ABSTRACT CRAIG, KELLY. Examination

ABSTRACT

CRAIG, KELLY. Examination of Pseudomonas sp. Response to Industrial Processing Stresses (Under the direction of Dr. Amy Grunden).

Members of the genus Pseudomonas have drawn interest for their biotechnological and agricultural potential along with their medical importance as plant and animal pathogens. Several species of Pseudomonas have been targeted for their ability to repress microbial plant pathogens, insects, and nematodes. To succeed as viable candidates for agricultural applications, biological control strains need to survive the formulation process, prolonged periods of storage, and challenging environmental conditions. During the formulation process, beneficial bacteria can be dried to halt metabolism and improve shelf-life stability, transportability, and ease of application in the field.

A high throughput screening strategy was developed to identify soil-associated microbes capable of surviving drying methods. The microbial diversity of soil bacterial communities was analyzed after exposure to spray drying and oven tray drying. In addition, a Gram-negative bacteria targeted isolation method was established to study the survival capabilities of asporulous

Gram-negative bacteria. Bacillus, a Gram-positive spore forming bacterium, has an advantage in surviving and quickly recovering from harsh drying methods such as spray drying, whereas

Pseudomonas sp. and other Gram-negative bacteria are capable of surviving milder formulation strategies such as oven tray drying.

A diverse set of Pseudomonas species was subjected to heat shock conditions to determine which species are capable of surviving heat shock. A second study assessed the ability of a panel of protectants to improve stress recovery of the Pseudomonas strains. P. thermotolerans and P. aeruginosa were the top two species capable of surviving a 60°C five- minute heat shock. Heat shock surviving strains were enriched with genes encoding for heat shock proteins and universal stress response proteins such as HSP70, GroEL/ES, and clp protease compared to strains that could not survive heat shock. A subset of the Pseudomonas diversity panel was treated with common protectants prior to exposure to heat shock temperatures. Skim milk improved the recovery of the broadest range of Pseudomonas strains subjected to heat shock.

These studies evaluated the capabilities of Pseudomonas species to survive formulation related stress, detected phylogenetic and genotypic predictors of heat shock survival, and determined the highest performing heat shock protectants for a diverse set of Pseudomonas species. It can take years of research to develop an efficacious and reliable product with a long shelf life. Developing a better understanding of optimal drying methods and protectants for

Pseudomonas species will accelerate the capabilities of these microorganisms as biological control products.

by Kelly Craig

A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science

Microbiology

Raleigh, North Carolina 2021

APPROVED BY:

______Dr. Amy Grunden Dr. Jose Bruno-Barcena Committee Chair

______Dr. Paul Hamilton Dr. James Kremer

DEDICATION

I would like to dedicate my thesis research to my husband, Chris, and my parents. Thank you for your never-ending support and encouragement.

BIOGRAPHY

Kelly Craig was born in Sayre, PA to parents Deborah and Brian Craig on February 4th,

1992. She was raised in Cary, NC with her three brothers and graduated from Athens Drive High

School in 2010. Kelly attended North Carolina State University and received her B.S. in agricultural and environmental technologies in 2015. During her final year at North Carolina

State University, Kelly worked at the Plant Disease and Insect Clinic where she discovered her passion for microbiology. After graduating, Kelly was offered a position at an agricultural biotechnology company named AgBiome. Kelly has worked at AgBiome for the past six years focusing on screening for biological control agents against plant pathogens and searching for probiotics to control pathogen outbreaks in newly weened piglets. Kelly joined the microbiology program at North Carolina State University to pursue a master’s degree under the mentorship of

Dr. Amy Grunden. Her research focused on the tolerance of Pseudomonas to heat stress and the translation of these adaptations to surviving the industrial formulation process. Kelly lives in

Durham, NC with her husband Chris and their two kittens, Sylphrena and Nyx.

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ACKNOWLEDGMENTS

First, I would like to thank my committee chair, Dr. Amy Grunden, for her support and guidance throughout my graduate career. You are brilliant, incredibly patient, and I am very grateful for your help over the years. I would also like to thank Dr. Brant Johnson and my committee members, Dr. James Kremer, Dr. Jose Bruno-Barcena, and Dr. Paul Hamilton, for their support and advice. A special thanks to Dr. James Kremer and Dr. Brant Johnson for supporting my graduate efforts at AgBiome. Attending graduate school while remaining full time at AgBiome was not easy and I was only successful because of your encouragement and mentorship.

I have had the opportunity to work with phenomenal scientists at AgBiome. This was my first time venturing into formulations and bioinformatics, and I would like to thank Alex

Schlesinger, Matthew Biggs, Jake Trimble, Chuck Pepe-Ranne, and Jessica Parks for answering my never-ending questions on experimental design and data analysis. Thank you to everyone in

Dr. Grunden’s lab for being so welcoming and supportive: Hannah Wapshott-Stehli, Mara

Cuebas-Irizarry, Jabeen Ahmad, Deaja Sanders, Enrique Garcia, Micaela Robson, and Jason

Whitham.

TABLE OF CONTENTS

LIST OF TABLES ...... viii LIST OF FIGURES ...... ix

Chapter 1: Leveraging Pseudomonas Stress Response Mechanisms for Industrial Applications ...... 1

1.1 Abstract ...... 2 1.2 Introduction ...... 3 1.3 Desiccation Stress ...... 7 1.4 Heat Stress ...... 17 1.5 Cold Stress ...... 27 1.6 General Stress Response ...... 33 1.7 Conclusions and Future Directions ...... 42 1.8 References ...... 44

Chapter 2: Diversity of Soil Bacterial Communities After Exposure to Formulation Stress ...... 62

2.1 Abstract ...... 63 2.2 Introduction ...... 64 2.3 Materials and Methods ...... 66 2.4 Results ...... 74 2.5 Discussion ...... 87 2.6 Conclusions ...... 89 2.7 References ...... 90

Chapter 3: Evaluation of Pseudomonas Heat Shock Survival in Response to Protectant Addition ...... 93

3.1 Abstract ...... 94 3.2 Introduction ...... 95 3.3 Materials and Methods ...... 97 3.4 Results ...... 104 3.5 Discussion ...... 120 3.6 Conclusions ...... 123 3.7 References ...... 124

Appendices ...... 128 Appendix A: Chapter 2 ...... 129 Appendix B: Chapter 3 ...... 134

LIST OF TABLES

Table 1.1 Examples of bacterial survival mechanisms against desiccation, heat, and cold stress ...... 5 Table 2.1 16S rRNA amplicon sequencing counts ...... 79 Table 3.1 Heat shock survivor list for gene set over representation analysis ...... 101 Table 3.2 Stress related genes enriched in the genomes of heat shock survivors ...... 112 Table 3.3 Strain list and impact of protectants on heat shock recovery ...... 116 Table 3.4 Peak biomass, generation time, and recovery time of heat shocked strains ...... 119

LIST OF FIGURES

Figure 1.1 Examples of cellular damage caused by desiccation, heat, and cold stress ...... 4 Figure 1.2 Examples of bacterial strategies to survive desiccation stress ...... 9 Figure 1.3 Examples of bacterial strategies to survive heat stress ...... 18 Figure 1.4 Examples of bacterial strategies to survive cold stress ...... 29 Figure 1.5 Examples of bacterial general stress response ...... 34 Figure 2.1 Formulation experiment design ...... 68 Figure 2.2 Effect of spray drying on the diversity of soil samples ...... 75 Figure 2.3 Effect of incubation time on the diversity of soil samples ...... 76 Figure 2.4 Relative abundance heatmap of top genera ...... 78 Figure 2.5 Bray-Curtis NMDS plot of soil, oven, and no stress samples ...... 80 Figure 2.6 Simpson diversity index of soil, oven, and no stress samples ...... 81 Figure 2.7 Total abundance of Bacillus in soil, oven, and no stress samples ...... 83 Figure 2.8 Total abundance of Pseudomonas in soil, oven, and no stress samples...... 84 Figure 2.9 The relative abundance of phyla for soil, oven, and no stress samples ...... 85 Figure 2.10 Heatmap of the top genera for oven and no stress samples ...... 86 Figure 3.1 Histogram of 55°C and 60°C heat shock survivors...... 105 Figure 3.2 Optical density readings of top survivors after heat shock above 60°C ...... 107 Figure 3.3 Phylogenetic tree of the Pseudomonas diversity panel ...... 109 Figure 3.4 Histogram of the change in strain growth after heat shock with glycerol ...... 117 Figure 3.5 Histogram of the change in strain growth after heat shock with skim milk ...... 118

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CHAPTER 1

Literature Review

Leveraging Pseudomonas Stress Response Mechanisms for Industrial Applications

Kelly Craig¹*, Brant R. Johnson¹, and Amy M. Grunden² ¹AgBiome Inc., 104 T. W. Alexander Drive, Research Triangle Park, NC 27709 ²Department of Plant and Microbial Biology, North Carolina State University, 4550A Thomas Hall, Campus Box 7612, Raleigh, NC, 27695, USA

Accepted for publication in Frontiers of Microbiology

1.1 Abstract

Members of the genus Pseudomonas are metabolically versatile and capable of adapting to a wide variety of environments. Stress physiology of Pseudomonas strains has been extensively studied because of their biotechnological potential in agriculture as well as their medical importance with regards to pathogenicity and antibiotic resistance. This versatility and scientific relevance led to a substantial amount of information regarding the stress response of a diverse set of species such as P. chlororaphis, P. fluorescens, P. putida, P. aeruginosa, and P. syringae. In this review, environmental and industrial stressors including desiccation, heat, and cold stress, are catalogued along with their corresponding mechanisms of survival in

Pseudomonas. Mechanisms of survival are grouped by the type of inducing stress with a focus on adaptations such as synthesis of protective substances, biofilm formation, entering a nonculturable state, enlisting chaperones, transcription and translation regulation, and altering membrane composition. The strategies Pseudomonas strains utilize for survival can be leveraged during the development of beneficial strains to increase viability and product efficacy.

1.2 Introduction

Members of the genus Pseudomonas have drawn interest for their biotechnology and agricultural potential along with their medical importance as plant and human pathogens. There are 122 recognized and validly published species according to the List of Prokaryotic Names with Standing in Nomenclature (Parte 2018). Some of the major species groupings are P. aeruginosa, P. chlororaphis, P. fluorescens, P. putida, and P. syringae (Gomila et al., 2015).

Several species including P. putida and P. fluorescens have been used for degradation of phenol and other environmental pollutants (Wasi et al., 2013). There are also plant beneficial species such as P. chlororaphis that have been utilized as biopesticides to control microbial pathogens, insects, and nematodes (Anderson and Kim, 2018). The plant pathogen P. syringae has been studied for its broad host range in commercial crops and the resulting yield reduction (Valencia-

Botín and Cisneros-López, 2012). The opportunistic pathogen P. aeruginosa is known for infecting immunocompromised individuals and has a natural resistance to many antibiotics

(Priebe and Goldberg, 2014).

Pseudomonas species have been isolated from all over the world from the cold deserts of the trans-Himalayas to deep-sea hydrothermal vents of Juan de Fuca Ridge (Vyas et al., 2009;

Wang et al., 2002). Pseudomonas species have not only been isolated from extreme environments, but they have also been found to colonize and promote plant growth under extreme temperature and drought conditions (Subramanian et al., 2016; Rolli et al., 2015).

Pseudomonas strains are exposed to drought, temperature extremes, and many other environmental stressors in nature and have evolved mechanisms to survive these harsh conditions (Figure 1.1). This review covers the impact of stress exposure on cellular functions and the stress response mechanisms Pseudomonas species have adapted to survive these harsh

conditions (Table 1.1). There is a focus on industrially relevant stresses and formulation strategies to improve viability and efficacy during product development.

Figure 1.1. Examples of cellular damage caused by desiccation, heat, and cold stress. Figure created with BioRender.com.

Table 1.1. Overview of bacterial survival mechanisms against desiccation, heat, and cold stress and examples of industrially relevant stress mitigation processes.

Industrially Effect of Effect of Mechanism of Relevant Stress Stress Resistance References Stress Resistance Mitigation Mechanism Process Accumulation Trehalose replaces Jain and Roy of Compatible water and forms et al. 2009; Addition of Solutes protective matrix. Mensink et al. protectants for Damage to (Trehalose, Glutamate 2017; formulation. cell Alpha- eliminates reactive Mailloux et al. membrane, ketoglutarate) oxygen species. 2009 accumulatio n of reactive Increases persister Fermentation Desiccation cells and forms an contamination López et al. oxygen Biofilms species, and exracellular matrix and 2010 loss of barrier. maintenance. protein Water retention function. Exopolysacch Polymer Gulez et al. and controls aride secretion encapulation for 2014; Chang biofilm (Alginate) formulation. et al. 2007 architecture. Chaperone Alix 2006; (GroEL/GroE Regulation of Georgescauld Correct misfolded S or cyclic et al. 2014; proteins. DnaK/DnaJ/G lipopeptides. Mayer et al. rpE systems) 2000 Meyer and Protease Catalyze the Regulation of Loss of Baker 2011; Systems breakdown of cyclic membrane Richter et al. (ClpAP) proteins. lipopeptides. permeability 2010 Heat , DNA damage, and Narberhaus Regulates Regulation of 2010; Nocker denatured Thermosensor expression of stress rhamnolipid et al. 2001; Proteins. (ROSE) response. production. Vinella et al. 2005

Controls Resistance to Alternative Potvin et al. transcription of desiccation in sigma factors 2008; Thakur stress related soil (sigma 32) et al. 2013 genes. environments.

Table 1.1 (continued).

Control of post- Trevors et al. Cold Shock Destabilize RNA harvest fungal 2012; Proteins (Csps secondary pathogens Nakaminami and Caps) structure. during cold An increase et al. 2006 in membrane storage. rigidity, over Provide freeze Venketesh and stabilized Cold Antifreeze Prevents ice protection to Dayananda RNA, and proteins crystalization. freeze sensitive 2008; Wang et impaired strains. al. 2017 protein folding. Membrane Provide freeze Shivaji and Composition Enhances protection to Prakash 2010; (unsaturated membrane fluidity. freeze sensitive Heipieper et fatty acids) strains. al. 2003 Viable but Ayrapetyan et Low metabolic Inaccurate Non- al. 2018; activity for enumeration of Culturable Lowder et al. survival. viable cells. General State 2000 stress Resistance to response can nutrient-limiting be triggered conditions and Alcántara et Energy storage and General by many Polyphosphate elevated al. 2018; stress regulation. stressors temperatures in Downey 2019 including soil desiccation, environments. heat, or cold. Regulates Stress induced Stringent expression of stress tolerance to Traxler et al., Response response. formulation. 2008

1.3 Desiccation Stress

Semi-arid regions cover approximately 15% of the earth’s land surface, and these regions are predicted to continue expanding due to climate change and desertification (Huang et al.,

2015). Desiccation stress is frequent in semi-arid regions, and it can temporarily occur in other regions due to seasonal changes, droughts, wet-dry cycles, or a number of other natural causes.

Bacteria that are able to survive under these conditions of reduced water availability are called xerotolerant. Some Pseudomonas species have moderate resistance to dehydration including many P. fluorescens endophytes that colonize roots and have developed mechanisms to persist through drought (Schnider-Keel et al., 2001; Pravisya et al., 2018; Ali et al., 2013).

Bacteria can be exposed to desiccation stress in the natural environment, but this stress can also hinder survival during industrial processes necessary for formulating beneficial, pathogen suppressing bacteria for application onto crops. During the formulation process, products containing beneficial bacteria can be dried to halt metabolism and improve shelf-life stability. Spray drying, fluidized bed drying, and freeze drying are all drying methods to preserve a product through the removal of moisture (Berninger et al., 2018). The stress of dehydration is taken into consideration when selecting a formulation process, and protectants can be added to improve viability (Bashan et al., 2014).

Using desiccation as a means to preserve bacteria is a delicate process. Water is critical for survival by providing cell structure and supporting vital molecules including proteins and nucleic acids (Esbelin et al., 2018). Dehydration can result in a loss of membrane integrity, a disruption of major biosynthesis and repair pathways, and a lethal accumulation of reactive oxygen species (Lebre et al., 2017). This common exposure in nature has led to the evolution of

desiccation survival mechanisms in bacteria including the production of compatible solutes and biofilm formation (Figure 1.2).

Figure 1.2. Examples of bacterial strategies to survive desiccation stress. Trehalose-P-synthase and trehalose-P-phosphatase synthesize trehalose. Trehalose protects the cell by encasing biomolecules in a glass sugar matrix and replacing water hydrogen bonds during desiccation.

Alpha ketoglutarate molecules protect the cell by scavenging reactive oxygen species. Alginate, levan, and PSI polysaccharides support biofilm architecture. Figure created with BioRender.com.

1.3.1 Compatible solutes overview

Compatible solutes including simple sugars, heterosides, and amino acids are accumulated intracellularly to protect cells from heating, freezing, desiccation, and oxidative stress (Welsh, 2000). Compatible solutes balance osmotic pressure, help maintain cell turgor pressure, can be used as an energy source, and protect cellular structures against changes in water availability. Studies have been conducted on many Pseudomonas strains demonstrating their ability to synthesize compatible solutes to overcome different types of stress (Kurz et al.,

2010; Sandhya et al., 2010).

1.3.2 Compatible solutes: trehalose

One of the most well studied compatible solutes is the disaccharide trehalose. Trehalose is a stable, colorless non-reducing disaccharide that is used to stabilize products in cosmetics, food, and pharmaceuticals (Schiraldi et al., 2002). There are two proposed mechanisms to explain the desiccation protection properties of trehalose. The first mechanism is that trehalose provides protection by encasing biomolecules in a glass sugar matrix which halts molecular mobility and degradation (Jain and Roy et al., 2009). The second is the water replacement hypothesis which explains that trehalose protects the native protein conformation during drying by replacing the hydrogen bonds formed between water and the protein with hydrogen bonds formed between the trehalose hydroxyl group and the protein (Mensink et al., 2017).

The ability to accumulate and synthesize trehalose has been identified in Pseudomonas and is accomplished through the action of trehalose-P-synthase and trehalose-P-phosphatase enzymes encoded by otsA and otsB homologs (Lee et al., 2005; Park et al., 2007). The accumulation of trehalose by Pseudomonas sp. BCNU 106 was determined by measuring the

total intracellular trehalose content, trehalase activity, and mRNA levels of the trehalose‐ biosynthetic genes (Park et al., 2007). Pseudomonas strains have been screened for the presence of trehalose synthase to produce trehalose from maltose. Trehalose synthase has been discovered in P. stutzeri and P. putida strains, and this enzyme has been cloned and expressed in

Escherichia coli (E. coli) for scaled-up production (Lee et al., 2005; Wang et al., 2014).

Trehalose has been demonstrated to protect Pseudomonas strains from toxic organic solvents, desiccation, salt stress, and other environmental stressors (Park et al., 2007; Freeman et al., 2010;

Mikkat et al., 2000).

1.3.3 Compatible solutes: glutamate

Glutamate and the derivatives of this amino acid play essential roles in nitrogen metabolism and stress tolerance. Alpha ketoglutarate, an intermediate of the TCA Cycle, is utilized by P. fluorescens for detoxification of reactive oxygen species (Mailloux et al., 2009).

Alpha ketoglutarate eliminates reactive oxygen species by reacting with hydrogen peroxide to form succinate, water, and carbon dioxide (Liu et al., 2018). During oxidative stress alpha ketoglutarate dehydrogenase is downregulated, leaving alpha ketoglutarate molecules to scavenge reactive oxygen species and protect the cell (Mailloux et al., 2009).

Glutamine synthetase was downregulated in P. syringae strains in response to osmotic shock (Freeman et al., 2013). Under these conditions, glutamine synthetase would no longer catalyze a conversion of glutamate to glutamine resulting in an accumulation of glutamate.

Freeman and associates theorize glutamate may function as a counterion to positively charged potassium ions before transitioning to glutamine synthesis. This hypothesis for glutamates function in osmotic stress has also been demonstrated in studies conducted on the model

organism E. coli (Cagliero and Jin, 2013). Exogenous potassium ions are brought into the cell to maintain the osmotic or turgor pressure after osmotic stress. To compensate for the net charge increase from the intake of potassium ions, glutamate is accumulated or synthesized so the net charge of the cytoplasm is preserved.

1.3.4 Formulation protectants

Pseudomonas strains have developed numerous mechanisms to survive exposure to stress in the natural environment. The knowledge gained from studying bacterial mechanisms of survival can be applied to industrial processing. Protectants can be externally applied to beneficial strains to increase viability especially during the formulation process. The formulation process is necessary to enhance the storage capability, transportability, and ease of application in the field. Typically, bacterial strains are grown and harvested from broth, protectants are externally added to the harvested cells, then the cells are dried using methods such as spray drying or freeze drying (Berninger et al., 2018). Protectants can also be added to the medium during growth to be taken up and accumulated in the cell for a protective effect.

There are a number of protectants available including sugars (e.g., sucrose, trehalose, maltose, and lactose), polyols (e.g., mannitol, sorbitol, and glycerol), and amino acids (e.g., L- leucine, Glycine, and L-Proline). Research has been conducted to identify optimal protectants for the formulation of Pseudomonas biological control agents. One study was carried out to identify optimal osmoprotectants for three P. fluorescens strains capable of reducing Fusarium dry rot of potatoes (Schisler et al., 2016). The osmoprotectants glucose, fructose, trehalose, raffinose and stachyose were applied to the strains before air drying. Fructose and trehalose were shown to be the most effective osmoprotectants overall though osmoprotectant influence on cell survival

varied between strains. Lactose proved to be an optimal lyoprotectant for freeze drying and cold storage of a P. fluorescens strain utilized for the control of fire blight disease (Cabrefiga et al.,

2014), whereas maltodextrin improved the viability after fluidized bed drying of a P. protegens strain capable of controlling bacterial wilt of tomatoes (Wang et al., 2020a). Elucidation of bacterial mechanisms of stress survival has informed the application of protectants during industrial fermentation and formulation processes and is an area of continued research interest for further refining and improving effectual protectant supplementation.

1.3.5 Biofilms and exopolysaccharide secretion

Biofilms are composed of a complex consortium of microorganisms that are adhered to a surface and are encased in an extracellular matrix. The extracellular matrix is composed of extracellular polymeric substances (EPSs) secreted by the cells living in the biofilm such as polysaccharides, proteins, glycoproteins, glycolipids, and extracellular DNA (López et al., 2010;

Flemming et al., 2007). The EPS primarily functions to maintain biofilm integrity and to provide protection against desiccation and other stresses. Biofilm formation supports the transition of formerly vegetative cells to non-dividing, antibiotic resistant persister cells, which further enhances their resistance to biotic and abiotic stresses (López et al., 2010).

Polysaccharides are a predominant component of biofilms and help facilitate aggregation, adherence, and surface tolerance (Mann and Wozniak, 2012). Alginate is an exopolysaccharide that plays a major role in Pseudomonas biofilm formation by affecting the biofilm structure and stress resistance (Hentzer et al., 2001). Alginate protects cells from water limitation by controlling biofilm architecture and hydration (Gulez et al., 2014). Alginate absorbs and retains water allowing cells to stay hydrated long enough to metabolically adjust to desiccation

conditions. Alginate production during water limiting stress in P. putida strain mt-2 resulted in taller biofilms covering less surface area with a thicker EPS layer compared to the algD mutant strain (Chang et al., 2007). Alginate deficiency was shown to decrease stress tolerance in biocontrol and plant growth promoting strains of P. putida and P. fluorescens (Marshall et al.,

2019; Svenningsen et al., 2018).

Several more polysaccharides are capable of supporting Pseudomonas sp. biofilm integrity including levan and the polysaccharide synthesis locus (psl) dependent polysaccharide.

Levan has been hypothesized as a source of nutrient stores to protect cells within the biofilm from starvation (Mann and Wozniak, 2012). Studies of P. syringae biofilm formation demonstrated an increase in the levan forming enzyme, levansucrase, during planktonic exponential growth and storage of levan in cell-depleted voids within microcolonies (Laue et al.,

2006). The psl-dependent polysaccharide consists of a repeating pentasaccharide containing D‐ mannose, D‐glucose and L‐rhamnose (Byrd et al., 2009). The deletion of psl genes has been demonstrated to dramatically decrease biofilm formation in P. aeruginosa (Ma et al., 2012). In addition, the comparison of alginate or Psl producing strains revealed that overproduction of alginate leads to mucoid biofilms, which occupy more space, while Psl‐dependent biofilms are densely packed.

1.3.6 Biofilm disruption of formulation methods

Detrimental biofilms have become a major problem in healthcare, food, and agricultural industries. The resilience of biofilms to stress has led to significant issues with contaminants persisting through sterilization procedures. P. aeruginosa has caused persistent infections in immunocompromised patients through biofilm formation on medical equipment (Hadi et al.,

2009). P. syringae has been isolated from irrigation water and epilithic biofilms (Morris et al.,

2008). Biofilms can become the source of infection in agricultural fields by contaminating irrigation systems with plant pathogens (Raudales et al., 2014). In addition, biofilms can build up in pipes, tubing, and tanks causing persistent contamination during the fermentation and processing of microbial products (Rich et al., 2015). Many strategies including chelating agents, peptide antibiotics, lantibiotics and synthetic chemical compounds have been developed to control biofilms in industrial processing (Roy et al., 2018).

1.3.7 Alginate encapsulation

Biological control agents can be encapsulated by surrounding the bacteria with coats of material such as polymers to create small capsules (Huq et al., 2013). Encapsulation can be used to protect biological control agents from stress and slowly release the bacteria over longer periods of time. Alginate is a common material for encapsulation because it is biodegradable, nontoxic, and easy to handle (Power et al., 2011). Several P. fluorescens strains have been encapsulated with alginate formulations to study the impact on storage survival and pathogen control (Pour et al., 2019; Kadmiri et al., 2020). P. fluorescens strains VUPF5 and T17-4 were encapsulated with an alginate-gelatin formulation which improved shelf life and control of dry rot induced by Fusarium solani under greenhouse conditions (Pour et al., 2019). The microbial fertilizer P. fluorescens Ms-01 was encapsulated with halloysite and alginate or montmorillonite and alginate formulations (Kadmiri et al., 2020). Both formulations were able to preserve bacterial survival after three months storage at room temperature or 4 °C. Encapsulation of microorganisms in a semipermeable membrane separates and protects the cells from the surrounding environment (Pour et al., 2019). This method protects biological control agents from

abiotic stresses allowing proliferation in the environment and effective deployment against plant pathogens.

1.4 Heat Stress

Bacteria are exposed to heat stress in the natural environment through various events including changes in weather and host responses. Pseudomonas species are typically mesophiles growing from a range of 20°C to 45°C, but there have been a few instances of thermotolerant species such as P. thermotolerans (Manaia et al., 2002). The opportunistic pathogen P. aeruginosa can infect immunocompromised individuals and has been isolated from patients experiencing fevers over 38°C (Han et al., 2019). Pseudomonas strains have been discovered all over the world including areas on the equator with elevated temperatures (Eheth et al., 2019;

Fernandes et al., 2018).

With advancements in food safety and other fields of research, bacteria are exposed to extreme temperatures during the manufacturing of products. Heat treatments involved in pasteurization and sterilization have widely been used for food safety (Cebrian et al., 2017).

Several Pseudomonas species including P. lundensis and P. fragi have heat-resistant proteases responsible for the spoilage of dairy products (Marchand et al., 2009; Stoeckel et al., 2016).

Similarly, beneficial bacteria are exposed to heat during the formulation process to create an easily distributed and shelf-stable dried product. During this process bacteria could be spray dried at temperatures above 100°C, but there are less harsh options available such as freeze drying that may be more suitable for temperature sensitive strains (Anekella and Orsat, 2013).

Heat stress affects many critical bacterial structures including the membrane, DNA, and proteins. Damage to lipopolysaccharides compromises outer and cytoplasmic membrane structures resulting in blebbing, meaning protrusions of the outer membrane, and a loss of permeability (Russell, 2003). The loss of permeability can lead to a leakage of intracellular compounds and an inability to transport substances into the cell. DNA can be impacted from heat

exposure by an increase in mutation frequency and both single and double stranded DNA breaks

(Cebrian et al., 2017). Protein denaturation is another potentially lethal effect of heat stress leaving structural proteins and enzymes damaged. Pseudomonas strains have evolved to rapidly respond to the harsh effects of heat stress through production of chaperones and proteases, and regulating with thermosensors and alternative sigma factors (Figure 1.3).

Figure 1.3. Examples of bacterial strategies to survive heat stress. Chaperones alter aggregated or misfolded proteins into functional proteins. RNA thermosensors regulate the heat shock sigma factor (σ32). Proteasomes break down denatured proteins into polypeptides. Figure created with

BioRender.com.

1.4.1 Chaperone overview

Bacteria must make rapid responses to the changing environment by forming and replacing cellular proteins. Molecular chaperones are proteins that play a critical role in this process by assisting with the correct folding and assembly or disassembly of other proteins

(Lawless and Hubbard, 2014). Chaperones remain important under non-stressed conditions as self-assembly of some proteins occur at slower rates and can lead to misfolding or aggregation of partially folded intermediates (Alix, 2006). A rapid increase in chaperone synthesis occurs under heat and other stress conditions to control the aggregation of denatured proteins.

1.4.2 Chaperone: GroEL/GroES

GroEL and DnaK are two well characterized chaperone systems conserved throughout many bacteria (Singh and Gupta, 2009). The heat shock protein system GroEL/GroES consists of the 60 kDa chaperonin GroEL that forms two stacked rings in a barrel-like structure and a 10 kDa cofactor GroES that forms a single ring structure (Alix, 2006). An unfolded protein binds to the hydrophobic amino acid residues on the interior rim of the GroEL ring opening

(Georgescauld et al., 2014). ATP binds to GroEL inducing a conformational change allowing

GroES to attach and close off GroEL. This formation results in an enclosed cage with a hydrophilic interior leading the unfolded protein to release from the rim and refold. ATP hydrolysis and protein binding to the opposite ring causes GroES and the protein to release.

Research on heat shock response in Pseudomonas has revealed a GroEL/GroES system similar to the system found in the model organism E. coli (Ito et al., 2014). The GroES/GroEL system was found in P. aeruginosa PAO1 through S1 nuclease mapping and northern hybridization with a two-fold upregulation under heat shock (Fujita et al., 1998). The groES and

groEL region is conserved amongst Pseudomonas species. In fact, the 536 bp region of the groE gene was used to develop a diagnostic PCR assay for detection of P. aeruginosa, P. putida, P. fluorescens and P. stutzeri (Clarke et al., 2003).

1.4.3 Chaperone: DnaK/DnaJ/GrpE system

The DnaK, DnaJ, and GrpE chaperone system prevents proteins that are damaged or stuck in an intermediate folded state from aggregating. The system consists of the chaperone

DnaK, the co-chaperone DnaJ, and the nucleotide exchange factor GrpE. DnaK is a 70 kDa chaperone with an ATP-binding N-terminal, a substrate binding domain, and a peptide binding

C-terminal domain (Mayer et al., 2000). The hydrophobic amino acid residue of misfolded proteins will bind to the substrate binding domain of DnaK. DnaJ will promote hydrolysis of

ATP to ADP at the N-terminal ATPase domain (Alderson et al., 2016). When ADP is bound, the

C-terminal domain locks down on the protein by closing off the substrate-binding site. DnaJ dissociates and the complex will continue to hold the protein. GrpE binds leading to a conformational change that releases ADP and shifts DnaK to an open conformation.

Several studies have been conducted to better understand the role of DnaK in the

Pseudomonas heat shock response. Transcriptome analysis was performed on P. aeruginosa

PAO1 grown at human body temperature (37°C) and an elevated temperature (46°C) (Chan et al., 2016). RNA sequencing revealed 133 genes were differentially expressed including the upregulation of dnaK, dnaJ and groEL at 46°C compared to human body temperature. The heat shock response of P. putida KT2442 was elucidated by constructing and testing null mutants of molecular chaperone genes (Ito et al., 2014). The P. putida KT2442 dnaJ mutant was

temperature-sensitive and formed more protein aggregates in response to heat stress compared to the wild-type.

1.4.4 Protease overview

Proteases are crucial for rapid responses to environmental changes when damaged proteins cannot be salvaged by chaperones or other heat shock proteins. Proteases are enzymes that catalyze the breakdown of proteins into amino acids or smaller polypeptides by cleaving peptide bonds. They contribute to the heat shock response by removing misfolded or damaged proteins and recovering the amino acids (Meyer and Baker, 2011). Proteases will also degrade functional proteins such as sigma factors, metabolic enzymes and structural proteins that are no longer needed as cells adapt to changes (Bittner et al., 2016; Kirstein et al., 2009). The Clp protease family is a highly conserved system that is vital for stress survival in many bacteria, including Pseudomonas species.

Clp complexes contain chaperones and proteases that belong to the heat shock protein family HSP100 and the ATPases Associated with diverse cellular Activities (AAA+) protein superfamily. The AAA+ superfamily has a highly conserved AAA+ module consisting of 250 amino acids, and they typically have motifs involved in ATP binding and hydrolysis (Ogura and

Wilkinson, 2001). ATP-dependent Clp protease (ClpP) consists of a ClpP serine peptidase subunit and a AAA+ ATPase subunit such as ClpA, ClpC, or ClpX (Richter et al., 2010). ClpP forms a barrel-like structure with two stacked heptameric rings of the ClpP serine peptidase subunit and is enclosed by one or two hexameric rings of the AAA+ ATPase subunit. The Clp

ATPase subunit uses ATP hydrolysis to unfold protein substrates then directs them through a central pore to the proteolytic chamber of ClpP for degradation (Snider et al., 2008).

Clp Peptidase has been found to control diverse aspects of cellular physiology in

Pseudomonas species. The proteolysis activity of ClpP helps regulate the stress response sigma factor RpoS (σ38) and other transcriptional regulators influencing many factors including motility, surface attachment, and antimicrobial activity (Bruijn and Raaijmakers, 2009). P. aeruginosa was found to have a second ClpP isomer which impacts stationary-phase cells, microcolony organization and biofilm formation (Hall et al., 2017). ClpP, clpX and clpP2 null mutants of P. aeruginosa PAO581 were created to study the regulation of exopolysaccharide alginate overproduction and the conversion to a mucoid phenotype which are markers for the onset of chronic lung infection in cystic fibrosis patients (Qiu et al., 2008). All null mutants demonstrated a decrease in transcriptional activity of the extracytoplasmic function sigma factor

AlgU which is responsible for alginate overproduction. The clpP and clpX null mutants were unable to maintain the mucoid phenotype.

1.4.5 Regulation by chaperones and proteases

Chaperones and Proteases such as DnaK and Clp control the regulation of cyclic lipopeptides in many Pseudomonas species. Cyclic lipopeptides have a role in biofilm formation, surface motility, and antimicrobial activity (Dubern et al., 2005). Putisolvin and massetolide have activity against oomycete plant pathogens by causing lysis of zoospores (Raaijmakers et al.,

2010). The DnaK complex is hypothesized to regulate putisolvin biosynthesis by controlling activity of other regulators or assisting with the assembly of putisolvin peptide synthetases. ClpP regulates massetolide biosynthesis by degradation of putative transcriptional repressors of massetolide biosynthesis genes (Bruijn et al., 2009). A range of growth temperatures (32°C,

28°C, 21°C, 16°C, and 11°C) were tested on a P. Putida strain and three dnaK, dnaJ and grpE

mutants to determine the effect on putisolvin production (Dubern et al., 2005). It was shown that putisolvin production decreased at high growth temperatures, but putisolvin production increased at low temperatures. DnaK and DnaJ are required to produce putisolvins at low temperatures.

These studies indicate that growth temperature can influence DnaK and DnaJ regulation of putisolvin synthesis, and growth at low temperatures may enhance putisolvin production.

Chaperones and proteases are key components to survival and efficacy for many Pseudomonas species. Understanding regulatory effects of these proteins is crucial to the development of

Pseudomonas strains as biocontrol products.

1.4.6 Thermosensors

Adverse environmental conditions such as elevated temperatures can damage proteins requiring molecular chaperones or proteases to help repair cells. Thermosensors are RNA, DNA, or protein molecules that react to changes in environmental conditions and regulate gene expression of the heat shock response and other stress responses. Temperature shifts cause thermosensors to change their secondary structure, and this change typically either exposes or blocks nucleic acid regions influencing expression of a gene. DNA topology can act as a stress sensor because transcription efficiency is sensitive to changes in DNA supercoiling (Klinkert and

Narberhaus, 2009). RNA thermosensors are translational control elements that are mostly located in the 5′ untranslated region (UTR) of messenger RNA encoding heat shock proteins

(Narberhaus, 2010). There are a diverse set of protein thermosensors including transcriptional repressors, sensor kinases, chaperones, and proteases (Collin and Schuch et al., 2009). The diversity is due to the sensitivity of tertiary and quaternary protein structures to temperature stress (Klinkert and Narberhaus, 2009).

Repression of heat shock gene expression (ROSE) elements are RNA thermosensors located in the 5′ untranslated region (UTR) of some heat shock protein mRNA (Nocker et al.,

2001). ROSE elements structures consist of two to four stem loops and they are characterized by a conserved G residue that pairs with the Shine Dalgarno sequence (AGGA) (Klinkert and

Narberhaus, 2009). Under normal conditions, the conserved G residue opposite the Shine

Dalgarno sequence closes the loop by a weak GG base pair preventing gene expression. The weak GG base pair breaks under temperature stress allowing the ribosome binding site to be accessed for translation.

Structural and functional analysis has uncovered the ROSE elements’ role in heat shock and virulence regulation for Pseudomonas species. P. putida and P. aeruginosa contain a simpler

ROSE element with only two hairpins compared to previously discovered Rhizobium sp. ROSE structures with three to four hairpin structures (Krajewski et al., 2013; Nocker et al., 2001). The

ROSE element precedes an ibpA gene which encodes the small heat shock protein IbpA

(inclusion body associated protein A). High sequence conservation of ibpA untranslated regions suggests ROSE thermosensors could be common for IbpA regulation in Pseudomonas species.

ROSE elements have also been studied for the temperature induced regulation of rhamnolipid production in Pseudomonas species. Bioremediation strains of Pseudomonas produce rhamnolipids which facilitate the uptake and degradation of hydrocarbons such as crude oil in polluted environments (Chrzanowski et al., 2012). P. aeruginosa is one of the most competent rhamnolipid producers and safe methods to produce rhamnolipids are being explored including strain engineering to diminish pathogenicity (Chong and Li, 2017). P. putida KT2440 rhamnolipid production increased by more than 60% when growth temperature was increased

from 30°C to 37°C (Noll et al., 2019). These studies demonstrate the potential to design a process for rhamnolipid production linked to temperature and ROSE regulation.

1.4.7 RNA polymerase sigma factors

Sigma factors are essential for regulation of both essential genes and conditional responses such as heat or cold shock. A sigma factor is a protein required for transcription initiation that allows RNA polymerase to bind to specific gene promoters. Primary sigma factor

(σ70) is named for its 70 kDa molecular weight, and it is responsible for regulation of essential genes during exponential growth. Sigma factor 70 binds to RNA polymerase allowing promoter recognition at -10 and -35 nucleotides upstream of the start site (Potvin et al., 2008). Alternative sigma factors are needed for transcription of genes linked to environmental or physiological changes. In response to stress RNA polymerases are redirected by alternative sigma factors to activate transcription of genes to help the cell respond to new conditions.

Genome analysis of P. aeruginosa PAO1 and P. syringae pv. syringae B728a revealed the presence of 24 and 15 putative sigma factors, respectively, including primary sigma factor

(σ70), heat shock sigma factor (σ32), stationary phase sigma factor (σ38), and nitrogen- limitation sigma factor (σ54) (Potvin et al., 2008; Thakur et al., 2013). The majority were classified as extracytoplasmic function (ECF) sigma factors which are involved in the regulation of a diverse set of functions including iron starvation, cell envelope stress, solvent tolerance, and oxidative stress (Otero-Asman et al., 2019). Stationary phase sigma factor RpoS (σ38) is a master stress response regulator active during stationary phase to protect against heat shock, starvation, and osmotic shock (Schuster et al., 2004). The heat shock sigma factor RpoH (σ32) was upregulated as temperature increased from 30°C to 42°C in P. aeruginosa (Nakahigashi et

al., 1998). RpoH regulates heat shock genes responsible for the expression of chaperones and proteases such as GroEL, GroES, DnaK, DnaJ, GrpE, and ClpP (Aramaki et al., 2001; Potvin et al., 2008). Sigma Factor AlgU (σ22) mutant strains of Pseudomonas fluorescens CHA0 were studied to determine the impact of AlgU on environmental stress survival (Schnider-Kell et al.,

2001). Schnider and colleagues found that AlgU is critical for survival against desiccation and hyperosmolarity in soil environments. Alternative sigma factors are critical to survival against major industrial and environmental stresses.

1.5 Cold Stress

Cold climates are widespread on earth, with ice covering 10% of the earth’s surface and a large percentage of the biosphere existing at temperatures below 15°C (Kuhn et al., 2012).

Bacteria that can colonize these extreme environments and grow at -20°C to 20°C are called psychrophiles (De Maayer et al., 2014). Several Pseudomonas psychrophiles have been isolated from glaciers in Antarctica and the Arctic Circle such as P. antarctica (Lee et al., 2017; Dziewit et al., 2013; Villeret et al., 1997). Cold stress survival is of particular importance for the control of post-harvest fruit and vegetable diseases. Many Pseudomonas species are adapted to cold stress and effectively control post-harvest fungal pathogens during cold storage (Aiello et al.,

2019; Wallace et al., 2017). The strain P. syringae ESC-10 was able to survive 30 days of 2°C cold storage and prevented post-harvest blue mold on apples (Errampalli and Brubacher, 2006).

Many strains of Pseudomonas, such as P. syringae ESC-10, are psychrotrophs or mesophiles that grow at more moderate temperatures and have adapted mechanisms to survive exposure to cold stress.

Freezing cultures, using processes such as freeze drying, is one of the most common methods for preserving bacterial inoculants. Freeze drying is a freezing and dehydration preservation technique that involves freezing, lowering pressure and removing ice through sublimation (Fellows, 2009). The food industry and the pharmaceutical industry rely on freeze drying for starter cultures, drug delivery, and long-term storage in culture collections.

Manufacturing of commercial foods requires vast amounts of starter cultures with cheese production alone requiring approximately one billion liters of bulk starter per year (Champagne et al., 1991). Freeze drying of biocontrol Pseudomonas strains has been explored as a method to deliver viable cells for crop protection (Cabrefiga et al., 2014).

Cold stress drastically affects cellular physiology by damaging the cell membrane, impairing protein folding and hindering transcription and translation (Kumar et al., 2020). Rapid chilling can induce phase separation of phospholipids within the lipid bilayer leading to membrane permeability and consequently cell death (Cao-Hoang et al., 2008). At low temperatures the secondary structures of RNA stabilizes which slows down transcription elongation and ribosomal movement on RNA (Phadtare, 2004). Cold tolerant microbes can survive these harsh conditions by adjusting the fatty acid composition in the membrane, producing specialized enzymes that are active at low temperatures, and by creating cryoprotective biomolecules (Figure 1.4).

Figure 1.4. Examples of bacterial strategies to survive cold stress. The membrane is altered by increasing the ratio of unsaturated fatty acids to saturated fatty acids. Antifreeze proteins are produced to prevent ice recrystallization. Cold shock proteins (Csps) and cold adaptive proteins

(Caps) destabilize RNA to prevent premature transcription termination. Figure created with

BioRender.com.

1.5.1 Cold shock proteins and cold acclimation proteins

Cold shock proteins (csp) and cold acclimation proteins (cap) are two strategies

Pseudomonas strains utilize to adapt to cold environments. Genes encoding cold shock proteins are expressed immediately after cold shock while genes encoding cold acclimation proteins are expressed during prolonged exposure to low temperatures (Trevors et al., 2012). Cold shock

proteins are transcriptional anti-terminators or translational enhancers that destabilize RNA secondary structure at low temperatures (Nakaminami et al., 2006). This destabilization of RNA secondary structure prevents premature transcription termination during cold shock. Cold acclimation proteins have been found to have a high level of amino acid sequence identity to members of the CspA family of E. coli and the homologous proteins of other microorganisms

(Berger et al., 1997). CapA contained highly conserved ribonucleoprotein (RNP1 and RNP2) involved with binding to single stranded DNA and RNA, suggesting similar functionality to Csp proteins.

The expression level of a cold shock protein from P. ﬂuorescens MTCC 103 (MW 14 kd) and a cold resistant protein (MW 35kd) from the P. ﬂuorescens mutant CRPF₈ were studied over a range of temperatures from 37°C down to 4°C (Khan et al., 2003). Expression of the cold shock protein and the cold resistant protein increased in response to decreased temperature with the rate of induction of protein synthesis reaching its maximum at 10°C. Eighteen Pseudomonas strains isolated from Antarctica were analyzed for the presence of cold shock proteins and cold accumulation proteins (Panicker et al., 2010). CapB was present in all Antarctic Pseudomonas isolates, but CspA was absent. The survival of these isolates in the perennially cold Antarctic environment could be attributed to the continuous expression of CapB and its regulatory role in transcription and translation of essential genes.

1.5.2 Antifreeze proteins

Antifreeze proteins also called ice structuring proteins are ice-binding proteins produced by certain organisms to enhance survival in freezing temperatures. Antifreeze proteins inhibit the spread of ice by lowering the freezing point and preventing ice recrystallization (Venketesh and

Dayananda, 2008). Antifreeze activity is theorized to occur by an adsorption inhibition mechanism where antifreeze proteins bind to ice crystals and curved ice structures form between the antifreeze proteins. Due to the Kelvin effect, the curved surface is energetically less favorable than flat surfaces which prevents additional water molecules from joining the ice formation

(Wang et al., 2017). Ice recrystallization, where ice crystals gradually grow larger in size at the expense of smaller ice crystals, is a lethal stress for cells in frozen conditions. Antifreeze proteins can prevent recrystallization by inhibiting water molecules from leaving ice crystals or by acting as a surfactant to reduce surface tension.

The freezing resistance of antifreeze proteins was discovered in several cold dwelling

Pseudomonas species including strains of P. ﬁcuserectae, P. fluorescens, and P. putida (Singh et al., 2014; Kawahara et al., 2004; Cid et al., 2016). Freeze sensitive mutants and a freeze resistant wild type strain of the plant growth-promoting rhizobacterium P. putida GR12-2 were tested for the ability to secrete antifreeze proteins (Kawahara et al., 2001). The freeze sensitive mutants secreted a much lower concentration of antifreeze protein compared to the wild type. Freeze resistance could be partially restored by adding purified antifreeze proteins to the freeze sensitive mutant’s cell suspension. The antifreeze gene afpA from P. putida GR12-2 was cloned into E. coli yielding a 72 kDa protein that exhibited low levels of antifreeze and ice nucleation activities compared to the native protein (Muryoi et al., 2004). The recombinant protein may not have been properly post translationally modified with previous work indicating the carbohydrate lipoglycoprotein was required for ice nucleation activity. These studies demonstrate the potential of accumulated antifreeze proteins to inhibit ice formation in both freeze resistant and freeze sensitive strains.

1.5.3 Membrane composition

Altering membrane composition is a critical strategy to combat the rigidity of the cell membrane during cold shock. Membrane fluidity is maintained by increasing the ratio of unsaturated fatty acids to saturated fatty acids or by altering the levels of anteiso branched chain fatty acids in membrane phospholipids (Shivaji and Prakash, 2010). Rigidity of the membrane signals the adjustment to membrane lipid composition through biosynthetic pathways including

FabAB and Des mediated pathways. The cis configuration of unsaturated fatty acids has double bonds which produce a bend in the fatty acid chain (Heipieper et al., 2003). These bends create more space between the unsaturated fatty acid chains producing a more flexible and fluid membrane during cold stress.

The synthesis of unsaturated fatty acids by fatty acid desaturase enzymes is essential for the survival of Pseudomonas during cold stress. The cold tolerant strains Pseudomonas sp.

AMS8 and Pseudomonas sp. A3 were found to produce large amounts of monounsaturated fatty acids at low temperatures (Garba et al., 2018; Garba et al., 2016). Δ9- fatty acid desaturase genes were isolated from each of the strains and were subsequently cloned and successfully expressed in E. coli. In both studies the recombinant E. coli had a functional putative Δ9-fatty acid desaturase capable of increasing the total amount of cellular unsaturated fatty acids. Functional genomics was used to study the cold stress response in the bacterial model organism P. putida

KT2440 (Frank et al., 2011). Transcriptome sequencing and proteome peptide profiling of

KT2440 revealed that the degradation pathway of valine to branched‐chain fatty acids (bkd operon) was upregulated during growth at low temperatures. These studies illustrate the importance of homeoviscous adaptation to support membrane fluidity during cold stress.

1.6 General Stress Response

The general stress response is vital to survival in nature when bacteria are challenged with a multitude of different stresses. In the environment bacteria must overcome many stresses including nutrient limitation, desiccation, temperature extremes, UV irradiation. While targeted stress responses typically help bacteria overcome a specific stress, the general stress response provides resistance against a diverse set of stresses (Storz and Hengge, 2011). General stress response provides cross-protection where defense against one stress can lead to enhanced tolerance to other stresses. This primes the cell for survival against other imminent stresses. The general stress response of Pseudomonas species includes transformation into the viable but non- culturable state, storage of polyphosphate, and induction of the stringent response (Figure 1.5;

Storz and Hengge, 2011).

Figure 1.5. Examples of bacterial general stress response. Stringent response is triggered by the

accumulation of uncharged tRNA. The alarmone ppGpp is synthesized and binds to RNA

polymerase leading to upregulation of nutrient acquisition and stress survival and

downregulation of translation machinery. Polyphosphate kinase catalyzes the formation of polyphosphate which accumulates and is stored for energy. The cell will convert to the viable but

non-culturable state with low metabolic activity to survive until hospitable conditions return.

Figure created with BioRender.com.

1.6.1 Viable but non-culturable state

Viable but non-culturable (VBNC) cells have low metabolic activity and intact membranes, but they are unable to replicate until they enter a more hospitable environment

(Ayrapetyan et al., 2018). Pseudomonas strains can enter this state as a response to unfavorable environmental conditions such as nutrient limitation, temperature extremes, and desiccation

(Lowder et al., 2000; Arana et al., 2010; Pazos-Rojas et al., 2019). Resuscitation methods that allow VBNC cells to repair and transition into a culturable state can vary greatly depending on the type of microorganism and environmental conditions (Bédard et al., 2014). The plant growth- promoting rhizobacterium P. putida KT2440 was found to return to a cultivable state after interaction with maize root exudates or a 48 hour rehydration in water (Pazos-Rojas et al., 2019).

The persistence of VBNC Pseudomonas in the natural environment was demonstrated with the biocontrol strain P. protegens CHA0 which survived in an uncovered field plot as a combination of dormant and VBNC cells for 72 days (Troxler et al., 2012).

Studies were conducted by Pazos-Rojas and colleagues to determine the survival of P. putida KT2440 exposed to desiccation stress. P. putida KT2440 was subjected to 18 days of desiccation then rehydrated with either maize root exudates for a short time or water for 48 hours

(Pazos-Rojas et al., 2019). Live/dead staining showed that bacterial counts for desiccated and rehydrated cells were just as high as the counts for the bacteria that did not get exposed to stress.

Cells exposed to desiccation stress that were neither rehydrated nor protected with trehalose stained red indicating death or a compromised membrane. The presence of VBNC cells after desiccation exposure was tested again with a GFP-tagged P. putida strain which exhibited active

GFP expression and housekeeping gene expression was confirmed with RT-PCR. A cytokine secreted by Micrococcus luteus called resuscitation-promoting factor (Rpf) has been discovered

which enables the resuscitation of VBNC cells in a wide range of Gram negative and Gram positive bacteria (Su et al., 2013). There is limited information on potential homologous genes in

Pseudomonas, but tests could be conducted to determine if adding Rpf to VBNC Pseudomonas cells improves resuscitation.

1.6.2 Biocontrol viability requirements

Biological control agents have a viability standard which is listed on the product label.

The company manufacturing the product determines the standard based on product efficacy and typically guarantees a minimum colony-forming unit (CFU) per gram (Anderson et al., 2018;

Cabrefiga et al., 2014). The stress of manufacturing live microbial products can lead a fraction of the population to enter a VBNC state (Davis 2014). VBNC cells can return to a culturable state in more hospitable environmental conditions or by resuscitating microbes through a rehydration process. A large population of VBNC cells which are more difficult to enumerate using traditional plating methods can complicate the process of ensuring the product meets the viability requirements on the label. Two methods to identify culturable and VBNC cells are live/dead staining with microscopic enumeration or detecting housekeeping gene expression by reverse transcription polymerase chain reaction (Li et al., 2014). Understanding the requirements for different metabolic states and improving enumeration techniques are both essential for enabling more accurate viability standards.

1.6.3 Polyphosphate

Polyphosphate is a polymer containing a few to several hundred phosphate residues linked by high energy phosphoanhydride bonds (Brown et al., 2004). Many bacteria can

intracellularly synthesize and degrade polyphosphate with polyphosphate kinases and exopolyphosphatase. Microorganisms accumulate polyphosphate to store energy, and this energy is released by ATP hydrolysis. Polyphosphate is also involved in stress responses including regulation of the stringent response, induction of the stress response sigma factor, and biofilm formation (Nikel et al., 2013; Gray and Jakob, 2014).

The role of polyphosphate was examined with Pseudomonas sp. B4 and P. fluorescens Pf0-1 polyphosphate kinase deficient mutants. Comparative proteomics on the Pseudomonas sp. B4 mutant revealed energy metabolism and nucleoside triphosphate formation were negatively impacted leading to an increase in energy generating metabolic pathways including oxidative phosphorylation and the tricarboxylic acid cycle (Varela et al., 2010). The P. fluorescens Pf0-1 mutant was 10-fold less competitive compared to the wild type strain in sterile soil low in inorganic phosphate, and it had an increased sensitivity to elevated temperatures in sterile soil

(Silby et al., 2009). These studies demonstrate the importance of polyphosphate in energy storage and stress response for Pseudomonas species.

1.6.4 Stringent response

Stringent response is a bacterial survival strategy using tRNAs as a sensor to overcome different stressors including nutrient starvation, iron limitation, and heat shock (Vinella et al.,

2005; Pletzer et al., 2016). Gene expression switches from growth promotion to survival during stationary phase when nutrients are scarce. The lack of available amino acids leads to an accumulation of ribosomal bound uncharged tRNAs. Binding of uncharged tRNA to the ribosome causes ribosome stalling which triggers RelA synthetase to convert Guanosine-5'- triphosphate (GTP) into the alarmone guanosine tetraphosphate (ppGpp) (Traxler et al., 2008).

SpoT also regulates the stringent response by modifying ppGpp concentration through hydrolysis of ppGpp to guanosine diphosphate (GDP) and pyrophosphate (Boes et al., 2008). ppGpp binds

RNA polymerase which halts so that the synthesis of translation machinery (such as ribosomal proteins, rRNA, and tRNA) is decreased, whereas transcription of genes involved in nutrient acquisition, amino acid biosynthesis and stress survival is increased (Wu et al., 2020).

Stringent response is a major factor in stress survival and virulence in both medically and biotechnologically relevant Pseudomonas strains. P. aeruginosa and P. putida stringent response mutants (ΔrelA ΔspoT) were created to test the influence of the stringent response on antioxidant defenses, antibiotic tolerance, biofilm formation, and quorum-sensing (Khakimova et al., 2013;

Schafhauser et al., 2014). Studies involving a P. aeruginosa mutant revealed that stringent response regulates catalase activity and hydrogen peroxide tolerance during both planktonic and biofilm growth (Khakimova et al., 2013). Another study on a P. aeruginosa stringent response mutant showed that (p)ppGpp significantly modulates the quorum-sensing hierarchy of a family of molecules controlling antibacterial and virulence functions (Schafhauser et al., 2014). A P. putida stringent response mutant was defective in biofilm dispersal indicating that the stringent response plays a role in relaying the nutrient stress signal to the biofilm dispersal machinery

(Díaz-Salazar et al., 2017).

Stringent response has been found to influence the production of secondary metabolites in agriculturally relevant Pseudomonas strains. Pseudomonas species produce antifungal compounds such as pyrrolnitrin and phenazine that inhibit fungal plant pathogens by suppressing mycelial growth (Huang et al., 2018). Stringent response mutants of the biological control agents

Pseudomonas sp. strain DF41 and Pseudomonas chlororaphis PA23 were created to stop the production of (p)ppGpp (Manuel et al., 2011; Manuel et al., 2012). The P. chlororaphis PA23

relA mutant had elevated pyrrolnitrin production while phenazine (PHZ) levels remained unchanged. Both P. chlororaphis PA23 and Pseudomonas sp. strain DF41 relA mutants exhibited increased antifungal activity against the pathogen Sclerotinia sclerotiorum (Lib.) de

Bary. Expression of secondary metabolites involved in antifungal activity are energetically costly and are reduced during the stringent response when nutrients are scarce (Manuel et al., 2012).

1.6.5 Stress induced tolerance

Exposure to sublethal stress is a strategy that has been used to induce the stress response and improve survival during formulation. Pseudomonas biocontrol agents have undergone hyperosmotic adaptation to improve formulation viability and efficacy against pathogens

(Cabrefiga et al., 2014; Wang et al., 2020a). Comparative transcriptomic analysis was conducted comparing P. protegens SN15-2 grown under hyperosmotic conditions or normal osmotic conditions then shocked with lethal temperatures (Wang et al., 2020b). The strains were exposed to cold or heat shock from a range of -15°C to 58°C. Exposure to the hyperosmotic environment increased the cytoplasmic concentration of potassium ions, altered the composition of the cell envelope, and increased trehalose and proline synthesis. The accumulation of potassium ions could be a strategy to maintain the osmotic or turgor pressure after osmotic shock (Cagliero and

Jin, 2013). Osmotic stress damaged the cell envelope due to salt-induced dehydration leading to an increase in glycerophospholipid metabolism to maintain membrane integrity (Wang et al.,

2020b). Triggering the stress response with osmotic stress prepared P. protegens SN15-2 for exposure to other stresses such as lethal temperatures.

This strategy of preparing cells with a sublethal shock translates to industrial formulation stress. Stress adaptation by exposure to osmotic shock has been conducted on P. fluorescens

EPS62e and P. protegens SN15-2 with improved viability after spray drying and fluidized-bed drying, respectively (Cabrefiga et al., 2014; Wang et al., 2020a). Intentionally triggering the stress response or targeting expression of specific survival mechanisms could enhance survival and colonization in the environment leading to better product performance. As described earlier in this review, Manuel and colleagues found that induction of stringent response can decrease the expression of some secondary metabolites involved in antifungal activity (Manuel et al., 2012).

When attempting this method, there needs to be an understanding of what mechanisms of survival are induced and if this will lead to a decrease in the production of the antifungal compound of interest.

1.7 Conclusions and Future Directions

Pseudomonas species are subjected to stress in the natural environment and have adapted mechanisms to survive these harsh conditions. Members of the Pseudomonas genus have been studied for their importance in bioremediation, plant pathogen control, antibiotic resistance, and pathogenicity against humans (Wasi et al., 2013; Chin-A-Woenget al., 2003; Folkesson et al.,

2012). Advances in next generation sequencing and previous work establishing the stress mechanisms of model organisms has accelerated the analysis of stress mechanisms in these important organisms (Ramos et al., 2001; Vorob'eva, 2004). Desiccation, heat, and cold stress can critically impact cells through protein and nucleic acid damage, disruption of major biosynthesis pathways, and loss of membrane integrity.

Bacteria have evolved a number of processes aimed at surviving or thriving under stressful conditions. There are both universal and targeted cellular responses designed to help cells cope with a variety of different stressors. Compatible solutes and polyphosphate can be accumulated as an energy source in the cell for use under critical conditions. Biofilm and EPS production protects the cell through an increased number of persister cells and an extracellular matrix barrier. The viable but non-culturable state is a strategy to persist through harsh conditions by keeping living cells at a low rate of metabolic activity. Chaperones, proteases, thermosensors, and alternative sigma factors rapidly respond to stress by controlling protein damage and regulating stress responses. In response to low temperatures, cold shock proteins and antifreeze proteins prevent premature transcription termination and lower the freezing point, respectively. Bacterial membrane composition changes in response to temperature stress to maintain a flexible and fluid membrane. Knowledge of bacterial stress survival has led to the incorporation of bacterial protectants in the formulation of beneficial microbial products.

New techniques are being established as the knowledge of stress survival mechanisms and availability of genome sequencing are becoming more accessible. Methods demonstrated on model organisms can be applied to industrially relevant Pseudomonas strains. Two techniques that have been demonstrated on model microorganisms are selective adaptation and genetic engineering (Sleight and Lenski, 2007; Billi et al., 2000). Selective pressure has been used in laboratory settings to adapt microorganisms to stress and select for more durable phenotypes. E. coli populations were subjected to over a hundred cycles of freezing and thawing. After many generations of adaptation to this stress the fitness of the E. coli strain was improved by 90%

(Sleight and Lenski, 2007). Adaptation is a tool that can be explored in more industrial relevant microorganisms although adaptation can also lead to undesirable traits. Microorganisms can be directly modified for improved resilience to abiotic stresses through genetic engineering. A sucrose-6-phosphate synthase gene from a resilient cyanobacterium was transformed into E. coli cells (Billi et al., 2000). Freeze drying, air drying, and desiccation survival was improved

10,000-fold in the transformed cells compared to the wild type cells. Studying the mechanisms of stress survival and trying new techniques to enhance survival is critical for improving beneficial applications and controlling pathogens.

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CHAPTER 2

Diversity of Soil Bacterial Communities After Exposure to Formulation Stress

2.1 Abstract

Biological control products have become a sustainable solution to prevent crop damage caused by plant pathogens, insects, and nematodes. Typically, biological control microorganisms have been isolated from plant-associated environments and have adapted to tolerate some level of environmental stress. For biological control agents to be effective, they should be able to thrive in field environments and survive the formulation process, which requires tolerance of drying, heat exposure, and long-term storage. The goal of this study was to find soil-associated microbes capable of surviving drying formulations and to establish a high throughput screening method to evaluate the performance of isolated microbes. In addition, a Gram-negative bacteria targeted isolation method was established to study the survival capabilities of asporulous Gram- negative bacteria. Bacillus, a Gram-positive bacterium, is a fast-growing durable microorganism capable of surviving spray drying without protectants. Pseudomonas sp. and other Gram- negative bacteria were capable of surviving milder formulation strategies such as oven tray drying.

2.2 Introduction

To ensure food security for a growing global population using sustainable and safe practices, the agricultural industry is turning to biological controls. Biological controls are microorganisms that are introduced to an environment to suppress a population of pests or pathogenic organisms (Begg et al., 2017, Heimpel and Mills., 2017). This practice is an alternative to chemical pesticides and antibiotics in crop and livestock production (Wilson et al.,

2013). Biocontrols can improve yield through beneficial interactions within a microbial community, such as antagonistic activity against plant pathogens or providing gastrointestinal microbiota hemostasis in livestock (Syed et al., 2018; Chaucheyras-Durand, 2010).

Biocontrols have been used for thousands of years dating back to 2000 BCE when

Egyptians spread the parasitoid Habrobracon hebetor to attack grain infesting caterpillars

(Heimpel and Mills, 2017). Technological advances have progressed to large-scale isolations, genome sequencing, and functional screening for beneficial microbes (Finkel et al., 2017). This search has led to biologicals that have outstanding potential with many members being Gram- negative bacteria (Santoyo et al., 2012). Biocontrol agents need to survive the formulation process, prolonged periods of storage, and challenging environmental conditions in order to be considered viable candidates for agricultural applications.

Endospore-forming, Gram-positive bacteria are stress tolerant and can be air-dried with minimal loss of viability (Schisler et al., 2004). Gram-negative bacteria are less resilient to air drying methods such as spray drying and are typically deployed as liquid or freeze-dried formulations (Peeran et al., 2014; Bisutti et al., 2015). Liquid or freeze-dried formulations are less economically feasible to commercially manufacture compared to air dried formulations due to storage and production costs (Schisler et al., 2016). The goal of this study is to understand the

diversity of soil bacterial communities after exposure to drying methods and identify bacteria capable of surviving these harsh stressors.

2.3 Materials and Methods

2.3.1 Sample processing and formulation

Soil samples were collected from Durham, North Carolina; Minburn, Iowa; and Clovis,

New Mexico. Soil samples were air dried overnight at room temperature then stored in a 4°C refrigerator for three days except the Iowa sample which was stored for one year. In preparation for spray drying, 15 g of the North Carolina and Iowa soil samples were suspended in 15 ml of

0.1M sodium phosphate buffer pH 7 (NaPO4), vortexed for one minute, and filtered through a

100-mesh screen. Samples were spray dried on the Buchi B-290 lab spray dryer (100% aspirator,

45 Q-flow, 35% pump speed, 200°C inlet temperature, 100°C outlet temperature). 0.5 g of spray dried powder was suspended in 2 ml of 0.1M NaPO4 (Figure 2.1A).

New Mexico samples were prepared for oven tray drying by suspending 2.5 g of soil and

2.5 g of sterile Sipernat 22 S in 7.5 ml of 0.1M NaPO4 and vortexing for one min (Figure 2.1B).

Sipernat 22 was sterilized by autoclaving at 121°C for 30 min and air dried overnight at room temperature. Samples were spread in a thin layer across a (100mm x 15mm) petri dish and dried in a 40°C oven till they reached 0.3 water activity (16 h). After oven drying, samples were resuspended to 20 ml with 0.1M NaPO4 and vortexed for one min.

North Carolina and Iowa samples were diluted to 10-4 and plated on Luria-Bertani agar then grown for both two days and one week at 30°C. Three replicates were made for each treatment. New Mexico samples were diluted to 10-3 and then grown at 30°C for one week on tryptic soy agar with and without 3 μg/ml vancomycin. New Mexico soil samples without plating were included in the experiment. Six replicates were made for each treatment. Colonies were collected from each plate, placed in tubes with 4 ml of 0.1M NaPO4, then pelleted by

centrifugation. North Carolina and Iowa samples were sent for metagenomic sequencing, and

New Mexico samples were sent for 16S rRNA Amplicon sequencing.

Figure 2.1. Formulation experiment design. A. Spray drying experiment design. Iowa and North

Carolina soil samples were exposed to spray drying or no stress conditions. Appropriate dilutions of the samples were used to inoculate LB plates that were incubated for two days or one week at

30°C. Iowa and North Carolina cell pellets were sent for metagenomic sequencing. B. Oven tray

drying experiment design. New Mexico soil samples were exposed to oven tray drying or no stress then appropriate dilutions of the samples were used to inoculate TSA + vancomycin plates

that were incubated for one week at 30°C. These samples and New Mexico soil samples were

sent for 16S amplicon sequencing.

2.3.2 Shotgun metagenomic sequencing and processing

Bacterial pellets were sent to MOgene (Maryland Heights, Missouri) for DNA extraction and shotgun metagenomic sequencing. Genomic DNA was extracted using the MO Bio DNA extraction kit according to the instructions of the manufacturer. Libraries were prepared using the

Nextera XT DNA Sample Preparation Kit, and DNA was amplified using a limited-cycle PCR program following the Nextera XT protocol. Sequencing was performed on the Illumina MiSeq platform using paired-end sequencing (2 x 150 bp).

2.3.3 Shotgun metagenomic sequence analysis and visualization

FASTQ files from shotgun metagenomic sequencing were uploaded to MG-RAST for sequence analysis. Sequences were dereplicated to remove artificial sequences produced by sequencing artifacts and screened to remove Homo sapiens (v36) sequences. Dynamic trimming was conducted to remove low quality sequences with 15 as the lowest phred score that was counted as a high-quality base and sequences were trimmed to contain at most 5 bases below the above specified quality. Taxonomy total abundance data was exported as excel files then converted to relative abundance at the genus level by dividing the read count for each genus by the total reads. Bar charts and a heat map were created in excel. The diversity was measured using the shannon diversity index. Reads above 0.1% were included in a genera relative abundance heat map.

2.3.4 16S amplicon sequencing

Soil and bacterial pellets were sent to MOgene for DNA extraction and 16S rRNA amplicon sequencing. Genomic DNA was extracted using the Qiagen Microbial DNEASY kit and PowerSoil DNA Extraction kit according to the instructions of the manufacturer. 16S rRNA

gene amplicon (V4 region) libraries were constructed using modified V4 SSU rRNA gene primers: 515f modified GTGYCAGCMGCCGCGGTAA and 806r modified

GGACTACNVGGGTWTCTAAT (Walters et al., 2016). PCR began with a thermal mix step

(50°C for 2 min); a hot start (70°C for 20 min; 95°C for 10 min); 10 cycles of denature (95°C for

15 s), annealing (60°C for 30 s), and elongation (72°C for 1 min); two cycles of denature (95°C for 15 s), C0t (80°C for 30 s), annealing (60°C for 30 s), and elongation (72°C for 1 min); eight cycles of denature (95°C for 15 s), annealing (60°C for 30 s), and elongation (72°C for 1 min); two cycles of denature (95°C for 15 s), C0t (80°C for 30 s), annealing (60°C for 30 s), and elongation (72°C for 1 min); eight cycles of (95°C for 15 s), annealing (60°C for 30 s), and elongation (72°C for 1 min); five cycles of denature (95°C for 15 s), C0t (80°C for 30 s), annealing (60°C for 30 s), and elongation (72°C for 60 s). Amplicon sequencing was performed on the Illumina MiSeq platform using paired-end sequencing (2 x 250 bp) based on the MiSeq

Reagents kit v3 (600-cycle). Forward primers included a 10 bp index specific to each sample to demultiplex reads by sample post-DNA sequencing.

2.3.5 Amplicon sequence read quality control (QC)

Processing and quality control of reads was performed using the R package dada2, version 1.16.0 (Callahan et al., 2016). The quality profiles were inspected for the reads. The dada2 filter function “trimLeft” was used to remove the primers. Forward reads were truncated at position 240 and reverse reads were truncated at position 160 to remove regions with low quality scores. Reads containing uncalled bases of more than two expected errors were removed. The

DADA2 parametric error model was used to denoise sequence data and infer true amplicon sequence variants (ASV). The forward and reverse reads that had an overlap of at least 12 bases

and were identical to each other in the overlap region were merged together. Chimeric sequences were identified and removed using the function “removeBimeraDenovo.” Taxonomy from kingdom to genus was assigned to the ASVs using the Silva reference 16S rRNA gene database version 123. The V4-region SSU rRNA gene of a mock community (ZymoBIOMICS, Irvine,

CA; Microbial Community Standard: product number D6305) was sequenced and failed with only 79 reads. There were 19,101,503 raw DNA sequence reads (an average of 1,061,195 per treatment with a standard deviation of 175,466) entered into this pipeline and after quality control 10,135,834 merged amplicon sequences remained (an average of 563,102 per treatment with a standard deviation of 129,910).

2.3.6 Bray Curtis Dissimilarity and Simpson Diversity Index

Analyses were performed using the program R, version 4.0.2 (R Core Team, 2020).

Differences in the microbial composition between treatments were measured using the Bray-

Curtis dissimilarity index and visualized using non-metric multidimensional scaling in phyloseq

(McMurdie and Holmes, 2013). Significance of the different treatment groups was tested using permutational analysis of variance (PERMANOVA) with the R package ‘vegan’ function

‘adonis’ (Jari et al., 2019). The community diversity was measured using the Simpson diversity index (SDI) with the R package phyloseq (McMurdie and Holmes, 2013).

2.3.7 16S rRNA amplicon community composition

The total abundance of Pseudomonas and Bacillus was determined before any taxonomic filtering. Species taxonomic designations were assigned for Pseudomonas isolates using Blastn

(Table A.2.1) and corresponding Pseudomonas DNA sequences are listed in Appendix A

Chapter 2 (Table A.2.2). For further community analysis, these data were filtered by phylum taxonomy. Reads with an unidentified phylum or that had a low prevalence were removed from the dataset. A prevalence threshold of 5% was set, and the phyloseq function ‘prune_taxa’ was used to remove reads below this threshold. Relative abundance at the phylum level was calculated by dividing the ASV counts for a phylum by the total ASV count. The genera heatmap was created using the plot_heatmap function in the phyloseq r package (method is "NMDS", distance is "bray"). Total abundance of ASV counts is displayed in the heatmap.

2.4 Results

2.4.1 Spray drying and a short growth duration decreases bacterial community diversity.

The microbial diversity of North Carolina and Iowa soil samples was tested under spray dried or no stress conditions. Dilutions of the soil samples were plated onto LB then incubated for two days or one week at 30°C. There were three replicates for each treatment. The Shannon diversity index was used to determine the diversity of each treatment. The Shannon diversity index takes into account the number and relative abundance of different species present in an area. A higher Shannon index score represents a greater diversity.

Spray-dried, North Carolina samples had a significantly lower diversity with a Shannon diversity index score of 0.5 (SD of 0.15) compared to no stress samples which had a score of 2.5

(SD of 0.09; Figure 2.2). This indicates that exposing a soil bacterial community to spray drying stress reduces the diversity of the community. Spray-dried, Iowa samples had a Shannon diversity score of 0.5 (SD of 0.19), while no stress samples had a score of 1.6 (SD of 0.34). The

Iowa soil was collected and stored at room temperature for one year while the North Carolina soil was collected fresh for this experiment. The long storage time of the Iowa soil could explain the lower Shannon diversity score of the no stress samples compared to the fresh North Carolina soil. North Carolina and Iowa soil both had a significant decrease in diversity after spray drying

(North Carolina p value of 0.008 and Iowa p value of 0.02).

The effect of a long incubation (one week) versus a short incubation (two days) was compared with the no stress North Carolina and Iowa samples (Figure 2.3). A longer incubation duration led to a significant increase in diversity for both locations. The diversity score of the

North Carolina weeklong incubation samples was 2.5 (SD of 0.09), while the two-day incubation diversity score was 1.6 (SD of 0.02). The Iowa weeklong incubation diversity score was 1.6 (SD

of 0.34), and the two-day incubation diversity score was 0.36 (SD of 0.22). A longer incubation time also led to more diversity observed for spray-dried North Carolina soil with a weeklong incubation diversity score of 0.5 (SD of 0.15) and a two-day incubation score of 0.08 (SD of

0.01; T-Test 0.04). The length of incubation makes a clear impact on the diversity of both spray- dried samples and samples that have not been exposed to stress.

Figure 2.2. Effect of spray drying on the diversity of soil samples. The Shannon Diversity Index was measured to determine the diversity of North Carolina and Iowa soil samples. North

Carolina and Iowa soil samples were spray dried or not exposed to stress. Samples were grown on LB agar for one week before isolation. *Paired T-test with p < 0.05.

Figure 2.3. Effect of incubation time on the diversity of soil samples. The Shannon Diversity

Index was measured to determine the diversity of North Carolina and Iowa soil samples.

Samples were grown for a long incubation (one week) or a short incubation (two days). Samples were not exposed to stress. *Paired T-test with p < 0.05.

2.4.2 Community composition after spray drying.

Bacillus comprised an average of 93% (SD of 6.89) of the bacterial community in all samples exposed to spray drying (Figure 2.4). Spray-dried samples with a short incubation (2 day) had a higher composition of Bacillus with a relative abundance of 96% (SD of 6.88) compared to spray-dried samples with a weeklong incubation which had a Bacillus relative abundance of 90% (SD of 5.7). Spray-dried samples that were incubated for a long duration had a more diverse community of Gram-positive bacteria with Paenibacillus, Brevibacillus,

Streptomyces, Lysinibacillus, and Rhodococcus identified. Spray drying reduced diversity, while a longer incubation period led to a greater diversity of Gram-positive bacteria.

The Iowa samples that did not undergo stress had similar trends as the spray-dried samples. The Iowa sample with the two-day incubation had Bacillus comprising over 88% of the bacterial community. A weeklong incubation increased the number of genera present in the Iowa samples, but a higher percentage of Bacillus was still present compared to the North Carolina soil after a long incubation. This trend toward more Gram-positive bacteria from the Iowa samples could be a result of a longer storage period (one year) or due to the different locations.

North Carolina samples that were not stressed and had a weeklong incubation had the highest diversity with Pseudomonas as the top genus, averaging 28% (SD of 2.1) of the bacterial community. Both Gram-negative and Gram-positive bacteria were present including Bacillus which had a relative abundance of 8% (SD of 2.5).

Long, Long, Short, Short, Long, NC, Long, IA, Short, NC, Short, IA, NC, No Spray IA, No Spray NC, No Spray IA, No Spray Genus Stress Dried Stress Dried Stress Dried Stress Dried Bacillus Pseudomonas Ochrobactrum Stenotrophomonas Achromobacter Aeromonas Paenibacillus Brevibacillus Brevundimonas Streptomyces Lysinibacillus Rhodococcus Xanthomonas Sinorhizobium

Genera Relative Abundance N/A 1% to 100%

Figure 2.4. Relative abundance heat map of top genera. Genera above 1% relative abundance were included in the heat map. The intensity of red represents the relative abundance of each genus. Treatments are long (one week) or short (two day) incubation time, Iowa (IA) or North

Carolina (NC), and spray dried or no stress.

2.4.3 Oven tray drying reduced bacterial community composition and diversity.

The microbial community composition was tested in three different treatment groups: soil with no stress exposure, cell pellet with no stress exposure, and cell pellet after exposure to oven

tray drying. Vancomycin (3 μg/ml) was added to the agar during growth to decrease the presence of Bacillus and other Gram-positive bacteria in the cell pellets. There were six replicates for each treatment which were sequenced averaging more than 500,000 high-quality V4 SSU rRNA gene amplicon sequences per treatment (Table 2.1).

The Bray-Curtis dissimilarity index and non-metric multidimensional scaling was used to visualize the differences in the microbial composition between replicates and treatments (Figure

2.5). Communities from the soil sample, heat exposed cell pellet, and no stress cell pellet differed in phylogenetic composition (Test PERMANOVA P value of 0.001). Treatment replicates clustered together indicating similar microbial compositions between replicates. All three treatment groups differed in microbial composition with separate clusters. Community diversity was measured using the Simpson diversity index (Figure 2.6). The Simpson diversity index ranges from 0 indicating low diversity to 1 indicating high diversity. The no stress soil samples had a high diversity score of 1 for all replicates. The average diversity score for the no stress cell pellet was 0.84 which was higher than the average diversity score of the oven replicates at 0.74.

Table 2.1. 16S rRNA amplicon sequencing counts. *Standard deviation of ASV counts per treatment group.

Sample Treatment Antibiotic Replicates Mean ASV ASV Count Type Counts SD*

Soil No Stress None 6 508,711 106,371

Cell Pellet No Stress Vancomycin 6 591,264 165,415

Cell Pellet Oven Vancomycin 6 589,330 86,680 Dried

Figure 2.5. Bray-Curtis NMDS plot of soil samples, oven cell pellet, and no stress cell pellet.

Nonmetric multidimensional scaling (NMDS) ordination of Bray-Curtis distances between samples based on amplicon sequence variant content. Color represents the different experimental treatments (No stress, oven, and soil samples). There are six replicates for each treatment.

Figure 2.6. Simpson diversity index of soil, oven, and no stress samples. Simpson diversity index of no stress soil, no stress cell pellet, and oven tray dried cell pellet treatments with six replicates. Color represents the different experimental treatments.

2.4.4 Targeted isolation with vancomycin decreased the presence of Bacillus and allowed the growth of Pseudomonas isolates.

Total abundance of Bacillus was determined for each treatment to test if vancomycin successfully prevented the growth of Bacillus. The total abundance of Bacillus in the cell pellet samples grown on TSA with vancomycin was negligible with no ASV counts present in the no stress oven cell pellets and an average total abundance of 1.3 reads (SD of 2.98) present in the oven cell pellet samples (Figure 2.7). Bacillus was present in the soil samples which did not have vancomycin with an average total abundance of 1,258.8 reads (SD of 759.49).

The total abundance of Pseudomonas was determined for each treatment to better understand the ability of this genus to survive oven tray drying. The total abundance of

Pseudomonas was highest in the no stress cell pellets averaging 203,779 total reads (SD of

55,236; Figure 2.8). Pseudomonas isolates survived oven tray drying with an average of 107,921 reads (SD of 83,509). Two oven replicates had low total ASV counts (below 250) and four oven replicates had total ASV counts over 100,000. No stress soil samples had the lowest total count with an average of 2,818 total reads (SD of 1,951). The ASVs present in the oven samples were identified as P. koreensis and unclassified Pseudomonas (Table A.2.1).

Figure 2.7. Total abundance of Bacillus in no stress and oven treatment groups with vancomycin and soil without vancomycin. Each sample is displayed along the x-axis. The y-axis has the abundance values for each ASV stacked in order from greatest to least separated by a horizontal line. This displays both the total value and the individual ASV abundances for each sample.

Figure 2.8. Total abundance of Pseudomonas in no stress and oven treatment groups with vancomycin and soil without vancomycin. Each sample is displayed along the x-axis. The y-axis has the abundance values for each ASV stacked in order from greatest to least separated by a horizontal line. This displays both the total value and the individual ASV abundances for each sample. Cell pellets were collected from the oven and no stress samples after growth with vancomycin. The soil was sequenced directly without vancomycin.

2.4.5 Microbial community composition after oven tray drying.

Proteobacteria and Bacteroidetes were the two major phyla for the no stress cell pellets

(Figure 2.9). The oven cell pellets had the lowest diversity of phyla and the majority of isolates were proteobacteria. The no stress soil samples contained the highest diversity of phyla with the

majority belonging to Proteobacteria, Actinobacteria, and Bacteroidetes. Both of the no stress and oven cell pellets were exposed to vancomycin leading to the isolation of Gram-negative phyla. The no stress soil samples were sequenced directly without an isolation step or antibiotic exposure which resulted in the presence of both Gram-negative and Gram-positive phyla.

Pseudomonas, Variovorax, Ensifer, Stenotrophomonas, Lysobacter were present in both the oven cell pellet and the no stress cell pellet samples (Figure 2.10). The abundance levels of

Chitinophaga and Pedobacter were greater for the no stress cell pellet compared to the oven cell pellet samples, whereas Microvirga, Phyllobacterium, Bosea had higher abundance levels in the oven cell pellet compared to the no stress cell pellet.

Figure 2.9. The relative abundance of bacterial phyla for the no stress soil, no stress cell pellet, and oven cell pellet treatment groups. There are six replicates for each treatment.

Figure 2.10. Heatmap of the top genera for oven and no stress samples. The top 15 bacterial genera are shown for the no stress cell pellet and oven cell pellet treatment groups. There are six replicates for each treatment.

2.5 Discussion

2.5.1 Spray drying enriches for Bacillus and other spore-forming bacteria.

Bacillus species are fast growing spore-forming microorganisms capable of surviving in harsh environments (McKenney et al., 2013;2012). Spores are stress resistant metabolically dormant cells protected by layers of protein shells (Dirks 2004). These characteristics enabled

Bacillus to dominate all microbial communities exposed to spray drying. Spray drying was set to

200°C for the inlet temperature and 100°C for the outlet temperature. The duration is typically only a few seconds of exposure at peak temperatures. Longer incubation periods (one week) led to a greater diversity of Gram-positive spore-forming bacteria with the exception of

Rhodococcus which is non-spore forming compared to the two-day incubation period. Spore formation is critical to survival from environmental stresses encountered in nature and gives these microbes an advantage in spray drying survival. Bacillus species resistance to stress and high efficacy against plant pathogens has led to tremendous potential as biological control products (Schisler et al., 2004).

2.5.2 Vancomycin reduces Bacillus growth.

Bacillus species’ ability to outcompete all other organisms through a combination of superior stress survival and faster growth led to inconclusive results on spray drying survival of

Gram-negative bacteria. The antibiotic vancomycin was added to the growth medium for oven tray drying experiments to prevent the growth of Bacillus and other vancomycin-sensitive Gram- positive bacteria. Vancomycin is a glycopeptide antibiotic that inhibits cell wall synthesis and causes damage to the cytoplasmic membrane (Ashford and Bew, 2012). Vancomycin does not affect Gram-negative bacteria because their outer membranes are impermeable to large glycogen

molecules (Vardanyan and Hruby, 2006). Bacillus growth was prevented by the addition of vancomycin to no stress and oven tray dried samples (Figure 2.7).

2.5.3 Gram-negative bacteria can survive oven tray drying.

Gram-negative bacteria are in beneficial products for the removal of environmental pollutants and for the control of plant pathogens, insects and nematodes (Wasi et al., 2013;

Anderson and Kim, 2018). Asporulous Gram-negative bacteria cannot form spores, but they still have mechanisms to survive environmental stressors including biofilm formation, the heat shock response, and altering membrane composition (Ramos et al., 2001). For oven tray drying experiments, a Gram-positive targeted antibiotic (vancomycin) and a less harsh formulation method were selected to enrich for Gram-negative bacteria. Oven tray drying was set to 40°C over a 16-hour drying period until samples reached a water activity of 0.3 which is a much lower temperature than spray drying at temperatures over 100°C. Gram-negative bacteria including

Pseudomonas were able to survive oven tray drying after Bacillus was prevented from growing.

Harsher formulations like spray drying can be revisited now that a method for Gram-negative targeted isolation has been established.

2.6 Conclusion

Understanding formulation stress survival is a critical step towards producing efficacious, viable biological control products. This study demonstrated the potential of beneficial microorganisms including Bacillus and Pseudomonas to survive different drying methods.

Bacillus has an advantage in surviving and quickly recovering from harsh drying methods such as spray drying. Pseudomonas and other Gram-negative bacteria are able to survive oven tray drying. A Gram-negative targeted isolation strategy was developed and can be utilized to test the survival capabilities of Gram-negatives to harsher drying methods. This method of testing survival rates of soil bacterial communities to drying can be expanded to include testing for optimal protectants. Gaining a better understanding of optimal drying methods and protectants for beneficial bacteria will accelerate the capabilities of these microorganisms as products.

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CHAPTER 3

Evaluation of Pseudomonas Heat Shock Survival in Response to Protectant Addition

3.1 Abstract

Pseudomonas species have drawn interest in industry due to the discovery of many beneficial species that can be utilized for bioremediation and plant pathogen control. Typically, beneficial bacteria are exposed to heat or cold during the formulation process to remove moisture and generate easily distributed and shelf-stable dried products. The goal of this study is to survey a diverse set of Pseudomonas species to determine which species are capable of surviving heat shock and identify which protectants can improve stress recovery. P. thermotolerans and P. aeruginosa were shown to be capable of surviving heat shock. Strains with high genomic similarity to the top heat shock survivors were also able to survive a five-minute heat shock at

60°C. Overall, heat shock tolerant strains clustered together based on phylogenetic similarity.

Gene enrichment analysis of heat shock surviving strains showed that heat shock proteins and universal stress response proteins such as HSP70, GroEL/ES, and clp protease were present at a higher frequency compared to the strains that could not survive heat shock. Skim milk was the highest performing protectant which benefited the broadest range of strains in the Pseudomonas diversity panel.

3.2 Introduction

Pseudomonas is a genus of Gram-negative, aerobic bacteria that are ubiquitous in nature.

There are 122 recognized and validly published species varying from species able to degrade environmental toxins to opportunistic animal pathogens (Parte, 2018). Pseudomonads have drawn interest in industry due to the discovery of many beneficial species that can be utilized for bioremediation and plant growth promotion (Wasi et al., 2013; Anderson and Kim, 2018). These biocontrol, biofertilization, and phytostimulation properties allow for an opportunity to transition away from traditional pesticides and chemical fertilizers to live microbial products (Hassen et al.,

2018). A benefit of biological control agents is the ability of strains to have multiple modes of action against pathogens through the secretion of siderophores and antimicrobial secondary metabolites. This benefit of P. putida, P. fluorescens, and other Pseudomonas species has been explored and a few examples of well-studied antimicrobial metabolites include pyrrolnitrin, phenazines, and pyoluteorin (Ligon et al., 2000). In addition, many Pseudomonas species have bioremediation potential to treat soil affected by environmental pollutants. Pseudomonas species such as P. aeruginosa and P. putida can remove insecticides, heavy metals, and engine oil from polluted environments (Cycoń et al., 2019; Chellaiah et al., 2018; Ramadass et al., 2018).

To be useful as products, these microbes must be able to survive industrial processing and be metabolically active once introduced into the environment. Typically, beneficial bacteria are exposed to heat or cold during the formulation process to remove moisture and generate easily distributed and shelf-stable dried products. Many Pseudomonas species have been discovered that are naturally resistant to extreme stress including P. thermoterans isolated from industrial cooking water and P. antarctica found in glaciers in Antarctica (Manaia and Moore,

2002; Lee et al., 2017). Most Pseudomonas species are mesophiles with optimal growth between

20°C to 45°C. However, even mesophilic bacteria have adapted to survive environmental stress such as seasonal temperature changes and drought. In addition to natural resistance to stress, protectants such as sugars, sugar alcohols, and amino acids can be applied to beneficial strains to increase viability during the formulation process (Strasser et al., 2009). Sugars protect the native protein conformation during desiccation by forming hydrogen bonds between proteins and sugar hydroxyl groups when water is removed. Sugar alcohols and amino acids prevent oxidative damage through antioxidant activity against free radicals (Mailloux et al., 2009). Adding protectants is a well-established method to improve the viability of beneficial microorganisms such as lactic acid bacteria and these strategies can be applied to formulating Pseudomonas products. The goal of this study was to survey a diverse set of Pseudomonas species to determine which species are capable of surviving heat shock and evaluate which protectants can improve stress recovery.

3.3 Materials and Methods

3.3.1 Microbial isolation

Samples were processed from soil, leaves, roots, insects, plants, amphibian skin, tubers, and excrement (Table A.3.1). These samples were collected in the United States and Africa from

2013 to 2020. Rhizosphere samples were collected by washing soil attached to the roots with 0.1

M sodium phosphate buffer (pH7) and filtering through a 40 μm filter. Insects and plant parts were ground with a mortar and pestle, mixed into a slurry with 0.1 M sodium phosphate buffer

(pH7) and filtered through a 40 μm filter. Environmental samples were plated on Oxoid

Brilliance Bacillus cereus agar (Thermo Fisher Scientific), Luria-Bertani agar (Thermo Fisher

Scientific), Potato Dextrose Agar (Thermo Fisher Scientific), Carrot Agar (Thomas Scientific),

M63 Agar, National Botanical Research Institute's Phosphate Growth Medium, or modified M9 minimal salts agar. Modified M9 salts consists of, per liter, 11.33 g Na₂HPO₄7H₂O, 3 g

KH₂PO₄, 1 g NH₄Cl, 10 g C₅H₈NNaO₄H₂O, 30 g Molasses (Grandma’s Unsulphured

Molasses), 2 mM MgSO₄7H₂O, 0.2 mM ZnSO₄7H₂O, 0.02 mM FeSO₄7H₂O. All M9 minimal salts materials were ordered from Sigma-Aldrich unless otherwise stated. Isolates were grown in

Luria-Bertani broth at 30°C for 24 h and frozen in 15% glycerol stocks. 86 Pseudomonas isolates were collected and stored.

3.3.2 Sequencing

Bacterial isolates were grown in Luria-Bertani broth then pelletized. DNA was extracted from cell pellets using MoBio microbial DNA isolation kits (Qiagen, Hilden, Germany). DNA was sheared enzymatically at 55°C for 5 min using the Illumina Nextera XT tagmentation enzyme (Illumina, San Diego, CA, USA) then tagmented DNA fragments were enriched by 10

cycles of PCR amplification. Libraries were sequenced on an Illumina HiSeq sequencing platform and Illumina paired-end reads were demultiplexed using Illumina software bcl2fastq v2.18.0.12. Paired-end reads were adapter and quality trimmed using cutadapt version 1.5 following Illumina recommendations. CLC Assembly Cell and CLC Mapper from Qiagen were used to assemble trimmed paired end reads and align back to a consensus sequence. Bacterial genomes were assigned taxonomic identifiers using the Genome Taxonomy Database (GTDB)

Toolkit version 1.0.2.

3.3.3 Strain selection

The 86 strain Pseudomonas panel was prioritized based on genome clustering to select representative strains from diverse clusters of Pseudomonas species. The total list consisted of

215 Pseudomonadales and was narrowed down to 86 diverse Pseudomonas strains. The similarity between strains was determined by hierarchical clustering using the Euclidean distance metric and single linkage method. The Jaccard distance was calculated from Eggnog functional annotations. Hierarchical clustering was plotted as a dendrogram and the cut point was selected at 94 clusters. One strain representative of each cluster was selected then the box was further narrowed down to only Pseudomonas isolates to create the 86 strain Pseudomonas diversity panel.

3.3.4 Heat shock without protectants

86 Pseudomonas isolates were grown in 96 well plates with 800ul of Tryptic Soy Broth

(TSB) at 30°C until stationary phase was reached. Then all strains were normalized to an OD595 of 0.06 in an 800 μl culture volume. 50 μl aliquots of cultures were exposed to heat shock for 5

minutes in a thermocycler at three temperatures of 55°C, 60°C, and 80°C. Three biological replicates were included per experiment and three independent experiments were conducted for a total of nine replicates per strain. Heat shocked strains were inoculated into 450 µl of TSB (10% inoculation) and incubated for 48 h at 30°C (225 rpm). After 48 hours, the optical density of the cultures was measured at 595 nm. Pseudomonas syringae DC3000, which cannot survive a 55°C five-minute heat shock, was included as a negative control. Pseudomonas thermotolerans CM3, which can survive a five-minute heat shock up to 62°C, was included as a positive control.

Strains were required to meet three conditions to be considered survivors: 1) the 55°C and/or 60°C OD595 reading had to be above 0.06, 2) the t test p value needed to be below 0.01, and 3) the strain was unable to survive the five min 80°C heat shock. The 0.06 OD595 read cutoff was selected based on the negative control OD595 readings (TSB media blank, DC3000 negative control, and 80°C heat killed strains). The p value of the t test needed to be below 0.01 to confirm that there was a statistically significant level of growth after heat shock. No known

Pseudomonas strains have been shown capable of surviving for 5 min at 80°C (Manaia and

Moore, 2002), so any strains able to survive were confirmed as contaminated by 16S rDNA PCR and removed from the diversity panel.

The top two surviving strains, P. thermotolerans CM3 and P. aeruginosa ID 35, were were exposed to heat shock at temperatures ranging from 60°C to 65°C to determine the maximum five min heat shock survival temperature. Isolates were grown in 800 μl of TSB at

30°C (225 rpm) until stationary phase was reached. Then all strains were normalized to an OD595 of 0.06 in an 800 μl culture volume. 50 μl aliquots of cultures were heat shocked for 5 min in a thermocycler at eight temperatures: 60°C, 60.3°C, 61°C, 62°C, 63.2°C, 64.2°C, 64.72°C, 65°C.

Three biological replicates were included for each strain. Heat shocked strains were inoculated

into 450 μl of TSB (10% inoculation) and incubated for 24 h at 30°C (225 rpm). After 24 h, the optical densities of the cultures were read at a wavelength of 595 nm. Pseudomonas syringae

DC3000 which cannot survive a 55°C five min heat shock was included as a negative control.

3.3.5 Gene enrichment analysis

Gene set over-representation analysis is an enrichment analysis that was used to determine if any genes from the heat shock survivors were enriched compared to genes from all the tested strains (Tokar et al., 2020). The hypergeometric test was used to statistically assess the significance of the overlap between the genes from heat shock survivors and genes from all the tested strains. Strains included as 55°C survivors, 60°C survivors, and strains that were excluded from analysis are listed in table 3.1. Excluded strains did not have available functional annotations at the time of analysis.

A total of 22,324 genes were present from the 60°C heat shock survivors, and a total of

78,073 genes were present from the 55°C heat shock survivors. 367 genes occurred above a frequency of 3 from the 55°C heat shock survivor gene list and 233 genes occurred above a frequency of 2 from the 60°C heat shock survival gene list. These two subsets of genes were evaluated using gene set over-representation analysis and hypergeometric testing. 190 genes from the 55°C heat shock survivor gene list and 159 genes from the 60°C heat shock survivor gene list were identified as enriched with p values below 0.05.

100

Table 3.1. Heat shock survivor list for gene set over representation analysis

55°C Survivors 60°C Survivors Strains Excluded from Analysis P. aeruginosa ID 9 P. aeruginosa ID 9 P. aeruginosa ID 76 P. aeruginosa ID 35 P. aeruginosa ID 35 P. aeruginosa ID 80 P. lundensis ID 55 P. lundensis ID 55 P. aeruginosa ID 79 P. otitidis ID 64 P. sp002386445 ID 75 P. thermotolerans CM3 P. sp001259595 ID 66 P. aeruginosa ID 77 P. citronellolis ID 26 P. citronellolis (NRRL B-2504) P. fragi ID 72 P. parafulva_B ID 43 P. helleri ID 47 P. taetrolens ID 57 P. endophytica ID 34 P. psychrophila ID 60 P. versuta ID 71 P. proteolytica ID 58 P. alcaligenes ID 65

3.3.6 Heat shock with protectants

A subset of the Pseudomonas diversity panel was selected for further experiments with protectants. 23 total strains were selected: 15 strains unable to survive a 55°C heat shock, 5 strains able to survive a 55°C heat shock, and 3 strains able to survive a 60°C heat shock. 23

Pseudomonas isolates were grown in 800 μl TSB medium at 30°C with shaking (225 rpm) until stationary phase was reached. Protectants were added to each culture, and the cultures were returned to the shaking incubator (30°C) for one hour. Cultures with no protectant were included as negative controls with two replicates per strain. Blank TSB and TSB with protectant added were included to determine the potential effect of the protectant on OD reads. The final

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concentration of each protectant added was as follows: 5% skim milk, 50 g/L trehalose, 50 g/L sucrose, 50 g/L xylitol, 1 mM proline, 30 mM glutamate and 15% glycerol. 50 μl of each sample was heat shocked for 5 min in a thermocycler with two replicates at 55°C. Heat shocked strains were inoculated into 450 μl TSB (10% inoculation) and incubated for 48 h at 30°C (225 rpm).

Optical density readings (595 nm) for each culture were determined immediately after heat shock and 48 h after heat shock. Two independent experiments were conducted with a total of four replicates for each treatment. The average and standard deviation were calculated across replicates. A one tailed t-test was performed to determine if there was a statistical significance in survival when protectants were added.

In addition, twelve heat shocked strains were inoculated into 1000 μl TSB (5% inoculation) in a 48 well FlowerPlate and incubated in the BioLector Pro system for 48 h at 30°C

(1,300 rpm), 85% humidity control, and 35% O2 regulation. Biomass was measured by light scatter at 620 nm, gain 4 (signal amplification of the photodiode) every 12 minutes. Two replicates of strains without protectant and strains with protectant were included. Two independent experiments were conducted for a total of four replicates per treatment. Delta peak biomass was calculated for each strain by subtracting the highest average biomass of the strain with protectant by the highest average biomass of the strain without protectant. The average generation time, which is the time it takes for a population of bacteria to double in number, was calculated for each strain with and without protectant. The delta average generation time was determined by subtracting the average generation time of the strain with protectant by average generation time of the strain without protectant. Delta lag in recovery was calculated by subtracting the average time of the start of exponential growth for strains with protectant by the average time of the start of exponential growth for strains without protectant. Glycerol did not

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have an impact on biomass reads. Skim milk did effect biomass reads which was corrected for in all calculations.

3.3.7 Phylogenetic analysis

The phylogenetic tree was constructed using GToTree v1.5.46 with default parameters

(Lee, 2019). 80 Pseudomonas genomes were included in the input dataset. All strains except for the following were included in the phylogenetic tree: P. fluorescens (ATCC 13525), P. putida

(ATCC 12633), P. resinovorans (NRRL B-2649), P. syringae DC3000, P. segetis ID 52, and P. poae_A ID 62. These strains did not have full genome sequences, so they were not included in the analysis. Prodigal v2.6.3 was used to predict the coding regions of the genomes. HMMER v3.3.2 was used to search against the Gammaproteobacteria Hidden Markov Model profile available on GToTree with 172 single-copy marker genes (Potter 2018). MUSCLE v3.8 was used to concatenate and align these market genes, then TrimAl v1.4 was used to trim the alignments (Edgar, 2004; Capella-Gutiérrez, 2009). The phylogenetic tree was constructed with

Interactive Tree Of Life v5 (Letunic 2019).

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3.4 Results

3.4.1 P. aeruginosa and P. thermotolerans strains are the top survivors of heat shock.

The 86 strains of the Pseudomonas diversity panel were exposed to a five min heat shock at 55°C, 60°C, and 80°C. All strains were normalized to 0.06 OD595 before heat shock. 21 strains recovered from a 55°C heat shock for five min, and 5 strains recovered from a 60°C heat shock for five min (Figure 3.1). Preliminary data (not shown) indicated heat shock survival of P. aeruginosa ID 35 and P. thermotolerans CM3 at 60°C which led to a search for genomically similar strains. P. aeruginosa ID 76, P. aeruginosa ID 79, and P. aeruginosa ID 80 are genomically similar to P. aeruginosa ID 35 and were also able to survive a 60°C five min heat shock (Table A.3.2). No genomically similar strains were found to P. thermotolerans CM3 to add to the diversity panel.

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Figure 3.1. Histogram of 55°C and 60°C heat shock survivors. An 86 strain Pseudomonas diversity panel was exposed to heat shock temperatures of 55°C and 60°C with nine replicates.

Optical density measurements (595 nm) were made 48 h after heat shock. All statistically significant heat shock survivors are included in this histogram (p value less than 0.01). The TSB media blank and P. syringae negative control are also displayed on the histogram.

3.4.2 Some strains had indications of heat shock survival but had high variation between replicates.

P. lundensis ID 55, P. citronellolis (NRRL B-2504), and P. aeruginosa ID 9 had indications of survival at 60°C with average OD595 reads of 0.22 (SD 0.32), 0.13 (SD 0.238), and

0.46 (SD 0.61), respectively but were not selected as survivors due to the high variability between replicates. All three strains consistently recovered from the 55°C heat shock with OD595 reads of 0.952 (SD 0.24), 0.949 (0.156), 1.292 (SD 0.166). Growth stayed consistent at 55°C for

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these three strains, but when the temperature increased to 60°C optical density readings varied between replicates and were not statistically significant. P. sp002386445 ID 75 had indications of survival at 55°C and 60°C with an average OD595 of 0.461 (SD 1.17) at 55°C and an average

OD595 of 0.171 (SD of 2.09) at 60°C but was not selected as a survivor at either temperature due to high variability between replicates leading it to be not statistically significant.

3.4.3 P. aeruginosa and P. thermotolerans can recover from heat shock above 60°C.

Two of the top performers, P. aeruginosa ID 35 and P. thermotolerans CM3, were normalized to 0.06 OD595 then exposed to heat shock temperatures from 60°C to 65°C for five min with two replicates (Figure 3.2). Optical densities of the cultures were read at a wavelength of 595 nm after 24 h of growth in fresh media. After recovering from a 60°C heat shock, P. aeruginosa ID 35 had an average OD595 of 0.91 (SD 0.10), and P. thermotolerans CM3 had an

OD595 of 0.42 (SD 0.058). After a 61°C heat shock, the recovery of P. aeruginosa ID 35 steeply dropped to 0.19 OD595 (SD 0.127). P. aeruginosa ID 35 failed to recover at 62°C or higher with

OD595 reads of 0.08 (SD 0.01). P. thermotolerans CM3 recovered from a 62°C heat shock with an OD595 of 0.14 (SD 0.07), but it was unable to recover from any temperature at or above 63°C with OD595 staying steadily at 0.06 (SD 0). The negative control Pseudomonas Syringae DC3000 remained at 0.05 OD595 (SD 0) at all temperatures. These results suggest a maximum five min heat shock survival temperature of 61°C for P. aeruginosa ID 35 and 62°C for P. thermotolerans

CM3.

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1.2 1

595 0.8 OD 0.6

0.4 Average Average 0.2 0 60°C 60.3°C 61°C 62°C 63.2°C 64.2°C 64.72°C 65°C Temperature

P. aeruginosa ID 35 P. Thermotolerans CM3 P. Syringae DC3000

Figure 3.2. Optical density readings of top survivors after heat shock above 60°C. Optical density reading were taken of the top two Pseudomonas strain survivors after exposure to heat shock temperatures on a gradient from 60°C to 65°C. The top two survivors and a P. syringae negative control were heat shocked at temperatures ranging from 60°C to 65°C and an OD595 reading was taken 24 h after heat shock. Two replicates were included for each strain.

3.4.4 Heat shock survivors tend to cluster together based on genomic similarity.

A phylogenetic tree was created based on 172 Gammaproteobacteria single-copy marker genes to determine the phylogenetic relationships of the Pseudomonas diversity panel (Figure

3.3). Heat shock survivors tended to cluster together with 18 strains grouping with one or more heat shock surviving strains. Three heat shock survivors (P. proteolytica ID 58, P. sp001259595

ID 66, and P. parafulva_B ID 43) did not cluster with other heat shock survivors. Seven 55°C heat shock survivors formed a clade in the phylogenetic tree (P. lundensis ID 55, P. endophytica

ID 34, P. helleri ID 47, P. psychrophila ID 60, P. fragi ID 72, P. versuta ID 71, P. taetrolens ID

57). All four of the P. aeruginosa 60°C survivors (P. aeruginosa ID 76, P. aeruginosa ID 79, P.

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aeruginosa ID 80, P. aeruginosa ID 35) formed a clade with the two 55°C surviving P. citronellolis strains (P. citronellolis ID 26 and P. citronellolis NRRL B-2504 ). These results show a trend that heat shock survivors cluster together based on phylogeny.

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Figure 3.3. Phylogenetic tree of the Pseudomonas diversity panel. A rooted dendrogram of the

phylogenetic relationships between the strains in the Pseudomonas diversity panel was

constructed by comparing 172 Gammaproteobacteria single-copy marker genes. Strains that recovered from 55°C heat shock are indicated in blue text, and strains that recovered from the

60°C heat shock are shown in red text.

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3.4.5 Stress survival genes were enriched in heat shock survivors.

Gene set over-representation analysis and the hypergeometric test were used to determine if any genes from the heat shock survivors were enriched compared to genes from all of the tested strains (Table A.3.3 and A.3.4). Stress related genes were significantly enriched in the

60°C and 55°C heat shock survivor gene list (Table 3.2). Gene lists from 55°C and 60°C heat shock survivors were enriched with Clp protease and GroEL/GroES. Heat shock protein 70 family was enriched with the 55°C heat shock gene list, while universal stress protein family

(uspA) and proteasome core complex were enriched with the 60°C heat shock gene list.

Interestingly, genes related to oxidative stress and cold stress were also enriched in the heat shock survivor gene list. The 55°C gene list was enriched for 1-Cys peroxiredoxin which protects cells against membrane oxidation. The 60°C gene list was enriched for the cold-shock

DNA-binding domain which helps the cell survive when temperatures drop below optimum growth temperatures.

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Table 3.2. Stress related genes enriched in the genomes of heat shock survivors

Heat Shock Sequence Temperature Protein Analysis Accession length pvalue Clp protease Pfam PF00574 213 0.000 Chaperonin 10 Kd subunit; GroES chaperonin family Pfam PF00166 97 0.005 55°C Heat shock protein 70 family Pfam PF00012 638 0.001 C-terminal domain of 1-Cys peroxiredoxin Pfam PF10417 187 0.000 C-terminal D2-small domain of ClpB protein Pfam PF10431 854 0.012 TCP-1/cpn60 chaperonin family; GroEL Pfam PF00118 546 0.006 Universal stress protein family 60°C UspA Pfam PF00582 167 0.006 Histidine kinase- DNA gyrase B- and HSP90-like ATPase Pfam PF02518 991 0.006 Proteasome subunit Pfam PF00227 209 0.012 Cold-shock' DNA-binding domain Pfam PF00313 70 0.012

3.4.6 Response of Pseudomonas strains to heat shock in the presence of protectants.

A subset of 23 Pseudomonas strains were exposed to a 55°C heat shock with seven different protectants and four replicates per strain. 5% skim milk, 50 g/L trehalose, 50 g/L sucrose, 50 g/L xylitol, 1mM proline, 30 mM Glutamate, and 15% glycerol were added to the cultures before the heat shock. The change in growth from immediately after heat shock to 48 h after heat shock was measured to assess the ability of the strains to recover from a five min 55°C heat shock when protectants were added. Glycerol and skim milk had the greatest impact on strain growth after heat shock. Proline and sucrose did not significantly improve the heat shock recovery of any strains, while glutamate, trehalose, and xylitol significantly improved the recovery of one strain (Table 3.3). Glutamate and trehalose both improved the recovery of P.

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aeruginosa ID 35 which was one of the top performing 60°C heat shock survivors. Comparing the recovery of the strain without protectant to the recovery with protectant there was an increase in the OD595 read by 0.15 and 0.16, respectively. Xylitol improved the recovery of P. putida

ATCC 12633 by OD595 0.75 when trehalose was added before heat shock.

Glycerol improved the growth of 10 strains, with 4 strains considered statistically significant (Figure 3.4; P. sp003050925 ID 5, P. aeruginosa ID 9, P. aeruginosa ID 35 and P. putida_M ID 50). In previous heat shock experiments without protectants, P. sp003050925 ID 5 and P. putida_M ID 50, were unable to significantly recover from 55°C heat shock. P. putida_M

ID 50 continued this trend and failed to recover without protectants, but it was able to recover when glycerol was added with average OD595 readings of 0.77 (SD 0.54). In previous experiments, it was observed that P. sp003050925 ID 5 had survived a 55°C heat shock in two out of nine replicates, but this recovery was not considered statistically significant. P. sp003050925 ID 5 survived a 55°C heat shock in this experiment with an average OD595 read of

1.07 (SD 0.06), and the addition of glycerol significantly improved recovery with an average

OD595 increase of 0.61 (SD 0.02). P. aeruginosa ID 35, which was one of the top 60°C survivors from previous experiments, and P. aeruginosa ID 9 survived at 55°C without protectants and recovery was significantly improved when glycerol was added with an OD595 increase of 0.21

(SD 0.05) and 0.10 (SD 0.08). Glycerol itself had no effect on OD595 readings, with the same

OD595 read of 0.03 for the TSB media sample and the TSB sample with glycerol.

Skim milk improved the growth of 12 strains with 10 of those strains considered statistically significant (Figure 3.5). P. fluorescens ATCC 13525, P. viridiflava_B ID 2, P. piscium ID 4, P. mohnii ID 27, P. trivialis ID 33, P. frederiksbergensis_A ID 46, and P. putida_M ID 50 were not able to recover from a 55°C heat shock without protectants, but

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significantly recovered when skim milk was added as a protectant. P. fluorescens ATCC 13525,

P. viridiflava_B ID 2, P. piscium ID 4, P. frederiksbergensis_A ID 46, and P. putida_M ID 50 recovered with OD595 reads of 1.11 (SD 0.07), 0.97 (SD 0.04), 0.89 (SD 0.06), 0.88 (SD 0.03) and 0.90 (SD 0.07), respectively. P. putida ATCC 12633, P. citronellolis NRRL B-2504, P. helleri ID 47, P. fragi ID 72, and P. sp002386445 ID 75 can survive a 55°C heat shock without protectants but adding skim milk significantly improved recovery with an OD595 increase of

0.68, 0.16, 0.39, 0.20 and 0.83, respectively. Adding skim milk does affect OD595 reads with the

TSB media sample having a OD595 reading of 0.05 and the TSB with skim milk having a OD595 reading of 0.07. This change in OD was accounted for in all samples with skim milk added.

To expand on this experiment and observe the growth over time, twelve strains were also grown in a microbioreactor (BioLector Pro) after heat shock and the biomass was measured every 12 minutes for 48 hours. Four strains were selected to compare the recovery of strains with glycerol added before heat shock and eight strains were selected to compare the recovery of strains with skim milk added before heat shock. Peak biomass, generation time, and lag in recovery were compared between strains with and without protectant to determine improvement in recovery after heat shock (Table 3.4). P. putida (ATCC 12633), P. aeruginosa ID 79, P. piscium ID 4, and P. sp003050925 ID 5 were improved when glycerol was added before heat shock with a higher peak biomass, a faster generation time, and a shorter recovery time to enter exponential phase growth (Figures A.3.1, A.3.2, A.3.3, A.3.4). This confirms the results of the previous experiment, but further testing needs to be done to include more of the strains that were previously identified as having a statistically significant recovery with glycerol. P. putida

(ATCC 12633), P. viridiflava_B ID 2, P. piscium ID 4, P. trivialis ID 33, and P. citronellolis

(NRRL B-2504) all had a higher peak biomass, a faster generation time, and a shorter recovery

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time to enter exponsential phase growth when skim milk was added before heat shock (Figures

A.3.5, A.3.6, A.3.7, A.3.8, A.3.9). P. syringae DC3000 was included as a negative control and had no growth after heat shock with or without skim milk (Figure A.3.12). P. lundensis ID 55 had improved generation time and a shorter recovery time, but the peak biomass was not improved (Figure A.3.11). P. helleri ID 47 did not confirm in this experiment and did not have an improved peak biomass, generation time, or recovery time (Figure A.3.10).

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Table 3.3. Strain list and impact of protectants on heat shock recovery. The ID color coding indicates strains unable to recover from a 55°C heat shock without protectant in red, strains able to recover from a 55°C heat shock without protectant in blue, and strains able to recover from a

60°C heat shock without protectant in green. The gray bar indicates significant improvement in recovery when the protectant was added (t test p value less than 0.05).

ID Skim Milk Glycerol Xylitol Trehalose Sucrose Glutamate Proline

P. aeruginosa ID 79 P. aeruginosa ID 35 P. thermotolerans CM3 P. citronellolis (NRRL B-2504) P. aeruginosa ID 9 P. helleri ID 47 P. lundensis ID 55 P. fragi ID 72 P. fluorescens (ATCC 13525) P. putida (ATCC 12633) P. batumici ID 1 P. viridiflava_B ID 2 P. piscium ID 4 P. sp003050925 ID 5 P. silesiensis ID 25 P. mohnii ID 27 P. trivialis ID 33 P. frederiksbergensis_A ID 46 P. putida_M ID 50 P. segetis ID 52 P. avellanae ID 68 P. sp002386445 ID 75 P. syringae DC3000 Total Strains Protected 10 4 1 1 0 1 0

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1.8 1.6 1.4 1.2

1 595

OD 0.8 0.6 0.4 0.2 0

Strain

Strain Only Strain with Glycerol

Figure 3.4. Histogram of the change in strain growth after heat shock with glycerol.

Change in strain growth was measured after a 55°C heat shock with glycerol. A subset of 23 strains from the Pseudomonas diversity panel were exposed to heat shock at 55°C with four replicates. The y-axis displays the change in optical density measurements (595 nm) from immediately after heat shock to 48h after heat shock. An asterisk represents statistically significant change in optical density measurements (595 nm) between heat shocked strain growth without protectants and heat shocked strain growth with glycerol added (t test p value less than

0.05). Strains that never grew after heat shock were excluded from the histogram.

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1.6

1.4

1.2

595 0.8 OD 0.6

0.4

0.2

Strains

Strain Only Strain with Skim Milk

Figure 3.5. Histogram of the change in strain growth after heat shock with skim milk.

Change in strain growth was measured after a 55°C heat shock with skim milk. A subset of 23 strains from the Pseudomonas diversity panel were exposed to heat shock at 55°C with four replicates. The y-axis displays the change in optical density measurements (595 nm) from immediately after heat shock to 48h after heat shock. An asterisk represents statistically significant change in optical density measurements (595 nm) between heat shocked strain growth without protectants and heat shocked strain growth with skim milk (t test p value less than 0.05).

Strains that never grew after heat shock were excluded from the histogram.

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Table 3.4. Delta peak biomass, delta generation time, and delta recovery time of heat shocked strains grown in the BioLector Pro. NA indicates a value could not be calculated due to not having a peak biomass or cells did not double during exponential phase growth to calculate generation time.

Delta Peak Biomass Delta Generation Delta Recovery Protectant Strain (arb. unit) Time (h) Time (h) Glycerol P. putida (ATCC 12633) 23.7 6.1 -2 Glycerol P. aeruginosa ID 79 6.435 9.6 -3 Glycerol P. piscium ID 4 8.875 3.6 25 Glycerol P. sp003050925 ID 5 15.2675 1.5 -8 Skim Milk P. putida (ATCC 12633) 13.4675 3 13 Skim Milk P. viridiflava_B ID 2 9.0125 7 27 Skim Milk P. piscium ID 4 6.715 NA 20 Skim Milk P. trivialis ID 33 12.9175 4.4 -11 P. citronellolis (NRRL B- Skim Milk 2504) 4.0725 1 -6 Skim Milk P. helleri ID 47 -1.6575 -3.4 0 Skim Milk P. lundensis ID 55 -0.58 2.2 -3 Skim Milk P. syringae DC3000 NA NA 0

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3.5 Discussion

3.5.1 Stress resilience of the top survivors: P. thermotolerans and P. aeruginosa

P. thermotolerans CM3 and several strains of P. aeruginosa were the top survivors of the five-minute 60°C heat shock. P. thermotolerans and P. aeruginosa are known to be some of the more heat resilient species of Pseudomonas. P. thermotolerans is the only thermotolerant species of Pseudomonas with a maximum growth temperature of 55°C. P. thermotolerans CM3, the

60°C heat shock survivor, was originally isolated from industrial cooking water (Manaia and

Moore, 2002). This bacterium has an optimal growth temperature of 47°C and at temperatures above 50°C the membrane phospholipids predominantly consist of saturated fatty acids (C16:0).

This transition of the phospholipid membrane to saturated fatty acids offsets the heat induced increase in membrane fluidity (Fan et al., 2015).

P. aeruginosa strains are mostly opportunistic human pathogens that cause disease in immunocompromised individuals, but they have also shown potential for the bioremediation of environmental pollutants (Chellaiah 2018). P. aeruginosa infections can be particularly dangerous due the adaptations against stress and the development of antibiotic resistance. For example, there is co-regulation of the heat shock response and the conversion into a mucoid colony morphology which is a characteristic of strains involved in chronic respiratory infections of cystic fibrosis patients (Schurr and Deretic, 1997). P. aeruginosa strains have a plethora of adaptations to survive stress including quorum sensing, biofilm formation, the stringent response, and the heat shock response (Moradali et al., 2017).

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3.5.2 Heat shock proteins and universal stress response proteins were enriched in the heat shock survivor gene lists.

The 55°C and 60°C heat shock survivor gene lists were significantly enriched with stress response genes. The Clp protease family, GroEL/ES chaperonin family, 70 kilodalton heat shock protein (Hsp70 or DnaK), and universal stress protein (USP) superfamily were enriched in the heat shock survivors and these are all well studied responses to heat stress. Clp proteases are enzymes that catalyze the breakdown of proteins and are vital to the removal of damaged or misfolded proteins (Hall et al., 2017). The GroEL/ES chaperonin family are molecular chaperones that facilitate the correct folding of proteins (Georgescauld et al., 2014). The hsp70

(or DnaK) system is a family of heat shock proteins that bind to damaged or incomplete proteins to prevent them from aggregating (Mayer et al., 2000). The universal stress protein superfamily defends against many different stresses by protecting against DNA damage (Gustavsson et al.,

2002).

The significance of heat shock protease encoding genes, such as the clp protease family, was tested in a strain of P. aeruginosa (Basta et al., 2020). When protease genes are deleted, protein aggregation becomes a major problem during growth arrest. Transcriptome analysis was performed on P. aeruginosa PAO1 grown at human body temperature (37°C) and an elevated temperature (46°C) (Chan et al., 2016). RNA sequencing revealed 133 genes were differentially expressed including the upregulation of Hsp70, ClpB and groEL at 46°C compared to human body temperature.

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3.5.3 Pseudomonas strain recovery with protectants

Protectants can be externally applied to beneficial strains to improve survival from stress exposure. In this study the disaccharides (trehalose and sucrose), sugar alcohols (xylitol and glycerol), amino acids (proline and glutamate), and the general protectant skim milk were added to culture before heat shock. Glycerol and skim milk protected the most strains out of 23

Pseudomonas strains tested. Glycerol is a cryoprotectant typically used for long-term bacterial preservation. Glycerol can diffuse across cellular membranes and protect cells by reducing the harmful effect of ice crystals (Cabrera et al., 2020). Although glycerol is a common cryoprotectant, in this experiment, it also significantly improved the recovery of four strains against heat shock.

Skim milk was the top performing protectant in this experiment by significantly improving the recovery of seven heat shocked Pseudomonas strains. Skim milk is an industrially important protectant consisting of lactose, casein, whey protein, fat, and inorganic salts that is commonly added for spray drying and freeze drying of beneficial bacteria (Hao et al., 2021; Zheng et al.,

2015). Zheng and colleagues tested the protective properties of the components of skim milk during drying. Calcium and milk proteins contribute to cell survival by stabilizing cellular structures and agglomerating to coat cells. Milk fat was found to be detrimental to the cellular membrane during dehydration. Lactose proved to be an optimal lyoprotectant for freeze drying and cold storage of a P. fluorescens strain utilized for the control of fire blight disease (Cabrefiga et al., 2014). Protectant studies have been conducted on beneficial Pseudomonas strains, and typically the optimal protectant varies depending on the strain (Schisler et al., 2016). Overall skim milk and glycerol were the highest performing protectants in this study, but other protectants such as xylitol and trehalose were still able to significantly benefit a few strains.

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3.6 Conclusion

P. thermotolerans and P. aeruginosa were the two top species capable of recovering from heat shock. Studies on P. aeruginosa strains have demonstrated the adaptability of this species to stress and how changes in stress regulation can lead to the mucoid colony morphology which is an indicator of chronic lung infection in cystic fibrosis patients (Schurr and Deretic, 1997). P. thermotolerans is the only thermotolerant species of Pseudomonas and has been discovered surviving in extreme conditions such as industrial cooking water (Manaia and Moore, 2002).

Strains with high genomic similarity to the top heat shock survivors were also able to recover from a five-minute heat shock at 60°C (P. aeruginosa ID 76, P. aeruginosa ID 79, and P. aeruginosa ID 80). Overall, heat shock tolerant strains clustered together based on phylogenetic similarity.

Gene lists of heat shock surviving strains were enriched with heat shock proteins and universal stress response proteins such as HSP70, GroEL/ES, and clp protease compared to the strains that could not recover from heat shock. A subset of the Pseudomonas diversity panel strains were treated with common protectants prior to exposure to heat shock temperatures, and skim milk was the protectant that benefited the highest number of strains. To become successful products, beneficial Pseudomonas strains must be resilient to formulation stresses such as heat shock. This study evaluated maximum limitations of heat shock survival for Pseudomonas species, detected phylogenetic and genotypic predictors of heat shock survival, and determined the highest performing heat shock protectants for a diverse set of Pseudomonas species.

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APPENDICES

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Appendix A: Chapter 2

Table A.2.1. Pseudomonas identification and ASV counts.

ASV Number Taxonomy from Blast Treatment Mean ASV Count SD ASV Count No Stress 18,226 16,819 Oven 107,708 83,371 ASV2 Pseudomonas koreensis Soil 527 383 No Stress 79,269 31,725 Oven 78 22 ASV4 Unclassified Pseudomonas Soil 600 569 No stress 68,771 71,114 ASV5 Pseudomonas fluorescens Soil 126 82 No stress 23,546 24,537 ASV17 Pseudomonas putida Soil 205 307 No stress 8,065 8,820 ASV26 Unclassified Pseudomonas Soil 43 75 No stress 5,159 11,536 ASV34 Unclassified Pseudomonas Soil 52 30 No stress 741 1,656 ASV136 Pseudomonas aeruginosa Soil 72 161 ASV245 Pseudomonas graminis Soil 467 487 ASV465 Unclassified Pseudomonas Soil 254 329 ASV500 Pseudomonas syringae Soil 239 171 ASV863 Pseudomonas koreensis Oven 135 302 ASV1103 Pseudomonas fluorescens Soil 101 105 ASV1977 Unclassified Pseudomonas Soil 43 51 ASV2297 Pseudomonas syringae Soil 34 48 ASV2537 Pseudomonas chlororaphis Soil 28 40

129

Table A.2.1 (continued).

Pseudomonas ASV2768 frederiksbergensis Soil 24 34 ASV5528 Pseudomonas syringae Soil 4 9 ASV7954 Pseudomonas fluorescens No stress 1 1 ASV8143 Pseudomonas fluorescens No stress 1 1 ASV8378 Pseudomonas fluorescens No stress 0 1

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Table A.2.2. ASV Sequences.

ASV Number DNA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV2 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATTC AAAACTGACAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV4 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTC AAAACTGACAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV5 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV17 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGAATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV26 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTCAGCAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATC CAAAACTACTGAGCTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGT AGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGA ASV34 CCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTCGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCGAGCTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV136 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA

131

Table A.2.2 (continued).

GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGAATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTAGGGCAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV245 CACCTGGGCTCATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTAGGGCAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV465 CACCTGGGCTCATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGAATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV500 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAGGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV863 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTC AAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV1103 CACCTGGACTAATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTCAGCAAGTTGAAGGTGAAATCCCCGGGCTCAACCTGGGAACTGCCTC CAAAACTACTGAGCTAGAGTACGGTAGAGGGTAGTGGAATTTCCTGTGT AGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGA ASV1977 CTACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTGGTTAAGTTGAATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCCAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV2297 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTCGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV2537 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA

132

Table A.2.2 (continued).

GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTCGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTC AAAACTGTCGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV2768 CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGAATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCC AAAACTGGCAAGCTAGAGTAGGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC ASV5528 CACCTGGACTCATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAA TCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTG GGCGTAAAGCGCGCGTAGGTGGTTTGTTAAGTTGGATGTGAAAGCCCCG GGCTCAACCTGGGAACTGCATTCAAAACTGACAAGCTAGAGTATGGTAG AGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAG GAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTG ASV7954 AGGTGCGAAAGCGTGGGGAGCAA GTTCTACAGTGCCAGCCGCCGCGGTAATACAGAGGGTGCAAGCGTTAAT CGGAATTACTGGGCGTAAAGCGCGCGTAGGAGGTTTGTTAAGTTGGATG TGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACAAGCTA GAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTA GATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATA ASV8143 CTGACACTGAGGTGCGAAAGCGTGGGGAGCAA GGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGG TTTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATTC AAAACTGACAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTA GCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGAC CACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCA ASV8378 AACAGGATTAGATACCCGGGTAGTCC

133

Appendix B: Chapter 3

Table A.3.1. Pseudomonas diversity panel isolation information. Table lists strain ID, isolation environment, year isolate was acquired, and isolation media.

Identifier Environment Date Isolation Media P. putida (ATCC 12633) Purchased 2020 Luria-Bertani Agar P. fluorescens (ATCC 13525) Purchased 2020 Luria-Bertani Agar P. citronellolis (NRRL B- 2504) Purchased 2020 Luria-Bertani Agar P. resinovorans (NRRL B- 2649) Purchased 2020 Luria-Bertani Agar P. thermotolerans CM3 Purchased 2018 Luria-Bertani Agar P. syringae DC3000 Purchased 2018 Luria-Bertani Agar P. batumici ID 1 Rhizosphere 2013 Luria-Bertani Agar P. viridiflava_B ID 2 Root Endophyte 2013 Modified M9 Minimal Salts Agar P. sp001655615 ID 3 Root Endophyte 2013 Modified M9 Minimal Salts Agar P. piscium ID 4 Rhizosphere 2013 Modified M9 Minimal Salts Agar P. sp003050925 ID 5 Rhizosphere 2013 Modified M9 Minimal Salts Agar P. sp900187635 ID 6 Root Endophyte 2013 Modified M9 Minimal Salts Agar P. sp900005815 ID 7 Root Endophyte 2013 Modified M9 Minimal Salts Agar P. oryzihabitans_B ID 8 Rhizosphere 2013 Modified M9 Minimal Salts Agar P. aeruginosa ID 9 Leaf 2013 Modified M9 Minimal Salts Agar P. moraviensis ID 10 Root Endophyte 2014 Modified M9 Minimal Salts Agar P. monteilii_A ID 11 Root Endophyte 2014 Modified M9 Minimal Salts Agar P. sp900187615 ID 12 Rhizosphere 2014 Modified M9 Minimal Salts Agar P. putida_C ID 13 Soil 2014 Modified M9 Minimal Salts Agar P. sp900110765 ID 14 Soil 2014 Modified M9 Minimal Salts Agar P. sp000282415 ID 15 Root Endophyte 2014 Modified M9 Minimal Salts Agar P. sp000282315 ID 16 Soil 2014 Modified M9 Minimal Salts Agar P. sp002338065 ID 17 Root Endophyte 2014 Modified M9 Minimal Salts Agar P. sp000282215 ID 18 Soil 2014 Modified M9 Minimal Salts Agar

134

Table A.3.1 (continued).

P. sp003050835 ID 19 Soil 2014 Modified M9 Minimal Salts Agar P. sp002980155 ID 20 Soil 2014 Modified M9 Minimal Salts Agar P. arsenicoxydans ID 21 Soil 2014 Modified M9 Minimal Salts Agar P. putida_J ID 22 Rhizosphere 2014 Modified M9 Minimal Salts Agar P. fuscovaginae_B ID 23 Rhizosphere 2014 Modified M9 Minimal Salts Agar P. fluorescens_E ID 24 Root Endophyte 2014 Modified M9 Minimal Salts Agar Pseudomonas_E ID 25 Rhizosphere 2014 Modified M9 Minimal Salts Agar P. citronellolis ID 26 Leaf 2014 Modified M9 Minimal Salts Agar P. mohnii ID 27 Phylloplane 2014 Modified M9 Minimal Salts Agar P. donghuensis ID 28 Soil 2014 Modified M9 Minimal Salts Agar P. sp002113025 ID 29 Soil 2014 Modified M9 Minimal Salts Agar P. sp002356535 ID 30 Soil 2014 Modified M9 Minimal Salts Agar P. fluorescens_W ID 31 Soil 2014 Modified M9 Minimal Salts Agar P. sp001547895 ID 32 Insect 2014 Oxoid Brilliance Bacillus Cereus Agar P. trivialis ID 33 Root Endophyte 2014 Oxoid Brilliance Bacillus Cereus Agar P. endophytica ID 34 Seed 2014 Oxoid Brilliance Bacillus Cereus Agar P. aeruginosa ID 35 Insect 2014 Modified M9 Minimal Salts Agar P. alkylphenolica ID 36 Soil 2015 Oxoid Brilliance Bacillus Cereus Agar P. sp003231305 ID 37 Soil 2016 Oxoid Brilliance Bacillus Cereus Agar P. sp002379585 ID 38 Soil 2016 Oxoid Brilliance Bacillus Cereus Agar P. sp001320045 ID 39 Soil 2016 Oxoid Brilliance Bacillus Cereus Agar P. sp000282535 ID 40 Soil 2016 Modified M9 Minimal Salts Agar P. sp900101695 ID 41 Soil 2016 Modified M9 Minimal Salts Agar P. sp002080045 ID 42 Soil 2016 Modified M9 Minimal Salts Agar P. parafulva_B ID 43 Leaf 2016 Modified M9 Minimal Salts Agar P. guariconensis ID 44 Rhizosphere 2016 Modified M9 Minimal Salts Agar P. sp002000165 ID 45 Rhizosphere 2016 Modified M9 Minimal Salts Agar P. frederiksbergensis_A ID 46 Unknown 2016 Luria-Bertani Agar P. helleri ID 47 Unknown 2016 Luria-Bertani Agar

135

Table A.3.1 (continued).

P. reinekei ID 48 Root Endophyte 2016 Modified M9 Minimal Salts Agar P. sp900113505 ID 49 Root Endophyte 2016 Modified M9 Minimal Salts Agar P. putida_M ID 50 Root Endophyte 2017 Modified M9 Minimal Salts Agar P. sp003253735 ID 51 Rhizosphere 2017 Modified M9 Minimal Salts Agar P. segetis ID 52 Soil 2017 Modified M9 Minimal Salts Agar P. moorei ID 53 Soil 2017 Modified M9 Minimal Salts Agar P. sp002419965 ID 54 Leaf 2017 Modified M9 Minimal Salts Agar P. lundensis ID 55 Root Endophyte 2017 Potato Media P. sp001421885 ID 56 Soil 2017 Luria-Bertani Agar P. taetrolens ID 57 Root Endophyte 2017 Potato Media P. proteolytica ID 58 Root Endophyte 2017 Potato Media P. veronii ID 59 Root Endophyte 2017 Carrot Agar P. psychrophila ID 60 Root Endophyte 2017 Carrot Agar P. viridiflava ID 61 Root Endophyte 2017 Carrot Agar P. poae_A ID 62 Tuber 2017 M63 Agar P. sp900108875 ID 63 Excrement 2017 Modified M9 Minimal Salts Agar P. otitidis ID 64 Amphibian 2017 Luria-Bertani Agar P. alcaligenes_A ID 65 Amphibian 2017 Luria-Bertani Agar P. sp001259595 ID 66 Amphibian 2017 Luria-Bertani Agar P. sp003205205 ID 67 Amphibian 2017 Luria-Bertani Agar P. avellanae ID 68 Purchased 2017 N/A P. ficuserectae ID 69 Purchased 2017 N/A P. sp001269545 ID 70 Soil 2017 Oxoid Brilliance Bacillus Cereus Agar P. versuta ID 71 Rhizosphere 2018 Luria-Bertani Agar P. fragi ID 72 Rhizosphere 2018 Luria-Bertani Agar P. coleopterorum ID 73 Leaf 2018 Modified M9 Minimal Salts Agar P. sp002699985 ID 74 Leaf 2018 Modified M9 Minimal Salts Agar National Botanical Research Institute's P. sp002386445 ID 75 Root Endophyte 2018 Phosphate Growth Medium P. aeruginosa ID 76 Crustacean 2013 Modified M9 Minimal Salts Agar

136

Table A.3.1 (continued).

P. aeruginosa ID 77 Leaf 2014 Modified M9 Minimal Salts Agar P. aeruginosa ID 78 Phylloplane 2014 Modified M9 Minimal Salts Agar P. aeruginosa ID 79 Soil 2014 Rice Enrichment P. aeruginosa ID 80 Soil 2014 Rice Enrichment

Table A.3.2. Genetically similar strains to P. aeruginosa ID 35. KID is Kmer identity meaning the percentage of shared Kmers between the query and subject genomes.

ID Hit ID KID Genus Species ID 76 ID 35 84.5 Pseudomonas aeruginosa ID 80 ID 35 70.8 Pseudomonas aeruginosa ID 79 ID 35 69.7 Pseudomonas aeruginosa

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Table A.3.3. 60°C enriched gene table (p value equal to or less than 0.01)

Sequence Start Stop ID Pvalue length Analysis Accession Description location location DAHP synthetase I 60CGene1 0.012 419 Pfam PF00793 family 102 401 Bacterial regulatory 60CGene2 0.006 244 Pfam PF00196 proteins luxR family 180 234 Thioesterase 60CGene3 0.002 181 Pfam PF03061 superfamily 43 116 Enoyl-Acyl carrier 60CGene4 0.012 247 Pfam PF13561 protein reductase 12 244 DnaB-like helicase C 60CGene5 0.006 479 Pfam PF03796 terminal domain 211 467 Aconitase C-terminal 60CGene6 0.002 217 Pfam PF00694 domain 1 132 ABC transporter transmembrane 60CGene7 0.006 722 Pfam PF00664 region 176 436 Uncharacterised protein family 60CGene8 0.006 86 Pfam PF06794 UPF0270 12 78 Type VI secretion 60CGene9 0.002 62 Pfam PF05638 system effector Hcp 6 62 Type VI secretion 60CGene10 0.002 62 Pfam PF05638 system effector Hcp 6 62 Phage portal protein 60CGene11 0.002 494 Pfam PF05136 lambda family 29 375 Gene 25-like 60CGene12 0.002 58 Pfam PF04965 lysozyme 14 52 Iron-containing 60CGene13 0.006 304 Pfam PF14518 redox enzyme 55 219 Cytochrome C oxidase cbb3-type 60CGene14 0.002 140 Pfam PF13442 subunit III 62 134 Transcription elongation factor 60CGene15 0.002 160 Pfam PF01272 GreA/GreB C-term 85 158 Putative zinc- or iron-chelating 60CGene16 0.006 134 Pfam PF03692 domain 25 125

138

Table A.3.3 (continued).

NO signalling/Golgi transport ligand- SUPERF SSF1111 binding domain 60CGene17 0.012 176 AMILY 26 superfamily 6 144 Iron/manganese superoxide dismutases C- 60CGene18 0.006 193 Pfam PF02777 terminal domain 89 189 Homoserine dehydrogenase NAD 60CGene19 0.012 490 Pfam PF03447 binding domain 10 127 Universal stress 60CGene20 0.006 167 Pfam PF00582 protein family 3 157 Outer membrane 60CGene21 0.012 414 Pfam PF13525 lipoprotein 105 307 Imidazoleglycerol- phosphate 60CGene22 0.012 210 Pfam PF00475 dehydratase 47 189 Haemagluttinin 60CGene23 0.002 87 Pfam PF05594 repeat 7 68 Phenazine biosynthesis protein 60CGene24 0.002 55 Pfam PF03284 A/B 1 55 Carbohydrate- selective porin OprB 60CGene25 0.002 32 Pfam PF04966 family 1 32 Protein of unknown 60CGene26 0.006 173 Pfam PF04751 function DUF615 12 163 BFD-like [2Fe-2S] 60CGene27 0.002 72 Pfam PF04324 binding domain 2 49 Protein of unknown 60CGene28 0.006 86 Pfam PF07023 function DUF1315 6 66 60CGene29 0.006 269 Pfam PF13614 AAA domain 9 184 Outer membrane 60CGene30 0.012 167 Pfam PF03938 protein OmpH-like 24 163 Ureidoglycolate 60CGene31 0.006 167 Pfam PF04115 lyase 2 158 Protein of unknown 60CGene32 0.006 158 Pfam PF07080 function DUF1348 11 140

139

Table A.3.3 (continued).

60CGene33 0.012 209 Pfam PF00227 Proteasome subunit 35 202 Uncharacterized protein conserved in 60CGene34 0.006 355 Pfam PF10095 bacteria DUF2333 31 355 60CGene35 0.012 335 Pfam PF04545 Sigma-70 region 4 268 321 Branched-chain amino acid ATP- binding cassette 60CGene36 0.012 241 Pfam PF12399 transporter 214 236 Transglycosylase 60CGene37 0.006 81 Pfam PF04226 associated protein 33 80 Proteolipid membrane potential 60CGene38 0.012 52 Pfam PF01679 modulator 2 49 Amino acid 60CGene39 0.002 461 Pfam PF00324 permease 17 425 RimP N-terminal 60CGene40 0.002 169 Pfam PF02576 domain 28 100 Cold-shock' DNA- 60CGene41 0.012 70 Pfam PF00313 binding domain 6 68 Glycine zipper 2TM 60CGene42 0.006 154 Pfam PF05433 domain 61 101 ATPase family associated with various cellular 60CGene43 0.002 281 Pfam PF00004 activities AAA 29 173 Glutamine synthetase 60CGene44 0.012 468 Pfam PF00120 catalytic domain 102 465 Aminoacyl-tRNA 60CGene45 0.002 156 Pfam PF04073 editing domain 32 140 60CGene46 0.002 460 Pfam PF07731 Multicopper oxidase 352 459 Inorganic 60CGene47 0.012 175 Pfam PF00719 pyrophosphatase 18 174 Respiratory-chain NADH dehydrogenase 30 60CGene48 0.006 593 Pfam PF00329 Kd subunit 44 171

140

Table A.3.3 (continued).

Glutamine synthetase 60CGene49 0.012 452 Pfam PF00120 catalytic domain 115 446 Isocitrate/isopropylm 60CGene50 0.002 360 Pfam PF00180 alate dehydrogenase 5 353 C-terminal D2-small domain of ClpB 60CGene51 0.012 854 Pfam PF10431 protein 764 843 SUPERF SSF5630 60CGene52 0.002 50 AMILY 0 15 50 60CGene53 0.002 369 Pfam PF04551 GcpE protein 13 352 SUPERF SSF5209 STAS domain 60CGene54 0.012 160 AMILY 1 superfamily 6 106 Transcriptional regulatory protein C 60CGene55 0.012 226 Pfam PF00486 terminal 149 221 Serine hydroxymethyltransf 60CGene56 0.002 417 Pfam PF00464 erase 9 385 Histidine kinase- DNA gyrase B- and 60CGene57 0.006 991 Pfam PF02518 HSP90-like ATPase 866 976 Quinolinate 60CGene58 0.012 352 Pfam PF02445 synthetase A protein 34 339 LysR substrate 60CGene59 0.006 307 Pfam PF03466 binding domain 92 290 Aminoacyl tRNA synthetase class II N- 60CGene60 0.012 338 Pfam PF02912 terminal domain 20 87 tRNA synthetases class I E and Q 60CGene61 0.006 583 Pfam PF00749 catalytic domain 94 410 Histidine kinase- DNA gyrase B- and 60CGene62 0.012 634 Pfam PF02518 HSP90-like ATPase 32 174 Tubulin/FtsZ family 60CGene63 0.012 398 Pfam PF00091 GTPase domain 14 173

141

Table A.3.3 (continued).

Succinate dehydrogenase/Fuma rate reductase transmembrane 60CGene64 0.012 122 Pfam PF01127 subunit 10 83 Transketolase C- 60CGene65 0.012 340 Pfam PF02780 terminal domain 203 325 LysR substrate 60CGene66 0.006 324 Pfam PF03466 binding domain 89 263 PPIC-type PPIASE 60CGene67 0.006 93 Pfam PF13616 domain 6 91 Ring hydroxylating alpha subunit 60CGene68 0.006 430 Pfam PF00848 catalytic domain 189 411 3-octaprenyl-4- hydroxybenzoate 60CGene69 0.012 488 Pfam PF01977 carboxy-lyase 11 431 Ribonuclease E/G 60CGene70 0.002 485 Pfam PF10150 family 115 389 Biotin carboxylase 60CGene71 0.002 451 Pfam PF00289 N-terminal domain 5 113 Sodium:dicarboxylat 60CGene72 0.012 462 Pfam PF00375 e symporter family 28 430 Putative endonuclease protein of unknown function 60CGene73 0.012 209 Pfam PF08682 DUF1780 2 209 Nucleoside-specific channel-forming 60CGene74 0.012 260 Pfam PF03502 protein Tsx 50 255 Ribosomal protein 60CGene75 0.012 130 Pfam PF00410 S8 5 130 TCP-1/cpn60 60CGene76 0.006 546 Pfam PF00118 chaperonin family 23 523 Helix-turn-helix 60CGene77 0.012 290 Pfam PF01418 domain rpiR family 6 80 50S ribosome- 60CGene78 0.006 366 Pfam PF01926 binding GTPase 5 113 60CGene79 0.006 65 Pfam PF02810 SEC-C motif 47 64

142

Table A.3.3 (continued).

Binding-protein- dependent transport system inner membrane 60CGene80 0.006 229 Pfam PF00528 component 32 220 Crossover junction endodeoxyribonuclea 60CGene81 0.002 174 Pfam PF02075 se RuvC 4 150 Binding-protein- dependent transport system inner membrane 60CGene82 0.012 223 Pfam PF00528 component 45 211 MraZ protein putative antitoxin- 60CGene83 0.012 151 Pfam PF02381 like 78 132 Outer membrane 60CGene84 0.012 205 Pfam PF03550 lipoprotein LolB 48 198 Glu-tRNAGln amidotransferase C 60CGene85 0.012 95 Pfam PF02686 subunit 19 90 Inhibitor of apoptosis-promoting 60CGene86 0.012 223 Pfam PF01027 Bax1 21 216 4Fe-4S binding 60CGene87 0.012 83 Pfam PF00037 domain 4 25

143

Table A.3.4. 55°C enriched gene table (p value equal to or less than 0.01).

Sequence Start Stop ID pvalue Length Analysis Accession Description Location Location Putative membrane protein insertion 55CGene1 0.000 85 Pfam PF01809 efficiency factor 7 72 55CGene2 0.006 101 Pfam PF07978 NIPSNAP 6 99 Iron-sulphur cluster 55CGene3 0.006 107 Pfam PF01521 biosynthesis 1 103 HNH 55CGene4 0.006 124 Pfam PF01844 endonuclease 40 88 Acetyltransferase 55CGene5 0.006 151 Pfam PF00583 GNAT family 35 123 Bacterial regulatory proteins luxR 55CGene6 0.000 233 Pfam PF00196 family 172 227 55CGene7 0.006 86 Pfam PF17209 Hfq protein 7 68 Transketolase pyrimidine 55CGene8 0.006 351 Pfam PF02779 binding domain 17 192 Multiple resistance and pH regulation protein 55CGene9 0.006 92 Pfam PF04066 F MrpF / PhaF 34 85 Coproporphyrinog 55CGene10 0.006 304 Pfam PF01218 en III oxidase 8 301 Aminotransferase 55CGene11 0.001 403 Pfam PF00155 class I and II 35 391 Glutathione S- transferase C- 55CGene12 0.006 205 Pfam PF13410 terminal domain 125 186 ATP-dependent protease La LON substrate-binding 55CGene13 0.006 798 Pfam PF02190 domain 6 197

144

Table A.3.4 (continued).

Phosphoribosyl- ATP pyrophosphohydr 55CGene14 0.006 110 Pfam PF01503 olase 7 95 55CGene15 0.006 293 Pfam PF00549 CoA-ligase 151 270 Proteasome 55CGene16 0.006 176 Pfam PF00227 subunit 2 169 55CGene17 0.001 182 Pfam PF01381 Helix-turn-helix 7 60 Ribosomal L18 of archaea bacteria mitoch. and 55CGene18 0.001 116 Pfam PF00861 chloroplast 3 116 55CGene19 0.005 150 Pfam PF00075 RNase H 4 141 NADH- ubiquinone/plasto quinone oxidoreductase 55CGene20 0.005 114 Pfam PF00420 chain 4L 5 109 55CGene21 0.005 314 Pfam PF00551 Formyl transferase 119 294 D-isomer specific 2-hydroxyacid dehydrogenase 55CGene22 0.005 458 Pfam PF00389 catalytic domain 63 375 Bacterial regulatory protein 55CGene23 0.006 119 Pfam PF02954 Fis family 90 119 ATP phosphoribosyltra 55CGene24 0.005 211 Pfam PF01634 nsferase 51 202 Iron-sulphur cluster 55CGene25 0.005 116 Pfam PF01521 biosynthesis 9 111 Phospho-N- acetylmuramoyl- pentapeptide- transferase 55CGene26 0.005 360 Pfam PF10555 signature 1 68 80

145

Table A.3.4 (continued).

Putative zinc- or iron-chelating 55CGene27 0.005 117 Pfam PF03692 domain 19 81 NADH- ubiquinone/plasto quinone oxidoreductase 55CGene28 0.001 102 Pfam PF00420 chain 4L 10 96 Inorganic 55CGene29 0.001 175 Pfam PF00719 pyrophosphatase 18 174 Ribosomal L29 55CGene30 0.000 63 Pfam PF00831 protein 4 59 55CGene31 0.005 296 Pfam PF01513 ATP-NAD kinase 6 271 55CGene32 0.012 79 Pfam PF01722 BolA-like protein 22 70 Ribosomal protein 55CGene33 0.001 156 Pfam PF00177 S7p/S5e 1 149 55CGene34 0.000 166 Pfam PF00691 OmpA family 66 160 55CGene35 0.001 193 Pfam PF08281 Sigma-70 region 4 132 181 Ribonucleotide reductase small 55CGene36 0.006 416 Pfam PF00268 chain 96 383 55CGene37 0.000 188 Pfam PF04545 Sigma-70 region 4 131 180 55CGene38 0.005 85 Pfam PF07130 YebG protein 1 71 55CGene39 0.006 224 Pfam PF00929 Exonuclease 32 206 ATP-grasp 55CGene40 0.001 388 Pfam PF08442 domain 2 203 MotA/TolQ/ExbB proton channel 55CGene41 0.006 231 Pfam PF01618 family 82 211 Protein of unknown function 55CGene42 0.006 96 Pfam PF11191 DUF2782 23 95 Iron-sulphur cluster 55CGene43 0.006 194 Pfam PF01521 biosynthesis 4 100 55CGene44 0.000 213 Pfam PF00574 Clp protease 29 208

146

Table A.3.4 (continued).

Glucose inhibited 55CGene45 0.006 630 Pfam PF01134 division protein A 9 399 Domain of unknown function 55CGene46 0.006 100 Pfam PF08980 DUF1883 1 76 Aminopeptidase I zinc metalloprotease 55CGene47 0.006 429 Pfam PF02127 M18 12 423 AIR synthase related protein C- 55CGene48 0.001 352 Pfam PF02769 terminal domain 177 344 Biotin carboxylase N-terminal 55CGene49 0.006 478 Pfam PF00289 domain 9 116 55CGene50 0.012 311 Pfam PF04545 Sigma-70 region 4 256 306 TENA/THI- 55CGene51 0.001 292 Pfam PF03070 4/PQQC family 56 264 AICARFT/IMPC 55CGene52 0.006 546 Pfam PF01808 Hase bienzyme 151 477 55CGene53 0.001 638 Pfam PF00012 Hsp70 protein 4 603 Respiratory-chain NADH dehydrogenase 51 55CGene54 0.006 451 Pfam PF01512 Kd subunit 62 234 Glycyl-tRNA synthetase alpha 55CGene55 0.001 317 Pfam PF02091 subunit 11 293 3' exoribonuclease 55CGene56 0.006 240 Pfam PF01138 family domain 1 11 141 Thioesterase 55CGene57 0.005 133 Pfam PF03061 superfamily 29 103 Domain of unknown function 55CGene58 0.006 108 Pfam PF08850 DUF1820 8 102 55CGene59 0.006 73 Pfam PF03621 MbtH-like protein 1 53

147

Table A.3.4 (continued).

Rho termination factor RNA- 55CGene60 0.000 419 Pfam PF07497 binding domain 52 125 55CGene61 0.006 146 Pfam PF07977 FabA-like domain 11 137 Aspartate/ornithin e carbamoyltransfer ase Asp/Orn 55CGene62 0.006 334 Pfam PF00185 binding domain 171 317 RuvA C-terminal 55CGene63 0.006 218 Pfam PF07499 domain 174 217 NO signalling/Golgi transport ligand- SUPERF SSF1111 binding domain 55CGene64 0.005 176 AMILY 26 superfamily 27 262 Elongation factor 55CGene65 0.001 606 Pfam PF00679 G C-terminus 397 477 Enolase C- terminal TIM 55CGene66 0.006 429 Pfam PF00113 barrel domain 144 425 Ribosomal protein 55CGene67 0.005 92 Pfam PF01649 S20 2 83 DNA gyrase B subunit insert 55CGene68 0.006 808 Pfam PF18053 domain 568 733 55CGene69 0.005 369 Pfam PF04551 GcpE protein 13 352 Ribosomal protein 55CGene70 0.006 128 Pfam PF01196 L17 20 116 RNA polymerase 55CGene71 0.000 87 Pfam PF01192 Rpb6 8 59 55CGene72 0.006 146 Pfam PF07977 FabA-like domain 11 137 TCP-1/cpn60 55CGene73 0.006 547 Pfam PF00118 chaperonin family 23 523 Chaperonin 10 Kd 55CGene74 0.005 97 Pfam PF00166 subunit 2 95

148

Table A.3.4 (continued).

6-pyruvoyl tetrahydropterin 55CGene75 0.005 118 Pfam PF01242 synthase 2 117 MraZ protein putative antitoxin- 55CGene76 0.000 151 Pfam PF02381 like 1 75 Respiratory-chain NADH dehydrogenase 30 55CGene77 0.005 594 Pfam PF00329 Kd subunit 44 172 Nucleoside- specific channel- forming protein 55CGene78 0.000 261 Pfam PF03502 Tsx 51 251 55CGene79 0.006 188 Pfam PF00692 dUTPase 80 184 Glu-tRNAGln amidotransferase 55CGene80 0.001 95 Pfam PF02686 C subunit 19 90 55CGene81 0.006 286 Pfam PF00231 ATP synthase 5 285 Phosphoribosyl- AMP 55CGene82 0.000 142 Pfam PF01502 cyclohydrolase 39 113 Protein of unknown function 55CGene83 0.000 66 Pfam PF07869 DUF1656 4 59 Ribosomal protein 55CGene84 0.006 130 Pfam PF00410 S8 5 130 Ribosomal protein 55CGene85 0.001 247 Pfam PF00318 S2 9 224 Ribosomal protein 55CGene86 0.000 110 Pfam PF00237 L22p/L17e 5 107 PPIC-type 55CGene87 0.001 91 Pfam PF00639 PPIASE domain 11 87 FtsJ-like 55CGene88 0.006 216 Pfam PF01728 methyltransferase 28 204 Protein of unknown function 55CGene89 0.000 54 Pfam PF07043 DUF1328 6 44

149

Table A.3.4 (continued).

Bacterial export 55CGene90 0.006 89 Pfam PF01313 proteins family 3 6 77 RimK PreATP- 55CGene91 0.001 301 Pfam PF18030 grasp domain 1 94 MerR HTH family 55CGene92 0.001 118 Pfam PF13411 regulatory protein 20 86 C-terminal domain of 1-Cys 55CGene93 0.000 187 Pfam PF10417 peroxiredoxin 156 181 Queuine tRNA- 55CGene94 0.001 376 Pfam PF01702 ribosyltransferase 16 368 Adenylosuccinate 55CGene95 0.005 456 Pfam PF08328 lyase C-terminal 332 446 Ribosomal protein 55CGene96 0.001 200 Pfam PF00573 L4/L1 family 11 197 ATP synthase 55CGene97 0.000 156 Pfam PF00430 B/B' CF0 6 136 55CGene98 0.000 71 Pfam PF03459 TOBE domain 8 67 Ribosomal Proteins L2 C- 55CGene99 0.000 274 Pfam PF03947 terminal domain 126 251 Biopolymer transport protein 55CGene100 0.005 141 Pfam PF02472 ExbD/TolR 14 133 Helix-turn-helix domain rpiR 55CGene101 0.000 289 Pfam PF01418 family 6 80 SecE/Sec61- gamma subunits of protein translocation 55CGene102 0.001 122 Pfam PF00584 complex 70 121 Prokaryotic Cytochrome C oxidase subunit 55CGene103 0.006 108 Pfam PF03626 IV 19 91 55CGene104 0.006 442 Pfam PF00344 SecY translocase 75 412

150

Table A.3.4 (continued).

Cell division 55CGene105 0.001 82 Pfam PF04999 protein FtsL 1 81 55CGene106 0.006 279 Pfam PF00571 CBS domain 63 118 Ribosomal protein 55CGene107 0.001 141 Pfam PF01250 S6 3 91 DNA polymerase III beta subunit 55CGene108 0.000 367 Pfam PF02767 central domain 130 244 Bacterial DNA- 55CGene109 0.001 98 Pfam PF00216 binding protein 1 91 Thioredoxin-like [2Fe-2S] 55CGene110 0.006 165 Pfam PF01257 ferredoxin 22 163 55CGene111 0.006 163 Pfam PF00731 AIR carboxylase 5 152 Ribosomal protein 55CGene112 0.005 166 Pfam PF00466 L10 6 101 SAICAR 55CGene113 0.006 237 Pfam PF01259 synthetase 7 232 NusA N-terminal 55CGene114 0.006 493 Pfam PF08529 domain 4 125 Transcription termination factor 55CGene115 0.001 177 Pfam PF02357 nusG 3 108 Bacterial regulatory protein 55CGene116 0.006 186 Pfam PF02954 Fis family 148 181 Ferritin-like 55CGene117 0.006 156 Pfam PF00210 domain 8 143 S1 RNA binding 55CGene118 0.000 564 Pfam PF00575 domain 19 87 55CGene119 0.001 159 Pfam PF00293 NUDIX domain 7 146 UvrA interaction 55CGene120 0.001 987 Pfam PF17760 domain 173 278 Ferritin-like 55CGene121 0.006 154 Pfam PF00210 domain 8 143

151

Table A.3.4 (continued).

NADH- ubiquinone/plasto quinone oxidoreductase 55CGene122 0.000 137 Pfam PF00507 chain 3 26 123 RimM N-terminal 55CGene123 0.006 179 Pfam PF01782 domain 14 95 Sigma 54 modulation protein / S30EA 55CGene124 0.006 102 Pfam PF02482 ribosomal protein 3 92 Carbamoyl- phosphate synthase L chain ATP binding 55CGene125 0.001 1073 Pfam PF02786 domain 673 875 PBP superfamily 55CGene126 0.006 334 Pfam PF12849 domain 39 296 tRNA Guanine-1- 55CGene127 0.001 250 Pfam PF01746 methyltransferase 26 223 ATPase family associated with various cellular 55CGene128 0.001 319 Pfam PF07726 activities AAA 37 167 Ribosomal protein 55CGene129 0.006 231 Pfam PF00687 L1p/L10e family 32 220 Lrp/AsnC ligand 55CGene130 0.001 162 Pfam PF01037 binding domain 77 151 Ribosomal protein 55CGene131 0.001 51 Pfam PF00471 L33 4 49 Ribosomal protein 55CGene132 0.006 231 Pfam PF00687 L1p/L10e family 35 220 Succinate dehydrogenase/Fu marate reductase transmembrane 55CGene133 0.003 122 Pfam PF01127 subunit 10 83

152

Table A.3.4 (continued).

4TM region of pyridine nucleotide transhydrogenase 55CGene134 0.003 107 Pfam PF12769 mitoch 12 95 Phosphoribosyl synthetase- 55CGene135 0.012 310 Pfam PF14572 associated domain 200 310 Ribosomal protein 55CGene136 0.003 91 Pfam PF00203 S19 3 83 Ribosomal protein 55CGene137 0.003 76 Pfam PF01084 S18 18 68

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Figure A.3.1. Biomass of heat shocked P. putida (ATCC 12633) with glycerol

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Figure A.3.2. Biomass of heat shocked P. aeruginosa ID 79 with glycerol

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Strain Only Strain with Glycerol

Figure A.3.3. Biomass of heat shocked P. piscium ID 4 with glycerol

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Strain Only Strain with Glycerol

Figure A.3.4. Biomass of heat shocked P. sp003050925 ID 5 with glycerol

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Strain Only Strain with Skim Milk

Figure A.3.5. Biomass of heat shocked P. putida (ATCC 12633) with skim milk

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Strain Only Strain with Skim Milk

Figure A.3.6. Biomass of heat shocked P. viridiflava_B ID 2 with skim milk

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Strain Only Strain with Skim Milk

Figure A.3.7. Biomass of heat shocked P. piscium ID 4 with skim milk

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Strain Only Strain with Skim Milk

Figure A.3.8. Biomass of heat shocked P. trivialis ID 33 with skim milk

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Strain Only Strain with Skim Milk

Figure A.3.9. Biomass of heat shocked P. citronellolis (NRRL B-2504) with skim milk

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Strain Only Strain with Skim Milk

Figure A.3.10. Biomass of heat shocked P. helleri ID 47 with skim milk

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Figure A.3.11. Biomass of heat shocked P. lundensis ID 55 with skim milk

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Strain Only Strain with Skim Milk

Figure A.3.12. Biomass of heat shocked P. syringae DC3000 with skim milk

159