Development of a Two-Stage Computational Modeling Method for Drinking Water

Microbial Ecology Effects on pneumophila Growth

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

By

David Augustus Hibler

Graduate Program in Public Health

The Ohio State University

2020

Thesis Committee

Dr. Mark Weir, Advisor

Dr. Michael Bisesi

Dr. Kerry Hamilton

Dr. Natalie Hull

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Copyrighted by

David A. Hibler

2020

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Abstract

Legionella pneumophila (L. pneumophila) has become a significant public health issue due to its growth in water distribution systems.1,2 In natural water systems L. pneumophila is often found in relatively low concentrations.1,3–12 However, in distribution systems it is able to thrive through the use of biofilms and invasion of larger host organisms such as protozoa.1–3,10,12–35 Additionally, the altered microbial ecology of water distribution systems seems to play a role in facilitating its ability to proliferate and persist.1,20,22,28,34,36–41

L. pneumophila can cause respiratory infections when contaminated water is aerosolized as it exits from distribution or premise plumbing systems and is then inhaled.2,42–45 Research has shown that some tap water organisms can exhibit inhibitory or commensal effects on L. pneumophila.11,13,28,34,37,40,46 Understanding more about these relationships will allow us to better estimate L. pneumophila concentrations in premise plumbing.

A systematic literature review was conducted to gather relevant information regarding the interactions of L. pneumophila with tap water biofilm microbial ecology.

From the resulting information a stochastic model has been produced to simulate (1) these interactions within a tap water biofilm and (2) the inhibitory or commensal effects on L. pneumophila concentrations. The model simulates the interactions of L.

ii pneumophila within a tap water biofilm. These interactions are used to calculate the resulting L. pneumophila concentrations in the biofilm and bulk tap water. Theses concentrations are then used in a quantitative microbial risk analysis (QMRA) of a 15- minute showering event and used to determine the exposure hazard to humans and associated risk of L. pneumophila infection based off this novel ecological modeling method. The models that my method develops are a means of improving the precision of estimates for exposure of after its growth in premise plumbing. From this, we can better understand how communities of microorganisms in biofilms affect the associated health risks, and thus use that to target intervention options.

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Dedication

To my incredibly loving family, who have always supported me, encouraged me to learn and think for myself, and who always show interest in my ideas and projects, even if they don’t understand what or why I am doing the things I am doing. Also, thanks and appreciation to those members of the Ohio Army National Guard, and including specifically the soldiers of the 285th Area Support Medical Company for their brotherhood, and encouragement to continue in my education. It was during our 2006-

2008 medical deployment to Baghdad, Iraq that the seeds of these ideas first started to take root. And to my myriad of close, patient friends who have allowed me to think out loud in their presence, bounce ideas off them, and willingness to spur on and enrich my thought process by offering their perspectives and feedback.

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Acknowledgments

I would like to thank my advisor, Dr. Mark Weir for giving me the opportunity to work under him. The tremendous amount of guidance, support, and especially motivation that he has supplied during the course of this program, goes well beyond this degree and impacts the totality of my life. Dr. Weir offered me the ability to explore research concepts that energized me, while helping me to encapsulate them within the framework of attainable and useful research projects, an invaluable skill. His advice and support always extended beyond the lab. I cannot over state how fortunate I am to have a mentor whose true concern was not just my performance in his lab, but my success with my own career and life goals. His perspective, knowledge, and dedication are uncommon in today’s world, and even more uncommonly valuable. I expected to learn a lot from him during this time, and I was not disappointed. I am extremely grateful for the mentorship that he provided.

I would like to thank Dr. Michael Bisesi, who served as a member of my committee, but took me under his wing far before that. His perspective and dedication is also an uncommon and treasured commodity in today’s world. His experience and knowledge in public health is staggering and may only be seconded by his dedication to his students. He has gone far out of his way to give me the best opportunities to succeed, and I am extremely grateful for his efforts and his mentorship.

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I would also like to specifically thank other members of my committee, Dr.

Natalie Hull and Dr. Kerry Hamilton, who have been willing to listen to my struggles, offer me advise, review my writing, provide me with insightful and knowledgeable feedback, and who have offered up their support. Their support is something that without which, this project could not have been accomplished. The challenges presented by the global situation during the last few months of this project would have crippled me if it were not for their help. I cannot express how grateful I am to have them as part of my committee.

I would also like to thank my friends, family and loved ones, who have encouraged me and offered me support and stress relief when needed. Specifically, I would like to that Anthony Cannizzaro, Ben Wheat, David Sabo, John Cannizzaro,

Derrick Whan, Chaz Perin, and Travis Grizzle, who have been the iron to my iron, sharpening my thoughts and providing structure and support for my pursuit of learning.

I also owe a great deal of gratitude to the Ohio State University College of Public Health and the Office of Military and Veteran Services, for not only giving me the opportunity to study topics that I am passionate about, but also allowing me to pursue my passion of helping veterans while I obtain my degrees.

And finally, I would like to thank my most persistent studying and writing partners, my four-legged companions, Orion and Atlas. These long stressful days and even harder nights would not have been bearable without the support (and too often distraction) of these dogs in my life. They ensure that I keep on schedule and get at least a short break out of the office every day. These distractions are invaluable, they help me

vi to take a moment away from the papers and computer screens and give me an opportunity to re-center and remember the bigger picture. I do not know where I would be without these loveable creatures.

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Vita

Education

B.S. Biology, Ohio State University………………………………………………...3/2012

B.S. Psychology, Ohio State University…………………………………………….3/2012

Publications

Smith, Laureen, Alai Tan, Janna D. Stephens, David Hibler, and Sonia A. Duffy.

"Overcoming Challenges in Multisite Trials." Nursing research 68, no. 3 (2019): 227-

236.

Fields of Study

Major Field: Public Health

Specialization: Environmental Health Sciences, Demography

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Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgments...... v Vita ...... viii List of Tables ...... xii List of Figures ...... xiii Chapter 1. Introduction ...... 1 Background on Legionella ...... 1 History and Health ...... 1 and Legionnaires’ Disease ...... 2 Public Health Impact...... 3 L. pneumophila in Water Distribution Systems ...... 6 Host Cells ...... 8 Biofilms...... 9 Problem Statement ...... 10 Chapter 2. Inhibitory Microorganisms ...... 14 Microorganisms ...... 14 Protozoa ...... 14 Biofilms...... 15 Chapter 3. Commensal Microorganisms...... 36 Microorganisms ...... 36 Protozoa ...... 36 Biofilms...... 39 Mechanisms of effects ...... 46 Host gene transfer and modulation ...... 46 ix

Intracellular replication ...... 48 Providing a protective environment ...... 49 Supplying nutrients and resources ...... 51 Chapter 4. Methods ...... 54 Modeling and Statistical Methods ...... 54 Quantitative Microbial Risk Analysis (QMRA) ...... 54 Stochastic Models ...... 56 Simulation Methodology ...... 58 Biofilm simulation ...... 59 Data for inhibitory effects ...... 61 Spot-on-lawn assays...... 61 Methods used to calculate inhibition percentage ...... 64 Data for commensal effects ...... 65 Modeling L. pneumophila Growth in Biofilm ...... 66 L. pneumophila Movement Within the Biofilm ...... 71 L. pneumophila Release from the Biofilm ...... 72 Non-Growth Change in L. pneumophila Concentration ...... 73 Model Flow and Summary...... 74 Showering Event Quantitative Microbial Risk Analysis (QMRA) ...... 77 Time to Exiting into Shower ...... 79 L. pneumophila Inactivation due to Residual Disinfectant ...... 79 Air-Water Partition Function ...... 80 Concentration of L. pneumophila in Air ...... 80 Calculation of Dose...... 81 Calculation of Risk ...... 81 QMRA Summary ...... 82 Chapter 5. Results and Discussion ...... 84 Overview ...... 84 Results ...... 84 Sensitivity Analysis ...... 86 Discussion ...... 90 Limitations ...... 95

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Chapter 6. Conclusion and Future Work ...... 100 Conclusion ...... 100 Future Work ...... 103 Bibliography ...... 109 Appendix A. Source Code for Biofilm Simulation Model and Shower QMRA ...... 133

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List of Tables

Table 1: Tap water microbiome organisms that have an inhibitory effect on L. pneumophila ...... 26 Table 2 Tap water microbiome organisms that have a commensal effect on Legionella. 40 Table 3 Spot-on-Lawn Assay data ...... 63 Table 4 Values and distribution for variables used in the model for which it is applicable ...... 75 Table 5 Values and distribution for variables used in the QMRA model for which it is applicable...... 78 Table 6 Validation of model outputs ...... 86

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List of Figures

Figure 1 Adaptation of the QRMA framework for greater inclusion in public health paradigms ...... 54 Figure 2 Section of pipe demonstrating dimensions of modeled biofilm...... 60 Figure 3 Two-dimensional representation of modeled internal water pipe biofilm...... 60 Figure 4 A Spot-On-Lawn assay, where rspot (yellow) denotes the radius of the impact spot of the producing bacteria, rinh (red) shows the radius of the inhibitory zone, and rT (blue) is the radius of the total potential growth area...... 65 Figure 5 Biofilm Simulation Algorithm ...... 76 Figure 6 QMRA Showering Event Flow Diagram ...... 77 Figure 7 Sensitivity analysis of independent variables in the calculation of L. pneumophila concentration in biofilm, where Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_PP are 3 non-inhibitory and non-commensal bacterial species, Bacterial Effect and Amoeba Effect are the total effect of those two organisms in the biofilm respectfully, Total Microbial Effect is the combined bacterial and amoeba effect across the biofilm, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively ...... 87 Figure 8 Sensitivity analysis of independent variables in the calculation of amount of L. pneumophila released from biofilm, Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_PP are 3 non-inhibitory and non-commensal bacterial species, Bacterial Effect and Amoeba Effect are the total effect of those two organisms in the biofilm respectfully, Total Microbial Effect is the combined bacterial and amoeba effect across the biofilm, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively ...... 87 Figure 9 Sensitivity analysis of independent variables in the calculation of risk of infection, where Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_PP are 3 non-inhibitory and non-commensal bacterial species, Bacterial Effect and Amoeba Effect are the total effect of those two organisms in the biofilm respectfully, Total Microbial Effect is the combined bacterial and amoeba effect across the biofilm, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively ...... 88 Figure 10 L. pneumophila released into bulk water under specific growth conditions .... 94 Figure 11 Biofilm Composition of Effect Organisms, heat maps showing the dispersal of the 6 potential levels of dominant organisms within the biofilm...... 95

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Chapter 1. Introduction

Background on Legionella

History and Health

Legionella is the genus name of a group of aerobic, non-spore-forming, gram- negative, flagellated, nutritionally fastidious and potentially pathogenic bacterial species that have become persistent inhabitants of water distribution systems.1,30,47–49 At least 51 species of Legionella exist, with over 60 different serogroups. Eighteen of those species are known to be pathogenic to humans.50,51

Legionella, specifically (L. pneumophila), acquired its name in 1976, when it was discovered after being identified as the causative agent responsible for an outbreak of community pneumonia centered around an American

Legion convention in Philadelphia, PA.52,53 The pathogen and the resulting disease were named respectively, Legionella and Legionnaires’ Disease (LD), after the Legionnaires attending the convention that were infected. This outbreak resulted in 182 cases of LD, and 29 fatalities.54,55 An additional outbreak of L. pneumophila infections was recorded nearly a decade earlier in 1968, in Pontiac, MI. However at the time no causal agent could be identified and the pathogen remained undetected.53 The outbreak in 1968 was eventually attributed incorrectly to toxin exposure. This was because L. pneumophila did not respond to traditional culture methods and, accordingly, the pathogen was found.

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Thus, it was not until after the American Legion outbreak in 1976, when a CDC scientist attempted to create a culture using embryonated eggs without antibiotics, that the actual infectious microbial agent could be identified as L. pneumophila.56 These outbreaks were the result of a specific strain of Legionella, Legionella pneumophila (L. pneumophila) and the infections associated are termed Legionellosis, which can lead to one of a set of two different respiratory diseases: Pontiac Fever and Legionnaires' disease (LD).57,58

Pontiac Fever and Legionnaires’ Disease

Pontiac Fever is known to be a non-pneumonia, flu-like respiratory illness that is generally nonfatal and will often self-limit in less than a week. Pontiac Fever often goes undiagnosed because of its milder symptoms and its tendency to resolve spontaneously.1,30,48,50,58–63 This disease was named for the city, Pontiac, MI, in which it was originally discovered. Multiple workers from the county’s health department reported contracting a fever and mild flu symptoms, without pneumonia, in 1968. As part of the investigation, blood samples from these workers were collected and screened for potential causes, though no source could be determined. Several years later after the 1976

LD outbreak at the Philadelphia American Legion convention, these archived blood samples were reexamined, and it was found that these health department employees had been infected with the L. pneumophila bacteria species “newly discovered” in

Philadelphia. Since then, several other strains of Legionella have been shown to cause

Pontiac Fever type infections, to include L. longbeachae in New Zealand and L. a micdadei in the United Kingdom.64–66

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LD is defined by an atypical pneumonia, it has initial signs and symptoms similar to Pontiac Fever, but then progresses to shortness of breath, fever, muscle pains and aches, headaches, nausea, vomiting, and diarrhea. These symptoms often will begin within 2 to 10 days after exposure, though late onset may be as many as 19 days.57,66–68

Unlike Pontiac Fever, LD has a mortality rate of up to 30% (as high as 80%, within vulnerable populations).1,30,48,50,59–61 Additionally LD can present in ways that are difficult to distinguish from other types of pneumonia, which can make accurate diagnosis difficult and delayed.69 LD can also be caused by several different strains of

Legionella spp., though roughly 90% of LD infections have been associated with L. pneumophila.13 This makes L. pneumophila an agent of specific concern.

Public Health Impact

L. pneumophila has become a significant public health issue due to its prolificity in water distribution systems.1,2 Because of its ability to grow to high concentrations in distribution system water, L. pneumophila poses an increased risk in water exiting plumbing fixtures.35,40,70,71 L. pneumophila can cause infection when contaminated water is aerosolized as it exits from distribution system plumbing fixtures and is then inhaled into human lungs, proliferates, cause pneumonia and potentially lead to mortality, through the infection of alveolar macrophages.1–3,6,30,42–45

Individuals that receive their water from non-municipal supplies, have had recent plumbing repair, work more than 8 hours a day, or use electric water heaters all have an increased exposure hazard and associated risk of infection and legionellosis.72–75 Non- municipal water supplies are known to suffer from higher rates of regulatory violations,

3 lower levels of disinfectant concentrations, higher rates of contaminants and older water age, all of which can aide in microbial growth.1,13,35,76–78 Construction or repair activities can potentially disturb L. pneumophila-infected biofilms inside plumbing systems’ pipes.

Subsequently, L. pneumophila released from those biofilms and increase planktonic cell concentrations in the water and subsequent exposure of L. pneumophila. Electric water heaters are also more expensive to maintain at temperatures high enough to kill L. pneumophila (60℃).75 Individuals with increased work hours (>40 hrs. a week) may also have increased exposure due to greater amounts of time around large building plumbing systems and industrial cooling units or towers.72

L. pneumophila poses an exposure as an opportunistic pathogen because it can cause significant infection and disease when it enters human lungs. Exposure occurs through inhalation of the pathogen contained in aerosolized water droplets, as contaminated water exits plumbing fixtures. Exposure can happen from showers, faucets, toilets, misters, fountains, hot tubs, air conditioners or any location where water is aerosolized, and animal studies have shown L. pneumophila to follow an exponential dose response model.68,79–84 These contaminated aerosol droplets can then be inhaled into the lungs allowing the bacteria to come in contact with alveolar macrophages. L. pneumophila has evolved the ability to evade amoeba and other phagocytic predator cells’ digestive mechanisms and utilize those mechanisms to aid in its reproduction.

Since macrophages are structurally and behaviorally similar to other phagocytic cells that

L. pneumophila regularly infects, it is also able to infect these immune cells, utilize them to reproduce and cause the development of pneumonia in human lungs.1,3,6,30,82,83

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Today, L. pneumophila is known as an opportunistic pathogen and a public health issue. This is in large part due to its ubiquitous presence in water distribution systems.1,30,47,48 Opportunistic pathogens such as L. pneumophila, are now considered the leading source of waterborne disease outbreaks in developed countries.35,66,71,85–91

Infections are rising dramatically, with the specific strain L. pneumophila causing more than 65% of water associated outbreaks.35,59,71,87 L. pneumophila is now the most common cause of all reported water-associated illnesses in the United States, with rates of infection continuing to increase globally.30,34,51,71,92–99 While several strains of

Legionella spp. are capable of causing disease in humans, L. pneumophila is generally recognized to be the most significant.1 Its propensity to cause disease, along with the seriousness of its infection (up to 30% mortality rates in normal populations) and its prevalence in the environment is why L. pneumophila was added as an important pathogen to the US Environmental Protection Agency Candidate Contaminant List and to the United States Waterborne Disease Outbreak Surveillance System in 2001.30

More than 40,000 waterborne disease cases are reported each year, the vast majority of which are attributed to L. pneumophila.35,59,71,87 8,000 to 18,000 suspected L. pneumophila hospitalizations are reported every year in the US, and L. pneumophila is estimated to be responsible for up to 9% of pneumonia cases acquired outside of hospitals.35,59,71,87,100 Between the years 2000 and 2015 the rate of reported cases in the

United States increased from 0.42 cases per 100,000 people to 1.89 cases per 100,000 as cited by the Immunization and Respiratory Diseases reports at the Centers for Disease

Control and Prevention (CDC), resulting in an increase of roughly 350%.59 Reasons for

5 this increase are multifactorial, and may in part be attributed to factors such as testing ability and reporting. However, evidence suggests that this increase is most influenced by increased susceptibility of the population due to aging, immunosuppression and the significant increased growth of Legionella in our environment.71,99

Though the overall burden of water born disease outbreaks in the U.S. has been declining since the 1970s, the proportion of outbreaks that are attributed to L. pneumophila has been rising.34,71 Since 2000, the rate of reported LD infections has grown by roughly five and half times in the U.S.66,101,102 In 2011 and 2012 there were 32 drinking water disease outbreaks in the U.S., which were responsible for a reported 431 cases of illness, 102 hospitalizations, and 14 deaths.71 From these incidents L. pneumophila was responsible for 66% (21) of the outbreaks, 26% (111) cases of illness,

89% (91) of the hospitalizations and all 14 of the deaths.71 In the U.S. estimated healthcare cost due to legionella are at $433.8 million a year and growing.40 This combined with governmental prevention and removal efforts of L. pneumophila cost governments billions of dollars a year globally.13,40

L. pneumophila in Water Distribution Systems

L. pneumophila is nutritionally fastidious, meaning that it has very specific nutrient requirements in order to replicate. This makes it notoriously difficult to grow in the lab. It also has an uncommon membrane structure with increased branched fatty acid chains and less dense ester group components than most gram-negative bacteria. These molecular structural components play an important role in L. pneumophila’s particular susceptibility to lysing and other biocidal effects by many antimicrobial

6 agents.2,13,28,103,104 In natural water systems L. pneumophila is often found in relatively low concentrations.1,3–12 However, in distribution systems it is able to survive and thrive via protective biofilms and the ability to invade larger host organisms such as native amoeba due to its evolution as an aquatic organism.1–3,10,12–35 Additionally, the altered microbial ecology of water distribution systems, in contrast to that of Legionella’s natural environment, plays a role in facilitating its ability to proliferate to higher concentrations than found in its natural environments.1,20,22,28,34,36–41

Several disinfectant processes have been tested to remove L. pneumophila from engineered water systems. These include oxidizing agents such as chlorine, chlorine dioxide, monochloramine, iodine, bromine, ozone, hydrogen peroxide, silver and copper ions, halogenated hydantoins, non-oxidizing agents such as aldehydes, amines, organotin compound, guanidine, thiocyanates, heterocyclic ketones, halogenated amide, halogenated glycols, thiocarbamates, and other treatments such as ultraviolet (UV) radiation.105,106 However, evident by the increasing number of L. pneumophila outbreaks and Legionellosis rates, and despite its particular susceptibility to many antimicrobial agents, these water treatment measures have not proven effective enough to eliminate or even maintain this public health threat.36,71,107 Much of this seems to be due to the protective effects that biofilms and host cell invasion provide to this pathogen.18,23,33,41,107

As stated earlier, L. pneumophila is a nutritionally fastidious organism. In order to culture it in the lab this pathogen requires specially designed medium infused with nutrients and amino acids that it cannot produce on its own. Some of these additional nutrients include

L-cysteine, arginine, isoleucine, leucine, threonine, valine, methionine, phenylalanine,

7 tyrosine, serine and trace elements such as Fe, Mn, Mg, Ca, Zn, K, Mg, Cu, and phosphate.2,13,104 In order to overcome this limitation in the oligotrophic environment of engineered water systems L. pneumophila relies on other organisms around it.13,34,108 L. pneumophila is capable of necrotrophic feeding on certain dead bacterial species around it, however its main way of acquiring nutrients is through its use of biofilms and intracellular replication within host cells. 1,2,34,35,35,39,40,70,109,110

Host Cells

L. pneumophila survives and replicates in water distribution systems by invading multiple hosts which span a range of phyla, such as Amoebozoa, Percolozoa and

Ciliophora. The three genera of Acanthamoeba, Hartmannella and Naegleria, are the most common hosts for L. pneumophila. All of these hosts have been found to aid in the reproduction and survival of L. pneumophila.3,14,25,34,46,111–117 Protozoan hosts such as these are more resistant to most disinfectant processes than bacteria like L. pneumophila.1,34,35 L. pneumophila, being a facultative intracellular bacterium, is able to utilize these host cells as shelter against disinfectants and environmental stressors.2,3,27,34,118,119 Following internalization by an amoeba or other phagocytic cell such as a macrophage, L. pneumophila is capable of forming a unique compartment called the Legionella containing vacuole (LCV). This compartment assists L. pneumophila in evading fusion with lysosomes. The LCV provides L. pneumophila with a protected and nutrient rich environment in which the bacteria can safely replicate.120

L. pneumophila contained within protozoan cysts are substantially more resistant to many environmental stressors and have shown the ability to endure environments with chlorine

8 concentration of 100 ppm and up to 80℃ for exposure periods of 10 minutes.2,121,122 Even when environmental conditions are hostile and host cells are not available, it is possible for L. pneumophila to enter into a viable but non-culturable (VBNC) state. In this VBNC state L. pneumophila is protected, does not replicate and becomes problematic to identify.

While VBNC, culturable colony forming unit (CFU) measurements can be as much as 2 orders of magnitude lower than numbers of actual viable cells.1,13,30,38,107,123–125 L. pneumophila will remain VBNC until they are in the presence of amoeba hosts cells or when environmental conditions improve, such as salinity, PH and nutrient levels.1,36,123 L. pneumophila’s intracellular invasion of protozoan host cells allows it to persist through water treatment and survive in water distribution systems. This is also facilitated by L. pneumophila’s reliance upon biofilms since protozoa concentrate in biofilms in order to feed on the other organisms that reside there.1–3,15–17,27,28,30,35,45,51,90,92,118,122,126–138

Biofilms

Biofilms are necessary for Legionella’s survival in water distribution systems.

Once L. pneumophila has entered into a water distribution system it can invade, survive and thrive within biofilms. Intrusion into biofilms, where 95% of the water distribution system’s bacterial biomass reside, is advantageous to microorganisms for multiple reasons.1,30,35 These include access to resources, protection from environmental hazards and proximity to other microorganisms.1,30,35 Depending on the environmental conditions,

L. pneumophila can persist and grow for days, weeks or even months inside of a distribution system biofilm.13,139,140 L. pneumophila’s growth within biofilms is largely

9 dependent upon its invasion of protozoan hosts, of which biofilms provide ready access.1,34,35

Other organisms associated with biofilms have also been found to influence the growth and survival of L. pneumophila. There are several ways in which this happens:

The creation of extracellular matrices and biofilms themselves can provide shelter and attachment points for L. pneumophila. Within biofilms and the bulk water, nitrifying bacteria may help L. pneumophila survive by depleting chloramine and other disinfectant residuals in the environment.141,142 Concurrently in the biofilms, nutritional symbiosis between different bacteria can aid in multiple ways such as the acquisition of essential amino acids and the excretion of extracellular compounds as carbon and energy sources.

Furthermore, many microbes can provide nutrients to L. pneumophila through necrotrophic means. However several organisms also demonstrate inhibitory effects on L. pneumophila through the production of extracellular molecules, such as surfactants, antagonistic diffusible molecules, volatile organic compounds (VOC) and other bacteriocin-like substance (BLS).1–3,11,13,20,22,28,34–41,43–46,104,107,124,135,135,143–155

Bacteriocins are proteinaceous compound produced by microorganisms to inhibit the growth of other bacteria through bactericidal or bacteriostatic modes of action. BLSs are antagonist substances that act as bacteriocins but are not completely defined or fit the typical criteria of bacteriocins.156

Problem Statement

The public health crisis from L. pneumophila is continuing to grow, despite our best efforts to control it.14,54,59,69,71,72,93,95,97,102,128,157–159 Evidence suggests that this may

10 be due to this pathogen’s specific relationship with other microorganisms in its environment.107,114,119,134,145 This forces the consideration of an ecological perspective on the relationship between L. pneumophila and human health risk. L. pneumophila, does not exist in isolation. Instead it is one of many microorganisms in a complex microbial ecology involved in an array of intricate ecological interactions.2,35,40,41,155 Recently, the concept of probiotic approaches to controlling L. pneumophila in our engineered water systems have been gaining popularity.38,40,42,143,149 In L. pneumophila’s natural environment competition has led to the evolution of multiple organisms and mechanisms that keep L. pneumophila concentrations low.11,36 In turn, the human health risk from diseases such as LD, in L. pneumophila’s natural environment is low in comparison to risk from engineered water distribution systems.10,11,107,160–162 It has been suggested that the ecology of L. pneumophila may play a crucial role in reducing the public health risk of LD through our water distribution systems.11,22,28,40 Increased knowledge of these relationships can help us to better predict concentrations of L. pneumophila, its exposure hazards and infection and disease risks to humans, and supply us with better control measures for future implementation.

My own long-term research aspirations are aimed towards understanding and modeling complexity across species and evolutionary stages. This includes group interactions and structure, from microbes to human beings. For example, how cooperation and competition work through communities and environmental or evolutionary pressures to develop specialized niches and organizational structures within groups. In public health engineering and environmental health sciences, my research has

11 taken me to a similar inspiration to model how L. pneumophila and the biofilm bacterial community interact to survive the pressures of engineered controls, and internal stresses of competition and growth limitations. Therefore, the modeling method descried here provides an ability to investigate this microbial community dynamics envisioned in a similar way to communities of higher order organisms.

In order to better understand the complex interactions between L. pneumophila and other organisms in water distribution systems literatures reviews were conducted to gather the available information on the inhibitory and commensal effects that other biofilm organisms have on L. pneumophila in these environments (chapters 2 and 3). This information was then used to construct a stochastic modeling method using mechanistic principles to simulate the interaction between these organisms and L. pneumophila in a simulated water distribution system biofilm, and their effects on L. pneumophila’s growth and the concentration of L. pneumophila released into the water flow (chapter 4). This model and a model of potential risk based on these simulated biofilms are discussed in chapter 4 and 5.

The hypothesis for this model is: While controlling for other water quality characteristics, the concentration of L. pneumophila can be estimated using localized microbial ecology through stochastic simulations. The model was developed using secondary data from the open literature, which have been outlined in the literature reviews discussed in chapters 2 and 3 and simulates the growth of L. pneumophila in a water distribution system biofilm. The specific elements investigated for this model were the interactions that L. pneumophila has with other microorganisms in these systems, and

12 the effects that those interactions have (commensal or inhibitory) on L. pneumophila’s growth and release into the water system.

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Chapter 2. Inhibitory Microorganisms

Microorganisms

Many microorganisms within water distribution systems have been found to interact with L. pneumophila in an inhibitory way. A majority of research into L. pneumophila ecological interactions falls into one of two areas: reproduction through parasitizing protozoan hosts, or extracellular growth within biofilms.2 This approach may veil the true complex nature of this pathogen’s survival strategies. L. pneumophila regularly interacts with protozoa, fungi, algae, bacteria and other biofilm constituents in more ways than just reproduction or inhibition due to the presence or absence of just one of these organisms.2,35,40,41,104,155 In fact the deleterious effects of certain organisms have been found to be muted when in the presence of other L. pneumophila-beneficial organisms.1,34,46

Protozoa

Protozoa can have significant interactions with L. pneumophila in tap water. As part of their lifecycle, protozoa often graze on bacteria in biofilms. Protozoa are sometime able to consume L. pneumophila as a food source but more often, L. pneumophila takes advantage of this ready access to protozoa in order to coopt their digestive mechanisms and increase its own ability to replicate.1

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Biofilms

L. pneumophila can also gain protection by integrating into biofilms, which are produced by a wide array of microorganisms in distribution systems.1,10,12,30,34,35,41,114,119

By incorporating into these biofilms, in addition to protection, L. pneumophila is also provided with higher concentrations of resources and close proximity to other microorganisms, such as grazing protozoa.1,34,35,39,41,163–169 These additional organisms often have an effect on L. pneumophila’s persistence and growth within the water system.1,2,10,11,16,17,19,25,28,30,34,35,37,46,104,112,114,138,149,170

Many organisms within distribution microbiomes are being found that have an inhibitory effect on L. pneumophila (Table 1).1,11,13,22,28,34,36,36–38,40,41,43,46,124,135,147,153 It has been proposed that many organisms that compete for resources with L. pneumophila may have evolved mechanisms to give them a competitive advantage by inhibiting the persistence and growth of L. pneumophila.11,40,41,124,171 Several organisms have been identified within tap water systems that do exert an inhibitory effect on L. pneumophila, and while many of the exact mechanisms have not yet been identified, a large portion of these effects appear to be dependent on the production of extracellular molecules, most frequently of which are BLS.1,11,13,22,28,34,36,36–38,40,41,43,46,124,135,147,153 Several studies have measured the effects of these BLSs through a phenotypic method called spot-on-lawn assays. Similar to Kirby Bauer disc diffusion,172 this is done when plates inoculated with

L. pneumophila has an organism or isolate of interest introduced to a localized center, and the resulting ring of inactivation around the introduction is measured as the zone of inhibition. Other studies utilized continuous-flow experimental chamber experiments to

15 noninvasively monitor the adherence of and biofilm formation by L. pneumophila under various conditions designed to simulate tap water systems.46

Biofilm microorganism interactions, research of interest summary Cotuk et al. (2005) examined the effects of Pseudomonas and Aeromonas strains on L. pneumophila growth by examining samples collected from municipal water distribution systems.37 For this study pure cultures of non-legionella bacteria (NLB) were collected from tap water. These samples were then suspended in sterile tap water and concentrated to 6(108) CFU/ml. Suspensions were centrifuged and filtered to eliminate live cells and create a cell-free supernatant (CFS). Spot-on lawn assay measuring was used to test the ability of the non-Legionella bacteria to inhibit L. pneumophila. 0.1 ml of suspensions of L. pneumophila at concentrations of 3(108) CFU/ml was inoculated onto the surface of individual buffered charcoal yeast extract agar (BCYEA) plates, which had been supplemented with glycine, polymyxin, vancomycin and cycloheximide (GVPC). In order to test the culture of non-Legionella bacteria, a 6 by 6 mm section of growth of the

NLB was patched onto the surface of the inoculated BCYEA plates. To test the CFS, 20

µl of CFS from corresponding NLB strains was deposited onto additionally inoculated

BCYEA plates. Inoculated plates were incubated under aerobic conditions for 3 days at

37℃. Inhibition zones were observed after the 3 days of incubation. Cotuk et al. (2005) found that, while some strains did inhibit L. pneumophila, many tested cultures of

Pseudomonas tended to have a stimulating effect on L. pneumophila. They also found that more than half of the tested Aeromonas species had an inhibiting effect on L. pneumophila. Only one CFS in this study, Aeromonas spp. was able to inhibit growth of

L. pneumophila alone. Inhibition generally required live cells. However, some CFS were 16 able to stimulate L. pneumophila growth without live cells, even when L-Cysteine was not supplemented. Cotuk et al. concluded that the natural waxing and waning growth of

NLB in tap water can have an effect on risks and outbreaks of LD.37

Mampel et al. (2006) analyzed surface adherence and biofilm formation by L. pneumophila in complex medium and under various flow conditions.46 This was accomplished in a flow chamber system inoculated with single bacterial species cultures.

Once microcolonies of these single species cultures were visible, the flow system was then inoculated with green fluorescent protein (GFP) tagged L. pneumophila. This allowed for the examination of L. pneumophila proliferation and adherence to mono- species biofilms. L. pneumophila was found to readily form biofilms under nutrient rich conditions that supported axenic replication. Biofilms were formed on glass surfaces and upright polystyrene wells, as well as on pins of inversed microtiters. This diversity indicates that sedimentation of L. pneumophila is not a requirement for biofilm formation. Similar to Gião et al. (2011), Mampel et al. (2006) found that L. pneumophila persisted in biofilms constructed by Macrobacterium spp. Mampel et al. (2006) also found that L. pneumophila will persist in biofilms created by Empedobacter breve.

However, in Mampel et al. (2006), L. pneumophila did not persist in biofilms formed by

Klebsiella pneuoniae. Results of this study emphasize that particular interactions between other bacteria in the environment have a modulating effect on L. pneumophila’s adherence.46,124

Guerrieri et al. (2008) examined bacterial interference on L. pneumophila biofilm development.22 This was done by investigating the interactions between L. pneumophila

17 and 80 other tap water bacteria, highlighting relationships within biofilms. Specifically, the authors were looking for evidence of the production of BLSs produced by non- legionella bacteria. The ability of the distribution water bacteria to inhibit L. pneumophila was measured by spot-on-lawn assay. Biofilm assays were also performed to assess BLS interaction with Legionella in biofilms produced by the selected distribution system bacteria. Confirmation of BLS production was identified by a zone of inhibition on the indicator lawn around the spot of the producer bacteria and quantification was done by measuring the size of the inhibition zones. Among the bacteria screened, 66.2% were found to be active against L. pneumophila. , Burkholderia cepacia,

Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas putida, and

Stenotrophomonas maltophilia, were all identified as producers of BLS and found to be active against L. pneumophila. However, Pseudomonas fluorescens was found to be the strongest BLS producer and had the largest negative effect on biofilm formation by L. pneumophila. P. fluorescens was also found to greatly enhance the detachment of L. pneumophila biofilms. Acinetobacter lwoffii proved to have the opposite effect. It did not produce any antagonistic agents towards L. pneumophila and was the only bacteria tested in this study that strongly enhanced L. pneumophila biofilm development. The results produced by Guerrieri et al. (2008) suggest that BLS production plays strong ecological a role in determining the fate of L. pneumophila in biofilms and through that in water systems as a whole.22

Gião et al. (2011) also examined the interaction of L. pneumophila with bacteria from drinking water systems124 In this study L. pneumophila was tested against strains of

18 bacteria collected and isolated from drinking water biofilms. These organisms included

Variovorax paradoxus, Mycobacterium chelonae, Acidovorax sp., Sphingomonas sp. and

Brevundimonas sp. Dual-species biofilms incorporating each of the different drinking water bacterial strains along with L. pneumophila were created and compared with mono- species biofilms produced by L. pneumophila. In order to investigate biofilm formation un-plasticized polyvinylchloride (uPVC) coupons were inoculated with 3.7 (107) total cells/ml of L. pneumophila. Inoculant for dual-species biofilm tests were prepared by mixing L. pneumophila with each of the selected drinking water bacteria in 50 ml of sterilized tap water, creating a final concentration of each of the microorganisms at 107 cells/ml. 4 ml of each mixture was transferred to microtiter plate wells, with each well containing one uPVC coupon. These wells were incubated, and coupons of each biofilm type were removed at intervals of 1, 2, 4, 8, 16 and 32 days. Coupons were measured to quantify sessile cells and observed under microscope to directly visualize the developed biofilms. No auto-aggregation or co-aggregation was observed between the pathogen and the isolated species from the drinking water. Both Acidovorax spp. and Sphingomonas spp. were found to have an inhibitory effect on the cultivability of L. pneumophila. In the case of Sphingomonas spp. this may be related to morphological changes that occurred in the dual-species biofilm, which can cause thickening and anaerobic zones.124,154,173

Despite these results however, Acidovorax spp. and Sphingomonas spp. did not have an effect on the viability of the pathogen. Gião et al. (2011) suggests that this may be related to the tendency of L. pneumophila to form VBNC cells in the face of an inhospitable environment, which may become viable later on. Similarly to Mampel et al. (2006), in

19 this study Gião et al. (2011) found that Mycobacterium spp., specifically Mycobacterium chelonae, did increase the cultivability of L. pneumophila and may have an important role in the survival of this pathogen in tap water systems.46,124

Stewart et al. (2011) examined a surfactant produced by L. pneumophila and its effect on other legionella species.174 By examining mutant strains, Stewart et al. (2011) was able identify the importance of the TolC outer membrane protein in surfactant production. This protein is a part of a trimolecular complex involved in efflux of detergents, antibiotics and type I protein secretion, as well as surfactant production. In order to test this surfactant’s effects Stewart et al. (2011) inoculated L. pneumophila into

BCYE broth, incubated them and measured their growth after 3 to 5 days. In order to evaluate the effect of the L. pneumophila surfactant on the growth of other bacteria, challenge bacteria were spotted onto various BCYE and the surfactant film excreted by L. pneumophila. After 1 to 3 days incubation at 37 °C, the growth of the bacteria was recorded. The surfactant, while not required for intrapulmonary survival, intracellular infection or extracellular growth, did show antimicrobial activity toward several other legionella species. One strain that was tested, , had its growth reduced by 106-fold when introduced to the surfactant, as compared with its control growth on BCYE agar alone. However, this surfactant was not active towards various non-Legionella species, making its antimicrobial activity uniquely designed to give it a selective advantage over other species of legionella.174

Stewart et al. (2012) examined the persistence of L. pneumophila in biofilms formed by various types of aquatic bacteria.34 Stewart et al. (2012) used a Centers for

20

Disease Control (CDC) bioreactor to grow biofilms and examine the ability of L. pneumophila to adhere and incorporate into those biofilms. This bioreactor was designed to simulate a tap water system with dynamic flow, steel surfaces, and low-nutrient conditions. L. pneumophila was able to adhere to and persist within mono-species biofilms formed by , Flavobacterium spp., and Pseudomonas fluorescens. Concentrations of L. pneumophila within these biofilms lasted for greater than 12 days and grew to as high as 4 (104) CFU/cm2. This study also found that L. pneumophila was not able survive within mono-species biofilms of . However, in more complex biofilms where both Pseudomonas aeruginosa and Klebsiella pneumoniae existed, L. pneumophila was able to persist. Results of this study show that the specific ecological makeup of L. pneumophila’s environment can have significant effects on its persistence and growth in water distribution systems.34

Loiseau et al. (2015) specifically investigated the unexpected anti-legionella effects of surfactin produced from Bacillus subtilis.135 As a contaminant, Bacillus subtilis was identified as a bacterial strain that was exhibiting a strong antagonistic effect against

L. pneumophila. PCR analysis identified the sfp gene, which is involved in the biosynthesis of surfactin. Spot-on-lawn assays were used to test its potency. 100 μl of a suspension of 108 CFU/ml of the L. pneumophila was inoculated onto a BCYE agar plate.

After that, 10 μl of an overnight culture of the tested Bacillus strain was introduced to the same agar plate and then incubated for 96 hrs. at 37℃. In order to determine the minimal inhibitory concentration (MIC) of surfactin, serial dilutions of antimicrobial peptides in sterile water were created, with a starting concentration of 50 μg/ml. These dilutions were

21 used to challenge the L. pneumophila. The reported MIC values were the lowest concentration of surfactin peptide that was required for inhibition of the growth of L. pneumophila. An additional experiment was run to examine the effects of surfactin on mature biofilms. For this test a 6-day old L. pneumophila biofilm was presented with increasing concentrations of surfactin. This process required 96 wells containing 200 μl per well of supplemented biofilm broth (SBB) medium with various concentrations of surfactin. These wells had 96 well plate lids deposited within them, which had been conditioned with L. pneumophila biofilms. They were then incubated for 2 hrs. at 37℃.

Following incubation, the lids were transferred into new wells filled with 200 μl of BYE.

After being sonicated, numbers of cells were quantified by CFU BCYE plating and counting. Additionally, to examine the effects that surfactin exhibited on L. pneumophila biofilm adhesion, a sub-MIC concentration of surfactin was introduced into a liquid medium containing a L. pneumophila biofilm. This mixture was then allowed 6 days to interact; after which time the cells adhesion was analyzed. It was found that this surfactin possessed an antibacterial spectrum that was almost completely isolated to legionella, with an additional weak effect against Acanthamoeba castellanii, a common host of legionella. The MIC of this surfactin was very low at 1-4 µg/ml, and 66 µg/ml was capable of eliminating up to 90% of a 6-day old biofilm. This study highlighted the potent effect of the surfactin produced by Bacillus subtilis against L. pneumophila and suggests that lipopeptides, like this surfactin, may be a strong contender for the control of

L. pneumophila in water systems.135

22

Loiseau et al. (2018), also in contrast to Cotuk et al. (2005), highlighted the strong anti-legionella surfactants produced by strains of Pseudomonas.28 Loiseau et al. (2018) tested various Pseudomonas strains collected from various sources such as water systems and hospital patients. These strains were evaluated for their antagonistic activity towards

L. pneumophila. Spot-on-lawn assays were used to assess the L. pneumophila inhibitory activity of these organisms. To accomplish this, 100 휇l of L. pneumophila was spread onto BCYE agar plates at an Optical density (OD600) of 0.1. Then, 10 휇l of each

Pseudomonas strain that had been cultured overnight was introduced to the L. pneumophila agar plates surface and incubation for 96 hrs. Antibacterial activity was identified as an inhibition area around the Pseudomonas. Additionally, this study also determined the MICs of the biosurfactants that were active against L. pneumophila. This was done by dilution similarly to Loiseau et al. (2015).28,135 A high amount of the tested strains were found to be active against L. pneumophila, and this activity was related to the presence of tensioactive agents (substances with a polar-non polar structure that can have an effect on surface tension) in their supernatants. Loiseau et al. (2018) extracted the

Pseudomonas BLSs from active strains and identified them as lipopeptides and rhamnolipids. All tested BLSs were found to be active towards L. pneumophila, and these compounds had a higher lower MICs toward L. pneumophila than towards any of the other various tested bacteria strains. These results suggest that these Pseudomonas anti- legionella BLSs may have potential as L. pneumophila control agents in water treatment while minimizing impact on the rest of the system’s ecology.28

23

Corre et al. (2018) examined possibilities in exploiting waterborne bacterial species in order to find natural competitors for L. pneumophila.11 Their goal was to examine microbial communities from five different freshwater environments, where natural competition may have led to the evolutionary development of antagonistic molecules as weapons against L. pneumophila. This was done in hopes of finding bacterial organisms that may be relevant in the control of L. pneumophila in manmade water distribution systems. Corre et al. (2018) conducted screening for anti-legionella activity in five environments: pond, swimming pool, river, tap water and well water.

Identified bacteria from the water sources were tested for anti-Legionella activity by spot- on-lawn assay. A 100 µl pre-culture of L. pneumophila at a concentration of 108 CFU/mL was inoculated onto BCYE agar plates. Then 10 µl of suspension of each isolated source bacteria was introduced to the center of the agar plate. The plates were then incubated for

48 hrs. at various temperatures up to 37℃, with a second incubation step conducted at

37℃ for 48 hrs. to allow L. pneumophila to grow. The diameter of the inhibition zones was then recorded and examined for BLS production. Corre et al. (2018) found 273 isolates in their samples and 65.2% (178) were observed to be antagonistic towards L. pneumophila with various ranges of inhibition diameters from .4 to 9cm. Tap water environment accounted for the lowest number of antagonistic samples. Additionally, a L. pneumophila long-range inhibition assay was performed for bacterial isolates that demonstrated total inhibition of L. pneumophila growth on agar plates. A 6-well plate assay was used to physically separate the bacterial isolates from L. pneumophila. This was done in order to find whether the bacterial isolates were emitting volatile organic

24 compounds (VOCs) that could disseminate through additional matrices such as air and inhibit the growth of L. pneumophila at a physically separated distance. Every well was given 5 mL of BCYE, and 10 mL of a suspension containing GFP-expressing L. pneumophila were introduced onto both upper sides of the plate. Then, 40 mL of bacterial isolate were added to the upper center of the plate. Plates were then incubated for 48 hrs. at 22, 30 or 37℃. VOC anti-legionella activity was measured using the fluorescence of

GFP-expressing L. pneumophila. In contrast to the Cotuk et al. (2005) study, Corre et al.

(2018) found several strains of Pseudomonas that inhibited the growth of L. pneumophila, and at least one anti-Legionella VOC that was expressed by multiple strains of Pseudomonas spp.11

Biofilm Organisms’ inhibition effects Over all, Acidovorax sp., Acinetobacter spp., Aeromonas spp., Aeromonas caviae,

Aeromonas hydrophila, Aeromonas sobria, Alacaligenes faecalis odorans, Bacillus spp.,

Burkholderia cepacian, Flabobacterium spp., Pseudomonas spp., Pseudomonas acidovorans, Pseudomonas aeruginosa, Pseudomonas aeruginosa ATCC 9027,

Pseudomonas fluorescens, Pseudomonas putida, Pseudomonas putida, Pseudomonas stutzeri, Sphingomonas spp. and Stenotrophomonas maltophilia were all found to be likely producers of antagonistic extracellular molecules that acted as a BLS, with in tap water systems.1,11,13,22,28,34,36–38,46,124,135,164,174–176 Several strains of Bacillus; Bacillus amyloliquefaciens M1, Bacillus subtilis C4, Bacillus subtilis G2III, Bacillus subtilis M1, were found to produce surfactants that contained BLSs with anti-legionella effects, and even Legionella pneumophila itself has been shown to produce a surfactant through the

TolC outer membrane protein, which is toxic to other legionella species.1,36,135,174,176 The 25

bacillus surfactants are believed to be effective by containing molecules that directly lyse

Legionella’s negatively charged membrane and by adversely affecting already formed

Legionella biofilms. These surfactants have also shown weak activity towards

Acanthamoebae castellanii, a regular host of L. pneumophila, which can affect L.

pneumophila’s reproduction.36,135,176

Table 1 is a summary of the various studied inhibitory organisms in water

distribution system microbiomes and their associated effects. The paragraphs remaining

in this section provide greater details on the experiments conducted, and the limitations

those may provide in using these data for mechanistic modeling purposes.

Table 1: Tap water microbiome organisms that have an inhibitory effect on L. pneumophila

Assays, Original Organism Inhibitory effect on Legionella experiments, sampling Mechanism ** Source or testing collection Spot-on-lawn Field 0 - 2 cm zone of inhibition. Production of BLS. 11 assay sample Biofilm Acidovorax sp. Five-fold reduction in number of formation on Field Competition for nutrients or production of 124,175 cultivable L. pneumophila cells. uPVC sample BLS. coupons Spot-on-lawn Field 2 - 6 cm zone of inhibition. Production of BLS. 11 assay sample Acinetobacter spp. Demonstrated ability to inhibit Spot-on-lawn Field Production of BLS. 22,175 growth. assay*177 sample Production of biofilm that is antagonistic for Prevention of L. pneumophila Continuous- Acinetobacter Field L. pneumophila colonization. Prevention of intrusion into A. baumannii flow chamber 34,46 baumanii sample L. pneumophila intrusion into A. baumannii biofilms. experiment biofilms. Spot-on-lawn Field 0 - 6 cm zone of inhibition. Production of BLS. 11 assay sample Aeromonas spp. Spot-on-lawn Field 1.4 - 5.0 cm zone of inhibition. Production of BLS. 37 assay sample

26

Assays, Original Organism Inhibitory effect on Legionella experiments, sampling Mechanism ** Source or testing collection Production of BLS, but only presence of Demonstrated ability to inhibit Spot-on-lawn Field Aeromonas caviae cells was able to inhibit legionella, cell free 37 growth assay sample supernatant (CFS) was not. Production of BLS, but only presence of Demonstrated ability to inhibit Spot-on-lawn Field cells was able to inhibit legionella, cell free 37 Aeromonas growth. assay sample supernatant (CFS) was not. hydrophila Spot-on-lawn Field 5 - 10 mm zone of inhibition. Production of BLS. 13,22,34,175 assay* 177 sample Production of BLS, but only presence of Demonstrated ability to inhibit Spot-on-lawn Field Aeromonas sobria cells was able to inhibit legionella, cell free 37 growth. assay sample supernatant (CFS) was not. Alacaligenes Demonstrated ability to inhibit Spot-on-lawn Field Production of BLS. 22 faecalis odorans growth. assay*177 sample Spot-on-lawn Field Bacillus spp. 0 - 6 cm zone of inhibition. Production of BLS. 11,175 assay sample Production of biosurfactants with antibacterial activity against legionella and Microtiter 1.5 - 4 µg/ml Minimum possible weak activity toward plate Culture Inhibitory Concentration (MIC) Acanthamoebae castellanii. Surfactants are 36,135,176 antimicrobial collections of surfactants. proposed to be effective by directly lysing assays negatively charged membranes of cells, and by degrading preformed legionella biofilms. Production of biosurfactants with Challenged antibacterial activity against legionella and 2 log reduction in L. Bacillus with possible weak activity toward pneumophila population, with Culture amyloliquefaciens increasing Acanthamoebae castellanii. Surfactants are 135 treatment for 1 hr. at 8 µg/ml of collections M1 concentrations proposed to be effective by directly lysing surfactant. of surfactant negatively charged membranes of cells, and by degrading preformed legionella biofilms. Production of biosurfactants with antibacterial activity against legionella and Surfactant possible weak activity toward 70% and 90% reduction in added to Culture Acanthamoebae castellanii. Surfactants are 135 biofilm attachment. liquid collections proposed to be effective by directly lysing medium. negatively charged membranes of cells, and by degrading preformed legionella biofilms. Microtiter Production of biosurfactants with 125 µg/ml MIC (Minimum Bacillus subtilis plate Culture antibacterial activity against legionella and Inhibitory Concentration) of 36,135 C4 antimicrobial collections possible weak activity toward surfactants. assays Acanthamoebae castellanii. Surfactants are

27

Assays, Original Organism Inhibitory effect on Legionella experiments, sampling Mechanism ** Source or testing collection proposed to be effective by directly lysing negatively charged membranes of cells, and by degrading preformed legionella biofilms.

Production of biosurfactants with antibacterial activity against legionella and Microtiter 250 µg/ml MIC (Minimum possible weak activity toward Bacillus subtilis plate Culture Inhibitory Concentration) of Acanthamoebae castellanii. Surfactants are 36,135 G2III antimicrobial collections surfactants. proposed to be effective by directly lysing assays negatively charged membranes of cells, and by degrading preformed legionella biofilms. Production of biosurfactants with antibacterial activity against legionella and Microtiter 1 mg/ml MIC (Minimum possible weak activity toward Bacillus subtilis plate Culture Inhibitory Concentration) of Acanthamoebae castellanii. Surfactants are 36,135 M1 antimicrobial collections surfactants. proposed to be effective by directly lysing assays negatively charged membranes of cells, and by degrading preformed legionella biofilms.

Burkholderia Spot-on-lawn Field 5 - 10 mm zone of inhibition. Production of BLS. 13,22,34,175 cepacia assay*177 sample

Exhibited strong negative Statistical Field Caldithrix correlations with legionella gene analysis of No proposed mechanism. 38 sample markers. gene markers Continuous- Corynebacterium L. pneumophila was not able to Field Production of biofilm that is antagonistic for flow chamber 34,46,147 glutamicum attach to biofilm. sample legionella colonization. experiment Exhibited strong negative Statistical Field Cyanobacteria correlations with legionella gene analysis of No proposed mechanism 38 sample markers. gene markers Flabobacterium Spot-on-lawn Field 0 - 6 cm zone of inhibition. Production of BLS. 11,175 spp. assay sample Accelerated detachment and Flow Flow chamber Production of biofilm that is antagonistic for reduced adherence of L. chamber 46 experiment legionella colonization. Klebsiella pneumophila to abiotic surfaces. system pneumoniae Flow L. pneumophila did not persist Flow chamber Production of biofilm that is antagonistic for chamber 22,46,147 within biofilm. experiment legionella colonization. system Growth of other legionella Production of TolC outer membrane protein Legionella Spot-on-lawn Culture species were reduced up to 106- that can secrete surfactant which is toxic to 1,174 pneumophila assay collections fold. other legionella species.

28

Assays, Original Organism Inhibitory effect on Legionella experiments, sampling Mechanism ** Source or testing collection Statistical Negative correlated with Field Naegleria fowleri analysis of No proposed mechanism 38 legionella gene markers. sample gene markers Exhibited strong negative Statistical Field NC10 correlations with legionella gene analysis of No proposed mechanism 38 sample markers. gene markers Exhibited strong negative Statistical Field Nitrospirae correlations with legionella gene analysis of No proposed mechanism 38 sample markers. gene markers Exhibited strong negative Statistical Field Parvarchaeota correlations with legionella gene analysis of No proposed mechanism 38 sample markers. gene markers Production of BLS. Rhamnolipid mixtures Demonstrated ability to totally Spot-on-lawn Field produced by Pseudomonas sp. are 11,22,28,147,175 inhibit growth.11 assay sample particularly active against legionella.28

Spot-on-lawn Field Pseudomonas spp. > 8 mm zone of inhibition. Production of BLS. 28 assay sample

Spot-on-lawn Field Production of BLS, including volatile 1.7 - 3.0 cm zone of inhibition. 37 assay sample compounds.

Pseudomonas Demonstrated ability to inhibit Spot-on-lawn Field Production of BLS. 22 acidovorans growth. assay*177 sample Spot-on-lawn Field 11 - 15 mm zone of inhibition. Production of BLS. 22 assay*177 sample Inhibition may be a result of the P. aeruginosa homoserine lactone quorums sensing (QS) molecule on biofilms of L. pneumophila. Biofilm formation by L. Pseudomonas pneumophila can be inhibited by the P. > 8mm zone of inhibition from aeruginosa Spot-on-lawn Culture aeruginosa quorum sensing autoinducer (3- both colony and concentrated 1,13,22,28,34,46 assay collections oxo-C12-HSL) and suggests that QS can supernatant. influence the degradation of L. pneumophila biofilms in later stages of development.13 P. aeruginosa also produces biofilm that are antagonistic for legionella colonization and may produce BLS.22,34 Production BLS, but only presence of cells Pseudomonas Culture demonstrated ability to Spot-on-lawn Field was able to inhibit legionella, cell free aeruginosa ATCC 37 inhibit growth. assay sample supernatant (CFS) was not (for tested strains 9027 N22).

29

Assays, Original Organism Inhibitory effect on Legionella experiments, sampling Mechanism ** Source or testing collection Spot-on-lawn Field 5 - 15 mm zone of inhibition Production of BLS. 22 assay*177 sample Production of BLS. Additionally, the negative influence of P. fluorescens was Negative effect on biofilm Biofilm Field strengthened by the simultaneous presence Pseudomonas formation and strongly enhanced formation 22 fluorescens sample of P. aeruginosa, causing a reduction of the detachment of legionella. assay legionella count of about 2 log over 3 days. 22

spot-on-lawn Field > 8 mm zone of inhibition. Production of BLS. 28 assay sample

Spot-on-lawn Field 5 – 10 mm zone of inhibition. Production of BLS. 22 Pseudomonas assay*177 sample putida spot-on-lawn Field 4 – 8 mm zone of inhibition. Production of BLS. 28 assay sample Production of BLS, but only presence of Pseudomonas Demonstrated ability to inhibit Spot-on-lawn Field cells was able to inhibit legionella, cell free 37 stutzeri growth. assay sample supernatant (CFS) was not (for tested strains N20 and N21). Spot-on-lawn Field 0 - 2 cm zone of inhibition. Production of BLS. 11 assay sample Sphingomonas Biofilm Competition for nutrients, production of spp. Four-fold reduction in numbers formation on Field BLS or morphology of biofilm that creates 13,34,124,175 of cultivable L. pneumophila. uPVC sample anaerobic zones. 124 coupons

Stenotrophomonas Spot-on-lawn Field 5 - 10 mm zone of inhibition. Production of BLS. 22,175 maltophilia assay*177 sample

* - Spot-on-lawn assay by modified deferred antagonism method

** - The proposed mechanism from the literature that are responsible for producing the observed inhibitory effect on Legionella.

Beyond just surfactants and BLSs, some organisms have also developed other

strategies to contend with L. pneumophila. Organisms such as, Acinetobacter baumanii,

Corynebacterium glutamicum, Klebsiella pneumoniae, Pseudomonas aeruginosa and

Sphingomonas spp. produce biofilms that are antagonistic for L. pneumophila or have a

morphology that creates anaerobic zones which also prohibit L. pneumophila 30 growth.13,22,34,46,46,124,147,175 Additionally, microbes such as Acidovorax sp. and

Sphingomonas spp. may hinder the growth and development of L. pneumophila by being major competitors for scares nutrients which L. pneumophila requires in order to grow.13,34,124,175 Pseudomonas fluorescens also shows a synergistic inhibitory effect towards L. pneumophila, through reduction of biofilm formation and enhanced detachment of already formed biofilms when it is paired with Pseudomonas aeruginosa.

In experimental settings this effect has shown as much as a 2 log reduction in L. pneumophila count over a 3 day period.22

Pseudomonas aeruginosa (P. aeruginosa) itself has an interesting interaction with

L. pneumophila. The inhibitory effect that P. aeruginosa has on L. pneumophila is thought to be the result of a homoserine lactone quorum sensing (QS) molecule. L. pneumophila biofilm formation can be inhibited by the aeruginosa quorum sensing autoinducer molecule (3-oxo-C12-HSL) that P. aeruginosa employs, and in later stages of L. pneumophila biofilm development this QS molecule may have a deteriorating effect against the biofilm itself.13 Many extracellular molecular inhibitor molecules may have been evolutionarily derived QS or other signaling type molecules.

The presence of several organisms was found to be antagonistic to the ability of L. pneumophila to form biofilms or to the attachment of previously established L. pneumophila biofilms. These organisms included Pseudomonas fluorescens, Klebsiella pneumoniae, and Bacillus amyloliquefaciens M1.22,34,36,46,135,147,176 The effect from

Pseudomonas fluorescens was found to be enhanced by the presence of Pseudomonas aeruginosa, and this was experimentally seen to cause close to a 2 log reduction in only 3

31 days.22 Klebsiella pneumoniae was reported to accelerate detachment of L. pneumophila from abiotic surfaces.22,46,147 Bacillus amyloliquefaciens M1 is especially potent in its effect on L. pneumophila biofilms. Its presence can cause up to a 1-log reduction in biofilm attachment, a 2-log reduction in population after one hour at a surfactin concentration of only 8 µg/ml, and a MIC of surfactin of only 1.5-4 µg/ml.36,135,176

Multiple mono-species biofilms have also been reported to restrict if not completely inhibit incorporation of L. pneumophila. These organisms included

Sphingomonas spp., Klebsiella pneumoniae, Corynebacterium glutamicum,

Acinetobacter baumanii, Acidovorax sp., and Pseudomonas aeruginosa.13,22,34,46,124,147,175

When compared to controls, biofilms of Sphingomonas spp. and Acidovorax sp. have been found to cause up to a four and five-fold reduction in the cultivability of L. pneumophila, respectively.13,34,124,175 Also, while mono-species biofilms of Klebsiella pneumoniae have been reported to inhibit L. pneumophila incorporation, it is important to note that Stewart et al. (2012) did report that Klebsiella pneumoniae biofilms under dynamic conditions and in complex scenarios when other organisms were present, allowed for the incorporation and persistence of L. pneumophila.22,34,46,147

Corynebacterium glutamicum and Acinetobacter baumanii both create biofilms that are antagonistic towards L. pneumophila, and after 14 days the pathogen was found to be completely eliminated from Acinetobacter baumanii monospecies biofilms.22,34,46,147

However, this impressive prevention from A. baumannii biofilms is not known to cross over to mixed species biofilm when A .baumannii is a constituent member.

32

Despite some findings from Cotuk et al. (2005), Pseudomonas was often observed to have a significant inhibitory effect on L. pneumophila.11,13,22,28,34,37,46,147,175 In fact

Pseudomonas strains were the only tap water bacteria found to have a total inhibitory effect on L. pneumophila and to produce VOCs that could inhibit L. pneumophila at a distance. Pseudomonas aeruginosa was also found to produce mono-species biofilms that prevented the incorporation of L. pneumophila. However, this effect was not seen when

Pseudomonas aeruginosa existed in multi-species biofilms with Legionella-permissive bacterial species, such as Klebsiella pneumoniae.34 Though many strains of Pseudomonas spp. are known to be opportunistic pathogens, this genes of bacteria may also represent a population worthy of further investigation for the potential to find quality candidates for the control of L. pneumophila in tap water.

Mechanisms of effects All mechanisms except for the structural development of biofilms, which created anaerobic zones that were antagonistic to L. pneumophila, were proposed to be from the production of extracellular molecules that had a BLS effect. One hypothesized mechanism for Pseudomonas aeruginosa was through a homoserine lactone QS molecule. The QS autoinducer (3-oxo-C12-HSL) inhibits L. pneumophila by restricting biofilm formation by L. pneumophila and suggests that QS can have a detrimental effect on L. pneumophila biofilms in later stages of development. 1,13,28,34,46

Very few other specific L. pneumophila inhibitory molecules produced by tap water organisms have been identified. Of the few that have been identified, BLSs in

Bacillus and Pseudomonas, these were found to be excreted as tensioactive agents, lipopeptides and rhamnolipids.28,135 Three antimicrobial peptides produced by 33

Staphylococcus warneri (not typically found in tap water) have been isolated and are similar to other Staphylococcus produced molecules known for their lytic action against red blood cells. They are short, cationic, highly hydrophobic and adopt α-helical structures.36,153 These molecules, like many lipopeptides, rhamnolipids and other BLSs produced by tap water bacteria, are highly membrane active and are thought to work on

L. pneumophila through a detergent-like effect. This same effect is suggested as a common mechanism for anti-legionella BLSs.28,36,43,153

L. pneumophila is thought to be particularly susceptible to detergent effects. It has been found to be up to 10- to 1000-fold more sensitive to detergent acting BLSs than other tested bacteria such as Pseudomonas aeruginosa, , Flavobacterium breve, and others.28,36,153 This is thought to be due to L. pneumophila’s uncommon membrane structure. Uncommon elements of its membrane, such as its thickness, fluidity, branched fatty acid chains, composition of lipopolysaccharides, less dense ester group components, and presence of phospholipids are all factors that play a role in L. pneumophila’s particular sensitivity to membrane active molecules and detergent effects.28,153 Because of this sensitivity the MIC of many BLSs toward L. pneumophila are significantly lower than the same BLS MICs for other bacterial species.11,22,28,34,36,43,135,144,153,174

It has become evident that there are many natural occurring mechanisms through which L. pneumophila may be influenced and controlled. Unfortunately, more research still needs to be done on the exact mechanisms that are being employed in the inhibition of L. pneumophila by tap water microorganism. Due to L. pneumophila’s high sensitivity

34 toward many BLSs produced by naturally occurring tap water microbes, MIC levels that are often drastically lower than those of other water system bacteria towards the same

BLS, and the specificity in which many tap water microorganisms target L. pneumophila, we are provided with several avenues for further research. Identifying specific substances and mechanisms through which we may be able to control L. pneumophila is of significant importance, especially in regard to the rising rates of LD. The identification of mechanisms employed by existing tap water microbes provides us with a unique opportunity to find and utilized a control that already exists in the environment, is significantly more effective towards our target pathogen than other present organisms, and can cause minimal disturbance to the ecosystem while mitigating the public health risks of L. pneumophila.11,22,28,40 Using knowledge of these microbial relationships will help us to better predict concentrations of L. pneumophila, it’s risks to human health, and will supply us with better control measures for future implementation.

One of the consistent limitations in the experiments conducted is that they were performed outside of a microbial ecology and with single organisms interacting with L. pneumophila, or single biomolecular compounds interacting with L. pneumophila. While this is sensible from a controlled experimental concern, computational modeling of a microbial ecological system would ideally have greater granularity regarding how these inhibitory mechanisms interact in a real environment. Therefore, despite this limitation the computational model derived in chapter 4 should likely be expanded upon in the future when this kind of dynamic system data can be collected.

35

Chapter 3. Commensal Microorganisms

Microorganisms

Because of the desire to reduce the public health hazard and impact associated with L. pneumophila, most research into intercellular interactions between L. pneumophila and other water distribution biofilm microorganisms, has focused on inhibitory interactions. However, many organisms also have been found to have commensal relationships with L. pneumophila.1,2,10,11,17,30,34,35,37,40,41,104,107,119,155,164,167

Much of the research examining commensal interactions with this pathogen, focus on L. pneumophilas’s intracellular reproductive behavior, especially with amoeba. Though, several other organisms that interact with L. pneumophila within water distribution system biofilms also have been found to possess commensal and promoting relationships with L. pneumophila.

Protozoa

While many protozoa have been reported to have relationships with L. pneumophila, free-living amoeba such as Acanthamoeba, are understood to have a significant important relationship with L. pneumophila in tap water. In order to feed, protozoa have been found to utilize biofilms where they graze on bacteria which are present in high concentrations there. L. pneumophila utilizes this ready access to protozoa in order to either be engulphed or force its way into the larger organisms, and increase its

36 ability to replicate.1 L. pneumophila is a facultative intracellular bacteria, which is capable of invading host cells, especially phagocytic cells, and utilizing their internal mechanisms for its own reproduction.1–3,15,15–17,19,26,27,30,34,35,90,92,111,123,127,160,171,178–184

Digestive vacuoles in many protozoa are rich in amino acids, such as L-cysteine, which are critical for L. pneumophila reproduction.38 In fact, it has been found that the amount of L. pneumophila in a tap water biofilm is strongly associated with the biomass of protozoa inhabiting that biofilm, reinforcing their importance in L. pneumophila reproduction.1

Additionally, through intercellular replication, L. pneumophila can be shielded against environmental stressors that the host cell is resistant to.1–3,15,15–

17,19,26,27,30,34,35,90,92,111,123,127,160,171,178–184 The supportive intracellular microenvironment provided by protozoa host cells protects L. pneumophila from adverse external environmental conditions, and provides a nutrient-rich replicative niche.185 L. pneumophila can survive in even particularly harsh environments, within the protective encasement of the host cell and utilizing the nutrient-rich environment for its own growth.3,11,15–17,27,30,92 These evolutionary adaptations have proven very effective for L. pneumophila to evade the effects of water treatment, disinfectants, and antibiotics.36,44,92,127,185,185,186

L. pneumophila involved in intercellular replication through host amoeba have also been found to have an increased ability to infect humans and increased antibiotic resistance. L. pneumophila cultured in association with amoeba also can exhibit alternative membrane chemistry which can aid in disinfection

37 resistance.2,13,15,36,92,125,127,185,187 This is in large part due to the exchanging of genes between the host and pathogen cells, as has been observed by comparing genomes of certain Legionella strains and their host amoebae.1,2,15–17,19,30,35,36,92,170,181 After being infected by L. pneumophila, amoeba and other protozoa can also release virulent membrane bound vesicles that contain multiple L. pneumophila cells. These vesicles continue to provide protection for the L. pneumophila cells encased within, and can be a human infection hazard that remains viable for months.92,127

Amoebae while protecting L. pneumophila from biocides, antibiotics, pH imbalances, or osmotic stress also play a further role in the spread of the pathogen in distribution systems.92,98,112,188 When environmental conditions are unfavorable for amoebae, they will differentiate into cysts that are highly resistant to environmental stressors in their dormant stage, and wherein L. pneumophila may be able to survive.20,92,107,189 When the environment once again becomes favorable amoebae cysts may excyst and release L. pneumophila back into the system. This may be a major issue in water disinfection processes, where L. pneumophila has been shown to quickly repopulate after disinfection.190–192

In adverse environments, if host cells are not available, it is possible for L. pneumophila to enter into a viable but non-culturable (VBNC) state, to protect itself. In a

VBNC state L. pneumophila does not replicate, drastically reduces its metabolic activity and becomes problematic to identify, with culturable colony forming unit (CFU) measurements being as much as 2 orders of magnitude lower than true viable cells.1,13,30,38,107,123–125 VBNC L. pneumophila will generally stay in that state until

38 environmental conditions improve or they are in the presence of amoebae. Amoebae are particularly capable of reviving L. pneumophila from a VBNC state and returning it to a metabolically active condition.1

Biofilms

L. pneumophila also gains protection from water distribution system stressors by integrating into biofilms.1,10,12,30,34,35,41,114,119 These biofilms can be produced by a wide variety of microorganisms that inhabit distribution systems.1,10,12,30,34,35,41,114,119 Within these biofilms, L. pneumophila is also provided access to higher concentrations of resources and close proximity to other microorganisms and host cells.1,34,35,39,41,163–169

These additional organisms can often effect L. pneumophila’s growth and persistence within water distribution systems.1,2,10,11,16,17,19,25,28,30,34,35,37,46,104,112,114,138,149,170 By multispecies biofilms and forming beneficial relationships with the bacterial community found within, L. pneumophila can overcome many of the environmental challenges presented in the water distribution system.3,11,13,34,108,147,149,151,154

In addition to forming beneficial relationships with microbes in biofilms, L. pneumophila can also overcome nutritional difficulties through necrophagy of certain microbial species, often gram negative bacteria.1,2,11,13,39,40,109 Experimental trials have shown that after 96 hours and a starting concentration of 100 dead Pseudomonas putida cells to every L. pneumophila cell, necrotrophic growth can be as high as 1.89 log units.39

This mode of reproduction has also been shown to be capable of out pacing protozoan- mediated growth, in the short term, and may be a major component of L. pneumophila’s persistent and rapid regrowth after water treatment.1,39

39

Biofilm Organisms’ commensal effects

Despite the many inhibitory interactions that have evolved between L.

pneumophila and competitive organisms in its environment, many organisms within

distribution microbiomes are being found that have beneficial effects on L.

pneumophila’s growth (Table 2). Within water distribution systems, these organisms

include Acanthamoeba spp., Hartmannella vermiformis, Naeglaeria spp., Acinetobacter

baumanii, Acinetobacter lwoffii, Aeromonas caviae, Alcaligenes spp., Brevundimonas

vesicularis, Empedobacter breve, Flavobacterum breve, Klebsiella pneumoniae,

Lentisphaerae, Mycobacterium spp., Nitrosomonas spp., Nitrosospira spp., Pseudomonas

spp. and Tenericutes. While many of the exact mechanisms have not yet been precisely

identified, a large portion of these beneficial effects appear to be dependent on providing

of nutrients or protection from environmental stressors.1,2,11,13,15–17,19,20,22,25,28,34,36–

38,40,41,43,44,46,92,109,112,119,123–125,127,135,143,145–147,149,153,178,185,187,193–195

Table 2 Tap water microbiome organisms that have a commensal effect on Legionella.

Promoting effect Assays, experiments or culture Organism Mechanism Source on Legionella testing collections

Increase the Culture virulence of L. Entry assays Host gene transfer 19,35 Collection pneumophila Promotes survival of L. pneumophila Culture Host gene transfer, membrane chemistry Biocide treatment 2,13,15,36,92,92,125,127,185,187 in hostile Collection alteration, protective encapsulation Amoebozoa environments Promotes resuscitation from viable but non- Providing a replicative safe environment and Culture Field sample 36,123 culturable (VBNC) unknown molecular mechanisms. state of L. pneumophila

40

Promoting effect Assays, experiments or culture Organism Mechanism Source on Legionella testing collections

Promotes survival growth of L. Disinfectant treatment pneumophila with most probable Culture Providing a replicative safe environment and through providing number (MPN) 1,11,13,185 Collection unknown molecular mechanisms. a habitat for procedure (Beattie et al., protection and 2003) replication Promotes survival and growth of L. pneumophila Direct through immunofluorescence Promotes survival growth of L. pneumophila Acanthamoeba Field sample 196 spp. intracellular assay and electron through intracellular replication. replication, up to 2 microscopy log CFU/ml within 4 days. Quantitative Promotes growth Supplying resource rich niche in oligotrophic Polymerase Chain Field sample 35,175 of L. pneumophila environments. Reaction (q-PCR) Supports growth through Culture Support intracellular replication of L. intracellular Microscopy 44,119 Collection pneumophila. replication of L. pneumophila Promotes survival and growth of L. pneumophila. Increased concentrations of Culture Support intracellular replication of L. Culture and microscopy 193 L. pneumophila 3- Collection pneumophila. 4 orders of magnitude in 48 - 72 hrs, after 1 hr exposure. Acanthamoeba Promotes growth castellanii and biofilm Biofilms which contain both Acanthamoeba colonization of L. castellanii and Pseudomonas aeruginosa Culture pneumophila in the Culture and microscopy increase the uptake of L. pneumophila within 1,20,36 Collection presence of A. castellanii and the colonization of L. Pseudomonas pneumophila in those biofilms. aeruginosa

Greatly promotes the growth of L. Promotes growth Culture and observation Field sample pneumophila within biofilms through 25,34,46,112 of L. pneumophila intracellular replication

Increased growth Presence increased growth rate of L. Acanthamoeba rate of L. Culture and colony Field ample pneumophila between 1.1 - 2.0, at 30, 32, 145 polyphaga pneumophila counting and 37 C, after 72 hrs. between 1.1 - 2.0. Increased growth Presence increased growth rate of L. rate of L. Culture and colony Field ample pneumophila between 2.3 - 7.6, at 32 and 72 145 pneumophila counting C, after 72 hrs. between 2.3 - 7.6. Promotes Hartmannella reproduction of vermiformis biofilm associated Greatly promote the growth of L. Batch test system with L. pneumophila, to Field sample pneumophila within biofilms through 25,34 PVCu or PVCp Maximum intracellular reproduction concentrations of L. pneumophila 6.3 41

Promoting effect Assays, experiments or culture Organism Mechanism Source on Legionella testing collections

± 0.1 log CFU/cm2 (PVCp) and 4.9 ± 0.1 log CFU/cm2 (PVCu) from starting concentrations of 2.4 log CFU/ml Promotes Presence aided in growth of L. pneumophila, reproduction and extracellular modulation of L. Culture and microscopy Field sample 145,146 VBNC state of L. pneumophila is likely to promote VBNC pneumophila state. Promotes reproduction and Assisted in the release of planktonic cells Rotating disc reactor Field sample 46,114 dissemination of L. from biofilms into bulk water. pneumophila Examination with a Promotes growth confocal FV-1000 Culture Support intracellular replication of L. Naeglaeria spp. 44,119 of L. pneumophila station installed on Collection pneumophila. inverted microscope Increased growth Presence increased growth rate of L. Naegleria rate of L. Culture and colony Field ample pneumophila between 4.8 - 7.2, at 24, 30, 32, 145 fowleri pneumophila counting and 37 C, after 72 hrs. between 4.8 - 7.2. Promotes growth of L. pneumophila Allowed for the development of satellite Acinetobacter in nutrient Satellitism Field sample colonies of L. pneumophila in pour plates of 13,149 sp. deficient BCYE lacking cysteine. environments Allows for attachment of L. Flow chamber Culture Produced biofilms favorable for L. 22,46 pneumophila to experiment Collection pneumophila, to a minimal extent biofilms Acinetobacter Promotes growth baumanii of L. pneumophila Cell-free supernatants (CFSs) had a Culture and colony Culture in nutrient stimulating effect on the growth of L. 197 counting Collection deficient pneumophila in sterilized tap water. environments Promotes growth spot-on-lawn assay Field sample Did not produce anti-legionella BLS 22 of L. pneumophila Acinetobacter Promote biofilm Strongly enhance L. pneumophila biofilm, lwoffii Biofilm Formation formation and Field sample maintaining biofilm counts higher than 22,34 Assays maintenance control.

Had a stimulating effect on the growth of L. Aeromonas Promotes growth Satellitism / colony Field sample pneumophila with only cell-free supernatants 37 caviae of L. pneumophila count assay (CFSs) Promotes growth of L. pneumophila Allowed for the development of satellite Alcaligenes in nutrient Satellitism Field sample colonies of L. pneumophila in pour plates of 13,149 spp. deficient BCYE lacking cysteine. environments Promotes growth of L. pneumophila In nutrient poor conditions presence of B. Brevundimonas Culture (Symbiosis in nutrient Field sample vesicularis was found to increase legionella 147 vesicularis experiment) deficient counts. environments

42

Promoting effect Assays, experiments or culture Organism Mechanism Source on Legionella testing collections

Allows for attachment and positively Production of extracellular matrix materials contribute to the Empedobacter Flow chamber Culture that support L. pneumophila adherence, or long-term 13,22,34,34,46,109,195,198,199 breve experiment Collection the production of factors that stimulate L. persistence and pneumophila growth. presence of L. pneumophila in biofilms Promotes growth Allowed for the development of satellite of L. pneumophila colonies of L. pneumophila in pour plates of in nutrient Satellitism Field sample 13,149,154 BCYE lacking cysteine, L-cysteine and deficient ferric pyrophosphate. environments Forms biofilms permissive to L. 104–105 pneumophila at high levels (e.g., 104–105 Flavobacterium Biofilm reactor Field sample 34 CFU/coupon CFU/coupon), and promotes the presence of sp. L. pneumophila in dynamic biofilms Positively contribute to the Production of capsular and extracellular long-term matrix materials which promote adherence Rotating disc reactor Field sample 13,34,46,109,114,195,198,199 persistence and or provide growth factors that stimulate presence of in growth. biofilms Promotes growth of L. pneumophila Flavobacterum Provides nutrients for the growth of L. in nutrient Satellitism Field sample 1,154 breve pneumophila satellite colonies. deficient environments Promotes growth of L. pneumophila Culture (Symbiosis Production of nutrients used by L. Green algae in nutrient Field sample 147 experiment) pneumophila. deficient environments Forms biofilms permissive to L. 104–105 pneumophila at high levels (e.g., 104–105 Biofilm reactor Field sample 1,34 CFU/coupon CFU/coupon), and promotes the presence of L. pneumophila in dynamic biofilms. Positively Klebsiella Production of capsular and extracellular contribute to the pneumoniae matrix materials which promote adherence, long-term or provide growth factors that stimulate persistence and Rotating disc reactor Field sample 13,34,46,109,114,195,198,199 growth, also allowed for the overcoming of presence of L. inhibitory effect of Pseudomonas pneumophila in aeruginosa. biofilms Exhibited positive correlations with Statistical analysis of Lentisphaerae Field sample No proposed mechanism. 38 legionella gene gene markers markers. Exhibited positive correlations with Statistical analysis of Field sample No proposed mechanism. 38 legionella gene gene markers Mycobacterium markers. spp. Promotes Flow chamber Culture Produced biofilms favorable to L. integration into 22,46 experiment Collection pneumophila biofilms.

43

Promoting effect Assays, experiments or culture Organism Mechanism Source on Legionella testing collections

Promotes Microbacterium Flow chamber Culture Produced biofilms favorable to L. integration into 34,46,109 sp. experiment Collection pneumophila biofilms. Promote biofilm Presence promoted biofilm formation by L. Mycobacterium Biofilm formation on formation by L. Field sample pneumophila and Increased cultivability of 22,34,124 chelonae uPVC pneumophila L. pneumophila in biofilms Promotes survival Membrane-intact Nitrosomonas of L. pneumophila fraction with PCR and Nitrifying bacteria deplete chloramine and Field sample 36,143,194 spp. in hostile denaturing gradient gel other disinfectant residuals environments electrophoresis (DGGE) Promotes survival Membrane-intact Nitrosospira of L. pneumophila fraction with PCR and Nitrifying bacteria deplete chloramine and Field sample 36,143,194 spp. in hostile denaturing gradient gel other disinfectant residuals environments electrophoresis (DGGE) Promotes growth of L. pneumophila Cell-free supernatants (CFSs) had a Proteus Culture and colony Culture in nutrient stimulating effect on the growth of L. 197 mirabilis counting Collection deficient pneumophila in sterilized tap water. environments Promotes growth of L. pneumophila Pseudomonas Shows growth of L. pneumophila satellite in nutrient Satellitism Field sample 13 spp. colonies deficient environments Allowed for the development of satellite Promotes growth colonies of L. pneumophila in pour plates of of L. pneumophila Pseudomonas BCYE lacking cysteine. Incubation in the in nutrient Satellitism Field sample 149 sp. presence of a symbiotic pseudomonas deficient improved the survival of L. pneumophila environments when compared to controls. Promotes growth Biofilms which contain both Acanthamoeba of L. pneumophila castellanii and Pseudomonas aeruginosa Culture in presence of Culture and microscopy increase the uptake of L. pneumophila within 1,20 Collection Acanthamoeba A. castellanii and the colonization of L. Pseudomonas castellanii pneumophila in those biofilms. aeruginosa Promotes growth of L. pneumophila Cell-free supernatants (CFSs) had a Culture and colony Culture in nutrient stimulating effect on the growth of L. 197 counting Collection deficient pneumophila in sterilized tap water. environments Demonstrated Presence had a stimulating effect on the Pseudomonas ability to stimulate Satellitism Field sample growth of L. pneumophila with culture and 37 alcaligenes growth cell-free supernatants (CFSs) Demonstrated ability to stimulate growth. Cell Free Supernatant (CFS) demonstrated Satellitism (culture) / Presence had a stimulating effect on the ability to support colony counting assay Field sample growth of L. pneumophila with culture and 37 the growth of L. (Cell Free Supernatant) cell-free supernatants (CFSs) Pseudomonas pneumophila up to fluorescens 70.4% when compared with controls. Forms biofilms permissive to L. 104–105 pneumophila at high levels (e.g., 104–105 Biofilm reactor Field sample 34 CFU/coupon CFU/coupon), and promotes the presence of L. pneumophila in dynamic biofilms 44

Promoting effect Assays, experiments or culture Organism Mechanism Source on Legionella testing collections

Positively contribute to the Synthetization of extracellular matrix long-term materials that support L. pneumophila persistence and Biofilm reactor Field sample 13,34,46,109,195,198,199 adherence, or the production of factors that presence of L. stimulate L. pneumophila growth. pneumophila in biofilms Promoted the number of L. Pseudomonas Heat treated water Produced biofilms favorable to L. pneumophila by 4 Field sample 22,34,109 putida disinfectant experiment pneumophila log units within biofilms Exhibited positive correlations with Statistical analysis of Tenericutes Field sample No proposed mechanism. 38 legionella gene gene markers markers.

* - The proposed mechanism from the literature that are responsible for producing the observed commensal effect on Legionella.

Pseudomonas aeruginosa (P. aeruginosa) is a bacterium that has an interesting

and complex relationship with L. pneumophila. Its presence in water distribution system

biofilms generally inhibits the growth of L. pneumophila. This is thought to be the result

of a homoserine lactone quorum sensing (QS) molecule that P. aeruginosa produces. L.

pneumophila biofilm formation can be inhibited by the P. aeruginosa quorum sensing

autoinducer molecule (3-oxo-C12-HSL) that P. aeruginosa employs, and in later stages

of development this QS molecule may present a deteriorating effect on L. pneumophila

biofilms.13 Additionally, when P. aeruginosa is in the presence of Pseudomonas

fluorescens (P. fluorescens) these two organisms show a synergistic inhibitory effect

towards L. pneumophila, through reduction of biofilm formation and enhanced

detachment in previously formed biofilms. In experimental settings this effect has been

found to cause as much as a 2-log reduction in L. pneumophila counts over a three day

period.22 However, cell-free supernatants collected from P. aeruginosa cultures that were

grown isolated from other bacteria, were found to aid in the multiplication of L. 45 pneumophila in sterilized tap water, the mechanism for this is hypothesized to be done through providing essential nutrients.197 Additionally, biofilms which contain both

Acanthamoeba castellanii (A. castellanii) and P. aeruginosa have been found to exhibit an increased uptake of L. pneumophila into A. castellanii. This results in more L. pneumophila undergoing intracellular reproduction which increases total amounts of L. pneumophila and colonization of L. pneumophila in those biofilms.1,20,36 This can result in significant growth of L. pneumophila, given that concentrations of L. pneumophila have been found to increase by three to four orders of magnitude in 48 to 72 hrs. after being exposed to A. castellanii for a single hour, and this is without the increased rate of uptake provided by the presence of P. aeruginosa.193

Mechanisms of effects

Over 30 categories of tap water organisms and their commensal or beneficial relationships with L. pneumophila, have been identified here and the previous chapter.

Though due to the different organisms used between studies and the vast array of analytical methods used, comparison between these findings is limited. However, the majority of commensal relationships between L. pneumophila and other organisms that make up the water distribution system microbiome seem to center around four overlapping themes: Host gene transfer and modulation, intracellular replication, provision of a protective environment, and supply of nutrients and resources.

Host gene transfer and modulation

L. pneumophila’s uncommon adaptation of utilizing host cells for reproduction provides this pathogen with multiple unique opportunities for enhanced survival. L.

46 pneumophila utilizes multiple hosts to accomplish this which include a range of phyla, such as Amoebozoa, Percolozoa and Ciliophora. The three amoeba Acanthamoeba,

Hartmannella and Naegleria, are recognized as L. pneumophila’s most common host.3,14,25,34,46,111–117 During the long evolutionary history of this process it has been hypothesized that coevolution of L. pneumophila with its various protists hosts has influenced its genomic content through interkingdom horizontal gene transfer.200–204,204–

212 This is supported by the comparisons of the genomes of certain legionella strains and their host amoebae.1,2,15–17,19,30,35,36,92,170,181 L. pneumophila genomes are known to be particularly plastic and have many effector regions which code for eukaryotic-like proteins, domains and motifs. These effectors are involved in the modulation of a large array of host cell processes including apoptosis, protein synthesis, signaling, vesicular trafficking, ubiquitination, histone modification, posttranslational modification, and many others.200 These effectors are thought to be the result of the combination of interkingdom horizontal gene transfer and the selective pressure of evading host cell digestive processes.200–204,204–212 Though many of these genes are highly conserved among legionella species, certain genes show high variation between species, which may indicate that these genes have been acquired more recently and that interkingdom horizontal gene transfer is not an uncommon occurrence for this pathogen.200

Additionally host gene transfer and modification are thought to be the main factors behind the increased disinfectant resistance, increased antibiotic resistance and increased ability to infect human cells, seen in L. pneumophila that have gone through intracellular replication over those that have not. L. pneumophila which have gone through

47 intracellular replication have been shown to exhibit alternative membrane structure and chemistry, which may aid in survival and persistence.2,13,15,36,92,125,127,185,187 This includes

L. pneumophila which reproduce through the infection of Hartmannella vermiformis, these cells have been found to possess extracellular modulations which are likely to aid in transformation into a VBNC state.145,146

Intracellular replication

While there is still debate on how many different methods L. pneumophila can employ for replication, it is evident that intracellular replication is by far the most advantageous method for the pathogen, especially in the long term. Intracellular replication can increase L. pneumophila resulting growth from a few hundred percent to several orders of magnitude.1,20,25,34–36,44,46,112,114,145,146,175,193,196,213 As stated previously, the most common eukaryotic hosts for L. pneumophila to use for intracellular replication are the three amoeba species Acanthamoeba, Hartmannella and Naegleria.3,14,25,34,46,111–

117 All of these amoebas can greatly enhance the growth of L. pneumophila through intracellular replication. Acanthamoeba spp. has shown to increase L. pneumophila’s growth up to 2 log CFU/ml within four days of exposure.196 Acanthamoeba castellanii alone was found to increase concentrations of L. pneumophila 3-4 orders of magnitude in

2-3 days after only a single hour of exposure and as mentioned earlier, has an even further increased rate of uptake when in the presence of P. aeruginosa.1,20,36,193

Acanthamoeba polyphaga, Hartmannella vermiformis and Naegleria fowleri increased L. pneumophila’s growth rate between 110 – 200, 230 – 760 and 480 - 720 percent, respectively, when compared to controls, at temperatures of 30, 32 and 37℃ after 72

48 hours of exposure.145 In a separate experiment, Hartmannella vermiformis was found to promote the growth of biofilm associated L. pneumophila from starting concentrations of

2.4 log CFU/ml (planktonic phase) up to maximum concentrations of 6.3 ± 0.1 log

CFU/cm2 and 4.9 ± 0.1 log CFU/cm2 in developing biofilms, depending on substrate used for biofilm construction. In this experiment the maximum growth rate of Hartmannella vermiformis was calculated as 0.15 h-1, which reached a maximum concentration of 5.3 ±

0.4 log protozoa/cm2 after 10 days, and the amount of L. pneumophila cells observed in amoebae before lysis ranged from 1 to 100.25,34

Providing a protective environment

L. pneumophila’s uncommon membrane structure plays a role in its higher level of susceptibility to certain environmental factors and biocidal agents than other microorganisms in the water distribution system’s microbiome.2,13,28,103,104 Because of this many of its survival strategies involves inhabiting a protective microenvironment that helps to alleviate external stressors. These protective environments can come in many forms. Protozoan are particularly more resistant to disinfectant processes than bacteria.1,34,35 These eukaryotes, particularly amoeba, and on occasion some algae and nematodes, are often used as hosts by L. pneumophila. While being used as hosts these larger organisms provide a protective microenvironment for L. pneumophila from external stressors.1,11,13,36,46,107,112,145,147,185,214,215 As stated earlier certain eukaryotes also can release membrane bound vesicles that can contain multiple L. pneumophila cells.

These vesicles continue to provide protection for L. pneumophila contained within, can remain viable for months, and can be virulent.92,127

49

Biofilms also play an important role in providing a protective environment for L. pneumophila. Biofilms create a safe haven from many disinfectants and environmental stressors, L. pneumophila has also been seen to survive within biofilms for months at a time.13,139,140 Several organisms commonly found in water distribution systems have been found to aid L. pneumophila in inhabiting protective biofilms. Acinetobacter baumanii has been shown to produce biofilms that are favorable for L. pneumophila to attach to and inhabit.22,46 The presence of Acinetobacter lwoffii was found to strongly enhance biofilm formation by L. pneumophila, and also allowed counts to grow higher than controls.22,34

The extracellular matrix materials produced by Empedobacter breve have been shown to allow for attachment of L. pneumophila and contribute to its long-term persistence and presence in biofilms.13,22,34,46,109,195,199,216 Pseudomonas fluorescens, Klebsiella pneumoniae and Flavobacterium sp. were all seen to create mono-species biofilms that are permissive to L. pneumophila colonization at high levels, and the capsular and extracellular matrix materials that they generate also promotes the long-term presence of

L. pneumophila in multi-species biofilms through increased adherence and stimulation of growth. Klebsiella pneumoniae has also been observed to mitigate the inhibitory effect of

P. aeruginosa.13,34,34,46,109,114,195,198,199,216 Mycobacterium spp. produce biofilms that are favorable for L. pneumophila integration, and Mycobacterium chelonae also has been shown to increase the cultivability of L. pneumophila in biofilms.22,34,46,124 Pseudomonas putida has also been shown to produce biofilms favorable to L. pneumophila to the extent that concentrations of L. pneumophila increased by 4 log units within these biofilms.22,34,109

50

Additionally, some microorganisms help to create a protective environment for L. pneumophila by eliminating environmental stressors. Both Nitrosomonas spp. and

Nitrosospira spp. are nitrifying bacteria species that have been found to promote the survival of L. pneumophila by reducing chloramine and other disinfectant residual concentrations in distribution system water.36,143,194

Supplying nutrients and resources

As mentioned earlier, L. pneumophila is difficult to grow in the laboratory due to its nutritional requirements.2,13,28,103,104 This pathogen requires specific amino acids and trace elements such as L-cysteine, arginine, isoleucine, leucine, threonine, valine, methionine, phenylalanine, tyrosine, serine, Fe, Mn, Mg, Ca, Zn, K, Mg and Cu, many of which it cannot produce itself.2,13,37 In the oligotrophic environments of water distribution systems L. pneumophila overcomes this nutritional scarcity through its relationship with other organisms in the system.3,11,13,34,108,147,149,151,154 During intracellular replication L. pneumophila will inhabit and inactivate the digestive vacuoles of protozoa, these vacuoles are rich in amino acids and nutrients, such as L-cysteine, which are crucial for

L. pneumophila’s survival and reproduction.38

Many microorganisms in tap water also supply nutrients to L. pneumophila through the production of extracellular molecules. The presence of Brevundimonas vesicularis was also found to promote L. pneumophila growth in nutrient poor conditions.147 Acinetobacter sp., Pseudomonas spp. and Alcaligenes spp. were shown to allow L. pneumophila to develop satellite colonies in pour plates that were lacking cysteine.13,149 Additionally, Flavobacterium sp., and specifically Flavobacterum breve,

51 allowed for the development of satellite colonies in nutrient poor conditions, such as pour plates that were lacking cysteine and ferric pyrophosphate.1,13,149,154 These studies provide evidence that these organisms supplemented environmental nutritional deficiency experienced by L. pneumophila.

Several organisms were found to stimulate L. pneumophila growth only through

Cell-free supernatants (CFS), but not when actual organisms were present. CFS from cultures of Acinetobacter baumanii, , Pseudomonas aeruginosa and

Aeromonas caviae were also shown to have a stimulating effect on the growth of L. pneumophila.37,197 The CFS and the presence of living cells of both Pseudomonas alcaligenes and Pseudomonas fluorescens were both found to stimulate the growth of L. pneumophila. The CFS from Pseudomonas fluorescens was also able to promote L. pneumophila’s growth up to 70.4 percent over that of controls.37

In addition to being able glean nutrients from the extracellular products other microorganisms, L. pneumophila has also demonstrated the ability derive nutrients through necrophagic means.39 Experimentally this has only been demonstrated with other heat killed gram negative bacteria, but results have shown growth rates as high as 1.89 log units after 96 hours.1,2,11,13,39,40,109 Experiments have also shown necrophagic growth rate to outpace protozoon-mediated growth in the short term, and may contribute to L. pneumophila’s persistent and rapid regrowth after certain water treatments.1,39

There is ample evidence that L. pneumophila has evolved multiple interactions and relationships with other water distribution system microorganisms though which it may be able to promote its survival and persistence. Unfortunately, more research still

52 needs to be done on the exact mechanisms that are being employed in these intercellular biofilm interactions. Better understanding of these interactions may provide opportunities where water distribution system ecology may be adjusted to help control the growing rates of L. pneumophila in these systems. Which in turn will help us to mitigate the public health risks from L. pneumophila, including LD. Using knowledge of these microbial relationships will help us to better predict concentrations of L. pneumophila, its risks to human health, and will supply us with better control measures for future implementation.

53

Chapter 4. Methods

Modeling and Statistical Methods

Quantitative Microbial Risk Analysis (QMRA)

A major component of this model’s development and its application is for the estimation of values for Quantitative Microbial Risk Analysis (QMRA). QMRA is a computational framework that characterizes the health risks due to exposure to microbiological agents.217 After the initial problem formation, QMRA typically consists of five key elements shown in figure 1: Hazard identification, dose-response, exposure modeling, risk characterization and risk management.217,218 These elements and the flow between them can also be significantly influenced by external pressures of the specific situations that they exist in, such as social, cultural, policy and economic pressures.

Figure 1 Adaptation of the QRMA framework for greater inclusion in public

health paradigms

54 Hazard identification is concerned with identifying what the microbial hazard is in relation to the health outcome that is being examined, why it is present and persisting in the environment and the specifics of its impact on human health. Dose-response relates to the pathogenesis and the estimates the probability of a health outcome dependent on the dose of pathogens that were inhaled to an infectious location. Exposure assessment is characterized by the details of how human beings are exposed to the pathogen. This can include the environmental matrices that relate to exposure, the time frame, frequency and duration of exposure driven by specific populations’ behavior and environmental dynamics. All of which impact the pathway and route(s) through which an individual is exposed. Risk characterization is formulated based on the interpretation of the risk, as results from the dose-response and exposure assessment steps. These processes are monitored, intervened upon, communicated to the population, and reassessed through the risk management phase. Both the dose-response and the exposure assessments are heavily impacted by the scenario being investigated. An important detail in a QMRA scenario is often the quantification of the microbial agent. This is because the amount of microbial agent in the scenario can heavily impact calculations in both does-response and exposure assessment steps which result in estimating the dose that an individual receives, and the resulting risk of disease. This makes quantification of the microbial agent particularly important in measuring risk.217–220

With this knowledge, the models that my method develops are a means of improving the precision of estimates for exposure of bacteria after its growth in plumbing. From this, we can better understand how communities of microorganisms in

55 biofilms affect the associated health risks, and thus use that to target and decide on intervention options.

Stochastic Models

In general, there are two broad categories of quantitative methods to develop mechanistic models of a physical system, deterministic or stochastic. The laws of conservation of mass and energy are crucial for both categories of modeling; however, the internal mechanics of these different methods vary significantly.

Deterministic models rely on specific inputs and produce fully determined outputs for specific initial and boundary conditions to the model. This is similar to a simple differential equation which will reproduce the same output for the same specific input variables and boundary conditions.221 Examples of deterministic models include timetables, sheet music, pricing structures, accounting, maps, linear programming models, and the economic order quantity model.222

Stochastic models introduce randomness (stochasticity) to facilitate the incorporation of system and parameter uncertainty in the model. This is accomplished by allowing at least one, if not more, of the model’s inputs to experience random variation typically using probability distributions. Typical operation of stochastic models involves using random variates of probability distributions optimized to data describing model variables or parameters are used to iteratively calculate estimates from the target function that is in the model. By using stochastic methods outputs will vary in relation to the uncertainty of the variables and parameters where uncertainty was included.221,223 For repeatability sake, when using stochastic methods, an initial randomization seed must be

56 set and recorded. Using a reported seed value will allow for model replication where exact values are not replicated but median and quartile values are replicated.

Stochastic methods were chosen to simulate the system due to the dominant need to manage the uncertainty of a computational model of biofilm dynamics. Within the stochastic methods there are a number of options, but to provide a simple framework that can be expanded upon easily to incorporate more complexity later, the Monte Carlo method was chosen.

Monte Carlo Simulation

The Monte Carlo method that was first developed for use in the Manhattan

Project and is named after a famous casino area, since it was inspired by the inherent uncertainty in gambling.224 A Monte Carlo simulation defines probability distributions to characterize the constraints of the uncertainty for each random variable in the scenario.225

These probability distributions are optimized to empirical data when possible, preconstructed data sets or using expert elicitation.226 While this method is computationally complex, today it can be easily performed by using an array of mathematical programs and software packages such as the R programming language. R, uses pseudo-random numbers to generate random variates from the probability distributions of uncertain variables, and use those variables in the model.223 In the Monte

Carlo method, a simulation is generally run through many iterations to utilize the law of large numbers in order to bring the simulation to convergence. This is because the law of large numbers states that as samples approach infinity, the sample mean approaches the population mean.226 This is why the Monte Carlo method is so effective for theoretically intractable problems.

57 Simulation Methodology

As discussed earlier, the hypothesis for the presented model is: While controlling for other water quality characteristics, the concentration of L. pneumophila can be estimated using localized microbial ecology through stochastic simulations. In order to accomplish this, a method for mechanistic risk modeling, and computational microbiology needed to be developed to account for the underlying mechanisms and uncertainties at the same time. Therefore, this was accomplished in a two-step method.

Step one, the derivation of a computational model to account for the growth, decay and biofilm interactions that impact the growth of L. pneumophila in a water distribution system biofilm, and release into the bulk water. This model will require inclusion of how

L. pneumophila interacts with other bacteria in the biofilm but should not be included solely in a deterministic model to account for the substantial system uncertainty.

Consequently, step two develops a stochastic simulation of a tap water biofilm to model the interactions that L. pneumophila has with other microorganisms in tap water systems, and the effects that those interactions have (commensal or inhibitory) on L. pneumophila’s growth.

This method models a process that needs to be viewed in the context of the system. The inspiration for this method came from my personal desire to understand how the complexity of interactions between groups or individuals can result in effects at higher orders of magnitude or even systemwide. Learning that the growing global health crisis of legionellosis is contingent upon the uncommonly vulnerable L. pneumophila being able to survive in the hostile environment of our water distribution systems, largely based on its interactions with other members of its biofilm communities, is what led me

58 to the desire to build this new type of model; a model that incorporates the complexity of the small-scale interactions taking place in these localized ecologies and reveals how those complexities can result in higher order, or even global impacts. This type of ecological based modeling gives us a much greater granularity when examining risks from microbial agents. Especially when the dose derived from those agents is based upon their interactions with other members of a complex community. In order to test my hypothesis, a method needed to be developed to converge L. pneumophila growth and decay within the context of the complex interactions of the biofilm communities. The system (the biofilm) and the complex processes happening within (interactions between

L. pneumophila and other microbes) cannot be separated in this type of computational model.

Biofilm simulation

Biofilms possess complex structures and interactions, can grow on almost any surface and are capable of lining the internal walls of water system pipes.41,70,175,227–232

For this simulation the modeled biofilm is assumed to have completely covered the internal surface of the pipe section being examined. From this the simulation is developed by making a two-dimensional matrix built with the length of the pipe and the width calculated from the circumference of the pipe. This is illustrated in Figures 2 and 3, and

2 Eqn. 1, where Ai is the surface area (cm ) of the biofilm matrix which is a function of pipe length (Li), and the circumference (Ci) based on the inside diameter (di,) of the pipe.

Distributions used in this calculation can be seen in table 4.

59 퐴푖 = 퐿푖 ∗ 퐶푖 = 퐿푖 ∗ (푑푖 ∗ 휋) Eqn. 1

Figure 2 Section of pipe Figure 3 Two-dimensional demonstrating dimensions of representation of modeled internal modeled biofilm. water pipe biofilm.

While the communities that make up distribution system biofilms are complex and consist of many different species of microorganisms. Both in-situ and in-vivo experimentation of water distribution system biofilms have found that at localized levels, biofilm heterogeneity can be characterized by the colonization up to three dominant (by percent makeup) bacterial species.231–233 In order to represent colonization trends described in the literature, each cm2 section of biofilm is modeled with up to three dominant biofilm bacterial species. The model randomly selects and assigns between one and three species of bacteria to each grid section at varying percentages. Nine species of bacteria were identified in the literature, with data that could be quantified, compared across studies and used in this model. The model randomly selects from these nine different species of inhibitory or commensal bacteria and an additional three species that have no effect on L. pneumophila (represented throughout the model as NA_K, NA_F, and NA_P), for a total of twelve different species that can be drawn from. Additionally, because of the significant influence that amoeba have on the growth of L. pneumophila, pertinent amoeba species and numbers234,235 are also randomly assigned to the simulated

60 biofilm grid sections. The probability that a gird section will contain a certain species of amoeba, and the resulting concentration of that amoeba species in that grid section, are determined from reported values in the literature from biofilm surveys of water distribution systems.236–238

Data for inhibitory effects

As discussed in chapter 2, there is a wealth of information in the literature on water distribution system microorganisms which have an inhibitory effect on L. pneumophila. This is likely because finding a probiotic solution to premise plumbing pathogens is becoming ever more popular as a concept to reduce health risks more sustainably.150,229 However, not all of the mechanisms behind these inhibitory effects are understood. A further concern is that studies which have examined the underlying mechanisms have not always presented their data in a way that that is conducive to modeling these effects.

Several studies have shown that select bacterial species inhibit L. pneumophila via an extracellular substance, such as a surfactant or a BLS. In many of these studies researchers have employed SOL assays, or other similar inhibition assays.11,177,239–243.

The data presented in these SOL assays allow for comparison of inhibitory effects across species. However, their use in computational model development is difficult since they are pseudo-quantitative methods, thus further necessitating stochastic methods for model development.

Spot-on-lawn assays

SOL assay involves the addition of a test microorganism, or cell free supernatant

(CFS), to a nutrient agar that has been inoculated with the target microorganism (in these

61 cases L. pneumophila). These agars are then incubated, and the target microorganisms grows in the presence of the test microorganism in the spot. This facilitates the observation of the test microorganism producing an inhibitory effect on the target microorganism. Data presented in these types of assays were able to be compared across studies, quantified, and used as inhibition measures for my model. Other methods of inhibition in the literature were as conducive to quantification or comparison across other studies of inhibition.

The secondary data collected from the SOL assays in the literature is presented in

Table 3. These methodological varieties are well represented in the studies used in the development of this model. Though the methods may vary slightly in their execution, the data produced can still be compared due to the basic mechanism being examined: the observation of zones of growth inhibition forming around inoculum of inhibitory bacteria, through the diffusion of extra-cellular products across the agar matrix. The following list outlines the variety of methods conducted for those data used in this model.

• Suspension: Corre et al. (2018) examined supernatant produced from bacteria obtained from water system samples and spotted them onto the center of 60mm

L. pneumophila inoculated plates. This gave the inhibiting inoculum a 10mm footprint.

The researchers then incubated these plates for 48hrs and compared to controls to examine inhibition against L. pneumophila.11

• Culture Patch: Cotuk et al. (2005) in conjunction with methods form

Toze et al. (1990), showed that a 6mm cutting of precultured bacterial growth patched onto the surface of an inoculated L. pneumophila plate can be used for similar SOL assays. These samples were then incubated for 72 hours and compared to controls.104,239

62 • Reverse Side Producer Spot Inoculation: Guerrieri et al. (2008) used a

technique referred to as a Reverse Side Producer Spot Inoculation or the Modified

Deferred Antagonism Method, where the natural diffusive properties of the agar is

utilized. The inhibitory bacteria are inoculated on the reverse side of the agar to the L.

pneumophila, leaving no footprint on the L. pneumophila inoculated side of the agar. And

the extracellular molecules that the inhibitory bacteria produce are allowed to interact

with the L. pneumophila as they diffuse through the agar during incubation.242

• Well Diffusion: Loiseau et al. (2018) specifically examined

Pseudomonas’s effects on L. pneumophila, through using spot-on-lawn assays in

conjunction with well diffusion assays. Here small wells were punched into the

inoculated agar and filled with concentrations of Pseudomonas or CFS. These plates were

then incubated to allow for growth, and inhibition zones were measured.240

Table 3 Spot-on-Lawn Assay data

Producer Inhibitory Method of SOL footprint Species Organism Source Zone Size (cm) assay spot size (mm) Acidovorax Acidovorax sp. 0 - 2 10 11

Acinetobacter Acinetobacter spp. 2 - 6 Suspension 10 11 0 - 6 10 11 Aeromonas spp. 1.4 - 5.0 Culture patch 6 239 Aeromonas Aeromonas Reverse side producer 0.005 – 0.010 0 175,242,244,245 hydrophila spot inoculation Bacillus Bacillus spp. 0 - 6 Suspension 10 11,175 Burkholderia Reverse side producer Burkholderia 0.005 – 0.010 0 175,242,244,245 cepacia spot inoculation Flabobacterium Flavobacterium 0 - 6 Suspension 10 11,175 spp. > 0.008 Well diffusion .01 240 Pseudomonas Pseudomonas spp. 1.7 - 3.0 Culture patch 6 239

63 Producer Inhibitory Method of SOL footprint Species Organism Source Zone Size (cm) assay spot size (mm) Reverse side producer 0.011 – 0.015 0 242 Pseudomonas spot inoculation aeruginosa > 0.008 Well diffusion .01 230,240,242–245 Reverse side producer 0.005 – 0.015 0 242 Pseudomonas spot inoculation fluorescens > 0.008 Well diffusion .01 240 Reverse side producer 0.005 – 0.010 0 242 Pseudomonas spot inoculation putida 0.004 – 0.008 Well diffusion .01 240 Sphingomonas Sphingomonas 0 - 2 Suspension 10 11 spp. Stenotrophomonas Reverse side producer Stenotrophomonas 0.005 – 0.010 0 175,242 maltophilia spot inoculation

Methods used to calculate inhibition percentage

The data produced by SOL assays provided an opportunity to calculate a proxy

for the intensity of inhibition on L. pneumophila’s growth. The method to accomplish this

is conducted by taking the proportion of the area in the zone of inhibition over the total

potential area for L. pneumophila growth. As visualized in Figure 4, Inhibition intensity

based on the zone of inhibition is calculated using Eqn. 2 – Eqn. 5, where: AT and rT are

respectively the total potential growth area (mm2) and radius (units of mm, blue line in

2 figure 4). The yellow line in Figure 4, corresponds with Aspot and rspot as the area (mm )

and radius (mm) of the producing bacteria footprint spot respectively. Lastly, the red line

2 in figure 4, corresponds to AInh and rInh which are the area (mm ) and radius (mm) where

inhibition was experienced. Here pEb (inh) is the percent of effect to L. pneumophila

growth, from the presence of an inhibitory bacteria species, as a function of: the total

potential area of growth (corrected for area taken by the spot), and area of inhibition.

Data in these studies were also presented as ranges of inhibition. For use in this model

64 stochastic distributions of these values were given triangular distributions with the minimum and maximum values set as the minimum and maximum inhibition radius values, and the most likely value set as the midpoint valued between those two distances.

2 퐴푇 = 푟푇 ∗ 휋 Eqn. 2

2 퐴푠푝표푡 = 푟푠푝표푡 ∗ 휋 Eqn. 3

2 퐴퐼푛ℎ = (푟푠푝표푡 − 푟푖푛ℎ) ∗ 휋 Eqn. 4

(퐴푇−퐴푠푝표푡)− (퐴퐼푛ℎ−퐴푠푝표푡) 푝퐸푏 (퐼푛ℎ) = Eqn. 5 (퐴푇− 퐴푠푝표푡)

Figure 4 A Spot-On-Lawn assay, where rspot (yellow) denotes the radius of the impact spot of the producing bacteria, rinh (red) shows the radius of the inhibitory zone, and rT (blue) is the radius of the total potential growth area. Data for commensal effects

As discussed in chapter 3, less information was present in the literature for bacteria that had a commensal effect on L. pneumophila. One commensal bacteria species, Pseudomonas fluorescens, and three species of amoeba, Acanthamoeba,

Hartmannella and Naegleria, were presented in the reviewed literature with data available that was comparable to effects from other studies, showed a quantifiable effect on the growth rate or over all growth of L. pneumophila, and could be used in this

65 model.234,239 Similarly to the inhibitory data, the data provide for commensal effects were standardized for effect per second and assumed to be triangular distributions.

Further complicating this part of the model, those data that could be used for bacterial commensal effects are presented in percent effect on L. pneumophila’s growth.239 This allows for the effect of commensal bacteria on L. pneumophila to be calculated by simply multiplying the effect of the bacteria by L. pneumophila’s growth.

The commensal effects on L. pneumophila growth from a commensal bacteria species, will be represented as pEb (Com). Likewise, pEa, will represent the commensal effects on L. pneumophila growth from a commensal amoeba species. These values will be used in later calculations for the overall effect on L. pneumophila. The effect that amoeba have on L. pneumophila was reported in the literature as an effect on L. pneumophila’s growth rate. L. pneumophila having a fist order growth rate, means that this effect is exponential.

Both commensal bacteria and commensal amoeba effects are directly reported from the literature and are measures of the reported increase in L. pneumophila’s growth and growth rate, respectfully.234,239

Modeling L. pneumophila Growth in Biofilm

The previously referenced matrix is used to simulate a premise plumbing biofilm that is randomly assigned bacteria that inhibitory and/or commensal data were found for, with the addition of three neutral bacteria, of which to draw from. This allows that for the effect on L. pneumophila in each grid section of biofilm, to be estimated by the combined effect of the biofilm bacteria and amoeba in that location, estimated based of the sum of the organisms’ effect on L. pneumophila and their percentage makeup of the organisms of their type (bacteria or amoeba) in that local area of the biofilm. Since this is operated

66 within a Monte Carlo, this biofilm is “reformed” at each iteration and the estimation is conducted again, for a total of 3,600 iterations. The resulting overall growth of L. pneumophila, in CFUs, in each grid section of the biofilm (CGBj) is calculated in Eqn. 6 as being equal to the growth of L. pneumophila (Cg) minus its decay (Cd). From this fundamental form, we will now progress through its further derivation in Eqn. 6 through

11.

퐶퐺퐵푗 = 퐶푔 − 퐶푑 Eqn. 6

The initial growth rate was aggregated from five studies of growth rates over a range of operational premise plumbing conditions.234,243,246–248 Similarly, the decay rate of

L. pneumophila is comprised of multiple studies’ results.249 By aggregating each of these and using a probability distribution to randomly sample from provides for growth rate uncertainty to be accounted for.

L. pneumophila’s decay in the biofilm is estimated by the starting concentration

249 of L. pneumophila (CLp0) multiplied by its rate of decay (rd,). Residual disinfectants such as Chlorine have also been found to cause inactivation of L. pneumophila within biofilms. Huang et al. (2020) calculated a first order range of this effect under simulated premise plumbing conditions with residual disinfectant levels of between .02 and 5 mg/L, to be a normal distribution with parameters shown in table 4. In this model decay from

84 residual disinfectants is represented as rClib. To identify the amount of decay that occurs in biofilms due to this residual disinfectant effect, the starting concentration of L.

푟 pneumophila (CLp0) is multiplied is multiplied by (1 - 푒 퐶푙푖푏). This allows us to model the overall decay of L. pneumophila in the biofilm (Cd) as shown in Eqn. 7.

푟퐶푙푖푏 퐶푑 = {퐶퐿푝0 ∙ (푟푑 + (1 − 푒 ) ∙ 푡)} = 퐶퐿푝0 ∙ 푅푑 Eqn. 7

67 84 The growth of L. pneumophila (Cg) is also assumed to be fist order growth and when growth is included as such Eqn. 8 estimates the concentration of L. pneumophila that grew in the biofilm during the time step simulated in the matrix,, where j is the index of the number of grid sections in the simulated biofilm.

푘∙푡 퐶퐺퐵푗 = (퐶퐿푝0 ∙ 푒 ) − [퐶퐿푝0 ∙ 푅푑] Eqn. 8

As outlined earlier, there are typically up to three dominant species of bacteria in a section of biofilm.231–233 Consequently, there will be up to three effects on L. pneumophila growth that will need to be accounted for in a grid section of biofilm (the simulation matrix). Additionally, since amoeba and commensal bacteria have a positive effect on the growth rate we can model the general effects of amoeba as a scaling factor for the growth rate k (Eqn. 8), assuming that the amoeba effect is not limited by the quantity of amoeba in the gird section of the biofilm and can be modeled linearly. This scaling factor (푝푎) is calculated in Eqn. 9, where i is the index of the number of amoeba

that are commensal in the simulation matrix during that iteration, 푝퐸푎푖 is the commensal

increase in growth rate and 푃푎푖 is the proportion of amoeba present. Heat maps depicting the overlaying bacterial and amoeba makeup of a simulated biofilm can be seen in figure

11.

∑3 푝푎 = 푖=1(푝퐸푎푖 ∙ 푃푎푖) Eqn. 9

Then we can develop a similar scaling factor acting on the overall L. pneumophila growth from bacteria (푝푏) using Eqn. 10, where i is the same index value, 푝퐸푏푖 is

calculated from the SOL data (Eqn. 5) or taken from the literature, and 푃푏푖 is the percent of each bacteria that are present. Because pEb represents the percent of L. pneumophila growth while in the presence of a specific bacteria when compared to a control, (ie. 0.80

68 in the presence of an inhibitory bacteria, or 1.70 in the presence of a commensal bacteria)

1 is subtracted from pEb to calculate the effect that the presence of the bacteria has on over all L. pneumophila growth (i.e. -0.20 for the same inhibitory bacteria, and positive

0.70 for the same commensal bacteria). This effect is them multiplied by the percent presence of the bacteria in that grid section of the simulated biofilm, in order to find the intensity of their effect (negative numbers for inhibitory effects and positive numbers for commensal effects). These intensities are then summed to calculate the total bacterial effect for that grid section. This value is then added to 1 so that the resulting value can be multiplied by the exponent of the growth rate for the overall bacterial effect on L. pneumophila’s growth in that grid section of the simulated biofilm (pb > 1 for commensal, 1> pb ≥ 0 for inhibitory effects), as can be seen in Eqn. 11. There is an assumption being made that the zone of inhibition calculations as described earlier, can be interpreted as modifier to growth L. pneumophila’s parameters on a localized scale.

3 푝푏 = 1 + {∑푖=1[(푝퐸푏푖 − 1) ∙ 푃푏푖]} Eqn. 10

Subsequently, we can include Eqns. 9 and 10 into Eqn. 11 as the factors acting on their associated variables. Therefore, Eqn. 9 is inserted as 푝푎 to scale the growth rate k and 푝푏 is included to scale the estimated concentration (Eqn. 11). This approach addresses that the growth of L. pneumophila in a biofilm is comprised of two main factors: growth effects from bacteria and from amoeba independently.11,84,234,239–243 These two factors act upon different portions of the L. pneumophila cells in the grid section. A certain percent of cells are taken up into amoeba hosts and experience the pa effects, and others experience the pb effects from exposure to bacteria in the biofilm grid section. The proportion of these two populations is determined by the rate of uptake into amoeba hosts

69 (ru). This is depicted in Eqn. 10 where Glp is the growth of L. pneumophila in the presence of bacteria and amoeba in the simulated biofilm grid section.

3 [푘∙∑ (푝 ∙푃 )∙푡] 푘∙푡 3 [(퐶 ∙ 푟 ) ∙ 푒 푖=1 퐸푎푖 푎푖 ] + [(퐶 ∙ (1 − 푟 )) ∙ 푒 ∙ (1 + ((푝 − 1) ∙ 푃 ))] 퐺푙푝 = { 퐿푝 푢 퐿푝 푢 ∑푖=1 퐸푏푖 푏푖 } Eqn. 11 0 0

Importantly, Van der Kooij et al. (2016, 2017) found that L. pneumophila reaches

7 7 a maximum concentration (CLp-max) in biofilms of roughly between 1(10) and 2(10)

CFU/cm.250,251 At which point microbial growth is limited. To account for this, Eqn. 12 is a split equation, where the first equation accounts for combined growth impacts which are included when amoebas (represented as concentration of amoebas - Ca) are present, and the CLp is less than the CLP-max. Additionally, for this model the starting concentration of L. pneumophila in the influent water is also an aggregate of multiple studies with a triangular distribution to describe uncertainty (table 4).84,252–256The first split in Eqn. 12 occurs when there are no amoeba in the grid section of the biofilm simulated, and the final split is when the CLp is at the ceiling as demonstrated by Van der Kooij et al. Thus, modeling the lack of amoeba present, and not forcing the simulation to include an amoeba in each grid section, and expand the generalizability of the model and method.

Note that this split removes 푝푎 but retains the 푝푏 to account for bacteria effects. The last split is when the ceiling of L. pneumophila cells in a biofilm grid section have been achieved, thus limiting changes in concentration to only decay.

{퐺푙푝} − {퐶퐿푝0 ∙ 푅푑} 푖푓 퐶푎 > 0 & 퐶퐿푝 < 퐶퐿푝−푚푎푥 퐶 = { [(푘∙푡)] Eqn. 12 퐺퐵푗 {퐶퐿푝0 ∙ (푒 ∙ 푝푏)} − {퐶퐿푝0 ∙ 푅푑} 푖푓 퐶푎 = 0 & 퐶퐿푝 ≤ 퐶퐿푝−푚푎푥

{퐶퐿푝0 ∙ 푅푑} 푖푓 퐶퐿푝 > 퐶퐿푝−푚푎푥

From this founding function in Eqn. 12, we can now include additional realistic dynamics regarding how L. pneumophila lives within biofilms. These expansions will

70 increase the fidelity to the real world, and thus the methods’ and associated model accuracy.

L. pneumophila Movement Within the Biofilm

Movement of L. pneumophila within the biofilm was also examined. Rice et al.

(2003) used the movement of Pseudomonas aeruginosa within a biofilm to model the movement of individual L. pneumophila within biofilms.257 They found that 44 percent of cells would move out of a defined area in a biofilm in one of six 3-dimentional directions over a period of a 0.1-4 hrs. These values were used to calculate an emigration rate per second (rem), as shown in table 4. This amount is then divided by 6 (for the 6 possible directions of movement in a cube) and used to calculate movement of L. pneumophila cells across different grid sections of the simulated biofilm. The concentration of cells within a section of biofilm is multiplied by rem, to find the number of cells that will be

th moving (Cem). 1/6 of the number of emigration of cells is subtracted from the current grid section’s number of L. pneumophila cells and added to the number of cells in an adjacent grid section, for each of the four physically adjacent locations on the biofilm.

Another 1/6th is subtracted and added to the bulk water as emigration out of the biofilm completely, and the final 1/6th is assumed to move vertically within the current biofilm grid section.

The impact of migration on the total concentration of L. pneumophila cells in each location of the biofilm is represented in Eqn. 13 and 14, where Cim is the concentration of cells immigrating from adjacent grid sections of the biofilm, Cem is the concentration of emigrating cells from the current grid section in the simulated biofilm and Cmig is the sum of the migrating (immigrating (+) and emigrating (-)) cells.

71 −퐶 ∗5 퐶 = (( 푒푚 ) + 퐶 ) Eqn. 13 푚푖푔 6 푖푚

{퐺푙푝} − {퐶퐿푝0 ∙ 푅푑} + {퐶푚푖푔} 푖푓 퐶푎 > 0 & 퐶퐿푝 < 퐶퐿푝−푚푎푥 퐶 = { [(푘∙푡)] 퐺퐵푗 {퐶퐿푝0 ∙ (푒 ∙ 푝푏)} − {퐶퐿푝0 ∙ 푅푑} + {퐶푚푖푔} 푖푓 퐶푎 = 0 & 퐶퐿푝 ≤ 퐶퐿푝−푚푎푥 Eqn.14

{퐶퐿푝0 ∙ 푅푑} + {퐶푚푖푔} 푖푓 퐶퐿푝 > 퐶퐿푝−푚푎푥

L. pneumophila Release from the Biofilm

In addition to cells that emigrate into bulk water (as discussed above), cells can also be released from the biofilm and enter into the bulk water through physical hydraulic forces. A simulation by Shen et al. (2017) found various hydraulic scenarios produced a range L. pneumophila cells being physically removed from stable drinking water biofilms

(1(10-3) - 0.012 CFU/min).258 Also, another way that L. pneumophila cells can enter into the bulk water from a biofilm, is through a sloughing event. This occurs when cell concentrations within sections of a biofilm reach a critical point (Ccrt-sl), causing instability within the structure of the biofilm, and hydraulic forces separate a portion of that section of biofilm and release it into the bulk water in mass.259,260 Studies by Schoen and Asholt (2011), have found this critical concentration for L. pneumophila to be roughly between 7.8(10)7 and 7.8(10)8 CFU/cm2.259 And the percent of biomass that is released through these events was found to be between 64.3 and 92.9.260

To model the amount of L. pneumophila released from the biofilm, into the bulk water, the following three variables are used: Cell emigration (Cem/6) (as discussed previously), cells removed by hydraulic forces (Cfl), and cells removed in sloughing events (Csl). This is shown in Eqn.s 15 and 16, where Cbw-i is the concentration of L. pneumophila released into the bulk water by a single section of biofilm, Cbw-T is the total concentration of L. pneumophila in the bulk water and Cbw-0 is the starting concentration

72 of L. pneumophila in the influent bulk water and Cdep-T is the total amount of L. pneumophila deposited into the biofilm from the bulk water.

퐶 퐶 = (퐶 + 퐶 + ( 푒푚)) Eqn. 15 푏푤−푖 푓푙 푠푙 6

퐶푏푤−푇 = (퐶푏푤−0 − 퐶푑푒푝−푇) + ∑(퐶푏푤−푖) Eqn. 16

The amount of L. pneumophila cells removed through these processes (cell emigration (Cem/6) (as addressed above), cells removed by hydraulic forces (Cfl), and cells removed in sloughing events (Csl = Psl · CLp0)) will need to be taken into consideration when calculating the change in L. pneumophila concentration that is not related to growth.

Non-Growth Change in L. pneumophila Concentration

If we allow develop a new function CNG to equal the change in L. pneumophila concentration in each section of biofilm, due to non-growth effects, as presented in Eqn.

17, then we can summarize the total change in each grid section of biofilm as depicted in

Eqn. 18, where Cdep is the number of L. pneumophila cells deposited from the influent bulk water per second. Cdep is a value taken from Shen et al. (2015), where the deposit rate of L. pneumophila cells on biofilms of various makeups were modeled under premise plumbing conditions.261

퐶 ∗5 퐶 = {퐶 ∙ 푅 } + 푒푚 + 퐶 + 퐶 − (퐶 + 퐶 ) Eqn. 17 푁퐺푖 퐿푝0 푑 6 푓푙 푠푙 푖푚 푑푒푝

{퐺푙푝} − {퐶푁퐺푖} 푖푓 퐶푎 > 0 & 퐶퐿푝0 < 퐶퐿푝−푚푎푥 [(푘∙푡)] 퐶퐺퐵푗 = {{퐶퐿푝0 ∙ (푒 ∙ 푝푏)} − {퐶푁퐺푖} 푖푓 퐶푎 = 0 & 퐶퐿푝0 ≤ 퐶퐿푝−푚푎푥 Eqn. 18 퐶 − {퐶 } 푖푓 퐶 > 퐶 퐿푝0 푁퐺푖 퐿푝0 퐿푝−푚푎푥

For the simulations Eqn. 19 is used to account for all of these processes. However,

it can be noted that each form of 퐶퐺퐵푗 was presented to demonstrate its derivation, but also

73 to provide for options in model use. This method is intended to be modular in nature, e.g. allowing for limited motility of the cells should that become an option in L. pneumophila control in the future.

Model Flow and Summary

Shen et al. (2015) simulated the deposition of L. pneumophila cells onto a drinking water biofilm in realistic flow regimes and calculated that between 5 and 16 percent of the number of cells in the influent bulk water deposited into their simulated biofilms. Based on the values from their simulations, this models uses a uniform distribution of 5 – 16 percent (rcol)to derive the number of L. pneumophila cells that are randomly deposited into the simulated biofilm from the concentration in the influent bulk

252,261,262 water flow (Cdep). The fate of these randomly deposited cells are then simulated dependent on the conditions described above. The reproduction, decay, and dispersal rates are calculated along with the resulting number of L. pneumophila in each section of the biofilm (CGB), and j is the index of the grid section of the simulated biofilm. Which is then summed to derive the total amount of L. pneumophila in the totality of the biofilm being modeled (CLp.total).

퐶퐿푝.푡표푡푎푙 = ∑(퐶퐺퐵푗) Eqn. 19

74

Table 4 Values and distribution for variables used in the model for which it is applicable

Data Variables Annotation Values Distribution Source

Concentration of L. pneumophila a min = 1; max = 3005 84,253–256 in influent bulk water (CFU/L) Cbw-0 Uniform min = 2.03*10-5; max = Growth rate (sec-1) k 1.39*10-3; mode: 4.63*10-5 Triangular 234 min = 2.8*10-6; max = -1 -6 249 Decay rate (sec ) rd 8.3*10 Uniform Cl2 inactivation rate in biofilms -1 84 (sec ) rClib  = -0.06;  = 0.02 Normal The maximum concentration of 2 7 7 250,251 LP (CFU/cm ) CLp-max min = 1*10 ; max =2*10 Uniform Critical concentration for LP min = 7.8*105; max = 2 8 7 259 sloughing event (CFU/cm ) Ccrt-sl 7.8*10 ; mode = 7.8*10 Triangular

The percent of biomass sloughed off during biofilm sloughing 260 events. (%) Psl min = 64.3; max = 92.9 Uniform

-1 261 Colonization rate (sec ) rcol min = 0.05; max = 0.16 Uniform

257 Emigration rate (%/hr) rem  = 44 ( = 0.46) Normal

min = 1*10-3; max = 1.2; 258,261 Release rate (CFU/min) rfl mode = 0.1 Triangular

263 Uptake rate (CFU/min) ru min = .0273; max = 97 Uniform a: parameterized as minimum and maximum values from data

75 The above described model is simulated by first generating a 2-dimetional grid as a simulation of a physical biofilm within a pipe segment (figures 2 and 3). Each grid section of this matrix is then assigned properties, which are all recorded in a second matrix. The information in the second matrix is used as the variable inputs to run the simulated growth of L. pneumophila within the simulated biofilm. The concentration, growth and number of cells released back into the bulk water of L. pneumophila is calculated for every grid section of the simulated biofilm for every second that the simulation is run. The biofilm grid sections are then summed to find the total concentration of L. pneumophila in the biofilm and that is released into to the bulk water flow, and the information for each iteration is recorded. This is depicted in figure 5.

Figure 5 Biofilm Simulation Algorithm The current model is designed to run for 900 seconds, representing a 15-minute timeframe. This model is run using the programing language R using a randomization starting seed of 37. This scenario is then run 3,600 times to create a distribution for these

76 values, and a sensitivity analysis is conducted to calculate the significance of the various elements in the model on the concentrations of L. pneumophila in and released from the simulated biofilms.

Showering Event Quantitative Microbial Risk Analysis (QMRA)

As stated above, a major component of the biofilm model’s development and its application is for the estimation of values for Quantitative Microbial Risk Analysis

(QMRA). To that end, the concentration of L. pneumophila released into the bulk water

(Cbw-T) from the modeled biofilm described above, is used to complete a QMRA model.

As depicted in Figure 6. This QMRA model simulates a 15-minute showering event where an individual is exposed to water contaminated with L. pneumophila, as a result of the previously described simulated biofilm. The applicable values and distributions of variables used in this QMRA are listed in Table 5.

Figure 6 QMRA Showering Event Flow Diagram

77 Table 5 Values and distribution for variables used in the QMRA model for which it is applicable.

Variables Annotation Values Distribution Description Reference Flow rate of water in 84 Flow rate Qf 7 L/min Point value the system. Cl2 inactivation Rate of inactivation of rate in bulk  = -0.1 min-1 LP in bulk water due 84,258 water rbw-inct ( = 0.03) Normal to residual Cl. Aerosol removal rate during showering Aerosol event, for particles removal rate <= less than or equal to -1 84,264,265 2µm rar1 0.35 min Point value 2µm. Aerosol removal rate during showering Aerosol event, for particles removal rate > between 2µm and -1 84,264,265 2µm rar2 1.24 min Point value 10µm. Fraction of total Fraction of total aerosolized L. aerosolized LP pneumophila in in aerosols of aerosols of size 0.95- size 0.95-1.6  = 0.038 ( Truncated 1.6 µm, during * 84,266 µm Fl2 = 0.054) normal showering event. Fraction of total aerosolized L. Fraction of total pneumophila in aerosolized LP aerosols of size 1.6- in aerosols of  = 0.037 ( Truncated 2.4 µm, during * 84,266 size 1.6-2.4 µm Fl3 = 0.082) normal showering event. Fraction of total aerosolized L. Fraction of total pneumophila in aerosolized LP aerosols of size 2.4-4 in aerosols of  = 0.078 ( Truncated µm, during showering * 84,266 size 2.4-4 µm Fl4 = 0.019) normal event. Fraction of total aerosolized L. Fraction of total pneumophila in aerosolized LP aerosols of size 4-6.8 in aerosols of  = 0.054 ( Truncated µm, during showering * 84,266 size 4-6.8 µm Fl5 = 0.076) normal event. Fraction of total Fraction of total aerosolized L. aerosolized LP pneumophila in in aerosols of aerosols of size 6.8- size 6.8-9.92  = 0.15 ( = Truncated 9.92 µm, during * 84,266 µm Fl6 0.054) normal showering event.

78 Volume of the shower 3 84 Shower Volume Vs 6 m Point Value in showering event. The rate at which min = 3.6 aerosolized particles Aerosol max = 5.7 are generated during generation rate G min-1 Uniform the showering event. 84,264,265 min = 0.06, max = 1.5 Mode = 0.72 Inhalation rate in Inhalation rate IR m3/hr Triangular showering event. 84,259 The does fitting parameter for the Does fit exponential dose- parameter k -0.0599 Point value response equation 68,79,84,219,267 * As described in the literature, Huang et al. (2020).

Time to Exiting into Shower

For this simulation, the L. pneumophila released from the biofilm, travels down a length of pipe (set at 5m) until it exits into a shower. The time that the L. pneumophila cells are in the pipes before exiting the shower is calculated by dividing the volume of water in the pipe between the biofilm and the shower, by the flow rate (7L/min).84 As can be seen in Eqn. 20 where texit is the time that the L. pneumophila cells are in the pipes before exiting the shower, Vsp is the volume of water in the pipes between the biofilm and the shower, and rf is the flowrate of the water.

푡푒푥푖푡 = 푉푠푝/푄푓 Eqn. 20

L. pneumophila Inactivation due to Residual Disinfectant

This is important because L. pneumophila will continue to be deactivated by residual disinfectants during this time, as shown in simulations such as those by Huang et al. (2020) and Shen et al. (2017).84,258 In these simulations the researchers found first order rate of inactivation across a range of simulated drinking water conditions, of -0.1 min-1 with a normal distribution and SD of 0.03.84,258 To calculate to amount of

79 Legionella exiting the shower, the concentration of L. pneumophila in the bulk water is multiplied by the exponent of the time to exit (texit) multiplied by bulk water inactivation rate. This can be seen in Eqn. 21, where rbw-inct is the inactivation rate in the bulk water, and Cexit is the concentration of L. pneumophila exiting the shower.

푡푒푥푖푡∗푟푏푤−푖푛푐푡 퐶푒푥푖푡 = 퐶푏푤−푇 ∗ 푒 Eqn. 21

Air-Water Partition Function

An air-water partition function (Ptf in m-3 for every liter of water) was used to model the accumulation of water droplets in the air that was generated during the shower.

This function was derived based on the aerosol accumulation in the air of a simulated showering event, done by Davis et al. (2016) and was determined by the shower room

3 -1 volume (Vs in m ), the aerosol generation rate (G in mg × sec ), aerosol removal rate (rar in sec-1, including removal from both deposition onto the wall and by fan air flow), and a water density of 106mg/L, as shown in equation 12.84,264 These parameters were taken from measured aerosol deposition at 1.5m above the ground over more than a 20 min duration of showering activity in a shower stall.84,265

퐺 −푟푎푟푢 푃푡푓푢 = 6 ∙ (1 − 푒 ) Eqn. 22 (10 ∙푟푎푟∙푉푠)

Concentration of L. pneumophila in Air

Only aerosols within a diameter size range of 1 – 10 µm were considered in the model, because only aerosols of these dimensions are considered capable of reaching the alveolar region by nose or mouth inhalation and allowing for infection.84,258 Due to the fact that removal rates for smaller aerosols differs from that of larger aerosols, two different removal rates were used.84,266 A rate of 0.35 sec-1 for aerosols of less than or

80 equal to 2µm, and a rate of 1.24 sec-1 for aerosols between 2µm and 10µm.84,264 These rates where then combined with experimentally derived fractions of total aerosolized L. pneumophila in aerosols within these size ranges84,266. This allowed for the determination of the concentration of L. pneumophila in the air (Cair) at timepoint i by multiplying the exiting concentration (Cexit) by the sum of Ptfu multiplied by corresponding fraction of aerosolized L. pneumophila (Flu), and adding it to the remaining aerosols that have not settled yet (Cns), as shown in Eqn. 23 and 24.

−푟푎푟 2 퐹푙푢 푢 퐶푛푠 = 퐶푎푖푟 − ∑푢=1((퐶푎푖푟 ∗ ) ) Eqn. 23 푖−1 푖−1 (퐹푙1+퐹푙2)

∑2 퐶푎푖푟푖 = [퐶푒푥푖푡 ∗ 푢=1(푃푡푓푢 ∗ 퐹푙푢)] + 퐶푛푠 Eqn. 24

Calculation of Dose

Dose (D) of L. pneumophila deposited into the alveolar region of the lungs per showering event, could then be determined by the summation of the concentration in the air multiplied by the inhalation rate (IR) for each time period. As shown in Eqn. 25.84,259

퐷 = ∑ 퐶푎푖푟 ∙ 퐼푅 Eqn. 25

Calculation of Risk

Animal studies have shown L. pneumophila to follow an exponential dose response model.68,79–81 In most cases guinea pigs, mice, rhesus monkeys and marmosets were used to model L. pneumophila infection in humans. These animal models are useful because they mimic the effects of LD in humans both pathologically and clinically. This method also allows for the use of the natural route of infection, through aerosol exposure, to be employed in order to study cell-mediated immune responses.79–81 The results of the

Muller et al. (1983) study are considered the most appropriate to use when extrapolating

81 to a human public health context, since this study’s examined response was infection, as opposed to death. In this study specific pathogen-free Hartley strain Guinea pigs were exposed to aerosols of Legionella Philadelphia 1 strain in an aerosol infection chamber which had been modified for the experiment. These animals were infected with either 5,

50 or 100 CFU of Legionella Philadelphia 1 strain. Of the animals exposed to 5, 50 and

100 CFU, roughly 28%, 94% and 100% became infected, respectfully. After ten thousand bootstrap iterations a reported k parameter of 5.99 (10-2) (95% CI 1.11 (10-1)) was able to be derived from the Muller et al. data, for use in the exponential dose response model of

L. pneumophila infection through aerosol exposure.68,79

Using this fitting parameter of 0.0599 (k) in an exponential dose response model, risk of infection from L. pneumophila (Pinf) in this QMRA was able to be identified. As

68,84,259 shown in Eqn. 26. Additionally, a per person per year risk (Pinf-pppy) is calculated, as shown in Eqn. 27, under the assumptions that the calculated risk from the biofilm simulation represents the average risk over the year and the individual averages taking one 15 minute shower once a day for the whole year.

−푘∗퐷 푃푖푛푓 = 1 − 푒 Eqn. 26

365 푃푖푛푓−푝푝푝푦 = 1 − (1 − 푃푖푛푓) Eqn. 26

QMRA Summary

In summary, this QMRA measures the amount of risk that is created due to the concentrations of L. pneumophila that are released into the bulk water flow, as depicted in Figure 6. The unique element of this QMRA is that the concentration of L. pneumophila in the bulk water is based off of the intercellular effects of organisms on the 82 growth of L. pneumophila in the simulated biofilm. This scenario is then run for each concentration produced from the biofilm simulations. Which allows for the assessment of risk of infection from the various values produced in the biofilm simulations.

83 Chapter 5. Results and Discussion

Overview

The results of the biofilm simulation model and the shower event QMRA demonstrate the significant complexity involved in the interactions between biofilm organisms and L. pneumophila, including its effects on human health. L. pneumophila, is a communal organism which does not naturally exist in isolation. It is one member in an array of microorganisms that exist in a complex microbial ecology which has the capability of significantly impacting human health.2,35,40,41,155 This is a concept that is beginning to gain traction in the literature, and which will require an increased granularity of focus upon interactions between microbial community members, in order to pursue. This method and model are initial steps in that direction. The model outputs for average growth within the biofilm ranging between 1.784 (104) and 4.009 (105)

CFU/cm2 are consistent with experimental results in the open literature of 2 (104) to 1

(107) CFU/cm2.250,251

Results

The results of the model produced averaged concentrations of L. pneumophila in the simulated biofilm of between 1.784 (104) and 4.009 (105) CFU/cm2, with a range of averaged L. pneumophila in the effluent water of between 4.184 (103) and 3.137 (106)

CFU/L over the 15 minutes of the simulation. These concentrations were then used in the showering QMRA. The QMRA produced inhaled doses of 9.01 (10-8) to 7.33 (10-5) 84 CFU, which produced a range of risk values of 5.40 (10-9) to 4.39 (10-6). This resulted in a per person per year (pppy) risk ranging from 1.97 (10-6) to 1.60 (10-3), with the upper portion outside of the acceptable risk range if L. pneumophila would be assessed under

Safe Drinking Water Act acceptable risk targets of 10-4.

As depicted in Table 6, these values were compared to respective ranges from the literature for validation of the model’s accuracy. The range of L. pneumophila per cm2 of biofilm, of 1.784 (104) to 4.009 (105) CFU/cm2, produced by this model, falls within the range of 2 (104) to 1 (107) CFU/cm2 reported by Van Der Kooij et al. (2017), in their tap water biofilm composition experiments, and well below the maximum concentrations of L. pneumophila reported in Van Der Kooij et al. (2016), or between 1

(107) and 2 (107) CFU/cm2.250,251 The effluent concentration of 4.184 (103) to 3.137 (106)

CFU/L produced by my model fell well within the range of reported values of L. pneumophila tap water concentrations from the literature between 1 to 9(108)

CFU/L.84,252–256,268 Additionally a recent publication from Huang et al. (2020), conducted a QMRA to evaluate risk from L. pneumophila released by biofilms grown under simulated premise plumbing conditions and found a pppy risk of between 2.0 (10-

4) and 4.0 (10-4).84 These values were more condensed than the pppy risk values created by the model from this thesis, ranging from 1.97 (10-6) to 1.60 (10-3). Likewise, the range of individual event risk values produced from my model, 5.40 (10-9) to 4.39 (10-6), also had a wider range than those produced by Huang et al. (2020), of 2.9 (10-7) to 5.8

(10-6). Though both maximum values were similar.84 The expansion of ranges for these values produced by my model, is likely a result of the higher magnitude of complexity represented in my model than in the current literature which traditionally models 85 microbial communities as simplistic biomasses. Huang et al. also used pppy values reported by Hamilton et al. (2019) of 10-6 to 10-2, to validate their model.84,269 Using a combination of these ranges, the values produced by the model from this research fall well within them.

Table 6 Validation of model outputs

Ranges Biofilm Model Values Values from the Literature Source min = 1.784(104), min = 2(104) L. pneumophila in Biofilm max = 4.009(105) CFU/cm2 max = 1(107) CFU/cm2 250,251 84,252– L. pneumophila in Effluent min = 4.184(103), min = 1, Water max = 3.137(106) CFU/L max = 9(108) CFU/L 256,268 min = 5.40(10-9), min = 2.9(10-7), Risk per 15 min shower event max = 4.39(10-6) max = 5.8(10-6) 84 min = 1.97(10-6), min = 2.0(10-4), pppy risk max = 1.60(10-3) max = 1.0(10-2) 84

Sensitivity Analysis

To determine the key variables in the biofilm ecology-based model, a partial rank correlation coefficient sensitivity analysis was conducted. Using Spearman’s rho (휌) values, the size of the 휌 for each independent variable are compared to each other. The

Spearman rho is calculated based on the value of the independent variable randomly sampled against each dependent variable independent of each other: L. pneumophila concentration in the biofilm, L. pneumophila released from the biofilm, and risk of infection, L. The results of these sensitivity analyses can be seen in figures 7 through 9, where Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_P are 3 non-inhibitory and non-commensal bacterial species, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively.

86 Figure 7 Sensitivity analysis of independent variables in the calculation of L. pneumophila concentration in biofilm, where Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_PP are 3 non-inhibitory and non-commensal bacterial species, Bacterial Effect and Amoeba Effect are the total effect of those two organisms in the biofilm respectfully, Total Microbial Effect is the combined bacterial and amoeba effect across the biofilm, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively

Figure 8 Sensitivity analysis of independent variables in the calculation of amount of L. pneumophila released from biofilm, Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_PP are 3 non-inhibitory and non-commensal bacterial species, Bacterial Effect and Amoeba Effect are the total effect of those two organisms in the biofilm respectfully, Total Microbial Effect is the combined bacterial and amoeba effect across the biofilm, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively 87 Figure 9 Sensitivity analysis of independent variables in the calculation of risk of infection, where Lp is shorthand for L. pneumophila, NA_K, NA_F, and NA_PP are 3 non-inhibitory and non-commensal bacterial species, Bacterial Effect and Amoeba Effect are the total effect of those two organisms in the biofilm respectfully, Total Microbial Effect is the combined bacterial and amoeba effect across the biofilm, and Pseudomonas + and Pseudomonas – represent commensal and inhibitory strains of Pseudomonas respectively

Besides factors used to directly calculate risk (such as dose and its predecessor the

amount of L. pneumophila per liter in effluent water), major factors involved in

determining risk were the inhalation rate, the amount of L. pneumophila in the influent

water, L. pneumophila released from the biofilm, the amount of L. pneumophila in the 88 biofilm, the fraction of particles between 1-2µm, and to a lesser extent the presence of

Pseudomonas + (Pseudomonas fluorescens, the only commensal bacteria strain). The presence of Pseudomonas fluorescens was also the dominant factor in determining the concentration of L. pneumophila in the biofilms, and the amount released into the effluent bulk water.

Particularly when examining the sensitivity analysis of independent variables in the calculation of risk of infection, a shift of importance to the exposure factors in the risk model can be seen. While there is general stability in the sensitivity to the biofilm model variables, there is a shift in the sensitivity of the model due to the influent concentration of L. pneumophila in the water, for risk of infection. All of these results from the risk of infection sensitivity plot demonstrates the importance of human and environmental exposure factors and attempts to continue to control the release and survival of L pneumophila in premise plumbing water.

Regression Analysis The results from the simulated biofilm model revealed several significant associations. A multiple regression analysis conducted over the important factors reviled in the sensitivity analysis, showed that the influent water concentration of L. pneumophila

(p-value < 2 (10-6)) in combination with the concentration of L. pneumophila in the biofilm (p-value = 0.00339) and the amount of L. pneumophila released (p-value < 2(10-

6)), are the best predictors of risk in this model (adjusted R2 0.706). These results inform that there may be evidence to assert the importance of monitoring the inlet of water from the distribution system to premise plumbing systems. This can allow for more rapid

89 intervention deployment to attempt to control the amount of L. pneumophila that can enter the biofilm.

As seen in the sensitivity analysis, the commensal strain of Pseudomonas

(Pseudomonas fluorescens), was a significant contributor to both the concentration of L. pneumophila in biofilms, and to L. pneumophila being released from the biofilm.

Pseudomonas fluorescens (P. fluorescens) was also the only commensal bacteria from the literature with data that could be utilized in the model. Through simple linear regression it was found that the presence of P. fluorescens was a strong predictor of both the concentration of L. pneumophila in biofilms, and to L. pneumophila being released from the biofilm, with p-values of < 2 (10-6) for both independent variables and respective R2 values of 0.5607 and 0.07779. This also demonstrates that despite the greater number of inhibitory organisms in the biofilm simulation, L. pneumophila’s evolution as a biofilm bacterium must not be discounted or overlooked for intervention investigations.

Furthermore, an inference that can begin to be made is that commensal bacteria have significant benefits to L. pneumophila thus potentially demonstrating an ability to develop an intervention strategy in control of commensal bacteria.

Discussion

Risk of L. pneumophila infection continues to be a significant issue in public health and water management.71,269–276 Yet despite considerable efforts put towards the control of Legionella in our water systems, the Centers for Disease Control and

Prevention (CDC) now report that it is the most common cause of waterborne disease outbreaks in the U.S.71,269,277–281 By all accounts L. pneumophila on its own is a particularly susceptible microorganism.279 Its nutritional requirements are challenging, 90 especially in the oligotrophic environment of engineered water systems.2,13,104 The uncommon molecular structure of its cell membrane makes it more susceptible to disinfectants and BLS than many other gram-negative bacteria.2,13,28,103,104 However, it is particularly adept at making up for its inadequacies and avoiding eradication through its interactions with other microorganisms.2,13,34,108,230 And its ability to interact with other microorganisms is drastically increased in biofilms, which line much of our engineered water systems.34,230

As illustrated in the recent publication by Huang et al. (2020), much of the current literature involved in microbiological risk assessment revolves around the treatment of complex microbial ecological communities, such as biofilms, as simplified biomasses that contribute to risks of infection at generally steady rates with low variability, complexity, or consideration of ecological community.84 These methods have done well for public health and environmental health science research to date. However, L. pneumophila has presented new challenges the traditional public health modeling and traditional water system pathogen intervention. To address the public health threat from

L. pneumophila we must incorporate the complex systems that are at the root of this pathogen’s maladies.

Through a better understanding of the interactions of biofilm community members and the effects that they produce, will allow us to better predict pathogen concentrations and define human health risks. This knowledge may allow for the development of much more effective and efficient means of control and elimination for waterborne pathogens, such as L. pneumophila. The current model was developed to demonstrate how better understanding of the interaction between microorganisms in 91 water distribution biofilms may lead to better predictions of pathogen concentrations and more accurate assessments of human health risk.

To demonstrate the relevance of this model, the generated results were compared with values reported in previous studies. As outlined above, the ranges produced by my model fall within the associated ranges publish in the literature. Huang et al. (2020) used a QMRA model to examine risk from L. pneumophila released from biofilms under premise plumbing conditions, in a similar shower event scenario. These researchers found through their sensitivity analysis that the rate of L. pneumophila detachment from biofilms and the decay of chlorine residual during stagnation were two of the more important factors that accounted from infection risk.84 Although the current model did not examine chlorine residual decay, it did examine the amount of L. pneumophila which was introduced to the water system. This variable was also found to be a strong factor in the prediction of risk.

Huang et al. modeled biofilms as singular biomasses that released L. pneumophila. My model treats the biofilm as a complex and varying system. Huang et al. allowed for changes in water chemistry, and residual disinfectant rates, while my model controlled for these factors. The result was a somewhat similar range for risk of infection.

However, the ranges produced by my model where wider, reflecting the increased complexity inherent modeling the interactions in these microbial communities.

Because there is a significant amount of uncertainty with how bacteria and amoeba interact within biofilm communities to affect the growth and release of L. pneumophila, this model was designed with a short time span (15 min, which is equal to

900 seconds). This allowed for greater numbers of iterations as opposed to longer periods 92 of growth cycles and allowed for a more in-depth examination of how the complex interactions within biofilms can affect risk of infection from L. pneumophila. It is well understood thorough the literature that amoeba are vital to L. pneumophila growth in engineered water systems. However, the sensitivity analysis conducted here does not portray this fact. Because of this a chart was created to examine a longer period of growth in these simulated biofilms. Figure 10 demonstrates how growth related to various organisms can affect the amount of L. pneumophila released into the water system. The chart shows the effects of the entire simulated biofilm (in black). As the sensitivity analysis portrays, this is dominated by bacterial effects (green). The red points show the effects under only amoeba influences. And the blue points represent the results if L. pneumophila was left alone to replicate under its normal growth rate without the effects of other bacteria or amoeba. It is clear that bacteria exert a strong effect in this model, this seems to be due to the presence of commensal strains of bacteria and their significant effect on the overall growth of L. pneumophila. Also, large releases in the form of sloughing events can be seen by the high release points speckled across the top of the chart. Additionally, the significant effect that amoeba have on Legionella growth, as reported in the literature, can be seen over taking the bacterial effect bacteria near the

1,000 second mark.

93 Figure 10 L. pneumophila released into bulk water under specific growth conditions

Through this examination we can see that the model does still hold fidelity to real world, in showing that amoeba are a significant influence to L. pneumophila growth in engineered water systems, even though the modeled timeframe may not accurately reflect a real-world timeframe. In a normal model scenario, the iteration would complete after

900 second (15 min), before the exponential growth rate effect of amoeba overtakes the bacterial growth rate effect. One reason why the effect from amoeba growth presents this way in the model, may be because while every grid section of the biofilm is populated with bacteria, only a certain percentage of the grid sections of the biofilm contain amoeba, since their presence was modeled on the percent of tap water biofilm samples in which certain species of amoeba were found to be present.236–238 This can be seen in figure 11, which depicts the effects in the simulated biofilm are derived from up to 3 94 dominant bacteria and amoeba. In figure 11, you can see 6 heatmaps which represent to

location of each of the potential dominant organisms for each grid section of the biofilm.

The different species are represented by varying colors, “0” denotes a grid section that

does not have an organism for that location. Every location in the biofilm is populated

with at least 1 species of bacteria, however not every grid section of the biofilm may be

populated with at least 1 amoeba species.

Figure 11 Biofilm Composition of Effect Organisms, heat maps showing the dispersal of the 6 potential levels of dominant organisms within the biofilm.

Limitations

There were several limitations to this study. A significant one was that there were

substantial hurtles in data collection. Due to these hurtles, a combination of personal

computers and online simulations from the Ohio Supercomputer Center were used to run

iterations for this model. The data from these iterations were then reviewed and combined

95 to form one consolidated data file. While many iterations were able to be conducted this way, it would still be desirable to be able to conduct more. A larger number of iterations of this model would better satisfy the law of large numbers and allow for a greater consensus in the identification of the variable in this model.

Along with the persistent inconsistency in experimental protocols and reporting across studies, another limitation of this study is its reliance upon several assumption regarding unknown details of processes within the complex ecological communities of biofilms. Many of these interactions are not completely understood, and more research is needed to understand the nuances of these details. For instance, it is assumed that growth seen in amoeba represents production of full survivable cells, which are released into to biofilm. We do not currently know if this is the case, if a portion of the produced cells are not viable, or what proportion of these cells are released into the biofilm. Recent research has found that some portion of these cells can be released in membrane bound vesicles.127,282,283 It may be the case that cells in this scenario may be influenced by different processes than illustrated in this model.

Additionally, the BLS produced by many inhibiting bacteria, and their mechanical process are not well descried in the literature. Many of these substances have not yet been identified along with the processes though which they inhibit L. pneumophila growth.

Much of the literature relays on the assumption that the BLS produced by inhibiting bacteria diffuse through the substrate of the biofilm to cause an effect on L. pneumophila.

However there is some evidence that these extracellular molecules may be transmitted through other routes.11,28,34,239 This could mean that some organisms may effect L. pneumophila outside of the biofilm, or possible even while residing within a host cell. 96 These possibilities where not incorporated into my model. My model separated the effects that amoeba and bacteria had on L. pneumophila’s growth dependent upon the uptake rate of L. pneumophila into amoeba hosts. It was assumed that once in the host cell, bacteria did not affect L. pneumophila, though we do not know this to be the case.

More research is needed to determine the exact mechanisms though which these inhibitory processes occur, and what the range of their influence may be. A future study which incorporates a larger number and range of bacteria may be able to help clarify this issue.

The microbial communities that exist within biofilms are also much more complex than this model depicts. Organisms in these communities can compete with, inhibit, or promote L. pneumophila in more ways than just through the production of

BLS. Many of these organisms can provide nutrients, may compete with L. pneumophila over limited resources such as nutrients, substrate utilization, or even physical space.13,22,34,46,124,147,175 As mentioned previously, some organisms have been found to have a synergistic effect on the effects of 3rd party organisms towards L. pneumophila.

This can effect L. pneumophila’s growth, its ability to colonize a biofilm, or even how it is taken up into amoeba hosts.11,34 But again, these complex interactions, while having been reported, are still not well understood. More work in understanding these interactions is needed and will add robustness to the type of total community system modeling.

My model also controls for other environmental facts, such as temperature, PH, and variable disinfectant concentrations. These environmental factors are another level of complexity that will need to be incorporated for a more accurate prediction of risk. 97 Additionally, my model portrays the biofilm as a 2-dimentional surface, where in reality biofilms also have depth. The element of depth can influence how organisms within the biofilm interact, not only with other organisms but with the environment of the water system as well. Disinfectant decay and nutrient distribution may cause changes in

L. pneumophila growth and would likely be significantly influenced by the 3-dimentional aspect of biofilms. Work done by Neu et al. (2019) demonstrated that even in uniform controlled systems, the 3-dimentional structures of biofilms (such as thickness and along with that total cell concentration and relative abundance) can exhibit significant variation, and even more so under real (uncontrolled) condistions.231 Additionally, Shen et al.

(2015) found that 3-dimentional aspects of biofilms, such as the biofilms roughness, contribute significantly to L. pneumophila’s rate of adhesion to and detachment from a biofilm.261 L. pneumophila has a higher rate of attachment and a lower rate of detachment on rougher biofilms than on smoother ones. This was found to be due to the increased surface area and the reduction of surface flow rates and shear stress that resulted from an increase in biofilm roughness.261 Furthermore, Shen et al. (2016, 2017) also demonstrated that biofilm thickness and stiffness can be affected by changes in disinfectant exposure.258,284 This could affect not only the microbial composition of biofilms, but also the ability of microbes to migrate in or out of them, and biofilm sloughing.258,284

Even with the identified limitations, this model still robustly outlines the importance of considering the complexity of the microorganism community when predicting health risks from pathogens, such as L. pneumophila. L. pneumophila would not be the significant public health issue that it is today, without the ecological influences surrounding it in our water distribution systems. This model is a first step, but it is an 98 important development for the more accurately identifying complex factors that can influence infection from L. pneumophila.

99 Chapter 6. Conclusion and Future Work

Conclusion

It is clear that L. pneumophila is a growing public health threat, especially within the United States.1,2 Its ability to grow to high concentrations in distribution system water, despite our increased efforts on elimination and disinfection, are of great concern.35,40,70,71 It is known that L. pneumophila is a naturally fragile organism,1,3–12 and that L. pneumophila utilizes the complex ecological community of engineered water systems to promote its growth in these environments.1,20,22,28,34,36–41 However most of our methods regarding prediction of this pathogen and the risks that it poses to human health, still revolve around treatment of the biofilm as a simplistic biomass, disregarding the extensive levels of complexity that it contains.84 This complexity is where L. pneumophila transforms from a nearly harmless background microbe, into a global public health hazard.1,20,22,28,34,36–41

Although we have made progress in understanding L. pneumophila’s impact on human health, major questions and knowledge gaps still persist. Some of the most important aspects of the risks that this pathogen can present revolve around the microorganism communities that surround it. The model constructed here, and the methods used within it were developed in the hopes that efforts to view this public health hazard in the light of the ecological community in which it becomes a hazard, would help us to find better ways to mitigate its risks. This model was developed with the inherent 100 complexity of these communities in mind, and the knowledge that is currently available on the interactions within these communities that can promote or inhibit L. pneumophila to undergo this public health transformation.

The results of this method and model maintain fidelity with the current understanding of the importance of amoeba to growth of L. pneumophila in water distribution system biofilms.15,20,70,111,127 Though, this method furthered our understanding by demonstrating how important the presence of commensal bacteria are to the growth of L. pneumophila. While it was demonstrated in chapter 5 that amoeba do contribute significantly to L. pneumophila’s growth and persistence over longer time scales, this model also demonstrated how significant of an impact commensal bacteria in the biofilm can have on L. pneumophila’s growth, persistence and its resulting risk to human health. Furthermore, this research demonstrated the need to balance both of these supportive microorganisms with inhibitory bacteria that L. pneumophila may encounter as well.

The results of this model and the QMRA analysis allowed for an introductory attempt at a novel method in the quantitative comparison of infection risks associated with the ecology of microbial communities in biofilms. This knowledge is important because legionellosis is now the most common waterborne infection in the United States with increasing prevalence globally.71,84,269,277–281 Further, the presence of L. pneumophila in our water distribution systems is heavily dependent upon its interactions with other microorganisms in its environment.2,13,34,108,230

The method and model developed through this research is a first step in using ecology and community influences to model pathogens’ impact on human health. 101 Therefore, it is important to continue to build upon with improved data and methods development. If we do not give credence to the factors that are the basis for allowing these microbes to become hazards, then our ability to purposefully effect change is hampered from the start. Ignoring the complex interactions in these complex microbial communities, will keep us from effectually addressing health risks, such as those presented by L. pneumophila in our water distribution systems. Our continued reliance on traditional water treatment methods to combat L. pneumophila, would be akin to a physician treating only his patient’s symptoms and never addressing the cause of the disease. This is why probiotic approaches to controlling L. pneumophila in our engineered water systems have recently been gaining conceptual popularity.38,40,42,143,149

However, it should be noted that the dynamics of the microbial ecology in drinking water systems are consistently in flux, and widely varied spatially. Consequently, this method and model, or an improved one should be used to account for changes in the microbial ecology and its impacts on L. pneumophila, lest we create unintended secondary or tertiary hazard increases. Further to this point without better understandings and prospective modeling of microbial ecology dynamics, probiotic approaches may simply replace the L. pneumophila for another potentially worse hazard such as Acinetobacter baumannii. Additionally, these complex interactions also exist in and are influence by other environmental factors. The model from this study held most environmental factors constant. However, future investigation about the environment in which these complex interactions occur, and how it affects those interactions, would aid greatly in modeling this type of complex system.

102 We understand that there is a difference between the microbial makeup of L. pneumophila’s natural environment, and the environment of engineered water systems where it becomes a threat.11,22,28,40 Now it is time for us to start incorporating this information into our tools for a better understanding of the risks of this pathogen. Though more research is still needed to understand the complex interactions in microbial communities, my model shows that we are now capable of incorporating this information into our tools of prediction and assessment, in order to create more-perfect systems for the control of this pathogen and the benefit of public health.

Future Work

While the exploration of this new method is an important first step in better understanding how complex interaction within water distribution system biofilms can impact the growth and resulting risk of L. pneumophila, there are still many areas where future research could improve upon the current understanding. These areas include: future research into the growth kinetics of L. pneumophila particularly in biofilms; Work on the dynamic demographic trends in biofilm communities; Investigation into the mechanisms used in L. pneumophila inhibition, including BLS production and the creation of biofilms that are antagonistic towards L. pneumophila.

While the resulting concentrations of L. pneumophila and the resulting levels of risk did fall within observed levels from the literature, this model had the propensity for rapid growth of the pathogen within biofilms. Possible explanations for this rapid growth, could be that substrate consumption was not taken into account, competition over limited resources was not be included in this model, nor changes in effects as community demographics shifted (L. pneumohpila could have become a dominant species in many 103 biofilm grid sections of the simulation, and altered the inhibitory or commensal effects being exhibited there. Additionally, carrying capacities or nutrient limiting factors would likely affect growth). Unfortunately, the data available will not support his level of granularity or differential effects in the commensal and inhibitory effects of the biofilm bacteria. More research examining these elements would likely aid in controlling some of the growth-related challenges in this model.

Additional trends in the demographics and composition of biofilm communities would also aid in better predictions of how these communities influence L. pneumophila growth. While this model did account for migration of L. pneumophila itself, it did not mode the migration or reproduction (and decay) of the other organisms in this system. L. pneumophila, does not exist in isolation, it is one member of a complex microorganism community with an intricate ecological network of interactions.2,35,40,41,155 As the populations of other microbes shift around L. pneumophila the effects upon L. pneumophila should shift as well. Not only that, but as L. pneumophila grows, its effects on the organisms around it and the system as a whole will develop as well. This is a living dynamic system, and it is important to understand how all of these elements are balanced especially in regard to L. pneumophila is growth. Again, current secondary data from the literature cannot support this type of dynamic model, thus demonstrating a crucial data gap.

The complexity of these systems is also not limited to microorganisms’ interactions with L. pneumophila. As discussed in chapters 2 and 3, many organisms were reported to have effects on other organisms that were affecting L. pneumophila, sometimes even in a synergistic nature. Pseudomonas aeruginosa was reported to 104 increase the uptake rate of L. pneumophila int Acanthamoeba castellanii.1,20,36

Additionally, Klebsiella pneumoniae has also been observed to mitigate the inhibitory effect of Pseudomonas aeruginosa against L. pneumophila.13,34,34,46,109,114,195,198,199,216

However, the mechanisms behind these effects have not yet been thoroughly investigated. More research into these second order effects would greatly help us to flesh out how the growth of L. pneumophila is being affected by the microbiological ecology of biofilms.

A very significant area of future work would be in defining the mechanisms behind inhibition. This would be particularly important regarding BLSs. The majority of these substances have yet to be identified, and their mechanisms of action are largely not understood. Because so much is still unknown about them, many assumptions must be made when incorporating them into models. The current model assumes that they diffuse to a limited degree within biofilm substrate, but there is evidence that these chemicals may utilize other matrixes and routs to effect L. pneumophila. This model also assumes that they do not affect L. pneumophila that is undergoing reproduction inside of a host cell. However, this assumption has not been confirmed and the presence of specific BLS producing bacteria may also affect L. pneumophila’s growth within host cells. This is an area that could contribute significantly to understanding the complexity within these communities. Further research in this area would greatly help in the development of better prospective models.

A major element of inhibitory action by biofilm bacteria presented in the literature, but which was not incorporated into the model was the production of biofilms that did not allow for the attachment of L. pneumophila.124,154,173 Much of this effect of 105 lack of attachment disappears when the biofilms were multi-species. However, it is still important to understand how the production of biofilm substrate can deny access to L. pneumophila, which would severely limit its access to the benefits that residing in biofilms offer to this pathogen. Understanding what elements of biofilm construction are antagonistic to L. pneumophila would allow researcher to more accurately identify what sections of biofilm L. pneumophila would inhabit and further isolating what specific interactions this pathogen would participate in within the complex community of the biofilm.

An ideal study would examine all of these factors. Though, a new concept developing in the literature is how the environmental forces exerted upon organisms living in large dense populations leads to the development of new behaviors and specializations. This trend has been seen across the evolutionary spectrum, assumable these factors would also apply to microorganisms in the large dense populations of biofilms.285–292 It would be very important to understand how these environmental pressures impact the behavior of not only L. pneumophila, but also the other organisms in the biofilm that influence L. pneumophila. Future work examining the processes behind this level of complexity is significantly important and may lead to new understanding on how to more effectively address public health issues that rise out of these complex interactions between microbial community members.

1.1.1 Summary

This model does represent a novel approach to understanding how the threat to human health can be determined by the complex ecologies that pathogens, such as L. pneumophila, develop in. It may be an introductory step, but it is a necessary step in 106 order to combat the rising risk from pathogens suck as L. pneumophila, whose root to their pathogenicity are buried deep within the complexities inherent in microbial communities. Yes, there are clearly areas where improvements can be made. Our research in public health is only now beginning to understand and collect the detailed data that is required for model development. Much of the data used for the current model was not originally collect for the purposes to which it was used here. Because of the limitations that this creates, we need more primary research to produce useable data for mechanistic models. Only then will we be able to begin modeling these complex systems more efficiently.

However, we cannot allow this current lack of specifically tailored data to hamper the development of models and methods that address these complex systems. As demonstrated with this model, current data can be modified to help obtain the inputs required to model this complexity at higher granularity. And as these methods are continued to be developed, we will gain a better understanding of what data is needed, and better data will again lead back to the development of more accurate models, as the cycle improves upon itself.

These methods may be able to be used across a spectrum of public health issues and are likely useful in other applications. However, L. pneumophila is a pertinent issue that demands addressing now and has provided an opportunity to begin developing methods to model these complex communities. The fact that L. pneumophila is such a growing public health risk that evades traditional treatment methods through its ecological interactions, is evidence that it requires a different approach to understanding than previous water borne pathogens have. In the process of better understanding how to 107 predict this pathogen, we will also find better ways to control and mitigate its effects on our health. As public health research progresses further into understanding the complex interactions in microbiological systems, we are being provided with the raw data and knowledge needed to construct more accurate simulations of these systems, well beyond the traditional approach of simple biomasses. Stochastic methods then give us the tools needed to take this raw data and turn it into ever more refined and more-perfect predictive models. Only though increasing our granularity of focus on the actual and complex nature behind the transition of L. pneumophila from a nearly harmless background microbe, into a global health crisis will we be able to gain control over this growing public health issue.

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132 Appendix A. Source Code for Biofilm Simulation Model and Shower QMRA

#======HEADER ======| # LP_biofilm_model_QMRA.R is an R source code developed for David A. Hibler's Thesis on L. pneumo phila in biofilms.| # This source code was developed in May, 2019 - July 2020 by David A. Hibler of the College of Public Health | # The Ohio State University | # This source code simulates a tap water biofilm and models L. pneumophila reproduction and dispe rsal as a result | # of interactions with other microbial organisms in the local ecology, and conducts a QMRA of a 15 m in showering | # event | #______|

#### Initial set up ##### set.seed(37) growth_cycles <- 900 # suggested 900 for 15 min shower event - number of growth cycles run in sec onds, per iteration num_iterations <- 10000 #number of full iterations to run this.dir <- dirname(parent.frame(2)$ofile) setwd(this.dir) dir.create("Testing_Outputs"); setwd("Testing_Outputs")

#### Packages check and instal #### if ("ggplot2" %in% rownames(installed.packages()) ==FALSE){install.packages("ggplot2"); require(ggplot2)} else{require(ggplot2)} if("readxl" %in% rownames(installed.packages())==FALSE){install.packages("readxl", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("readxl")}else{require("readxl")} if("lattice" %in% rownames(installed.packages())==FALSE){install.packages("lattice", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("lattice")}else{require("lattice")} if("gplots" %in% rownames(installed.packages())==FALSE){install.packages("gplots", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("gplots")}else{require("gplots")} if("readr" %in% rownames(installed.packages())==FALSE){install.packages("readr", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("readr")}else{require("readr")} 133 if("dplyr" %in% rownames(installed.packages())==FALSE){install.packages("dplyr", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("dplyr")}else{require("dplyr")} if("scales" %in% rownames(installed.packages())==FALSE){install.packages("scales", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("scales")}else{require("scales")} if("truncnorm" %in% rownames(installed.packages())==FALSE){install.packages("truncnorm", dependencies = TRUE, repos = "http://cran.us.r-project.org"); require("truncnorm")}else{require("truncnorm")}

require('truncnorm') require('moments') require('NMF') require("pheatmap") require("dplyr") library("lattice") library("gplots") library(readr) library(dplyr) library(ggplot2) library(scales) library(truncnorm) require(stats) TriRand <- function(minValue, likeValue, maxValue) { z = runif(1); .Random.seed[1:1]; t = sqrt(z*(maxValue-minValue)*(likeValue-minValue))+minValue tt = maxValue-sqrt((1-z)*(maxValue-minValue)*(maxValue-likeValue)) if (tt < likeValue) {return(t)} else{return(tt)} }

##### Iterations ##### cuml_iterations <- matrix(0, nrow=(num_iterations), ncol=(48)) # colnames(cuml_iterations) <- c("Iteration", "starting_lp", "growth_rate", "decay_rate", "colonization_ rate", "uptake", "cl_inact_bf", "cl_inact_bw", "pipe_l", "pipe_w", "pipe_v", "time_exit", "aerosol_gen_r", " aerosol_remov2", "aerosol_remov10", "frac_aero1", "frac_aero2", "frac_aero3", "frac_aero5", "frac_aer o8", "shower_vol", "IR", "Acidovorax_n", "Acinetobacter_n", "Bacillus_n", "Burkholderia_n","Flavobact erium_n", "Sphingomonas_n", "Stenotrophomonas_n","Aeromonas_n", "Pseudomonas_n","Pseudomon as_p", "NA_P", "NA_F", "NA_K", "Acanthamoeba", "Hartmannella", "Naegleria", "lp_biofilm", "lp_release d", "bac_effect", "amoeba_effect", "tot_effect", "dose", "risk", "ave_lp_sh_l", "risk_pppy","ave_lp_grid") combined_matrixes <- matrix()

for (u in 1:num_iterations) { # iterations of lp growth in biofilm scenarios and showering events

####### Vectors ####### 134 x_colmn <- vector() amoeba_type_r <- vector() amoeba_type <- vector() biofilm_sl <- vector() biofilm_sw <- vector() biofilm_type <- vector() biofilm_type_r <- vector() org_name <- vector() Org_number <- vector() lp_lp <- vector() lp_bacteria <- vector() lp_amoeba <- vector() lp_from_ab <- vector() lp_leave_bf <- vector() lp_tot_inbf <- vector() tot_lp_system <- vector() lp_tot_die <- vector() lp_self_growth <- vector() lp_dep_gr <- vector() growthrate_dif <- vector() cell_inter <- vector() cell_track <- vector() ef_release <- vector() lp_leave_bfjb <- vector() lp_leave_bfja <- vector() lp_leave_bfjlp <- vector() lp_tot_inbfjb <- vector() lp_tot_inbfja <- vector() lp_tot_inbfjlp <- vector() tot_lp_systemjb <- vector() tot_lp_systemja <- vector() tot_lp_systemjlp <- vector() ef_releasejb <- vector() ef_releaseja <- vector() ef_releasejlp <- vector() lp_bulk_sim <- vector() lp_bulk_jbv <- vector() lp_bulk_jav <- vector() lp_bulk_jlpv <- vector() lp_exit <- vector() air_lp_con <- vector() lp_dose <- vector() infect_r <- vector()

####### Variables and matrixies ######## uptake <- ((runif(1, .0273, 97)/100)/60) # uptake rate of LP in amoeba from Mraz 2018, she report ed per min, /60 for per sec pd <- 60 #maximum agar plate diameter in mm, from SOL inhibition measures pipe_l <- round(50, 0) #length of pipe in cm round(time_exit) pipe_w <- 5.08 #width of pipe in cm pipe_circ <- round((pipe_w * (pi)),0) 135 pipe_vol <- round((((pipe_w/2)^2)*(pi)*pipe_l)/100) # pipe volume in Liters pipe_feild <- matrix(nrow=pipe_l, ncol=pipe_circ) #matrix for physical spaced out biofilms pipe_l_pbf <- 500 # length of pipe after biofilm, before exiting system, in cm. pipe_v_pbf <- round(((pipe_w/2)^2)*(pi)*pipe_l_pbf)/1000 # volume in pipes past biofilm before ex iting the system, in Liters flow_r <- 7 # Liters per min time_exit <- pipe_v_pbf/(flow_r/60) # time in system till exit (in sec) at a flow rate of L/min lp <- round(TriRand(1, 5500, 30054))*pipe_vol # the distributions of the L. pneumophila concentrat ions ranging from 1 to 30054 CFU/L, as reported in previous studies - Borella et al., 2004; Borella et a l. 2005; LeChevallier et al. 2019; Totaro et al. 2017, Huang et al. 2020, with mode from Tison, 1983. a dditional ref Prussin, 2017 lp_col <- runif(1, .05, .16) # LP colonization # probability of LP attaching to biofilm - Shen et al. 2015 lp_gr <- (TriRand((0.0203), 0.0463, 1.39)/1000)# range of growth rates from various studies. - Buse and Ashbolt 2011, Cervero-Arago et al. 2015, Sharaby et al. 2017 lp_dieoff <- runif(1,2.8*10^-6,8.3*10^-6) # s^-1 From Mraz 2018, Servais et al. 1985 resid_cl_con <- TriRand(.02, 1, 5) # amount of residual Cl in water, mg/L - Huang et al. 2020, AWWA, 2018 cl_inactivation <- (rnorm(1,-0.06,0.02)*(1/60)) # rate of inactivation per sec. of LP in biofilms due t o residual Cl, from an analysis for chlorine concentrations ranging from 0.02 to 5mg/L, calculated in Huang et al. 2020 (Rhoads et al. 2016, Salehi et al. 2018) bulk_cl_inact <- rnorm(1,-.1,.03)*(1/60) # rate of inactivation per sec. of LP in bulk water do to resid ual Cl, from an analysis for chlorine concentrations ranging from 0.02 to 5mg/L, calculated in Huang et al. 2020 (Rhoads et al. 2016, Salehi et al. 2018) lp_bulk <- lp lp_bulkjb <- 0 lp_bulkja <- 0 lp_bulkjlp <- 0 lp_biofilm <- 0 Heatm_org_eft <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_org_name <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_amb_eft <- matrix(nrow=pipe_l, ncol=pipe_circ) lp_bfeft <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_org1 <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_org2 <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_org3 <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_amb1 <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_amb2 <- matrix(nrow=pipe_l, ncol=pipe_circ) Heatm_amb3 <- matrix(nrow=pipe_l, ncol=pipe_circ) tot_eft <- matrix(nrow=pipe_l, ncol=pipe_circ) lp_count <- matrix(nrow=pipe_l, ncol=pipe_circ) lp_count_loop <- matrix(nrow=pipe_l, ncol=pipe_circ) ww <- matrix(nrow = growth_cycles, ncol = 2) frac_aero1 <- rtruncnorm(1, 0, 1, .038, .054) #Fraction of total aerosolized LP in aerosols of size 0.95 -1.6 um - Huang et al. 2020, Allegra et al. 2016 frac_aero2 <- rtruncnorm(1, 0, 1, .037, .082) #Fraction of total aerosolized LP in aerosols of size 1.6- 2.4 um - Huang et al. 2020, Allegra et al. 2016 frac_aero3 <- rtruncnorm(1, 0, 1, .078, .019) #Fraction of total aerosolized LP in aerosols of size 2.4- 4 um - Huang et al. 2020, Allegra et al. 2016 frac_aero5 <- rtruncnorm(1, 0, 1, .054, .076) #Fraction of total aerosolized LP in aerosols of size 4-6. 8 um - Huang et al. 2020, Allegra et al. 2016 frac_aero8 <- rtruncnorm(1, 0, 1, .15, .054) #Fraction of total aerosolized LP in aerosols of size 6.8-9. 136 92 um - Huang et al. 2020, Allegra et al. 2016 shower_v <- 6 # volume of shower in m^3 - huang et al. 2020, (Davis et al., 2016; Xu and Weisel, 200 3) aerosol_gen <- runif(1, 3.6, 5.7)/60 # aerosol generation rate in shower mg/sec - huang et al. 2020, ( Davis et al., 2016; Xu and Weisel, 2003) ir <- TriRand(.06, .72, 1.5) # inhalation rate in shower, m^3/hr - Huang et al. 2020, (Schoen and Ash bolt, 2011) aerosol_remov2 <- 0.35/60 # Aerosol remaval rate for diameter <= 2 um, durning showering event, first order decay, min^-1, /60 for per sec - Huang et al. 2020, (Davis et al.. 2016; Xu and Weisel 2003) aerosol_remov10 <- 1.24/60 # Aerosol remaval rate for diameter > 2 um, durning showering event, f irst order decay, min^-1, /60 for per sec - Huang et al. 2020, (Davis et al.. 2016; Xu and Weisel 2003) dose_fit <- -0.0599 # Dose-response fitting parameter Huang et al. 2020, (Armstrong and Hass 2007; Muller et al. 1983) lp_num_ext <- 0 ptf2 <- 0 ptf10 <- 0 lp_air <- 0 dose <- 0 infect_risk <- 0 amoeba_n <- 3 #number of amoeba in pipe amoeba_con <- 100 #concentration of amoeba in ml maxiter <- (pipe_l*(pipe_circ)) #number of cells in pipe_feild matrix percell_growth <- matrix(0, nrow=(maxiter), ncol=(growth_cycles)) percell_be <- matrix(0, nrow=(maxiter), ncol=(1)) percell_ae <- matrix(0, nrow=(maxiter), ncol=(1)) percell_te <- matrix(0, nrow=(maxiter), ncol=(1)) lp_tot_bf <- 0 lp_tot_bf_b <- 0 lp_tot_bf_a <- 0 lp_tot_detach <- 0 lp_tot_death <- 0 lp_self_gr <- 0 lp_nat_gr <- 0 lp_inbf <- 0 lp_decay <- 0 combined_ab <- 0 lp_tot_bf_lptot <- 0 nglp_decay <- 0 nglp_inbf <- 0 nglp_tot_death <- 0 lpbiofilm_gr <- 0 lpbiofilm_grjb <- 0 lpbiofilm_grja <- 0 lpbiofilm_grjlp <- 0 lp_detach_bf <- (TriRand((.003/(30*60)),(.02/(2*60)),(.06/(5*60)))*100) # Shen et al. 2017 - Nor malized by number of initially adhered LP, simulated release of LP from drinking water biofilms. Mult iplied by 100 because Shen study reports as number of cells leaving biofilm per mm^2 lp_move <- ((((rnorm(1,44, .46))/6)/10000)/(TriRand(.1, 2, 4)*60*60)) # Rice et al. 2003 - emigrat ion rate of Pseudomonas aeruginosa PAO1 cells being used as a model for bacteria cells in a biofilm - 137 divided by 6 for the 6 sides that the cell could leave. max_con_lp <- runif(1,(1*10^7),(2*10^7)) # determines the maximum concentration of LP allowed i n this section of the biofilm CFU/cm^2 - Van der Kooij et al. 2016 crit_lp_slough <- TriRand((7.8*10^5), (7.8*10^7), (7.8*10^8)) # critical density of lp in CFU/cm^2 f or sloughing event - Schoen et al. 2011 slough_per <- runif(1, .643, .929) # percent of biomass sloughed off calculated in Garny et al. 2009 tot_rels <-0 nglp_tot_detach <- 0 ng_release <- 0 ngtot_rels <- 0 lp_tot_bfjb <- 0 lp_tot_bfja <- 0 lp_tot_bfjlp <- 0 lp_inbfjb <- 0 lp_inbfja <- 0 lp_inbfjlp <- 0 lp_grjb <- 0 lp_grja <- 0 lp_grjlp <- 0 lp_decayjb <- 0 lp_decayja <- 0 lp_decayjlp <- 0 lp_tot_detachjb <- 0 lp_tot_detachja <- 0 lp_tot_detachjlp <- 0 tot_relsjb <- 0 tot_relsja <- 0 tot_relsjlp <- 0 num_detach <- 0 lpinsection <- 0 lp_leave <- 0 lp_leavejb <- 0 lp_leaveja <- 0 lp_leavejlp <- 0 lp_tot_bf_btot <- 0 lp_tot_bf_atot <- 0 depos <- matrix(0, nrow=(maxiter), ncol=(growth_cycles)) # creates matrix of where LP will deposi t in biofilm for each growth cycle # sets up Lp depositing for each growth cycle for (i in 1:growth_cycles){ lp_biofilm <- round(((lp*lp_col)+.4999), digits=0) # selects legionella from system that will coloniz e the biofilm for (b in 1:lp_biofilm){ w <- round(runif(1, 0.5, (maxiter+0.49999999)), digits=0) depos[w,i] <- (depos [w,i] + 1) } }

################### Bacteria and Amoeba Setup ##################### # creates and fills in bacteria and amoeba property matrices for potential assignment in biofilm 138 inh_Acidovorax <- matrix(nrow=maxiter, ncol=3) #1 inh_Acinetobacter <- matrix(nrow=maxiter, ncol=3) #2 inh_Bacillus <- matrix(nrow=maxiter, ncol=3) #3 inh_Burkholderia <- matrix(nrow=maxiter, ncol=3) #4 inh_Flabobacterium <- matrix(nrow=maxiter, ncol=3) #5 inh_Sphingomonas <- matrix(nrow=maxiter, ncol=3) #6 inh_Stenotrophomonas <- matrix(nrow=maxiter, ncol=3) #7 inh_Aeromonas <- matrix(nrow=maxiter, ncol=3) #8 ic_Pseudomonas <- matrix(nrow=maxiter, ncol=3) #9 nic_p <- matrix(nrow=maxiter, ncol=3) #10 nic_f <- matrix(nrow=maxiter, ncol=3) #11 nic_k <- matrix(nrow=maxiter, ncol=3) #12 com_Acanthamoeba <- matrix(nrow=maxiter, ncol=3) #13 com_Hartmannella <- matrix(nrow=maxiter, ncol=3) #14 com_Naegleria <- matrix(nrow=maxiter, ncol=3) #15 for (i in 1:maxiter){ inh_Acidovorax[i,1] <- "Acidovorax -" inh_Acidovorax[i,2] <- 1 iter<- round(runif(1, .5, 1.4999), digits=0) #randomly selects an integer 1-1 if (iter == 1) {inh_Acidovorax[i,3] <- (((((pd^2 * pi)-(10^2 * pi))-((TriRand(10, 15, 20)^2 * pi)-(10 ^2 * pi)))/((pd^2 * pi)-(10^2 * pi))))} # inhibition or commensal effect for this organism -ZOI (ZOI/ 60 gives percent of plate where growth was inhibitd) inh_Acinetobacter[i,1] <- "Acinetobacter -" inh_Acinetobacter[i,2] <- 2 inh_Acinetobacter[i,3] <- (((((pd^2 * pi)-(10^2 * pi))-((TriRand(20, 40, 60)^2 * pi)-(10^2 * pi)))/(( pd^2 * pi)-(10^2 * pi)))) # inhibition or commensal effect for this organism inh_Bacillus[i,1] <- "Bacillus -" inh_Bacillus[i,2] <- 3 inh_Bacillus[i,3] <- (((((pd^2 * pi)-(10^2 * pi))-((TriRand(10, 35, 60)^2 * pi)-(10^2 * pi)))/((pd^2 * pi)-(10^2 * pi)))) # inhibition or commensal effect for this organism -ZOI inh_Burkholderia[i,1] <- "Burkholderia -" inh_Burkholderia[i,2] <- 4 inh_Burkholderia[i,3] <- (((((pd^2 * pi))-((TriRand(5, 7.5, 10)^2 * pi)))/((pd^2 * pi)))) # inhibition or commensal effect for this organism inh_Flabobacterium[i,1] <- "Flavobacterium -" inh_Flabobacterium[i,2] <- 5 inh_Flabobacterium[i,3] <- (((((pd^2 * pi)-(10^2 * pi))-((TriRand(10, 35, 60)^2 * pi)-(10^2 * pi)))/ ((pd^2 * pi)-(10^2 * pi)))) # inhibition or commensal effect for this organism inh_Sphingomonas[i,1] <- "Sphingomonas -" inh_Sphingomonas[i,2] <- 6 iter<- round(runif(1, .5, 1.4999), digits=0) #randomly selects an integer 1-1 if (iter == 1) {inh_Sphingomonas[i,3] <- (((((pd^2 * pi)-(10^2 * pi))-((TriRand(10, 15, 20)^2 * pi)-( 10^2 * pi)))/((pd^2 * pi)-(10^2 * pi))))} # inhibition or commensal effect for this organism -ZOI inh_Stenotrophomonas[i,1] <- "Stenotrophomonas -" inh_Stenotrophomonas[i,2] <- 7 139 inh_Stenotrophomonas[i,3] <- (((((pd^2 * pi))-((TriRand(5, 7.5, 10)^2 * pi)))/((pd^2 * pi)))) # inhi bition or commensal effect for this organism inh_Aeromonas[i,1] <- "Aeromonas -" inh_Aeromonas[i,2] <- 8 iter<- round(runif(1, .5, 3.4999), digits=0) #randomly selects an integer 1-3 if (iter == 1) {inh_Aeromonas[i,3] <- (((((pd^2 * pi)-(10^2 * pi))-((TriRand(10, 35, 60)^2 * pi)-(10 ^2 * pi)))/((pd^2 * pi)-(10^2 * pi))))} if (iter == 2) {inh_Aeromonas[i,3] <- (((((pd^2 * pi)-(6^2 * pi))-((TriRand(14, 32, 50)^2 * pi)-(6^2 * pi)))/((pd^2 * pi)-(6^2 * pi))))} if (iter == 3) {inh_Aeromonas[i,3] <- (((((pd^2 * pi))-((TriRand(5, 7.5, 10)^2 * pi)))/((pd^2 * pi))) )} ic_Pseudomonas[i,1] <- "Pseudomonas -" ic_Pseudomonas[i,2] <- 9 iter<- round(runif(1, .5, 9.4999), digits=0) #randomly selects an integer 1-8 if (iter == 1) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi)-(.01^2 * pi))-((TriRand(8, 34, 60)^2 * pi)-(.01 ^2 * pi)))/((pd^2 * pi)-(.01^2 * pi))))} #inhibition or commensal effect for this organism if (iter == 2) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi)-(6^2 * pi))-((TriRand(17, 23.5, 30)^2 * pi)-(6 ^2 * pi)))/((pd^2 * pi)-(6^2 * pi))))} if (iter == 3) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi))-((TriRand(11, 13, 15)^2 * pi)))/((pd^2 * pi)) ))/64800} if (iter == 4) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi)-(.01^2 * pi))-((TriRand(8, 34, 60)^2 * pi)-(.01 ^2 * pi)))/((pd^2 * pi)-(.01^2 * pi))))} if (iter == 5) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi))-((TriRand(5, 10, 15)^2 * pi)))/((pd^2 * pi))) )/64800} if (iter == 6) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi)-(.01^2 * pi))-((TriRand(8, 34, 60)^2 * pi)-(.01 ^2 * pi)))/((pd^2 * pi)-(.01^2 * pi))))} if (iter == 7) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi))-((TriRand(5, 7.5, 10)^2 * pi)))/((pd^2 * pi)) ))/64800} if (iter == 8) {ic_Pseudomonas[i,3] <- (((((pd^2 * pi)-(.01^2 * pi))-((TriRand(4, 6, 8)^2 * pi)-(.01^2 * pi)))/((pd^2 * pi)-(.01^2 * pi))))} if (iter == 9) { ic_Pseudomonas[i,1] <- "Pseudomonas +" ic_Pseudomonas[i,2] <- 10 ic_Pseudomonas[i,3] <- 1 + (TriRand(0, .352, .704))} # commensal effect for this organism, promot es growth up to 70.4%, nic_p[i,1] <- "NA.P" # non inhibitory or commensal bacteria species nic_p[i,2] <- 10.1 nic_p[i,3] <- 1 # nic_f[i,1] <- "NA.F" # non inhibitory or commensal bacteria species nic_f[i,2] <- 11 nic_f[i,3] <- 1 nic_k[i,1] <- "NA.K" # non inhibitory or commensal bacteria species nic_k[i,2] <- 12 nic_k[i,3] <- 1 com_Acanthamoeba[i,1] <- "Acanthamoeba" com_Acanthamoeba[i,2] <- 13 com_Acanthamoeba[i,3] <- TriRand(1.1, 1.55, 2) # increases growth rate 140 com_Hartmannella[i,1] <- "Hartmannella" com_Hartmannella[i,2] <- 14 com_Hartmannella[i,3] <- TriRand(2.3, 4.3615, 7.6) # increases growth rate com_Naegleria[i,1] <- "Naegleria" com_Naegleria[i,2] <- 15 com_Naegleria[i,3] <- TriRand(4.8, 6, 7.2) # increases growth rate }

############# Create Biofilm Property Matrix ############# biofilm_props <- matrix(0, nrow=(maxiter), ncol=(33)) #matrix with rows of properties for each cel l of biofilm matrix colnames(biofilm_props) <- c("x_cor", "y_cor", "biofilm_org_1", "biofilm_org_1num","effct_biofilm_or g_1", "biofilm_org_2", "biofilm_org_2num","effct_biofilm_org_2", "biofilm_org_3", "biofilm_org_3num", "effct_biofilm_org_3","amoeba_org_1", "amoeba_org_1num", "effct_amoeba_1", "amoeba_org_2", "amo eba_org_2num", "effct_amoeba_2", "amoeba_org_3", "amoeba_org_3num", "effct_amoeba_3", "percent_ biofilm1", "percent_biofilm2", "percent_biofilm3", "con_amoeba_1", "con_amoeba_2", "con_amoeba_3" , "loc_biofilm_cuml_eft", "loc_amoeba_cuml_eft", "Tot_loc_cum_eft", "lp", "released", "max_lpcon", "crit _slough") biofilm_props_jb <- matrix(0, nrow=(maxiter), ncol=(4)) #matrix for only bacterial growth colnames(biofilm_props_jb) <- c("x_cor", "y_cor", "lp", "released") biofilm_props_ja <- matrix(0, nrow=(maxiter), ncol=(4)) #matrix for only amoeba growth colnames(biofilm_props_ja) <- c("x_cor", "y_cor", "lp", "released") biofilm_props_jlp <- matrix(0, nrow=(maxiter), ncol=(4)) #matrix for only normal LP growth colnames(biofilm_props_jlp) <- c("x_cor", "y_cor", "lp", "released")

############# Fill in Biofilm Property Matrix ############# pf_row <- 0 pf_col <- 0 ab_row <- 0 for (i in 1:nrow(biofilm_props)) #sets up vector that will be used to create matrix that will track cell growth over time {cell_track [i] <- 0}

#fills in x and y cor section of biofilm_props matrix for (i in 1:pipe_l) {pf_row <- pf_row + 1 for(j in 1:pipe_circ) { pf_col <- pf_col + 1 ab_row <- ab_row + 1 biofilm_props[ab_row,1] <- pf_row #first column is pipe_feild row numbur biofilm_props[ab_row,2] <- pf_col #second column is pipe_feild column number

biofilm_props_jb[ab_row,1] <- pf_row #first column is pipe_feild row numbur biofilm_props_jb[ab_row,2] <- pf_col #second column is pipe_feild column number

biofilm_props_ja[ab_row,1] <- pf_row #first column is pipe_feild row numbur biofilm_props_ja[ab_row,2] <- pf_col #second column is pipe_feild column number

141 biofilm_props_jlp[ab_row,1] <- pf_row #first column is pipe_feild row numbur biofilm_props_jlp[ab_row,2] <- pf_col #second column is pipe_feild column number } pf_col <- 0 } for (i in 1:maxiter) # assigning dominant biofilm organisms to each cell { #If there is one dominant bacteria in this grid section iter<- round(runif(1, .5, 3.4999), digits=0) #randomly selects an integer 1-3 if (iter == 1) {iter2 <- round(runif(1, .5, 12.4999), digits=0) #randomly selects an integer 1-12 to d etermin single dominant biofilm organism if (iter2 == 1) {biofilm_props[i,3] <- inh_Acidovorax[i,1] # pulls name of biofilm bacteria for this gri d section biofilm_props[i,4] <- inh_Acidovorax[i,2] #pulls number associated with biofilm bacteria for this grid section biofilm_props[i,5] <- inh_Acidovorax[i,3] #pulls effect of biofilm bacteria for this grid section biofilm_props[i,21] <- runif(1, 70, 100)/100} #sets the percent of the biofilm in this area composed of this domminant species if (iter2 == 2) {biofilm_props[i,3] <- inh_Acinetobacter[i,1] biofilm_props[i,4] <- inh_Acinetobacter[i,2] biofilm_props[i,5] <- inh_Acinetobacter[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 3) {biofilm_props[i,3] <- inh_Bacillus[i,1] biofilm_props[i,4] <- inh_Bacillus[i,2] biofilm_props[i,5] <- inh_Bacillus[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 4) {biofilm_props[i,3] <- inh_Burkholderia[i,1] biofilm_props[i,4] <- inh_Burkholderia[i,2] biofilm_props[i,5] <- inh_Burkholderia[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 5) {biofilm_props[i,3] <- inh_Flabobacterium[i,1] biofilm_props[i,4] <- inh_Flabobacterium[i,2] biofilm_props[i,5] <- inh_Flabobacterium[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 6) {biofilm_props[i,3] <- inh_Sphingomonas[i,1] biofilm_props[i,4] <- inh_Sphingomonas[i,2] biofilm_props[i,5] <- inh_Sphingomonas[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 7) {biofilm_props[i,3] <- inh_Stenotrophomonas[i,1] biofilm_props[i,4] <- inh_Stenotrophomonas[i,2] biofilm_props[i,5] <- inh_Stenotrophomonas[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 8) {biofilm_props[i,3] <- inh_Aeromonas[i,1] biofilm_props[i,4] <- inh_Aeromonas[i,2] biofilm_props[i,5] <- inh_Aeromonas[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 9) {biofilm_props[i,3] <- ic_Pseudomonas[i,1] biofilm_props[i,4] <- ic_Pseudomonas[i,2] biofilm_props[i,5] <- ic_Pseudomonas[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 10) {biofilm_props[i,3] <- nic_p[i,1] biofilm_props[i,4] <- nic_p[i,2] biofilm_props[i,5] <- nic_p[i,3] 142 biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 11) {biofilm_props[i,3] <- nic_f[i,1] biofilm_props[i,4] <- nic_f[i,2] biofilm_props[i,5] <- nic_f[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} if (iter2 == 12) {biofilm_props[i,3] <- nic_k[i,1] biofilm_props[i,4] <- nic_k[i,2] biofilm_props[i,5] <- nic_k[i,3] biofilm_props[i,21] <- runif(1, 70, 100)/100} }

#If there are two dominant bacteria in this grid section if (iter == 2){iter2 <- round(runif(1, .5, 12.4999), digits=0) #randomly selects an integer 1-12 to de termine top 2 dominant biofilm organism org_percent <- runif(1, .70, 1.00) org1_percent <- runif(1, .10, .30) org2_percent <- runif(1, 0.01, .10) #first organism if (iter2 == 1) {biofilm_props[i,3] <- inh_Acidovorax[i,1] biofilm_props[i,4] <- inh_Acidovorax[i,2] biofilm_props[i,5] <- inh_Acidovorax[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 2) {biofilm_props[i,3] <- inh_Acinetobacter[i,1] biofilm_props[i,4] <- inh_Acinetobacter[i,2] biofilm_props[i,5] <- inh_Acinetobacter[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 3) {biofilm_props[i,3] <- inh_Bacillus[i,1] biofilm_props[i,4] <- inh_Bacillus[i,2] biofilm_props[i,5] <- inh_Bacillus[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 4) {biofilm_props[i,3] <- inh_Burkholderia[i,1] biofilm_props[i,4] <- inh_Burkholderia[i,2] biofilm_props[i,5] <- inh_Burkholderia[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 5) {biofilm_props[i,3] <- inh_Flabobacterium[i,1] biofilm_props[i,4] <- inh_Flabobacterium[i,2] biofilm_props[i,5] <- inh_Flabobacterium[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 6) {biofilm_props[i,3] <- inh_Sphingomonas[i,1] biofilm_props[i,4] <- inh_Sphingomonas[i,2] biofilm_props[i,5] <- inh_Sphingomonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 7) {biofilm_props[i,3] <- inh_Stenotrophomonas[i,1] biofilm_props[i,4] <- inh_Stenotrophomonas[i,2] biofilm_props[i,5] <- inh_Stenotrophomonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 8) {biofilm_props[i,3] <- inh_Aeromonas[i,1] biofilm_props[i,4] <- inh_Aeromonas[i,2] biofilm_props[i,5] <- inh_Aeromonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 9) {biofilm_props[i,3] <- ic_Pseudomonas[i,1] biofilm_props[i,4] <- ic_Pseudomonas[i,2] biofilm_props[i,5] <- ic_Pseudomonas[i,3] 143 biofilm_props[i,21] <- org1_percent} if (iter2 == 10) {biofilm_props[i,3] <- nic_p[i,1] biofilm_props[i,4] <- nic_p[i,2] biofilm_props[i,5] <- nic_p[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 11) {biofilm_props[i,3] <- nic_f[i,1] biofilm_props[i,4] <- nic_f[i,2] biofilm_props[i,5] <- nic_f[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 12) {biofilm_props[i,3] <- nic_k[i,1] biofilm_props[i,4] <- nic_k[i,2] biofilm_props[i,5] <- nic_k[i,3] biofilm_props[i,21] <- org1_percent} #second organism iter2 <- round(runif(1, .5, 12.4999), digits=0) # randomly selects an integer 1-12 to determine top 2 dominant biofilm organism if (iter2 == 1) {biofilm_props[i,6] <- inh_Acidovorax[i,1] biofilm_props[i,7] <- inh_Acidovorax[i,2] biofilm_props[i,8] <- inh_Acidovorax[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 2) {biofilm_props[i,6] <- inh_Acinetobacter[i,1] biofilm_props[i,7] <- inh_Acinetobacter[i,2] biofilm_props[i,8] <- inh_Acinetobacter[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 3) {biofilm_props[i,6] <- inh_Bacillus[i,1] biofilm_props[i,7] <- inh_Bacillus[i,2] biofilm_props[i,8] <- inh_Bacillus[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 4) {biofilm_props[i,6] <- inh_Burkholderia[i,1] biofilm_props[i,7] <- inh_Burkholderia[i,2] biofilm_props[i,8] <- inh_Burkholderia[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 5) {biofilm_props[i,6] <- inh_Flabobacterium[i,1] biofilm_props[i,7] <- inh_Flabobacterium[i,2] biofilm_props[i,8] <- inh_Flabobacterium[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 6) {biofilm_props[i,6] <- inh_Sphingomonas[i,1] biofilm_props[i,7] <- inh_Sphingomonas[i,2] biofilm_props[i,8] <- inh_Sphingomonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 7) {biofilm_props[i,6] <- inh_Stenotrophomonas[i,1] biofilm_props[i,7] <- inh_Stenotrophomonas[i,2] biofilm_props[i,8] <- inh_Stenotrophomonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 8) {biofilm_props[i,6] <- inh_Aeromonas[i,1] biofilm_props[i,7] <- inh_Aeromonas[i,2] biofilm_props[i,8] <- inh_Aeromonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 9) {biofilm_props[i,6] <- ic_Pseudomonas[i,1] biofilm_props[i,7] <- ic_Pseudomonas[i,2] biofilm_props[i,8] <- ic_Pseudomonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 10) {biofilm_props[i,6] <- nic_p[i,1] 144 biofilm_props[i,7] <- nic_p[i,2] biofilm_props[i,8] <- nic_p[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 11) {biofilm_props[i,6] <- nic_f[i,1] biofilm_props[i,7] <- nic_f[i,2] biofilm_props[i,8] <- nic_f[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 12) {biofilm_props[i,6] <- nic_k[i,1] biofilm_props[i,7] <- nic_k[i,2] biofilm_props[i,8] <- nic_k[i,3] biofilm_props[i,22] <- org2_percent} } #If there are three dominant bacteria in this grid section if (iter == 3) {iter2 <- round(runif(1, .5, 12.4999), digits=0) #randomly selects an integer 1-12 to d etermine top 3 dominant biofilm organism org_percent <- runif(1, .70, 1.00) org1_percent <- runif(1, .10, .30) org2_percent <- runif(1, 0.01, .10) org3_percent <- runif(1, 0.001, .01) #first organism if (iter2 == 1) {biofilm_props[i,3] <- inh_Acidovorax[i,1] biofilm_props[i,4] <- inh_Acidovorax[i,2] biofilm_props[i,5] <- inh_Acidovorax[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 2) {biofilm_props[i,3] <- inh_Acinetobacter[i,1] biofilm_props[i,4] <- inh_Acinetobacter[i,2] biofilm_props[i,5] <- inh_Acinetobacter[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 3) {biofilm_props[i,3] <- inh_Bacillus[i,1] biofilm_props[i,4] <- inh_Bacillus[i,2] biofilm_props[i,5] <- inh_Bacillus[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 4) {biofilm_props[i,3] <- inh_Burkholderia[i,1] biofilm_props[i,4] <- inh_Burkholderia[i,2] biofilm_props[i,5] <- inh_Burkholderia[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 5) {biofilm_props[i,3] <- inh_Flabobacterium[i,1] biofilm_props[i,4] <- inh_Flabobacterium[i,2] biofilm_props[i,5] <- inh_Flabobacterium[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 6) {biofilm_props[i,3] <- inh_Sphingomonas[i,1] biofilm_props[i,4] <- inh_Sphingomonas[i,2] biofilm_props[i,5] <- inh_Sphingomonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 7) {biofilm_props[i,3] <- inh_Stenotrophomonas[i,1] biofilm_props[i,4] <- inh_Stenotrophomonas[i,2] biofilm_props[i,5] <- inh_Stenotrophomonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 8) {biofilm_props[i,3] <- inh_Aeromonas[i,1] biofilm_props[i,4] <- inh_Aeromonas[i,2] biofilm_props[i,5] <- inh_Aeromonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 9) {biofilm_props[i,3] <- ic_Pseudomonas[i,1] 145 biofilm_props[i,4] <- ic_Pseudomonas[i,2] biofilm_props[i,5] <- ic_Pseudomonas[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 10) {biofilm_props[i,3] <- nic_p[i,1] biofilm_props[i,4] <- nic_p[i,2] biofilm_props[i,5] <- nic_p[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 11) {biofilm_props[i,3] <- nic_f[i,1] biofilm_props[i,4] <- nic_f[i,2] biofilm_props[i,5] <- nic_f[i,3] biofilm_props[i,21] <- org1_percent} if (iter2 == 12) {biofilm_props[i,3] <- nic_k[i,1] biofilm_props[i,4] <- nic_k[i,2] biofilm_props[i,5] <- nic_k[i,3] biofilm_props[i,21] <- org1_percent} #second organism iter2 <- round(runif(1, .5, 12.4999), digits=0) # randomly selects an integer 1-12 to determine top 2 dominant biofilm organism if (iter2 == 1) {biofilm_props[i,6] <- inh_Acidovorax[i,1] biofilm_props[i,7] <- inh_Acidovorax[i,2] biofilm_props[i,8] <- inh_Acidovorax[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 2) {biofilm_props[i,6] <- inh_Acinetobacter[i,1] biofilm_props[i,7] <- inh_Acinetobacter[i,2] biofilm_props[i,8] <- inh_Acinetobacter[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 3) {biofilm_props[i,6] <- inh_Bacillus[i,1] biofilm_props[i,7] <- inh_Bacillus[i,2] biofilm_props[i,8] <- inh_Bacillus[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 4) {biofilm_props[i,6] <- inh_Burkholderia[i,1] biofilm_props[i,7] <- inh_Burkholderia[i,2] biofilm_props[i,8] <- inh_Burkholderia[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 5) {biofilm_props[i,6] <- inh_Flabobacterium[i,1] biofilm_props[i,7] <- inh_Flabobacterium[i,2] biofilm_props[i,8] <- inh_Flabobacterium[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 6) {biofilm_props[i,6] <- inh_Sphingomonas[i,1] biofilm_props[i,7] <- inh_Sphingomonas[i,2] biofilm_props[i,8] <- inh_Sphingomonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 7) {biofilm_props[i,6] <- inh_Stenotrophomonas[i,1] biofilm_props[i,7] <- inh_Stenotrophomonas[i,2] biofilm_props[i,8] <- inh_Stenotrophomonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 8) {biofilm_props[i,6] <- inh_Aeromonas[i,1] biofilm_props[i,7] <- inh_Aeromonas[i,2] biofilm_props[i,8] <- inh_Aeromonas[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 9) {biofilm_props[i,6] <- ic_Pseudomonas[i,1] biofilm_props[i,7] <- ic_Pseudomonas[i,2] biofilm_props[i,8] <- ic_Pseudomonas[i,3] 146 biofilm_props[i,22] <- org2_percent} if (iter2 == 10) {biofilm_props[i,6] <- nic_p[i,1] biofilm_props[i,7] <- nic_p[i,2] biofilm_props[i,8] <- nic_p[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 11) {biofilm_props[i,6] <- nic_f[i,1] biofilm_props[i,7] <- nic_f[i,2] biofilm_props[i,8] <- nic_f[i,3] biofilm_props[i,22] <- org2_percent} if (iter2 == 12) {biofilm_props[i,6] <- nic_k[i,1] biofilm_props[i,7] <- nic_k[i,2] biofilm_props[i,8] <- nic_k[i,3] biofilm_props[i,22] <- org2_percent} #Third organism iter2 <- round(runif(1, .5, 12.4999), digits=0) # randomly selects an integer 1-12 to determine top 2 dominant biofilm organism if (iter2 == 1) {biofilm_props[i,9] <- inh_Acidovorax[i,1] biofilm_props[i,10] <- inh_Acidovorax[i,2] biofilm_props[i,11] <- inh_Acidovorax[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 2) {biofilm_props[i,9] <- inh_Acinetobacter[i,1] biofilm_props[i,10] <- inh_Acinetobacter[i,2] biofilm_props[i,11] <- inh_Acinetobacter[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 3) {biofilm_props[i,9] <- inh_Bacillus[i,1] biofilm_props[i,10] <- inh_Bacillus[i,2] biofilm_props[i,11] <- inh_Bacillus[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 4) {biofilm_props[i,9] <- inh_Burkholderia[i,1] biofilm_props[i,10] <- inh_Burkholderia[i,2] biofilm_props[i,11] <- inh_Burkholderia[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 5) {biofilm_props[i,9] <- inh_Flabobacterium[i,1] biofilm_props[i,10] <- inh_Flabobacterium[i,2] biofilm_props[i,11] <- inh_Flabobacterium[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 6) {biofilm_props[i,9] <- inh_Sphingomonas[i,1] biofilm_props[i,10] <- inh_Sphingomonas[i,2] biofilm_props[i,11] <- inh_Sphingomonas[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 7) {biofilm_props[i,9] <- inh_Stenotrophomonas[i,1] biofilm_props[i,10] <- inh_Stenotrophomonas[i,2] biofilm_props[i,11] <- inh_Stenotrophomonas[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 8) {biofilm_props[i,9] <- inh_Aeromonas[i,1] biofilm_props[i,10] <- inh_Aeromonas[i,2] biofilm_props[i,11] <- inh_Aeromonas[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 9) {biofilm_props[i,9] <- ic_Pseudomonas[i,1] biofilm_props[i,10] <- ic_Pseudomonas[i,2] biofilm_props[i,11] <- ic_Pseudomonas[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 10) {biofilm_props[i,9] <- nic_p[i,1] 147 biofilm_props[i,10] <- nic_p[i,2] biofilm_props[i,11] <- nic_p[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 11) {biofilm_props[i,9] <- nic_f[i,1] biofilm_props[i,10] <- nic_f[i,2] biofilm_props[i,11] <- nic_f[i,3] biofilm_props[i,23] <- org3_percent} if (iter2 == 12) {biofilm_props[i,9] <- nic_k[i,1] biofilm_props[i,10] <- nic_k[i,2] biofilm_props[i,11] <- nic_k[i,3] biofilm_props[i,23] <- org3_percent} }

####Amoeba assignment to grid sections iter<- round(runif(1, .5, 3.4999), digits=0) #randomly selects an integer 1-3, for number of possible amoeba in this location if (iter >= 1) { iter2 <- runif(1, 0, 100) #randomly selects the percent chance to assign Amoeba species if ( iter2 <= 3.8) {biofilm_props[i,12] <- com_Acanthamoeba[i,1] # biofilm occurrence of 3.8% Wan g-2012 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas ae ruginosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems biofilm_props[i,13] <- com_Acanthamoeba[i,2] biofilm_props[i,14] <- com_Acanthamoeba[i,3] biofilm_props[i,24] <- TriRand(1, 30000, 82000)} # amoebas biofilm concentration Wang - 2102 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aeruginosa , and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems if ( iter2 > 3.8 && iter2 <= 30.7) {biofilm_props[i,12] <- com_Hartmannella[i,1] # biofilm occurren ce of 26.9% Wang-2012 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aeruginosa, and Amoeba Hosts in Two Chloraminated Drinking Water Distribution Sys tems biofilm_props[i,13] <- com_Hartmannella[i,2] biofilm_props[i,14] <- com_Hartmannella[i,3] biofilm_props[i,24] <- TriRand(1, 1800000, 5320000)}# amoebas biofilm concentration Wang - 21 02 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aerugi nosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems if ( iter2 > 30.7 && iter2 <= 35.11) {biofilm_props[i,12] <- com_Naegleria[i,1] # sample percent De lafont-2013 - Microbiome of free-living amoebae isolated from drinking water biofilm_props[i,13] <- com_Naegleria[i,2] biofilm_props[i,14] <- com_Naegleria[i,3] biofilm_props[i,24] <- runif(1, 1, 104712.8548) # amoebas biofilm concentration in Chinese treated distribution water biofilm, Lin-2014 } if (iter2 > 54.7) {biofilm_props[i,12] <- 0 biofilm_props[i,13] <- 0 biofilm_props[i,14] <- 0 biofilm_props[i,24] <- 0} } if (iter >= 2) { iter2 <- runif(1, 0, 100) #randomly selects the percent chance to assign Amoeba species if ( iter2 <= 3.8) {biofilm_props[i,15] <- com_Acanthamoeba[i,1] # biofilm occurrence of 3.8% Wan g-2012 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas ae ruginosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems 148 biofilm_props[i,16] <- com_Acanthamoeba[i,2] biofilm_props[i,17] <- com_Acanthamoeba[i,3] biofilm_props[i,25] <- TriRand(1, 30000, 82000)} # amoebas biofilm concentration Wang - 2102 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aeruginosa , and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems if ( iter2 > 3.8 && iter2 <= 30.7) {biofilm_props[i,15] <- com_Hartmannella[i,1] # biofilm occurren ce of 26.9% Wang-2012 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aeruginosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Syste ms biofilm_props[i,16] <- com_Hartmannella[i,2] biofilm_props[i,17] <- com_Hartmannella[i,3] biofilm_props[i,25] <- TriRand(1, 1800000, 5320000)}# amoebas biofilm concentration Wang - 21 02 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aerugi nosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems if ( iter2 > 30.7 && iter2 <= 35.11) {biofilm_props[i,15] <- com_Naegleria[i,1] # sample percent De lafont-2013 - Microbiome of free-living amoebae isolated from drinking water biofilm_props[i,16] <- com_Naegleria[i,2] biofilm_props[i,17] <- com_Naegleria[i,3] biofilm_props[i,25] <- runif(1, 1, 104712.8548) # amoebas biofilm concentration in Chinese treated distribution water biofilm, Lin-2014 } if (iter2 > 54.7) {biofilm_props[i,15] <- 0 biofilm_props[i,16] <- 0 biofilm_props[i,17] <- 0 biofilm_props[i,25] <- 0} } if (iter >= 3) { iter2 <- runif(1, 0, 100) #randomly selects the percent chance to assign Amoeba species if ( iter2 <= 3.8) {biofilm_props[i,18] <- com_Acanthamoeba[i,1] # biofilm occurrence of 3.8% Wan g-2012 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas ae ruginosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems biofilm_props[i,19] <- com_Acanthamoeba[i,2] biofilm_props[i,20] <- com_Acanthamoeba[i,3] biofilm_props[i,26] <- TriRand(1, 30000, 82000)} # amoebas biofilm concentration Wang - 2102 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aeruginosa , and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems if ( iter2 > 3.8 && iter2 <= 30.7) {biofilm_props[i,18] <- com_Hartmannella[i,1] # biofilm occuranc e of 26.9% Wang-2012 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aeruginosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Syste ms biofilm_props[i,19] <- com_Hartmannella[i,2] biofilm_props[i,20] <- com_Hartmannella[i,3] biofilm_props[i,26] <- TriRand(1, 1800000, 5320000)}# amoebas biofilm concentration Wang - 21 02 - Molecular Survey of the Occurrence of Legionella spp., Mycobacteriumspp., Pseudomonas aerugi nosa, and Amoeba Hosts in Two Chlorinated Drinking Water Distribution Systems if ( iter2 > 30.7 && iter2 <= 35.11) {biofilm_props[i,18] <- com_Naegleria[i,1] # sample percent De lafont-2013 - Microbiome of free-living amoebae isolated from drinking water biofilm_props[i,19] <- com_Naegleria[i,2] biofilm_props[i,20] <- com_Naegleria[i,3] biofilm_props[i,26] <- runif(1, 1, 104712.8548) # amoebas biofilm concentration in Chinese treated distrobution water biofilm, Lin-2014 } if (iter2 > 54.7) {biofilm_props[i,18] <- 0 149 biofilm_props[i,19] <- 0 biofilm_props[i,20] <- 0 biofilm_props[i,26] <- 0} }

}

# determining max concentration and critical sloughing concentrations of LP in each grid section for (i in 1:nrow(biofilm_props)){ max_con_lp <- runif(1,(1*10^7),(2*10^7)) # determines the maximum concentration of LP allowed i n this section of the biofilm - Van der Kooij et al. 2016 crit_lp_slough <- TriRand((7.8*10^5), (7.8*10^7), (7.8*10^8)) #critical density of lp in CFU/cm^2 fo r sloughing event - Schoen et al. 2011 biofilm_props[i,32] <- max_con_lp biofilm_props[i,33] <- crit_lp_slough } write.csv(biofilm_props, file="biofilm_props.csv") biofilm_props <- read.csv("biofilm_props.csv", header = TRUE) biofilm_props <- biofilm_props <- biofilm_props[ -c(1) ] write.csv(biofilm_props_jb, file="biofilm_props_jb.csv") biofilm_props_jb <- read.csv("biofilm_props_jb.csv", header = TRUE) biofilm_props_jb <- biofilm_props_jb <- biofilm_props_jb[ -c(1) ] write.csv(biofilm_props_ja, file="biofilm_props_ja.csv") biofilm_props_ja <- read.csv("biofilm_props_ja.csv", header = TRUE) biofilm_props_ja <- biofilm_props_ja <- biofilm_props_ja[ -c(1) ] write.csv(biofilm_props_jlp, file="biofilm_props_jlp.csv") biofilm_props_jlp <- read.csv("biofilm_props_jlp.csv", header = TRUE) biofilm_props_jlp <- biofilm_props_jlp <- biofilm_props_jlp[ -c(1) ]

######### Cummulative Microorganism Effects Calculation for each grid section ########## for (i in 1:nrow(biofilm_props)){ #Cummulative effect of local biofilm organisms on legionella biofilm_props$loc_biofilm_cuml_eft[i] <- (1+((biofilm_props$effct_biofilm_org_1[i] - 1) * biofilm_pr ops$percent_biofilm1[i]) + ((biofilm_props$effct_biofilm_org_2[i] - 1) * biofilm_props$percent_biofil m2[i]) + ((biofilm_props$effct_biofilm_org_3[i] - 1) * biofilm_props$percent_biofilm3[i])) #Cummulative effect of local amoeba on legionella if (biofilm_props$effct_amoeba_1[i] == 0 && biofilm_props$effct_amoeba_2[i] == 0 && biofilm_pro ps$effct_amoeba_1[i] == 0) { biofilm_props$loc_amoeba_cuml_eft[i] <- 1 } if (biofilm_props$effct_amoeba_1[i] != 0 || biofilm_props$effct_amoeba_2[i] != 0 || biofilm_props$ef fct_amoeba_1[i] != 0) { biofilm_props$loc_amoeba_cuml_eft[i] <- (((biofilm_props$effct_amoeba_1[i] * ((biofilm_props$co n_amoeba_1[i])/(biofilm_props$con_amoeba_1[i]+biofilm_props$con_amoeba_2[i]+biofilm_props$c on_amoeba_3[i]+(10^-9)))) + (biofilm_props$effct_amoeba_2[i] * ((biofilm_props$con_amoeba_2[i] )/(biofilm_props$con_amoeba_1[i]+biofilm_props$con_amoeba_2[i]+biofilm_props$con_amoeba_3[ i]+(10^-9)))) + (biofilm_props$effct_amoeba_3[i] * ((biofilm_props$con_amoeba_3[i])/(biofilm_pro ps$con_amoeba_1[i]+biofilm_props$con_amoeba_2[i]+biofilm_props$con_amoeba_3[i]+(10^-9))))) 150 ) #+(10^-9)) so that not dividing by 0, and adding 1 because it is an increase in growth rate } #Total cummulative local effect on legionella biofilm_props$Tot_loc_cum_eft[i] <- (exp(biofilm_props$loc_amoeba_cuml_eft[i] * lp_gr)) * (biofilm _props$loc_biofilm_cuml_eft[i]) }

#############LP Colonization and Growth ############## c <- 0 count_it <- 0 track <- cell_track bf_x <- 0 bf_y <- 0 lp_rnd <- 0 lp_tot_bf <- 0 lp_tot_bfjlp <- 0 lp_tot_bfja <- 0 lp_tot_bfjb <- 0 lp_tot_bf_b <- 0 lp_tot_bf_a <- 0 combined_ab <- 0 for (j in 1:growth_cycles) { # run multiple iterations of LP colonizing and growing in biofilm # resetting of appropriate values for each growth cycle lp_bulk <- lp lp_bulk <- (lp_bulk - lp_biofilm) lp_bulkjb <- lp_bulk lp_bulkja <- lp_bulk lp_bulkjlp <- lp_bulk lp_tot_bf_btot <- 0 lp_tot_bf_atot <- 0 lp_tot_bf_lptot <- 0

c <- c+1 print(u) print(j) print(lp_tot_bf) print(lp_tot_bfjlp) print(count_it) lp_tot_bf <- 0 lp_tot_bfjlp <- 0 lp_tot_bfja <- 0 lp_tot_bfjb <- 0 lp_tot_bf_b <- 0 lp_tot_bf_a <- 0 combined_ab <- 0 lp_tot_detach <- 0 lp_tot_death <- 0 lp_nat_gr <- 0 ng_release <- 0

# Calculations for each grid section per growth cycle. 151 for (i in 1:nrow(biofilm_props)){

#Lp depositing onto biofilm biofilm_props$lp[i] <- (biofilm_props$lp[i] + depos[i,j]) # adds LP deposited from water flow for t his growth cycle at this grid space biofilm_props_ja$lp[i] <- (biofilm_props_ja$lp[i] + depos[i,j]) biofilm_props_jb$lp[i] <- (biofilm_props_jb$lp[i] + depos[i,j]) biofilm_props_jlp$lp[i] <- (biofilm_props_jlp$lp[i] + depos[i,j]) percell_growth[i,j] <- biofilm_props$lp[i] # records amount of lp in each cell over each growth cycl e

cell_inter[i] <- 0 if (biofilm_props$lp[i] < 0 ) {biofilm_props$lp[i] <- 0} if (biofilm_props_jb$lp[i] < 0 ) {biofilm_props_jb$lp[i] <- 0} if (biofilm_props_ja$lp[i] < 0 ) {biofilm_props_ja$lp[i] <- 0} if (biofilm_props_jlp$lp[i] < 0 ) {biofilm_props_jlp$lp[i] <- 0}

lp_inbf <- biofilm_props$lp[i] lp_inbfjb <- biofilm_props_jb$lp[i] lp_inbfja <- biofilm_props_ja$lp[i] lp_inbfjlp <- biofilm_props_jlp$lp[i] lp_uptake <- uptake * lp_inbf lp_uptakejb <- uptake * lp_inbfjb lp_uptakeja <- uptake * lp_inbfja lp_uptakejlp <- uptake * lp_inbfjlp

lp_decay <- 0 slough_per <- runif(1, .643, .929) # percent of biomass sloughed off calculated in Garny et al. 2009 - "Sloughing and limited substrate conditions trigger filamentous growth in heterotrophic biofilms-m easurements in flow-through tube reactor"

# calculating bacterial and amoeba effects on growth per grid section per growth cycle. if (biofilm_props$lp[i] > 0 && biofilm_props$lp[i] <= biofilm_props$max_lpcon [i]) { bac_gr <- lp_inbf * (exp(lp_gr)*biofilm_props$loc_biofilm_cuml_eft[i]) am_gr <- 0 if (biofilm_props$loc_amoeba_cuml_eft[i] != 1){ bac_gr <- (lp_inbf - lp_uptake) * (exp(lp_gr)*biofilm_props$loc_biofilm_cuml_eft[i]) am_gr <- lp_uptake * (exp(biofilm_props$loc_amoeba_cuml_eft[i] * lp_gr)) } lp_tot_bf_b <- (bac_gr - (lp_inbf * exp(lp_gr))) # LP growth due to biofilm bacteria effect lp_tot_bf_a <- 0 # LP growth due to amoeba effect if (am_gr > 0) {lp_tot_bf_a <- (am_gr - (lp_uptake * exp(lp_gr))); lp_tot_bf_b <- (bac_gr - ((lp_inbf - lp_uptake) * exp(lp_gr)))} # growth due if amoeba is present lp_nat_gr <- (lp_inbf*exp(lp_gr)) # How much LP would grow without commensal or inhibitory ef fects lp_tot_bf_lptot <- lp_tot_bf_lptot+(lp_inbf*exp(lp_gr)) lp_tot_bf_btot <- lp_tot_bf_btot + lp_tot_bf_b lp_tot_bf_atot <- lp_tot_bf_atot + lp_tot_bf_a combined_ab <- combined_ab + (lp_tot_bf_a + lp_tot_bf_b) #Total growth effect on LP (bacteria + amoeba) }

#assinging growth if not above max concentration 152 if (biofilm_props_jb$lp[i] > 0 && biofilm_props_jb$lp[i] <= biofilm_props$max_lpcon [i]) { lp_grjb <- lp_grjb + (lp_inbfjb*(exp(lp_gr)*biofilm_props$loc_biofilm_cuml_eft[i]))} if (biofilm_props_ja$lp[i] > 0 && biofilm_props_ja$lp[i] <= biofilm_props$max_lpcon [i]) { lp_grja <- lp_grja + (lp_inbfja*(exp(biofilm_props$loc_amoeba_cuml_eft[i] * lp_gr)))} if (biofilm_props_jlp$lp[i] > 0 && biofilm_props_jlp$lp[i] <= biofilm_props$max_lpcon [i]) { lp_grjlp <- lp_grjlp + (lp_inbfjlp*(exp(lp_gr)))}

# LP decay cell_inter[i] <- biofilm_props$lp[i] if (biofilm_props$lp[i] > 0 ) { lp_decay <- ((lp_inbf * lp_dieoff) + (lp_inbf - (lp_inbf * exp(cl_inactivation)))) lp_tot_death <- lp_tot_death + lp_decay } if (biofilm_props_jb$lp[i] > 0 ) { lp_decayjb <- ((lp_inbfjb * lp_dieoff) + (lp_inbfjb - (lp_inbfjb * exp(cl_inactivation)))) } if (biofilm_props_ja$lp[i] > 0 ) { lp_decayja <- ((lp_inbfja * lp_dieoff) + (lp_inbfja - (lp_inbfja * exp(cl_inactivation)))) } if (biofilm_props_jlp$lp[i] > 0 ) { lp_decayjlp <- ((lp_inbfjlp * lp_dieoff) + (lp_inbfjlp - (lp_inbfjlp * exp(cl_inactivation)))) }

# LP migration and leaving biofilm lp_detach_bf <- (TriRand((.003/(30*60)),(.02/(2*60)),(.06/(5*60)))*10) # Shen et al. 2017 - Nor malized by number of initially adhered LP, simulated release of LP from drinking water biofilms. Mult iplied by 10 because Shen study reports as number of cells leaving biofilm per mm lp_move <- ((((rnorm(1,44, .46))/6)/10000)/(TriRand(.1, 2, 4)*60*60)) # Rice et al. 2003 - emigr ation rate of Pseudomonas aeruginosa PAO1 cells being used as a model for bacteria cells in a biofilm - divided by 6 for the 6 sides that the cell could leave. num_detach <- 0 lp_leave <- 0 lp_leavejb <- 0 lp_leaveja <- 0 lp_leavejlp <- 0

if (biofilm_props$lp[i] > 0) { # simulated biofilm lp_move_bf <- biofilm_props$lp[i]*lp_move lp_tot_detach <- lp_tot_detach + (lp_detach_bf * biofilm_props$lp[i]) + lp_move_bf # LP leaving bi ofilm tot_rels <- tot_rels + lp_tot_detach

#add migrating lp cells to adjacent grid locations q <- (i-1) if (q < 1 ) {q <- maxiter} biofilm_props$lp[q] <- biofilm_props$lp[q] + (lp_move_bf) q <- (i+1) if (q > maxiter ) {q <- 1} biofilm_props$lp[q] <- biofilm_props$lp[q] + (lp_move_bf) q <- (i-pipe_circ) if (q < 1 ) {q <- (maxiter + q)} 153 biofilm_props$lp[q] <- biofilm_props$lp[q] + (lp_move_bf) if (q > maxiter ) {q <- (1 + (q - maxiter))} biofilm_props$lp[q] <- biofilm_props$lp[q] + (lp_move_bf)

num_detach <- ((lp_detach_bf * biofilm_props$lp[i])+lp_move_bf) if (biofilm_props$lp[i] > biofilm_props$crit_slough[i]) {num_detach <- ((lp_detach_bf * biofilm_pr ops$lp[i])+lp_move_bf + (biofilm_props$lp[i] * slough_per)) tot_rels <- tot_rels + (biofilm_props$lp[i] * slough_per)} if (num_detach > (biofilm_props$lp[i] - (lp_move_bf*4))) {num_detach <- (biofilm_props$lp[i] - (l p_move_bf*4))} lp_leave <- (((lp_move_bf*4)+(num_detach))) biofilm_props$released[i] <- biofilm_props$released[i] + (num_detach) if (biofilm_props$lp[i] < 0 ) {biofilm_props$lp[i] <- 0} lp_bulk <- (lp_bulk + num_detach) }

if (biofilm_props_jb$lp[i] > 0) { # Just bacteria effect biofilm lp_move_bfjb <- biofilm_props_jb$lp[i]*lp_move lp_tot_detachjb <- lp_tot_detachjb + (lp_detach_bf * biofilm_props_jb$lp[i]) + lp_move_bfjb # LP l eaving biofilm tot_relsjb <- tot_relsjb + lp_tot_detachjb ##add migrating lp cells to adjacent grid locations q <- (i-1) if (q < 1 ) {q <- maxiter} biofilm_props_jb$lp[q] <- biofilm_props_jb$lp[q] + (lp_move_bfjb) q <- (i+1) if (q > maxiter ) {q <- 1} biofilm_props_jb$lp[q] <- biofilm_props_jb$lp[q] + (lp_move_bfjb) q <- (i-pipe_circ) if (q < 1 ) {q <- (maxiter + q)} biofilm_props_jb$lp[q] <- biofilm_props_jb$lp[q] + (lp_move_bfjb) q <- (i+pipe_circ) if (q > maxiter ) {q <- (1 + (q - maxiter))} biofilm_props_jb$lp[q] <- biofilm_props_jb$lp[q] + (lp_move_bfjb)

num_detach <- ((lp_detach_bf * biofilm_props_jb$lp[i])+lp_move_bfjb) if (biofilm_props_jb$lp[i] > biofilm_props$crit_slough[i]) {num_detach <- ((lp_detach_bf * biofilm _props_jb$lp[i])+lp_move_bfjb + (biofilm_props_jb$lp[i] * slough_per)) tot_relsjb <- tot_relsjb + (biofilm_props_jb$lp[i] * slough_per)} if (num_detach > (biofilm_props_jb$lp[i] - (lp_move_bfjb*4))) {num_detach <- (biofilm_props_jb$ lp[i] - (lp_move_bfjb*4))} lp_leavejb <- (((lp_move_bfjb*4)+(num_detach))) biofilm_props_jb$released[i] <- biofilm_props_jb$released[i] + (num_detach) if (biofilm_props_jb$lp[i] < 0 ) {biofilm_props_jb$lp[i] <- 0} lp_bulkjb <- (lp_bulkjb + num_detach) }

if (biofilm_props_ja$lp[i] > 0) { # Just amoeba effect biofilm lp_move_bfja <- biofilm_props_ja$lp[i]*lp_move lp_tot_detachja <- lp_tot_detachja + (lp_detach_bf * biofilm_props_ja$lp[i]) + lp_move_bfja # LP le aving biofilm tot_relsja <- tot_relsja + lp_tot_detachja ##add migrating lp cells to adjacent grid locations 154 q <- (i-1) if (q < 1 ) {q <- maxiter} biofilm_props_ja$lp[q] <- biofilm_props_ja$lp[q] + (lp_move_bfja) q <- (i+1) if (q > maxiter ) {q <- 1} biofilm_props_ja$lp[q] <- biofilm_props_ja$lp[q] + (lp_move_bfja) q <- (i-pipe_circ) if (q < 1 ) {q <- (maxiter + q)} biofilm_props_ja$lp[q] <- biofilm_props_ja$lp[q] + (lp_move_bfja) q <- (i+pipe_circ) if (q > maxiter ) {q <- (1 + (q - maxiter))} biofilm_props_ja$lp[q] <- biofilm_props_ja$lp[q] + (lp_move_bfja)

num_detach <- ((lp_detach_bf * biofilm_props_ja$lp[i])+lp_move_bfja) if (biofilm_props_ja$lp[i] > biofilm_props$crit_slough[i]) {num_detach <- ((lp_detach_bf * biofilm _props_ja$lp[i])+lp_move_bfja + (biofilm_props_ja$lp[i] * slough_per)) tot_relsja <- tot_relsja + (biofilm_props_ja$lp[i] * slough_per)} if (num_detach > (biofilm_props_ja$lp[i] - (lp_move_bfja*4))) {num_detach <- (biofilm_props_ja$l p[i] - (lp_move_bfja*4))} lp_leaveja <- (((lp_move_bfja*4)+(num_detach))) biofilm_props_ja$released[i] <- biofilm_props_ja$released[i] + (num_detach) if (biofilm_props_ja$lp[i] < 0 ) {biofilm_props_ja$lp[i] <- 0} lp_bulkja <- (lp_bulkja + num_detach) }

if (biofilm_props_jlp$lp[i] > 0) { # Just LP natural growth biofilm lp_move_bfjlp <- biofilm_props_jlp$lp[i]*lp_move lp_tot_detachjlp <- lp_tot_detachjlp + (lp_detach_bf * biofilm_props_jlp$lp[i]) + lp_move_bfjlp # L P leaving biofilm tot_relsjlp <- tot_relsjlp + lp_tot_detachjlp ##add migrating lp cells to adjacent grid locations q <- (i-1) if (q < 1 ) {q <- maxiter} biofilm_props_jlp$lp[q] <- biofilm_props_jlp$lp[q] + (lp_move_bfjlp) q <- (i+1) if (q > maxiter ) {q <- 1} biofilm_props_jlp$lp[q] <- biofilm_props_jlp$lp[q] + (lp_move_bfjlp) q <- (i-pipe_circ) if (q < 1 ) {q <- (maxiter + q)} biofilm_props_jlp$lp[q] <- biofilm_props_jlp$lp[q] + (lp_move_bfjlp) q <- (i+pipe_circ) if (q > maxiter ) {q <- (1 + (q - maxiter))} biofilm_props_jlp$lp[q] <- biofilm_props_jlp$lp[q] + (lp_move_bfjlp)

num_detach <- ((lp_detach_bf * biofilm_props_jlp$lp[i])+lp_move_bf) if (biofilm_props_jlp$lp[i] > biofilm_props$crit_slough[i]) {num_detach <- ((lp_detach_bf * biofilm _props_jlp$lp[i])+lp_move_bf + (biofilm_props_jlp$lp[i] * slough_per)) tot_relsjlp <- tot_relsjlp + (biofilm_props_jlp$lp[i] * slough_per)} if (num_detach > (biofilm_props_jlp$lp[i] - (lp_move_bfjlp*4))) {num_detach <- (biofilm_props_jl p$lp[i] - (lp_move_bfjlp*4))} lp_leavejlp <- (((lp_move_bfjlp*4)+(num_detach))) biofilm_props_jlp$released[i] <- biofilm_props_jlp$released[i] + (num_detach) if (biofilm_props_jlp$lp[i] < 0 ) {biofilm_props_jlp$lp[i] <- 0} 155 lp_bulkjlp <- (lp_bulkjlp + num_detach) }

#Total LP growth calculations lpinsection <- 0 if (biofilm_props$lp[i] > 0 ) {lpbiofilm_gr <- ((biofilm_props$lp[i] * exp(lp_gr*biofilm_props$loc_a moeba_cuml_eft[i]) * biofilm_props$loc_biofilm_cuml_eft[i]) - (lp_decay+lp_leave)) if (biofilm_props$loc_amoeba_cuml_eft[i] != 1) { lpbiofilm_gr <- ((((biofilm_props$lp[i] * uptake) * exp(lp_gr*biofilm_props$loc_amoeba_cuml_eft[i])) + (biofilm_props$loc_biofilm_cuml_eft[i] * (exp(l p_gr) * (biofilm_props$lp[i] - (biofilm_props$lp[i] * uptake))))) - (lp_decay+lp_leave)) } lpinsection <- biofilm_props$lp[i] if (biofilm_props$lp[i] <= biofilm_props$max_lpcon [i]) {biofilm_props$lp[i] <- (lpbiofilm_gr)} if (biofilm_props$lp[i] > biofilm_props$max_lpcon [i]) {biofilm_props$lp[i] <- (biofilm_props$lp[i] - (lp_decay+lp_leave))} }

lpinsection <- 0 if (biofilm_props_jb$lp[i] > 0 ) {lpbiofilm_grjb <- ((lp_inbfjb*(exp(lp_gr)*biofilm_props$loc_biofil m_cuml_eft[i])) - (lp_decayjb+lp_leavejb)) lpinsection <- biofilm_props_jb$lp[i] if (biofilm_props_jb$lp[i] <= biofilm_props$max_lpcon [i]) {biofilm_props_jb$lp[i] <- (lpbiofilm_gr jb)} if (biofilm_props_jb$lp[i] > biofilm_props$max_lpcon [i]) {biofilm_props_jb$lp[i] <- (biofilm_props _jb$lp[i] - (lp_decayjb+lp_leavejb))} }

lpinsection <- 0 if (biofilm_props_ja$lp[i] > 0 ) {lpbiofilm_grja <- ((lp_inbfja*(exp(biofilm_props$loc_amoeba_cuml _eft[i] * lp_gr))) - (lp_decayja+lp_leaveja)) lpinsection <- biofilm_props_ja$lp[i] if (biofilm_props_ja$lp[i] <= biofilm_props$max_lpcon [i]) {biofilm_props_ja$lp[i] <- (lpbiofilm_gr ja)} if (biofilm_props_ja$lp[i] > biofilm_props$max_lpcon [i]) {biofilm_props_ja$lp[i] <- (biofilm_props _ja$lp[i] - (lp_decayja+lp_leaveja))} }

lpinsection <- 0 if (biofilm_props_jlp$lp[i] > 0 ) {lpbiofilm_grjlp <- ((lp_inbfjlp*(exp(lp_gr))) - (lp_decayjlp+lp_leav ejlp)) lpinsection <- biofilm_props_jlp$lp[i] if (biofilm_props_jlp$lp[i] <= biofilm_props$max_lpcon [i]) {biofilm_props_jlp$lp[i] <- (lpbiofilm_ grjlp)} if (biofilm_props_jlp$lp[i] > biofilm_props$max_lpcon [i]) {biofilm_props_jlp$lp[i] <- (biofilm_prop s_jlp$lp[i] - (lp_decayjlp+lp_leavejlp))} } lpinsection <- 0

if (biofilm_props$lp[i] < 0 ) {biofilm_props$lp[i] <- 0} if (biofilm_props_jb$lp[i] < 0 ) {biofilm_props_jb$lp[i] <- 0} if (biofilm_props_ja$lp[i] < 0 ) {biofilm_props_ja$lp[i] <- 0} if (biofilm_props_jlp$lp[i] < 0 ) {biofilm_props_jlp$lp[i] <- 0}

156 # Summing growth from individual grid sections lp_tot_bf <- lp_tot_bf + biofilm_props$lp[i] lp_tot_bfjb <- lp_tot_bfjb + biofilm_props_jb$lp[i] lp_tot_bfja <- lp_tot_bfja + biofilm_props_ja$lp[i] lp_tot_bfjlp <- lp_tot_bfjlp + biofilm_props_jlp$lp[i]

}

######## Showering event #########

#####LP exiting shower lp_num_ext <- (((lp_bulk * exp((bulk_cl_inact)*time_exit))/pipe_vol) * (flow_r/60)) #concentration of LP exiting faucet per second

#####LP in aerosolized particles ptf2 <- ((aerosol_gen/((10^6) * aerosol_remov2 * shower_v))*(1 - exp((-1*aerosol_remov2)))) # a ir-water partition function where water density is 10^6 mg/L ptf10 <- ((aerosol_gen/((10^6) * aerosol_remov10 * shower_v))*(1 - exp((-1*aerosol_remov10)))) # air-water partition function where water density is 10^6 mg/L

lp_air <- (lp_num_ext * ((ptf2*(frac_aero1 + frac_aero2)) + (ptf10*(frac_aero3 + frac_aero5 + frac_ aero8)))) + (lp_air - (((lp_air*((frac_aero1 + frac_aero2)/(frac_aero1 + frac_aero2 + frac_aero3 + fr ac_aero5 + frac_aero8)))*exp(-1*aerosol_remov2)) + (lp_air*((frac_aero3 + frac_aero5 + frac_aero8 )/(frac_aero1 + frac_aero2 + frac_aero3 + frac_aero5 + frac_aero8)))*exp(-1*aerosol_remov10)))

##### dose inhaled dose <- dose + (lp_air * (ir/3600)) # dose inhaled per second

#### risk of infection infect_risk <- (1 - exp(dose_fit * dose))

######## Tallying vectors ######## if (count_it > 0 ) { inter_c <- cell_inter track <- cbind(track,inter_c) } count_it <- count_it + 1 x_colmn [c] <- count_it lp_lp [c] <- lp_tot_bf_lptot lp_bacteria [c] <- lp_tot_bf_btot lp_amoeba [c] <- lp_tot_bf_atot lp_from_ab [c] <- combined_ab lp_leave_bf [c] <- lp_tot_detach lp_tot_inbf [c] <- lp_tot_bf tot_lp_system [c] <- lp_tot_bf + lp_tot_detach lp_tot_die [c] <- lp_tot_death lp_self_growth [c] <- lp_nat_gr growthrate_dif [c] <- (lp_tot_bf - lp_tot_bfjlp)

lp_leave_bfjb [c] <- lp_tot_detachjb lp_leave_bfja [c] <- lp_tot_detachja lp_leave_bfjlp [c] <- lp_tot_detachjlp lp_tot_inbfjb [c] <- lp_tot_bfjb 157 lp_tot_inbfja [c] <- lp_tot_bfja lp_tot_inbfjlp [c] <- lp_tot_bfjlp tot_lp_systemjb [c] <- lp_tot_bfjb + lp_tot_detachjb tot_lp_systemja [c] <- lp_tot_bfja + lp_tot_detachja tot_lp_systemjlp [c] <- lp_tot_bfjlp + lp_tot_detachjlp ef_release [c] <- tot_rels ef_releasejb [c] <- tot_relsjb ef_releaseja [c] <- tot_relsja ef_releasejlp [c] <- tot_relsjlp

lp_bulk_sim [c] <- lp_bulk lp_bulk_jbv [c] <- lp_bulkjb lp_bulk_jav [c] <- lp_bulkja lp_bulk_jlpv [c] <- lp_bulkjlp

lp_exit [c]<- lp_num_ext air_lp_con [c] <- lp_air lp_dose [c] <- dose infect_r [c] <- infect_risk }

############# write excel files to record across iterations ##############

combined_matrixes <- as.matrix(data.frame(combined_matrixes, biofilm_props)) write.csv(combined_matrixes, file="combined_matrixes.csv")

write.csv(cuml_iterations, file="cuml_iterations.csv") cuml_iterations <- read.csv("cuml_iterations.csv", header = TRUE) cuml_iterations <- cuml_iterations <- cuml_iterations[ -c(1) ] for (h in 1:nrow(biofilm_props)){ if (biofilm_props$biofilm_org_1[h] == "Acidovorax -") {cuml_iterations$Acidovorax_n[u] <- cuml_i terations$Acidovorax_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Acinetobacter -") {cuml_iterations$Acinetobacter_n[u] <- c uml_iterations$Acinetobacter_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Bacillus -") {cuml_iterations$Bacillus_n[u] <- cuml_iteratio ns$Bacillus_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Burkholderia -") {cuml_iterations$Burkholderia_n[u] <- cu ml_iterations$Burkholderia_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Flavobacterium -") {cuml_iterations$Flavobacterium_n[u] <- cuml_iterations$Flavobacterium_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Sphingomonas -") {cuml_iterations$Sphingomonas_n[u] <- cuml_iterations$Sphingomonas_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Stenotrophomonas -") {cuml_iterations$Stenotrophomona s_n[u] <- cuml_iterations$Stenotrophomonas_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Aeromonas -") {cuml_iterations$Aeromonas_n[u] <- cuml_i terations$Aeromonas_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Pseudomonas -") {cuml_iterations$Pseudomonas_n[u] <- c uml_iterations$Pseudomonas_n[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "Pseudomonas +") {cuml_iterations$Pseudomonas_p[u] <- cuml_iterations$Pseudomonas_p[u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "NA.P") {cuml_iterations$NA_P[u] <- cuml_iterations$NA_P[ u] + biofilm_props$percent_biofilm1[h]} 158 if (biofilm_props$biofilm_org_1[h] == "NA.F") {cuml_iterations$NA_F[u] <- cuml_iterations$NA_F[ u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$biofilm_org_1[h] == "NA.K") {cuml_iterations$NA_K[u] <- cuml_iterations$NA_K [u] + biofilm_props$percent_biofilm1[h]} if (biofilm_props$amoeba_org_1[h] == "Acanthamoeba") {cuml_iterations$Acanthamoeba[u] <- c uml_iterations$Acanthamoeba[u] + ((biofilm_props$con_amoeba_1[h])/(biofilm_props$con_amoeba _1[h]+biofilm_props$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))} if (biofilm_props$amoeba_org_1[h] == "Hartmannella") {cuml_iterations$Hartmannella[u] <- cum l_iterations$Hartmannella[u] + ((biofilm_props$con_amoeba_1[h])/(biofilm_props$con_amoeba_1[h ]+biofilm_props$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))} if (biofilm_props$amoeba_org_1[h] == "Naegleria") {cuml_iterations$Naegleria[u] <- cuml_iterati ons$Naegleria[u] + ((biofilm_props$con_amoeba_1[h])/(biofilm_props$con_amoeba_1[h]+biofilm_p rops$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))}

if (biofilm_props$biofilm_org_2[h] == "Acidovorax -") {cuml_iterations$Acidovorax_n[u] <- cuml_i terations$Acidovorax_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Acinetobacter -") {cuml_iterations$Acinetobacter_n[u] <- c uml_iterations$Acinetobacter_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Bacillus -") {cuml_iterations$Bacillus_n[u] <- cuml_iteratio ns$Bacillus_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Burkholderia -") {cuml_iterations$Burkholderia_n[u] <- cu ml_iterations$Burkholderia_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Flavobacterium -") {cuml_iterations$Flavobacterium_n[u] <- cuml_iterations$Flavobacterium_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Sphingomonas -") {cuml_iterations$Sphingomonas_n[u] <- cuml_iterations$Sphingomonas_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Stenotrophomonas -") {cuml_iterations$Stenotrophomona s_n[u] <- cuml_iterations$Stenotrophomonas_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Aeromonas -") {cuml_iterations$Aeromonas_n[u] <- cuml_i terations$Aeromonas_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Pseudomonas -") {cuml_iterations$Pseudomonas_n[u] <- c uml_iterations$Pseudomonas_n[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "Pseudomonas +") {cuml_iterations$Pseudomonas_p[u] <- cuml_iterations$Pseudomonas_p[u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "NA.P") {cuml_iterations$NA_P[u] <- cuml_iterations$NA_P[ u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "NA.F") {cuml_iterations$NA_F[u] <- cuml_iterations$NA_F[ u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$biofilm_org_2[h] == "NA.K") {cuml_iterations$NA_K[u] <- cuml_iterations$NA_K [u] + biofilm_props$percent_biofilm2[h]} if (biofilm_props$amoeba_org_2[h] == "Acanthamoeba") {cuml_iterations$Acanthamoeba[u] <- c uml_iterations$Acanthamoeba[u] + ((biofilm_props$con_amoeba_2[h])/(biofilm_props$con_amoeba _1[h]+biofilm_props$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))} if (biofilm_props$amoeba_org_2[h] == "Hartmannella") {cuml_iterations$Hartmannella[u] <- cum l_iterations$Hartmannella[u] + ((biofilm_props$con_amoeba_2[h])/(biofilm_props$con_amoeba_1[h ]+biofilm_props$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))} if (biofilm_props$amoeba_org_2[h] == "Naegleria") {cuml_iterations$Naegleria[u] <- cuml_iterati ons$Naegleria[u] + ((biofilm_props$con_amoeba_2[h])/(biofilm_props$con_amoeba_1[h]+biofilm_p rops$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))}

if (biofilm_props$biofilm_org_3[h] == "Acidovorax -") {cuml_iterations$Acidovorax_n[u] <- cuml_i terations$Acidovorax_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Acinetobacter -") {cuml_iterations$Acinetobacter_n[u] <- c 159 uml_iterations$Acinetobacter_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Bacillus -") {cuml_iterations$Bacillus_n[u] <- cuml_iteratio ns$Bacillus_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Burkholderia -") {cuml_iterations$Burkholderia_n[u] <- cu ml_iterations$Burkholderia_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Flavobacterium -") {cuml_iterations$Flavobacterium_n[u] <- cuml_iterations$Flavobacterium_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Sphingomonas -") {cuml_iterations$Sphingomonas_n[u] <- cuml_iterations$Sphingomonas_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Stenotrophomonas -") {cuml_iterations$Stenotrophomona s_n[u] <- cuml_iterations$Stenotrophomonas_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Aeromonas -") {cuml_iterations$Aeromonas_n[u] <- cuml_i terations$Aeromonas_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Pseudomonas -") {cuml_iterations$Pseudomonas_n[u] <- c uml_iterations$Pseudomonas_n[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "Pseudomonas +") {cuml_iterations$Pseudomonas_p[u] <- cuml_iterations$Pseudomonas_p[u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "NA.P") {cuml_iterations$NA_P[u] <- cuml_iterations$NA_P[ u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "NA.F") {cuml_iterations$NA_F[u] <- cuml_iterations$NA_F[ u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$biofilm_org_3[h] == "NA.K") {cuml_iterations$NA_K[u] <- cuml_iterations$NA_K [u] + biofilm_props$percent_biofilm3[h]} if (biofilm_props$amoeba_org_3[h] == "Acanthamoeba") {cuml_iterations$Acanthamoeba[u] <- c uml_iterations$Acanthamoeba[u] + ((biofilm_props$con_amoeba_3[h])/(biofilm_props$con_amoeba _1[h]+biofilm_props$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))} if (biofilm_props$amoeba_org_3[h] == "Hartmannella") {cuml_iterations$Hartmannella[u] <- cum l_iterations$Hartmannella[u] + ((biofilm_props$con_amoeba_3[h])/(biofilm_props$con_amoeba_1[h ]+biofilm_props$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))} if (biofilm_props$amoeba_org_3[h] == "Naegleria") {cuml_iterations$Naegleria[u] <- cuml_iterati ons$Naegleria[u] + ((biofilm_props$con_amoeba_3[h])/(biofilm_props$con_amoeba_1[h]+biofilm_p rops$con_amoeba_2[h]+biofilm_props$con_amoeba_3[h]))}

cuml_iterations$lp_biofilm[u] <- cuml_iterations$lp_biofilm[u] + biofilm_props$lp[h] cuml_iterations$lp_released[u] <- cuml_iterations$lp_released[u] + biofilm_props$released[h] cuml_iterations$bac_effect[u] <- cuml_iterations$bac_effect[u] + biofilm_props$loc_biofilm_cuml_e ft[h] cuml_iterations$amoeba_effect[u] <- cuml_iterations$amoeba_effect[u] + biofilm_props$loc_amoe ba_cuml_eft[h] cuml_iterations$tot_effect[u] <- cuml_iterations$tot_effect[u] + biofilm_props$Tot_loc_cum_eft[h]

percell_be[h,1] <- biofilm_props$loc_biofilm_cuml_eft [h] percell_ae[h,1] <- biofilm_props$loc_amoeba_cuml_eft [h] percell_te[h,1] <- biofilm_props$Tot_loc_cum_eft [h]

} cuml_iterations$growth_rate[u] <- lp_gr cuml_iterations$decay_rate[u] <- lp_dieoff cuml_iterations$pipe_l[u] <- pipe_l cuml_iterations$pipe_w[u] <- pipe_w cuml_iterations$pipe_v[u] <- pipe_vol cuml_iterations$time_exit[u] <- time_exit cuml_iterations$starting_lp[u] <- lp 160 cuml_iterations$dose[u] <- dose cuml_iterations$risk[u] <- infect_risk cuml_iterations$Iteration[u] <- u cuml_iterations$colonization_rate[u] <- lp_col cuml_iterations$cl_inact_bf[u] <- cl_inactivation cuml_iterations$cl_inact_bw[u] <- bulk_cl_inact cuml_iterations$aerosol_gen_r[u] <- aerosol_gen cuml_iterations$shower_vol[u] <- shower_v cuml_iterations$aerosol_remov2[u] <- aerosol_remov2 cuml_iterations$aerosol_remov10[u] <- aerosol_remov10 cuml_iterations$frac_aero1[u] <- frac_aero1 cuml_iterations$frac_aero2[u] <- frac_aero2 cuml_iterations$frac_aero3[u] <- frac_aero3 cuml_iterations$frac_aero5[u] <- frac_aero5 cuml_iterations$frac_aero8[u] <- frac_aero8 cuml_iterations$IR[u] <- ir cuml_iterations$uptake[u] <- uptake cuml_iterations$ave_lp_sh_l[u] <- (cuml_iterations$lp_released[u]/((growth_cycles/60)*flow_r*pip e_vol))*exp(bulk_cl_inact*time_exit) #average lp released in shower per L cuml_iterations$risk_pppy[u] <- (1-((1-infect_risk)^(365))) # per person per year risk cuml_iterations$ave_lp_grid[u] <- (cuml_iterations$lp_biofilm[u]/(pipe_l * pipe_w * (pi))) # Averag e LP in a grid section

write.csv(cuml_iterations, file="cuml_iterations.csv") cuml_iterations <- read.csv("cuml_iterations.csv", header = TRUE) cuml_iterations <- cuml_iterations <- cuml_iterations[ -c(1) ]

write.csv(percell_be, file="percell_be.csv") write.csv(percell_ae, file="percell_ae.csv") write.csv(percell_te, file="percell_te.csv")

write.csv(biofilm_props, file="biofilm_props.csv") write.csv(biofilm_props_jb, file="biofilm_props_jb.csv") write.csv(biofilm_props_ja, file="biofilm_props_ja.csv") write.csv(biofilm_props_jlp, file="biofilm_props_jlp.csv") write.csv(percell_growth, file="percell_growth.csv") percell_growth <- read.csv("percell_growth.csv", header = TRUE)

}

###########Plots############ write.csv(track, file="cell_tracking.csv")

x11() #1 plot(lp_dose, pch = 20, col = "blue", main="1. Dose", xlab="Time", ylab="Dose") dev.copy(png,'Dose.png') dev.off()

x11() #2 plot(infect_r, pch = 20, col = "red", main="2. Risk of Infection", 161 xlab="Time", ylab="Risk") dev.copy(png,'Risk.png') dev.off()

x11() #6a plot(lp_bulk_jav, pch = 20, col = "red", main="6a. L. pneumophila in bulk water, by simulated biofil m (black), by only bacterial effects (green), only amoeba effects (red) and just LP (blue).", xlab="Time", ylab="LP") points(lp_bulk_jbv, pch = 15, col = "green") points(lp_bulk_jlpv, pch = 10, col = "blue") points(lp_bulk_sim, pch = 2, col = "black" ) dev.copy(png,'LP in bulk water -all.png') dev.off()

x11() #7a plot(lp_bulk_jav, pch = 20, col = "red", main="7a. L. pneumophila in bulk water, by simulated biofil m (black), by only bacterial effects (green), only amoeba effects (red) and just LP (blue) (log plot).", xlab="Time", ylab="LP", log="y") points(lp_bulk_jbv, pch = 15, col = "green") points(lp_bulk_jlpv, pch = 10, col = "blue") points(lp_bulk_sim, pch = 2, col = "black" ) dev.copy(png,'LP in bulk water -all.png') dev.off()

x11() #6 plot(ef_releaseja, pch = 20, col = "red", main="6. L. pneumophila released into bulk water, by simul ated biofilm (black), by only bacterial effects (green), only amoeba effects (red) and just LP (blue).", xlab="Time", ylab="LP") points(ef_releasejb, pch = 15, col = "green") points(ef_releasejlp, pch = 10, col = "blue") points(ef_release, pch = 2, col = "black" ) dev.copy(png,'LP in bulk water -all.png') dev.off()

x11() #7 plot(ef_releaseja, pch = 20, col = "red", main="7. L. pneumophila released into bulk water, by simul ated biofilm (black), only bacterial effects (green), only amoeba effects (red) and just L. pneumophila (blue) (log plot).", xlab="Time", ylab="LP", log="y") points(ef_releasejb, pch = 15, col = "green") points(ef_releasejlp, pch = 10, col = "blue") points(ef_release, pch = 2, col = "black") dev.copy(png,'LP in bulk water -all-lp.png') dev.off()

x11() #8 plot(lp_tot_inbfja, pch = 20, col = "red", main="8. Amount of L. pneumophila in biofilm, by simulate d biofilm (black), by only bacterial effects (green), only amoeba effects (red) and just L. pneumophila 162 (blue).", xlab="Time", ylab="LP") points(lp_tot_inbfjb, pch = 15, col = "green") points(lp_tot_inbfjlp, pch = 10, col = "blue") points(lp_tot_inbf, pch = 2, col = "black") dev.copy(png,'LP in biofilm -all.png') dev.off()

x11() #9 plot(lp_tot_inbfja, pch = 20, col = "red", main="9. Amount of L. pneumophila in biofilm, by simulate d biofilm (black), by only bacterial effects (green), only amoeba effects (red) and just LP (blue) (log p lot).", xlab="Time", ylab="LP", log="y") points(lp_tot_inbfjb, pch = 15, col = "green") points(lp_tot_inbfjlp, pch = 10, col = "blue") points(lp_tot_inbf, pch = 2, col = "black") dev.copy(png,'LP in biofilm -all-lp.png') dev.off()

x11() #10 plot(lp_amoeba, pch = 20, col = "red", main="10. Simulation Growth: Normal (black), Due to Bacter ia (green), Due to Amoeba (red)", xlim = c(-1, 1000), ylim = c(-1.5*10^-5, 1.5*10^5), xlab="Time", ylab="LP") points(lp_bacteria, pch = 5, col = "green") points(lp_lp, pch = 2, col = "black") dev.copy(png,'simulation growth break down.png') dev.off()

x11() #33 hist(biofilm_props$lp, main="33. Amount of L. pneumophila in biofilm WITH Microbial effects") dev.copy(png,'Hist - LP in biofilm with effects.png') dev.off()

x11() #34 par(mfrow = c(2,2)) hist(biofilm_props$lp, main="34. Amount of L. pneumophila in biofilm With all microbial effects") hist(biofilm_props_jb$lp, main="Amount of L. pneumophila in biofilm with only bacteria effects") hist(biofilm_props_jlp$lp, main="Amount of L. pneumophila in biofilm with just normal LP growth" ) hist(biofilm_props_ja$lp, main="Amount of L. pneumophila in biofilm with only amoeba effects") dev.copy(png,'Hist - LP in biofilm effects combo.png') dev.off()

x11() #35 par(mfrow = c(2,2)) hist(biofilm_props$released, main="35. Amount of LP released into bulk water per biofilm section With all microbial effects") hist(biofilm_props_jb$released, main="Amount of LP released into bulk water per biofilm section w ith only bacteria effects") 163 hist(biofilm_props_jlp$released, main="Amount of LP released into bulk water per biofilm section with just normal LP growth") hist(biofilm_props_ja$released, main="Amount of LP released into bulk water per biofilm section w ith only amoeba effects") dev.copy(png,'Hist - LP released effects combo.png') dev.off()

x11() #35a par(mfrow = c(2,2)) hist(biofilm_props$released, main="35a. Amount of LP released into bulk water per biofilm section With all microbial effects", ylim = c(0, 100)) hist(biofilm_props_jb$released, main="Amount of LP released into bulk water per biofilm section w ith only bacteria effects", ylim = c(0, 100)) hist(biofilm_props_jlp$released, main="Amount of LP released into bulk water per biofilm section with just normal LP growth", ylim = c(0, 100)) hist(biofilm_props_ja$released, main="Amount of LP released into bulk water per biofilm section w ith only amoeba effects", ylim = c(0, 100)) dev.copy(png,'Hist - LP released effects combo-short.png') dev.off()

x11() #35-cuml par(mfrow = c(2,2)) hist(cuml_iterations$risk, main="35-cuml. Risk") hist(cuml_iterations$growth_rate, main="Base Growth Rate") hist(cuml_iterations$lp_released, main="L. pneumophila Released") hist(cuml_iterations$lp_biofilm, main="L. pneumophila in Biofilm") dev.copy(png,'Hist - cuml_iter.png') dev.off()

#####################Heat Maps########################## library(ggplot2)

######Dispersal of Organisms in Biofilm plots heat_org_out <- ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_p rops$biofilm_org_1)) x11() #36 ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_props$biofilm_org_ 1)) + geom_tile() + theme(axis.text.x = element_text(face = "bold", color = "black", size = 10, angle = 45)) + labs (x = c(expression(bold("Biofil X"))), y = expression(bold("Biofilm Y")), fill="Bacteria 1") + guides (fill = guide_legend(overrride.aes = list(shape = NA))) ggsave("Biofilm_org_HEAT1.png", heat_org_out)

heat_org_out <- ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_pr ops$biofilm_org_2)) x11() #37 ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_props$biofilm_org_ 164 2)) + geom_tile() + theme(axis.text.x = element_text(face = "bold", color = "black", size = 10, angle = 45)) + labs (x = c(expression(bold("Biofil X"))), y = expression(bold("Biofilm Y")), fill="Bacteria 2") + guides (fill = guide_legend(overrride.aes = list(shape = NA))) ggsave("Biofilm_org_HEAT2.png", heat_org_out)

heat_org_out <- ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_p rops$biofilm_org_3))

x11() #38 ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_props$biofilm_org _3)) + geom_tile() + theme(axis.text.x = element_text(face = "bold", color = "black", size = 10, angle = 45)) + labs (x = c(expression(bold("Biofil X"))), y = expression(bold("Biofilm Y")), fill="Bacteria 3") + guides (fill = guide_legend(overrride.aes = list(shape = NA))) ggsave("Biofilm_org_HEAT3.png", heat_org_out)

heat_org_out <- ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_p rops$amoeba_org_1))

x11() #39 ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_props$amoeba_or g_1)) + geom_tile() + theme(axis.text.x = element_text(face = "bold", color = "black", size = 10, angle = 45)) + labs (x = c(expression(bold("Biofil X"))), y = expression(bold("Biofilm Y")), fill="Amoeba 1") + guides (fill = guide_legend(overrride.aes = list(shape = NA))) ggsave("Amoeba_HEAT1.png", heat_org_out)

heat_org_out <- ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_p rops$amoeba_org_2))

x11() #40 ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_props$amoeba_or g_2)) + geom_tile() + theme(axis.text.x = element_text(face = "bold", color = "black", size = 10, angle = 45)) + labs (x = c(expression(bold("Biofil X"))), y = expression(bold("Biofilm Y")), fill="Amoeba 2") + guides (fill = guide_legend(overrride.aes = list(shape = NA))) ggsave("Amoeba_HEAT2.png", heat_org_out)

heat_org_out <- ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_p rops$amoeba_org_3)) 165

x11() #41 ggplot(biofilm_props, aes(biofilm_props$x_cor, biofilm_props$y_cor, fill= biofilm_props$amoeba_or g_3)) + geom_tile() + theme(axis.text.x = element_text(face = "bold", color = "black", size = 10, angle = 45)) + labs (x = c(expression(bold("Biofil X"))), y = expression(bold("Biofilm Y")), fill="Amoeba 3") + guides (fill = guide_legend(overrride.aes = list(shape = NA))) ggsave("Amoeba_HEAT3.png", heat_org_out)

heat_org_out <- ggplot(percell_growth)

#########Biofilm Organisms effects on legionella in biofilm plots a <- 1 b <- 0 for (i in 1:nrow(biofilm_props)) { a <- a b <- b+1 Heatm_org_eft[a,b] <- biofilm_props$loc_biofilm_cuml_eft [i] Heatm_org_name[a,b] <- biofilm_props$biofilm_org_1 [i] Heatm_amb_eft[a,b] <- biofilm_props$loc_amoeba_cuml_eft [i] tot_eft[a,b] <- biofilm_props$Tot_loc_cum_eft [i] lp_bfeft[a,b] <- biofilm_props$loc_biofilm_cuml_eft [i] * biofilm_props$lp[i] lp_count[a,b] <- biofilm_props$lp [i] if (b == ncol(pipe_feild)) {b <- 0; a <- (a+1)} } write.csv(Heatm_org_eft, file="Heatm_org_eft.csv") write.csv(Heatm_org_name, file="Heatm_org_name.csv") write.csv(Heatm_amb_eft, file="Heatm_amb_eft.csv") write.csv(tot_eft, file="tot_eft.csv") write.csv(lp_count, file="lp_count.csv")

hm_org_eft <- read.csv("Heatm_org_eft.csv", header = TRUE) hm_org_name <- read.csv("Heatm_org_name.csv", header = TRUE) hm_amb_eft <- read.csv("Heatm_amb_eft.csv", header = TRUE) hm_org_eft <- hm_org_eft[ -c(1) ] hm_org_name <- hm_org_name[ -c(1) ] hm_amb_eft <- hm_amb_eft[ -c(1) ] hm_orgeft_mtx <- data.matrix(hm_org_eft, rownames.force = NA) hm_ornm_mtx <- data.matrix(hm_org_name, rownames.force = NA) hm_amb_eft_mtx <- data.matrix(hm_amb_eft, rownames.force = NA)

hm_tot_eft <- read.csv("tot_eft.csv", header = TRUE) hm_tot_eft <- hm_tot_eft[ -c(1) ] hm_tot_eft_mtx <- data.matrix(hm_tot_eft, rownames.force = NA)

hm_lp_count <- read.csv("lp_count.csv", header = TRUE) hm_lp_count <- hm_lp_count[ -c(1) ] 166 hm_lp_count_mtx <- data.matrix(hm_lp_count, rownames.force = NA)

hm_percell_growth <- read.csv("percell_growth.csv", header = TRUE) hm_percell_growth <- hm_percell_growth[ -c(1) ] hm_percell_growth_mtx <- data.matrix(hm_percell_growth, rownames.force = NA) hm_percell_be <- read.csv("percell_be.csv", header = TRUE) hm_percell_be <- hm_percell_be[ -c(1) ] hm_percell_be_mtx <- data.matrix(hm_percell_be, rownames.force = NA) hm_percell_ae <- read.csv("percell_ae.csv", header = TRUE) hm_percell_ae <- hm_percell_ae[ -c(1) ] hm_percell_ae_mtx <- data.matrix(hm_percell_ae, rownames.force = NA) hm_percell_te <- read.csv("percell_te.csv", header = TRUE) hm_percell_te <- hm_percell_te[ -c(1) ] hm_percell_te_mtx <- data.matrix(hm_percell_te, rownames.force = NA)

x11() #48 levelplot(hm_orgeft_mtx, colorkey=list(labels=list(cex=1,font=2,col="black"),height=1,width=1.4 ),main=list('Biofilm Bacteria Effect',side=1,line=0.5)) trellis.focus("legend", side="right", clipp.off=TRUE, highlight=FALSE) trellis.unfocus() dev.copy(png,'Hm - bac effect.png') dev.off()

x11() #49 levelplot(hm_amb_eft_mtx, colorkey=list(labels=list(cex=1,font=2,col="black"),height=1,width=1 .4),main=list('Amoeba Effect',side=1,line=0.5)) trellis.focus("legend", side="right", clipp.off=TRUE, highlight=FALSE) trellis.unfocus() dev.copy(png,'Hm - amoeba effect.png') dev.off()

x11() #50 levelplot(hm_lp_count_mtx, colorkey=list(labels=list(cex=1,font=2,col="black"),height=1,width= 1.4),main=list('L.pneumophila in Biofilm',side=1,line=0.5)) trellis.focus("legend", side="right", clipp.off=TRUE, highlight=FALSE) trellis.unfocus() dev.copy(png,'Hm - LP in biofilm.png') dev.off()

x11() #51 levelplot(hm_tot_eft_mtx, colorkey=list(labels=list(cex=1,font=2,col="black"),height=1,width=1. 4),main=list('Effect on L.pneumophila growth after total microbial effect',side=1,line=0.5)) trellis.focus("legend", side="right", clipp.off=TRUE, highlight=FALSE) trellis.unfocus() dev.copy(png,'Hm - effect fon LP growth.png') dev.off()

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