 mL of 10 mM phosphate buffer (pH = 7.4). Overlay agarose was prepared by adding

5 g of low EEO agarose and 30 g of TSB to 500 mL of 10 mM phosphate buffer (pH =

7.4). Both solutions were heated until the agarose melted and then autoclaved. To a rectangular, one-well plate, 20 mL of underlay agarose, inoculated with 8x106 CFUs of bacteria, was added. A sterile, 96-well plate replicator from Sigma-Aldrich (Darmstadt,

Germany) was set in the molten agarose and removed once the agarose solidified.

Antibiotic was prepared at 4 times the final desired concentration. For the antibiotic standard, a serial dilution of 3:4 across a 96-well plate was performed followed by 1:4 dilution with PBS. Otherwise, the peptide was diluted to 20 μM with cells and/or serum to give between 2% (1x108 cells/mL) and 20% (1x109 cells/mL). Solutions were incubated with gentle shaking for 30 min at 37 °C, prior to the addition of 10 μL to the wells in the underlay. Inverted plates were incubated at 37 °C for 3 h. Overlay was added, and the plate was incubated upside down overnight. Surface growth was cleared; the plates were sterilized with 25% methanol and 5% acetic acid. Zones of inhibition were photographed and analyzed using ImageJ.

Hemolysis 38

Peptide was serially diluted in PBS starting at a concentration of 100 μM. The final volume of peptide in each well was 50 μL. To each well, 50 μL of RBCs in PBS at 2x10 8 cells/mL was added. As a positive lysis control, 1% triton was used. The mixtures were incubated at 37 °C for 1 hour, after which they were centrifuged at 1000xg for 5 minutes.

After centrifugation, 10 μL of supernatant was transferred to 90 μL of DI H2O in a fresh

96-well plate. The absorbance of released hemoglobin at 410 nm was recorded and the fractional hemolysis was calculated based on the 100% and 0% lysis controls.

CFU reduction

Peptide was prepared at 5x the final concentration in 0.025% AcOH and 30 μL was added to a single well of a 96-well plate. PBS or red blood cells at 2.5x10 9 cells/mL were added to peptide in a volume of 60 μL. A bacterial suspension was prepared in PBS at 2.5x106

CFU/mL and 60 μL of the suspension was added to the experimental wells. The plates were then incubated at 37 °C for 1 hour. The mixtures were then serially diluted 1:10 and the dilutions were spotted on TSB agar. The agar plates were incubated at 37 °C overnight and colonies were counted the next day to determine the efficacy of peptides in the presence and absence of RBCs.

Results

Because we hypothesized that interactions with eukaryotic cells may cause AMP activity loss, we first sought to study these interactions by measuring hemolysis.

Hemolysis is a phenomenon where red blood cells rupture and release hemoglobin into the extracellular space70. It naturally occurs at low levels in the human body, but may be heightened by infection with certain microbes (e.g. S. aureus) or exposure to exogenous compounds15,70,71. The destruction of a large number of blood cells, in vivo, can be 39 problematic for the host, leading to conditions such as anemia and jaundice 72. Thus, it is important to measure hemolysis of potential therapeutics in vitro. In the case of AMPs, it also reports on selectivity for bacterial membranes.

Here, we measured the toxicity of a set of natural (Figure 2-1A) and synthetic

(Figure 2-1B) antimicrobial peptides in the presence of 1x108 red blood cells/mL. With the exception of cecropin A, an isolate of the moth Hyalophora cecropia21, and magainin

2 from the frog, Xenopus laevis23, all of the peptides tested induced a significant amount of hemolysis. It should be noted that the most lytic peptides, melittin and melP5, are a peptide toxin derived from honey bee venom and a derivative of said toxin, respectively73,74. While they have potent antimicrobial activity and often serve as proxies for AMPs, the template sequence evolved in nature for an entirely different purpose and they should not be expected to be selective.

Interestingly, the synthetically derived AMPs caused more hemolysis than the naturally occurring peptides. The peptides L-ARVA, D-ARVA, VVRG, and NATT are

AMPs that were derived from a synthetic vesicle-based screen75. They have a number of sequence similarities and all cause a notable amount of hemolysis. Because they were discovered in a screen against synthetic bilayers, it follows that they do not have a high level of selectivity.

The only human-derived AMP in this study, the cathelicidin, LL-3776, causes a moderate amount of hemolysis. While known and studied for its antimicrobial activity, it is likely more important in human biology for its immunomodulatory effects76. A more potent AMP, based on LL-37, is WLBU2. The minimal antimicrobial motif of LL-37 was discovered, expanded, and substituted to yield an ideal amphipathic helix that bears little 40 resemblance to the parent sequence77. Unlike LL-37, it is a potent AMP, but also displays significant cytotoxicity.

A final interesting observation is derived from the comparison of magainin 2 and

MSI-78. Magainin 2 was used as the template sequence for the rational design of MSI-78, one of the first AMPs to ever reach clinical trials65. While the antimicrobial activity of

MSI-78 is generally superior to that of magainin 2, it is also more hemolytic. It should be noted that the concentration of RBCs, 1x108 cells/mL, is only 2% of the human physiological value. The higher concentration of RBCs present in vivo would likely lead to lower levels of peptide-induced hemolysis.

Having shown, via hemolysis, that the peptides do interact with RBCs, we next sought to understand how these interactions impact AMP potency. To accomplish this, we leveraged several assays that have been used to assess antimicrobial activity and modified them to contain red blood cells. The first such assay is known as the broth dilution assay.

In the standard setup, the antibiotic of interest is prepared in serial dilutions and then challenged with a suspension of bacteria78. The mixtures are incubated and growth of the microbe is monitored, usually overnight. The lowest concentration of antibiotic that is able to effectively limit growth is known as the minimum inhibitory concentration (MIC).

We modified the assay such that it contains human red blood cells at three different concentrations (107, 108, 109 cells/mL) and also reports on the MIC in the absence of extraneous cells (Figure 2-2). We performed this assay with E. coli (Table 1) and S. aureus (Table 2) for the full panel of peptides presented in Figure 2-1, as well as the small molecule antibiotics streptomycin and vancomycin. The results demonstrate convincingly that antimicrobial peptide activity can be inhibited by concentrated (109 cells/mL) host 41 cells. With the exception of cecropin A and D-indolicidin, all of the AMPs tested lost activity in the presence of RBCs. It is notable that the only L-peptide to retain its antimicrobial activity, cecropin A, is also one of the only peptides that didn’t cause significant hemolysis. However, this does not appear to be a reliable pattern, as magainin

2, which also affects limited hemolysis, loses activity in the presence of RBCs. We were especially interested in the behavior of WLBU2, which had previously been reported to maintain activity in all human biological fluids77. The results presented here did not support the initial findings, as WLBU2 lost activity in the presence of RBCs. With respect to organismal differences, the potency of each AMP differs against E. coli and S. aureus, but the effect of RBCs is not dependent on the target bacterial species. Unlike the antimicrobial peptides studied, the small molecule antibiotics were largely unaffected by the presence of RBCs.

Using the broth dilution assay, we also wanted to study the effect of human serum on AMP activity and compare it to the effect of RBCs. We selected a subset of peptides

(melittin, L-ARVA, D-ARVA, and L-indolicidin) and performed broth dilution experiments with human serum at concentrations analogous to those used in the RBC experiments. The results, reported in Figure 2-3, show that the effects of serum and RBCs are similar, with serum being a slightly more potent inhibitor of antimicrobial activity.

The second assay that we modified to study the effect of RBCs was the radial diffusion assay. In these experiments, peptide is introduced to a small well in an agarose plate and allowed to diffuse into the surrounding medium78. The plate, which has been seeded with bacterial cells, is incubated overnight. It is then examined for microbial growth and if the antibiotic was effective, a small zone of growth inhibition is apparent 42 around the wells. As with broth dilution, we modified these experiments to contain RBCs at concentrations from 1x108 to 1x109 cells/mL (Figure 2-4). We used the same subset of peptides as in Figure 2-3 (melittin, L-ARVA, D-ARVA, and L-indolicidin) and also included streptomycin and vancomycin. The activity of each antibiotic was measured by determining the radius of the zone of growth inhibition in the absence of RBCs. The radius of the zone of inhibition in the presence of a particular concentration of RBCs was also measured and compared to the no RBC condition. The reduction in radius (in most cases) compared to the activity in the absence of cells and/or serum can be described as relative activity. As with the broth dilution, we sought to compare the effects of RBCs to serum and also study the effect of both combined.

The results in radial diffusion assays corroborate those from the broth dilution experiments, demonstrating that every AMP tested loses activity in the presence of RBCs

(Figure 2-5). In contrast, the small molecule antibiotics did not lose activity. Interestingly, the effect of serum was less dramatic in the radial diffusion experiments as compared to broth dilution. Further, the effect of red blood cells alone was greater than the combined effect of serum and RBCs. This is an unexpected result and suggests that different inhibitory mechanisms may be at work, with respect to serum and cells.

To gain a better understanding of the behavior of AMPs in the presence of RBCs, we employed an assay known as colony forming unit (CFU) reduction. The conditions in this assay are similar to broth dilution, however, instead of observing all-or-nothing growth/sterilization, the number of viable bacterial cells remaining in suspension after a fixed incubation period with antibiotic is calculated by plating and counting CFUs in dilutions of the mixtures. We used fixed concentrations of 5, 10, 15, and 20 μM. For this 43 assay, we studied an additional microorganism, Pseudomonas aeruginosa, which is an opportunistic human pathogen and will be included as a model Gram-negative organism in the coming chapters.

We chose to study of the differences in behavior between L- and D-amino acid peptides using the CFU reduction assay. It is interesting to note that the behavior of

L-ARVA and D-ARVA are very similar in the assays performed thus far. In contrast, the

D-amino acid variant of indolicidin appears to perform better in broth dilution than its

L-isomer counterpart (Figure 2-3). The results (Figure 2-6) indicate that both L- and

D-ARVA have similar performance profiles with respect to colony reduction, with the

D-stereoisomer having a slightly better activity retention profile. The difference between the -/+ RBC conditions was significant for every combination of organism and concentration for the L-peptide (p < 0.05). The D-peptide only had significant differences at 5 and 10 micromolar with Gram-negative organisms. The behavior is different for indolicidin, where there is less activity inhibition by RBCs, even for the L-peptide. The

D-peptide showed no significant difference under any of the measured conditions.

However, it should be noted that even in the absence of statistical significance, the presence of RBCs always leads to decreased microbial killing, except in cases were full sterilization was achieved. Importantly, it seems like the inhibitory effect of RBCs is more pronounced at concentrations near the MIC and can be overcome by increasing the peptide concentration. The differences between the stereoisomers of both peptides suggest that proteolysis is likely a factor in activity loss in the presence of RBCs, but does not entirely explain the phenomenon. The determinants of activity loss will be explored more deeply in the next chapter. 44

Discussion

The results presented in this chapter confirm our initial hypothesis: the presence of host cells (human RBCs) reduces the antimicrobial activity of AMPs. The phenomenon is introduced by showing that a large cross-section of antimicrobial peptides have toxic interactions with red blood cells. We continue by showing that in three distinct, commonly-employed assays of antibacterial activity, AMPs are less potent when host cells are present. While not investigating the mechanistic details in this chapter, the differences in behavior between L and D-amino acid peptides suggests that proteolysis is partially responsible for the activity loss. Still, there appear to be other factors involved, given that the D-peptides also display activity loss under some conditions. In agreement with our initial hypothesis, we believe that a combination of proteolysis and host cell binding can explain the activity loss we observed.

An interesting result across the experiments in this chapter is the qualitatively superior performance of naturally occurring AMPs. Starting with hemolysis, the natural peptides are generally less hemolytic than their synthetic counterparts. They also appear to suffer less activity loss in the antimicrobial assays when RBCs are present. This observation is emphasized by the results in the CFU reduction experiments, where the naturally occurring indolicidin isomers have fewer significant differences. A reasonable explanation for this behavior is that naturally occurring AMPs have been acted on by evolution over millions of years33,79. Continuing with this reasoning, it would seem that peptides that damage host cells in any significant way would be strongly selected against.

This may explain why natural AMPs display less hemolysis and seem less perturbed by the presence of eukaryotic cells. 45

In contrast to the naturally occurring peptides, the synthetically derived AMPs,

ARVA, NATT, and VVRG, were discovered in a screen of synthetic vesicles75. While synthetic membranes are crucial to the study of membrane active peptides, they only model the lipid bilayer and do not account for other features like protein and carbohydrate. Thus, there was not strong pressure to select for peptides that preferentially interact with microbial cells. The result seems to be the discovery of a peptide family with potent antimicrobial activity, but poor ability to discriminate between bacterial and eukaryotic membranes.

The observation of differential behavior and selectivity of naturally occurring and synthetically derived peptides elicits fundamental questions about the way in which synthetic peptides are designed and discovered. It is often the case that newly discovered

AMPs are tested first for antimicrobial activity in the absence of any other environmental factors. They are then independently tested for cytotoxicity against one or more eukaryotic cell types. While this process yields useful information, it does not accurately characterize AMP behavior when both cell types are present together. In truth, this is the only scenario that matters with respect to AMP utility in clinical medicine.

The work in this chapter suggests that a paradigm shift is needed in research and discovery strategies for AMPs. We believe that host cells should be included in antimicrobial assays from the beginning of the development process. Whether the approach is screening-based or employs rational design, knowing whether a peptide will be active in the presence of host cells is critical. The absence of studies like this might explain the failure of AMPs when they reach clinical trials. By incorporating host cells 46 into discovery strategies, researchers will be including natural selective pressures in synthetic AMP development.

Later in this work, we will detail a screening strategy that flows naturally into rational design and includes host cells at every step in the development process. This approach has led to the emergence of several promising AMP sequences that retain their activity in the presence of RBCs. If strategies like these are widely-incorporated into the

AMP development process, it may finally lead to development of clinically useful antimicrobial peptides. 47

Figure 2-1. Hemolysis experiments with antimicrobial peptides

Hemolysis induced by (A) naturally-discovered antimicrobial peptides and (B) synthetically-derived antimicrobial peptides. Peptides were incubated with 1x108

RBCs/mL for 1 hour at 37 °C. The cells were removed from suspension by centrifugation and a dilution of the supernatant was measured for absorbance at 410 nm. Error bars represent standard deviation. (N = 3) 48 49

Figure 2-2. Design of the broth dilution assay with RBCs

This broth dilution assay has been designed such that AMP activity will be measured in the presence of 0, 1x107, 1x108, and 1x109 RBCs/mL. Peptide is serially diluted from right to left, after which RBCs are added. Following a short incubation, bacterial cells at

2x105 CFU/mL are added. After the addition of bacteria, the plate is incubated overnight at 37 °C. Because the RBCs make the determination of growth/sterilization difficult, a small aliquot from each well is transferred to a fresh plate and incubated overnight, again at 37 °C. This plate is read at 600 nm to determine whether bacterial growth has occurred.

Plates with OD600 < 0.1 are considered sterilized. 50 51

Figure 2-3. Comparing AMP inhibition by RBCs and serum in broth dilution

Four representative AMPs were chosen for these experiments (melittin, L-ARVA,

D-ARVA, and L-indolicidin). The assays for serum and red blood cells were performed under analogous physiological conditions. Bacteria were added at 2x105 CFU/mL.

Inhibition was determined by reading the secondary plate for optical density at 600 nm after overnight incubation. Inhibited wells had an OD600 < 0.1. (A) AMPs vs. E. coli +

RBCs. (B) AMPs vs. S. aureus + RBCs. (C) AMPs vs. E. coli + serum. (D) AMPs vs. S. aureus + serum. Error bars represent standard deviation. (N = 6-22) 52 53

Figure 2-4. Schematic of the radial diffusion assay with RBCs and serum

The standard radial diffusion assay is modified for use with RBCs and serum. A serial dilution of peptide starting at 20 μM and reduced by 1/3 for each well is prepared from right to left in row 1. Rows 2-5 have fixed 20 μM peptide incubated with increasing concentrations of RBCs, serum, or both, from left to right. Rows 6-8 are RBC and serum only controls. The serial dilution is used to determine relative activity of the antibiotic when incubated with biological fluids. 54 55

Figure 2-5. Relative antibiotic activity in the presence of biological fluids

The antimicrobial peptides (melittin, L-ARVA, D-ARVA, and L-indolicidin) and small molecule antibiotics (streptomycin and vancomycin) were assessed in the radial diffusion assay as described in figure 2-4. (A) E. coli + RBCs. (B) S. aureus + RBCs. (C) E. coli + serum. (D) S. aureus + serum. (E) E. coli + RBCs + serum. (F) S. aureus + RBCs + serum. Error bars represent standard deviation. (N = 5) 56 57

Figure 2-6. CFU reduction assay with L and D-stereoisomers of ARVA and indolicidin

The L and D-amino acid variants of ARVA and indolicidin were tested in a CFU reduction assay in the absence and presence of 1x109 RBCs/mL against E. coli, S. aureus, and P. aeruginosa. Peptides were prepared at 5, 10, 15, and 20 μM and incubation was in

1% TSB in PBS for 1 hour at 37 °C. Following incubation, the suspensions were serially diluted and plated. * represents statistical significance at p < 0.05. (A) L-ARVA. (B)

D-ARVA. (C) L-indolicidin. (D) D-indolicidin. Error bars represent standard deviation.

(N = 3) 58 59 60

Table 2-1. MIC of AMPs against E. coli in the presence of RBCs 61

Table 2-2. MIC of AMPs against S. aureus in the presence of RBCs 62

CHAPTER 3: The determinants of inhibition of antimicrobial peptide activity by human red blood cells

Introduction

In the preceding chapter, we investigated the effect of human erythrocytes on the activity of antimicrobial peptides. We utilized the well-known antimicrobial assays, broth dilution, radial diffusion, and CFU reduction, and modified them to include the presence of red blood cells (RBCs). These experiments demonstrated that as RBCs become increasingly concentrated, the ability of AMPs to selectively target microbes is compromised and as a result, potency is reduced. This was particularly apparent at the highest concentration of RBCs (1x109 cells/mL), where many peptides did not retain any discernible activity at the concentrations assayed. Given that the highest concentration of

RBCs assayed is only 20% of physiological cell density, red blood cells represent a significant barrier to the prospect of achieving a systemically active antimicrobial peptide. In previous chapters, the experimental focus has been on the phenomenon of activity loss, rather than the mechanisms that underlie this behavior. Nonetheless, our measurement of hemolysis (Figure 2-1) are strongly suggestive of direct interactions with the erythrocyte plasma membrane, and the differential activity of the L- and

D-stereoisomers in CFU reduction (Figure 2-6) is indicative of biological peptide degradation.

The association of proteolytic activity with erythrocytes is a well-characterized phenomenon. Several decades ago, a number of studies were published on the degradation of proteins (e.g. hemoglobin) in the context of human red blood cells80–84.

This proteolytic activity was characterized as being independent of both ubiquitination 63 and the chemical energy of ATP80. Additionally, proteolysis was enhanced in the presence of oxidizing agents and oxidatively damaged proteins80,81. Further efforts to characterize the enzyme(s) responsible led to the isolation of a 670-700 kDa multisubunit protein complex that was originally named macroxyproteinase (M.O.P.)82,83. Temporally parallel to this work, was the study and characterization of the 26S proteasome: the major molecular complex responsible for the turnover of proteins inside the cells of eukaryotes and archaea85–87. Of interest to this discussion, the 20S subunit of the proteasome has a molecular weight of 673 kDa, well within the range of the complex described in early work with RBCs86. While protein degradation by the proteasome is often associated with the ATP-dependent activity of the 19S subunits, it has been shown that 20S core complex is capable of degrading proteins in an energy-independent manner88. Thus, it is likely that the activity of macroxyproteinase can ultimately be ascribed to the 20S proteasomal core complex. This assertion is strengthened by later studies that identified active, 20S complex subunits in the erythrocyte cytosol89. Proteomics approaches have provided further support for the presence proteasomal subunits, and have also predicted the presence of a number of serine, cysteine, and metalloproteases90–92. While the proteolytic activity of the RBC cytosol has been studied in great detail, we have not found evidence of studies in the context of antimicrobial peptides or exogenously administered therapeutic peptides. Here, we will begin this investigation.

In addition to peptide degradation, there is strong evidence for host cell binding as a major determinant of antimicrobial peptide activity loss. While it has been demonstrated that AMPs are selective for bacterial membranes due to electrostatic interactions, it is also clear that this selectivity is not sufficient to prevent binding of 64

AMPs to host cells. AMP interactions with membranes have been studied extensively, often through the use of synthetic phospholipid bilayers93–95. These studies employ a variety of analytical techniques, including fluorescence partitioning, circular dichroism, analyte leakage, calorimetry, and many others96. Approaches like these are indispensable in the study of peptide and membrane interactions, but it is important to remember that the biological membrane is much more complex than a simple phospholipid bilayer.

Specifically, the incorporation of proteins modifies the biophysical properties of the bilayer substantially97. Many cells also have carbohydrate-based macromolecules decorating the extracellular surface, which have been shown to interact with AMPs5,12,98.

Finally, because antimicrobial peptides induce cell lysis, a number of intracellular macromolecules, particularly anionic species like nucleic acids, have the ability to influence AMP behavior39,99. These structural differences highlight the importance of expanding binding studies from synthetic systems to include interactions with real cells.

Fluorescence-based approaches are often used to study the interactions of antimicrobial peptides with living cells, with the goal of detecting a signal that is distinct from naturally occurring compounds. As an indirect measure of peptide-cell interactions, the DNA intercalating fluorescent dye, SYTOX green, is often used100. When the bacterial

(or eukaryotic) membrane is compromised, the dye enters and associates with nucleic acids, increasing its fluorescence by several orders of magnitude. Recently, advances in imaging technology have permitted the use of SYTOX and other dyes to visualize the activity of antimicrobial peptides in real-time, as they disrupt biological membranes101,102.

To directly measure binding, it is common to covalently label the AMP of interest with a fluorophore. Recently, a group used a dansyl-labeled variant of the porcine AMP, 65

PMAP-23, to study the association of peptide with E. coli and erythrocyte membranes34,103. These studies led to interesting observations on the number of AMPs required to kill a bacterium (106-107 peptides/cell), as well as insight into the behavior of

AMPs in the presence heterogenous cell populations. However, the relative size and charge state of fluorescent dyes is not negligible in the context of antimicrobial peptide behavior. In the case of the studies referenced above, the dansyl moiety accounts for

~10% of the mass of the compound. Dansylated peptides also contain chemical groups that are not normally present in peptides, potentially altering the physicochemical properties of the peptide in unpredictable ways. Because of these complications, it is preferable to study binding using peptides that have not been modified to contain any additional chemical groups. An approach similar to the one that we will employ was used to study rhesus ϴ-defensin binding to E. coli104. In that study, HPLC was used to monitor the concentration of the AMP remaining in solution after incubation with varying concentrations of bacterial cells. We will adapt this approach and utilize the native fluorescence of tryptophan to study binding with multiple cell types.

In this chapter, we will pursue the aforementioned determinants of AMP activity loss using analytical methods. We will quantify the behavior several AMPs, including

ARVA and indolicidin, with a focus on solution stability and availability in the presence of human erythrocytes. First, we explore RBC-associated degradation of AMPs and investigate the nature and origins of the proteolytic activity observed. Later, we investigate the affinity of ARVA for the membranes of E. coli, S. aureus, and human

RBCs, using cell density-based binding experiments. Finally, we will use the data from

D-ARVA binding experiments and measurements of antimicrobial activity loss in the 66

MIC assays from Chapter 2 to construct a computational model of MIC changes driven by cell binding interactions.

Materials and methods

Peptides

All peptides used in this study were synthesized using solid-phase FMOC chemistry and purified to >95% by Bio-synthesis Inc (Lewisville, TX). Peptides were dissolved in

0.025% acetic acid solution and concentrations were determined by absorbance at 280 nm, if possible. In the absence of tryptophan or tyrosine residues, concentrations were determined based on measured weight of peptide and volume of solvent.

Red blood cell and serum preparation for proteolysis

Human red blood cells and serum were purchased from Interstate Bloodbank (Memphis,

TN). Red blood cells were from O+ donors and were collected in a citrate phosphate dextrose (CPD) anticoagulant solution. Upon receipt, the cells were washed three times with sterile PBS and stored at 4 °C at a density of 5x109 cells/mL until use, collecting and storing the supernatant with each wash. Before experiments were performed, cells were diluted to a working concentration of 1.33x108 cells/mL. Serum was devoid of clotting factors (OTC) and was stored at -80 °C until use. It was sterile filtered with a 0.22-micron vacuum filter to remove any particulates and precipitates. Serum was diluted to 2.66% of the initial concentration in PBS in order to match the physiological values of the RBC preparations.

Preparation of red blood cell cytosolic extracts and membrane ghosts

Red blood cell ghosts were prepared per the method published by Steck and Kant105.

Approximately 1 mL of washed, packed RBCs was added to a 50-mL centrifuge tube and 67

40 mL of cold, 5 mM phosphate buffer (pH 8.0) was added to lyse the cells. The lysed cells were rocked while incubating on ice for 30 minutes and then centrifuged at

18,000xg to separate the membrane fraction from the cytosolic components. At this point, the supernatant was collected to serve as the cytosolic extract for analysis. The membrane fractions were subjected to two additional rounds of washing with cold, 5 mM phosphate buffer and centrifugation at 18,000xg. The membrane fractions were then resuspended in warm PBS (pH 8.0) and incubated with rocking for 45 minutes at 37 °C to reseal the membranes. The resealed membranes were washed three times with room temperature

PBS and centrifuged at 18,000xg following each wash. Both cytosolic and membrane fractions were stored at 4 °C until use.

Peptide degradation experiments with cells

For each time point, a washed cell suspension was mixed with peptide to give final concentrations of 20 µM peptide and 1.0x108 cells/mL. All experiments contained 5 µM

FMOC-aspartic acid as an internal HPLC standard. The mixtures were incubated at 37 °C with agitation. At the appropriate time points, the mixtures were spun down at 1000xg to pellet the RBCs and the supernatant was removed for analysis by HPLC.

Peptide degradation with erythrocyte ghosts and cytosolic extracts

Membrane ghosts or cytosolic extracts were prepared as above. To match the cell-based experiments, peptide and cytosol or ghosts were mixed to give 20 M peptide and a final concentration of cytosol/ghosts equivalent to 1.0x108 cells/mL. The mixtures were incubated at 37 °C with agitation. At the appropriate time points, the mixtures were analyzed via HPLC.

Peptide degradation with serum 68

Serum was diluted to 2% in PBS to match the dilution of the cells used in other degradation experiments. As with other experiments, the final peptide concentration was

20 M. The mixtures were incubated at 37 °C with agitation. At the appropriate time points, the mixtures were analyzed via HPLC.

HPLC analysis of peptide degradation

Analysis of peptides and degradation products was performed using reversed-phase chromatography. The stationary phase was a 100 mm x 4.6 mm C-18 column from

Kromasil (Bohus, Sweden). The mobile phase was composed of a gradient of distilled water (0.1% trifluoroacetic acid) and acetonitrile (0.1% trifluoroacetic acid) with a flow rate of 1 mL/min. Where possible, peptide and peptide fragments were analyzed using tryptophan fluorescence (285ex/340em). In the absence of tryptophan residues, peptide was analyzed by absorbance at 220 nm.

Fragment collection and mass spectrometry

HPLC was used to collect peptide fragments eluted during analysis. Each potential fragment was subjected to MALDI-TOF mass spectrometry using a

α-Cyano-4-hydroxycinnamic acid (CHCA) as a matrix. Masses observed in this analysis were compared to a list of all potential cleavage products. An error of less 0.5 Da was considered acceptable for peptide identification.

Bacterial Binding

Cells were grown to high density (OD600 = 1.0 or greater), pelleted at 10000xg and

8 resuspended in 1x PBS. CFU density was determined by OD600, as above (1.0 = 1x10

CFU/mL for S. aureus, 5x108 CFU/mL for E. coli). Bacteria and peptide mixtures were rocked for 30 minutes at RT, and centrifuged at 10000xg. The resulting supernates were 69 injected onto an analytical reverse phase HPLC column. The stationary phase was a 100 mm x 4.6 mm C-18 column from Kromasil (Bohus, Sweden). The mobile phase was composed of a gradient of distilled water (0.1% trifluoroacetic acid) and acetonitrile

(0.1% trifluoroacetic acid) with a flow rate of 1 mL/min. The native tryptophan fluorescence of these peptides was used to determine how much peptide remained in solution following incubation with cells.

RBC Binding

Stock RBC solutions were prepared and mixed with stock peptide solutions in a 3:1 ratio.

The solutions were rocked gently for 30 minutes at RT prior to centrifugation at 10000xg.

The resulting supernates were analyzed using HPLC as previously described for bacterial binding experiments.

Results

Proteolysis

To begin our investigation of the contribution of proteolysis to AMP activity loss in the presence of RBCs, we incubated ARVA and indolicidin with 1x108 red blood cells/mL for 4 hours at 37 °C. We centrifuged the mixtures to remove the cells and proceeded to analyze the results. The results (ARVA: Figure 3-1A, indolicidin: Figure

3-2A) are highly suggestive of peptide degradation, as indicated by the emergence of multiple new peaks following incubation. To collect further evidence, we performed the same experiments with peptides containing all D-amino acids (Figure 3-1B, Figure

3-2B), which should not be susceptible to degradation by biological proteases. As expected, no additional peaks appeared in the experiments with D-peptides. We collected the putative peptide degradation products of the L-peptides using HPLC and identified 70 them with MALDI-TOF mass spectrometry. Figures 3-1C and 3-2C show that the peaks observed after incubation of the L-peptides with RBCs were indeed products of AMP degradation. We identified seven and eleven peptide fragments for ARVA and indolicidin, respectively. Because we used tryptophan fluorescence to collect the fragments, it is likely that additional fragments without tryptophan residues were present. Finally, we did time course experiments to monitor the rate of degradation for both sets of peptides

(Figure 3-1D, Figure 3-2D). ARVA was degraded rapidly, with no L-peptide remaining after eight hours. The D-form of the peptide rapidly decreased in concentration by 50% and then stabilized. This behavior is consistent with the binding experiments described later in this chapter. Indolicidin was more stable; more than 20% of the peptide was intact after 24 hours. D-indolicidin was present at nearly 100% of its starting concentration for the duration of the experiment.

Having confirmed the presence of proteolytic activity in our RBC preparations, we sought to discern its source. A preliminary concern was that residual serum proteases were present in the RBC suspensions. To test this idea, we performed a series of incubation experiments with the supernatant solutions collected during the RBC washing process. RBCs, at a concentration of 5x109 cells/mL, were washed three times with sterile

PBS, followed by gentle centrifugation at 1000xg. ARVA and indolicidin were incubated with each of the wash supernates, as well as RBCs and human serum, both at 2% physiological concentrations (Figure 3-3A: ARVA, Figure 3-3B: indolicidin). Both peptides were degraded by the first wash supernatant, but not as quickly as with either

RBCs or serum. With both the second and third wash supernates, both peptides remained intact for 24 hours. These results suggest that while residual, serum proteases are likely 71 present in unprocessed RBC suspensions, our washing procedure is necessary and sufficient for their removal.

To further differentiate the activity of serum and RBC-associated proteases, we studied the degradation patterns and products of both solutions. In Figure 3-3, we compare the rates of degradation for incubations with serum and RBCs. ARVA (Figure

3-3A) is degraded more quickly in RBC suspensions than in serum suspensions; the converse is true for indolicidin (Figure 3-3B). This observation suggests that different proteases are active in the serum and RBC conditions. In addition to rates of degradation, we also studied the pattern of product generation by examining the HPLC chromatograms. Figures 3-4A and 3-4B display tryptophan fluorescence in the peptide elution region during a time course incubation with RBCs for L-ARVA and L-indolicidin, respectively. Parallel experiments were performed for L-ARVA (Figure 3-4C) and

L-indolicidin (Figure 3-4D) in preparations of human serum. Comparison of these experiments reveals that for both peptides, the processes of degradation are unique for the serum and RBC incubations. In addition to these qualitative assessments, we sought quantitative evidence of differential degradation-product generation. We collected the fragments from serum experiments and identified them using MALDI-TOF mass spectrometry. The results, presented in Table 3-1, reveal that while a number of the peptide fragments from the serum and RBC incubations are overlapping, there are also unique products that are only present in one experiment or the other. Taken together, the results in Figures 3-3 and 3-4 show that the proteolytic activity in RBC preparations is not an artifact of residual serum proteases, and is exerted by enzymes that are at least partially unique from those found in serum. 72

With the knowledge that the proteolytic activity was RBC-associated, we examined the specific, subcellular localization. Because RBCs lack nuclei and most other cellular organelles, we were interested in whether the protease activity was intracellular or membrane-associated-extracellular. Our approach to this question was to use the well-established method of RBC ghost preparation (Figure 3-5A)105. Here, the RBCs are thoroughly washed and then lysed in a hypotonic phosphate buffer. The suspensions are centrifuged at high speeds to separate the membrane fraction from bulk solution. The first centrifugation allows for collection of supernatant liquid containing the cytosolic components of the erythrocytes. The membrane fractions are subjected to additional washing and centrifugation cycles to remove any residual solutes. The final step is ghost membrane resealing via a short incubation in warm PBS. The entire preparation procedure was performed to maintain the supernatant and ghosts at the same relative concentrations as in the incubations with whole cells (1.0x108 cells/mL). We incubated

L-ARVA and L-indolicidin with either the cytosolic fraction or membrane fraction and monitored degradation for 24 hours. When incubated with membrane ghosts (Figure

3-5B), and then separated by centrifugation, the indolicidin recovered from the supernatant was essentially equal to the starting concentration, while the

ARVA-containing solution quickly lost approximately 40% of the peptide and then stabilized. In both cases, there was no evidence of degradation products on the HPLC chromatogram. The decrease in ARVA concentration is consistent with the strong membrane affinity and binding discussed later in this chapter. When either peptide was incubated in the cytosolic fraction, they were degraded (Figure 3-5C). These results suggest that the enzymatic activity is sequestered in the cytosol. In the case of intact cells, 73 degradation can be explained by moderate hemolytic induction by these AMPs, causing enzyme release into the extracellular space.

Our final inquiry with respect to proteolysis was to examine the degradation rates of the peptides studied in the previous chapter, in addition to ARVA and indolicidin. To standardize for differing levels of hemolysis between the peptides, we performed these experiments in the cytosolic extracts of RBCs. We were able to study all of the peptides from the previous chapter except for melittin and LL-37, which could not be chromatographically separated from the large hemoglobin peak in the preparations. All of the peptides studied, both natural (Figure 3-6B) and synthetic (Figure 3-6A), were significantly degraded over the 24-hour observation period.

An interesting comparison can be made between the degradation rates of magainin 2 and MSI-78. As previously stated, MSI-78 is a rationally engineered sequence variant of the anuran derived magainin 2, and was the first AMP to reach clinical trials23,65,66. The sequence of the natural peptide was modified to replace five polar residues with cationic lysine residues. Based on the experimental data, these substitutions confer increased resistance to degradation by RBC-associated proteases. Indeed, it takes only two hours for 50% of magainin 2 to be degraded, whereas eight hours of incubation are required to observe a congruent decline in MSI-78 concentration. The differences between these peptides suggest that susceptible peptide bonds are not defined by cationic residues, as would be expected from trypsin-like enzymatic activity.

Another interesting result of comparing the degradation rates is the relative stability of WLBU2. A perfect amphipathic α-helix by design, it is likely that the secondary structure of this peptide contributes to its stability. An additional stabilizing 74 factor may be the presence of eight valine, three tryptophan, and thirteen arginine residues in the sequence77. These amino acids have relatively bulky side chains that can obscure access to the peptide backbone. WLBU2 is particularly noteworthy as it has been reported to retain activity in both serum and RBC suspensions as well as in animal models77,106. Although we did not observe full activity retention in our assays, this peptide did perform well relative to the others studied51.

Cell binding

Convinced of the presence of proteolytic activity derived from cytosolic RBC components, we remained interested in the phenomenon of host cell binding. While proteolysis might explain activity loss by L-peptides, it does not account for the activity loss of D-ARVA. To explore this phenomenon, we performed a series of binding experiments with both L- and D-ARVA with E. coli, S. aureus, and RBCs. We incubated peptide at 5 µM and 20 µM with each cell type for a series of increasing cell densities.

After a 30-minute incubation of peptide and cells, the cells were removed from suspension by centrifugation and the supernates were analyzed with HPLC. In an effort to minimize alterations of the physicochemical properties by the conjugation of a fluorophore, we used the native tryptophan fluorescence of ARVA to quantitate the amount of peptide remaining in solution. The results for bacterial cells and RBCs are presented in Figure 3-7 and Figure 3-8, respectively. The behavior of each peptide isomer was similar in bacterial binding experiments for both organisms studied. There was a marked difference in the data for the 5 µM and 20 µM concentrations, suggesting that binding to bacterial cells is a saturable phenomenon under these conditions. This behavior is in contrast to the red blood cell binding experiments, where peptides at both 75 concentrations were bound at similar fractions for the same cell counts, which is more in agreement with a partitioning equilibrium. It is notable that D-ARVA appears to have a slightly reduced affinity for RBCs, which is likely an experimental artifact of the proteolytic activity previously discussed. Taken together, the results of the binding experiments reveal that as expected, the ARVA variants have a higher affinity for bacterial cells than for RBCs. It takes an order of magnitude increase in RBC density to achieve ~100% bound peptide, as compared to the bacterial cells. When the surface area of the cells in question is considered (E. coli: 6 µm2, S. aureus: 2.3 µm2, RBC: 200 µm2), this observation is especially striking107–109. Still, the highest concentration of RBCs assayed is only 20% of physiological concentrations and is sufficient to sequester all of the peptide in solution, indicating that while selectivity is apparent, it needs to be improved if AMPs are to be employed as systemic therapeutics.

Our final objective with respect to cell binding was to use the peptide binding and

MIC data to construct a computational model of D-ARVA activity loss. A critical parameter for development of this model was the number of peptide molecules required to kill each individual bacterium. We acquired this data in a series of mock binding experiments. These were executed in the exact manner as those displayed in Figure 3-7, however, instead of analyzing the supernates with HPLC, we plated the suspensions to determine the number of surviving CFUs after a one hour incubation. The bacteria were considered inhibited by the presence of peptide if fewer than 0.1% of the initial CFUs were remaining (Figure 3-9). Because the data lacked a clear mathematical relationship, we performed simple interpolation between the two points where the inhibitory threshold was crossed and assigned 1.13x108 CFU/mL as this critical value. The binding data 76 allowed us to calculate the number of peptides bound to each cell at each concentration

(Figure 3-9). Using the calculated critical cell density and interpolation of the binding data, we experimentally determined that 1.03x108 peptides/cell are required for D-ARVA to kill an E. coli cell.

We began our construction of the MIC simulation using a simple hyperbolic binding equation to model association of peptide with each cell type (Equation 3-1). This equation was rearranged such that it yields the fraction of peptide bound (fb), given knowledge of the initial peptide concentration ([PT]) concentration of binding sites ([ST]), and the dissociation constant (KD). We note that several assumptions are made in order to apply this equation: (1) all binding sites on the cell surface are the same, (2) binding can be described by a reversible equilibrium, and (3) binding behavior is consistent over a range of peptide and binding site concentrations. Having already obtained experimental data on the fraction of peptide bound at a given cell density, we set the unknown coefficients, the dissociation constant and concentration of binding sites, such that the calculated fraction of bound peptide approximated the experimental data for both E. coli

(Figure 3-10A) and red blood cells (Figure 3-10B). The constants used to calculate these fits are reported in Table 3-2. We note that the number of binding sites calculated for each cell type is somewhat arbitrary, however, these values are constrained by the surface areas of the cells, relative to each other. Although we collected binding data at 5 µM and

20 µM, the fit was only determined for the 20 µM condition.

To simulate the MIC, we used the binding equation with unique coefficients for each cell type, combined with our knowledge of the E. coli cell density sterilization threshold determined in Figure 3-9. The sterilization threshold is used in the binding 77 equation to calculate the fraction of peptide bound to bacteria. This fraction can then be used to calculate the total number of peptide molecules that are bound to bacteria. When this quantity was divided by the number of bacteria in suspension, we calculated that

9.84x107 peptide molecules are required to kill one microbe. This is very close to the experimentally determined value of 1.03x108 peptides/cell. The number of peptides to kill a cell can be used with the binding equation to calculate the MIC for any microbial cell count. For the number of cells in our assays, 2x105 CFU/mL, we calculated an MIC of

1.5 µM. Again, this value is close to the experimentally determined MIC of 3.0 µM. We then expanded our theoretical calculations to include the presence of RBCs in the MIC calculation. For this usage, we first calculated the amount of peptide bound to the RBCs in solution and determined how much peptide is left to interact with E. coli. We then calculated how much of the remaining peptide will bind to E. coli and how much will remain free. Because this calculation shifts the equilibrium for RBCs, we recalculated the amount of peptide bound to RBCs. This process is repeated until the amount of

RBC-bound, bacteria-bound, and free peptide are all stable. We then determined whether the amount of peptide bound to the bacteria is enough to reach the critical number of peptides per cell needed for sterilization. By doing these calculations for a series of RBC counts (1x105 to 1x1010) and a range of peptide concentrations (0.1 µM to 250 µM), we generated the simulated MIC curve shown in Figure 3-10C. This MIC curve matches the experimentally determined MIC values quite accurately. It also demonstrates that as the

RBC count approaches physiological values (5x109 RBC/mL), the MIC value for

D-ARVA exceeds 100 µM. 78

Having mapped the simulation to our experimental values, we sought to explore its behavior when the cell dissociation constants were modified. The results of a log-fold decrease in E. coli KD or a log-fold increase in RBC KD are shown in Figure 3-10D. Both modifications significantly reduce the MIC at physiological concentrations to values that would be considered useful for a clinical AMP. It should be noted that decreasing the bacterial KD reduces the MIC to a sub-micromolar concentration, a phenomenon that is rarely observed in the field of AMP research. The KD reduction also does not mitigate the problem of host cell toxicity and hemolysis because it does not alter the affinity for

RBCs. Thus, attempting to increase the KD for RBCs is a better engineering strategy than trying to increase affinity for E. coli. Whether it is possible to alter the dissociation constant for one cell type without impacting the affinity for the other cell type is an open question.

Discussion

In this chapter, we have investigated what we believed were the two most important determinants of AMP activity loss in the presence of RBCs: proteolysis and host cell binding. We started by exploring the activity of proteases associated with red blood cells and found them to be potent catalysts of AMP degradation, even at 2% of physiological cell densities. We demonstrated that the activity is distinct from that found in serum, based on the progression of peptide degradation with time and by identification of the degradation products. We showed that the protease activity is in the RBC cytosol and posited that the hemolytic activity of AMPs facilitates their release into the extracellular space. Finally, we tested a cross-section of AMPs in degradation 79 experiments with RBC cytosolic extracts and showed that all of them are degraded, albeit at different rates.

To study cell binding, we performed a series of incubation experiments with

D-ARVA and E. coli, S. aureus, and RBCs. The data revealed that while D-ARVA has a higher affinity for bacterial cells than RBCs, binding of peptide to erythrocytes can explain the phenomenon observed in our MIC experiments. Further, nearly all of the free peptide was associated with RBCs at cell densities five times lower than normal human physiological values. We used these data to perform a computational simulation of MIC values in the presence of varying RBC densities, which predicted MIC values of higher than 100 µM at physiological RBC densities. Ultimately, we demonstrated that a log-fold increase in RBC KD could lead to a viable, systemically-active AMP. These studies have led us to construct a qualitative model of the barriers to in vivo activity for AMPs (Figure

3-11).

A central theme in the development of any peptide therapeutic is ensuring stability in biological environments. As such, there are already a number of strategies that have been developed to prevent peptide proteolysis, many of which will likely be effective in protecting from degradation by RBC-associated proteases. Among these strategies are amino acid composition changes, substitution of non-natural amino acids, termini modifications, and crosslinking or cyclization50. An effective approach, especially for

AMPs, where receptor binding is not a concern, is the substitution of D-amino acids in the peptide sequence69. Often, a scanning approach is useful in order to monitor which positions are most susceptible to cleavage and which substitutions, if any, have an effect on activity. Nguyen and colleagues performed a study using short, tryptophan-rich AMPs 80 in which they investigated the impact of termini modifications on peptide stability110.

They report a modest stabilizing effect from either N-terminal acylation or C-terminal amidation alone, but a synergistic phenomenon when both modifications are combined. A more extreme mode of terminus modification was also examined: end-to-end cyclization.

This modification conferred an impressive increase in stability, likely due to the elimination of exopeptidase susceptibility and perhaps decreased availability of internal peptide bonds110,111. Another method of cyclization, disulfide crosslinking, has also been shown to protect peptides from degradation112,113. It is interesting to note that one of the most pervasive, naturally-occurring AMP families, the defensins, are characterized by conserved cysteine residues and disulfide crosslinking25. It is possible that they are able to exert potent antimicrobial activity in vivo because they are chemically stable in those environments.

It is clear that the study of therapeutic peptide proteolysis and the prevention thereof has been a subject of great interest for a number of research groups. Indeed, the literature and approaches cited above are only a small subset of the research that has been performed with stability as an end goal. Conversely, the study of antimicrobial peptide association with eukaryotic cells has not been studied as intently.

As previously detailed, the majority of mechanistic studies of AMP activity have been performed in synthetic systems. Studies, like that which led to the development of the Wimley-White interfacial hydrophobicity scale, provide a framework for the propensity of peptides of different compositions to partition into hydrophobic environments114. Other groups have used methods like calorimetric approaches to study the physical properties of peptide-membrane interactions115. This information is 81 invaluable to the understanding of the biophysical properties of AMPs and other membrane active peptides. Yet, the behavior of peptides in simple, synthetic systems is not perfectly predictive of interactions with biological membranes, probably due to the presence of proteins in the bilayer and extensive polymer networks on or in the vicinity of the membrane (peptidoglycan, LPS, sialic acids for Gram-positive, Gram-negative, and RBCs, respectively).

The knowledge deficit between synthetic and biological membrane systems has made it difficult to develop explicit sequence-structure-function relationships for antimicrobial peptides and natural cell membranes. Thus, it is difficult to predict how alterations in the sequence of a given AMP will affect its behavior. For our particular problem of modifying eukaryotic cell affinity while retaining potent antimicrobial activity, an understudied problem to begin with, a strategy for rational sequence engineering is not evident. A more pragmatic approach to discovering improved peptide sequences is the use of high-throughput screening. By employing a screening-based development strategy, a relatively broad sequence-space can be explored and more information can be generated than with targeted modifications. Later, when the sequence-activity relationships are more firmly established and understood, rational sequence engineering can be used to fine-tune activity. The following chapters will detail our efforts to find peptides with increased hemocompatibility using first, a screening-based approach, followed by targeted sequence engineering. 82

Figure 3-1. Incubation of ARVA with RBCs

(A) L-ARVA was incubated with 1x108 RBCs/mL for 4 hours. The HPLC trace demonstrates substantial degradation of the parent peptide and the emergence of peptide fragments. (B) D-ARVA remains intact after incubation with RBCs suggesting that degradation can be attributed to biological proteases, and that the peaks observed for

L-ARVA are not peptides or other compounds released by the RBCs. (C) The peptide fragments resulting from degradation were collected and analyzed via MALDI-TOF mass spectrometry. They were confirmed to be components of the full-length parent peptide, within 0.5 Da. (D) Time course of the degradation of ARVA in the presence of 1x108

RBCs/mL. L-ARVA is rapidly broken down, while D-ARVA remains intact. The loss of

D-ARVA can be explained by membrane binding. Error bars represent standard deviation.

(N = 3) 83 84 85

Figure 3-2. Incubation of indolicidin with RBCs

(A) L-indolicidin was incubated with 1x108 RBCs/mL for 4 hours. The HPLC trace demonstrates substantial degradation of the parent peptide and the emergence of peptide fragments. (B) D-indolicidin remains intact after incubation with RBCs suggesting that degradation can be attributed to biological proteases and that the peaks present after incubation with L-indolicidin are not peptides released by the RBCs. (C) The peptide fragments resulting from degradation were collected and analyzed via MALDI-TOF mass spectrometry. They were confirmed to be components of the full-length parent peptide, within 0.5 Da. The increase in fragment count as compared to L-ARVA can be attributed to both the length of indolicidin and the large number of tryptophan residues. (D) Time course of the degradation of indolicidin in the presence of RBCs. L-indolicidin is degraded, while D-indolicidin remains intact. Compared to ARVA, the degradation of

L-indolicidin was slower, and the binding of the D-peptide to RBC membranes was not apparent. Error bars represent standard deviation. (N = 3) 86 87

Figure 3-3. Comparison of the degradation of peptides with RBCs, serum, and RBC wash supernates

Peptides were incubated with 2% serum, 1x108 RBC/mL, and RBC wash supernatants.

These data demonstrate that neither ARVA (A) nor indolicidin (B) are being degraded by residual serum proteases associated with the RBCs. This is evidenced by the progressive decrease in degradation rate with each wash step until the third wash where the entire peptide remains fully intact for the duration of the incubation. Error bars represent standard deviation. (N = 3). 88 89

Figure 3-4. Comparison of the degradation patterns of serum and RBC-associated proteases

The HPLC chromatographs for incubation of peptide with RBCs or serum for 24 hours at

37 °C show the pattern of parent peptide reduction and the emergence of degradation products. Panels (A) and (C) show the degradation of ARVA over 24 hours with RBCs and serum, respectively. The same is true of (B) and (D) for indolicidin. For both peptides, there are distinct differences in the progression of peptide degradation, suggesting that the enzymes responsible for degradation in RBC preparations are different from those in serum. 90 91

Figure 3-5. Localization of the proteolytic activity to the cytosol of erythrocytes

(A) RBCs are lysed with a hypotonic buffer in order to separate the cytosolic and membrane fractions of the cells. (B) Incubation with RBC ghost membranes for 24 hours at 37 °C reveals no proteolytic degradation. The small amount of L-ARVA lost is consistent with membrane binding. (C) Incubation in the RBC cytosolic fraction under the same conditions causes rapid degradation of both L-ARVA and L-indolicidin. Error bars represent standard deviation. (N = 3). 92 93

Figure 3-6. Incubation of a cross-section of synthetic and naturally-occurring AMPs with RBC cytosolic extracts

These plots detail the rate of degradation for (A) synthetic and (B) natural antimicrobial peptides. Peptides were incubated with RBC cytosolic extracts equivalent to 1x108 cells/mL at 37 °C. Reverse-phase HPLC was used to monitor the progression of degradation. Error bars represent standard deviation. (N = 3) 94 95

Figure 3-7. Binding of ARVA to bacterial cells

The binding of L- and D-ARVA to bacterial cells at increasing cell densities following a

30-minute incubation at room temperature. Cells were removed from solution by centrifugation and the supernates were analyzed using reverse-phase HPLC. (A) L-ARVA

+ E. coli. (B) L-ARVA + S. aureus. (C) D-ARVA + E. coli. (D) D-ARVA + S. aureus.

Experiments were done at both 5 µM and 20 µM. Error bars represent standard deviation.

(N = 3) 96 97

Figure 3-8. The binding of ARVA to red blood cells

Binding of (A) L-ARVA and (B) D-ARVA to increasing concentrations of red blood cells.

The incubation was for 30 minutes at room temperature. Cells were removed from solution by centrifugation and the supernates were analyzed using reverse-phase HPLC

Experiments were done at both 5 µM and 20 µM. Error bars represent standard deviation.

(N = 3) 98 99

Figure 3-9. Measurement of the surviving CFUs in mock binding experiments and number of peptides bound per cell

We performed mock binding experiments with D-ARVA and E. coli and determined the number of surviving cells after incubation. These experiments were used to determine an inhibition threshold of 1.13x108 CFU/mL for 20 µM peptide, which, based on experimentally measured binding, corresponds to 1.03x108 peptides/cell. (N = 3) 100 101

Figure 3-10. Simulation of MIC of D-ARVA vs. E. coli based on experimentally derived parameters

The analytical solution to the single-site binding equation was used to fit the experimental data for D-ARVA binding to (A) E. coli and (B) RBCs. The parameters used in the binding equations can be found in Table 3-2. (C) The fits for the experimentally-derived binding data for both E. coli and RBCs were used to predict the

MIC for a broad range of RBC concentrations. The calculated MIC values closely reflect the experimentally determined MIC values. (D) Expansion of the scope of the model to see predicted changes in MIC based on modification of the dissociation constant for E. coli and RBCs. The shifted KD values can also be seen in Table 3-2. Error bars represent standard deviation. 102 103

Figure 3-11. A qualitative model of the barriers to systemic activity for AMPs

In this qualitative depiction of the barriers to AMP activity in vivo, we account for a number of known and previously unexplored barriers to AMP activity. Our contribution is the study of AMP interactions with red blood cells, which can lead to reduced activity through proteolysis and cell binding. This figure was adopted from Starr, C. G., He, J., and Wimley, W. C. (2016)51. 104 105

Table 3-1: Serum-derived antimicrobial peptide degradation products

ARVA Indolicidin RGWALRLVLAY ILPWKWPWWPWR GWALRLVLAY LPWKWPWWPWR RGWALRLVL LPWKWPWWPWRR WALRLVLAY WKWPWWPWRR ALRLVLAY WKWPWWPWR GWALRLVL KWPWWPWRR LRLVLAY WPWWPWRR RRGWALR ILPWKWPW RRGWAL/RGWALR WPWWPWR W WWPWRR WWPWR W *Bold species are unique to serum.

Table 3-2: Constants used in modeling of MIC 106

Constant Value

-6 -1 E. coli KD 1.1x10 M E. coli sites per cell 1.7x108 M/cell

-3 -1 RBC KD 3.0x10 M RBC sites per cell 2.5x1010 M/cell

-7 -1 Decreased E. coli KD 1.1x10 M

-2 -1 Increased RBC KD 3.0x10 M

Equation 3-1. Single-site binding relationship with analytical solution using the quadratic formula

KD = dissociation constant 107

[S] = concentration free binding sites [P] = concentration free peptide [SP] = concentration peptide/binding site complex

[ST] = concentration total binding sites

[PT] = concentration total peptide (always 20 µM) fb = fraction bound peptide 108 109

CHAPTER 4: High-throughput screening of a combinatorial peptide library for antimicrobial peptides with stable activity in the presence of erythrocytes

Introduction

In this work so far, we have studied the behavior of well-characterized antimicrobial peptides in the presence of human erythrocytes. We began by measuring the antimicrobial efficacy of twelve peptides in a broth dilution MIC assay, ultimately demonstrating that concentrated RBCs (1x109 cells/mL) strongly inhibit AMP activity51.

We proceeded to study the antimicrobial peptides, ARVA and indolicidin, more intently by confirming the results of the broth dilution assays in radial diffusion and CFU reduction experiments. The results of these standard antimicrobial assays prompted investigation of the mechanistic details of AMP inhibition by RBCs. We focused on two probable effectors of antimicrobial activity loss: proteolytic degradation of AMPs and host cell binding of AMPs to erythrocytes. The studies of AMP proteolysis revealed that there are cytosolic enzyme(s) associated with red blood cells that rapidly degrade antimicrobial peptides. We showed that these proteases are at least partially unique from those associated with serum. While sequence was important to the rate of degradation, we did not observe any L-form peptides that were resistant to proteolysis over a twenty-four-hour time course. In cell binding experiments, we showed that although

ARVA has a high affinity for bacterial cells, it also binds strongly to RBCs at cell densities many-fold lower than physiological conditions. We used the data acquired in cell binding experiments to conduct a computational simulation of MIC values, which closely matched the experimentally determined values. By expanding the scope of the simulation, we demonstrated that increasing, by an order of magnitude, the K D of ARVA 110 for erythrocytes, MIC values at physiological cell concentrations would remain in the low micromolar range. The work described above and in the previous chapters demonstrates the need for antimicrobial peptide development under conditions that will be encountered in vivo.

For many classes of membrane-active peptides, the sequence-structure-function relationships are not well understood74. This is especially true in discussions of antimicrobial peptide activity, where theories on mechanism revolve around the broad physicochemical properties of electrostatic attraction (basic amino acid residues and negatively charged phospholipids head groups at the bilayer interface) and hydrophobic interactions (hydrophobic amino acids and long-chain hydrocarbons of the bilayer interior)28,30,31,47. Aside from these general drivers of material interactions, the mechanistic details of AMP action on a molecular level are not well characterized. In a recent review by Nguyen and colleagues, no fewer than 11 mechanisms of membrane disruption by

AMPs36 were discussed. Although these models make sense in the context of electrostatic and hydrophobic interactions, few of them are supported by strong experimental evidence.

There are many differences in the structures of bacterial and eukaryotic membranes, but aligning with the canonical theories of AMP selectivity, the focus is usually on electrostatic differences; bacteria have a negatively charged phospholipid head groups, whereas eukaryotic cells generally present a neutral charge on the extracellular surface5,6. However, discussions of eukaryotic membrane structure often fail to account for other anionic structures, like sialic acids, which are abundant, cell-surface glycoconjugates116. In addition to charge, the other driver of AMP activity, hydrophobic 111 interactions, is similar between the membranes of prokaryotes and eukaryotes. These factors, combined with a vast sequence space that yields potent antimicrobial molecules, render our understanding of AMP selectivity tenuous, at best.

Because the factors defining AMP selectivity are nebulous, it remains difficult to develop selective, antimicrobial peptide sequences de novo. A reasonable strategy may be to start with a peptide sequence that already displays some level of selectivity, and systematically modify it until it displays the desired characteristics. This process, often referred to as rational sequence engineering, has been used to successfully improve AMP activity in numerous studies66,77,117,118. While useful, this approach only permits the exploration of small sequence spaces and makes difficult the study of the contributions and interplay of different combinations of amino acids. It is also better served in applications where the system is well understood. For the goal of engineering a more selective antimicrobial peptide, a better approach is the employment high-throughput screening. The advantages of screening are clear; it allows for the exploration of a much larger sequence space and as such, yields more information about the system of interest and increases the potential of discovering a compound that fulfills the desired criteria.

Before initiating a high-throughput screen for any purpose, one must have a library of molecules to study. Chemical libraries come in many (molecular) shapes and sizes, some exceeding 106 unique compounds119. While it may seem advantageous to screen as many compounds as possible, time and resource constraints dictate that as throughput increases, return on investment per compound, in the form of data, generally decreases. Thus, a balance must be struck between compound throughput and the quality of the information recovered. In working with natural peptide sequences, we are at least 112 somewhat constrained by the 20 naturally-occurring amino acids. Still, the number of possible sequence variants is described by 20x, where x is the length of the sequence. To manage the potential exponential growth of the sequence space, we will use combinatorial peptide library synthesis templated by the well-characterized AMP, ARVA.

Combinatorial peptide library synthesis, based on the solid-phase peptide synthesis (SPPS) method developed by Merrifield in 1963, is a robust means of creating diverse molecular libraries120. Combinatorial libraries can be broadly divided into two categories: indexed and non-indexed. This designation refers to whether the sequence identity of each library member is readily available following synthesis. Indexing of library peptides greatly simplifies post-screening analysis and permits deeper sequence analysis because every sequence assayed is easily identified. Indexing methods are diverse and include binary masking during synthesis, SPOT synthesis, multi-pin synthesis, and oligonucleotide barcoding121–124. The tradeoff for total sequence identity is that indexed methods are usually only compatible with small libraries (~102-103 compounds).

In contrast, non-indexed libraries sacrifice a priori knowledge of sequence for an expanded sequence space. Early non-indexed approaches used pool-based syntheses to explore the properties of heterogenous peptide mixtures125. While useful for screening large numbers of compounds, repeated rounds of synthesis are required for sequence deconvolution and results are often unpredictable because of the interactions between different peptide species.

A more refined method of non-indexed combinatorial synthesis, known as the

“one-bead, one-sequence” approach, might be described as “semi-indexed”126. Here, a 113 synthesis routine known as “split and recombine” is used to create a library that does not have readily available sequence information, but does keep the sequences physically separated on distinct polystyrene spheres (Figure 1). This strategy permits the assessment of individual sequences but requires post-screening sequence determination by methods such as Edman degradation or peptide LC-MS/MS127. “One-bead, one-sequence” has been employed extensively in peptide research for a number of applications, including antimicrobial peptide development, translocating peptide discovery, ligand-receptor identification, MHC-affinity screening, and antibody development75,127–130.

Of interest to the work herein, several of the peptides tested in chapter 2, including ARVA, were derived from a “one-bead, one-sequence” library75,128,131.

Importantly, these peptides were discovered in a screen assessing membrane disruption of synthetic lipid bilayers. This may explain why ARVA is a potent, but relatively non-selective effector of biological membrane disruption. In this work, we refined the screening approach to identify peptides that are potent antimicrobials, but do not have a high affinity for or toxicity towards eukaryotic cells. We have already developed assays that report on the activity of antimicrobial peptides in the presence of red blood cells.

Here, we adapted these assays to a high-throughput format such that each library member was assessed in a single well of a 96-well plate. We attempted three screening approaches that used a combination of radial diffusion and broth dilution in the presence of RBCs, as well as a measurement of hemolysis. This strategy allowed us to select nine peptides that retain some antimicrobial activity in the presence of RBCs and show low levels of toxicity. We obtained sequence data for the screen positives and synthesized them for post-screening validation. 114

Methods

Library synthesis

The combinatorial peptide library was synthesized on TentaGel mega beads from Rapp

Polymere (Tuebindin, Germany). Before library synthesis, the loading capacity of the resin was amplified by the conjugation of lysine dendrimers. After each lysine is added, the side-chain and N-terminal protecting groups were removed such that two additional lysine residues could be added. This process was repeated three times to increase the loading capacity eight-fold. Prior to addition of amino acids, a UV-sensitive photo-labile linker was added to allow the peptides to be cleaved from the resin by exposure to

UV-light. The library was synthesized using the split-recombine strategy for combinatorial sites (Figure 4-2)132. Residues were added using standard solid-phase peptide synthesis principles for FMOC protected amino acids133. Briefly, FMOC is removed from the N-terminus of the growing peptide by treatment with 30% piperidine in DMF. The C-terminus of the next residue is activated in situ with HBTU/HOBt in

DIPEA/DMF. When synthesis is complete, the side-chains are deprotected with Reagent

B (trifluoroacetic acid (88%), phenol (5%), water (5%), triisopropylsilane (2%)).

Peptide Extraction

Beads were affixed to petri dishes by adding a small amount of methanol to a pool of resin beads and allowing it to evaporate. The beads are then “pre-cleaved” by exposure to

UV light for five hours. Prior to use in an antimicrobial assay(s), 30 µL of 0.025% acetic acid is added to each well of a 96-well plate. Individual beads are picked from the petri dish using forceps and placed in a well (one bead per well). To each well, 30 µL of HFIP was added, and the plate was then incubated under UV-light with shaking for two hours 115

(until the solvent evaporated). Next, 35 µL of PBS was added to all wells and the plates were placed on a shaker overnight. Prior to an assay, the peptide solutions were transferred to a fresh 96-well plate to separate peptide solution from synthesis resin and the extraction plates were stored for indexing. The average concentration of extracted peptide was ~15 µM.

Bacterial Strains and Growth Conditions

E. coli ATCC 25922, S. aureus ATCC 25923, and P. aeruginosa PA01 were used in this study. Subcultures, prepared by inoculating 25 mL of fresh tryptic soy broth (TSB) with

200 μL of an overnight culture, were grown to log phase (OD600 = 0.3−0.6), after which cell counts were determined by measuring the OD600 (1.0 = 1.5x108 CFU/mL for S. aureus, 5x108 CFU/mL for E. coli, 4x108 for P. aeruginosa). Bacterial cells were diluted to appropriate concentrations in TSB.

Pre-screening antimicrobial assays

The appropriate organism was cultured as previously described. The organisms were diluted to 2x the specified concentration in 1% TSB in PBS. In each well, 25 µL of bacterial suspension was mixed with 25 uL of extracted peptide. For assays that included

RBCs, they were prepared at the specified concentration in the minimal growth media and added to the plates with the bacteria. The peptide bacteria mixture was incubated at

37 °C for 2 hours. Next, 50 µL of 2x TSB was added to all wells, and the plates were incubated overnight at 37 °C. The plates were read for optical density at 600 nm; wells with OD600 < 0.1 were considered inhibited.

Pre-screening hemolysis assays 116

RBCs were prepared at 6x107 cells/mL in PBS. To 25 µL of extracted peptide, 125 µL of

RBC suspension was added. The plates were gently shaken for 1 hour. The cells were then removed from suspension by centrifugation at 1000xg for 5 minutes. From the experimental plate, 100 µL of the supernatant was withdrawn from each well and added to a new 96-well plate. The absorbance of the solutions in each well was measured at 410 nm and each well was compared to a no-treatment control and a 100% lysis control (10

µM melittin).

Radial diffusion screen

Radial diffusion plates were prepared as described in chapter 2. Extracted peptide from a given well was split into 2-15 µL aliquots. To one aliquot, 15 µL of sterile PBS was added; to the other, 15 µL of RBCs at 2x108 cells/mL was added. For each assay, 10 µL of pure peptide was added to two radial diffusion plates, each harboring a different bacterial strain. Likewise, 10 µL of the peptide/RBC mixture was added to another set of two radial diffusion plates. The plates were incubated upside down for 3 hours at 37 °C.

After the incubation, 20 mL of a nutrient-rich overlay agar (described in chapter 2), was added to each plate. After solidification, the plates were incubated upside down overnight at 37 °C. The plates were photographed and the zones of inhibition were assessed using

ImageJ.

Mixed radial diffusion broth dilution screen

To 30 µL of extracted peptide, 6 µL of RBCs at 6x109 cells/mL was added and the plates were incubated for 1 hour with shaking at room temperature. Following incubation, the plates were centrifuged at 1000xg for five minutes to pellet the cells. Five microliters of supernatant was removed from the plate and added to 60 µL of PBS. This plate was read 117 at 410 nm for hemolysis. Next, the cells were resuspended and 10 µL of the solution were added to a radial diffusion plate harboring E. coli. These plates were processed as described above in the radial diffusion screen section. To the remaining 20 µL of peptide/RBC solution, 20 µL of P. aeruginosa at 5x105 CFU/mL in 1% TSB in PBS was added. The plate was allowed to incubate for 3 hours at 37 °C. Following incubation, 40

µL of 2x TSB was added and the plate was incubated at 37 °C overnight. Because of the presence of dense RBCs, inhibition of bacterial growth was initially detected by a lack of deoxygenation in the wells (as evidenced by RBC coloration). Wells that were suspected to have been inhibited had an aliquot removed and spread on a TSA plate. These plates were incubated at 37 °C to verify the presence/absence of microbes.

Broth dilution screen

To 30 µL of extracted peptide in PBS, 35 µL of RBCs at 3.7x10 9 cells/mL was added.

This mixture was allowed to incubate for 30 minutes, after which it was centrifuged at

1000xg. A 5 µL aliquot was removed for hemolysis measurements, as described for the mixed screen. The remaining solution was split into 2-30 µL aliquots in 2 separate

96-well plates. E. coli was prepared at 2x104 CFU/mL and P. aeruginosa at 2x106

CFU/mL in 1% TSB in PBS. To one plate, 30 µL of the E. coli suspension was added to the RBC/peptide solution; to the other plate, 30 µL of the P. aeruginosa suspension was added. The plates were incubated at 37 °C for 3 hours, after which 60 µL of 2x TSB was added. The plates were incubated overnight. Because of the presence of dense RBCs, inhibition of bacterial growth was initially detected by a lack of deoxygenation in the wells (as evidenced by RBC coloration). Wells that were suspected to have been inhibited 118 had an aliquot removed and spread a TSA plate. These plates were incubated at 37 °C to verify the presence/absence of microbes.

Sequence identification

The synthesis resin of peptides selected from the screen was retained from the indexing plate. Each bead was sent to John’s Hopkins for sequence analysis via Edman degradation.

Peptide synthesis for post-screen characterization

All peptides used in this study were synthesized using solid-phase FMOC chemistry and purified to >95% by Bio-synthesis Inc (Lewisville, TX). Peptides were dissolved in

0.025% acetic acid solution and concentrations were determined by absorbance at 280 nm, if possible. In the absence of tryptophan or tyrosine residues, concentrations were determined based on measured weight of peptide and volume of solvent.

Post-screen broth dilution assays

Peptides were prepared at 5-times the final concentration needed in 0.025% acetic acid.

The antibiotics were serially diluted by a factor of 2:3 horizontally across a 96-well, conical-bottomed plate from Corning, 25 μL per well. One column was reserved for controls. RBCs at 0 or 2.5x109 cells/mL were added in 50 μL aliquots to all wells.

Following a 30-minute incubation, 50 μL of TSB, inoculated with 5x105 CFU/mL, was added to all wells, and plates were incubated overnight at 37 °C. To assess bacterial growth, a second inoculation was performed with 10 μL of solution from the original plate added to 100 μL of sterile TSB. Following overnight incubation at 37 °C, the

OD600 was measured (values of less than 0.1 were considered sterilized).

Post-screen radial diffusion assays 119

Underlay and overlay agarose were prepared as described in chapter 2. Both solutions were heated until the agarose melted, and then autoclaved. To a rectangular, one-well plate from Nunc, 15 mL of underlay agarose, inoculated with 6x106 CFUs of bacteria, was added. Peptide was prepared at 80 µM and serially diluted 2:3 in Eppendorf tubes (8 concentrations prepared). To a 96-well plate, 15 µL of each dilution was added to an entire row. RBCs were prepared at 10 concentrations from 1.33x108 to 1.33x109 cells/mL.

To the plate with peptide, 45 µL of each RBC concentration was added to each column.

There was also a no RBC column to which PBS was added. The peptide/RBC solutions were incubated for 30 minutes prior to the assay. To the radial diffusion plate, 10 µL from each well was transferred. Inverted plates were incubated at 37 °C for 3 h. Overlay was added, and the plate was incubated upside down overnight. Surface growth was cleared; the plates were sterilized with 25% methanol and 5% acetic acid. Zones of inhibition were photographed and analyzed using ImageJ. To determine MIC, the radius of each zone of inhibition was calculated and plotted against the log10 of the concentration. The best-fit linear relationship of these data was computed and used to determine the x-intercept, which is the MIC.

Post-screen hemolysis assays

Peptide was serially diluted in PBS starting at a concentration of 100 μM. The final volume of peptide in each well was 50 μL. To each well, 50 μL of RBCs in PBS at 2x10 8 cells/mL was added. As a positive lysis control, 1% triton was used. The mixtures were incubated at 37 °C for 1 hour, after which they were centrifuged at 1000xg for 5 minutes.

After centrifugation, 10 μL of supernatant was transferred to 90 μL of DI H2O in a fresh 120

96-well plate. The absorbance of released hemoglobin at 410 nm was recorded and the fractional hemolysis was calculated based on the 100% and 0% lysis controls.

Post-screen cytotoxicity assays

CCLP-1 cells were grown to confluency in T-75 flasks in complete DMEM (10% FBS).

The day prior to cytotoxicity experiments, cells were trypsinized, removed from the flask, and pelleted at 1000xg. The trypsin and spent media were discarded and the cells were resuspended in complete DMEM. The cell count was obtained using a standard hemocytometer. The cells were then seeded at a density of 3.5x104 cells/well in a 96-well tissue-culture plate. In a separate 96-well plate, peptide was serially diluted in serum-free

DMEM starting at a concentration of 100 μM. The final volume of peptide in each well was 100 μL. To perform the cytotoxicity assay, media was removed from the wells and replaced with the peptide/DMEM solutions. No peptide and 20 µM melittin in serum-free media were used as negative and positive controls, respectively. The cells were then incubated for one hour in a standard tissue-culture incubator. After this incubation, 10 µL of alamar blue assay reagent was added to each well in the plate and the plate was returned to the incubator. After two hours of additional incubation, the plate was read for fluorescence with an excitation wavelength of 530 nm and an emission wavelength of

590 nm. The cytotoxicity was calculated based on the 100% and 0% lysis controls.

Results

A combinatorial library of prospective antimicrobial peptides was designed based on the sequence template of ARVA (Figure 4-1). The library contains peptides varying in length between 11 and 15 residues and a minimum of 33% sequence of identity with

ARVA. The major themes in library design were variations in the hydrophobic and 121 electrostatic properties of its members. Indeed, a wide range of charge states was possible, with a minimum of -3 and maximum of +8 (including the positively charged amino terminus). With respect to hydrophobicity, substitutions of different aromatic residues were explored, as well as exchanging some hydrophobic residues for cationic arginine residues. The residues glycine and proline were incorporated at position seven to potentially eliminate β-sheet propensity, a structural feature that can promote aggregation, in some cases. In sum, the library is composed of 28,800 unique peptides. A full recounting of the library design principles can be reviewed in Figure 4-1.

The library was synthesized using standard FMOC-based solid-state peptide synthesis (SPPS) reactions with the split and recombine strategy for combinatorial library generation (Figure 4-2)132,133. Briefly, for each position in the library template, the pool of synthesis resin is divided into a number of pools equal to the number of amino acid variations at that position. A unique residue is coupled to each pool, after which, the pools are recombined. This strategy ensures that every possible combination of residues will be found in the finished library. Prior to library synthesis, the resin was modified with third generation lysine dendrimers, in an effort to increase the molar loading capacity of the beads by a factor of eight. Peptide release from the resin was tested post-synthesis and found to average approximately 0.60 nanomoles per bead.

Before initiating a large-scale screening program, we wanted to verify the presence of peptides with antimicrobial activity in the library and study activity against both bacteria and RBCs. We performed small scale (176 library members) broth dilution experiments against E. coli and S. aureus and varied the density of bacterial cells (Figure

4-3A). As expected, increasing cell densities led to smaller fractions of active library 122 members. The peptides tested also seemed to be more active against E. coli, suggesting that the library peptides may be most effective against Gram-negative organisms. Next, we introduced red blood cells to the experiments and varied the RBC density (Figure

4-3B). Again, we observed the expected decrease in active library members with increasing RBC counts. Finally, we measured hemolysis of the library peptides (Figure

4-3C). More than half of the peptides tested caused no detectable hemolysis and most caused less than 2%.

Having validated the presence of antimicrobial peptides among our library members, we initiated screening. In our initial approach, we tested each peptide against two different microbes in radial diffusion assays. Radial diffusion was chosen because it yields continuous, quantitative data, as opposed to broth dilution, which yields discreet, binary classifications. We measured the zone of inhibition in both the absence and presence of 1x108 RBCs/mL, resulting in four radial diffusion measurements per peptide.

We did two iterations of the screen (384 peptides each), one against P. aeruginosa and E. coli (Figure 4-4A, B) and one against P. aeruginosa and methicillin-resistant S. aureus

(Figure 4-4C, D). In both cases, we saw potent activity when RBCs were absent and a substantial decline when they were added. We selected a single peptide from each iteration of the screen to be sequenced. The selection criteria did not focus on the largest zones of inhibition when RBCs were absent, but instead, activity retention upon RBC addition. We obtained purified samples of the isolated peptides, CHUK1 and CHUK2

(Table 4-1), and tested them in antimicrobial assays (Figure 4-7, Figure 4-8B, C). The results suggested that these initial isolates were not as potent as ARVA in either the absence or presence of RBCs. The exception was the microbicidal activity of CHUK2 123 against P. aeruginosa in the broth dilution assay, where ARVA is inactive. Overall, we took the results to suggest that our screening approach was not stringent enough to select for potent AMPs. Additionally, the poor performance in broth dilution led us to include that assay in the next screening scheme.

Our second screening approach included assays for radial diffusion against E. coli in the presence of 1x109 RBCs/mL, broth dilution against P. aeruginosa with 5x108

RBCs/mL, and hemolysis measurements (Figure 4-5A). We did not include any assays in the absence of RBCs because they were not useful in selecting potent AMPs in the initial screen. Here, we included ARVA in every assay so its performance could be compared to the library peptides. This was our largest screen, with 10,560 library members assessed.

Increasing the RBC concentration had noticeable effects on the performance of the library peptides, especially in radial diffusion; over 93% of the sequences showed no activity (Figure 4-5C). The broth dilution assay was also very stringent, with only 1.3% of the peptides sterilizing P. aeruginosa suspensions. The hemolysis distribution shows that nearly all of the library members induced hemolysis at levels lower than 5%, a substantial improvement over ARVA (13%) (Figure 4-5B). Ultimately, we isolated two additional peptides from this screen and determined their sequences. The peptides were named GNS1 and GNS2 (Table 4-1), a reference to the screen containing only

Gram-negative (GN) organisms. GNS2 was similar in nature to the peptides isolated from the initial screen and had a similar performance in post-screening characterization assays

(Figure 4-7, Figure 4-8E). GNS1 showed very little activity in post-screening assays and was likely a false-positive hit (Figure 4-7, Figure 4-8D). Again, the lack of performance of these peptides post-screen led us to question the power of this screening approach. We 124 observed that a library member being able to sterilize bacteria in broth was a good predictor of activity in radial diffusion assays, but that the converse is not true. Along with the relatively high positive rate in the radial diffusion assay (~7%), we concluded that we were not obtaining maximally useful additional information from this assay.

Our final screening scheme incorporated hemolysis and broth-based sterilization assays against both E. coli and P. aeruginosa in the presence of 1x109 RBCs/mL (Figure

4-6). This screening program was the most stringent with only 0.23% of the peptides screened meeting our criteria (sterilizing activity in both assays). Here, we screened

3,840 peptides and isolated 5 additional unique sequences (Table 4-1). These peptides,

DBS1-5 (DBS is shorthand for double-broth screen), were the most powerful antimicrobials in post-screening characterization (Figure 4-7, 4-8F-J). The DBS series of peptides were particularly effective against P. aeruginosa, an organism that the parent peptide, ARVA, does not kill.

As mentioned previously, after each round of screening, we subjected the library isolates to antimicrobial assays like those described in chapter 2. The purpose of this was to collect a more robust, quantitative dataset than the small amount of unreplicated screening data, and to compare this new data to the parent peptide. In Figure 4-7, we tested the MIC of the isolated peptides in broth dilution against E. coli, P. aeruginosa, and S. aureus in the presence and absence of 1x109 RBCs/mL. Against E. coli, the template peptide, ARVA, remained the most potent AMP in the absence of RBCs (Figure

4-7A). However, when RBCs are present, CHUK2, DBS2, and DBS5 have lower MIC values. For experiments with P. aeruginosa, we observed that CHUK2 and the entire

DBS series are improvements over ARVA in both the absence and presence of RBCs 125

(Figure 4-7B). Unfortunately, Figure 4-7C reveals that all of the isolated peptides are inactive against S. aureus, a Gram positive bacterial species, even when RBCs are absent.

In addition to the broth dilution assays, we also measured the MIC in radial diffusion assays at 10 concentrations of RBCs between 1x108 and 1x109 RBCs/mL. This is a departure from our usage of radial diffusion in chapter 2, where we calculated activity loss based on the relative decrease in the size of the zone of inhibition following

RBC incubation. We made this change based on our observation that peptides that create large zones of inhibition are not necessarily the most potent in other measures of activity

(broth dilution, CFU reduction). Qualitatively, none of the peptides isolated from the screen were an improvement over ARVA against E. coli and none of them had MICs less than 20 µM in the presence of 1x109 RBCs/mL (Figure 4-8). Surprisingly, although they lacked activity against S. aureus in broth, several peptides were active against S. aureus in radial diffusion. Still, they rapidly lost activity as RBCs were added and were not substantial improvements over ARVA in this assay.

Finally, we wanted to assess the toxicity of the library isolates towards mammalian cells. We used both red blood cells for hemolysis assays (Figure 4-9A) and the epithelial liver cancer cell line, CCLP-1, for cytotoxicity assessments using alamar blue (Figure 4-9B). In the hemolysis assays, every peptide isolated from the library screen was an improvement over ARVA. None of the isolated peptides induced hemolysis at levels greater than 10%, even at 100 µM. Against epithelial cells, ARVA, CHUK series, and GNS series peptides showed no toxicity. In contrast, the entire DBS series of peptides induced at least 30% cell death at the highest concentration tested. 126

To analyze the library sequence results, we examined the propensity for a given residue to be selected at each site. We accomplished this by using binomial statistics to calculate the probability of a residue being selected at its experimental frequency if the likelihood of selecting each residue is completely equal. Significance was established at p

< 0.05, with a multiple comparison correction for each site. We observed statistically significant selection of a specific residue at five of the eight combinatorial sites (Figure

4-10). Four of the five significant conservations involved cationic arginine residues.

Perhaps most notable, is the presence of double arginine residues at every N-terminus and seven of the nine C-termini.

Discussion

An examination of the post-screening characterization of the isolated peptides reveals that we did not select for peptides that retain potent antimicrobial activity in the presence of RBCs. Moreover, we did not discover any sequences that were substantial improvements over ARVA, with respect to raw antimicrobial activity. Still, there were several minor successes achieved in the screening program, including the discovery of multiple peptides with activity against P. aeruginosa and a significant reduction in toxicity to RBCs. In addition to the immediately apparent successes, we also gained important information from the sequences in the library. In the next chapter, the sequences discovered here will be rationally engineered to yield an antimicrobial peptide that retains potent activity in the presence of RBCs. Here, we will examine some of the details of the library design and screening that contributed to the results we have observed thus far. 127

In the initial screen where we utilized only radial diffusion assays, we isolated two peptides. Here, the concentration of RBCs, 1x108 cells/mL, was likely too low to effectively isolate peptides that retain activity in the presence of concentrated host cells.

Examining the results in Table 2-1 and 2-2, it is clear that there is a small effect of RBCs at 1x108 cells/mL, but a much more profound loss of activity at 1x10 9 cells/mL. In addition to the concentration issue, we screened a relatively small fraction of the library in this screen (2.7% at most). Perhaps with a larger sample size, we would have isolated more effective peptides. We have also concluded that the raw size of the zone of inhibition is not the best assessment of antimicrobial activity and is perhaps more suggestive of the peptide’s ability to diffuse in a porous, semi-solid medium. We addressed this factor in our post-screening characterization, but it was not applied in the actual screen.

In the mixed broth dilution and radial diffusion screen, we also isolated two peptides, one of which (GNS1) seemed to be a false positive. GNS1 is an interesting case study as it was the only isolated peptide to not include RR at both termini. This observation underscores the importance of having RR at both termini in this library. In this screen, we increased the concentration in radial diffusion assays to 1x10 9 cells/mL and 5x108 cells/mL in broth dilution in an effort to increase stringency. This was successful in terms of the number of positives in radial diffusion assays, but it is likely that the concentration in broth dilution was still too low. This screen was the largest in terms of peptides analyzed, examining the behavior of more than 10,000 sequences.

Because of the sample size relative to the double radial diffusion screen, it is possible that 128 we should have attempted to isolate more sequences, had the two sequences that we did isolate performed better in post-screening characterization.

In the final screen with only broth dilution assays, we isolated five peptides, all of which performed substantially better than the previous library isolates. The concentration of RBCs in both broth sterilization assays was 1x109 cells/mL, likely contributing to our ability to isolate more effective peptides. Interestingly, these peptides were also the most toxic toward eukaryotic cells, especially in the alamar blue assay against epithelial cells.

This raises questions about the relationship between prokaryotic and eukaryotic toxicity and whether they can be extricated sufficiently to discover a peptide that maintains potent activity against bacteria when eukaryotic cells are present.

While it is possible to identify further questions about the suitability of the screening conditions, it is also valuable to examine the library design itself. Much of the work in this chapter was done in parallel with the studies presented in Chapters 2 and 3.

In particular, when this library was initially designed, we were unaware of the extent of the proteolytic activity derived from red blood cells. In retrospect, it would have been prescient to synthesize the library using only D-amino acids. This idea will be explored in the next chapter. In addition to the degradation issue, examining the peptides that were isolated and the preference for certain residues at some positions is enlightening. The presence of “RR” at both termini reflects the necessity of cationic residues in effective antimicrobial peptides, especially given that these two combinatorial sites had the most opportunities for variability. If we take the high level of statistical significance (p <

0.001) to suggest that this sequence characteristic is essential, the number of viable peptides in the library is reduced to 4%. Taking this logic a step further and fixing the 129 other residues that were found to have statistically significant bias, the number of viable peptides is reduced to 36, a mere 0.125% of the library members. While this analysis may be extreme, it is useful to the understanding of what we observed in our screens and could help inform future library designs. This information will also be useful for rational sequence engineering of the peptides that we have already isolated.

Figure 4-1. Design of an ARVA-derived combinatorial library

We designed a unique combinatorial library based on the antimicrobial peptide, ARVA.

The library has 8 potential combinatorial sites yielding a total of 28,800 unique members.

It was synthesized using solid-phase peptide synthesis principles for FMOC-protected amino acids. We used a macro-sized solid support resin to enable easy separation of individual peptides. The major concepts in library design were modulation of the hydrophobic and cationic characteristics of the members. A full list of library design principles is shown in the figure. 130 131 132

Figure 4-2. The split and recombine scheme for combinatorial library synthesis

The library was synthesized using the split and recombine approach. Library synthesis begins with a single pool of synthesis resin that is physically composed of small (~0.3 mm) spherical beads. When residues are added, the full pool is divided into a set of sub-pools equal to the number of variable residues at that site. A unique residue is coupled to each sub-pool, after which the pools are recombined into a single unit. In this example, we follow synthesis through three rounds with three variable residues at each site. The end result is a small library with 27 (3x3x3) peptides. This image was adapted from Starr, C. G. and Wimley, W. C. (2017)134. 133 134

Figure 4-3. Pre-screening characterization of the antimicrobial peptide library

We profiled the activity of the peptide library by determining the number of peptides with sterilizing activity as we varied both the (A) bacterial and (B) red blood cell densities. We observed in both cases, that as cell density increases, the fraction of sterilizing peptides decreases. We also measured the (C) hemolytic activity of the library peptides and observed, for the most part, a very low propensity to induce hemolysis. However, there were a few outliers that caused substantial damage in red cell populations. 135 136

Figure 4-4. Antimicrobial peptide library screening using radial diffusion assays

Extracted peptides were divided into equal aliquots and incubated for 30 minutes with either PBS or red blood cells at a concentration of 1x108 cells/mL. After incubation, the solutions were transferred to a 96-well agarose plate to assess their ability to inhibit the growth of bacteria. Two iterations of the screen were performed, one against E. coli and

P. aeruginosa and the other against P. aeruginosa and methicillin-resistant S. aureus

(MRSA). A total of 768 peptides were assayed, 384 in each screen. Two peptides were isolated and are indicated by star symbols in these plots. (A) Library against EC and PA in the absence of RBCs. (B) Library against EC and PA in the presence of RBCs. (C)

Library against PA and MRSA in the absence of RBCs. (D) Library against PA and

MRSA in the presence of RBCs. 137 138

Figure 4-5. AMP library screening with radial diffusion and broth dilution

Extracted peptides were incubated with 1x109 RBCs/mL for 30 minutes. Following incubation, an aliquot of the solution was removed to assess hemolysis. An additional aliquot was removed for a radial diffusion assay against E. coli. The remaining peptide/RBC solution was used for a broth sterilization assay against P. aeruginosa. In this screening approach, we tested 10,560 peptides. We isolated two peptides that showed low hemolysis, large zones of inhibition in radial diffusion, and successfully sterilized P. aeruginosa in broth. (A) Analysis of the peptide library members based on all three assays. The template peptide, ARVA, is indicated with a red circle. The peptide sequences isolated in this screen are indicated with yellow stars. (B) Distribution of the hemolysis induction observed in the screen. Most peptides were below 5%. (C) Distribution of zones of inhibition in radial diffusion assays. As compared to assays without RBCs, the positive rate and average size of the zones is much smaller. 139 140

Figure 4-6. Library screening using only broth dilution in the presence of RBCs

We determined that the radial diffusion assay was not stringent enough and was not providing us with useful information beyond the broth dilution assays. In our final screening approach, we chose to use hemolysis and broth dilution assays against both P. aeruginosa and E. coli in the presence of 1x109 RBCs/mL. The symbols in the plot denote whether the peptide was able to sterilize neither, one, or both microbes. It also displays the level of hemolysis for each library member. We isolated five peptides using this screening approach, which are denoted with yellow stars on the plot. 141 142

Figure 4-7. Post-screening characterization of isolated AMPs in broth dilution assays

After screening, it was necessary to test the newly discovered peptides in broth dilution assays in the same manner as used in Chapter 2. The peptides were assayed for MIC in the absence and presence of 1x109 RBCs/mL. ARVA is also displayed on the plots for comparison of the isolates to the template sequence. (A) Peptides vs. E. coli. (B) Peptides vs. P. aeruginosa. (C) Peptides vs. S. aureus. Error bars represent standard deviation. (N

= 3) 143 144

Figure 4-8. Post-screening MIC in radial diffusion of library isolates

For post-screening characterization, we measured the MIC of each library isolate in radial diffusion assays against S. aureus and E. coli. In a departure from the method used in

Chapter 2, we sought to explicitly define the MIC by plotting the radius of inhibition (in mm) against the log of the peptide concentration. The x-intercept of this linear relationship reveals the MIC. This measurement was done in single 96-well agar plates with concentrations of RBCs ranging from 1x108 to 1x109 cells/mL. We also assessed

ARVA in this version of the radial diffusion assay for comparison with the library isolates. (A) ARVA. (B) CHUK1. (C) CHUK2. (D) GNS1. (E) GNS2. (F) DBS1. (G)

DBS2. (H) DBS3. (I) DBS4. (J) DBS5. Error bars represent standard deviation. (N = 4) 145 146

Figure 4-9. Post-screening cytotoxicity assays of library isolates

The peptides isolated from the library were assayed for toxicity against (A) human red blood cells and (B) CCLP-1 cells, a human liver cancer cell line. For the RBCs, we measured hemolysis in the presence of 1x108 RBCs/mL. For the epithelial cell line, we used alamar blue to assess metabolic activity. Alamar blue is a fluorescent dye that is activated via reduction reactions caused by actively respiring mitochondria. The library peptides were non-toxic to RBCs, but the DBS series of peptides was somewhat toxic towards CCLP-1 cells. ARVA was also included for the purpose of comparison. Error bars represent standard deviation. (N = 3) 147 148

Figure 4-10. Binomial statistical analysis of the frequency of residue appearance at each combinatorial site

We analyzed the sequences isolated from the library and counted the number of times each residue was found at each combinatorial site. We then calculated the probability of this frequency occurring by random chance if there were no selective pressures for any of the residues. We observed that at five of the eight combinatorial sites, a particular residue was chosen at a statistically significant probability of p < 0.05. 149 150

Table 4-1. Sequence information for peptides isolated from the combinatorial library

Name Sequence Screen CHUK1 RRGWALRPVLAFGRR Radial diffusion CHUK2 RRGWARRLAAAYGRR Radial diffusion GNS1 RGWARRRFFASG Mixed GNS2 RRGWAFRRALAYGRR Mixed DBS1 RRGWARRLFFAYGRR Broth dilution DBS2 RRGWAARLFAAFGRR Broth dilution DBS3 RRGWARRLFAAFGRR Broth dilution DBS4 RRGWARRLVFAFGRR Broth dilution DBS5 RRGWARALAFAFGR Broth dilution 151

CHAPTER 5: Rational sequence engineering of antimicrobial peptides based on sequences isolated in high-throughput screening with RBCs

Introduction

We have shown, thus far, that a diverse cross-section of antimicrobial peptides lose activity in the presence of concentrated erythrocytes51. In the previous chapter, we designed and screened a combinatorial peptide library for antimicrobial peptides that retain bactericidal activity in the presence of human red blood cells. Over the course of three screening programs, we isolated nine unique, but related, peptides with antimicrobial activity against Gram-negative pathogens. The selected peptides were subjected to post-screening characterization assays and were found to lose activity in the presence of red blood cells, even though they displayed activity in stringent screens.

While the results did not meet expectations, the selected peptides were significantly less toxic to erythrocytes than the template peptide, ARVA. Additionally, many of the isolated peptides were active against P. aeruginosa, an opportunistic human pathogen of serious concern in the fight against multi-drug resistant bacteria, against which ARVA has poor activity135–138.

Although we did not achieve all of our goals by screening, we acquired important information about the sequences in the library. Of the eight variable positions in the combinatorial design, five had a statistically significant preference for a single residue.

Importantly, four of these biases dictate that the consensus library sequence will have at least six arginine residues. Because the sequence space of the library was strongly constrained, we also explored sequence variants of this family of peptides in a hypothesis driven manner using rational sequence engineering. 152

The utility of peptides as signaling molecules, enzyme activity modulators, transcriptional regulators, and effectors of biophysical processes is ubiquitous in every domain of life139–143. As such, a substantial amount of research capital has been invested in engineering peptides that can be administered as exogenous therapeutics. A common theme in the engineering of therapeutic peptides is the improvement biological stability.

The most familiar and perhaps, successful, of these efforts has been sequence engineering of the peptide hormone, insulin, for the treatment of diabetes mellitus144. Indeed, engineering efforts have yielded a large family of synthetic insulin analogues with variable half-lives that can be used in tandem for improved management of diabetes145,146.

Another signaling peptide that has been the subject of extensive sequence modification efforts is pituitary adenylate cyclase-activating polypeptide 38 (PACAP38). PACAP38 is widely distributed in the central nervous system and digestive tract and has been implicated in a number of disease states147. Efforts here have focused on increasing biological residence times and modulating receptor affinity through substitutions of non-natural amino acids148,149. While the modes of action of these engineered peptides differ significantly from canonical antimicrobial peptides, the principles of biological stability are closely related for all therapeutic peptides.

Antimicrobial peptides have also been the subject of numerous efforts to engineer improved activity or increased biological stability. Both MSI-78 and WLBU2, studied in

Chapters 2 and 3, were derived from natural peptides via rational sequence engineering.

MSI-78 is an analog of magainin that was engineered by replacing five polar, but uncharged, amino acids with cationic lysine residues66,150. Although ultimately failing to receive approval by the FDA or EMA, these efforts helped make MSI-78 the first 153 antimicrobial peptide to reach human clinical trials29,65. WLBU2 was derived from the human cathelicidin, LL-37, although the extent of sequence engineering has left few recognizable sequence features between the two peptides. Engineering of WLBU2 and related peptides began with the identification of a minimal antimicrobial motif in the sequence of LL-3777,151. This “lytic base unit” (LBU) was then subjected to amplification and residue substitution with multiple tryptophan residues to create a perfect amphipathic helix, 24 residues in length77,151. Although it has yet to reach clinical trials, WLBU2 has displayed potent antimicrobial activity in biological fluids and some efficacy in animal models77,106.

A study by Chen and colleagues approached sequence engineering in a manner opposite the development of WLBU2152. Here, the researchers started with an ideal amphipathic helix with good antimicrobial activity and studied the impact of substituting polar residues on the hydrophobic face and hydrophobic residues on the polar face.

Surprisingly, these substitutions improved antimicrobial activity while reducing hemolytic effects. While the increased bactericidal activity was not well understood, it was concluded that reducing the hydrophobicity of the sequence was directly related to a decrease in toxic interactions between the peptide and red cell membrane.

Chemical modification of either terminus is a common means of tuning both antimicrobial activity and biological stability. A number of naturally occurring AMPs are known to exist in vivo with amidated C-termini, probably for the purpose of anionic charge elimination21,153,154. This modification, along with N-terminal acetylation, have been shown to increase serum stabilities of antimicrobial peptides110. Non-canonical terminus modification has included the conjugation of p-hydroxy cinnamic acid, acetic 154 anhydride, cinnamic acid, and 3-(4-hydroxyphenyl) propionic acid155. An improvement in

MIC was observed, but their interactions with eukaryotic cells were not studied. There has also been substantial interest in the conjugation of fatty acid moieties to the termini and side chains of antimicrobial peptides156,157. These modifications are likely to enhance antimicrobial activity by virtue of increased hydrophobicity, but also create analogs that are predisposed to strong interactions with eukaryotic membranes.

Several groups have forgone residue and terminus modifications completely and focused on the structure of the peptide backbone. These strategies include the incorporation of β-amino acids, peptoids, and combinations thereof158–160. Modifications to the peptide backbone generally yield compounds of similar potency to their canonical amino acid counterparts, with the added benefit of resistance to proteolysis. However, the implications of introducing these biological mimics into living systems is unclear.

The farthest we will venture from standard antimicrobial peptides is an inspection of brilacidin (also known as PMX 30063). An arylamide foldamer, brilacidin bears little resemblance to any peptide, except for the incorporation of two arginine side chains flanking a fluorinated aromatic core moeity161. However, this molecule was designed based on the interactions AMPs with biological membranes and retains the same antimicrobial mechanism. Brilacidin is an exciting direction in antibiotic research as it has been shown to be an improvement on both the activity and selectivity of MSI-78 and has rapidly advanced to clinical trials48,162.

Although we have discussed a wide range of approaches in the field of rational peptide design, in this work we have restricted our studies to the use of the 20 naturally occurring amino acids. Our most significant departure from these natural building blocks 155 is the incorporation of D-amino acids. Here, we asked pointed questions about the effects of specific amino acid substitutions on the behavior of AMP sequence, with the goals of understanding the impact of sequence elements on AMP behavior and more importantly, developing an AMP that retains activity in the presence of RBCs. The most common route of inquiry was the modulation of the hydrophobic and cationic properties of the peptides, but we also questioned the importance of glycine residues in these sequences.

Ultimately, the most important design principle was the incorporation of D-amino acid variants.

Methods

Peptides

All peptides used in this study were synthesized using solid-phase FMOC chemistry and purified to >95% by Bio-synthesis Inc (Lewisville, TX). Peptides were dissolved in

0.025% acetic acid solution and concentrations were determined by absorbance at 280 nm, if possible. In the absence of tryptophan or tyrosine residues, concentrations were determined based on measured weight of peptide and volume of solvent.

Bacterial Strains and Growth Conditions

Escherichia coli (ATCC 25922), Staphylococcus aureus (ATCC 25923), Pseudomonas aeruginosa (PA01), Enterococcus faecium (ATCC 19434), Klebsiella pneumoniae subsp. pneumoniae (ATCC 13883), Acinetobacter baumannii (ATCC 19606), and Salmonella enterica subsp. enterica (ATCC 14028) were used in this study. Subcultures, prepared by inoculating 25 mL of fresh tryptic soy broth (TSB) with 200 μL of an overnight culture, were grown to log phase (OD600 = 0.3−0.6), after which cell counts were determined by measuring the OD600 (1.0 = 5x108 CFU/mL for E. coli, 1.5x108 CFU/mL for S. aureus, 156

4x108 CFU/mL for P. aeruginosa, 3x108 CFU/mL for E. faecium, 4x108 CFU/mL for K. pneumoniae, 3.5x108 CFU/mL for A. baumannii, 1x109 CFU/mL for S. enterica).

Bacterial cells were diluted to appropriate concentrations in TSB. E. faecium was grown in BHB.

Broth dilution assays

Antibiotics were prepared at 5-times the final concentration needed in 0.025% acetic acid. The antibiotics were serially diluted by a factor of 2:3 horizontally across a 96-well, conical-bottomed plate from Corning, 25 μL per well. One column was reserved for controls. PBS or RBCs at 2.5x109 cells/mL were added in 50 μL aliquots to all wells.

Following a 30-minute incubation, 50 μL of TSB, inoculated with 5x105 CFU/mL, was added to all wells, and plates were incubated overnight at 37 °C. To assess bacterial growth, a second inoculation was performed with 10 μL of solution from the original plate added to 100 μL of sterile TSB. Following overnight incubation at 37 °C, the

OD600 was measured. Values of less than 0.1 were considered sterilized.

Radial diffusion assays

Underlay and overlay agarose were prepared as described in Chapter 2. Both solutions were heated until the agarose melted, and then autoclaved. To a rectangular, one-well plate from Nunc, 15 mL of underlay agarose, inoculated with 6x106 CFUs of bacteria, was added. Peptide was prepared at 80 µM and serially diluted 2:3 in Eppendorf tubes (8 concentrations prepared). To a 96-well plate, 15 µL of each dilution was added to an entire row. RBCs were prepared at 10 concentrations from 1.33x108 to 1.33x109 cells/mL.

To the plate with peptide, 45 µL of each RBC concentration was added to each column.

There was also a no RBC column to which PBS was added. The peptide/RBC solutions 157 were incubated for 30 minutes prior to the assay. To the radial diffusion plate, 10 µL from each well was transferred. Inverted plates were incubated at 37 °C for 3 h. Overlay was added and the plate was incubated upside down overnight. Surface growth was cleared; the plates were sterilized with 25% methanol and 5% acetic acid. Zones of inhibition were photographed and analyzed using ImageJ. To determine MIC, the radius of each zone of inhibition was calculated and plotted against the log10 of the concentration. The best-fit linear relationship of these data was computed and used to determine the x-intercept, which is the MIC.

Hemolysis assays

Peptide was serially diluted in PBS starting at a concentration of 100 μM. The final volume of peptide in each well was 50 μL. To each well, 50 μL of RBCs in PBS at 2x10 8 cells/mL was added. As a positive lysis control, 1% triton was used. The mixtures were incubated at 37 °C for 1 hour, after which they were centrifuged at 1000xg for 5 minutes.

After centrifugation, 10 μL of supernatant was transferred to 90 μL of DI H2O in a fresh

96-well plate. The absorbance of released hemoglobin at 410 nm was recorded and the fractional hemolysis was calculated based on the 100% and 0% lysis controls.

Cytotoxicity assays

CCLP-1 cells were grown to confluency in T-75 flasks in complete DMEM (10% FBS).

The day prior to cytotoxicity experiments, cells were trypsinized, removed from the flask, and pelleted at 1000xg. The trypsin and spent media were discarded and the cells were resuspended in complete DMEM. The cell count was obtained using a standard hemocytometer. The cells were then seeded at a density of 3.5x104 cells/well in a 96-well tissue-culture plate. In a separate 96-well plate, peptide was serially diluted in serum-free 158

DMEM starting at a concentration of 100 μM. The final volume of peptide in each well was 100 μL. To perform the cytotoxicity assay, media was removed from the wells and replaced with the peptide/DMEM solutions. No peptide and 20 µM melittin were used as negative and positive controls, respectively. The cells were then incubated for 1 hour in a standard tissue-culture incubator. After this incubation, 10 µL of alamar blue assay reagent was added to each well in the plate and the plate was returned to the incubator.

After 2 hours of additional incubation, the plate was read for fluorescence with an excitation wavelength of 530 nm and an emission wavelength of 590 nm. Cytotoxicity was calculated based on the 100% and 0% lysis controls.

Results

We started our rational sequence engineering experiments by designing eight novel sequence variants based on several sequences from the previous two antimicrobial library screens131 (Figure 4-1). The hypotheses for the design of these peptides, the parent sequences, and the rationally designed sequences can be found in Table 5-1. We subjected these peptides to our characteristic, antimicrobial assays in the presence of RBCs and performed cytotoxicity measurements.

The first modification was the removal of the ubiquitous glycine (G) spacer that separates the doubly charged arginine terminal cassettes from the core peptide. The glycine residues were not individually varied in any library, and we believed that because glycine does not contribute to hydrophobic or electrostatic properties, it may not be necessary in the sequence. We tested this modification with two different peptides, DBS1 from the most recent library screen (Chapter 4) that was done in the presence of RBCs, and NATT from the original combinatorial library128. We named the rationally designed 159 peptides RAT1 and RAT2, respectively. This sequence alteration did not diminish antimicrobial activity. Instead, it led to small improvements in antimicrobial potency in the absence of RBCs, few of which were significant (Figure 5-1, Figure 5-2, Table 5-1).

More importantly, removal of the glycine residues did not lead to a decrease in activity in the presence of RBCs. Nor did it lead to substantial improvements in broth when RBCs were present, although the small improvement was statistically significant for RAT2.

RAT1 had improved antibacterial activity in the radial diffusion assay against both E. coli and S. aureus, but unfortunately, this improvement did not extend to activity in the presence of 1x109 RBCs/mL (Figure 5-2A). An unanticipated result of this modification was a substantial increase in hemolysis induced by RAT2 (Figure 5-3A). Aside from this complication, the removal of the G-spacer did not seem particularly impactful. While it did not deliver the activity improvement in the presence of RBCs that we sought, a shorter peptide sequence with one fewer amino acid species is advantageous with respect to synthesis complexity and cost.

The second major sequence variation tested was the addition of -GRR to the library template peptide, ARVA. We reasoned that because the majority of the library isolates had -GRR at both termini, adding the motif to the C-terminal of ARVA may improve its activity. The results for this variant, RAT3, were not positive; we observed significant activity loss against E. coli and S. aureus in the absence of RBCs, although there was improvement against S. aureus when RBCs were present (Figure 5-1, Table

2-1). There was effectively no change in radial diffusion (Figure 5-2C). Interestingly, there was a large increase in hemolysis caused by this peptide compared to its parent. 160

This result was unanticipated as the aim of the modification was to make ARVA more like the library isolates, which all cause less hemolysis than ARVA.

Next, we sought to explore the behavior of a consensus sequence based on the library isolates. We did not include GNS1 when constructing the consensus because it appeared to be a false positive. We took the amino acid selected most frequently at each position and synthesized this sequence, RAT4, in both the L and D amino acid forms

(Figure 4-10, Table 5-1). At variable sites 5 and 6, there was an equal number of alanine and phenylalanine residues. We selected A at position 5 and F at position 6 in order to create the motif AFAF as the hydrophobic core. The D-amino acid variant was included to test the hypothesis that proteolysis was causing significant interference in our assays.

For comparison to a “parent sequence”, we decided that it was most appropriate to compare L-RAT4 to L-ARVA, and D-RAT4 to L-RAT4. With respect to L-RAT4, we saw results very similar to what would be expected from an average of all of the library peptides. Performance against E. coli and S. aureus in broth was reduced, but performance against P. aeruginosa was improved (Figure 5-1). As with the rest of the library members, it was much less hemolytic than L-ARVA (Figure 5-3A). The most significant breakthrough in our effort to engineer a hemocompatible peptide came with the development of D-RAT4. Activity against E. coli and P. aeruginosa in the presence of

RBCs increased significantly, almost to the point of being equal to activity without RBCs

(Figure 5-1). The peptide had a small, measurable efficacy against S. aureus, an improvement over the other library members. In the radial diffusion assay, D-RAT4 retained activity against both E. coli and S. aureus, even as the RBC concentration reached 1x109 cells/mL (Figure 5-2E). As opposed to the increased toxicity to bacterial 161 cells, using all D-amino acids did not significantly alter hemolysis or cytotoxicity (Figure

5-3). These results signified that we were indeed isolating desirable sequences during our library screen, they were simply being degraded by RBC-associated proteases.

The remaining sequence variants in the first round of rational engineering were modifications of the electrostatic and hydrophobic properties of the consensus sequence

(L-RAT4) by addition or removal of residues. For RAT5, we added an additional RL motif to the center of the sequence because this pair of residues had been conserved from the template peptide, ARVA. In the design of RAT6 and RAT7, we experimented with the addition and subtraction, respectively, of one “AF unit” of the AFAF motif. The effects of the modifications in RAT5 and RAT6 were very similar. We observed an increase in antimicrobial potency in broth against E. coli in the absence of RBCs and P. aeruginosa in the presence of RBCs (Figure 5-1). RAT5 was improved against E. coli in the radial diffusion screen and this improvement was maintained at 1x109 cells/mL (Figure 5-2F).

Conversely, RAT6 was improved against S. aureus in radial diffusion, but did not retain activity at 1x109 cells/mL (Figure 5-2G). Both peptides were significantly more hemolytic

(Figure 5-3A). With respect to RAT7, we observed a loss of antimicrobial activity against both E. coli and P. aeruginosa in broth, and against E. coli in radial diffusion (Figure 5-1,

Figure 5-2H). There was a significant reduction in hemolysis and cytotoxicity; this peptide appeared to be completely non-toxic (Figure 5-3A).

Having identified a peptide with potent antimicrobial activity in the presence of

RBCs (D-RAT4), we sought to explore further, the potential of D-amino acid variants.

The underlying hypotheses and sequence modifications for this set of four peptides can be found in Table 5-2. Because the library consensus sequence was so successful when 162 synthesized with D-amino acids, we sought to test two of the library isolates, GNS2 and

DBS1, as D-form peptides. D-GNS2 was more effective than its L-form counterpart against E. coli in broth in both the presence and absence of RBCs (Figure 5-4). This peptide did not display activity against either S. aureus or P. aeruginosa in broth. In radial diffusion, D-GNS2 was substantially improved and its activity was maintained through the gradient of RBC concentrations (Figure 5-5A). Unlike the D-RAT4, D-GNS2 was more toxic to red blood cells than the L-isomer (Figure 5-6A). The other library variant,

D-DBS1, was a much more potent antimicrobial. Here, we observed increased activity in broth against all organisms tested in both the presence and absence of RBCs (Figure 5-4).

Like D-GNS2, we also measured significant performance improvements in radial diffusion across all RBC concentrations (Figure 5-5B). Again, like D-GNS2, D-DBS1 has an increased propensity to cause hemolysis. The study of these two peptides confirmed our belief that the library screen had yielded antimicrobials that could retain potent activity in the presence of RBCs.

In the interest of studying the mechanistic details of the activity of this AMP family, we synthesized and tested a D-form consensus sequence where all six arginine residues were replaced with lysine residues (D-KON). The hypothesis was that if electrostatic interactions are the only consideration, these substitutions should have little impact on antimicrobial activity. We were proven incorrect in this regard, as the lysine-containing variant had a significantly higher MIC against E. coli and P. aeruginosa in broth in both the absence and presence of RBCs (Figure 5-4). Additionally, it had inferior performance to D-RAT4 in the radial diffusion assay against both organisms (Figure 5-5C). These results show that the electrostatic determinants of 163 antimicrobial activity are more complex than simply having a positive charge(s) present in the sequence.

Our final, rationally designed peptide incorporated the two most successful variations from the first set of peptides. We used the D-form of the consensus sequence,

D-RAT4, and modified it to remove the glycine spacers. We reasoned that because this modification increased activity in DBS1 and NATT, it could be effective for the consensus sequence as well. Although we observed an increase in cytotoxicity for the

NATT variant, we theorized that the consensus sequence would behave more like DBS1 because they were derived from the same library. The results of these modifications were the improvement of an already excellent antimicrobial. In the broth dilution assays,

D-NOGCON (“NOG” = no glycine, “CON” = consensus) was a significant improvement, compared to D-RAT4, against E. coli, S. aureus, and P. aeruginosa in both the presence and absence of RBCs (Figure 5-4). Likewise, D-NOGCON was improved in radial diffusion against both organisms across the range of RBC concentrations (Figure 5-5D).

Finally, D-NOGCON showed no significant change in toxicity towards eukaryotic cells

(Figure 5-6).

With D-NOGCON, we had succeeded in engineering a much more hemocompatible peptide that retains potent antibacterial activity in the presence of RBCs.

Additionally, D-NOGCON displays antimicrobial activity at very low concentrations, comparable to the best-known AMPs and some clinically useful antibiotics. To reflect on the successes of our peptide engineering strategy, we compared D-NOGCON to D-ARVA in the standard panel of characteristic assays (Figure 5-7). In broth dilution, we added the additional Gram-negative human pathogens S. enterica, A. baumannii, and K. 164 pneumoniae. Both D-ARVA and D-NOGCON displayed potent antimicrobial against all species analyzed (Figure 5-7A). However, when RBCs are added to the assay, D-ARVA loses a significant amount of activity against all organisms (Figure 5-7A). Conversely,

D-NOGCON retains the ability to sterilize all organisms at low concentrations, even when 1x109 RBCs/mL are present (Figure 5-7A). The results were similarly spectacular in the radial diffusion assessment, where D-NOGCON was a potent antimicrobial across the gradient of RBC concentrations tested (Figure 5-7B, C). D-ARVA shows noticeable activity loss as RBCs are added to the assay (Figure 5-7B, C). With respect to toxicity towards eukaryotic cells, D-NOGCON is significantly less toxic to human erythrocytes

(Figure 5-7D). When incubated with the human epithelial cell line, CCLP-1, there was no difference in toxicity between the two peptides (Figure 5-7E). The measured values for

MIC in broth, MIC in radial diffusion, cytotoxicity, and solubility for all peptides in

Chapters 4 and 5 are displayed in Tables 5-3 and 5-4.

Discussion

In this chapter, we used the knowledge and sequence information that we obtained from combinatorial library screening to make small, focused changes to several antimicrobial peptide sequences isolated from the library. We initially modified the amino acid composition, with an emphasis on hydrophobicity and charge. In most cases, the sequence variants did not improve in the principle performance metric of retaining stable

MIC values in the presence of concentrated erythrocytes. However, a major advancement was made when we synthesized a consensus sequence based on the screening isolates using all D-amino acids. In this particular case, the peptide, D-RAT4, was a potent AMP against Gram-negative organisms and retained nearly all of its activity when 1x109 165

RBCs/mL were present. We took this information and applied one of our other successful modifications, removal of two glycine residues, to synthesize the peptide D-NOGCON.

This sequence displayed improved antimicrobial activity against all organisms tested, including the restoration of low micromolar antimicrobial activity against S. aureus, even in the presence of RBCs. Additionally, there was not a significant change in cytotoxicity towards RBCs or epithelial cells. These characteristics make D-NOGCON an excellent candidate to move forward to in vivo trials of antibiotic efficacy.

Aside from the success of developing D-NOGCON, these rational engineering experiments yielded important information about the sequence determinants of peptide antimicrobial activity and toxicity towards eukaryotic cells. One such example is the variation in the hydrophobic motif, “AF”, between L-RAT4, RAT6, and RAT7 (AFAF,

AFAFAF, AF). There is a critical balance that must be struck with respect to hydrophobicity. If there are too few hydrophobic residues, antimicrobial activity is eliminated (RAT7 – “AF”). However, if there are too many hydrophobic residues, peptides become more toxic towards eukaryotic cells (RAT6 – “AFAFAF”). L-RAT4 seems to find the median as it has good antimicrobial activity and is relatively non-toxic.

In the future, it may be informative to vary the aromatic residues of L-RAT4 to tyrosine

(slightly more hydrophilic) and tryptophan (slightly more hydrophobic)114.

Another interesting phenomenon in the rational engineering experiments was the importance of arginine as the cationic moiety. We used the D-form consensus sequence

(D-RAT4) to design a variant containing six lysine residues in place of the arginine residues in the parent. This peptide, D-KON, had reduced potency towards all bacterial species assessed. A similar phenomenon has been observed in a study of bovine 166 lactoferricin analogs, suggesting that the phenomenon is not unique to this family of peptides163. It has been suggested that arginine is especially prone to interacting with the plasma membrane because of the unique chemical nature of the guanidinium group164.

Indeed, the distributed cationic charge of arginine’s side chain is capable of forming up to six hydrogen bonds with the negatively charged head groups of membrane phospholipids165. Supporting its role in membrane interaction is abundant representation at the interfacial domain in transmembrane proteins165. A clever illustration of this principle was the substitution of a dimethylated arginine residue in the antimicrobial peptide PG-1, rendering the side-chain more lysine-like166. This study revealed decreased antimicrobial efficacy against several bacterial species. Using P31 solid-state NMR, they demonstrated that substantially less membrane disorder was induced by the methylated arginine variant. Still, an analysis of a comprehensive antimicrobial peptide database reveals that lysine residues are actually more prevalent in AMPs than arginine 27. A possible explanation for this phenomenon are differing mechanisms of action between lysine-rich and arginine-rich antimicrobial peptides. An interesting corollary that may explain the mechanism of arginine-rich AMPs is the abundance of arginine residues in membrane translocating peptides165,167. It is possible that arginine-rich AMPs exert their effect by both disrupting the bacterial membrane and translocating into the cytoplasm and disrupting intracellular processes. This concept has been demonstrated in an AMP by substituting a single lysine residue with arginine and observing a shift from antimicrobial to membrane translocating properties168. It is unknown whether this behavior extends to multiple classes of AMPs. 167

In recent years, as the prevalence of computational biology approaches has increased, an interesting approach to peptide engineering has emerged. Instead of screening in vitro, initial peptide development is done using in silico methods33. The advantages to this approach are time and resource conservation and the exploration of massive sequence spaces. Unfortunately, machine learning methods rely on data derived from physical studies of AMP activity compiled from disparate laboratories and methodologies, which leaves serious questions about the consistency of the datasets used

27,33. Additionally, these methods have failed to yield AMPs with better antimicrobial activity than peptides discovered in nature or those engineered by physical screening or rational engineering approaches. Moreover, our approach to testing antimicrobial peptide efficacy in the presence of concentrated host cells is rare. Because of this, there is not enough input data available to derive coherent results from machine learning approaches.

However, it would be interesting to develop a method to integrate data from an AMP database and a cytotoxicity database to develop a potent, but non-toxic AMP27,169.

Because cytotoxicity is indicative of interactions with eukaryotic cells, such a peptide might be a good candidate to retain activity in the presence of RBCs.

In this study, we combined peptide library screening with rational engineering to generate a potent AMP with potential to retain activity in vivo. The results demonstrate the power of combining a screening approach and using the derivative information to fine-tune peptide activity. Our research group has used this approach in the past with both membrane-lytic and membrane-translocating peptides74,130,170,171. These studies fit into a larger paradigm known as synthetic molecular evolution. The process starts with identifying a peptide with desirable biological properties that also displays attributes that 168 are not compatible with the desired applications. A combinatorial library is then designed with a limited sequence space that is derived from various hypotheses about sequence-function relationships. The library is synthesized and screened in assays designed to elicit the desired properties of the next-generation peptide. The peptides may then undergo rational engineering to further improve activity and gain a deeper understanding of mechanisms of action. We have reached this point with the peptide

D-NOGCON, a potent antimicrobial that retains activity in the presence of concentrated

RBCs. A potential next step could be using D-NOGCON as the template for another combinatorial library, possibly to identify peptides with activity in whole blood. This would be a logical course, given that D-NOGCON was developed at sub-physiological cell counts in the absence of serum components. However, the performance of

D-NOGCON is comparable to the best antimicrobial peptides discovered to date. Thus, we believe that D-NOGCON is at the point in development that warrants assessment in an in vivo model of infection. These studies will reveal the translational value of

D-NOGCON and provide insight on the utility of the antimicrobial assays and screening approaches we have designed to mimic in vivo conditions. 169

Figure 5-1. Comparison of the first set of rationally engineered AMPs in the absence and presence of RBCs in broth-based MIC assays

Peptides were tested to determine minimum inhibitory concentrations in the absence and presence of 1x109 RBCs/mL. The MIC values of the parent peptides are plotted on the x-axis and the engineered sequences on the y-axis. Improvements in peptide MIC are apparent in points plotted below the gray-dashed line (y = x). These experiments revealed that the D-form consensus sequence (D-RAT4) retained potent antimicrobial activity in the presence of RBCs. (A) E. coli in the absence of RBCs. (B) E. coli in the presence of

RBCs. (C) S. aureus in the absence of RBCs. (D) S. aureus in the presence of RBCs. (E)

P. aeruginosa in the absence of RBCs. (F) P. aeruginosa in the presence of RBCs. Error bars represent standard deviation. (N = 6-22) 170 171

Figure 5-2. Comparison of radial diffusion MIC values for the first set of rationally engineered peptides

The rationally engineered and parent peptides were assayed in radial diffusion assays against E. coli and S. aureus in the presence of 1x108 to 1x109 RBCs/mL. MIC values were determined by calculating the x-intercept of the linear relationship between radius of inhibition and log10(peptide concentration). The results are plotted as (MIC of parent) –

(MIC of engineered) such that improvements (decreases) in MIC are plotted as a positive value. These experiments confirmed the results of broth dilution, revealing that the

D-consensus sequence (D-RAT4) was a significant improvement and retained activity in the presence of RBCs. (A) DBS1/RAT1. (B) NATT/RAT2. (C) L-ARVA/RAT3. (D)

L-ARVA/L-RAT4. (E) L-RAT4/D-RAT4. (F) L-RAT4/RAT5. (G) L-RAT4/RAT6. (H)

L-RAT4/RAT7. Error bars represent standard deviation. (N = 4). 172 173

Figure 5-3. Comparison of the first set of engineered AMPs and parent sequences in eukaryotic cytotoxicity assays

The peptides were assayed for cytotoxicity in hemolysis assays against 1x108 RBCs/mL and in alamar blue viability assays against 5x105 CCLP-1 cells/mL. The data are plotted as (toxicity of engineered) – (toxicity of parent) such that engineered peptides that display increased toxicity are plotted as positive values. (A) Cytotoxicity against human

RBCs (N = 3). (B) Cytotoxicity against human CCLP-1 epithelial cells. Error bars represent standard deviation. (N = 3) 174 175

Figure 5-4. Comparison of the second set of rationally engineered AMPs in the absence and presence of RBCs in broth-based MIC assays

Peptides were tested to determine minimum inhibitory concentrations in the absence and presence of 1x109 RBCs/mL. The MIC values of the parent peptides are plotted on the x-axis and the engineered sequences on the y-axis. Improvements in peptide MIC are apparent in points plotted below the gray-dashed line (y = x). The D-form consensus sequenced modified to remove glycine residues (D-NOGCON) showed excellent antimicrobial activity against all challenge organisms. The experiments exchanging

L-form library members for D-form variants also led to improvements. (A) E. coli in the absence of RBCs. (B) E. coli in the presence of RBCs. (C) S. aureus in the absence of

RBCs. (D) S. aureus in the presence of RBCs. (E) P. aeruginosa in the absence of RBCs.

(F) P. aeruginosa in the presence of RBCs. Error bars represent standard deviation. (N =

6-10) 176 177 178

Figure 5-5. Comparison of radial diffusion MIC values for the second set of rationally engineered peptides

The rationally engineered and parent peptides were assayed in radial diffusion assays against E. coli and S. aureus in the presence of 1x108 to 1x109 RBCs/mL. MIC values were determined by calculating the x-intercept of the linear relationship between radius of inhibition and log10(peptide concentration). The results are plotted as (MIC of parent) –

(MIC of engineered) such that improvements (decreases) in MICs are plotted as a positive value. The improved D-consensus (D-NOGCON) showed improved activity against both organisms. We also observed that other D-form variants had improved activity relative to the L-forms. (A) L-GNS2/D-GNS2. (B) L-DBS1/D-DBS1. (C)

D-RAT4/D-KON. (D) D-RAT4/D-NOGCON. Error bars represent standard deviation. (N

= 4) 179 180 181

Figure 5-6. Comparison of the second set of engineered AMPs and parent sequences in eukaryotic cytotoxicity assays

The peptides were assayed for cytotoxicity in hemolysis assays against 1x108 RBCs/mL and in alamar blue viability assays against 5x105 CCLP-1 cells/mL. The data are plotted as (toxicity of engineered) – (toxicity of parent) such that engineered peptides that display increased toxicity are plotted as positive values. Importantly, D-NOGCON did not have a significant increase in cytotoxicity compared to D-RAT4. Conversely, the

D-form library variants, D-DBS1 and D-GNS2, were more toxic than the L-form variants. (A) Cytotoxicity against human RBCs. (B) Cytotoxicity against human CCLP-1 epithelial cells. Error bars represent standard deviation. (N = 3) 182 183

Figure 5-7. Comparing D-NOGCON, the result of screening and sequence engineering, to the template sequence, D-ARVA

(A) The peptides were compared in broth dilution assays against six different microorganisms in the absence and presence of 1x109 RBCs/mL. The results were compared using a two-sample t-test, in the presence of RBCs, D-NOGCON performed significantly better against all organisms except for K. pneumoniae. It also had improved activity against E. coli, P. aeruginosa, S. enterica, and A. baumannii in the absence of

RBCs (N = 6-10). The peptides were also tested against (B) E. coli and (C) S. aureus in radial diffusion assays in the presence 1x108 to 1x109 RBCs/mL. The results were compared using a paired two-sample t-test and significant improvements were made against both organisms (N = 4). We tested the cytotoxicity of the peptides against (D) human RBCs and (E) human CCLP-1 epithelial cells. When tested in a paired t-test, there was a significant improvement for D-NOGCON against RBCs and no difference against

CCLP-1 cells (N = 3). Error bars represent standard deviation.

* = p < 0.05

** = p<0.01

*** = p<0.001 184 185

Table 5-1. Sequence design, underlying hypotheses, and statistical analysis for the first set of rationally engineered peptides

The engineered peptide sequences and their derivation from parent sequences is shown and the underlying hypotheses described. We performed statistical testing for performance in antimicrobial assays in the presence and absence of RBCs, as well as for cytotoxicity. The broth dilution results were tested using a two-sample t-test (N = 6-22).

The radial diffusion results were tested using a paired t-test (N = 4). The cytotoxicity results were tested using a paired t-test (N = 3). The ↑ represents the improvement in performance that would be desired for a viable in vivo antimicrobial peptide. This corresponds to a decrease in MIC values (increased potency) and decreased cytotoxicity.

The ↓ symbol represents a regression in desired behavior.

* = p < 0.05

** = p<0.01

*** = p<0.001 186

Table 5-1

Peptide Sequence Hypothesis Significant Results The presence of glycine in the sequence of DBS1 is not necessary and increases the complexity of (DBS1) ↑Broth vs. S. aureus (-RBC)** synthesis. Removal of G will not RRGWARRLFFAYGRR ↑Radial Diffusion vs. S. aureus** RAT1 reduce the activity of the peptide and ↓ ↑Radial Diffusion vs. E. coli* may actually increase the activity by RRWARRLFFAYRR ↑Cytotoxicity vs. CCLP-1** increasing both hydrophobicity and positive charge, relative to the length of the sequence. The presence of glycine in the sequence of NATT is not necessary and increases the complexity of (NATT) synthesis. Removal of G will not ↑Broth vs. P. aeruginosa (-RBC)* RRGWNLALTLTYYGRR RAT2 reduce the activity of the peptide and ↑Broth vs. P. aeruginosa (+RBC)* ↓ may actually increase the activity by ↓Hemolysis* RRWNLALTLTYYRR increasing both hydrophobicity and positive charge relative to the length of the sequence. A library based on the sequence of ARVA yielded all peptides with two arginine residues at both termini. (ARVA) These new peptides are less potent, ↓Broth vs. E. coli (-RBC)*** RRGWALRLVLAY but also less toxic, and some perform RAT3 ↓Broth vs. S. aureus (-RBC)*** ↓ better in the presence of RBCs. Adding ↓Hemolysis* RRGWALRLVLAYGRR a cassette to ARVA will reduce its toxicity and interaction with RBCs while leaving the potent antimicrobial activity intact. The consensus sequence will retain ↓Broth vs. E. coli (-RBC)*** important features from each peptide in ↓Broth vs. S. aureus (-RBC)*** the library and thus perform better in ↑Broth vs. P. aeruginosa (-RBC)** (Library Consensus) terms of potency and minimizing RAT4 ↑Broth vs. P. aeruginosa RRGWARRLAFAFGRR interactions with RBCs. Note that (+RBC)*** residues 9 and 10 both had the ↑Radial Diffusion vs. S. aureus* potential to be A or F; we selected A at ↑Hemolysis* position 9 and F at position 10. ↑Broth vs. E. coli (-RBC)*** The library peptides are being ↑Broth vs. E. coli (+RBC)*** impacted by proteolysis. Synthesizing ↓Broth vs. P. aeruginosa (-RBC)** (Library Consensus) the consensus sequence with D-amino D-RAT4 ↑Broth vs. P. aeruginosa rrgwarrlafafgrr acids will yield a peptide that retains (+RBC)*** activity more faithfully in the presence ↑Radial Diffusion vs. E. coli** of RBCs. ↑Radial Diffusion vs. S. aureus*** The presence of RL at positions 7 and 8 was often conserved in the (Library Consensus) ↑Broth vs. E. coli (-RBC)* consensus sequence from the RRGWARRLAFAFGRR ↑Broth vs. P. aeruginosa (+RBC)* RAT5 template, ARVA. Expanding this to ↓ ↑Radial Diffusion vs. S. aureus* include another RL in series may RRGWARRLRLAFAFGRR ↓Hemolysis* increase activity and/or reduce RBC interactions. (Library Consensus) Alternating AF residues appear twice in ↑Broth vs. E. coli (-RBC)** RRGWARRLAFAFGRR the consensus sequence. Expanding ↑Broth vs. P. aeruginosa (+RBC)** RAT6 ↓ this to include another AF in series may ↑Radial Diffusion vs. E. coli** RRGWARRLAFAFAFGRR increase activity. ↓Hemolysis* ↓Broth vs. E. coli (-RBC)*** (Library Consensus) Alternating AF residues appear twice in ↓Broth vs. P. aeruginosa (-RBC)*** RRGWARRLAFAFGRR the consensus sequence. Contract this ↓Broth vs. P. aeruginosa RAT7 ↓ to remove an AF in series may reduce (+RBC)*** RRGWARRLAFGRR RBC interactions. ↓Radial Diffusion vs. E .coli*** ↑Hemolysis** 187

Table 5-2. Sequence design, underlying hypotheses, and statistical analysis for the second set of rationally engineered peptides

The engineered peptide sequences and their derivation from parent sequences is shown and the underlying hypotheses described. We performed statistical testing for performance in antimicrobial assays in the presence and absence of RBCs, as well as for cytotoxicity. The broth dilution results were tested using a two-sample t-test (N = 6-22).

The radial diffusion results were tested using a paired t-test (N = 4). The cytotoxicity results were tested using a paired t-test (N = 3). The ↑ represents the improvement in performance that would be desired for a viable in vivo antimicrobial peptide. This corresponds to a decrease in MIC values (increased potency) and decreased cytotoxicity.

The ↓ symbol represents a regression in desired behavior.

* = p < 0.05

** = p<0.01

*** = p<0.001 188

Table 5-2

Peptide Sequence Hypothesis Significant Results

↑Broth vs. E. coli (-RBC)*** (GNS2) ↑Broth vs. E. coli (+RBC)*** RRGWAFRRALAYGRR D-GNS2 ↑Radial diffusion vs. E. coli*** ↓ Radial diffusion vs. S. aureus*** rrgwafrralaygrr In the first round of rational design, ↑ changing the consensus sequence ↓Hemolysis** from the L-form to the D-form led to a huge improvement in antimicrobial activity and activity retention in the presence of RBCs. Because the ↑Broth vs. E. coli (-RBC)*** sequences are similar, it is possible ↑Broth vs. E. coli (+RBC)*** that some of the library isolates may ↑Broth vs. S. aureus (-RBC)*** (DBS1) see performance increases when ↑Broth vs. S. aureus (+RBC)*** RRGWARRLFFAYGRR D-DBS1 they are synthesized as D-peptides. ↑Broth vs. P. aeruginosa (-RBC)*** ↓ ↑Broth vs. P. aeruginosa (+RBC)*** rrgwarrlffaygrr ↑Radial diffusion vs. E. coli*** ↑Radial diffusion vs. S. aureus** ↓Hemolysis*

The nature of the sidechain is important in addition to just the presence of a positive charge. By changing arginine to lysine, we should be able to learn something about the importance of using one ↓Broth vs. E. coli (-RBC)*** residue vs. the other in antimicrobial (Consensus sequence) ↓Broth vs. E. coli (+RBC)*** peptide design. Additionally, lysine RRGWARRLAFAFGRR ↓Broth vs. P. aeruginosa (-RBC)*** D-KON is modestly more soluble in ↓ ↓Broth vs. P. aeruginosa (+RBC)*** aqueous buffers than arginine. It will KKGWAKKLAFAFGKK ↓Radial diffusion vs. E. coli*** also be interesting to see if the ↓Radial diffusion vs. S. aureus*** effect of the discretely positively charged primary amine of lysine will have an effect on potency as compared to the distributed charge of the guanidinium group on arginine.

In antimicrobial peptide canon, activity is driven by electrostatic and hydrophobic interactions, to which glycine does not contribute. Still, conglomerate data on AMPs shows that glycine is statistically ↑Broth vs. E. coli (-RBC)*** overrepresented, making its role ↑Broth vs. E. coli (+RBC)*** (Consensus sequence) somewhat ambiguous. In the ↑Broth vs. S. aureus (-RBC)*** RRGWARRLAFAFGRR preliminary round of rational ↑Broth vs. S. aureus (+RBC)*** D-NOGCON ↓ engineering, we tried removal on ↑Broth vs. P. aeruginosa (-RBC)*** RRWARRLAFAFRR DBS1 and NATT. Neither peptide ↑Broth vs. P. aeruginosa (+RBC)*** suffered any activity change against ↑Radial diffusion vs. E. coli*** P. aeruginosa or E. coli, and both ↑Radial diffusion vs. S. aureus** gained a modest amount of activity against S. aureus. The modification did not have a noticeable effect with respect to antimicrobial activity in the presence of RBCs. 189

Table 5-3. Broth MIC values in the absence and presence of RBCs

MIC values for each peptide in broth were measured in the absence or presence of 1x109

RBCs/mL. (N ≥ 3). The MIC without RBCs is in the left column for each organism. MIC in the presence of RBCs is in the right column. All values are micromolar concentration measurements.

EC = E. coli

PA = P. aeruginosa

KP = K. pneumoniae

AB = A. baumannii

SE = S. enterica

SA = S. aureus 190

Table 5-3

EC PA KP AB SE SA L-ARVA 1.7 22.9 26.0 >30 2 29.1 5.4 >30 9.9 >30 3.6 >30 D-ARVA 3.0 19.6 >30 >30 1.4 8.6 7.5 >30 6.6 >30 1.3 20.0 CHUK1 >30 >30 14.8 >30 >30 >30 >30 >30 >30 >30 >30 >30 CHUK2 18.1 >30 5.1 191

27.1 >30 >30 25.4 >30 >30 >30 >30 >30 GNS1 >30 >30 >30 >30 >30 >30 >30 >30 >30 >30 >30 >30 GNS2 22.2 >30 >30 >30 >30 >30 >30 >30 14.8 >30 >30 >30 DBS1 7.3 >30 4.0 19.2 5.4 8.6 1.7 1.5 1.5 2.2 >30 >30 DBS2 5.7 26.6 192

3.4 27.1 >30 >30 5.0 >30 7.0 >30 >30 >30 DBS3 11.5 >30 2.3 20.0 >30 >30 5.4 >30 10.6 >30 >30 >30 DBS4 15.1 26.6 3.6 19.0 >30 >30 5.4 >30 4.1 >30 >30 >30 DBS5 6.6 15.5 3.8 22.1 14.8 >30 2.6 >30 7.0 >30 >30 >30 RAT1 5.2 193

>30 4.2 26.2 >30 >30 6.6 >30 3.1 >30 18.7 >30 RAT2 1.4 9.5 1.5 22.9 1.2 >30 2.6 >30 1.6 >30 4.2 >30 RAT3 17.5 >30 16.3 >30 7.3 >30 12.9 >30 18.1 >30 26.2 >30 L-RAT4 12.5 >30 2.8 13.3 >30 >30 13.8 22.2 6.6 >30 >30 >30 D-RAT4 194

1.6 1.3 4.5 6.8 14.8 9.9 7.5 8.6 1.6 2.0 26.2 28.0 RAT5 7.8 >30 2.0 7.8 >30 >30 2.7 >30 2.9 >30 >30 >30 RAT6 5.5 28.0 2.5 5.2 2.6 >30 6.1 >30 1.5 >30 26.2 >30 RAT7 >30 >30 >30 >30 >30 >30 >30 >30 >30 >30 >30 >30 195

D-GNS2 2.7 2.6 >30 >30 14.8 14.8 >30 >30 5.2 5.2 >30 >30 D-DBS1 0.9 1.3 5.9 4.6 5.4 6.6 1.4 1.5 1.3 1.8 9.6 9.3 D-KON 7.3 8.9 20.7 23.7 >30 >30 >30 >30 22.2 22.2 >30 >30 D-NOGCON 1.0 0.8 1.9 2.8 4.4 3.8 4.4 4.4 1.3 1.9 5.7 196

7.0

Table 5-4. Radial diffusion MIC values, cytotoxicity, and solubility

Radial diffusion MIC measured in the absence and presence of 1x109 RBCs/mL (N ≥ 4).

The MIC without RBCs is in the left column for each organism. MIC in the presence of

RBCs is in the right column. MIC values are micromolar concentration measurements.

Cytotoxicity toward eukaryotic cells (RBCs and CCLP-1 cells) at 100 µM is also shown

(N = 3). Values represent percentage of dead cells. Solubility in PBS is displayed as micromolar concentration in the last column (N = 1). 197

Table 5-4

Radial Diffusion (MIC) Cytotoxicity (100 µM)

EC SA RBCs CCLP-1 Solubility (µM) L-ARVA 2.9 17.8 7.8 >20 19.2 0.0 100.0 D-ARVA 2.5 12.4 3.5 >20 25.1 14.0 100.0 198

CHUK1 3.0 15.6 4.7 >20 1.4 0.0 >1000 CHUK2 2.0 >20 10.0 >20 1.7 0.0 >1000 GNS1 5.9 >20 12.4 >20 0.6 0.0 >1000 GNS2 2.6 >20 2.7 >20 1.0 0.0 >1000 DBS1 2.8 17.9 4.8 >20 6.1 93.1 >1000 DBS2 3.0 >20 4.7 >20 10.9 62.1 >1000 DBS3 3.1 >20 199

5.8 >20 5.2 63.5 >1000 DBS4 3.7 >20 9.8 >20 5.3 48.7 >1000 DBS5 2.2 15.4 6.7 >20 10.5 35.0 350.0 RAT1 2.2 15.8 3.5 >20 10.4 74.5 >1000 RAT2 1.9 10.3 6.4 >20 89.7 2.0 350.0 RAT3 2.4 16.2 14.3 >20 60.5 54.2 >1000 L-RAT4 2.6 16.4 3.7 >20 10.0 200

35.8 >1000 D-RAT4 1.6 2.2 2.8 8.6 10.0 74.8 >1000 RAT5 3.7 16.0 3.7 >20 16.0 71.7 >1000 RAT6 2.6 11.2 4.6 >20 37.4 82.9 >1000 RAT7 3.2 >20 2.7 >20 2.7 0.0 >1000 D-GNS2 1.0 3.4 1.7 11.0 4.4 0.0 >1000 D-DBS1 1.7 2.9 2.2 7.1 13.1 92.3 >1000 D-KON 201

0.9 2.3 8.6 >20 4.2 0.0 >1000 D-NOGCON 1.1 1.8 1.8 5.3 6.1 68.3 >1000

CHAPTER 6: Assessment of a novel antimicrobial peptide in a murine model of bacterial pneumonia

Introduction

We began this work with an antimicrobial peptide, ARVA, that displayed potent antimicrobial activity against several pathogenic bacterial species, but was rendered less effective when concentrated human erythrocytes were present51. We observed this behavior for both the L- and D-amino acid variants of the peptide. Our study was expanded to look at a cross-section of well-studied AMPs, and the results were similar in 202 most cases. We used HPLC-based binding experiments to demonstrate that ARVA has a strong affinity for eukaryotic membranes, partially explaining the activity loss observed in the presence of RBCs. We also identified the activity of RBC-associated, intracellular proteases as an important factor in AMP inhibition. These studies prompted the synthesis of a combinatorial library based on ARVA, and the development of screens to select for peptides that retain activity in the presence of RBCs. We identified nine unique peptide sequences during library screening and synthesized larger quantities for post-screening characterization. The peptides did not retain activity in the presence of RBCs: an unexpected result. To refine the library-derived sequences, we initiated a program of rational sequence engineering. A major breakthrough occurred with the synthesis and characterization of a D-amino acid consensus sequence based on the screening isolates.

This sequence, D-RAT4, retained activity in the presence of concentrated erythrocytes and was less toxic to human cells than ARVA. We further engineered this sequence to develop D-NOGCON, an antimicrobial peptide with activity comparable to the best

AMPs identified to date that does not lose activity in the presence of RBCs. In this chapter, we sought to test the ability of D-NOGCON to protect mice from acute pneumonia induced by aspiration of P. aeruginosa.

An opportunistic human pathogen, Pseudomonas aeruginosa is a serious concern in the escalating crisis of antibiotic resistant bacteria. Even before considering resistance mechanisms acquired through mutation and horizontal gene transfer, P. aeruginosa has extensive intrinsic antibiotic resistance mechanisms172. To start, the outer membrane of this organism has very low permeability relative to other pathogens, up to 100-fold lower than E. coli173. P. aeruginosa also utilizes multi-drug efflux pumps and porins to eject a 203 broad-spectrum of antibiotic classes from the cytosol and periplasmic space, including cephalosporins, carbapenems, aminoglycosides, and quinolones172,174. Conferring resistance to a number of additional compounds, P. aeruginosa produces a chromosomally encoded β-lactamase, AmpC, that is not susceptible to common classes of

β-lactamase inhibitors173,175. In addition to intrinsic resistance mechanisms, P. aeruginosa is known to utilize several acquired resistance genes172. Among these are a variety of extended spectrum β-lactamases, which have rendered some strains completely resistant to the entire class of antibiotics173,176. P. aeruginosa is also known to acquire or develop resistance genes for several other classes of antibiotics, including aminoglycosides and fluoroquinolones173,177,178. Taken together, these resistance phenotypes make treatment of infections caused by P. aeruginosa a complex clinical undertaking.

With respect to antimicrobial peptides, the prospects for treating P. aeruginosa are somewhat promising. Numerous reports have demonstrated good antimicrobial activity for numerous peptides against P. aeruginosa, in vitro. Although there are not clinical successes to present at this time, it is appropriate to mention the polymyxin family of antibiotics as a parallel. Modified cyclic peptides, polymyxins are known to act through the same mechanisms as canonical antimicrobial peptides: interaction with LPS and disruption of the bacterial membrane179. These antibiotics have fallen out of favor for systemic administration because of toxicity concerns, but it is telling that they are often used for the treatment of extensively drug-resistant Gram-negative pathogens, including

P. aeruginosa42,180–182. While the successes of treatment with polymyxins are encouraging, it should be noted that resistance to polymyxin antibiotics has been observed in P. aeruginosa. In these cases, a mutation in the two-component regulatory system, PmrAB, 204 leads to the conjugation of amino-arabinose residues to LPS, effectively neutralizing the anionic charge that is present in wild-type strains13. This same mechanism of resistance renders antimicrobial peptides ineffective.

Further contributing to P. aeruginosa pathogenesis, is the organism’s ability to survive in a variety of resource-poor environments172. This characteristic can be attributed to the size and flexibility of its genome. At more than 6 Mbp, the genome of P. aeruginosa is exceptionally large among known human pathogens172,183. In addition to size, the expression of genes is remarkably plastic. This can be attributed, at least in part, to two-component regulatory systems, which are abundant within the species172,184. These factors are responsible for the rapid adaptation of P. aeruginosa strains from natural environments like soil and water, to the hostile environment of the human body.

Usually an opportunistic pathogen, P. aeruginosa infections are usually only a concern to individuals with compromised immune systems, or other complicating conditions185. Indeed, P. aeruginosa infections are common in patients with HIV

(compromised T-cell mediated immunity), cancer patients undergoing chemotherapy

(neutropenia), and victims of severe burns (compromised immune barrier)185. P. aeruginosa is also commonly isolated from the lungs of susceptible individuals. These infections are often classified into two subtypes, chronic colonization as seen in cystic fibrosis, and acute infection, often associated with ventilator acquired pneumonia. In patients with cystic fibrosis, a defective chloride ion channel (CFTR) causes mucus secretions to become abnormally viscous186. In the lung, secretions are not able to be cleared by the normal movement of epithelial cilia, and as such, mucus accumulates over time187. This pathology leads to the emergence of a nutrient-rich environment that 205 supports colonization by diverse microbial species188. As the time progresses, the microbial population becomes dominated by P. aeruginosa, which is generally associated with disease exacerbations and the progressive decline in overall pulmonary function189.

Because cystic fibrosis is an incurable genetic condition, suppression of P. aeruginosa infection is paramount to disease management strategies185. Acute lung infection by P. aeruginosa is commonly observed in patients receiving respiratory support via mechanical ventilation190. These infections may be difficult to treat and cause significant mortality due to complications arising from bacterial sepsis190,191. Because of inherent resistance to many classes of antibiotics and the difficulties of resolving pulmonary infections, new treatment options are highly sought for the treatment of P. aeruginosa-associated lung infections.

In this chapter, we will attempt to translate the in vitro success of the novel antimicrobial peptide D-NOGCON, to an in vivo model of acute P. aeruginosa pneumonia. We will use a murine (mouse) model in which the animals are challenged intratracheally and treated by aspiration of the AMP192. There is precedent for the use of respired antimicrobial therapies for pulmonary P. aeruginosa infections, as aerosolized administration of the aminoglycoside, tobramycin, is one of the most common treatments in both chronic and acute cases193,194. While initially effective, resistance to this antibiotic often emerges, especially in chronic infections where the patient may need to receive treatment indefinitely137. Because of this, alternative treatments are needed.

Previous efforts to utilize antimicrobial peptides to treat P. aeruginosa in the lung have not been successful192,195. In the case of iseganan, a human clinical trial revealed that

P. aeruginosa infections of ventilated individuals could not be prevented by prophylactic 206 administration of the peptide195. Alarmingly, there was increased mortality in individuals treated with the peptide and the trial was discontinued prior to completion 40,195. Recently, a group attempted to use D-BMAP18 to treat acute pneumonia in a mouse model of P. aeruginosa infection. Although the peptide showed excellent in vitro activity, it was not able to improve the outcomes of infected animals192. Despite these unsuccessful studies, we hypothesize that D-NOGCON will be effective in this model because it was developed under conditions that were designed to mimic the in vivo milieu. This peptide shows excellent activity against a panel of Gram-negative pathogens, including P. aeruginosa, and is relatively non-toxic to eukaryotic cells (Figure 5-7). The results of these experiments will inform on the effectiveness of our development methods and screening conditions and guide the development of future therapeutic antimicrobial peptides.

Methods

Peptide

All peptides used in this study were synthesized using solid-phase FMOC chemistry and purified to >95% by Bio-synthesis Inc (Lewisville, TX). Peptides were dissolved in

0.025% acetic acid solution and concentrations were determined by absorbance at 280 nm.

Prior to administration, the peptide was diluted to the appropriate concentration in PBS.

Animals

Female C57BL/6 mice, aged five weeks, were obtained from Charles River Laboratories.

Upon receipt, they were allowed at least one week to recover from transport and acclimate to the new housing environment. Mice were maintained on a standard chow 207 diet. Experiments were performed with mice no younger than six weeks and no older than nine weeks.

Peptide administration

The mice were anesthetized using isoflurane and the peptide and vehicle controls were administered via intratracheal instillation. Briefly, the anesthetized mice were suspended from wire by their front teeth and blunt forceps was used to extract the tongue. The tongue was pulled out and downward. The treatment was deposited in the back of the throat with a micropipette. The nares of the animal were then covered while the mouse recovered from anesthesia. The chest was gently rubbed to promote inhalation. The mice were made to inhale 15 times before the tongue was released and the mouse was removed from the wire. The animals were then allowed a minute to fully awaken and then returned to their cages.

Bacterial infection

P. aeruginosa PA01 was cultured overnight in tryptic soy broth (TSB) at 37 °C and shaken at 220 RPM. Prior to infecting the mice, the overnight culture was diluted

100-fold into 25-mL of fresh TSB. The culture was grown for three hours before the optical density was determined. Previous growth curve experiments allowed us to dilute this newly expanded culture to 1.4x108 CFU/mL. To infect an animal, 50 µL of the bacterial suspension (7x106 CFUs) were instilled in the trachea, exactly as described for the administration of peptide. Control animals were treated with sterile PBS.

Weight measurements

Mice were weighed every 24 hours during experimental trials.

Criteria for euthanasia 208

Mice were euthanized if they had lost 20% of their original body weight at any point during the trial. Mice were also euthanized if they displayed disequilibrium significant enough to prevent acquisition of food and water or failed to right themselves after being picked up by the tail. Euthanasia was performed by exposure to 100% CO2 for five minutes, followed by cervical dislocation.

CFU burden determination

Immediately after an animal was euthanized, a necropsy was performed and the lungs were extracted and placed in two milliliters of sterile PBS. The lung tissue was homogenized with a standard tissue homogenizer. The homogenized tissue was serially diluted and plated on P. aeruginosa isolation agar (PIA) to determine the bacterial burden in the lungs.

Results

Before attempting to treat a P. aeruginosa infection of the lung, we sought to establish a safe dose for administration of the peptide via aspiration. Based on the in vitro toxicity data (Figure 5-7D, E), D-NOGCON begins to induce cytotoxicity at 100 µM against CCLP-1 epithelial cells, but not RBCs. We chose this concentration for the initial safety studies and administered 50 µL every 8 hours for 3 days (9 doses). As a no treatment control, we administered 50 µL of PBS on the same schedule. To monitor the toxicity of the treatment regimen, we observed animal behavior and measured body weight changes. We observed that after each dosing of either peptide or vehicle solution, the animals seemed markedly distressed and took several minutes to recover. This behavior indicated that the effects of anesthesia and forced respiration of a volume of liquid equivalent to 25% of the total murine lung volume was detrimental. Further, the 209 animals had worsening reactions with each dose, suggesting that they were not fully recovering between administrations. These observations of negative effects were corroborated by the weight change data, collected every 24-hours. After the first day, the treated animals lost approximately 10% of their body weight, whereas the control animals lost 5% (Figure 6-1A). Following the 48-hour timepoint, the treated animals had lost over

20% of their body weight, meeting the criterion for euthanasia and early study termination (Figure 6-1A). Weight loss of this magnitude indicates that the animals were not eating or drinking an appropriate amount and were likely very distressed.

Because the initial dosing regimen proved too toxic for the animals to tolerate, we reduced both the peptide concentration and frequency of administration. In the second safety trial, we administered 50 µL of peptide at 50 µM for each dose. We reduced the frequency of administration from three doses per day to two doses per day, while maintaining the original three-day design. Under these conditions, the mice appeared to be far more tolerant of the peptide. The animals recovered much more rapidly after each administration and did not appear to experience cumulative effects of repeated dosings.

When we measured weight change, these animals maintained 95% of their body weight for the duration of the trial (Figure 6-1B). We determined that this dosing regimen was sufficiently safe to proceed with in bacterial challenge experiments.

Our initial experiment was survival-based. Previous work with the model had revealed that aspiration of 7x106 CFUs of P. aeruginosa was invariably fatal after 72 hours. The goal of the study was to keep the mice in the peptide treatment group alive for the course of the 72-hour study. We infected 8 mice (4 for peptide treatment, 4 for vehicle) with this bacterial load in 50 µL of PBS and allowed 2 hours of recovery prior to 210 the initial treatment administration. After the recovery period, dosing proceeded as it had for the second safety study (50 µL, 50 µM, 2x daily). All mice from both treatment groups survived the initial 24 hours. They appeared sluggish after infection and dosing, but did not show other outward signs of distress. When they were weighed at the 24-hour time point, mice from both groups had lost the same amount of body weight, ~12%

(Figure 6-2A). Between the 24 and 48-hour time points, 3 controls and 1 peptide treated animal were sacrificed (Figure 6-2B). While not meeting the weight loss criterion for euthanasia, these animals displayed outward signs of severe systemic infection. Most significantly, they lost the ability to maintain their balance and displayed excessive

“rolling” behavior. Euthanasia is required in these cases because the animals are clearly suffering and are not able to obtain the nutrients required for survival. On day 3, the weight measurements indicated that all of the animals had lost more than 20% of their body weight (Figure 6-2A). Because of this, all of the remaining animals were euthanized

(Figure 6-2B). The lungs of 3 animals from each treatment group were removed, homogenized, and plated on PIA plates to determine the P. aeruginosa CFU burden.

These experiments revealed a nearly identical number of bacteria present in both groups

(Figure 6-2C). It is important to mention that the animals in the CFU burden studies were not time-matched, limiting the conclusions that can be drawn from those analyses.

Because peptide treatment was not able to rescue mice from a fatal P. aeruginosa lung infection, we attempted to reduce the infectious burden in time-matched animals over a short time-course of 24 hours. As before, the mice were infected with 7x10 6 CFUs of P. aeruginosa in 50 µL of PBS. For this study, we increased the concentration of administered peptide solution to 100 µM, while maintaining a volume of 50 µL and a 211 frequency of 2 doses per day (2 doses total, in this case). After 24 hours, the animals were sacrificed and their lungs homogenized and plated to determine the infectious burden. As in the previous experiment, both treated and untreated animals lost 10-12% of their body weight after 24 hours (Figure 6-3A). Unexpectedly, we observed a non-significant increase in CFU burden in the treated animals (Figure 6-3B).

Discussion

In this chapter, we sought to translate the success of our in vitro peptide development strategy to an in vivo model of infection. We began by assessing the toxicity of our peptide in the lungs of laboratory mice. We found that the in vitro toxicity data aligned well with the in vivo toxicity studies, revealing that the peptide was toxic at a concentration of 100 µM upon repeated dosing, but safe at 50 µM. Using this information, we developed a dosing regimen to treat mice that were presented with a lethal P. aeruginosa challenge in the lungs. In this study, treatment did not extend survival of the peptide treated group beyond 72 hours. We then sought to determine whether peptide treatment could reduce infectious burden over a 24-hour period, but unexpectedly observed an increase in infectious burden in the treated animals.

The results in this section suggest that direct application of the antimicrobial peptide, D-NOGCON, to the lungs is not an effective means of controlling acute pneumonia induced by respired P. aeruginosa. These results are somewhat surprising, given that naturally occurring AMPs play an important role in the defense of the airway epithelium196,197. However, similar results have been observed with other antimicrobial peptides in both animal models and human clinical trials40,192. The lung has a highly complex structure and it remains possible that exogenously administered peptide is not 212 reaching all of the infected spaces. It is also possible that exogenously administered peptide is toxic to host cells in the lung. This possibility seems especially realistic when the formulation involves D-stereoisomers, which cannot be degraded and cleared by normal biological processes192. Cytotoxicity would be especially detrimental if the immune cells that normally function to control infection were being negatively impacted by the peptide.

An overlooked facet of this animal model is the propensity for infection to spread to other tissues. We observed symptoms consistent with this possibility in our experiments with animals that were unable to maintain physical balance and displayed

“spinning” and “rolling” behavior. These symptoms can be caused by otitis media (OM) which can be caused by bacterial infection or exposure to lipopolysaccharide, either of which are feasible outcomes in this model198,199. The outward behavior of the mice aside, this animal model of P. aeruginosa pneumonia has been used in the past to study bacterial sepsis200. A life-threatening condition, sepsis occurs when the body commands a massive inflammatory response to an acute infection or other immune trigger201. The magnitude of the immune response is such that it is detrimental to the health of the organism. Closely related, but distinct from sepsis, is the phenomenon of bacteremia. Here, viable bacteria are detected in the blood. The P. aeruginosa pneumonia model employed in this study has also been used in studies of bacteremia202. Because of issues and concerns about distribution of peptide therapeutics to disparate tissues, it is likely that antimicrobial peptide administered to the lung would not be effective in controlling an infection that has spread to other areas of the body203.

Future Directions 213

Although our lead peptide, D-NOGCON, was not effective in the P. aeruginosa pneumonia model, we will test its efficacy in other mouse models of bacterial infection.

The first model that we will test is a wound excisional model with P. aeruginosa204,205.

Here, a lesion is created on the back of the animal and infected with the bacterial species of interest205. This mouse model is used as a surrogate to simulate topical infections, which can occur naturally for many reasons, but can also develop from surgical wounds.

The surgical wound scenario is especially relevant because they are often deeper than normal dermal injuries and are also prone to exposure to antibiotic resistant pathogens, which are commonly acquired in nosocomial environments. Additionally, because surgical wounds are often predictable, antimicrobial peptide treatment could be used prophylactically to prevent wound colonization. The bacterial infection in this model is usually not fatal. Thus, we will be attempting to observe an expedited clearance of the infection with respect to control animals. We are optimistic about the potential for success in topical wound models, as other antimicrobial peptides have performed well as topical therapeutics in past studies64. Because D-NOGCON outperforms many known AMPs in vitro and is relatively non-toxic, we believe that it is an excellent candidate for translational efficacy, here.

Traditionally, the infection in this wound model is monitored by mechanically sampling the lesion and plating the CFUs to determine bacterial burden206. While this method has been commonly utilized, it has the disadvantages of perturbing the site of injury and yielding inconsistent results, due to infected wounds having heterogenous bacterial distributions. Further, if the wound is covered by dried exudate (scabbing), the site becomes unavailable for CFU sampling. Thus, we will utilize a less invasive method 214 of monitoring the infection: in vivo bioluminescence measurements206. For this approach, a bacterial strain expressing fluorescent or bioluminescent compound is introduced to the wound and the progress of the infection can be monitored using the IVIS® imaging system206.

In addition to the topical infection model, we will also test the efficacy of

D-NOGCON in a systemic model of Salmonella enterica infection. In this model, the mice are infected orally with a small aliquot of bacterial culture. Over the course of 2-3 weeks, the infection spreads throughout the body, eventually overwhelming the animal and causing mortality207. With this model, we will test the efficacy of a systemic administration regimen (intravenous (IV) or intraperitoneal (IP)). Because this model of infection is fatal in almost all cases, we will be attempting to increase survival time, at the very least, and hopefully, prevent mortality altogether. We are especially interested in peptide efficacy in this system because it closely matches the conditions under which the peptide was developed. In the other models discussed thus far, peptide will primarily encounter epithelial cells with perhaps a few disparate immune cells. In the S. enterica systemic model, D-NOGCON will encounter concentrated red blood cells and will need to target bacteria within that context. This is the exact scenario in which we screened for and isolated the peptides that led to the development of D-NOGCON. Perhaps matching the in vivo usage with the in vitro development strategy will yield the translational success that we have been attempting to achieve.

The goal of this work was to create an in vitro environment for AMP characterization that was more like that encountered in vivo. We did this by adding eukaryotic cells to antimicrobial assays with the goal of discovering novel AMPs that 215 retain activity even when concentrated host cells are present. We were ultimately able to isolate an AMP, D-NOGCON, with impressive activity against a broad-spectrum of bacterial species. Still, when introduced to a true in vivo environment, the murine lung, the peptide was unable to clear a bacterial infection. This suggests that other factors are influencing peptide activity besides peptide-cell interactions. We can eliminate proteolysis as a potential complicating factor because D-amino acid peptides cannot be degraded by naturally occurring proteases. Another potential impediment is the solvating ability of biological fluids. However, the peptide is soluble in PBS at concentrations exceeding 1000 µM, leaving doubts about solubility issues occurring at concentrations ten-fold lower.

A more likely cause of peptide activity loss is interaction with in vivo structures that are independent of cells. A common concern for any potential therapeutic is interaction with serum components, which were not included in this library screen. In particular, the protein, human serum albumin (HSA), has a number of hydrophobic sites that are known to strongly bind some therapeutic molecules208. Other structures that may inhibit antimicrobial peptide activity are components of the extracellular matrix (ECM).

A complex network of polysaccharide polymers and proteins, the ECM contains many molecules that carry anionic charges (e.g. heparan sulfate)47. Antimicrobial peptides carrying net positive charges will be prone to interactions with these structures.

Although no experimental demonstration was provided here, it is a logical next step that AMPs will interact with components that carry anionic charges or have hydrophobic surfaces, other than cells. Thus, a potential route for this research to follow would be implementing a screening program in the presence of both host cells and 216 non-cell detriments to activity. This could include a screen in which serum components are also included and perhaps even elements of the extracellular matrix. Alternatively, it may be important to explore means of formulating and targeting antimicrobial peptides more specifically. Because the mechanism of AMP killing is very general, it would be advantageous to mask the lethal attributes of the peptides until they reach the desired target. This might be accomplished by entrapping the AMP in liposomes and using a specific receptor/ligand on the liposomal surface to target the peptide to the locus of infection.

The preceding discussion serves to highlight the crux of translating antimicrobial peptides into clinically useful therapeutics. Their global mechanism of action, driven by electrostatic and hydrophobic interactions, leads to broad spectrum activity. However, these same characteristics are the cause of the off-target effects and host-cell interactions that were the focus of this work. While the host cell interaction problem has been minimized by the development of D-NOGCON, preliminary in vivo trials suggest that there are more issues to overcome. Whether all of the factors that inhibit AMP activity in vivo can be overcome with a single, unique sequence is an open question.

Figure 6-1. Safety study for aspirated D-NOGCON by C57BL/6 mice 217

(A) Weight loss by mice treated with 50 µL of 100 µM D-NOGCON, every 8 hours for 3 days. The mice were visibly distressed by this treatment regimen and lost enough weight to warrant euthanasia after seven doses. (N = 2 per group) (B) Weight loss by mice treated with 50 µL of 50 µM D-NOGCON, every 12 hours for 3 days. These mice remained relatively healthy and survived the three-day trial. (N = 2 per group) 218 219

Figure 6-2. Treatment with D-NOGCON of acute pneumonia induced by P. aeruginosa aspiration

(A) Weight loss of mice infected in the lungs with 7x106 CFUs of P. aeruginosa. Mice were treated with 50 µL of 50 µM D-NOGCON or 50 µL of PBS every 12 hours. Both the treatment and vehicle groups lost approximately the same amount weight. (N = 4 per group) (B) Survival curves for mice infected with P. aeruginosa. While the treatment animals appeared to survive marginally longer than vehicle animals, none of the animals survived to the 72-hour endpoint. (N = 4 per group) (C) CFU burdens in the lungs of peptide-treated and vehicle-treated animals. There was no significant difference (N = 3 per group). Error bars represent standard deviation. 220 221

Figure 6-3. CFU burden analysis for mice receiving two doses of D-NOGCON over

24 hours

(A) Weight loss after 24 hours for animals infected in the lungs with 7x106 CFUs of P. aeruginosa. Animals were treated with 50 µL of 100 µM D-NOGCON or 50 µL of PBS every 12 hours. (N = 4 per group) (B) CFU burdens in the lungs of peptide-treated and vehicle-treated animals. There was no significant difference (N = 4 per group). Error bars represent standard deviation. 222 223

LIST OF REFERENCES

1. Singer, A. S. J. & Nicolson, G. L. The Fluid Mosaic Model of the Structure of Cell Membranes. Science (80-. ). 175, 720–731 (1972).

2. Robertson, J. D. The ultrastructure of cell membranes and their derivatives. Biochem. Soc. Symp. 16, 3–43 (1959).

3. Danielli, J. F. & Davson, H. A contribution to the theory of permeability of thin films. J. Cell. Physiol. 5, 495–508 (1935).

4. Beveridge, T. Use of the Gram stain in microbiology. Biotech. Histochem. 76, 111–118 (2001).

5. Silhavy, T. J., Kahne, D. & Walker, S. The bacterial cell envelope. Cold Spring Harb Perspect Biol 2, 1–16 (2010).

6. Epand, R. M. & Epand, R. F. Bacterial membrane lipids in the action of antimicrobial agents. J. Pept. Sci. 17, 298–305 (2011).

7. Navarre, W. W. & Schneewind, O. Surface proteins of gram-positive bacteria and mechanisms of their targeting to the cell wall envelope. Microbiol. Mol. Biol. Rev. 63, 174–229 (1999).

8. Peschel, A. et al. Inactivation of the dlt operon in Staphylococcus aureus confers sensitivity to defensins, protegrins, and other antimicrobial peptides. J Biol Chem 274, 8405–8410 (1999).

9. Strominger, J., Park, J. & Thompson, R. Composition of the Cell Wall of Staphylococcus aureus: Its Relation to the Mechanism of Action of Penicillin. J. Biol. Chem. 234, 3263–68 (1959).

10. Gan, L., Chen, S. & Jensen, G. J. Molecular organization of Gram-negative peptidoglycan. Proc. Natl. Acad. Sci. 105, 18953–18957 (2008).

11. De Duve, C. & Wattiaux, R. Functions of Lysosomes. Annu. Rev. Physiol. 28, 435–492 (1966).

12. Raetz, C. R. H. & Whitfield, C. Lipopolysaccharide Endotoxins. Annu. Rev. Biochem. 71, 635–700 (2002).

13. Moskowitz, S. M., Ernst, R. K. & Miller, S. I. PmrAB, a Two-Component Regulatory System of Pseudomonas aeruginosa That Modulates Resistance to Cationic Antimicrobial Peptides and Addtion of Aminoarabinose to Lipid A. J. Bacteriol. 186, 575–579 (2004). 224

14. Guo, L. et al. Lipid A acylation and bacterial resistance against vertebrate antimicrobial peptides. Cell 95, 189–98 (1998).

15. Yeaman, M. R. & Yount, N. Y. Mechanisms of antimicrobial peptide action and resistance. Pharmacol. Rev. 55, 27–55 (2003).

16. Mohandas, N. & Gallagher, P. G. Red cell membrane : past, present, and future. Blood 112, 3939–3948 (2009).

17. Bloom, J. A. Monitoring Respiration and Circulation. (2004).

18. Krause, M. R. & Regen, S. L. The Structural Role of Cholesterol in Cell Membranes: From Condensed Bilayers to Lipid Rafts. Acc. Chem. Res. 47, 3512–3521 (2014).

19. Phillips, G. B. & Dodge, J. T. Composition of phospholipids and of phospholipid fatty acids in human red cells. J. Lipid Res. 8, 667–675 (1967).

20. Fernandes, H. P., Cesar, C. L. & Barjas-Castro, M. de L. Electrical properties of the red blood cell membrane and immunohematological investigation. Rev. Bras. Hematol. Hemoter. 33, 297–301 (2011).

21. Steiner, H., Hultmark, D., Engström, A., Bennich, H. & Boman, H. G. Sequence and specificity of two antibacterial proteins involved in insect immunity. Nature 292, 246–248 (1981).

22. Holak, T. A. et al. The solution conformation of the antibacterial peptide cecropin A: a nuclear magnetic resonance and dynamical simulated annealing study. Biochemistry 27, 7620–7629 (1988).

23. Zasloff, M. Magainins, a class of antimicrobial peptides from Xenopus skin: isolation, characterization of two active forms, and partial cDNA sequence of a precursor. Proc. Natl. Acad. Sci. 84, 5449–5453 (1987).

24. Patterson-Delafield, J., Szklarek, D., Martinez, R. J. & Lehrer, R. I. Microbicidal cationic proteins of rabbit alveolar macrophages: amino acid composition and functional attributes. Infect. Immun. 31, 723–731 (1981).

25. Ganz, T. Defensins: antimicrobial peptides of innate immunity. Nat. Rev. Immunol. 3, 710–720 (2003).

26. Selsted, M. E., Harwig, S. S., Ganz, T., Schilling, J. W. & Lehrer, R. I. Primary structures of three human neutrophil defensins. J. Clin. Invest. 76, 1436–9 (1985).

27. Wang, G., Li, X. & Wang, Z. APD3 : the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 44, 1087–1093 (2016). 225

28. Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature 415, 389–395 (2002).

29. Jenssen, H., Hamill, P. & Hancock, R. E. Peptide antimicrobial agents. Clin. Microbiol. Rev. 19, 491–511 (2006).

30. Wimley, W. C. Describing the mechanism of antimicrobial peptide action with the interfacial activity model. ACS Chem. Biol. 5, 905–917 (2010).

31. Shai, Y. Mode of action of membrane active antimicrobial peptides. Biopolymers 66, 236–248 (2002).

32. Tossi, A., Sandri, L. & Giangaspero, A. Amphipathic, alpha-Helical Antimicrobial Peptides. Pept. Sci. 55, 4–30 (2000).

33. Fjell, C. D., Hiss, J. A., Hancock, R. E. & Schneider, G. Designing antimicrobial peptides: form follows function. Nat. Rev. Drug Discov. 11, 37–51 (2012).

34. Roversi, D. et al. How many antimicrobial peptide molecules kill a bacterium? The case of PMAP-23. ACS Chem. Biol. 2014, 2003–2007 (2014).

35. Hurdle, J. G., O’Neill, A. J., Chopra, I. & Lee, R. E. Targeting bacterial membrane function: an underexploited mechanism for treating persistent infections. Nat. Rev. Microbiol. 9, 62–75 (2011).

36. Nguyen, L. T., Haney, E. F. & Vogel, H. J. The expanding scope of antimicrobial peptide structures and their modes of action. Trends Biotechnol. 29, 464–472 (2011).

37. Koller, D. & Lohner, K. The role of spontaneous lipid curvature in the interaction of interfacially active peptides with membranes. Biochim. Biophys. Acta - Biomembr. 1838, 2250–2259 (2014).

38. Jang, S. a. et al. Mechanism of action and specificity of antimicrobial peptides designed based on buforin IIb. Peptides 34, 283–289 (2012).

39. Hsu, C. H. et al. Structural and DNA-binding studies on the bovine antimicrobial peptide, indolicidin: Evidence for multiple conformations involved in binding to membranes and DNA. Nucleic Acids Res. 33, 4053–4064 (2005).

40. Marr, A. K., Gooderham, W. J. & Hancock, R. E. Antibacterial peptides for therapeutic use: obstacles and realistic outlook. Curr. Opin. Pharmacol. 6, 468–472 (2006).

41. Peschel, A. & Sahl, H.-G. The co-evolution of host cationic antimicrobial peptides and microbial resistance. Nat. Rev. Microbiol. 4, 529–536 (2006). 226

42. Falagas, M. E. & Kasiakou, S. K. Colistin: the revival of polymyxins for the management of multidrug-resistant gram-negative bacterial infections. Clin. Infect. Dis. 40, 1333–1341 (2005).

43. Johansson, J., Gudmundsson, G. H., Rottenberg, M. E., Berndt, K. D. & Agerberth, B. Activity of the Naturally Occurring Human Conformation-dependent Antibacterial Activity of the Naturally Occurring Human Peptide LL-37. J. Biol. Chem. 273, 3718–3724 (1998).

44. Fasano, M. et al. The extraordinary ligand binding properties of human serum albumin. IUBMB Life (International Union Biochem. Mol. Biol. Life) 57, 787–796 (2005).

45. Kooi, C. & Sokol, P. A. Burkholderia cenocepacia zinc metalloproteases influence resistance to antimicrobial peptides. Microbiology 155, 2818–2825 (2009).

46. Silbernagl, S. The renal handling of amino acids and oligopeptides. Physiol. Rev. 68, 911–1007 (1988).

47. Matsuzaki, K. Control of cell selectivity of antimicrobial peptides. Biochim. Biophys. Acta - Biomembr. 1788, 1687–1692 (2009).

48. Fox, J. L. Antimicrobial peptides stage a comeback. Nat. Biotechnol. 31, 379–382 (2013).

49. Trotti, A. et al. A multinational, randomized phase III trial of iseganan hcl oral solution for reducing the severity of oral mucositis in patients receiving radiotherapy for head-and-neck malignancy. Int. J. Radiat. Oncol. Biol. Phys. 58, 674–681 (2004).

50. Werle, M. & Bernkop-Schnurch, A. Strategies to improve plasma half life time of peptide and protein drugs. Amino Acids 30, 351–367 (2006).

51. Starr, C. G., He, J. & Wimley, W. C. Host Cell Interactions Are a Significant Barrier to the Clinical Utility of Peptide Antibiotics. ACS Chem. Biol. 11, 3391–3399 (2016).

52. Hendler, R. W. Biological Membrane Ultrastructure. Physiol. Rev. 51, 66–97 (1971).

53. Tripathi, P. et al. Towards a nanoscale view of lactic acid bacteria. Micron 43, 1323–1330 (2012).

54. Jeon, D. et al. Investigation of cationicity and structure of pseudin-2 analogues for enhanced bacterial selectivity and anti-inflammatory activity. Sci. Rep. 7, 1–14 (2017). 227

55. Classen, D. C. et al. The timing of prophylactic administration of antibiotics and the risk of surgical-wound infection. N. Engl. J. Med. 326, 281–286 (1992).

56. Gould, I. M. Antibiotic resistance: the perfect storm. Int. J. Antimicrob. Agents 34, S2–S5 (2009).

57. Allen, H. & Donato, J. Call of the wild: antibiotic resistance genes in natural environments. Nat. Rev. Microbiol. 8, 251–259 (2010).

58. Newman, D. J., Cragg, G. M. & Snader, K. M. The influence of natural products upon drug discovery. Nat. Prod. Rep. 17, 215–234 (2000).

59. Davies, J. & Davies, D. Origins and Evolution of Antibiotic Resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).

60. Fishman, N. Antimicrobial stewardship. Am. J. Infect. Control 34, 55–63 (2006).

61. Bauchner, H., Pelton, S. I. & Klein, J. O. Parents, Physicians and Antibiotic Use. Pediatrics 103, 395–401 (1999).

62. Phillips, I. et al. Does the use of antibiotics in food animals pose a risk to human health? A critical review of published data. J. Antimicrob. Chemother. 53, 28–52 (2004).

63. Talbot, G. H. et al. Bad bugs need drugs: an update on the development pipeline from the Antimicrobial Availability Task Force of the Infectious Diseases Society of America. Clin.Infect.Dis. 42, 657–668 (2006).

64. Hancock, R. E. W. & Sahl, H. G. Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nat. Biotechnol. 24, 1551–1557 (2006).

65. Hancock, R. E. Peptide antibiotics. Lancet 349, 418–422 (1997).

66. Hallock, K. J., Lee, D.-K. & Ramamoorthy, A. MSI-78, an Analogue of the Magainin Antimicrobial Peptides, Disrupts Lipid Bilayer Structure via Positive Curvature Strain. Biophys. J. 84, 3052–3060 (2003).

67. Juretic, D. et al. Magainin-2 Amide and Analogs. Febs Lett. 249, 219–223 (1989).

68. Gordon, Y. J., Romanowski, E. G. & McDermott, A. M. A review of antimicrobial peptides and their therapeutic potential as anti-infective drugs. Curr. Eye Res. 30, 505–515 (2005).

69. Vlieghe, P., Lisowski, V., Martinez, J. & Khrestchatisky, M. Synthetic therapeutic peptides: science and market. Drug Discov. Today 15, 40–56 (2010). 228

70. Beutler, E. Drug-induced hemolytic anemia. Pharmacol. Rev. 21, 73–103 (1969).

71. Bhakdil, S. & Tranum-Jensen, J. Alpha-Toxin of Staphylococcus aureus. Microbiol. Rev. 55, 733–751 (1991).

72. Maisels, M. J. & Kring, E. The contribution of hemolysis to early jaundice in normal newborns. Pediatrics 118, 276–279 (2006).

73. Dempsey, C. E. The actions of melittin on membranes. Biochim. Biophys. Acta - Rev. Biomembr. 1031, 143–161 (1990).

74. Krauson, A. J., He, J. & Wimley, W. C. Gain-of-function analogs of the pore-forming peptide melittin selected by orthogonal high-throughput screening. J. Am. Chem. Soc. 134, 12732–12741 (2012).

75. Rathinakumar, R. & Wimley, W. C. Biomolecular engineering by combinatorial design and high-throughput screening: Small, soluble peptides that permeabilize membranes. J. Am. Chem. Soc. 130, 9849–9858 (2008).

76. Dürr, U. H. N., Sudheendra, U. S. & Ramamoorthy, A. LL-37 , the only human member of the cathelicidin family of antimicrobial peptides. Biochim. Biophys. Acta 1758, 1408–1425 (2006).

77. Deslouches, B. et al. Activity of the de novo engineered antimicrobial peptide WLBU2 against Pseudomonas aeruginosa in human serum and whole blood: Implications for systemic applications. Antimicrob. Agents Chemother. 49, 3208–3216 (2005).

78. Wiegand, I., Hilpert, K. & Hancock, R. E. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 3, 163–175 (2008).

79. Zelezetsky, I. & Tossi, A. Alpha-helical antimicrobial peptides-Using a sequence template to guide structure-activity relationship studies. Biochim. Biophys. Acta - Biomembr. 1758, 1436–1449 (2006).

80. Fagan, J. M., Waxman, L. & Goldberg, A. L. Red blood cells contain a pathway for the degradation of oxidant-damaged hemoglobin that does not require ATP or ubiquitin. J. Biol. Chem. 261, 5705–5713 (1986).

81. Davies, K. J. & Goldberg, A. L. Proteins damaged by oxygen radicals are rapidly degraded in extracts of red blood cells. J. Biol. Chem. 262, 8227–8234 (1987).

82. Cells, B., Pacifici, R. E., Salo, D. C. & Davies, K. J. A. Macroxyproteinase (M.O.P): A 670 kDa proteinase complex that degrades oxidatively denatured proteins in red blood cells. Free Radic. Biol. Med. 7, 521–536 (1989). 229

83. Fagan, J. M. & Waxman, L. Purification of a protease in red blood cells that degrades oxidatively damaged haemoglobin. Biochem. J. 277, 779–786 (1991).

84. Fagan, J. M. & Waxman, L. The ATP-independent pathway in red blood cells that degrades oxidant-damaged hemoglobin. J. Biol. Chem. 267, 23015–23022 (1992).

85. Ciechanover, A. The ubiquitin-proteasome proteolytic pathway. Cell 79, 13–21 (1994).

86. Lowe, J. et al. Crystal Structure of the 20S Proteasome from the Archaeon acidophilum at 3.4 angstrom Resolution. Science (80-. ). 268, 533–539 (1995).

87. Groll, M. et al. Structure of 20S proteasome from yeast at 2.4 A resolution. Nature 386, 463–471 (1997).

88. Fenteany, G. et al. Inhibition of Proteasome Activities and Subunit-Specific Amino-Terminal Threonine Modification by Lactacystin. Science (80-. ). 268, 726–731 (1995).

89. Neelam, S., Kakhniashvili, D. G., Wilkens, S., Levene, S. D. & Goodman, S. R. Functional 20S proteasomes in mature human red blood cells. Exp. Biol. Med. 236, 580–591 (2011).

90. Pasini, E. M., Mann, M. & Thomas, A. W. Red blood cell proteomics. Transfus. Clin. Biol. 17, 151–164 (2010).

91. Roux-Dalvai, F. et al. Extensive Analysis of the Cytoplasmic Proteome of Human Erythrocytes Using the Peptide Ligand Library Technology and Advanced Mass Spectrometry. Mol. Cell. Proteomics 7, 2254–2269 (2008).

92. Pasini, E. M. E. et al. In-depth analysis of the membrane and cytosolic proteome of red blood cells. Blood 108, 791–801 (2006).

93. Wimley, W. C., Selsted, M. E. & White, S. H. Interactions between human defensins and lipid bilayers : Evidence for formation of multimeric pores. Protein Sci. 3, 1362–1373 (1994).

94. Gazit, E., Boman, a, Boman, H. G. & Shai, Y. Interaction of the mammalian antibacterial peptide cecropin P1 with phospholipid vesicles. Biochemistry 34, 11479–11488 (1995).

95. Oren, Z., Lerman, J. C., Gudmundsson, G. H., Agerberth, B. & Shai, Y. Structure and organization of the human antimicrobial peptide LL-37 in phospholipid membranes: relevance to the molecular basis for its non-cell-selective activity. Biochem. J. 341, 501–513 (1999). 230

96. Shai, Y. Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by alpha-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim. Biophys. Acta - Biomembr. 1462, 55–70 (1999).

97. Hong, K. & Hubbell, W. L. Preparation and Properties of Phospholipid Bilayers Containing Rhodopsin. Proc. Natl. Acad. Sci. 69, 2617–2621 (1972).

98. Varki, A. Glycan-based interactions involving vertebrate sialic-acid-recognizing proteins. Nature 446, 1023–1029 (2007).

99. Lee, H. S. et al. Mechanism of anticancer activity of buforin IIb, a histone H2A-derived peptide. Cancer Lett. 271, 47–55 (2008).

100. Makovitzki, A., Avrahami, D. & Shai, Y. Ultrashort antibacterial and antifungal lipopeptides. Proc. Natl. Acad. Sci. U. S. A. 103, 15997–16002 (2006).

101. Sochacki, K. A., Barns, K. J., Bucki, R. & Weisshaar, J. C. Real-time attack on single Escherichia coli cells by the human antimicrobial peptide LL-37. Proc. Natl. Acad. Sci. 108, E77–E81 (2011).

102. Rangarajan, N., Bakshi, S. & Weisshaar, J. C. Localized permeabilization of E. coli membranes by the antimicrobial peptide cecropin A. Biochemistry 52, 6584–6594 (2013).

103. Savini, F. et al. Cell-density dependence of host-defense peptide activity and selectivity in the presence of host cells. ACS Chem. Biol. 12, 52–56 (2017).

104. Tran, D. et al. Homodimeric theta-Defensins from Rhesus macaque Leukocytes. J. Biol. Chem. 277, 3079–3084 (2002).

105. Steck, T. L. & Kant, J. A. Preparation of impermeable ghosts and inside-out vesicles from human erythrocyte membranes. Methods Enzymol. 31, 172–180 (1974).

106. Deslouches, B. et al. De novo-derived cationic antimicrobial peptide activity in a murine model of Pseudomonas aeruginosa bacteraemia. J. Antimicrob. Chemother. 60, 669–672 (2007).

107. Nikaido, H. & Vaara, M. in Escherichia coli and Salmonella typhirmurium (eds. Neidhardt, F. C. et al.) 7–8 (American Society for Microbiology, 1987).

108. Maass, S. et al. Efficient, global-scale quantification of absolute protein amounts by integration of targeted mass spectrometry and two-dimensional gel-based proteomics. Anal. Chem. 83, 2677–2684 (2011).

109. Marcelli, G., Parker, K. H. & Winlove, C. P. Thermal fluctuations of red blood cell 231

membrane via a constant-area particle-dynamics model. Biophys. J. 89, 2473–2480 (2005).

110. Nguyen, L. T. et al. Serum stabilities of short tryptophan- and arginine-rich antimicrobial peptide analogs. PLoS One 5, 1–8 (2010).

111. Molhoek, E. M., Van Dijk, A., Veldhuizen, E. J. A., Haagsman, H. P. & Bikker, F. J. Improved proteolytic stability of chicken cathelicidin-2 derived peptides by d-amino acid substitutions and cyclization. Peptides 32, 875–880 (2011).

112. Fazio, M. et al. Structure-Activity Relationship Studies of Gomesin: Importance of the Disulfide Bridges for Conformation, Bioactivities, and Serum Stability. Biopolymers 84, 205–218 (2006).

113. He, J. et al. Ebola Virus Delta Peptide is a Viroporin. J. Virol. (2017). doi:10.1128/JVI.00438-17

114. Wimley, W. C. & White, S. H. Experimentally determined hydrophobicity scale for proteins at membrane interfaces. Nature structural biology 3, 842–848 (1996).

115. Lohner, K. & Prenner, E. J. Differential scanning calorimetry and X-ray diffraction studies of the specificity of the interaction of antimicrobial peptides with membrane-mimetic systems. Biochim. Biophys. Acta - Biomembr. 1462, 141–156 (1999).

116. Marchesi, V. T. & Furthmayr, H. The red cell membrane. Annu. Rev. Biochem. 45, 667–698 (1976).

117. Hai, Y., Bang, J. & Yub, S. Peptides Design of novel indolicidin-derived antimicrobial peptides with enhanced cell specificity and potent anti-inflammatory activity. Peptides 30, 832–838 (2009).

118. Rozek, A., Powers, J. P. S., Friedrich, C. L. & Hancock, R. E. W. Structure-Based Design of an Indolicidin Peptide Analogue with Increased Protease Stability. Biochemistry 42, 14130–14138 (2003).

119. Lipinski, C. & Hopkins, A. Navigating chemical space for biology and medicine. Nature 432, 855–861 (2004).

120. Merrifield, R. Solid Phase Peptide Synthesis. I. The Synthesis of. J. Am. Chem. Soc. 85, 2149–2154 (1963).

121. Brenner, S. & Lerner, R. A. Encoded combinatorial chemistry. Proc. Natl. Acad. Sci. 89, 5381–5383 (1992).

122. Frank, R. The SPOT-synthesis technique: Synthetic peptide arrays on membrane 232

supports - Principles and applications. J. Immunol. Methods 267, 13–26 (2002).

123. Fodor, S. P. et al. Light-directed, spatially addressable parallel chemical synthesis. Science 251, 767–773 (1991).

124. Geysen, H. M., Meloent, R. H. & Bartelingt, S. J. Use of peptide synthesis to probe viral antigens for epitopes to a resolution of a single amino acid. Biochemistry 81, 3998–4002 (1984).

125. Houghten, R. A. et al. Generation and use of synthetic peptide combinatorial libraries for basic research and drug discovery. Nature 354, 84–86 (1991).

126. Lam, K. S. et al. A new type of synthetic peptide library for identifying ligand-binding activity. Nature 354, 82–84 (1991).

127. Lam, K. S., Lebl, M. & Krchnák, V. The ‘One-Bead-One-Compound’ Combinatorial Library Method. Chem. Rev. 97, 411–448 (1997).

128. Rathinakumar, R. & Wimley, W. C. High-throughput discovery of broad-spectrum peptide antibiotics. FASEB J. 24, 3232–3238 (2010).

129. Krauson, A. J., He, J., Wimley, A. W., Hoffmann, A. R. & Wimley, W. C. Synthetic molecular evolution of pore-forming peptides by iterative combinatorial library screening. ACS Chem. Biol. 8, 823–831 (2013).

130. Marks, J. R., Placone, J., Hristova, K. & Wimley, W. C. Spontaneous membrane-translocating peptides by orthogonal high-throughput screening. J. Am. Chem. Soc. 133, 8995–9004 (2011).

131. Rathinakumar, R., Walkenhorst, W. F. & Wimley, W. C. Broad-spectrum antimicrobial peptides by rational combinatorial design and high-throughput screening : The importance of interfacial activity. J. Am. Chem. Soc. 131, 7609–7617 (2009).

132. Hogan, J. C. Directed combinatorial chemistry. Nature 384, 17–19 (1996).

133. Fields, G. & Nobel, R. Solid phase peptide synthesis utilizing 9-fluorenylmethoxycarbonyl amino acids. Int J Pept Protein Res 35, 161–214 (1990).

134. Starr, C. & Wimley, W. in Antimicrobial Peptides: Discovery, Design and Novel Therapeutic Strategies, 2nd version (2017).

135. Keen III, E. F. et al. Changes in the Incidences of Multidrug‐ Resistant and 233

Extensively Drug‐ Resistant Organisms Isolated in a Military Medical Center. Infect. Control Hosp. Epidemiol. 31, 728–732 (2010).

136. Blair, J. M., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2015).

137. Folkesson, A. et al. Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nat. Rev. Microbiol. 10, 841–51 (2012).

138. Calhoun, J. H., Murray, C. K. & Manring, M. M. Multidrug-resistant organisms in military wounds from Iraq and Afghanistan. Clin. Orthop. Relat. Res. 466, 1356–1362 (2008).

139. Meier, U. & Gressner, A. M. Endocrine regulation of energy metabolism: Review of pathobiochemical and clinical chemical aspects of leptin, ghrelin, adiponectin, and resistin. Clin. Chem. 50, 1511–1525 (2004).

140. Pihlanto-Leppälä, A. Bioactive peptides derived from bovine whey proteins. Trends Food Sci. Technol. 11, 347–356 (2000).

141. Nicolas, P. & El Amri, C. The dermaseptin superfamily: A gene-based combinatorial library of antimicrobial peptides. Biochim. Biophys. Acta - Biomembr. 1788, 1537–1550 (2009).

142. Kleerebezem, M., Quadri, L. E., Kuipers, O. P. & de Vos, W. M. Quorum sensing by peptide pheromones and two-component signal-transduction systems in Gram-positive bacteria. Mol. Microbiol. 24, 895–904 (1997).

143. Simonson, M. S. & Dunn, M. J. Cellular signaling by peptides of the endothelin gene family. FASEB J. 4, 2989–3000 (1990).

144. Dewitt, D. E., Hirsch, I. B., Care, R. & Pro, P. Outpatient Insulin Therapy in Type 1 and Type 2 Diabetes Mellitus. JAMA 289, 2254–2264 (2003).

145. Wang, F., Carabino, J. M. & Vergara, C. M. Insulin glargine: A systematic review of a long-acting insulin analogue. Clin. Ther. 25, 1541–1577 (2003).

146. Plank, J. et al. Systematic review and meta-analysis of short-acting insulin analogues in patients with diabetes mellitus. Arch. Intern. Med. 165, 1338–1344 (2005).

147. Moody, T. W., Ito, T., Osefo, N. & Jensen, R. T. VIP and PACAP. Recent insights into their functions/roles in physiology and disease from molecular and genetic studies. Curr Opin Endocinol Diabetes Obes 18, 61–67 (2011). 148. Ramos-Alvarez, I. et al. A structure-function study of PACAP using 234

conformationally restricted analogs: Identification of PAC1 receptor-selective PACAP agonists. Peptides 66, 26–42 (2015).

149. Igarashi, H. et al. Development of Simplified Vasoactive Intestinal Peptide Analogs with Receptor Selectivity and Stability for Human Vasoactive Intestinal Peptide / Pituitary Adenylate Cyclase-Activating Polypeptide Receptors. J. Pharmacol. Exp. Ther. 315, 370–381 (2005).

150. Fuchs, P. C., Barry, A. L. & Brown, S. D. In Vitro Antimicrobial Activity of MSI-78 , a Magainin Analog. Antimicrob. Agents Chemother. 42, 1213–1216 (1998).

151. Deslouches, B. et al. De Novo Generation of Cationic Antimicrobial Peptides : Influence of Length and Tryptophan Substitution on Antimicrobial Activity. Antimicrob. Agents Chemother. 49, 316–322 (2005).

152. Chen, Y. et al. Rational design of alpha-helical antimicrobial peptides with enhanced activities and specificity/therapeutic index. J. Biol. Chem. 281, 30242–30250 (2006).

153. Selsted, M. E. et al. Indolicidin, a novel bactericidal tridecapeptide amide from neutrophils. J. Biol. Chem. 267, 4292–4295 (1992).

154. Terwilligert, T. C. & Eisenbergg, D. The Structure of Melittin. J. Biol. Chem. 257, 6016–6022 (1982).

155. Bisht, G. S., Rawat, D. S., Kumar, A., Kumar, R. & Pasha, S. Antimicrobial activity of rationally designed amino terminal modified peptides. Bioorganic Med. Chem. Lett. 17, 4343–4346 (2007).

156. Mak, P. et al. The increased bactericidal activity of a fatty acid-modified synthetic antimicrobial peptide of human cathepsin G correlates with its enhanced capacity to interact with model membranes. Int. J. Antimicrob. Agents 21, 13–19 (2003).

157. Majerle, A., Kidrič, J. & Jerala, R. Enhancement of antibacterial and lipopolysaccharide binding activities of a human lactoferrin peptide fragment by the addition of acyl chain. J. Antimicrob. Chemother. 51, 1159–1165 (2003).

158. Chongsiriwatana, N. P. et al. Peptoids that mimic the structure, function, and mechanism of helical antimicrobial peptides. Proc. Natl. Acad. Sci. U. S. A. 105, 2794–2799 (2008).

159. Olsen, C. A. et al. Alpha-Peptide/Beta-Peptoid Chimeras. Org. Lett. 9, 1549–1552 (2007). 235

160. Porter, E. A., Wang, X., Lee, H. S., Weisblum, B. & Gellman, S. H. Non-haemolytic beta-amino-acid oligomers. Nature 404, 565 (2000).

161. Choi, S. et al. De novo design and in vivo activity of conformationally restrained antimicrobial arylamide foldamers. Proc. Natl. Acad. Sci. 106, 6968–6973 (2009).

162. Mensa, B., Howell, G. L., Scott, R. & DeGrado, W. F. Comparative mechanistic studies of brilacidin, daptomycin, and the antimicrobial peptide LL16. Antimicrob. Agents Chemother. 58, 5136–5145 (2014).

163. Vogel, H. J. et al. Towards a structure-function analysis of bovine lactoferricin and related tryptophan- and arginine-containing peptides. Biochem. Cell Biol. 80, 49–63 (2002).

164. Chan, D. I., Prenner, E. J. & Vogel, H. J. Tryptophan- and arginine-rich antimicrobial peptides: Structures and mechanisms of action. Biochim. Biophys. Acta - Biomembr. 1758, 1184–1202 (2006).

165. Hristova, K. & Wimley, W. C. A Look at Arginine in Membranes. J. Membr. Biol. 239, 49–56 (2011).

166. Tang, M., Waring, A. J., Lehrer, R. I. & Hong, M. Effects of guanidinium-phosphate hydrogen bonding on the membrane-bound structure and activity of an arginine-rich membrane peptide from solid-state NMR spectroscopy. Angew. Chemie - Int. Ed. 47, 3202–3205 (2008).

167. Futaki, S. Membrane-permeable arginine-rich peptides and the translocation mechanisms. Adv. Drug Deliv. Rev. 57, 547–558 (2005).

168. Nakase, I. et al. Transformation of an antimicrobial peptide into a plasma membrane-permeable, mitochondria-targeted peptide via the substitution of lysine with arginine. Chem. Commun. 48, 11097–11099 (2012).

169. Gautam, A. et al. Hemolytik: A database of experimentally determined hemolytic and non-hemolytic peptides. Nucleic Acids Res. 42, 444–449 (2014).

170. Fuselier, T. & Wimley, W. C. Spontaneous Membrane Translocating Peptides: The Role of Leucine-Arginine Consensus Motifs. Biophys. J. 113, 835–846 (2017).

171. Krauson, A. J. et al. Conformational Fine-Tuning of Pore-Forming Peptide Potency and Selectivity. J. Am. Chem. Soc. 137, 16144–16152 (2015).

172. Gellatly, S. L. & Hancock, R. E. W. Pseudomonas aeruginosa: New insights into pathogenesis and host defenses. Pathog. Dis. 67, 159–173 (2013).

173. Strateva, T. & Yordanov, D. Pseudomonas aeruginosa - A phenomenon of bacterial 236

resistance. J. Med. Microbiol. 58, 1133–1148 (2009).

174. Livermore, D. M. Of Pseudomonas, porins, pumps and carbapenems. J. Antimicrob. Chemother. 47, 247–250 (2001).

175. Nordmann, P. & Guibert, M. Extended-spectrum beta-lactamases in Pseudomonas aeruginosa. J. Antimicrob. Chemother. 42, 128–132 (1998).

176. Nordmann, P., Naas, T., Fortineau, N. & Poirel, L. Superbugs in the coming new decade; multidrug resistance and prospects for treatment of Staphylococcus aureus, Enterococcus spp. and Pseudomonas aeruginosa in 2010. Curr. Opin. Microbiol. 10, 436–440 (2007).

177. El’Garch, F., Jeannot, K., Hocquet, D., Llanes-Barakat, C. & Plésiat, P. Cumulative effects of several nonenzymatic mechanisms on the resistance of Pseudomonas aeruginosa to aminoglycosides. Antimicrob. Agents Chemother. 51, 1016–1021 (2007).

178. Hooper, D. C. Emerging mechanisms of fluoroquinolone resistance. Emerg. Infect. Dis. 7, 337–341 (2001).

179. Hsuchen, C. C. & Feingold, D. S. The Mechanism of Polymyxin B Action and Selectivity toward Biologic Membranes. Biochemistry 12, 2105–2111 (1973).

180. Pastewski, A. A. et al. Parenteral polymyxin B use in patients with multidrug-resistant gram-negative bacteremia and urinary tract infections: A retrospective case series. Ann. Pharmacother. 42, 1177–1187 (2008).

181. Evans, M. E., Feola, D. J. & Rapp, R. P. Polymyxin B sulfate and colistin: Old antibiotics for emerging multiresistant gram-negative bacteria. Ann. Pharmacother. 33, 960–967 (1999).

182. Falagas, M. E. & Kasiakou, S. K. Toxicity of polymyxins: a systematic review of the evidence from old and recent studies. Crit. Care 10, 1–13 (2006).

183. Silby, M. W., Winstanley, C., Godfrey, S. A. C., Levy, S. B. & Jackson, R. W. Pseudomonas genomes: Diverse and adaptable. FEMS Microbiol. Rev. 35, 652–680 (2011).

184. Gooderham, W. J. & Hancock, R. E. W. Regulation of virulence and antibiotic resistance by two-component regulatory systems in Pseudomonas aeruginosa. FEMS Microbiol. Rev. 33, 279–294 (2009).

185. Lyczak, J. B., Cannon, C. L. & Pier, G. B. Establishment of Pseudomonas 237

aeruginosa infection: Lessons from a versatile opportunist. Microbes Infect. 2, 1051–1060 (2000).

186. LiPuma, J. J. The changing microbial epidemiology in cystic fibrosis. Clin. Microbiol. Rev. 23, 299–323 (2010).

187. Regnis, J. A. et al. Mucociliary clearance in patients with cystic fibrosis and in normal subjects. Am J Respir Crit Care Med 150, 66–71 (1994).

188. Sibley, C. D. et al. A polymicrobial perspective of pulmonary infections exposes an enigmatic pathogen in cystic fibrosis patients. Proc. Natl. Acad. Sci. 105, 15070–1575 (2008).

189. Zhao, J. et al. Decade-long bacterial community dynamics in cystic fibrosis airways. Proc. Natl. Acad. Sci. 109, 5809–5814 (2012).

190. Brewer, S. C., Wunderink, R. G., Jones, C. B. & Leeper, K. V. Ventilator-associated pneumonia due to Pseudomonas aeruginosa. Chest 109, 1019–1029 (1996).

191. Kurahashi, K. et al. Pathogenesis of septic shock in Pseudomonas aeruginosa pneumonia. J. Clin. Invest. 104, 743–50 (1999).

192. Mardirossian, M. et al. D-BMAP18 Antimicrobial Peptide Is Active In vitro, Resists to Pulmonary Proteases but Loses Its Activity in a Murine Model of Pseudomonas aeruginosa Lung Infection. Front. Chem. 5, 1–9 (2017).

193. Brown, R. B. et al. Double-blind study of endotracheal tobramycin in the treatment of gram-negative bacterial pneumonia. The Endotracheal Tobramycin Study Group. Antimicrob. Agents Chemother. 34, 269–72 (1990).

194. Ratjen, F., Döring, G. & Nikolaizik, W. H. Effect of inhaled tobramycin on early Pseudomonas aeruginosa colonisation in patients with cystic fibrosis. Lancet Infect. Dis. 358, 983–984 (2001).

195. Kollef, M. et al. A randomized double-blind trial of iseganan in prevention of ventilator-associated pneumonia. Am. J. Respir. Crit. Care Med. 173, 91–97 (2006).

196. Bals, R., Wang, X., Zasloff, M. & Wilson, J. M. The peptide antibiotic LL-37/hCAP-18 is expressed in epithelia of the human lung where it has broad antimicrobial activity at the airway surface. Proc. Natl. Acad. Sci. 95, 9541–9546 (1998).

197. Bals, R. & Hiemstra, P. S. Innate immunity in the lung: How epithelial cells fight 238

against respiratory pathogens. Eur. Respir. J. 23, 327–333 (2004).

198. Lim, D. J. et al. Cell biology of tubotympanum in relation to pathogenesis of otitis media - A review. Vaccine 19, S17–S25 (2000).

199. Golz, A., Angel-Yeger, B. & Parush, S. Evaluation of balance disturbances in children with middle ear effusion. Int. J. Pediatr. Otorhinolaryngol. 43, 21–26 (1998).

200. Coopersmith, C. M. et al. Inhibition of intestinal epithelial apoptosis and survival in a murine model of pneumonia-induced sepsis. JAMA 287, 1716–21 (2002).

201. Nguyen, H. B. et al. Severe Sepsis and Septic Shock: Review of the Literature and Emergency Department Management Guidelines. Ann. Emerg. Med. 48, 28–54 (2006).

202. Pearson, J. P., Feldman, M., Iglewski, B. H. & Prince, A. Pseudomonas aeruginosa Cell-to-Cell Signaling Is Required for Virulence in a Model of Acute Pulmonary Infection. Infect. Immun. 68, 4331–4334 (2000).

203. Almeida, A. J. & Souto, E. Solid lipid nanoparticles as a drug delivery system for peptides and proteins. Adv. Drug Deliv. Rev. 59, 478–490 (2007).

204. Fila, G. et al. Murine model imitating chronic wound infections for evaluation of antimicrobial photodynamic therapy efficacy. Front. Microbiol. 7, 1–11 (2016).

205. Wang, X., Ge, J., Tredget, E. E. & Wu, Y. The mouse excisional wound splinting model, including applications for stem cell transplantation. Nat. Protoc. 8, 302–309 (2013).

206. Guo, Y. et al. In vivo bioluminescence imaging to evaluate systemic and topical antibiotics against community-acquired methicillin-resistant staphylococcus aureus-infected skin wounds in mice. Antimicrob. Agents Chemother. 57, 855–863 (2013).

207. Hapfelmeier, S. & Hardt, W. D. A mouse model for S. typhimurium-induced enterocolitis. Trends Microbiol. 13, 497–503 (2005).

208. Svenson, J., Brandsdal, B., Stensen, W. & Svendsen, J. S. Albumin binding of short cationic antimicrobial micropeptides and its influence on the in vitro bactericidal effect. J. Med. Chem. 50, 3334–3339 (2007).

BIOGRAPHY 239

Charles Gannon Starr was born on August 12, 1987 in Madison, WI, the first child of Mark and Susan Starr. He received his primary education at St. Aloysius school in Sauk Prairie, WI and attended secondary school in the Sauk Prairie School District public education system. After graduating from Sauk Prairie High School as the salutatorian of the class of 2006, Charles attended the University of Wisconsin-Madison, ultimately receiving a Bachelor’s degree in Biochemistry in 2010. It was during his time at Madison that he first developed an interest in research. After a two-year hiatus, Charles started the Graduate Program in Biomedical Sciences at Tulane University in the fall of

2012.