1 Possible Drivers in Endophyte Diversity and Transmission in The

Possible Drivers in Endophyte Diversity and Transmission in the Tomato Plant Bacterial

Microbiome

Thesis

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

the Graduate School of The Ohio State University

Ana María Vázquez, B.S.

Graduate Program in Plant Pathology

The Ohio State University

2020

Thesis Committee

Dr. María Soledad Benítez-Ponce, Advisor

Dr. Christine Sprunger

Dr. Jonathan M. Jacobs

Copyrighted by

Ana María Vázquez

2020

Abstract

It has been documented that beneficial plant-associated bacteria have contributed to disease suppression, growth promotion, and tolerance to abiotic stresses. Advances in high-throughput sequencing have allowed an increase in research regarding bacterial endophytes, which are microbes that colonize the interior of plants without causing disease. Practices associated with minimizing the use of off-farm resources, such as reduced tillage regimes and crop rotations, can cause shifts in plant-associated bacteria and its surrounding agroecosystem. Integrated crop–livestock systems are an option that can provide environmental benefits by implementing diverse cropping systems, incorporating perennial and legume forages and adding animal manure through grazing livestock. It has been found that crop-livestock systems can increase soil quality and fertility, reduce cost of herbicide use and improve sustainability, especially for farmers in poorer areas of the world. This work explores how crop-livestock systems that integrate chicken rotations can impact tomato plant growth, as well as soil and endophytic bacterial communities. Tomato plants were subjected to greenhouse and field studies where biomass was assessed, and bacterial communities were characterized through culture- dependent and -independent approaches.

In greenhouse experiments, the greater percent of chicken grazed soil incorporated in the planting substrate, the greater the stunting of tomato seedlings. In the field study, bacterial communities differed significantly by sample origin and plant development stage, regardless of chicken grazing history. Our findings suggest stronger contribution of agricultural management practices during early plant stages on endophytic microbiome, as opposed to later on in the host lifecycle. Taxonomic composition of dominant groups of recovered endophytic bacterial isolates were consistent with those found by amplicon sequencing. Plots with history of chicken grazing had a significant increase of soil fertility and differentially abundant ASVs. Dominant phyla

(Actinobacteria, Proteobacteria, Bacteroidetes and Firmicutes) of bacterial taxa identified were uniform between culture-dependent and -independent approaches from both greenhouse and field experiments. Most of the recovered isolates were found to be phylogenetically similar to formerly cultured plant endophytes and bacteria found in soil.

However, some isolates were similar to bacteria recovered in human clinical settings.

Further directions should focus on identification and elucidation of transmission of possible plant growth promoting bacteria, as well as human pathogens, found in crops grown in this type of agricultural management strategy. Moreover, the influence of stage in plant lifecycle on bacterial community composition under specific management strategies remains to be explored. This work provides insight into the influence of crop- livestock rotations on soil- and tomato plant-associated bacterial communities.

iii

Dedication

This thesis is dedicated to the memory of my biggest fan, my grandmother

Carmen Leticia Cabrera Amador. Regardless of adversities, Abuela lived by seeing beauty in science, art and human connections. I will always honor her by emulating her tenacity and persistence.

Acknowledgments

First, I would like to acknowledge my advisor, María Soledad Benítez-Ponce for her support and commitment in mentoring me during these past two years. Thank you for always stimulating an intellectually challenging and collaborative environment where I could thrive as a scientist. In addition, thank you for encouraging me to integrate other aspects to my professional skillset.

Moreover, I would like to thank the members of my student advisory committee,

Christine Sprunger and Jonathan Jacobs, for their valuable and constructive suggestions. I would also like to thank Suranga Basnagala for his advice in setting up my first field experiment at Mellinger Research Farm, as well as his assistance in looking out for my tomato plants and answering all of my questions.

I would like to offer my special thanks to Leslie Taylor for all her help with my experiments and much needed cheer during this journey. Through countless emails exchanged, assistance with computational analysis provided by former lab member

Matthew Willman was greatly appreciated. In addition, I would like to thank lab members and fellow graduate students Daowen Huo and Ranjana Rawal for their inspiring motivation and persistence. A special thanks to the whole Benitez lab, current v and former members, for making my time in Wooster memorable and cherished. Thank you to undergraduates Nedas Matulionis, Emmily Moses and Yesenia Velez for helping in different aspects of my research project, in and outside the lab.

My grateful thanks are also extended to three very special people that have helped me make Wooster a home away from home, Marlia Bosques, Madeline Horvat and

Edwin Navarro. In addition, a big thanks to all my friends, back home and in different places, that have encouraged me from afar.

Finally, the biggest thanks are for the first supporters and mentors I ever had, my family. Mamá, Michelle, Berto, Carlos, José, Tommy and Tito. Thanks to my abuela for being my biggest and, arguably, my most outspoken fan.

Vita

1995………………………………………… Born, Arecibo, Puerto Rico

2013-2018……………………………………B.S. Biology and B.S. Industrial

Microbiology, University of Puerto Rico at Mayagüez

2018 to present………………………………Graduate Research Associate, Department of Plant Pathology, The Ohio State University

Fields of Study

Major Field: Plant Pathology

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vii

List of Tables ...... xii

List of Figures ...... xvi

Chapter 1. Literature Review ...... 1

Introduction ...... 1

The Plant Microbiome ...... 3

Bacterial Endophytes in Aboveground Plant Tissues ...... 5

Bacteria in Soil and Rhizosphere of Plants ...... 6

Transmission and Colonization of Plant-Associated Bacterial Communities ...... 8

Role of Bacterial Endophytes ...... 12

Summary and Conclusions ...... 15

viii

References ...... 18

Chapter 2. Characterization of the Bacterial Endophytic Microbiome of Tomato

Seedlings Grown in a Soil Diversity Gradient Using Chicken-Grazed Soils ...... 35

Introduction ...... 35

Materials and Methods ...... 40

Field Site Description ...... 40

Soil Sampling ...... 41

Planting Substrate, Plant Materials and Growth Conditions ...... 42

Seedling Sampling and Evaluation ...... 44

Plant Tissue Sampling and Isolation of Endophytic Bacteria from Tomato Seedling

Stem ...... 45

DNA Extraction, PCR Amplification and Sequencing of the 16S rRNA and rpoB

Gene Regions from Individual Bacterial Isolates ...... 45

Isolate Sequence Data Analysis ...... 48

Plant Growth Data Analysis and Statistics ...... 49

Results ...... 50

Effect of Soil Dilution Treatments on Aboveground Biomass and Height of Tomato

Seedlings ...... 50

Effect of Soil Slurry Dilution Treatments on Aboveground Biomass, Root Biomass,

Height and Number of Compound Leaves of Tomato Seedlings ...... 50 ix

Impact of Dilution Treatments on Cultured Endophytic Bacteria Composition ...... 51

Discussion ...... 53

Tables ...... 82

Figures...... 93

Chapter 3. Characterization of the Endophytic Bacterial Microbiome of Tomato Plants

Grown in a Chicken-Grazed Plots ...... 100

Introduction ...... 100

Materials and Methods ...... 104

Field Site Description ...... 104

Preparation of Tomato Seedlings for Transplant ...... 105

Soil Health Measurements ...... 106

Tomato Plant Sampling and Evaluation ...... 107

Plant Tissue Sampling, Surface Sterilization and Isolation of Endophytic Bacteria

from Tomato Plant Stem ...... 108

Cultured Endophyte DNA Extraction, PCR Amplification and Sequencing of the

16S rRNA Gene Region ...... 109

Isolate Sequence Data Analysis ...... 111

Culture-Independent Characterization of Bacterial Endophytes ...... 111

Amplicon Sequence Data Processing and Analysis ...... 113

Amplicon Data Analysis and Statistics ...... 114

Plant Growth and Soil Health Data Analysis and Statistics ...... 116

Results ...... 117

Impact of Chicken Grazing History on Soil Health ...... 117

Effect of Chicken Grazing History on Plant Tissue Measurements of Tomato Plants

Sampled in July and September ...... 117

Impact of Chicken Grazing History on Cultured Endophytic Bacteria Composition

...... 118

Tomato-Associated Bacterial Communities and Diversity Analyses ...... 119

Discussion ...... 123

References ...... 136

Tables ...... 154

Figures...... 174

Bibliography ...... 192

Appendix A. Supplementary Table ...... 225

List of Tables

Table 2.1. History of management strategy rotations implemented in plots selected for soil sampling of 2018 and 2020 greenhouse experiments. For 2018 and 2019 plots sampled are indicated in bold. P/C: Pasture/chickens. P/V: Pasture/vegetables...... 82

Table 2.2. Impact of dilution of chicken grazed soils on aboveground biomass and height, as estimated by analysis of variance, of Celebrity and Moneymaker tomato seedlings.

Seedlings were grown under greenhouse conditions in chicken-grazed soils diluted with a sterile soil substrate in 2018...... 83

Table 2.3. Number of CFU/0.1mL for each chicken-grazed soil slurry dilution. The first inoculation of soil slurry was done prior to tomato seed germination. The second inoculation was added to germinated seedlings...... 84

Table 2.4. Impact of dilution of chicken grazed soils on aboveground biomass, root biomass, height, number of compound leaves and root to shoot ratio of tomato seedlings grown under greenhouse conditions in sterile sand-turface and composted soil substrate with an addition of diluted chicken-grazed soil slurry, for 2020 experiment...... 85

xii with an addition of diluted chicken-grazed soil slurry. Means in a column without a common superscript letter differ (p < 0.05), as analyzed by Fisher’s LSD test...... 86

Table 2.6. Bacterial endophytes recovered from the stem of tomato seedlings grown in chicken-grazed soil dilutions. Identification is based on partial 16S sequence. The number of isolates recovered per each sequence type is shown for each treatment and tomato genotype. Abbreviations: CGS: Chicken-grazed soil, SSS: Sterile soil substrate,

C: Celebrity, M: Moneymaker...... 87

Table 2.7. Subset of bacterial endophytes recovered from the stem of tomato seedlings grown in chicken-grazed soil dilutions. Identification is based in rpoB gene region...... 90

Table 2.8. Number of isolates recovered from surface sterilized stem of tomato seedlings grown across all chicken-grazed soil slurry dilutions...... 92

Table 3.1. Effect of chicken grazing history on active pools of organic matter in soil collected from plots prior to planting of the tomato...... 154

Table 3.2. Impact of block and chicken-grazing history on aboveground biomass and number of compound leaves of tomato plants sampled in July...... 155

Table 3.3. Impact of block and chicken-grazing history on aboveground biomass and number of compound leaves of tomato plants sampled in July. Means in a column without a common superscript letter differ (p < 0.05), as analyzed by Fisher’s LSD test.

...... 156

Table 3.4. Impact of block and chicken-grazing history on above-ground biomass, number of compound leaves, total biomass of tomatoes harvested, number of

xiii symptomatic tomatoes harvested, number of total tomatoes harvested and root biomass of tomato plants sampled in September...... 157

Table 3.5. Impact of block and chicken-grazing history on aboveground biomass, number of compound leaves, total biomass of tomatoes harvested, number of symptomatic tomatoes, number of total tomatoes harvested and root biomass of tomato plants sampled in September. Means in a row without a common superscript letter differ (p < 0.1), as analyzed by Fisher’s LSD test...... 159

Table 3.6. Identification and distribution of bacterial endophytes recovered from the stem and tomato flesh of tomato plants grown in plots with and without history of chicken grazing. Abbreviations: J: July, SP: September, S: Stem, TF: Tomato flesh, CG: Chicken grazed, PO: Pasture only...... 160

Table 3.7. Recovery of amplicon sequencing reads from different tomato plant tissues, rhizosphere soil and baseline samples (bulk soil and chicken manure). Using Cutadapt software, each primer and adapter was removed...... 163

Table 3.8. Recovery of amplicon sequencing reads from different tomato plant tissues, rhizosphere soil and baseline samples (bulk soil and chicken manure). Using DADA2, reads were processed and filtered, per sample sequenced...... 164

Table 3.9. Recovery of amplicon sequencing reads from different tomato plant tissues, rhizosphere soil and baseline samples (bulk soil and chicken manure). Using phyloseq, reads belonging to unwanted taxa were removed...... 165

Table 3.10. Increase in bacterial community richness, evenness and diversity in samples obtained from chicken-grazed pasture plots. Alpha diversity metrics (Chao1 species

xiv richness, Shannon’s diversity index and Faith’s Phylogenetic diversity) of bacterial communities from bulk soil and chicken manure collected in May and September, respectively...... 166

Phylogenetic diversity) of bacterial communities from rhizosphere soil, root, stem and leaf of tomato plants sampled in June and Septem ...... 167

Table 3.12. Variations in bacterial community richness, evenness and diversity in tomato fruit and seed samples obtained from chicken-grazed pasture plots and pasture only plots.

Alpha diversity metrics (Chao1 species richness, Shannon’s diversity index and Faith’s

Phylogenetic diversity) of bacterial communities from tomato flesh and seed sampled in

September...... 168

Table 3.13. Alpha diversity metrics (Chao1 species richness, Shannon’s diversity index and Faith’s Phylogenetic diversity) of bacterial communities from rhizosphere soil and leaf from both sampling months and treatments...... 169

Table 3.14. Differentially abundant taxa (p<0.1) between chicken grazed pasture plots and pasture only plots within sampling months. All taxa shown, except (indicate which) were more abundant in chicken grazed plots...... 171

Table A.1. Identification of differentially abundant taxa (p<0.1) by comparing chicken grazed pasture plots with pasture only plots...... 226

List of Figures

Figure 2.1. Field layout of the SMSFG plots at Mellinger Research Farm at OARDC,

Wooster, Ohio. The 0.6 hectare field is divided in four replicates of five plot treatments installed west of the farmstead...... 93

Figure 2.2. Gradient of diluted soil from chicken-grazed pasture plots. Five treatments were generated by diluting soil from chicken-grazed pasture plots with autoclaved soil substrate: 100% chicken-grazed soil, 75%, 50% and 25% dilutions and 100% autoclaved soil substrate...... 94

Figure 2.3. Gradient of filtered chicken-grazed soil slurry dilution. Filtered soil slurry was diluted to 10-1, 10-2, 10-3 and 10-4. Sterile water was used as a control...... 95

Replicates of each treatment were placed in the greenhouse for six weeks and these were measured before being destructively sampled. ANOVA and Fisher’s LSD tests were calculated, and replicate measurements were graphed as boxplots. Black horizontal lines indicate the median, the lower portion of the box is the first quartile, the upper portion of the box is the third quartile, the whiskers are maximum and minimum, and the outliers xvi are black dots. Uppercase letters indicate significant difference across soil dilutions according to Fisher’s LSD test (p < 0.05) for the Celebrity seedlings and the lowercase letters for Moneymaker seedlings. 2018 experiment...... 96

Figure 2.5. Total number of bacterial endophytes recovered by genera. Pie chart indicating the percentage recovery of different bacterial genera from tomato seedling stems grown in different soil dilutions (total = 87 isolates)...... 97

Figure 2.6. Impact of dilution of chicken grazed soils on taxa of bacterial endophytes recovered from two tomato varieties, grown in different soil dilution treatments. The bar plot shows the number of bacterial isolates recovered from tomato seedling stems (total =

87 isolates). Different colors indicate the taxa to which each isolate belongs...... 98

Figure 2.7. Phylogenetic reconstruction of unique endophytic bacterial taxa recovered from the stem of tomato seedlings based on 16S rRNA gene sequences. Taxa labeled as

AVC represent unique taxa recovered in this study and are color coded according to their treatment origin. Reference sequences obtained from the NCBI Database are labeled according to the sample origin. The tree was created using the Maximum Likelihood method based on the Tamura-Nei model and generated in MEGA. The bootstrap consensus tree inferred is from 1000 replicates and branches corresponding to partitions reproduced in less than 60% bootstrap replicates are collapsed. Ferroplasma acidiphilum and Methanobacter cuticularis were used as outgroups...... 99

Figure 3.1. Overview of the field trial conducted in the Summer of 2019 at Mellinger

Research Farm. Plots grazed by chickens in August 2017 and 2018 were used as

xvii vegetable plots in 2019. Tomato plants were grown in soils with differing chicken grazing history...... 174

Figure 3.2. Overview of the three sampling months, involving the bulk soil, tomato plant and chicken manure sampling. At each sampling time, different samples were collected and analyzed for bacterial diversity, plant vigor and soil characteristics...... 175

Figure 3.3. Soil sampling and homogenization for subsequent use in amplicon sequencing, as planting substrate and soil health tests. Soil obtained from chicken-grazed pasture plots and pasture only plots at Mellinger Research Farm. Soil was bagged in separate plastic bags per split plot. In the greenhouse, soil was poured into plastic containers, separated by treatment and plot, and mixed-together and homogenized by hand...... 176

Figure 3.4. Three-week old tomato seedling transplant. On June 14th, 10 tomato seedlings were selected per treatment and plot to be transplanted to the corresponding split plot were soil was originally collected from...... 177

Figure 3.5. Amplicon sequence data processing and analysis pipeline adapted from Huo,

2020...... 178

Figure 3.6. Endophytic bacterial isolates recovered from stem tissue of tomato plants grown in plots with or without history of chicken grazing. Different colors indicate the taxa to which each isolate belongs...... 179

Figure 3.7. Endophytic bacterial isolates recovered from tomato flesh of tomatoes harvested from plants grown in plots with or without history of chicken grazing. Different colors indicate the taxa to which each isolate belongs...... 180

xviii

Figure 3.8. Sequencing depth for each sample origin displayed using rarefaction curves.

Each sample is divided by treatment. Legend represents sampling months...... 181

Figure 3.9. The relative abundance of major phyla in bulk soil samples collected in May.

Each bar represents a sample within a treatment...... 182

Figure 3.10. The relative abundance of major phyla in tomato root samples. Each bar represents a sample within a treatment in a sampling month...... 183

Figure 3.11. The relative abundance of major phyla in tomato stem samples. Each bar represents a sample within a treatment in a sampling month...... 184

Figure 3.12. The relative abundance of major phyla in tomato leaf samples. Each bar represents a sample within a treatment in a sampling month...... 185

Figure 3.13. The relative abundance of major phyla in tomato flesh samples. Each bar represents a sample within a treatment in a sampling month...... 186

Figure 3.14. The relative abundance of major phyla in tomato seed samples. Each bar represents a sample within a treatment in a sampling month...... 187

Figure 3.15. The relative abundance of major phyla in tomato rhizosphere soil samples.

Each bar represents a sample within a treatment in a sampling month...... 188

Figure 3.16. The relative abundance of major phyla in chicken manure samples. Each bar represents a sample within a plot...... 189

Figure 3.17. Bacterial communities were significantly separated by sampling month and sample origin. Principal coordinate analysis of bacterial communities of rhizosphere soil, root and stem samples. Weighted normalized UniFrac distance matrix was calculated from ASV distribution...... 190

xix

Figure 3.18. The top five phyla (Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria) were distributed among all samples with the exception of

Acidobacteria being the most clustered with rhizosphere samples. Principal coordinate analysis of bacterial communities of rhizosphere soil, root and stem samples and top five phyla. A: Distribution of bacterial communities by treatment, sample origin and sampling month. B: Distribution of top five phyla across samples. Samples that cluster close together share a greater similarity in composition...... 191

Chapter 1. Literature Review

Introduction

The science that elucidates abundance, diversity, distribution, and functions of microorganisms in ecosystems is known as microbial ecology. Microorganisms are known to have the vast majority of the genetic and metabolic diversity on the planet while engaging in critical ecosystem processes (Gray & Head, 2008). Consequently, their intrinsic roles in the ecosystem allows microorganisms to evolve alongside and within multicellular forms of life, such as humans and plants. It is known that plants are influenced by the complex microbial communities they host and this shapes plant’s ecology, evolution and phenotype, a concept also known as the plant holobiont (Simon et al., 2019).

Research regarding beneficial plant-microbe interactions has been primarily focused on symbiotic associations of rhizobia, which are bacteria that form nodules in roots of legumes and fix nitrogen, and the role of arbuscular mycorrhizal fungi in nutrient uptake and plant growth (Parniske, 2008; Udvardi & Poole, 2013). In the past decades, microbial-based agricultural management efforts typically inoculate individual bacteria and fungi in crops to promote growth and development, nitrogen and phosphorous uptake, and disease resistance (Busby et al., 2017). However, these inoculants have met 1 variable success of efficacy due to various factors, such as microbial competition and survival in soil (R. de Souza et al., 2015). For example, inconsistent yield responses of single strain inoculations of Azospirillum, a plant-growth-promoting bacterial inoculant of choice, have been reported (Bashan et al., 2004). In addition, inoculation of two foreign isolates of Funneliformis mosseae successfully excluded members of the natural arbuscular mycorrhizal fungal community as root colonizers, which raises concerns about possible biodiversity losses (Pellegrino et al., 2012).

Focusing research efforts from the use of single strains to understanding and harnessing whole plant microbiomes can provide solutions for increasing plant resilience and yields in sustainable agroecosystems research. Agricultural management strategies can impact, positively or negatively, soil microbiomes, ultimately affecting the host plant’s susceptibility to disease, growth and development (Crowder et al., 2010;

Santhanam et al., 2015; Voort et al., 2016). Increased fertilization and reduced crop diversity negatively affect the abundance of bacteria in soil. Practices associated with minimizing the use of off-farm resources, such as reduced tillage regimes and crop rotations, can cause shifts in plant-associated bacteria and its surrounding agroecosystem

(Degrune et al., 2017; Schmidt et al., 2019). Depending on the practice, bacterial diversity, and in some cases abundance, in soil is promoted (Hartman et al., 2018;

Hartmann et al., 2015; Peralta et al., 2018). Sun et al. (2004) demonstrated that cattle manure application in soil increased soil bacterial diversity and had a more even distribution than soil with inorganic fertilizer applications and untreated control soil.

Achieving a better understanding of the impact of agricultural management strategies on the plant microbiome, specifically the beneficial functions of its endophytes, could pave way for the development of solutions to control current and emerging pathogens, as well as alleviate abiotic and biotic stresses.

Bacteria, fungi, oomycetes, algae, protozoa, nematodes, and viruses compose the plant microbiota (Hacquard et al., 2015; Jones, 2014; Kemen, 2014). Techniques from culturing isolates to metagenomic approaches, have helped shed light on uncovering the diversity and abundance of microbial species in all plant tissues and raise questions on their functions. Culture‐dependent techniques, which involves phenotypic and genomic fingerprinting analyses of bacterial isolates, can help elucidate mechanisms and physiological aspects that contribute to the microbe-host interactions within a specific ecosystem (Laforest‐Lapointe and Whitaker, 2019). With the help of next generation sequencing and statistical approaches, our understanding of the phylogeny and functional genes of bacteria has improved and has provided new understanding into the structural shifts of impacted microbial communities (Wakelin et al. 2013). Therefore, these techniques have shown that bacterial microbiomes form a vast mutualist network with their plant hosts.

The Plant Microbiome

Plants host beneficial, commensal and pathogenic microbial communities in all its compartments such as the rhizosphere, phyllosphere, spermosphere, and endosphere 3

(Turner et al. 2013). Beneficial plant-associated bacteria have demonstrated to help in disease suppression, growth promotion, and tolerance to abiotic stresses (Mueller and

Sachs, 2015). These microbes can facilitate plant growth by producing phytohormones, increasing accessibility of nutrients and protection from plant pathogens (Gaiero et al.

2013). Additionally, each plant tissue provides selective pressures that affect the assembly of microbial communities residing near, on, or inside plants. Studies on the bacterial plant microbiota have demonstrated that there are few dominant phyla, mainly

Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes (Müller et al., 2016;

Remus‐Emsermann et al., 2014). However, differences in bacterial community composition can be observed by plant compartment. Studies on Arabidopsis thaliana and rice found significantly different communities between bulk soil, the rhizosphere, and the root compartments (Bulgarelli et al., 2012; Edwards et al., 2015; Lundberg et al., 2012).

In sugarcane, a distinction was found in bacterial communities between belowground organs (rhizosphere, endophytic root and bulk soil) and aboveground organs (stalks and leaves) (de Souza et al., 2016). A study on tomato plants found that roots have significantly enriched microbial diversity in comparison to aboveground surfaces

(Ottesen et al., 2013). The microbial communities are considered endophytic, which live inside the plant, or epiphytic, which reside on the plant surface (Bacon & White, 2016).

In the last decade, there has been an increase in research regarding bacterial endophytes, which are microbes that colonize the interior of plants without causing disease.

Bacterial Endophytes in Aboveground Plant Tissues

Bacterial endophytes can be found in above-ground compartments of the plant such as the stem, leaves and seeds. Endophytic communities found in each of these compartments are distinct and can reside in the vascular system, in intracellular spaces or intercellular spaces (Koskimäki et al., 2015; Thomas et al., 2007; Thomas & Sekhar,

2014; Zinniel et al., 2002). By residing in these spaces, endophytic bacteria have access to an abundance of carbohydrates, amino acids, and inorganic nutrients (Dong et al.,

1994; Elbeltagy et al., 2001). Additionally, these bacteria are more protected from environmental fluctuations, in comparison to bacteria that reside in the rhizosphere

(Rosenblueth & Martínez-Romero, 2006). However, bacteria that thrive in endophytic compartments possess physiological advantages to be able to move through the plant.

Bacterial movement can be assisted through the plant transpiration stream and having flagella (Compant et al., 2005; James et al., 2002). Secretion of cell-wall degrading enzymes like cellulases and pectinases are also helpful for bacterial movement through intercellular spaces (Compant et al., 2010; Naveed et al., 2014; Reinhold-Hurek et al.,

2006). The xylem has perforated plates that allow bacteria to pass through these large pores without using cell-wall degrading enzymes (Sapers et al., 2005).

Some of the functions described for bacterial endophytes in aboveground plant tissues include alleviating abiotic stress, defending from biotic stress and increasing accessibility of nutrients such as nitrogen, phosphorus and iron (Bacon & White, 2016; Deyett et al.,

2017). For example, four species of gfp-tagged rhizobia were observed to colonize healthy rice plant tissues, from the root to the stem and leaves, and plants produced 5 significantly higher root and shoot biomass, increased their photosynthetic rate and accumulated higher levels of indoleacetic acid and gibberellin growth-regulating phytohormones (Chi et al., 2005). And severity of angular leaf spot and naturally occurring anthracnose were significantly reduced by inoculation of Bacillus pumilus strain INR7, which was isolated from a surface-sterilized stem of a surviving cucumber plant in a field heavily infested with cucurbit wilt disease (Wei, 1996).

Bacteria in Soil and Rhizosphere of Plants

Although various physical and chemical soil factors can increase nutrient availability and plant productivity, soil microbial communities play a huge role in plant development (Bergna et al., 2018; Hartmann et al., 2015; López et al., 2018; Shaik &

Thomas, 2019). A single gram of bulk soil can contain up to 10 billion bacterial cells

(Raynaud & Nunan, 2014). On the other hand, a gram of rhizosphere soil can contain up to 100 billion bacterial cells (Kennedy & de Luna, 2005). Through altering resources, such as deposits of exudates, metabolites and induced changes to the abiotic soil environment, within the rhizosphere of an individual plant host, plant diversity is a significant driver of soil microbial community structure, richness and interactions

(Bardgett & Wardle, 2010; Schlatter et al., 2015; Wardle et al., 2004). Land use, including agricultural management strategies, soil resource stoichiometry, climate, soil spatial heterogeneity and soil pH are other factors that can impact bacterial diversity and composition in soil (Delgado‐Baquerizo et al., 2017; Ding et al., 2013; Wang et al.,

2016). 6

The rhizosphere is the area where plant roots deposit exudates, mucilage, and sloughed cells, that influence the surrounding soil and the large array and diversity of organisms within (Lakshmanan et al., 2014). Microorganisms in the rhizosphere that have been studied for their beneficial effects on plant growth and health include nitrogen- fixing bacteria, mycorrhizal fungi, plant growth-promoting rhizobacteria, biocontrol microorganisms, mycoparasitic fungi, and protozoa (Zinniel et al., 2002). The rhizosphere hosts a plethora of bacteria with plant growth promoting and disease suppressive abilities (Adesemoye et al., 2009; Srour et al., 2017; Weller et al., 2002).

Plants are known to manipulate the bulk soil and rhizosphere microbiome to their advantage, by recruiting beneficial microorganisms. Mechanisms plants employ to recruit beneficial bacteria include the production of plant root exudates and volatile organic compounds. Various studies have observed how these exudates attract specific bacteria with functions ranging from plant growth promotion to production of pathogen-inhibitory compounds (Rudrappa et al., 2008; Schulz-Bohm et al., 2018). On the other hand, these compounds can also signal plant pathogens (Walker et al., 2003). In a study by Rudrappa et al. (2008), the authors provide biochemical evidence that malic acid secreted from roots of Arabidopsis thaliana selectively signals and recruits the beneficial rhizobacterium Bacillus subtilis FB17 in a dose-dependent manner after infection with foliar pathogen Pseudomonas syringae pv. tomato. Different strains of B. subtilis have also been reported to produce cytokinins and increases the phytohormone content in the plants (Arkhipova et al., 2005).

Transmission and Colonization of Plant-Associated Bacterial Communities

To understand the role endophytes play in plants and its ecosystem, it is important to understand how endophytic communities assemble. Although plant compartments are studied separately, it is important to remember that each of these systems can share bacterial communities (Bai et al., 2015). Endophytes are transmitted horizontally, by entering the plant from an environmental origin, or vertically, from generation to generation via seed (Frank et al., 2017). Sources for horizontal transmission of bacterial endophytes include the air, rain, soil, pollinators or other insects. The aerial surface of plants, which include stem, leaves, flowers and fruits, can have bacteria deposited from bioaerosols, which are particles released by wind from terrestrial and marine environments into the atmosphere and include viable and metabolically active bacteria

(Fröhlich-Nowoisky et al., 2016; Hill et al., 2007). The surfaces of floral organs are visited by pollinators, herbivores and predators in search of prey (Wardhaugh et al.,

2012). In turn, these visits influence floral microbiomes in areas such as nectar, petals, pollen and fruit (Aizenberg-Gershtein et al., 2013; Fürnkranz et al., 2012; Glassner et al.,

2015; Madmony et al., 2005; McFrederick et al., 2017; Ushio et al., 2015).

Although, bacteria colonizing the surface of leaves do not penetrate the leaf cuticle, bacteria can enter through openings in the plant epidermis such as stomata, lenticels and hydathodes (Hugouvieux et al., 1998; Melotto et al., 2008; Scott et al.,

1996). The stomata is known to recognize and exclude specific bacteria, as well as allow non-pathogenic bacteria to enter (Baldotto et al., 2011). Also known as guard cells, the 8 stomata can sense microbe-associated molecular patterns (MAMPs) and close in response

(Arnaud & Hwang, 2015). Pathogens are known to possess mechanisms to open the stomatal pore through virulence factors, including phytotoxins that prevent MAMP- triggered closure and effectors that prevent closure or induce opening of the stomata

(Hurley et al., 2014; Melotto et al., 2006). Diazotrophic species of bacterial endophytes have similarly shown to alter stomatal opening. A study demonstrated the effect of abscisic acid (ABA), key hormone of stomatal control and its diurnal fluctuation, production by diazotrophs, such as Rhizobium and Burkholderia, in assisting in water use efficiency of rice (Rho et al., 2018; Tallman, 2004).

Some endophytes spread systemically to various areas of the plant like stem, leaves, and fruits via the xylem (Compant et al., 2010; James et al., 2002). Bacterial endophytes can move between vegetative parts of the plant, including the developing seed, through vascular connections (Agarwal & Sinclair, 1996). These bacteria can also move via the shoot apical meristem, which consists of cells that give rise to all the post- embryonic aerial organs, including reproductive organs (Clark, 1997). For example, leaf- nodulating nitrogen-fixing Burkholderia symbionts reside in the vegetative shoot tip and colonize each new leaf in Rubiaceae species (Sinnesael et al., 2018). Burkholderia symbionts are then transferred into the floral shoot tip, into the developing ovule and eventually the embryo where they become enclosed in the shoot tip of the seedling

(Miller, 1990). The movement of the endophyte B. phytofirmans strain PsJN from the rhizosphere to the grapevine’s reproductive organs following soil inoculation was studied

(Compant et al., 2008). This study helped elucidate an example of transmission of a plant growth promoting bacteria, originating from the soil, via xylem colonization.

The soil contains a microbial seed bank which is a plethora of active microorganisms, as well as dormant ones (Lennon & Jones, 2011). The rhizosphere creates a selective environment that supports higher bacterial abundance and activity compared to other plant compartments and bulk soil, while at the same time lowering bacterial diversity (Kowalchuk et al., 2002; Smalla et al., 2001). In turn, the rhizosphere recruits distinct microbial communities that influence the structure and assembly of communities transmitted to the inside of the root (Peiffer et al., 2013). Bacteria, including plant pathogens, can persist and overwinter in plant residues (Togashi et al., 2001).

Bacteria enter the roots through cracks, root tips and wounds, where lateral roots emerge from (Hurek et al., 1994). For this, bacteria engage in mechanisms to approach and colonize roots and compete with other microorganisms, such as motility, chemotaxis and quorum sensing (Podile et al., 2014; Scharf et al., 2016). Additionally, bacteria can secrete cell wall-degrading enzymes, such as endoglucanase and endopolygalacturonase, to assist in penetration into the root endosphere (Compant et al., 2005). These colonization routes are similar between bacterial endophytes and bacterial plant pathogens. The bacterial plant pathogen Ralstonia solanacearum causes bacterial wilt, a soil-borne vascular disease that affects many economically important crops, such as tomato. R. solanacearum can survive and overwinter in plant root debris, or roots of weeds (Pradhanang et al., 2000). Similar to a bacterial endophyte, R. solanacearum uses

10 plant cell wall-degrading enzymes to colonize and enter the plant host (Digonnet et al.,

2012). Virulent strains of R. solanacearum compared with mutant strains lacking production of these enzymes have shown reduced virulence in tomato plants (Huang &

Allen, 2000; Saile et al., 1997). Other than roots, bacteria are known to colonize germinating seeds in the soil. The zone surrounding a germinating seed is known as the spermosphere (Schiltz et al., 2015). The zone is composed of carbon exudates in the form of sugars, proteins, and fatty acids that serve as energy sources for bacteria, eventually shaping the bacterial composition near the seed (Roberts et al., 2009).

Sources for vertical transmission of bacterial endophytes include the seed and pollen. One of the least researched plant tissues is the seed and its indigenous microbial communities. Bacterial seed endophytes can be selected by the soil microbial community or the seed genotype and serve as a vehicle for vertical transmission through generations

(Nelson, 2004). After seed germination, genetic and physiological changes in the plant contribute to significant differences in bacterial endophyte colonization that can vary between cultivars (Adams and Kloepper, 2002). Certain seed endophytes have shown to be beneficial to the host, through plant growth promotion, antibiotic production and promote resilience to stress (Shahzad et al. 2018). Considering sources for horizontal transmission, it has been difficult to prove vertical transfer of obligate bacterial endophytes. It has also been difficult to obtain culturable bacterial endophytes from the seed. A study found that surface-sterilized seeds hosted only one PDA-culturable bacterial or fungal endophyte per seed, or none (Newcombe et al., 2018). The authors in

11 this study could explain this occurrence with the Primary Symbiont Hypothesis. This hypothesis states that plants host primary, sole, or dominant, endophytic symbionts in individual seeds and its presence or absence and identity of the primary symbiont affects survival of the host during seed dispersal, germination, emergence and young growth

(Mundt & Hinkle, 1976). Although culture-independent techniques reveal several operational taxonomic units per seed, it is thought that a primary symbiont could be as dominant as to have a larger effect on fitness of seedlings (Truyens et al., 2015).

Role of Bacterial Endophytes

Endophytic bacteria inhabit plants without causing harm. They are known to mediate, enhance or alter plant functional traits that influence the plant’s interactions with competitors, mutualists, herbivores, and pathogens and can play a role in niche range, abundance, growth and development (Friesen et al., 2011). Bacterial endophytes can be efficient for phytoremediation (Barac et al., 2004). Plant endophytes have also been found to be closely related to human pathogens or are human pathogens. For example, plant growth promoting traits have been identified in Bacillus cereus, a plant endophyte also known as a human pathogen, associated to food poisoning (Bottone, 2010; Vendan et al., 2010).

A study compared the genomes of 40 well-described plant-associated bacteria, including endophytes, to understand their potential functional and mechanistic interactions (Hardoim et al., 2015) . The 40 bacterial strains studied included strict 12 endophytes, symbionts, plant pathogens with endophytic lifestyle, rhizosphere and soil bacteria. They found that motility and chemotaxis and the generation of detoxification and stress-related enzymes were important properties driving colonization of bacteria in plants. The plant endosphere can be inhospitable for aerobic microorganisms due to it being a microaerophilic environment (Das & Roychoudhury, 2014). Therefore, endophytes need to be able to generate enzymes with detoxification capacities to survive in plants. Genes associated to type IV conjugal DNA-protein transfer secretion systems, which are involved with host colonization and conjugation of DNA, adhesion to the host via twitching motility and type I pilus assembly, which assists in attaching to host cells, were detected among endophytes. Furthermore, genes involved in plant growth promotion, such as nitrogenase (nifH), involved in fixation of atmospheric N2, and 1- aminocyclopropane-1-carboxylase (ACC) deaminase (acdS), involved in plant stress alleviation, were more common in endophytes. However, functions associated to signal transduction, bacterial cell communication, nutrient transport and transcriptional regulators varied between functional groups depending on what suited their survival in different environmental niches and conditions. Another study compared 3,837 bacterial genomes to identify genes that contribute to bacterial adaptation to plants (Levy et al.,

2018). The authors found numerous plant-associated functions, such as those that facilitate plant colonization, consistent across phylogenetically diverse bacterial taxa, and shared with plant-associated eukaryotes.

Plant-growth promoting activities have been mostly studied on bacteria associated with the rhizosphere (Berendsen et al., 2012; Kwak et al., 2018; Lugtenberg & Kamilova,

2009; Zhao et al., 2017). However, there are examples of endophytes shown to promote plant growth (Miliute et al., 2015). In the case of legumes, they can host rhizobia, which are nitrogen-fixing bacteria, in nodules in their roots, with in turn help plants in nitrogen limited environments (Oldroyd, 2013). B. phytofirmans PsJN is a well characterized plant growth promoting endophyte. Mutants of this bacteria lacking ACC deaminase activity ceased to promote canola seedling root elongation (Y. Sun et al., 2009). Endophytic bacteria can also increase host tolerance to abiotic stresses (Bresson et al., 2013;

Coleman-Derr & Tringe, 2014; Lucas et al., 2014). Pseudomonas fluorescens YsS6 and

Pseudomonas migulae 8R6, endophytes with ACC deaminase activity, promoted tomato plant growth in high salinity (Ali et al., 2014). Endophytic Azospirillum spp. alleviated water stress tolerance in maize plants by producing ABA and promoted plant growth through the production of indole acetic acid and gibberellins (Cohen et al., 2009).

Through various mechanisms, endophytic bacteria can decrease or prevent the destructive effects of bacterial and fungal plant pathogens (Eljounaidi et al., 2016).

Endophytic Pseudomonas fluorescens was shown to produce volatile organic compounds, antibiotics and lytic exoenzymes that inhibit Athelia rolfsii, a soil‐borne fungal pathogen of Atractylodes lancea (Zhou et al., 2014). Seed endophyte Bacillus subtilis SG_JW.03 suppressed fungal pathogen Fusarium moniliforme growth on Arabidopsis thaliana seedlings through the production of lipopeptides and showed the induction of

14 pathogenesis-related genes, including ones that relate to plant defense against fungal pathogens (Gond et al., 2015). Pavlo et al. (2011) demonstrated that Pseudomonas sp. increased growth and resistance of potato plant shoots towards soft rot disease caused by

Pectobacterium atrosepticum.

Summary and Conclusions

Gaps in research should be considered when understanding the effects of bacterial microbiome on host’s performance. A specific group of endophytes that tends to be overlooked are the commensals, which are the most common endophytes with unknown or yet unknown functions in plants (Berlec, 2012). Increasing knowledge of the functionality of this group can shift our understanding on the role of endophytic communities and how these might me impacting the plant host. Studies on bacterial communities in agroecosystems have been mainly focused on the rhizosphere. This is due to the fact that intensively managed soils can affect soil health and microbial abundance and diversity, ultimately decreasing disease suppression (Chaparro et al., 2012; Havlin et al., 1990; Mendes et al., 2011; Voort et al., 2016). For example, high use of pesticides and fungicides used to target plant pathogens can reduce soil microbial community diversity and evenness (Liu et al., 2007). However, there are not as many studies on the impact this might have on above-ground endophytes. Moreover, there is a lack of research on model non-pathogenic bacterial endophytes and how these factors impact their colonization, horizontal and vertical transmission, and function in plants. Most of

15 the research used to understand endophytic lifestyle is obtained from plant pathogenic bacteria.

When studying drivers in endophyte colonization, many experiments grow host plants under controlled conditions (Cordovez et al., 2018; Hardoim et al., 2012). While this is necessary, there is a lack of studies assessing variable field conditions that might impact endophytic community composition and function. This premise can apply to studies involving agroecosystems. Additionally, there is a lack of studies regarding the impact of management strategies associated to diversified farming systems, such as those that employ crop-livestock rotation, on the plant microbiome. The development of high yielding varieties of crops, increased use of fertilizers and other management strategies associated to the “green revolution” have resulted in lack of sustainability, loss of biodiversity and pollution (Lemaire et al. 2005). It has been found that crop-livestock systems can increase soil quality and fertility, reduce cost of herbicide use and improve sustainability, especially for farmers in poorer areas of the world (de Faccio Carvalho et al. 2010; Devendra and Thomas, 2002). Studies involving the impact of this type of management strategies on microbial communities are not common. One study showed an increase in microbial biomass and enzyme activities in a crop-livestock system compared to a monoculture (Acosta-Martínez et al. 2010). Although crop-livestock systems have demonstrated their efficiency and improved sustainability, the impact of this strategy on the plant microbiome is not completely known. It is important to address these gaps in knowledge to be able to understand this complex ecological network of bacterial

16 endophytes in plants and how the information gained can be transformed into sustainable solutions for agroecosystems.

For this thesis, a characterization of the endophytic bacterial microbiome of the tomato plant and its surrounding rhizosphere soil was done by using culture-dependent and culture independent approaches. The aim of this research project was to explore what influences bacterial diversity in soil and tomato plant tissues at different stages of its life cycle, using an integrated crop and livestock systems with chickens as a model.

The two objectives for this thesis are as follow:

1. Characterize the cultured bacterial endophytic microbiome of tomato

seedlings grown in a soil diversity gradient using chicken-grazed soils.

2. Characterize the endophytic bacterial microbiome of tomato plants

grown until harvest in a chicken grazed plot.

We hypothesize that bacterial endophyte diversity will increase in chicken-grazed soils compared to soils with pasture, and that endophyte diversity will decrease (and be more similar to the seed source) as soil biodiversity decreases. This project will help understand the potential of integrating crop-livestock strategies from the perspective of tomato endophyte diversity and beneficial effects in tomato growth. Elucidating more about the plant microbiome can pave way in the development of sustainable agricultural practices and eventually, employ these systems, close information gaps, increase energy

17 efficiency and decrease negative environmental impact to further enhance food production.

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Chapter 2. Characterization of the Bacterial Endophytic Microbiome of Tomato

Seedlings Grown in a Soil Diversity Gradient Using Chicken-Grazed Soils

Introduction

Endophytic bacteria reside inside plant organs and are considered important components of the plant microbiome (Bacon & White, 2000). The establishment of endophytic and rhizosphere microbiome is influenced mainly by the plant host and soil.

Many bacterial endophytes are attracted by root exudates and rhizodeposits, enabling their colonization of plant hosts through the roots (Kawasaki et al., 2016). Other areas of plant entry include stem and leaf, surfaces, flowers, fruits, stomata, wounds and hydathodes (Compant et al., 2010). These bacteria have exhibited functional, as well as taxonomic diversity. Bacterial endophytes with plant growth promoting activities can positively impact host growth through indole acetic acid production, nitrogen fixation and phosphate solubilization (Ma et al., 2011, 2016; Sexton et al., 2020; Tariq et al., 2014).

Additionally, these bacteria can also engage in antagonistic activity with plant pathogens and promote disease suppression (Shahzad et al., 2017; Xu et al., 2019; Yanti et al.,

2019). Bacterial phyla Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes are dominant across plant environments, harboring bacteria with diverse ecological relationships ranging from mutualists to pathogenic (Cordero et al., 2020; Deng et al.,

2019; Szymańska et al., 2018; Wang et al., 2019). Some of these bacteria can also be 35 pathogens to humans while providing benefits to the plant host (Chelius & Triplett,

2000; E. T. Wang et al., 2006).

Diverse forces drive the multitrophic interactions surrounding said bacterial endophyte establishment and composition. Soil type and host plant genotype has been observed to shape bacterial endophyte community composition, with soil type possibly being a stronger driver (Ding et al., 2013; Pablo R. Hardoim et al., 2015; Rasche et al.,

2006). Furthermore, plant growth stage and agricultural management strategies can shift community composition (Gdanetz & Trail, 2017; Ren et al., 2015; Xia et al., 2015).

Crop-livestock systems are gaining interest due to greater energy efficiency, nutrient recycling, perenniality and opportunity to diversify rotations (McGilloway, 2005).

Integrated crop-livestock systems are a type of mixed farming practice where farmers can have different enterprises and save resources by interchanging them on the farm (Schiere

& Kater, 2001). In crop-livestock systems, small farmers can cash income, improve quality and quantity of food produced and exploitation of unutilized resources by using the byproduct of one system as the input for other (Gupta et al., 2012). It has been found that crop-livestock systems can increase soil quality and fertility, reduce cost of herbicide use and improve sustainability, especially for farmers in poorer areas of the world (de

Faccio Carvalho et al. 2010; Devendra and Thomas, 2002). Studies involving the impact of this type of management strategies on microbial communities are not common. Among these few studies, one showed an increase in microbial biomass and enzyme activities in soil samples from a crop-livestock system compared to a monoculture (Acosta-Martínez

36 et al. 2010). Others have shown the enhancement of soil fertility, productivity and bioremediation with chicken manure applications (Dikinya and Mufwanzala, 2010; Liu et al. 2009). Additionally, crop-livestock systems include the use of cover crops or forage which provide a grazing source for animals (Gardner et al., 1991; Luo et al., 2017). As cover crops increase plant diversity, they also have positive effects on microbial biomass

(Chen et al., 2019; Michalk et al., 2017). Although crop-livestock systems have demonstrated their efficiency and improved sustainability, the impact of this strategy on the endophytic plant microbiome is not completely known.

To further research microbiomes and account for the various forces driving bacterial community composition, it is necessary to establish simplified model plant- microbiome systems. Conducting controlled experiments can assist in streamlining plant- microbiome interactions and aid in uncovering taxonomic and functional diversity of microbes (Chihaoui et al., 2015; Pavlova et al., 2017; Wicaksono et al., 2018). Various factors in these experiments are modified, such as growth conditions, planting substrate type and quality, temperature, and duration of experiment. Tomato plants have already been used to obtain insight on the role of endophytes and the microbial composition of different plant tissues (Romero et al., 2014; Tian et al., 2015). Other than being one of the top ten most important crops in the world, the tomato plant’s short lifecycle and ability to adapt to different conditions, makes it an appropriate research model for our purposes

(Bergougnoux, 2014). By simplifying interactions, greater understanding can be obtained from the impact of agricultural management strategies on crops and associated microbes.

Advances in next-generation sequencing, alongside traditional culturing approaches, have facilitated recovery, detection and identification of endophytes

(Quambusch & Winkelmann, 2018). Although it is often time consuming, culturing bacteria remains an important step to understand their biological processes and ecological roles as well as obtaining novel insights for application (Palleroni, 1997). Coupled with advances in culture-independent approaches, information provided by obtaining bacterial isolates presents the opportunity to delve into unknown biosynthetic pathways and biochemical characteristics as well as understanding adaptations towards environmental changes (Connon & Giovannoni, 2002; Overmann et al., 2017). However, not meeting all the nutritional requirements of bacteria present creates a bias. For endophytic bacteria, two of the most commonly used media are tryptic soy agar (TSA) and Reasoner's 2A

Agar (R2A). The former supports growth of a broad range of bacteria and the latter targets bacteria that require a lower amount of nutrients (J. M. Gardner et al., 1982;

Reasoner & Geldreich, 1985). It is not suggested to use TSA at full strength due to its high nutrient concentration which allows fast growing bacteria to overgrow those that are slower growing (Hallmann et al., 2006). The bacterial ribosomal marker 16S rRNA gene region is used to identify bacteria at a genus level. Because of its functional stability, high conservation, and its ubiquitous nature, the 16S rRNA gene is an effective molecular marker for taxonomic purposes (Ramasamy et al., 2014). The rpoB gene region has been suggested as an alternative, as well as a compliment, to the 16S rRNA gene due to its comparable phylogenetic resolution (Case et al., 2007). Additionally, the available databases, such as National Center of Biotechnology Information (NCBI) nucleotide

38 database and the Ribosomal Database Project (RDP), assist in sequence identification

(Altschul et al., 1990; Wang et al., 2007).

The aim of this chapter is to address the impact of diluted chicken-grazed soil, as well as the associated bacterial microbiome, on tomato seedling growth and stem endophyte composition. Diluting soil from the field in a greenhouse setting will assist in reducing the impact of a subset of driving forces in bacterial endophyte community composition and plant growth. Driving forces, such as soil’s available nutrients, physical and chemical characteristics, and abundance of microorganisms, can be affected by dilution. The objectives are as follows: (a) determine the impact of chicken-grazed soil dilution treatments on aboveground biomass and height of tomato seedlings, (b) determine the impact of diluted chicken-grazed soil slurry on aboveground and root biomass, number of compound leaves and height of each tomato seedling and (c) evaluate the impact of dilution treatments on cultured endophytic bacteria composition obtained from the stem of tomato seedlings. We hypothesize that as planting substrate contains higher amounts of chicken-grazed soil, tomato seedlings will have higher plant biomass, as well as bacterial endophyte diversity. Furthermore, bacterial endophytes recovered from tomato seedlings grown in planting substrate containing chicken-grazed soil will be similar to those commonly found in plants, and possibly to those associated to animal sources, such as chicken manure. This project will help understand the potential of integrating crop-livestock strategies from the perspective of tomato endophyte diversity and beneficial effects in tomato plant growth.

Materials and Methods

Two experiments were conducted in the greenhouse with the objective to determine the impact of diluted chicken-grazed soils on tomato seedling growth and stem endophyte composition. Even though the goals were similar, different approaches were used in 2018 and 2020 and are described below.

Field Site Description

In the year 2016, the project Diversification of Small and Medium Sized Farms

Grant (SMSFG) was established at Mellinger Research Farm at the Ohio Agricultural

Research and Development Center (OARDC), The Ohio State University (OSU). The

0.61-hectare experimental field includes integrated crop and livestock systems as part of a rotation and its impact is evaluated on subsequent vegetable yield (Figure 2.1). Each plot undergoes a management strategy with rotations every year (Table 2.1). For this dissertation, we focused on the pasture and chicken grazing rotation (split plot pasture and pasture chicken). The pasture plots were planted with a mixture of grass and clover for first year establishment and were mowed prior to the growing season for the second- year pasture. The composition of the seed mixture for pasture plots was 13.61 kg of HQ-

R Pasture Mix (JDS Seeds Orrville, OH) with 2.27 kg of white (JDS Seeds Orrville, OH) and red clover (RKO). HQ-R Pasture Mix contains 29% Niva orchard grass, 29% Laura meadow fescue, 20% premium perennial rye grass, 10% Calibra perennial rye grass and

10% Climax timothy. This seed mixture was added to new pasture plots established in

2017, 2018 and 2019. In August 2018 and 2019, broiler chickens were placed in tractors and moved across selected pasture split plots for 32 and 35 days, respectively. Chickens grazed the mixture of available pasture and chicken diet was supplemented with feed.

Soil Sampling

2018 Experiment. In October, 20 soil cores were collected from four chicken grazed pasture plots (CGPPs) R1r5, R4r3, R2r1 and R3r1 (Figure 2.1, Table 2.1), approximately ~ four weeks after chickens were removed from the plots, using a 1” soil core sampler and cleaning core sampler with ethanol between plots. Soil cores were bagged in separate plastic bags per plot. All soil recovered from CGPPs were mixed- together and homogenized.

2020 Experiment. In January 29 approximately ~ 20 weeks after chickens were removed from the plots), five soil cores were collected from four CGPPs (R4r2, R3r2,

R4r4, R3r5 (Figure 2.1, Table 2.1)) using a 1” soil core sampler and mixed in one plastic bag. A week later, five soil cores were collected from the same CGPPs using a 1” soil core sampler and mixed in one plastic bag. As in 2018, all soil recovered from CGPPs were mixed-together and homogenized.

Planting Substrate, Plant Materials and Growth Conditions

2018 Experiment. Five treatments were generated by diluting homogenized soil from CGPPs with autoclaved soil substrate: 100% chicken-grazed soil, 75%, 50% and

25% dilutions and 100% autoclaved soil substrate (Figure 2.2).

The soil substrate was prepared by mixing 0.0816 metric tons of field soil, 0.0272 metric tons of peat moss, 454 g of lime and top fertilizer, and was autoclaved two times at 121oC. Tomato seeds were sown in 10.16 cm plastic pots filled with soil from each dilution treatment (Figure 2.2). One tomato seed was sown for each pot. Tomato seeds from two varieties were used: Celebrity (Seeds from September 11, 2018 originally from tomatoes grown in CGPPs at Mellinger Research Farm Plot R3r3) and Moneymaker

(Everwilde Farms), with five pots per tomato variety per treatment for a total of 50 pots.

The experiment was setup as a randomized complete block design, where each block represented a different tomato variety. Pots were evaluated for germination 12 days after sowing and no significant differences in seed germination were observed. Greenhouse conditions ranged from a maximum of 25oC and a minimum of 21oC.

2020 Experiment. To develop a controlled inoculation, while reducing the impact of soil’s available nutrients, physical and chemical characteristics, and abundance of microorganisms, a chicken-grazed soil slurry was prepared. For this, 60g of soil obtained from CGPPs was mixed with 400mL of sterile water in a sterile 1L bottle for 10 minutes,

42 creating a soil slurry. The soil slurry was passed through a stainless-steel filter with 47 mm pore size, disinfected with bleach, using a vacuum pump. 10 mL of the collected filtered soil slurry was added to a sterile beaker with 100mL of water, diluting it to 10-1.

The dilution was repeated three times generating four soil slurry treatments: 10-1, 10-2, 10-

3 and 10-4 (Figure 2.3). Sterile water was used as a control.

To measure the concentration of colony forming units (CFUs) of each slurry at the time of application, 100 µL of each dilution, including the control, was spread on a

1/10 TSA plate and colonies were counted after 24 hours. Seventy-five Celebrity F1 tomato seeds (Johnny’s Seeds) were surface sterilized in 0.5% NaOCl and 0.1% Tween for 1 minute and washed using sterile water. For surface sterilization, tomato seeds were contained in a plastic tissue cassette (Tissue Path, Thermo Scientific). For each treatment, autoclaved germination paper was moistened inside the beaker. Approximately 20 surface-sterilized seeds were placed 2.54 cm apart on one piece of autoclaved germination paper for each treatment, including control. To facilitate the establishment of microorganisms originating from chicken-grazed soils, tomato seeds were inoculated with diluted soil slurry treatments, prior to germination. Papers were rolled and placed in separate beakers for each treatment. Each beaker was covered with aluminum foil and placed in a growing chamber with lights on for 16 hours of the day for one week. Six days later, seedlings were transferred to pots containing a growing substrate of sand, turface, and composted soil. The substrate was prepared by mixing sand:turface 1:1 followed by a final mixture of 2/3 sand-turface and 1/3 composted soil. The planting

43 substrate was autoclaved two times at 121oC. Fifty 10.16 cm pots were sprayed with ethanol and filled with planting substrate. Pots were watered and after 24 hours two pre- germinated tomato seedlings were sowed per pot. By using newly sampled chicken- grazed soil, a second soil slurry treatment was prepared, filtered and diluted using the same method as previously described. A total of five treatments were prepared: 10-1, 10-2,

10-3 and 10-4, including sterile water as a control. After sowing seedlings, 30ml of each treatment was drenched on pots corresponding to the original treatment of the pre- germinated seeds. Pots were placed in a randomized block design and grown for six weeks in the greenhouse. After a week, seedlings in each pot were thinned to one. After two weeks, 10ml of NPK (20-20-20) fertilizer was added to each pot once a week.

Greenhouse conditions ranged from a maximum average of 25oC and a minimum average of 23oC.

Seedling Sampling and Evaluation

2018 Experiment. Seedlings were sampled at the 4-leaf stage, approximately six weeks after sowing. Seedlings had the aboveground plant tissues separated from the below-ground by cutting with scissors. Roots were rinsed to remove planting substrate.

Aboveground plant tissues were measured for biomass and height.

2020 Experiment. Seedlings were sampled at the 4-leaf stage, approximately six weeks after sowing. Seedling had the aboveground plant tissues separated from the

44 below-ground by cutting with scissors. Roots were rinsed to remove planting substrate.

Aboveground plant tissues were measured for biomass, height and number of compound leaves. Root biomass was also weighed.

Plant Tissue Sampling and Isolation of Endophytic Bacteria from Tomato Seedling Stem

For both 2018 and 2020 experiments, stems were surface sterilized through a stepwise procedure by immersing tissue in 70% ethanol (1 min), 20% bleach mixed with

0.1% Tween (1 min), 70% ethanol (1 min) and rinsed three times with sterile distilled water. Edges of the surface sterilized stem were cut off. Surface-sterilized stem was cut into several 1-cm section pieces. Sections were plated on 1/10 TSA and R2A and incubated at ambient temperature (approximately 22oC) for 48 hours. Emerging bacterial colonies from the stem sections were transferred up to five times to obtain pure cultures.

DNA Extraction, PCR Amplification and Sequencing of the 16S rRNA and rpoB Gene Regions from Individual Bacterial Isolates

To characterize bacterial endophytes recovered, molecular techniques were employed. A boiling lysis method, according to Sanders and Miller (2010), was used to extract genomic DNA from individual bacterial isolates. A single colony was selected and immersed in 200µL of sterile water in a 1.5mL tube. The tube was then incubated in a water bath at 100oC for 10 minutes, transferred to ice for 10 minutes followed by

45 centrifugation at 10,000 rpm for 10 minutes. 150µL of the supernatant containing the genomic DNA was transferred to a new 1.5mL tube. PCR amplification of the 16SrRNA gene for bacterial endophytes was performed using primers 8F (5’-

TGGAGAGTTTGATCCTGGCTCAG-3’) and 1492R (5'-

GGTTACCTTGTTACGACTT-3') (Weisburg et al., 1991).

The PCR reaction mixture consisted of 2 μL of DNA template, 1× GoTaq Flexi

Buffer (Promega), 0.2 mM dNTP mix (Promega), 2 mM of MgCl2 (Promega), 1 mg/ml of bovine serum albumin (BSA), 0.03 GoTaq DNA polymerase (Promega), 0.5 μM each primer, and molecular grade water (Hyclone). PCR reaction conditions were an initial denaturation at 95oC for 5 minutes, 35 cycles of denaturation at 94oC for 1 minute, annealing at 54oC for 45 seconds and elongation at 72oC for 1 minute and a final elongation cycle of 72oC for 8 minutes. 5µL of each amplification product was analyzed by 1% agarose gel (1%, containing 5 μL of 10,000X SYBR Safe, Thermo Fisher

Scientific) electrophoresis in 0.5 Tris borate EDTA (TBE) buffer with 100 bp Plus DNA ladder (Promega).

To identify recovered isolates at a species level, amplification of the rpoB gene region was performed. The rpoB region was selected to be amplified due to it exhibiting higher levels of divergence compared to 16S rRNA gene region (Mollet et al., 1997; Vos et al., 2012). First, bacterial genomic DNA was purified using Wizard Genomic DNA

Purification Kit (Promega). PCR amplification of the rpoB gene for bacterial endophytes

46 of the genus Bacillus was performed using primers rpoB‐f (5′‐

AGGTCAACTAGTTCAGTATGGAC‐3′) and rpoB‐r (5′‐

AAGAACCGTAACCGGCAACTT‐3′). The PCR reaction mixture of 25 μL for rpoB genes using rpoB-f and rpoB-r primer pair was adapted from Özdemir & Arslan

(2019), which included 2 μL of DNA template, 1× GoTaq Flexi Buffer (Promega), 0.2 mM dNTP mix (Promega), 2 mM of MgCl2 (Promega), 1 mg/ml of BSA, 0.03 GoTaq

DNA polymerase (Promega), 0.4 μM each primer, and molecular grade water (Hyclone).

PCR amplification of the rpoB gene for the remaining bacterial endophytes was performed using primers Univ_rpoB-F_deg (5′‐GGYTWYGAAGTNCGHGACGTDCA‐

3′) and Univ_rpoB_R_deg (5′‐TGACGYTGCATGTTBGMRCCCATMA‐3′) (Ogier et al., 2019). The PCR reaction mixture of 25 μL for rpoB genes using Univ_rpoB-F_deg and Univ_rpoB_R_deg primer pair included 3 μL of DNA template, 1× GoTaq Flexi

Buffer (Promega), 0.2 mM dNTP mix (Promega), 1.5 mM of MgCl2 (Promega), 1 mg/ml of BSA, 0.03 GoTaq DNA polymerase (Promega), 0.2 μM each primer, and molecular grade water (Hyclone). The PCR reaction conditions for both primer pairs were adapted from Clerck and Vos (2004) and it consisted of denaturation at 94°C for 5 minutes, 40 cycles of denaturation at 94°C for 30 seconds, annealing at 53°C for 45 seconds and elongation at 72°C for 1 minute with a final elongation cycle at 72°C for 5 minutes. Two additional rpoB primer pairs were tested for PCR amplification: rpoB-985-F (5’-

ACNGAYATGATMAAGGG-3’) and rpoB-2362-R (5’-GCCATYARMCCSCGCAT-3’) and RpoB-F (5’-AACCAGTTCCGCGTTGGCCTGG-3’) and RpoB-R (5’-

CCTGAACAACACGCTCGGA-3’) (Hoffmann & Roggenkamp, 2003; Jiao, 2013).

These were not successful in amplifying the isolate collection from tomato.

PCR products were evaluated through gel electrophoresis. The gels were visualized under UV transillumination (Gel Logic 112 Imaging System, Kodak).

Enzymatic PCR cleanup was done by adding 5µL of PCR product, 1µL of Alkaline

Phosphatase and 1µL Exonuclease I (Ilustra) into a new 0.2ml tube. PCR cleanup conditions consisted incubating at 37°C for 15 minutes followed by incubating at 80°C for 15 minutes. Preparation of cleaned PCR products for sequencing consisted of adding

10.75µL of clean PCR product and 1.25µL of 6.4 pmol of one of two primers previously used for PCR reaction. Samples were sequenced at the Genomics Shared Resource at The

Ohio State University Comprehensive Cancer Center, Columbus, OH. Partial nucleotide sequence was determined with Sanger sequencing using forward and reverse primers.

Isolate Sequence Data Analysis

To evaluate the sequences, chromatograms were visually revised and trimmed for quality control using 4Peaks software (Gerberastraat & Aalsmeer, 2001). Sequenced ends were trimmed based on quality. Forward and reverse reads for each isolate were merged with CLC Sequence Viewer (Genomics Workbench Version 20.0, 2020). Sequences were compared to the NCBI (National Center of Biotechnology Information) nucleotide database using the basic local alignment search tool (BLAST, blastn) (Altschul et al.,

1990), and to the Ribosomal Database Project (RDP) (Wang et al., 2007) using Classifier to classify sequences to genus. Phylogenetic analysis was performed using MEGA software version 7 (Kumar et al., 2016). Recovered isolate sequences were aligned and pairwise distance was computed using the Maximum Composite Likelihood model

(0.000; sequences had 100% nucleotide identity) to identify unique isolates (Tamura et al., 2004). A phylogenetic tree was created based on the partial 16S rRNA sequences of the unique isolates recovered and reference sequences. Reference sequences were obtained by targeting a known cultured isolate using the SeqMatch tool in RDP and retrieving the sequence from NCBI database. The final tree was constructed using the

Maximum Likelihood method based on the Tamura-Nei model (K. Tamura & Nei, 1993).

Bootstrap consensus tree inferred is from 1000 replicates.

Plant Growth Data Analysis and Statistics

Root to shoot ratio was obtained in 2020 only by dividing root biomass by aboveground biomass. The Shapiro-Wilks normality test was performed to examine normality of the different variables tested. Levene’s test was performed to determine homogeneity of variance. Log transformation was used on non-normal data. One-way analysis of variance (ANOVA) was used to determine significant differences in measurements between the treatments and blocks. Kruskal-Wallis test was performed on data that did not meet parametric assumptions after transformation. Significant F-values

(p<0.05) obtained led to using Fisher’s least square difference (LSD) test to determine

49 differences between treatments. R (R Core Team, 2014) was used to perform the statistical analyses and figures using “agricolae” (de Mendiburu, 2020), “car” (Fox &

Weisberg, 2019), “ggplot2” (H. Wickham, 2016), "lsmeans" (Lenth, 2016) and "readr"

(Wickham et al., 2018) packages.

Results

Effect of Soil Dilution Treatments on Aboveground Biomass and Height of Tomato Seedlings

Two greenhouse experiments were conducted in an aim to determine the impact of chicken-grazed soil dilutions on tomato seedlings and stem endophytes. In the 2018 experiment, there were significant differences in aboveground biomass and height of the seedlings of both varieties grown in the different soil dilutions (Table 2.2). The most diluted chicken-grazed soil treatments (100% sterile soil substrate, 25% and 50% chicken-grazed soil) had significantly higher aboveground biomass and height compared to the least diluted soil treatments (75% and 100% chicken-grazed soil) (Figure 2.4).

Effect of Soil Slurry Dilution Treatments on Aboveground Biomass, Root Biomass, Height and Number of Compound Leaves of Tomato Seedlings

In 2020, for each chicken-grazed soil slurry dilution preparation CFUs were quantified. For each dilution, the amount of CFUs decreases, showing how there is less quantifiable bacteria as it is closer to the control treatment, as well as a decreasing

50 concentration of soil bacteria that can establish on the growing substrate and transmit into the plant (Table 2.3). There was a significant treatment effect on tomato seedling height, number of compound leaves and root to shoot ratio (Table 2.4). According to the Fisher's

LSD test, the most diluted treatment (10-4) had a significantly higher height compared to the other treatments and the non-inoculated control (Table 2.5). Except for height, no significant differences were observed between the non-inoculated control and the soil slurry dilutions for all measurements across treatments. Overall, the highest aboveground biomass and height was observed on the most diluted treatment (10-4) and the lowest measurements were seen in the least diluted treatment (10-1). However, a significant block effect was observed on aboveground and root biomass. Measurements from tomato seedlings grown in block B3 were the highest.

Impact of Dilution Treatments on Cultured Endophytic Bacteria Composition

Endophytic bacteria were recovered from surface-sterilized stem of tomato seedlings from all treatments using two sources of nutrient media. In 2018, a total of 87 endophytic bacteria were recovered and identified at a genus level. By computing the pairwise distance of each 87 bacterial sequences, 35 of these represent unique taxa. The classification of these 35 taxa, based on >97% similarity to GenBank database entries queried through blastn is shown in Table 2.6. Recovered isolates belong to 15 genera and four phyla: Actinobacteria, Firmicutes, Proteobacteria and Bacteroidetes (Figure 2.5).

One isolate could only be identified to the family level as Enterobacteriaceae

51 consistently in NCBI and RDP. However, these differed at genus level. An unknown bacterium in NCBI was the closest match to one of the isolates, whereas RDP classified it as Enterobacter sp. Hence, this isolate will be referred as Enterobacteriaceae 1. The top three most abundant genera recovered were Bacillus, Microbacterium and Paenibacillus, representing 60%, 14% and 7% of isolates, respectively. Taxa belonging to Bacillus were found on all treatments (Figure 2.6). Many of the taxa were present in 1-3 treatments.

Bacterial endophytes assigned to the genera Ochrobactrum, Chryseobacterium and

Cellusimicrobium were only found in 100% sterile soil substrate.

A phylogenetic reconstruction using the unique taxa recovered and reference sequences was generated based on 16S rRNA gene sequence alignments (Figure 2.7).

Most of the taxa recovered were closely related to known bacteria associated to soil, rhizosphere and plant sources. However, some taxa, such as Bacillus 3, Paenibacillus 2,

Microbacterium 1-4, Cellulosimicrobium 1, and Enterobacteriaceae 1 were closely related to bacteria of clinical relevance. Some of these bacteria were only recovered from treatments containing chicken-grazed soils (25% through 100% chicken-grazed soil).

Lysinibacillus 1-2, Arthrobacter 1, Streptomyces 1, Pelomonas 1, Pseudomonas 1,

Ochrobactrum 1, Sphingobium 1-2, Chryseobacterium 1 and Flavobacterium 1-2 did not have closely related reference taxa that originated from clinical settings.

To differentiate within species of each recovered genera, the rpoB region was selected. Two primer pairs were used to target rpoB: rpoB-f/rpoB-r and Univ rpoB-

F/Univ rpoB-R. The former targets Bacillus spp. and the latter is a universal primer.

When the bacterial isolates were sequenced by targeting the rpoB gene region, 25 out of

87 amplified. From the sequences recovered from the remaining 25 isolates, 11 amplified using primer pair rpoB-f/rpoB-r and 14 amplified using primer pair Univ rpoB-F/Univ rpoB-R (Table 2.7). All isolates amplified using primer pair rpoB-f/rpoB-r showed a high similarity (>97%, blastn) to NCBI database entries. Most of these isolates belong to the

Bacillus 3 taxa. Only 9 isolates using primer pair Univ rpoB-F/Univ rpoB-R showed a high similarity (>97%, blastn) to GenBank database entries. The rest of the isolates showed lower similarities (91–95%) to the closest match in NCBI database. Overall, only

29% of the isolates recovered were amplified with rpoB, while 16SrRNA was successful with 100% of the isolates, and with the studied primers, rpoB did not provide greater taxonomic resolution of the tested isolates.

In 2020, a total of 263 endophytic bacterial isolates were recovered from surface sterilized stem tissue of tomatoes grown in substrate inoculated with different soil slurry dilutions. Isolates were stored at -80oC (Table 2.8). Future work will require the identification of these isolates.

Discussion

In our experiments, chicken-grazed soil introduced in the planting substrate impacted both tomato seedling vigor and bacterial endophytes. The greater percent of

53 chicken grazed soil, the greater the stunting of tomato seedlings. Similarly, a subset of isolates recovered in these experiments differed by treatment; however, most of the recovered isolates identified in 2018 consisted of two dominant genera, Bacillus and

Microbacterium, the former being the only genus present across all treatments. Most of the recovered isolates were found to be phylogenetically similar to formerly cultured plant endophytes and bacteria found in soil. However, some isolates were similar to bacteria recovered in human clinical settings. Microbial community selection is known to be influenced by plant genotype, favoring microorganisms that confer beneficial effects to the host (Wickham, 2016). However, our results suggest the importance of planting substrate over plant genotype.

In the 2018 experiment, there was a significant decrease in aboveground biomass and height of both tomato seedling varieties grown in the dilutions containing high percentages of soil from CGPPs. In 2018, soils from CGPPs was mixed with an autoclaved soil substrate. Changes in biomass in 2018 might be due to nutrient availability being enhanced in the autoclaved soil substrate. Autoclaving soil is known to alter physicochemical characteristics of soil, such as changes in soil organic matter structure, pH and nutrient availability (Alphei & Scheu, 1993; Berns et al., 2008;

Serrasolsas & Khanna, 1995; Wolf et al., 1989). Miransari et al. (2009). observed a significant increase of nutrient uptake of mycorrhizal wheat grown in soil sterilized by autoclaving, compared to non-autoclaved soil. Although most studies on autoclaved soils focus on its effect in microbial activity, a few do provide information on autoclaving

54 effect on plant growth. For example, Mahmood et. al. (2014) found that wheat seedlings grown in soils sterilized through autoclaving and γ-irradiation, produced higher root biomass, length, surface area, volume and number of tips compared to those grown in the unsterilized soil.

The modifications of the experiment conducted in 2020 were an effort to develop a controlled inoculation protocol of diluted chicken-grazed soil slurry. The soil slurry assisted in reducing the impact of available nutrients, soil’s physical and chemical characteristics, and abundance of microorganisms. Previous studies have been successful at incorporating soil slurry inoculations, which assist in harnessing the benefits of soil and its microbiome. In other studies, upon inoculation with a soil slurry, microbial shifts in the rhizosphere and subsequently, in the plant host were observed (Jack et al., 2019;

Panke-Buisse et al., 2015). Similarly, previous studies have added a suspension of an inoculum to germinated seeds, to control microorganisms being introduced during germination (Armanhi et al., 2018; Niu et al., 2017).

While modifications to the protocol were performed in 2020, similar results were observed regarding tomato biomass, compared to the 2018 experiment. The least diluted soil slurry treatments had significantly lower tomato plant growth. Other than stunting, the tomato seedlings did not present any other symptomatology in above- or belowground tissues. Low tomato vigor observed in both 2018 and 2020 experiments might be due to the presence of chicken manure in the studied soils and its effect on nutrient availability

55 and phytotoxicity. Past research has shown that application of chicken manure caused stunting of potato plants (Conn & Lazarovits, 1999). Conn and Lazarovits (1999) also observed the effect to be site and soil specific and considered effects in pH as a possible explanation for site to site variation in response to chicken manure application and stunting. Riegel and Noe (2000) also observed a decreased of cotton plant growth in chicken manure amended pots compared with nonamended pots. The authors for both papers suggested the use of lower amounts of chicken manure to reduce potential phytotoxic effects. Chicken manure is known to be a nutrient rich medium, containing significant quantities of micronutrients, P, K and N, in forms of ammonia, nitrate, and nitrite, that can lead to a reduction in seed germination or a decrease in emergence (Moe et al., 2017; Roy and Kashem, 2014).

For tomato plants, nutrients such as nitrogen (N), phosphorous (P), potassium (K), calcium (Ca), magnesium (Mg) and sulfur (S), are needed in large amounts for normal growth and reproduction. Other nutrients, such as manganese (Mn), copper (Cu), zinc

(Zn), chlorine (Cl), boron (B), iron (Fe), and molybdenum (Mo), are needed in smaller amounts (Sainju et al., 2003). Chicken manure is known to contain all 13 of the essential tomato plant nutrients (Chastain et al., 2001). Excess level of P is not as harmful to tomato plants, but it can reduce the availability of some micronutrients, such as Fe, Zn,

Mn, and Cu. This is achieved by decreasing the nutrient’s solubility in the soil and translocation within the plant (Ismail et al., 1996; Needham, 1973). Deficiency of Fe can cause plant growth stunting (Sainju et al., 2003). A study found phytotoxic effects of

56 chicken manure on Banksia spinulosa due to high phosphorus concentrations (Aryantha et al., 2000). Ravindran et al. (2017) found chicken manure to have phytotoxic properties due to a recorded germination index of less than 50% on commercial vegetable crops, including tomato. The authors used a germination index to quantify phytotoxicity on commercial vegetable crops. The germination index is generated using measurements related to the performance of crop seedlings, relative seed germination and relative root elongation during the assay period. Crops, including tomato, with a germination index of less than 50% indicated a phytotoxic medium, compared to crops with 50% and 80% values which indicate moderate phytotoxicity. Nonetheless, germination was not affected in experiments conducted in 2018 and 2020. Incorporation of chicken manure, however, has shown improvement on plant growth depending on the rate and form of incorporation. Dikinya and Mufwanzala (2010) found initial increases and subsequent decreases of the dry biomass of spinach with application of increasing rate of chicken manure. Demir et al. (2010) found that application of pelletized poultry manure improved tomato shoot growth and fruit yield and increased leaf N concentrations at the harvest stage. Some studies lack specific information on rate and form of incorporated chicken manure, although results show an increase in plant growth (Chellemi et al., 2013; Maru et al., 2015). Therefore, the management of chicken-grazed soils through the protocols for

2018 and 2020 might impact the ideal amount of nutrient levels for tomato seedlings. We hypothesize that phytotoxic effects could be driving stunting in tomato seedlings grown in these experiments. However, for these experiments, nutrient availability was not measured.

Other than phytotoxicity, physical and chemical factors related to soil can also impact stunting. Soil compaction can affect root system architecture by limiting expansion and exploration of soil, ultimately affecting shoot growth (Lipiec & Hatano,

2003). Tomato plants grown in columns containing higher soil bulk density had reduced root surface area, total root volume and total root length (Tracy et al., 2012). Reduced crop growth due to compaction can be attributed to reduced water infiltration and crop water use efficiency (Radford et al., 2001). However, hypothesizing that soil compaction affected stunting of tomato seedlings would only apply to the 2018 experiment. Tomato seedlings from the 2020 experiment grew in the same substrate. Moreover, root to shoot ratio had a higher trend in treatments with highest dilutions. Plants are known to allocate its resources to aboveground compartments when growing in nutrient-rich environments due to competition for light. On the other hand, resources are allocated to belowground compartments when growing in nutrient-poor environments (Mašková & Herben, 2018).

Higher root to shoot ratios for tomato seedlings inoculated with the higher dilutions might reflect a nutrient-poor planting substrate.

An imbalance of plant hormones can impact stunting in plants. Abscisic acid

(ABA) production is related to stunting in plants, especially under water stress.

Regarding the 2018 experiment, there might be a reduced water infiltration in soil treatments with higher amounts of chicken-grazed soil, leading to water stress and eventually, increased amount of ABA in the tomato seedlings. ABA production increases during water stress, preventing excess ethylene production and subsequently, inhibiting

58 shoot growth (Sharp & LeNoble, 2002). Additionally, ABA induces stomatal closure and reduces water loss (Herrera-Medina et al., 2007). Increased concentrations of ABA are known to stunt growth of Brassica, maize and French beans (Cramer & Quarrie, 2002;

He & Cramer, 1996; Montero et al., 1998). There are microbes, such as Azospirillum spp. and Bacillus amyloliquefaciens RWL-1, involved in the production of ABA when plants are under water stress (Cohen et al., 2009; Shahzad et al., 2017). A smaller number of different taxa were recovered from plants grown in 100% chicken-grazed soil compared to other treatments. Bacillus constituted the majority of the endophytes recovered from this treatment. These microbes might have been facilitating in ABA production, causing stunting in growth.

Changes in soil physical and chemical factors, such as the addition of organic amendments, have an impact on endophytic communities (J. Hallmann et al., 1997). In

2018, a total of 87 endophytic bacteria belonging to 15 genera and four phyla

(Actinobacteria, Proteobacteria, Bacteroidetes and Firmicutes) were recovered from our experiment. Predominant plant endophytes typically belong to these major phyla

(Rosenblueth & Martínez-Romero, 2006). The origin of the isolates recovered from the stem, whether it is the soil or the tomato seed, is unknown. 60% of the bacterial endophytes were Bacillus and it was the only genus found across all treatments. Bacillus is as ubiquitous as it is studied for its beneficial modes of action, such as antagonism towards plant pathogens and pests and its plant growth promoting activity (Bai et al.,

2002; Hu et al., 2010; L. Ma et al., 2013; Melnick et al., 2008; Raymond et al., 2010).

Correspondingly, beneficial endophytic Bacillus have also been recovered from tomato plant tissues (Chauhan et al., 2014; Kefi et al., 2015; Thomas & Upreti, 2016).

Most of the endophytes recovered were closely related to known bacteria associated to soil, rhizosphere, and plant sources. Lysinibacillus 1-2, Arthrobacter 1,

Streptomyces 1, Pelomonas 1, Pseudomonas 1, Ochrobactrum 1, Sphingobium 1-2,

Chryseobacterium 1 and Flavobacterium 1-2 did not have reference 16S rDNA sequences that originated from clinical settings. Bacteria from some of these genera have already been identified as endophytes or rhizospheric microbes with beneficial properties towards plant hosts, such as tomato, and disease suppression (Abbamondi et al., 2016; Compant et al., 2011; Kunova et al., 2016; Palmieri et al., 2017). Isolates recovered from seedlings grown in 100% sterile soil substrate might be originally from the tomato seed.

Additionally, the only treatment where Bacillus was not as predominant was the 100% sterile soil substrate. Bacterial endophytes assigned to the genera Ochrobactrum,

Chryseobacterium and Cellusimicrobium were only found in 100% sterile soil substrate.

Although not reported to be common in tomato plants, these genera have been recovered from the rhizoplane to the phyllosphere of various crops (Akbaba & Ozaktan, 2018;

Akinsanya et al., 2015; Hardoim et al., 2012; Jeong et al., 2017; Lebuhn et al., 2000; Shi et al., 2010).

Based on a 16S rDNA sequence phylogeny, a subset of isolates were closely related to bacteria of human clinical relevance. These taxa, Bacillus 3, Paenibacillus 2,

Microbacterium 1-4, Cellulosimicrobium 1 and Enterobacteriaceae 1 also belong to genera with known species as plant endophytes as well as human pathogens (Beesley et al., 2010; Glaeser et al., 2013; Pablo Rodrigo Hardoim et al., 2013; McNeil et al., 2004;

Park et al., 2006; Yoon et al., 2007; Zhang et al., 2017). Various studies on the effect of chicken manure focused on the presence of human and animal pathogenic bacteria. Yang et. al has conducted studies on the presence of multiple antibiotic-resistant bacteria in chicken manure and manure fertilized vegetables (Qingxiang Yang et al., 2016; Yang et al., 2014). The implementation of chicken manure, as a source of nutrients and pathogenic microorganisms, can also pose environmental concerns due to runoff from agricultural land (Sistani et al., 2009).

One isolate recovered from a tomato seedling grown in 50% chicken-grazed soil treatment could only be identified to the family level as Enterobacteriaceae. While this family is predominantly known to contain taxa associated to human pathogens, other taxa in Enterobacteriaceae have been identified as plant endophytes (Bulgari et al., 2009; de

Abreu et al., 2017; Magnani et al., 2010). Bacteria in the family Enterobacteriaceae have shown a wide functional diversity, by expressing traits such as auxin production, phosphate solubilization, siderophore production and nitrogen-fixation (Khalaf &

Raizada, 2016; T. Luo et al., 2016; Zimmer et al., 1994). Enterobacteriaceae has also been found to be part of the endophytic community of tomato plants (Romero et al.,

2014). Additionally, studies have been conducted attempting to understand colonization patterns of bacteria from this family (Leite et al., 2013; Lopez-Fernandez et al., 2016).

Although some studies indicate beneficial roles of bacteria in this family, there is concern surrounding the presence of these bacteria and their ability to cause infectious diseases in animals and humans. For example, bacteria from the family Enterobacteriaceae were isolated from various wild and cultivated plants, including tomato, and were shown to have a broad spectrum of resistance to antibiotics and were highly adhesive to human erythrocytes (Markova et al., 2005).

Considering that certain endophytic bacteria are closely related to known human and animal pathogens, it is of importance to identify species and strain level differences.

Although the identification of 16S rRNA gene region allows for successful discrimination of diverse microbial taxa, it has shown difficult to assign species level taxonomy (Kim et al., 2011). The use of other marker genes, such as rpoB (encodes the

RNA polymerase β Subunit) or gyrB (encodes the subunit B protein of DNA gyrase), can better assist in defining phylogenetic relationships by (Arfaoui et al., 2019; Yamamoto &

Harayama, 1995). In an attempt to determine the recovered endophytic bacteria’s species, the rpoB region was selected to be amplified due to it exhibiting higher levels of divergence compared to 16S rRNA gene region (Mollet et al., 1997; Vos et al., 2012).

Two primer pairs were used where one targets Bacillus spp. and the other is a universal primer. When the bacterial isolates were sequenced by targeting the rpoB gene region, 25 out of 87 amplified. The primers that target the genus Bacillus were not as specific as expected and the universal primers were not as broad considering the low number of recovered isolates with an amplification product for the rpoB gene. Alternatively, other

62 universally conserved genes can be used to obtain genetic information from the recovered isolates (Santos & Ochman, 2004). Methods, such as multi locus sequence analysis, can also assist in understanding phylogenetic relationships of species within a genus or genera within a family, especially in cases where the genus has species that are endophytes and human and animal pathogens (Brady et al., 2008; Glaeser & Kämpfer,

2015; Gonçalves et al., 2018).

The bacterial endophytes recovered in this study can be further studied to uncover their potential as plant growth promoting agents or antagonists of plant pathogens.

However, further work must be done in first differentiating possible plant and human pathogens. Changes in management of chicken-grazed soil could potentially assist in controlling bacteria detrimental to human and plant health. Composting chicken manure, for example, lead to a decrease in potential pathogenic bacteria, antibiotics and antibiotic resistance genes (Deng et al., 2020). With the proper protocols in place, the effectiveness of chicken grazing on subsequent plant growth and yield, as well as endophyte diversity and composition, can be determined. Our findings also indicate the need to assess parameters such as rate and form of incorporation of chicken manure for success in managing soil fertility and minimizing phytotoxicity. Experiments with controlled inoculation, as well as controlled incorporation, of chicken-grazed soils are helpful in interpreting these parameters and their impact on plant growth, yield, and its associated microbiome. However, it is important to determine soil factors such as nutrient availability as well as chemical and physical characteristics to truly create reproducible

63 conditions. Consequently, these conditions can assist in obtaining causal links between bacterial endophytes and their potential impact on plant health.

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Tables

Year of Rotation Year of Plots Experiment 2016 2017 2018 2019

2018 Split plot Split plot Split plot R1r5 Sunflowers with pasture with pasture with pasture and P/C and P/C and P/V

Split plot Split plot Split plot R4r3 Sunflowers with pasture with pasture with pasture

and P/C and P/C and P/V

Split plot Split plot Split plot R2r1 Sunflowers with pasture with pasture with pasture

and P/C and P/C and P/V

Split plot Split plot Split plot R3r1 Sunflowers with pasture with pasture with pasture

and P/C and P/C and P/V

2020 Split plot Split plot R3r5 Oats Sunflowers with pasture with pasture and P/C and P/C Split plot Split plot R4r4 with pasture Oats Sunflowers with pasture and P/V and P/C Split plot Split plot R3r2 with pasture Oats Sunflowers with pasture and P/V and P/C Split plot Split plot R4r2 Oats Sunflowers with pasture with pasture and P/C and P/C

Sum of Mean Source of Variation df F Value Pr(>F) Squares Square

Celebrity Aboveground Biomass Treatment 4 680.09 170.02 14.27 5.36x10-5 *** Rep 4 40.89 10.22 0.86 0.51 Residuals 15 178.78 11.92

Height Treatment 4 194.64 48.66 11.93 1.47x10-4 *** Rep 4 21.94 5.49 1.35 0.30 Residuals 15 61.16 4.08

Moneymaker Aboveground Biomass Treatment 4 361.46 90.37 6.36 2.93x10-3 ** Rep 4 7.36 1.84 0.13 0.97 Residuals 16 227.50 14.22

Height Treatment 4 187.74 46.94 10.00 2.97x10-4 *** Rep 4 19.24 4.81 1.03 0.42 Residuals 16 75.06 4.69

*** significant at P=0 ** significant at P=0.001

Table 2.2. Impact of dilution of chicken grazed soils on aboveground biomass and height, as estimated by analysis of variance, of Celebrity and Moneymaker tomato seedlings. Seedlings were grown under greenhouse conditions in chicken-grazed soils diluted with a sterile soil substrate in 2018.

Replicates First Inoculation Mean 1 2 3 10-1 TNTC* TNTC* TNTC* 10-2 TNTC* TNTC* TNTC* 10-3 1.7x104 1.3x104 9.5x103 1.3x104 10-4 5.1x103 2.3x103 4.4x103 3.9x103 Sterile Water (Control) 0 0 0 0

Second Inoculation

10-1 TNTC* TNTC* TNTC* 10-2 2.3x104 2.4x104 1.1x104 1.9x104 10-3 3.4x103 3x103 3.8x103 3.4x103 10-4 6x101 3x101 5x101 4.7x101 Sterile Water (Control) 0 0 0 0

*Colony counts above 250 are considered "Too numerous to count" (TNTC)

Sum of Mean F Source of Variation df Pr(>F) Squares Square Value Aboveground Biomass Treatment 4 22.83 5.71 1.47 0.24 Block 2 19.23 9.62 2.47 0.10 Rep 7 12.87 1.84 0.47 0.85 Treatment:Block 8 10.74 1.34 0.34 0.94 Residuals 27 105.17 3.90

Height1 Treatment 4 0.07 0.02 4.64 0.01 ** Block 2 2.06x10-3 1.03x10-3 0.27 0.77 Rep 7 0.01 1.67x10-3 0.43 0.87 Treatment:Block 8 0.02 2.28x10-3 0.59 0.78 Residuals 27 0.10 3.84x10-3

Root Biomass Treatment 4 10.25 2.56 1.74 0.17 Block 2 8.01 4.01 2.72 0.08 . Rep 7 4.04 0.58 0.39 0.90 Treatment:Block 8 4.15 0.52 0.35 0.94 Residuals 27 39.79 1.47

Number of Compound Leaves2 Treatment 0.06 . Block 0.42

Root to Shoot Ratio2 Treatment 2.58x10-3 *** Block 0.65

. significant at less than P=0.1 ** significant at P=0.001 1Data was transformed due to non-normality. 2Data was measured using Kruskal-Wallis test.

Aboveground Number of Root to Shoot Height Root Biomass Biomass Compound Leaves Ratio Treatment Standard Standard Standard Standard Standard Mean Mean Mean Mean Mean Deviation Deviation Deviation Deviation Deviation 10-1 6.31 2.06b 14.45 2.53b 2.82 1.40b 5.00 0.67b 0.42 0.12a 10-2 7.04 1.29ab 14.17 1.27b 3.19 0.85ab 5.78 0.44a 0.45 0.07a 10-3 7.84 2.28ab 15.45 1.69b 3.85 1.09a 5.50 0.71ab 0.50 0.06a 10-4 8.20 1.84a 17.70 2.32a 3.92 1.29a 5.50 0.53ab 0.48 0.09a Sterile Water 6.93 1.46ab (Control) 13.95 1.67b 3.94 0.88a 5.00 0.82b 0.57 0.05b

Block*

B1 6.30 1.55b 14.86 1.97 2.95 1.03b 5.14 0.66 0.46 0.11 B2 7.52 1.80ab 15.30 1.25 3.65 1.16ab 5.40 0.63 0.48 0.10

86 B3 7.76 2.00a 15.28 3.16 3.90 1.17a 5.45 0.76 0.50 0.08

*Block arrangement in the greenhouse: B1 (block closest to the wall of the greenhouse), B2 (block in the middle of B1 and B2) and B3 (block closest to the middle of the greenhouse.)

Table 2.5. Soil dilution treatment and block effect on aboveground biomass, root biomass, height, number of compound leaves and root to shoot ratio of tomato seedlings grown under greenhouse conditions in sterile sand-turface and composted soil substrate with an addition of diluted chicken-grazed soil slurry. Means in a column without a common superscript letter differ (p < 0.05), as analyzed by Fisher’s LSD test.

Number of isolates recovered per soil dilutions and tomato varieties 100% 75% 50% 25% 100% Number of Closest match in NCBI Recovered isolate ID1 CGS CGS CGS CGS SSS isolates Identity Database with exact %3 C M C M C M C M C M (Accession number) sequences2 Arthrobacter sp. Anthrobacter 1 (AVC.064) 1 1 (MH699257.1) 98.96% Bacillus 1 (AVC.025) 6 5 3 2 3 4 3 5 1 1 33 Bacillus sp. (MN969919.1) 99.71% Bacillus 2 (AVC.038) 1 1 Bacillus sp. (MH683121.1) 99.71% Bacillus 3 (AVC.051) 1 1 1 1 4 Bacillus sp. (MN865961.1) 99.93% Bacillus 4 (AVC.054) 1 2 5 1 9 Bacillus sp. (MN966966.1) 99.85% Bacillus 5 (AVC.061) 1 1 Bacillus sp. (MF948297.1) 99.28% Bacillus 6 (AVC.063) 1 1 Bacillus sp. (MT355654.1) 99.89% Bacillus 7 (AVC.089) 1 1 Bacillus sp. (MT350130.1) 100% 87

Bacillus 8 (AVC.098) 1 1 Bacillus sp. (MT328683.1) 99.90% Bacillus 9 (AVC.110) 1 1 Bacillus sp. (MN447143.1) 100% Cellulomonas sp. Cellulomonas 1 (AVC.104) 1 1 (GQ369082.1) 100% Cellulosimicrobium sp. Cellulosimicrobium 1 (AVC.023) 1 1 (MN904887.1) 99.84% Chryseobacterium sp. Chryseobacterium 1 (AVC.024) 1 1 (FJ169958.1) 99.41% Enterobacteriaceae 1 (AVC.057) 1 1 Unknown bacteria4 Continued

Table 2.6 Continued

Number of isolates recovered per soil dilutions and tomato varieties 100% 75% 50% 25% 100% Number of Closest match in NCBI Recovered isolate ID1 Identity CGS CGS CGS CGS SSS isolates with Database %3 C M C M C M C M C M exact sequences2 (Accession number) Flavobacterium sp. Flavobacterium 1 (AVC.055) 1 1 2 (MH883902.1) 98.87% Flavobacterium sp. Flavobacterium 2 (AVC.087) 1 1 (MG786610.1) 99.48% Lysinibacillus sp. Lysinibacillus 1 (AVC.045) 1 1 (MN795736.1) 99.42% Lysinibacillus sp. Lysinibacillus 2 (AVC.094) 1 1 (MK414879.1) 100% Microbacterium sp. Microbacterium 1 (AVC.022) 1 1 2 (JQ660113.1) 99.55% 88

Microbacterium sp. Microbacterium 2 (AVC.026) 2 2 (MN923509.1) 99.89% Microbacterium sp. Microbacterium 3 (AVC.027) 1 1 (MK425667.1) 100% Microbacterium sp. Microbacterium 4 (AVC.030) 1 2 3 (MG232347.1) 99.89% Microbacterium sp. Microbacterium 5 (AVC.066) 1 1 (MG016448.1) 100% Microbacterium sp. Microbacterium 6 (AVC.068) 1 1 (KC810832.1) 100% Microbacterium sp. Microbacterium 7 (AVC.093) 1 1 (MH127624.1) 99.78% Continued

Table 2.6 Continued

Number of isolates recovered per soil dilutions and tomato varieties 100% 75% 50% 25% 100% Number of Closest match in NCBI Recovered isolate ID1 CGS CGS CGS CGS SSS isolates with Identity Database exact %3 C M C M C M C M C M (Accession number) sequences2 Microbacterium 8 Microbacterium sp. (AVC.105) 1 1 (MG016448.1) 100% Ochrobactrum sp. Ochrobactrum 1 (AVC.033) 1 1 (MG550982.1) 99.91% Paenibacillus sp. Paenibacillus 1 (AVC.056) 1 1 2 (MG963217.1) 99.83% Paenibacillus sp. Paenibacillus 2 (AVC.086) 1 2 3 (AB680470.1) 99.64% 89

Paenibacillus sp. Paenibacillus 3 (AVC.106) 1 1 (MN704412.1) 99.07% Pelomonas sp. Pelomonas 1 (AVC.037) 1 1 (KM253124.1) 99.84% Pseudomonas sp. Pseudomonas 1 (AVC.041) 2 2 (MN326729.1) 99.76% Sphingobium sp. Sphingobium 1 (AVC.065) 1 1 (MF582325.1) 98.10% Sphingobium sp. Sphingobium 2 (AVC.102) 1 1 (MF582325.1) 98.22% Streptomyces sp. Streptomyces 1 (AVC.088) 1 1 (MN704433.1) 100%

Closest match in NCBI Length Primer pair Recovered isolate ID1 database (Accession Identity %2 of query3 number)

rpoB-f / rpoB-r Bacillus 3 (AVC.034) Bacillus pumilis (LT906438.1) 100% 562 Bacillus 3 (AVC.039) Bacillus sp. (CP043559.1) 100% 526 Bacillus 3 (AVC.040) Bacillus sp. (CP043559.1) 100% 562 Bacillus 3 (AVC.043) Bacillus sp. (CP043559.1) 100% 575 Bacillus 3 (AVC.048) Bacillus sp. (CP043559.1) 100% 541 Bacillus 4 (AVC.054) Bacillus sp. (CP043559.1) 100% 581 Bacillus 3 (AVC.079) Bacillus sp. (CP043559.1) 100% 575 Bacillus 3 (AVC.099) Bacillus sp. (CP043559.1) 100% 566 Bacillus 3 (AVC.109) Bacillus sp. (CP043559.1) 100% 582 90 Bacillus licheniformis Bacillus 7 (AVC.089) (CP045814.1) 100% 578 Bacillus 8 (AVC.098) Bacillus sp. (CP043559.1) 100% 578

Continued

1AVC: Reference isolates from tomato endophyte culture collection. 2The identity percent describes how similar the query sequence is to the target sequence. The higher the percent identity is, the more significant the match. 3Length of query from recovered isolate.

Table 2.7. Subset of bacterial endophytes recovered from the stem of tomato seedlings grown in chicken-grazed soil dilutions. Identification is based in rpoB gene region.

Table 2.7 Continued

Closest match in NCBI database Identity Length of Primer pair Recovered isolate ID1 (Accession number) %2 query3

Univ rpoB-F / Univ rpoB-R

Chryseobacterium carnipullorum Chryseobacterium 1 (AVC.024) (CP033921.1) 91.29% 401 Enterobacteriaceae 1 (AVC.057) Enterobacter cloacae sp. (JX425238.1) 99.74% 389 Flavobacterium 1 (AVC.055) Flavobacterium johnsoniae (CP000685.1) 99.62% 264 Flavobacterium 2 (AVC.087) Flavobacterium johnsoniae (CP000685.1) 99.23% 259 Lysinibacillus 1 (AVC.045) Lysinibacillus sp. (CP026007.1) 99.75% 401

91 Microbacterium 6 (AVC.084) Paenibacillus vulneris (MH087423.1) 98.29% 401

Ochrobactrum 1 (AVC.033) Ochrobactrum anthropi (CP044970.1) 99.75% 395 Paenibacillus 1 (AVC.086) Paenibacillus vulneris (MH087423.1) 98.29% 245 Paenibacillus cellulositrophicus Paenibacillus 2 (AVC.106) (CP045295.1) 94.88% 410 Pelomonas 1 (AVC.037) Roseateles depolymerans (CP013729.1) 91.77% 414 Pseudomonas 1 (AVC.041) Pseudomonas fluorescens (HM234618.1) 99.24% 394 Pseudomonas 1 (AVC.044) Pseudomonas fluorescens (HM234618.1) 99.24% 396 Sphingobium 1 (AVC.065) Sphingobium herbicidovorans (CP020538.1) 93.39% 243 Sphingobium 2 (AVC.102) Sphingobium herbicidovorans (CP020538.1) 93.70% 254

Number of Isolates Soil Slurry Dilution Recovered

10-1 57 10-2 51 10-3 59 10-4 45 Sterile Water (Control) 51

Table 2.8. Number of isolates recovered from surface sterilized stem of tomato seedlings grown across all chicken-grazed soil slurry dilutions.

Figures

Figure 2.1. Field layout of the SMSFG plots at Mellinger Research Farm at OARDC, Wooster, Ohio. The 0.6 hectare field is divided in four replicates of five plot treatments installed west of the farmstead.

Figure 2.3. Gradient of filtered chicken-grazed soil slurry dilution. Filtered soil slurry was diluted to 10-1, 10-2, 10-3 and 10-4. Sterile water was used as a control.

Figure 2.4. Differences in aboveground biomass and height of tomato seedlings grown in chicken-grazed soil dilutions after six weeks (n=5, except for Celebrity 75% chicken-grazed soil n=4). A. Aboveground biomass. B. Height. Celebrity and Moneymaker seeds were sowed in a dilution series of chicken-grazed soils using sterile soil substrate. Replicates of each treatment were placed in the greenhouse for six weeks and these were measured before being destructively sampled. ANOVA and Fisher’s LSD tests were calculated, and replicate measurements were graphed as boxplots. Black horizontal lines indicate the median, the lower portion of the box is the first quartile, the upper portion of the box is the third quartile, the whiskers are maximum and minimum, and the outliers are black dots. Uppercase letters indicate significant difference across soil dilutions according to Fisher’s LSD test (p < 0.05) for the Celebrity seedlings and the lowercase letters for Moneymaker seedlings. 2018 experiment.

Figure 2.7. Phylogenetic reconstruction of unique endophytic bacterial taxa recovered from the stem of tomato seedlings based on 16S rRNA gene sequences. Taxa labeled as AVC represent unique taxa recovered in this study and are color coded according to their treatment origin. Reference sequences obtained from the NCBI Database are labeled according to the sample origin. The tree was created using the Maximum Likelihood method based on the Tamura-Nei model and generated in MEGA. The bootstrap consensus tree inferred is from 1000 replicates and branches corresponding to partitions reproduced in less than 60% bootstrap replicates are collapsed. Ferroplasma acidiphilum and Methanobacter cuticularis were used as outgroups. 99

Chapter 3. Characterization of the Endophytic Bacterial Microbiome of Tomato Plants

Grown in a Chicken-Grazed Plots

Introduction

Intensification of cropping systems through increased fertilization and reduced crop diversity negatively affect the abundance of bacteria in soil (Hole et al., 2005;

Postma-Blaauw et al., 2010). On the other hand, practices associated with minimizing the use of off-farm resources, such as organic approaches, reduced to no tillage and crop diversification, are known to promote diversity, and in some cases abundance, of bacteria in soil (Hartman et al., 2018; Hartmann et al., 2015; Peralta et al., 2018). Integrated crop- livestock systems are a type of mixed farming practice where farmers can have different enterprises and save resources by interchanging them on the farm (Schiere & Kater,

2001). These systems provide environmental benefits by implementing diverse cropping systems, incorporating perennial and legume forages and adding animal manure through grazing livestock (Russelle et al., 2007). Most studies on the use of grazing livestock on this type of farming system in the U.S. focus on the benefits of using ruminants (Sulc &

Franzluebbers, 2014). On the other hand, there is a lack of emphasis on the use of chickens for grazing. The use of chickens for meat and egg production as part of crop- 100 livestock systems have been studied mostly outside the U.S. (Bishop, 1995; Devendra &

Thomas, 2002; Udo et al., 2011).

The use of incorporated chicken manure in farming systems, however, has been studied extensively. The chicken manure as a source of nutrients and organic matter, has shown to increase plant yield, suppress disease and improve soil quality and health (Araji et al., 2001; Chellemi et al., 2013; Conn & Lazarovits, 1999; Demir et al., 2010; Riegel &

Noe, 2000). However, concerns have been raised over chicken manure containing human pathogens and antibiotic resistant bacteria (Kyakuwaire et al., 2019). Efforts to mitigate these risks by the use of chicken manure include controlling rate and form of incorporation, composting and mixing with other substrates (Ch’ng et al., 2013; Maru et al., 2015; Ye et al., 2016). Chickens can be integrated to crop-livestock systems by having them graze on pasture plots that can later be used for crops. Pastured poultry is considered a sustainable livestock production system that can be integrated with other farm operations (DEFRA, 2001). This strategy can assist in the incorporation of chicken manure in soil while maintaining chicken meat production.

Tomato (Solanum lycopersicum L.) is one of the world’s most important vegetable crops, surpassing production of 182 million tonnes worldwide in 2018 (FAO,

2018). In 2019, the total value of fresh market tomatoes in the U.S. was estimated at approximately $1.60 billion (USDA-AMS, 2019). Although it is mostly known for its culinary versatility, tomato plants have become a model organism for basic and applied

101 research programs. Scientists use these plants to assess its physiology, breeding, fruit quality and plant-microbe interactions (Bergougnoux, 2014). The bacterial tomato plant microbiome has been explored in efforts to characterize communities and select taxa with functionality important for production (Dong et al., 2019; Ottesen et al., 2013). However, this type of research also unravels the mechanisms for transmission and colonization of bacterial communities from the rhizosphere, to the seed (Bergna et al., 2018; López et al.,

2018; Shaik & Thomas, 2019).

Studies that uncover the richness and diversity of bacterial communities associated to the tomato plant compartments have been more common within the last decade. Most of these studies focus on rhizosphere and root tomato plant microbiome, and how it is affected by soil, plant genotype, and root system architecture (Cheng et al.,

2020; Kwak et al., 2018; Lee et al., 2016; Saleem et al., 2018; Tian et al., 2017). To a lesser extent, there are studies that focus on bacterial communities on tomato seed and aerial parts of the plant, such as leaves. (Morella et al., 2019; Romero et al., 2014; Simon et al., 2001; Toju et al., 2019). Allard et al. (2016), Dong et al. (2019) and Ottesen et al.

(2013) have characterized the rhizosphere, phyllosphere and endosphere of roots, stems, leaves, fruits and seeds of tomato plants grown in greenhouse and field conditions. Still, there is a lack of knowledge on the impact of diverse experimental sites and land- management practices on bacterial community composition and variation between tomato plant tissues. Moreover, understanding the impact of agricultural management strategies can assist in modulating the plant microbiome to promote plant health.

102

Studies of integrated crop–livestock systems using chicken grazing impacting the bacterial plant microbiome remain sparse. The aim of this chapter is to characterize the endophytic bacteria of tomato plants grown in pasture plots a year after chicken grazing.

Culture-dependent and culture-independent techniques will provide insight into the impact of crop-livestock rotation in tomato and soil microbiomes. The objectives are to:

(a) determine the impact of chicken grazed pasture plots (CGPPs) on soil heath and tomato plant tissue biomass, (b) evaluate cultured endophytic bacteria composition obtained from the stem of tomato plants grown in CGPPs and (c) using amplicon sequencing, determine the effect of chicken grazing history on bacterial communities of different tomato plant tissues and soil. We hypothesize that bacterial endophyte diversity is higher in plants grown in CGPPs, as opposed to those grown in pasture only plots. To test this hypothesis, a field trial was conducted in the Summer of 2019 where tomato plants were grown in soils with differing chicken grazing history (Figure 3.1). During the field trial, tomato plant tissues (stem, leaves, roots, fruit) and rhizosphere soil were collected at two time points: before flowering and at harvest (Figure 3.2). This project will help acquire a greater understanding of the impact of agricultural management strategies that increase soil quality on the microbiome of this highly demanded commodity.

103

Materials and Methods

Field Site Description

In the year 2016, the project Diversification of Small and Medium Sized Farms

Grant (SMSFG) was established at Mellinger Research Farm at the Ohio Agricultural

Research and Development Center (OARDC), The Ohio State University (OSU). The

0.61-hectare experimental field includes integrated crop and livestock systems as part of a rotation and its impact is evaluated on subsequent vegetable yield (Figure 2.1). Each plot undergoes a management strategy with rotations every year. For this dissertation, we focused on the pasture and chicken grazing rotation (split plot pasture and pasture chicken). The plots were 105 (R1r5), 403 (R4r3), 201 (R2r1) and 301 (R3r1). In 2017, all plots were planted with a mixture of grass and clover for first year establishment and were mowed prior to the growing season for the second-year pasture in 2018. The composition of the seed mixture for pasture plots was 13.61 kg of HQ-R Pasture Mix

(JDS Seeds Orrville, OH) with 2.27 kg of white (JDS Seeds Orrville, OH) and red clover

(RKO). HQ-R Pasture Mix contains 29% Niva orchard grass, 29% Laura meadow fescue,

20% premium perennial rye grass, 10% Calibra perennial rye grass and 10% Climax timothy.

Chicken grazing was setup as split-plot within each pasture plot. For both years

(2017 and 2018) broiler chickens were placed in 1.5 m x 3.5 m tractors and allowed to

104 graze in pasture plots. In August 2017 and 2018, tractors were moved once daily across the selected pasture split plots for 37 and 32 days, respectively. Chickens grazed the mixture of available pasture and chicken diet was supplemented with feed. Pasture/ pasture-chicken split plots were terminated on May 6th 2019 with application of

Roundup® with Ammonium sulfate. On May 16th 2019 plots were tilled prior to preparing vegetable beds for planting. Between May 20th and May 30th, landscaping fabric and drip-tape were layed on all four plots, and plots were fenced. A mixture of pasture and clover seeds was hand spread in the exposed areas between vegetable beds.

During the same week, other vegetables were planted in addition to the tomatoes for this experiment. For each split plot, two 2.3 m rows were used to plant tomato seedlings.

Preparation of Tomato Seedlings for Transplant

On May 20th, 6 L of soil was collected from each CGPP half and pasture only

(POP) half from split plots 105, 403, 201 and 301. The shovel was cleaned with ethanol between each half and each plot. Soil was bagged in separate plastic bags per split plot. In the greenhouse, soil was poured into plastic containers, separated by treatment and plot, and mixed-together and homogenized by hand. This soil was used as substrate for germination of tomato plants for the field experiment to avoid introduction of endophytes from potting mix (Figure 3.3).

105

The field soil was added to twelve 10.16 cm pots, representing each split plot

(CGPP and POP) in the field (four field replications), for a total of 96 pots. The remaining soil was stored in plastic bags and paper bags. Plastic bags were placed in a -

80oC freezer and paper bags were left open inside the greenhouse for drying. One

Celebrity (Johnny’s Selected Seeds) tomato seed was added for each pot. Pots were evaluated for germination 12 days after sowing and those that did not have germination, had a new seed planted. No significant differences in seed germination were observed.

Tomato seedlings were grown in the greenhouse for three weeks. Greenhouse conditions ranged from a maximum of 35oC and a minimum of 18oC. On June 14th, 10 tomato seedlings were selected per treatment and plot to be transplanted to the corresponding split plot were soil was originally collected from (Figure 3.4). For each split plot, two 2.3 m rows were used to plant tomato seedlings. Each row had five tomato seedlings planted

70 cm apart. After each seedling was planted, water and Bonide® Slug Killer were added around the bottom of the stem. On July 17th, tomato plants were staked by using bamboo stakes and twine and further checked throughout the rest of the season for growth and disease.

Soil Health Measurements

Air dried soil samples from each split plot were sieved to 2mm. Soil samples were analyzed for permanganate oxidizable carbon (POXC), mineralizable carbon and soil protein by the Soil Fertility Lab in the School of Environment and Natural Resources at

106 the OARDC (Soil Fertility Lab, 2019). POXC is a measurement for readily available carbon. Soil protein is a measurement of readily-available nitrogen (Hurisso et al., 2018).

Respiration is a measurement of mineralizable carbon by microbial activity by rewetting dried soil and incubating for 24 hours (Franzluebbers et al., 2000). These measurements were obtained to compare the effect of chicken grazing prior to planting of the tomato on the active pool of organic matter in soil which is rapidly cycled and acquired by crops

(Wander et al., 1994; Woods, 1989). Therefore, they are sensitive indicators of fertility for recently altered management practices (McCarty et al., 1998). Larger active carbon pools reflect greater soil organic matter and enhanced soil fertility (Wander, 2004).

Tomato Plant Sampling and Evaluation

Tomatoes were sampled at two time points, for both biomass and endophyte characterization: July 5th and September 5th.

On July 5th, when the plants were about to the reach the flowering stage (around five compound leaves per plant), four tomato plants were sampled for each split plot. A shovel was introduced in the soil 15.24 cm away from the stem and around the plant to loosen the soil around the roots. The whole plant was removed from the plot and placed in a paper bag. Aboveground plant tissues were separated from the below-ground by cutting with scissors at the base of the stem. Aboveground plant tissues were measured for biomass and number of compound leaves.

107

On September 5th, when the plant had mature fruits, four tomato plants were sampled for each split plot, as described above. However, in September, all plants were infected with septoria leaf spot of tomato, caused by Septoria lycopersici. Plants were diagnosed by the Vegetable Pathology Lab at OARDC. Some plants had leaves severely dried up and the compound leaf count considered the leaf node. Aboveground plant tissues were separated from the below-ground by cutting with scissors at the base of the stem. Aboveground plant tissues, including tomatoes, were measured for biomass, number of compound leaves, number of symptomatic tomatoes and number of total tomatoes harvested.

Plant Tissue Sampling, Surface Sterilization and Isolation of Endophytic Bacteria from Tomato Plant Stem

For both July and September tomato plants, individual plant tissues were sampled.

First, stems were separated from leaves and excess soil loosely attached was removed from roots. Rhizosphere soil (soil attached to the roots) was collected by shaking roots gently over aluminum foil. Root, stems and leaves were surface sterilized by immersing tissue in 70% ethanol (1 min), 20% bleach mixed with 0.1% Tween (1 min), 70% ethanol

(1 min) and rinsed three times with sterile distilled water. One healthy fruit from one plant per split plot was surface sterilized by immersing in 70% ethanol and drying in a laminar flow hood for 30 minutes. After, fruit flesh was cut open using a sterile scalpel

108 and seeds were removed. Seeds were placed on paper, dried in laminar flow hood and stored in paper envelopes.

For culturing of bacterial endophytes, one healthy (or healthier) plant per split plot was selected. The edges of the surface sterilized stem were cut off with a sterile scalpel, and the stem and tomato, was cut into several 1-cm section pieces. Tissue sections were plated on 1/10 TSA and yeast dextrose carbonate agar and incubated at ambient temperature (approximately 22oC) for 48 hours. Emerging bacterial colonies from the stem and tomato flesh sections were transferred up to five times to obtain pure cultures. Remaining tomato flesh, stem, leaves, roots and rhizosphere soil were stored in aluminum foil and placed in -80oC freezer.

Cultured Endophyte DNA Extraction, PCR Amplification and Sequencing of the 16S rRNA Gene Region

The 16S rRNA Gene was used to characterize bacterial endophytes recovered from tomato tissues. A boiling lysis method, according to Sanders and Miller (2010), was used to extract genomic DNA from individual bacterial isolates. A single colony was selected and immersed in 200µL of sterile water in a 1.5mL tube. The tube was then incubated in a water bath at 100oC for 10 minutes, transferred to ice for 10 minutes followed by centrifugation at 10,000 rpm for 10 minutes. 150µL of the supernatant containing the genomic DNA was transferred to a new 1.5mL tube. PCR amplification of the 16SrRNA gene for bacterial endophytes was performed using primers 8F (5’- 109

TGGAGAGTTTGATCCTGGCTCAG-3’) and 1492R (5'-

GGTTACCTTGTTACGACTT-3') (Weisburg et al., 1991). The PCR reaction mixture consisted of 2 μL of DNA template, 1× GoTaq Flexi Buffer (Promega), 0.2 mM dNTP mix (Promega), 2 mM of MgCl2 (Promega),1 mg/ml of Bovine serum albumin (BSA),

0.03 GoTaq DNA polymerase (Promega), 0.5 μM each primer, and molecular grade water (Hyclone). PCR reaction conditions were an initial denaturation at 95oC for 5 minutes, 35 cycles of denaturation at 94oC for 1 minute, annealing at 54oC for 45 seconds and elongation at 72oC for 1 minute and a final elongation cycle of 72oC for 8 minutes.

5µL of each amplification product was analyzed by agarose gel (1%, containing 5 μL of

10,000X SYBR Safe, Thermo Fisher Scientific) electrophoresis in 0.5 Tris borate EDTA

(TBE) buffer with 100 bp Plus DNA ladder (Promega). PCR products were evaluated through gel electrophoresis. The gels were visualized under UV transillumination (Gel

Logic 112 Imaging System Kodak). Enzymatic PCR cleanup was done by adding 5µL of

PCR product, 1µL of Alkaline Phosphatase and 1µL Exonuclease I (Ilustra) into a new

0.2ml tube. PCR cleanup conditions consisted incubating at 37°C for 15 minutes followed by incubating at 80°C for 15 minutes. Preparation of cleaned PCR products for sequencing consisted of adding 10.75µL of clean PCR product and 1.25µL of 6.4 pmol of one of two primers previously used for PCR reaction. Samples were sent to sequence at the Genomics Shared Resource at The Ohio State University Comprehensive Cancer

Center, Columbus, OH. Partial nucleotide sequence was determined with Sanger sequencing using forward and reverse primers.

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Isolate Sequence Data Analysis

For individual sequences, chromatograms were visualized and trimmed for quality using 4Peaks software (Gerberastraat & Aalsmeer, 2001). Forward and reverse reads for each isolate were merged with CLC Sequence Viewer (Genomics Workbench Version

20.0, 2020). Sequences were compared to the NCBI (National Center of Biotechnology

Information) nucleotide database using the basic local alignment search tool (BLAST, blastn) (Altschul et al., 1990), and the Ribosomal Database Project (RDP) (Wang et al.,

2007) Classifier tool. Recovered isolate sequences were aligned and pairwise distance was computed using the Maximum Composite Likelihood model (0.000; sequences had

100% nucleotide identity) to identify unique isolates (Tamura et al., 2004).

Culture-Independent Characterization of Bacterial Endophytes

Total DNA was extracted from the different sampled sources. DNA extraction of all plant tissue (stem, leaf, roots, tomato flesh and tomato seeds), chicken manure and rhizosphere soil samples were processed with the DNAeasy Plant Mini Kit (Qiagen) according to the manufacturer’s instructions, but with the following tissue specific modifications (for grinding and maceration). For tomato tissue, surface sterilized stem, leaf, root and tomato samples were macerated using mortar and pestle. Tomato seed samples were submerged in liquid nitrogen and macerated using mortar and pestle. Prior to extraction, bulk and rhizosphere soil samples were dried using a centrifugal evaporator

111 for at 60°C 30 minutes. Chicken manure samples were lyophilized and macerated by adding sample into a 2 ml tube with two stainless steel beads and then the tube was vortexed for 30 seconds at 4 M/S high speed by a high-speed benchtop homogenizer

(FASTPREP-24™ 5G, MP BIO). DNA extraction of bulk soil samples was processed with SurePrep Soil DNA Isolation Kit (Fisher BioReagents). Negative extraction controls were processed for each kit.

To assess the DNA quantity and purity, 3µL of each DNA sample was analyzed by agarose gels (1%, containing 5 μL of 10,000X SYBR Safe, Thermo Fisher Scientific) electrophoresis in 0.5 TBE buffer at 70 mV for 90 min with GeneRuler 1 kb Plus DNA

Ladder (Thermo Fisher Scientific). The gels were visualized under UV transillumination

(Gel Logic 112 Imaging System Kodak). ImageJ (Schneider et al., 2012) was used to determine relative PCR product concentration from gel images, using Gene Ruler ladder as a reference. DNA concentration was adjusted to 5 ng/μL.

Samples were submitted to the Molecular, Cellular and Imaging Center (MCIC) at

OARDC, Wooster, OH for preparation and sequencing of 16S rRNA Amplicon libraries.

There, DNA fragments of the bacterial 16S rRNA gene were amplified using a modified semi-nested PCR previously described to limit amplification of plant products (Beckers et al., 2016). The first PCR was run with the primers 799F (5′-

AACMGGATTAGATACCCKG-3′) and 1391R (5’-GACGGGCGGTGWGTRCA-3′), for 15 cycles (Chelius & Triplett, 2001; Walker & Pace, 2007). Then, a 1:20 dilution of

112

PCR product was used as template for the second PCR with pair 967F (5′-

AACMGGATTAGATACCCKG -3′) and 1391R (5′-GACGGGCGGTGWGTRCA-3′) fused with Illumina MiSeq adapters (Sogin et al., 2006). The second PCR was run for 10 of cycles. Individual sample indexes were added through a dual-indexing approach with a final 8 cycles. PCR products were then pooled in equimolar concentration and the pooled library was size selected using Blue Pippin Prep (Sage Science) with a 2% agarose gel cassette and set to collect products of ~ 600 bp, to enrich for bacteria (and exclude plant mitochondrial amplification with a band of ~ 1000 bp with Illumina adapter). Amplicon libraries were then sequenced in a MiSeq 2x300 (Illumina) platform. A total of 154 samples were sequenced in one sequencing run, including 149 field samples and 5 DNA extraction controls.

Amplicon Sequence Data Processing and Analysis

Amplicon sequence data was processed and analyzed (Figure 3.5). Primer and adapter trimming for raw paired reads were done using Cutadapt version 1.18 (Martin,

2011). R (R Core Team, 2014) was used to perform the sequence data processing and statistical analysis. The “DADA2” package (Callahan et al., 2016) was used to trim and filter reads, infer sequence variants, and merge the resulting sequence variants to obtain amplicon sequence variants (ASVs). ASVs were aligned to the SILVA reference database version 132 release (Quast et al., 2013) and taxonomy was assigned. Following sequence alignment using the “DECIPHER” package (Wright, 2016), phylogenetic relatedness of

113 all ASVs was estimated by fitting a generalized time-reversable, with Gamma rate variation (GTR+G+I), maximum likelihood tree using the "phangorn" package (Schliep et al., 2017).

Amplicon Data Analysis and Statistics

Data was imported to “phyloseq” (McMurdie & Holmes, 2013) and was analyzed as a “phyloseq” object to explore ecological metrics and identify differentially abundant taxa. The “decontam” package was used to determine any possible contaminants in the samples submitted for sequencing (Davis et al., 2018). Using this package, the abundance of ASVs considered contaminants were then compared to those in control samples. None of the ASVs tagged as contaminants overlapped with ASVs in control samples, which suggest they were real reads. Sequences matching “Chloroplast”, “Mitochondria” and

“Eukaryota” were removed. Using the “ampvis2” package (Andersen et al., 2018), rarefaction curves were generated to estimate sampling efforts. Samples from different origins were analyzed separately due to differences in sequencing depth, differences in sample handling, DNA extraction procedures, and expected differences in DNA extraction efficiency for each sample type. Cumulative Sum Scaling (CSS) was used as a normalization method for our data due to its ability in detecting differences in the rare members of the communities (McKnight et al., 2019). CSS normalization was performed using the “metagenomeSeq” package (Paulson et al., 2013). To observe distribution of relative abundance of ASVs per sample at phylum level, plot abundances were generated

114 using the “phyloseq” package. Three different alpha diversity metrics were selected to interpret bacterial community compositions: Chao1 Species Richness, Shannon's

Diversity Index and Faith's Phylogenetic Diversity. Chao1 index is used to estimate bacterial richness (abundance of ASVs in a given sample) (Chao et al., 2006). Shannon's

Index is used to estimate bacterial richness as well as evenness of the community

(frequency of ASVs in a given sample) (Shannon, 1948). Faith's Phylogenetic Diversity addresses biodiversity using phylogenetic information (Faith, 2018). Chao1 Species

Richness and Shannon's Diversity Index were estimated using the “breakaway” package

(Willis & Bunge, 2015). Faith's Phylogenetic Diversity was estimated using the

“metagMisc” package (McMurdie & Holmes, 2013; Oksanen et al., 2019). Using the

“agricolae” package (de Mendiburu, 2020), Kruskal-Wallis test was used to determine significant index differences.

Due to variability of diversity indexes and sample size, stem, root and rhizosphere samples were the only ones selected for beta diversity analysis. For beta diversity analysis, Principal Coordinates Analysis (PCoA) was performed based on weighted

UniFrac distance matrices, to observe changes in taxon abundance and taxa distribution between treatments, sample origin and sampling month (Lozupone & Knight, 2005). A two-dimensional ordination plane was plotted with the “phyloseq” package (McMurdie &

Holmes, 2013) and the “ampvis2” package (Andersen et al., 2018). The statistical significance of the differences in bacterial community composition between sampling month (July and September), sample origin (root, stem and rhizosphere soil), and

115 treatment (chicken grazing and pasture only) was assessed through Permutational

Multivariate Analysis of Variance (PERMANOVA) with 1000 permutations (Anderson,

2017). PERMANOVA was performed using the “vegan” package (Oksanen et al., 2019).

Using “DESeq2” package (Love et al., 2014), differentially abundant taxa were analyzed to observe treatment effects between CGPPs and POPs.

Plant Growth and Soil Health Data Analysis and Statistics

The Shapiro-Wilks and Levene’s tests for normality and homogeneity of variance were run in R (Oksanen et al., 2019) to determine any non-normality and equal variance of sample groups. Log 10 transformation was used on data that did not meet parametric assumptions. Mixed design analysis of variance (ANOVA) was used to determine significant differences in measurements between the treatments and blocks. Kruskal-

Wallis test was performed on data that did not meet parametric assumptions after transformation. Significant F-values (p<0.1) obtained led to using Fisher’s least square difference (LSD) test to determine differences between treatments. R (R Core Team,

2014) was used to perform the statistical analyses using “agricolae” (de Mendiburu,

2020), “car” (Fox & Weisberg, 2019), "lsmeans" (Lenth, 2016) and "readr" (Wickham et al., 2018) packages.

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Results

Impact of Chicken Grazing History on Soil Health

In May 2019, prior to tomato planting, soil samples were collected to test for permanganate oxidizable carbon (POXC), soil protein and respiration. Soil protein and respiration were shown to be significantly different between treatments at p<0.1 (Table

3.1). The means for POXC, soil protein and respiration measurements of soil from

CGPPs increased by 7.6%, 13.1% and 32%, respectively, compared to POPs.

Effect of Chicken Grazing History on Plant Tissue Measurements of Tomato Plants Sampled in July and September

During the field trial at Mellinger Farm, tomato plant tissues (stem, leaves, roots, fruit) were collected and measured at two time points: before flowering (July 5th) and at harvest (September 5th). For tomato plants collected in July, above-ground biomass and number of compound leaves were measured. In July, no significant differences were observed in tomato biomass between CGP and POP, however significant block effects were observed for aboveground biomass and number of compound leaves (Table 3.2).

Fisher's LSD tests showed that the biomass and number of leaves of plants grown on plots 201 and 301 were significantly higher from those grown in 105 and 403 (Table 3.3).

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For tomato plants collected in September, no significant differences were observed for any of the tomato measurements, however, the means for plants grown in

CGPPs were larger than for those grown in POPs. In September, there were significant differences in above-ground biomass, total biomass of tomatoes harvested, number of symptomatic tomatoes harvested, number of total tomatoes harvested and root biomass of the blocks where the tomato plants grew (Table 3.4). Fisher's LSD tests showed that the above-ground biomass, number of compound leaves, number of total tomatoes harvested and root biomass plots 201 and 301 were significantly higher from those grown in 105 and 403 (Table 3.5).

Impact of Chicken Grazing History on Cultured Endophytic Bacteria Composition

A total of 124 endophytic bacteria were recovered from surface sterilized stems and tomato flesh of tomato plants from July and September. By computing the pairwise distance of each 124 bacterial sequences, 29 of these represent unique taxa. The classification of these 29 taxa, based on >97% similarity to GenBank database entries queried through blastn is shown in (Table 3.6). Recovered isolates belong to 19 genera within four phyla: Actinobacteria, Firmicutes, Proteobacteria and Bacteroidetes. Of the

124 isolates recovered, the stem community had 90 isolates belonging to 27 taxa; the tomato flesh had 34 isolates belonging to 9 taxa. 14 taxa were recovered exclusively in

September. In July, more than 30% of the bacterial endophytes recovered were Bacillus spp. Pseudomonas spp. compromised 26% of the bacterial endophytes recovered in July. 118

In September, nearly half of the bacterial endophytes recovered were Bacillus spp.

Pseudomonas spp. compromised 27% of the bacterial endophytes recovered in

September. The majority of the taxa found across all treatments, tomato plant tissues and sampling months belong to Bacillus 1, followed by Pseudomonas 1. In July and

September, Bacillus spp. and Pseudomonas spp. were the only genera found on all treatments in all blocks (Figure 3.6 and 3.7). Additionally, taxa belonging to Bacillus represented 67% of the isolates recovered from tomato flesh.

The only genera recovered in both time points and treatments, were Bacillus,

Pseudomonas, Microbacterium, and Pantoea. The rest of the isolates, that form around

25%, were present in one of two time points or treatments. Taxa belonging to Bacillus,

Pseudomonas and Microbacterium were the only ones found on both stem and tomato flesh. Taxa belonging to Comamonas, Massilia and Stenotrophomonas were only recovered from CGPPs. Taxa belonging to the Enterobacteriaceae family were found in both treatments.

Tomato-Associated Bacterial Communities and Diversity Analyses

Due to significant block effect and high incidence of septoria leaf spot on tomato, only plant tissue samples obtained from plots 201 and 301 were used for amplicon sequencing. The DNA sequencing of 16S rRNA gene region from bacterial communities in soil, tomato plant tissues and chicken manure resulted in a total of 8,658,317 reads

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(Table 3.7). After primer removal, filtering, and removal of chloroplast, mitochondria and eukaryotic reads, 981,385 reads remained with an average of 6,372 reads per sample

(Tables 3.8 and 3.9). 148 samples remained after filtering. Control samples had an average of 16 reads and ASVs found in these samples were not abundant in any of the soil, plant tissue and chicken manure samples. Due to these findings, there were no concerns for contamination of the rest of the samples. Control samples were removed from the dataset for analysis. After removing controls, 18,958 ASVs were identified in

143 samples.

ASV richness was plotted using rarefaction curves by displaying the total reads sequenced for each sample by their origin (Figure 3.8). Rarefaction curves for bulk soil, stem, tomato seed and tomato flesh samples leveled off at less than 8,000 reads.

Rarefaction curves for leaf samples leveled off at less than 2,000 reads. Rhizosphere soil and root samples had a sequencing depth surpassing 30,000 reads and the rarefaction curve leveled off at around 30,000 and 45,000 reads, respectively. Rarefaction curves for chicken manure samples surpassed 20,000 reads, and the curve leveled off below 20,000.

Overall, the curves of samples from rhizosphere soil, root and chicken manure leveled off at a higher sequencing depth, therefore had higher species richness, than the curves of samples from bulk soil, stem, leaf, tomato seed and tomato flesh.

The most abundant phyla in all samples were Proteobacteria, Actinobacteria,

Firmicutes and Bacteroidetes (Figures 3.9-3.13). Proteobacteria dominated more than half

120 of the bacterial normalized reads from the bulk soil, root, stem, leaf, tomato flesh and seed samples (60%, 77%, 77%, 86%, 75% and 58% respectively) (Figures 3.9-3.14).

Actinobacteria was the second most abundant phyla in bulk soil, root, stem, tomato flesh and seed (21%, 10%, 9%, 10% and 22% respectively) (Figures 3.9-3.11, 3.13 and 3.14).

However, rhizosphere soil samples contained an even amount of Proteobacteria (34%) and Actinobacteria (34%) (Figure 3.15). In chicken manure, Firmicutes dominated 82%, followed by Proteobacteria (17%) (Figure 3.16). In addition to phyla, abundance of families were evaluated within each sample origin. Burkholderiaceae was abundant in bulk soil (20%), leaf (11%), rhizosphere soil (4%), root (21%), stem (14%) and tomato flesh (3%). Pseudomonadaceae was among most abundant families in bulk soil (3%), leaf (45%), root (6%), seed (5%), stem (25%) and tomato flesh (11%).

Enterobacteriaceae was found in high abundance in all plant tissues, dominating tomato flesh (42%) and tomato seed (37%). Chicken manure was mainly composed of

Lactobacillaceae (56%), Enterobacteriaceae (15%) and Enterococcaceae (10%).

Alpha diversity metrics (Chao1 species richness, Shannon’s diversity index and

Faith’s Phylogenetic diversity) were estimated for bacterial communities from bulk soil, chicken manure, rhizosphere soil and plant tissue samples collected in May, June and

September. (Tables 3.10-3.12). In May and July, there was a trend of higher richness, evenness and phylogenetic diversity for soil and plant tissue samples obtained from

CGPPs. In September, richness, evenness and phylogenetic diversity was lower for all samples obtained from CGPPs, with the exception of seed and leaf samples. The bacterial

121 communities in the rhizosphere soil showed the highest estimates among all of the tested samples across sampling months and treatments. There were significant differences in the diversity of bacteria recovered from rhizosphere soil and leaf samples collected from tomato plants grown between plots 201 and 301 (Table 3.13). Rhizosphere soil samples collected from tomato plants grown in plot 201 had higher species richness than plot 301 in both sampling months and treatments. Leaf samples belonging to CGPP 301 had the highest richness.

Due to variability of diversity indexes and sample size, stem, root and rhizosphere samples were the only ones selected for further analysis. PCoA ordination, from weighted normalized UniFrac distance matrix, was performed to evaluate the effect of sample origin, sampling month and treatment, on bacterial community structure (Figure 3.17).

The first and second principal coordinates explained 47.2% and 20.9% of the total variation, respectively. Bacterial community structure had significant differences in bacterial communities based on sample origin (PERMANOVA, p value = 9.99x10-5) and sampling month (PERMANOVA, p value = 1.40x10-3). The strongest clustering was observed for rhizosphere soil samples followed by stem and root, indicating similarity in bacterial community profiles, regardless of the treatment. Additionally, bacterial communities differ between the stem and root. Another PCoA ordination plot was generated to show the distribution of the five most dominant phyla (Figure 3.18). In this plot, data points representing one of the top five most dominant phyla are plotted within the same ordination space, therefore taxa distribution can be correlated with samples

122 using the same ordination space (Pavoine et al., 2004). The top five phyla (Acidobacteria,

Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria) were distributed among all samples with the exception of Acidobacteria being the most clustered with rhizosphere samples.

Differentially abundant taxa in tomato tissue, in response to chicken grazing history, were identified by comparing samples of CGPPs and POPs within sampling months (Table 3.14), using DeSEq. A total of 67 ASVs were differentially abundant between treatments, across all tissue samples (Table 3.14, Table A.1). Only one ASV from Actinobacteria found in rhizosphere soil from tomato plants grown in CGPPs had significantly lower relative abundance, compared to POP. The majority of the ASVs shown to be more abundant in CGPPs belonged to Proteobacteria, followed by

Actinobacteria.

Discussion

This study found that plots with history of chicken grazing resulted in significant increases in soil fertility. Although not significant, there was an increasing trend of biomass measurements from tomato plants grown in CGPPs compared to those from

POPs. Taxonomic composition of dominant groups of recovered endophytic bacterial isolates were consistent with those found by amplicon sequencing. Higher amounts of C in CGPPs could favor copiotrophic bacteria, such as dominant phyla in this study, alpha

123

Proteobacteria and Bacteroidetes. A total of 65 ASVs were differentially abundant in soil and tomato plant samples from CGPPs. High abundance of Enterobacteriaceae in the endophytic community of all plant tissues could raise concerns for the use of chicken manure, which is known to harbor human pathogens that can potentially contaminate fresh produce. Isolates recovered from tomato plants grown in CGPPs could possess potentially antagonistic properties towards plant pathogens. Sampling month and origin of sample were the main drivers in bacterial community composition and diversity, regardless of chicken grazing history. These findings indicate that plant- and soil- associated microbiomes might have a stronger response to management strategies in earlier plant development stages.

Soils with a history of chicken grazing had significantly higher microbial activity and readily available nitrogen, although no significant increase in tomato plant biomass.

However, the means for POXC measurements in soil and tomato plant biomass sampled from CGPPs in September are higher than those grown in POPs. The active pools of organic matter measured serve as indicators of available carbon and nitrogen that is eventually taken up by microbes (Sahoo et al., 2019). Measurements for active carbon, mineralizable carbon and soil protein tend to be most sensitive to changes in the environment (Culman et al., 2012; Hurisso et al., 2018; Lai et al., 2012). The practice of chicken grazing might be increasing fertility in soil. Hilimire et al. (2013) conducted a similar study where they compared soil fertility of plots with recent history of pastured poultry, as well as biomass changes of crop plants grown in greenhouse experiment with

124 those soils. Although labile C and N were not measured, pastured poultry plots had significantly higher total C, total N, and organic matter compared to soils without pastured animal inputs. Additionally, biomass and height of sunflowers and beans grown in those soils was greater but varied by farm. Unlike our study, the authors were also able to evaluate the amount of chicken manure incorporated in the soil. Studies evaluating varying application rates of chicken manure have reported variable results (Conn &

Lazarovits, 1999; Maru et al., 2015; Riegel & Noe, 2000). Therefore, while chicken grazing might be beneficial for soil fertility and plant biomass, it is important to know the application rate of chicken manure to further distinguish its effects.

Variations in plant biomass observed might be due to field topography and precipitation during the growing season, regardless of treatment. Tomato plants grown in plots 201 and 301 from both sampling times had a significant increase in biomass compared to other plots. Weather during the 2019 growing season had a high precipitation with a total of 53.44 cm, which is a 51% increase of the average precipitation from other years (CFAES, 2020). Plots 105 and 403 are closest to the north of the field. According to Web Soil Survey, this area has less drainage (Soil Survey Staff,

2019). Therefore, there could be differences in soil compaction and infiltration within the field that can increase flooding in plots where tomato plants that had significantly lower biomass (Alaoui et al., 2018).

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Dominant phyla (Actinobacteria, Proteobacteria, Bacteroidetes and Firmicutes) of bacterial taxa identified were uniform between culture-dependent and -independent approaches. According to amplicon sequencing data, Proteobacteria dominated more than half of the bacterial community in samples from plant tissue origin. Moreover, cultured endophytes had a predominance of Proteobacteria, as well as Firmicutes. Both phyla have been consistently shown as abundant across tomato plant tissues in other studies (Santoyo et al., 2016; Tian et al., 2017; Xia et al., 2015). Across all treatments, blocks, plant tissue and sampling points, taxa belonging to Bacillus and Pseudomonas were the only ones with high abundance. Bacillus 1 was the most abundant taxa recovered from surface sterilized tomato plant stems, in greenhouse and field experiments (Chapter 2 and 3 of this dissertation). Pseudomonas 1 was recovered in higher amount from tomato plants grown in the field. These genera are frequently isolated from agricultural crops (Miliute et al., 2015; Souza et al., 2013). Additionally, these genera have been isolated from tomato plants and exhibited beneficial activities towards various plant hosts, such as indole acetic acid production, nitrogen fixation and disease suppression (Duijff et al.,

1997; Nawangsih et al., 2011; Santoyo et al., 2016; Shahzad et al., 2017; Tamošiūnė et al., 2018; Tian et al., 2017).

Proteobacteria and Actinobacteria were the most abundant between tomato plant tissue and rhizosphere soil samples. Abundance of these phyla is common in plants, including tomato (Ding & Melcher, 2016; Dong et al., 2019; Rosenzweig et al., 2011;

Zhang et al., 2013). After assessing the bacterial diversity in the tomato rhizosphere

126 through culture-dependent and -independent approaches, Lee et al. 2016 found the majority of isolates and operational taxonomic units belonging to Proteobacteria and

Actinobacteria. Antoniou et al. 2017 evaluated the impact of compost on tomato plant growth by isolating bacteria from compost and the rhizosphere and found Proteobacteria and Actinobacteria highly enriched in the rhizosphere. In our study, taxa belonging to

Microbacterium, from the phylum Actinobacteria, was recovered across all treatments, plant tissues and sampling points. Microbacterium is predominant in various plants, including tomato, and has been studied for growth promotion in plants, including tomato

(Cordovez et al., 2018; Lin et al., 2012; Madhaiyan et al., 2010; Marquez-Santacruz et al., 2010; Zazou et al., 2016). Predominance of these taxonomic groups across soil and plant tissues might be due to horizontal and vertical transmission of microorganisms.

Seeds and roots could be colonized by bacteria in the soil, and eventually migrate to aboveground compartments such as stems and leaves (Compant et al., 2005; Delmotte et al., 2009).

As the active organic matter pool was higher in CGPPs, higher bacterial richness, and diversity in soil and plant tissue samples was observed in May and July. Increase in microbial diversity has been associated to the application of organic amendments

(Chaudhry et al., 2012; Sun et al., 2004). Enrichment in organic matter due to incorporation of chicken manure can increase microbial activity in soil (Gomez et al.,

2006). A study comparing an organic farming system with rich substrate in soil through the introduction of cattle farmyard manure, biological practices without the use synthetic

127 compounds and the presence of weed species, demonstrated higher microbial diversity compared to a conventional system (Lupatini et al., 2017). Due to availability of organic carbon and nitrogen, soils with higher amounts of C should favor copiotrophic bacteria, which thrive in environments with higher organic matter, as opposed to soils with lower amounts of C where oligotrophic bacteria, which thrive in environments with lower organic matter, should predominate (Ding et al., 2014; Hartmann et al., 2015). Soil and tomato plants grown in CGPPs could be harboring more copiotrophic bacteria, in comparison to POPs which had a lower active organic matter pool. Fierer et al. 2007 found a positive correlation of abundances of alpha Proteobacteria and Bacteroidetes with

C mineralization rates, hence, being considered copiotrophic bacteria. Similar to our results, these phyla were the most abundant in soil samples, regardless of chicken-grazing history. According to Ho et al. (2017), Acidobacteria and Verrucomicrobia are more competitive under oligotrophic conditions. One of the least abundant phyla found in our study was Acidobacteria.

Although history of chicken grazing did not produce a significant shift in bacterial communities, it did affect relative abundance of individual taxa, as revealed by amplicon sequencing. A total of 65 ASVs were differentially abundant in stem and rhizosphere soil samples collected in July from CGPPs compared to POPs. Seven ASVs identified in rhizosphere soil in July belonged to the genus Tumebacillus. This genus has been previously found as part of rhizospheric communities (Agnolucci et al., 2019; Jin et al.,

2020; Thokchom et al., 2017). Two ASVs from the Chitinophagaceae family were

128 differentially abundant in rhizosphere soil. These bacteria have been previously isolated from soil samples, and are known to encode several proteins predicted to have β- glucosidase activity, which is important in cellulose composition (Bailey et al., 2013).

Moreover, there was an increase of ASVs from the Rhizobiales order. Bacteria in the

Rhizobiales are associated to C-cycling in soil and N-cycling in leguminous plants (Fan et al., 2014). Five ASVs identified in rhizosphere soil and stem in July belong to the genus Sphingomonas. This genus has been found as a plant endophyte, including the culturing surveys in this dissertation, and has growth promoting effects in tomato plants

(Khan et al., 2014). It would be important to evaluate application rate of chicken manure and determine the extent of its impact on differentially abundant ASVs and their possible direct and indirect effects on the plant host.

Considering chicken manure is known to harbor human pathogens that can potentially contaminate fresh produce, it might be the source of high abundance of

Enterobacteriaceae in the endophytic community of all plant tissues (Chen & Jiang, 2014;

Omeira et al., 2006). Chicken manure had 82% Firmicutes followed by 17%

Proteobacteria, which is consistent with reports from previous studies (Gong et al., 2007;

Zhang et al., 2018). Additionally, chicken manure samples contained families such as

Lactobacillaceae (56%), Enterobacteriaceae (15%) and Enterococcaceae (10%) which have been previously found in chicken’s intestinal and litter microbiota (Cressman et al.,

2010). According to amplicons sequencing data, Enterobacteriaceae was found in high abundance in all plant tissues, dominating tomato flesh (42%) and tomato seed (37%),

129 regardless of chicken grazing history. Bacteria in this family are best known for their pathogenic effect on humans, as well as their presence in fresh and processed tomato (Al-

Kharousi et al., 2016; Eribo & Ashenafi, 2003). Recovered isolates from taxa belonging to the Enterobacteriaceae family was present in both treatments, sampling months, and plant tissues. Increased abundance of Enterobacteriaceae can also be attributed to elevated temperature and humidity, as seen by Allard et al. (2020) in epiphytic communities on tomato fruit. There are bacteria within Enterobacteriaceae include plant endophytes with plant growth promoting activity in tomato (Bendaha & Belaouni, 2019;

Gupta et al., 2020; Mayak et al., 2001). Examples of taxa belonging to

Enterobacteriaceae recovered in our study include Pantoea and Enterobacter. Pantoea was recovered across all treatments, plant tissues and sampling points. Pantoea has studied for its nitrogen fixation and plant growth-promoting capabilities in tomato

(Dursun et al., 2010; Enya et al., 2007; Enya et al., 2007; Sharon et al., 2016; Walpola &

Yoon, 2013; Walterson & Stavrinides, 2015). On the other hand, Pantoea also known as a plant and human pathogen (Coutinho & Venter, 2009; Cruz et al., 2007). Enterobacter has been studied as a beneficial plant endophyte in tomato and banana (Bendaha &

Belaouni, 2019; Macedo-Raygoza et al., 2019; Telias et al., 2011). On the other hand,

Enterobacter had been studied as an opportunistic human pathogen, as well as exhibiting multiple antibiotic resistance (Mishra et al., 2017). Further research is needed to identify possible harmful bacterial endophytes in soil and tomatoes plant tissues.

130

Implementing chicken manure in agricultural practices has shown to reduce plant disease incidence (Pan et al., 2017), therefore isolates recovered from tomato plants grown in CGPPs have potentially antagonistic properties towards plant pathogens. In this study, a variety of bacteria were isolated, some of which belong to genera with previously described antagonistic properties, such as Bacillus, Pseudomonas, Paenibacillus and

Stenotrophomonas (Massart et al., 2015). Nandhini et al. (2012) recovered endophytic bacteria from surface sterilized root, stem, leaves and fruits of healthy tomato plants.

Through a screening process, only 50% of the bacterial isolates showed antagonistic activity, including Bacillus and Pseudomonas. Conn and Lazarovits (1999) observed a reduction in verticillium wilt, potato scab incidence and plant-parasitic nematodes in potato crops in different field sites incorporated with chicken manure. A study conducted by Zhang et al. (2020) incorporated a mixture of biochar with fresh chicken manure and observed a significant decrease of Fusarium spp and Phytophthora spp. They used high- throughput gene sequencing to monitor changes in the soil’s bacterial and fungal communities and found a significant decrease of the diversity of these communities.

Consequently, the authors observed greater relative abundance of Actinobacteria and the genera Pseudomonas, Bacillus and Chaetomium, which are known to have antagonistic properties.

Sample origin and sampling month were significant drivers in clustering of endophytic bacterial composition in stem, root and rhizosphere samples. In this study, we were able to observe how bacterial communities inside the plant host are organ specific.

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A study by Allard et al. (2016) assessed rhizosphere and phyllosphere bacterial communities of tomato plants that were grown in soils with different organic amendments, including fresh poultry litter, and had similar findings. However, Allard et al. (2016) limited their sampling to one time point and were not able to address differences during plant development. In a different study, Gao et al. (2015) observed higher soil microbial diversity in cucumber rhizosphere during earlier plant stages compared to the following months. Richness and diversity of bacterial communities in soil and plant tissue samples varied in response to sampling month. In May and July, there was a trend of higher bacterial richness, evenness and phylogenetic diversity for soil and plant tissue samples obtained from CGPPs. In September, however, richness, evenness and phylogenetic diversity was lower for all samples obtained from CGPPs, with the exception of seed and leaf. According to plant development stage, leaf, stem, root and rhizosphere-associated microbial populations can vary in abundance community composition (Carper et al., 2018; Chaparro et al., 2012; Copeland et al., 2015; Wagner et al., 2016). Changes varying by plant development stage could be attributed to shifts in the host plant’s nutritional needs (Hacquard et al., 2015; Panke-Buisse et al., 2015).

Considering its significantly higher microbial activity, soil in CGPPs might harbor more bacteria with functional diversity, such as plant growth promotion, affecting the tomato plants initial selection during early plant development stages. Edwards et al. (2018) found the root microbiota of rice plants to be highly dynamic during the vegetative phase of plant growth and followed by stabilization of its composition for the remainder of the life cycle.

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Variations in amplicon sequencing data within sample origin can be improved through better standardization. Plant microbiome surveys tend to use high-throughput amplicon sequencing. This method, coupled with well-designed sampling strategies, unveils the composition, organization, and spatial distribution of microbial communities in a given sample (Knief et al., 2012). Amplicon sequencing can be used for targeting single groups of microbes, rare organisms, as well as functional genes (Herbold et al.,

2015). However, its sensitivity makes it more prone to contamination and the addition of positive and negative controls is necessary (Glassing et al., 2016). In this study, a positive control was not included with the samples sent to sequencing. Including positive controls is important to ensure there are no false positive or false negative results. The lack of positive controls is common in microbiome research. Hornung et al. (2019) reviewed all publications from the 2018 issues of Microbiome and the ISME journal and found that from the 265 publications utilizing high-throughput community sequencing of any type, only 30% reported using any type of negative control, and only 10% reported using a positive control. It is difficult to compare results of cultivation-independent approaches due to the use of different sampling protocols, primers, and sequencing pipelines. When profiling communities by targeting the 16S rRNA gene using NGS techniques, a recurrent problem is the contamination of samples with undesired sequences, such as chloroplast or mitochondrial DNA (Lutz et al., 2011). Some studies in plant microbiomes employ the use of primers that minimize contamination (Ghyselinck et al., 2013). In this study, we used primer pair 799F/1391R which have been found to amplify the

133 hypervariable regions V5, V6 and V7 of the 16S gene and exclude chloroplast and mitochondrial DNA (Beckers et al., 2016; Bodenhausen et al., 2013; Horton et al., 2015).

Due to differences in sequencing depth for samples from different origins, normalization had to be performed. Although common in microbiome research, rarefying counts was not used to normalize data. Rarefying counts is known to produce a high rate of false positives in tests for species that are differentially abundant across sample classes, as well as discards samples (McMurdie & Holmes, 2014). CSS is the most appropriate normalization method to be able to determine differences in rare ASVs between treatments, sampling months and plots.

Of all samples processed, rhizosphere soil and root samples had the highest sequencing depth, and the highest richness and diversity. This is consistent with a study by Ottesen et al. (2013), where the authors saw a higher number of OTU's observed for roots compared to the rest of the tomato plant tissues. Bergna et al. (2018) also recorded higher alpha diversity measurements for rhizosphere soil and root and lower in tomato seed. Although significant differences were found between plots, leaf samples recorded an unexpectedly high richness and diversity. Therefore, higher richness and diversity in bacterial communities from leaf samples in this study might be due to inefficiency in the surface sterilization process, which in turn can affect the detection of epiphytes, and therefore increasing the diversity of bacteria recovered in the leaf sample. Although not common in plant microbiome studies, we used chloroplast and mitochondria excluding primers, which increases the coverage of bacterial sequences, ultimately affecting

134 diversity (Lucaciu et al., 2019). Selection of primers can be a source of bias, therefore, comparisons between studies must take this into account.

With this study, we were able to characterize the endophytic bacterial microbiome of tomato plants grown in CGPPs. Through the use of culture-dependent and culture- independent techniques, we were able to determine the richness and abundance of bacterial communities in soil, tomato plant tissues and chicken manure. Additionally, we were able to assess the impact of management strategy, sample origin and sampling point on these bacterial communities, as well as soil health and plant growth. Our results showed that the sample origin, whether it is soil or above- and belowground plant compartments has a larger influence on bacterial richness and abundance, than agricultural management strategy. Our findings suggest stronger contribution of agricultural management practices during early plant stages on endophytic microbiome, as opposed to later on in the host lifecycle. Improvements in standardization are important preparing samples for sequencing to avoid any biases and incorrect assumptions when analyzing the data. Additionally, maintaining consistency with samples size are essential when attempting to interpret data with sufficient statistical support. Further directions should focus on identification and elucidation of transmission of possible plant growth promoting bacteria, as well as human pathogens, found in crops grown in this type of agricultural management strategy. Moreover, the influence of stage in plant lifecycle on bacterial community composition under specific management strategies remains to be explored.

135

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Tables

Soil health measurements1

POXC2 Soil protein Respiration Strategy P P P Mean Mean Mean value value value Chicken Grazed 765.62 8.28a 279.37a Pasture 0.56 0.08 . 0.04 * Pasture Only 711.16 7.32b 210.67b

* significant at p = 0.05 . significant at p < 0.1 1Log 10 transformation was done on all measurements that did not meet parametric assumptions due to non-normality and non-homogeneity of variance. 2Permanganate oxidizable carbon.

Table 3.1. Effect of chicken grazing history on active pools of organic matter in soil collected from plots prior to planting of the tomato.

154

Sum of Mean F Source of Variation df Pr(>F) Squares Square value

Aboveground Biomass1 Rep 3 0.03 0.01 0.07 0.97 Block 3 2.93 0.98 7.96 0.01 ** Error (a) (Main Plot) 9 1.11 0.12 Treatment 1 0.02 0.02 0.29 0.60 Block:Treatment 3 0.24 0.08 1.38 0.30 Error (b) (Subplot) 12 0.68 0.06

Number of Compound Leaves Rep 3 2.63 0.88 0.40 0.76 Block 3 58.38 19.46 8.81 0.00 ** Error (a) (Main Plot) 9 19.88 2.21 Treatment 1 0.13 0.13 0.06 0.80 Block:Treatment 3 10.38 3.46 1.77 0.21 Error (b) (Subplot) 12 23.50 1.96

*** significant at P=0 ** significant at P=0.001 1Data was transformed due to non-normality.

Table 3.2. Impact of block and chicken-grazing history on aboveground biomass and number of compound leaves of tomato plants sampled in July.

155

Aboveground Number of Compound

Biomass Leaves Treatment Standard Standard Mean Mean Deviation Deviation Chicken Grazed 13.77 Pasture 19.10 7.25 1.77 Pasture Only 19.42 15.38 7.38 2.12

Block1

105 8.49 5.88a 6.38 1.60a 201 29.39 12.87b 8.38 1.06b 301 30.90 12.17b 8.88 1.25b 403 8.26 4.74a 5.63 1.69a

1Each block represents a whole plot.

156

Sum of Mean Source of Variation df F value Pr(>F) Squares Square

Aboveground Biomass1 Rep 3 0.10 0.03 0.66 0.60 Block 3 0.91 0.30 5.77 0.02 * Error (a) (Main Plot) 9 0.47 0.05 Treatment 1 0.06 0.06 1.54 0.24 Block:Treatment 3 0.17 0.06 1.38 0.30 Error (b) (Subplot) 12 0.49 0.04

Number of Compound Leaves1 Rep 3 0.02 0.01 0.18 0.91 Block 3 0.25 0.08 2.77 0.10 Error (a) (Main Plot) 9 0.27 0.03 Treatment 1 0.03 0.03 1.92 0.19 Block:Treatment 3 0.06 0.02 1.33 0.31 Error (b) (Subplot) 12 0.17 0.01

Total Biomass of Tomatos Harvested1 Rep 3 0.29 0.10 1.24 0.35 Block 3 0.69 0.23 3.00 0.09 . Error (a) (Main Plot) 9 0.70 0.08 Treatment 1 0.01 0.01 0.04 0.85 Block:Treatment 3 0.07 0.02 0.14 0.94 Error (b) (Subplot) 12 2.01 0.17

Continued *** significant at P=0 ** significant at P=0.001 . significant at P < 0.1 1Data was transformed due to non-normality.

Table 3.4. Impact of block and chicken-grazing history on above-ground biomass, number of compound leaves, total biomass of tomatoes harvested, number of symptomatic tomatoes harvested, number of total tomatoes harvested and root biomass of tomato plants sampled in September.

157

Table 3.4 Continued

Sum of Mean Source of Variation df F value Pr(>F) Squares Square

Number of Symptomatic Tomatoes Harvested1 Rep 3 0.16 0.05 3.50 0.06 . Block 3 0.71 0.24 15.53 6.65x10-4 *** Error (a) (Main Plot) 9 0.14 0.02 Treatment 1 0.01 0.01 0.03 0.86 Block:Treatment 3 0.04 0.01 0.08 0.97 Error (b) (Subplot) 12 2.02 0.17

Number of Total Tomatoes Harvested1 Rep 3 0.21 0.07 2.12 0.17 Block 3 0.60 0.20 6.01 0.02 * Error (a) (Main Plot) 9 0.30 0.03 Treatment 1 0.02 0.02 0.16 0.69 Block:Treatment 3 0.07 0.02 0.20 0.90 Error (b) (Subplot) 12 1.41 0.12

Root Biomass1 Rep 3 0.01 2.10x10-3 0.08 0.97 Block 3 1.22 0.41 13.97 9.81x10-4 *** Error (a) (Main Plot) 9 0.26 0.03 Treatment 1 0.06 0.06 2.61 0.13 Block:Treatment 3 0.04 0.01 0.54 0.67 Error (b) (Subplot) 12 0.27 0.02

*** significant at P=0 ** significant at P=0.001 . significant at P < 0.1 1Data was transformed due to non-normality

158

Treatment Block1 Measurements Chicken Pasture Only 105 201 301 403 Grazed Pasture Mean 128.48 89.18 68.42 175.42 121.15 70.33 Aboveground Biomass Standard Deviation 98.05 39 34.92a 96.24b 72.98ab 31.90a

Number of Compound Mean 34 28 25.63 40.38 33.75 24.25 Leaves Standard Deviation 15.36 7.9 8.80a 11.11b 15.89ab 5.75a

Total Biomass of Mean 617.42 540.35 297.50 826.71 708.92 482.43 Tomatoes Harvested Standard Deviation 526.07 368.58 128.46 475.85 655.67 244.13

Number of Mean 4.00 3.81 2.25 5.38 5.63 2.38 159 Symptomatic Standard Deviation 3.36 3.06 1.04 3.5 4.27 1.06 Tomatoes

Number of Total Mean 6.19 5.44 3.25 8.00 7.50 4.50 Tomatoes Harvested Standard Deviation 4.89 3.67 1.28a 4.87b 5.78ab 1.93a

Mean 16.72 13.97 9.74 22.21 21.04 8.37 Root Biomass Standard Deviation 9.27 7.42 4.51a 4.73b 8.93b 2.47a

1Each block represents a whole plot.

159

Sampling Plant Number of Month Tissue Treatment Isolates Closest Match in NCBI Identity Recovered Isolate ID1 Recovered Recovered with Exact Database (Accession Number) %3 J SP S TF CG PO Sequences2

Bacillus 1 (AVC.025) 9 31 26 14 20 20 40 Bacillus sp. (MN969919.1) 99.71% Bacillus 2 (AVC.038) 4 4 4 4 Bacillus sp. (MH683121.1) 99.71% Bacillus 3 (AVC.051) 5 6 8 3 6 5 11 Bacillus sp. (MN865961.1) 99.93% Bacillus 4 (AVC.054) 2 4 2 4 2 4 6 Bacillus sp. (MN966966.1) 99.85% Bacillus 10 (AVC.146) 1 1 1 1 Bacillus sp. (MN793064.1) 100% Chryseobacterium 1 Chryseobacterium sp. 1 1 1 1 97.57% (AVC.135) (MN081010.1)

160 Comamonas 1 1 1 1 1 Comamonas sp. (KX461916.1) 100%

(AVC.114) Curtobacterium 1 Curtobacterium sp. 1 1 1 1 99.93% (AVC.177) (MK883133.1) Enterobacter 1 2 2 2 2 Enterobacter sp. (KP792627.1 ) 99.85% (AVC.156) Enterobacteriaceae 1 2 2 2 2 Enterobacter sp. (MN641475.1) 99.70% (AVC.057)

Continued 1AVC: Reference isolates from tomato endophyte culture collection. 2Recovered isolate sequence considered exactly the same were determined by computing pairwise distance (0.000; sequences were the same). 3The identity percent describes how similar the query sequence is to the target sequence. The higher the percent identity is, the more significant the match.

160

Table 3.6. Continued

Sampling Plant Number of Month Tissue Treatment Isolates Closest Match in NCBI Identity Recovered Isolate ID1 Recovered Recovered with Exact Database (Accession Number) %3 J SP S TF CG PO Sequences2

Frigoribacterium 1 Frigoribacterium sp. (AVC.125) 1 1 1 1 (MN989058.1) 99.93% Massilia 1 (AVC.234) 1 1 1 1 Massilia sp. (AM237367.1) 97.64% Microbacterium 1 Microbacterium sp. (AVC.022) 2 3 4 1 4 1 5 (JQ660113.1) 99.55% Microbacterium 5 Microbacterium sp. (AVC.066) 1 1 1 1 (MG016448.1) 100% Paenibacillus 3 161 (AVC.149) 1 1 1 1 Paenibacillus sp. (GU647099.1) 99.52% Paenibacillus 4 (AVC.172) 1 1 1 1 Paenibacillus sp. (KP279976.1) 99.63% Pantoea 1 (AVC.130) 1 1 1 1 1 1 2 Pantoea sp. (MK618629.1) 99.93% Pantoea 2 (AVC.218) 1 1 1 1 Pantoea sp. (CP048033.1) 99.11% Pseudomonas 1 Pseudomonas sp. (AVC.041) 8 21 24 5 17 12 29 (MN326729.1) 99.76% Pseudomonas 2 Pseudomonas sp. (AVC.116) 1 1 1 1 (MH769577.1) 99.21%

161

Table 3.6. Continued

Sampling Plant Number of Month Tissue Treatment Isolates Closest Match in NCBI Identity Recovered Isolate ID1 Recovered Recovered with Exact Database (Accession Number) %3 J SP S TF CG PO Sequences2

Pseudomonas 3 (AVC.154) 1 1 1 1 Pseudomonas sp. (AB720144.1) 99.86% Pseudomonas 4 (AVC.160) 1 1 1 1 Pseudomonas sp. (KU305727.1) 100% Pseudomonas 5 (AVC.169) 1 1 1 1 Pseudomonas sp. (KU305727.1) 100% Rhizobium 1 (AVC.221) 1 1 1 1 Rhizobium sp. (MG836173.1) 100% 162 Sphingobacterium 1 Sphingobacterium sp. (AVC.136) 1 1 1 1 (KY767658.1) 98.89% Sphingomonas 1 Sphingomonas sp. (AVC.153) 1 1 1 1 (MN932317.1) 98.73% Sphingomonas 2 Sphingomonas sp. (NR (AVC.157) 1 1 1 1 109167.1) 97.56% Sphingomonas 3 Sphingomonas sp. (AVC.203) 3 2 1 3 3 (MH769434.1) 100% Stenotrophomonas 1 Stenotrophomonas sp. (AVC.111) 1 1 2 2 2 (JN638427.1) 99.93%

1AVC: Reference isolates from tomato endophyte culture collection. 2Recovered isolate sequence considered exactly the same were determined by computing pairwise distance (0.000; sequences were the same). 3The identity percent describes how similar the query sequence is to the target sequence. The higher the percent identity is, the more significant the match.

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Number After Origin of Total Reads / Forward/Reverse of Primer/Adapter Sample Reads1 Sample Sequence Samples Removal2 Reads 25~343,954 13~167,531 All samples 8,658,317 154 Average 56,222 27,458 Chicken Reads 21,649~127,185 10,580~62,473 271,411 4 Manure Average 68,103 33,475 Reads 4,458~21,210 2,201~10,384 Bulk Soil 98,475 7 Average 14,067 6,921 Rhizosphere Reads 3,763~101,284 1,808~49,847 1,332,727 36 Soil Average 37,020 18,189 Reads 2,191~338,413 1,039~164,552 Root 2,343,792 35 Average 66,965 32,738 Reads 35,669~343,954 17,360~167,531 Stem 2,831,813 32 Average 88,494 43,062 Reads 6,958~164555 3,438~80,445 Leaf 1,383,756 27 Average 51,250 25,020 Tomato Reads 27,525~79,945 13,386~38,633 229,887 4 Flesh Average 57,472 27,895 Tomato Reads 23,839~68,399 11,515~33,257 164,271 4 Seed Average 41,068 19,977 Reads 26~876 13~434 Controls 1,263 5 Average 253 112

1The total reads of all samples in 2019 before processing and filtering. 2Primer and adapter removal performed in Cutadapt software.

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Origin of Reads / Quality filtered1 Denoised F2 Denoised R3 Merged Pairs4 Nonchimeric5 Sample Sample Reads 9~143,262 9~143,142 9~143,232 6~54,709 6~53,964 All samples Average 23,584 23,451 23,508 10,197 9,892 Chicken Reads 9,334~55,432 9,173~55,283 9,236~55,394 8,799~54,709 8,745~53,964 manure Average 29,593 29,497 29,544 29,095 28,781 Reads 1,927~8,919 1,923~8,737 1,924~8,815 1,901~7,247 1,900~7,197 Bulk soil Average 5,970 5,858 5,904 4,256 4,132 Rhizosphere Reads 1,559~42,574 1,513~41,777 1531~42,131 1,361~30,447 1,353~30,088 soil Average 15,593 15,245 15,384 11,489 11,122 Reads 898~139,197 896~138,860 894~139,025 847~53,785 845~49,826 164 Root Average 28,192 28,045 28,106 14,631 14,031 Reads 14,903~143,262 14,898~143,142 14,896~143,232 1,899~30,859 1,894~29,693 Stem Average 36,941 36,900 36,926 5,974 5,713 Reads 1,711~68,455 1,711~68,425 1,711~68,451 1,696~32,719 1,696~32,451 Leaf Average 21,352 21,343 21,351 10,313 10,251 Reads 11,674~32,553 11,656~32,489 11,667~32,526 1,037~8,841 1,033~8,590 Tomato flesh Average 24,070 24,041 24,060 3,778 3,687 Reads 9,306~28,615 9,302~28,606 9,305~28,615 291~8,971 285~8,821 Tomato seed Average 17,097 17,079 17,092 3,347 3,276 Reads 9~375 9~372 9~375 6~366 6~366 Controls Average 93 92 93 79 79

1-5Processing done using DADA2 software. Low-quality reads were filtered out, forward and reversereads were denoised and merged, chimeric reads were removed.

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Before After Number of Origin of Reads / Removing Removing Samples After Sample Sample Unwanted Unwanted Removing Ones Taxa1 Taxa2 with 0 Reads Reads 0~53,988 0~53,759 148 All samples Average 7,108 6,372 Reads 8,754~53,988 8,641~53,749 4 Chicken Manure Average 28,838 28,730 Reads 685~7,189 167~7,107 7 Bulk Soil Average 3,913 3,788 Reads 1,375~30,464 1,360~30,081 35 Rhizosphere Soil Average 11,163 11,008 Reads 30~48,938 29~40,813 33 Root Average 12,941 9,967 Reads 15~8,848 15~8,467 32 Stem Average 2,240 2,184 Reads 0~1,841 0~1,790 24 Leaf Average 285 277 Reads 72~6,157 71~5,948 4 Tomato Flesh Average 2,158 2,101 Reads 17~8,732 17~8,732 4 Tomato Seed Average 2,226 2,224 Reads 6~142 6~48 5 Controls Average 35 16

1-2Unwanted taxa removed belong to chloroplast, mitochondria and eukaryotic DNA.

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Indices means by treatment Sample origin and indices Chicken grazed Pasture only Bulk soil1 Chao1 Species Richness 4848.68 3277.01 Shannon's Diversity Index 7.29 5.37 Faith's Phylogenetic Diversity 116.98 83.61

Chicken Manure2 Chao1 Species Richness 423.99 Shannon's Diversity Index 3.00 Faith's Phylogenetic Diversity 23.24

1There were four samples for chicken grazed pasture plots and three samples from pasture only plots. Bulk soil samples were collected in May. 2There were four chicken manure samples, all collected in September.

Table 3.10. Increase in bacterial community richness, evenness and diversity in samples obtained from chicken-grazed pasture plots. Alpha diversity metrics (Chao1 species richness, Shannon’s diversity index and Faith’s Phylogenetic diversity) of bacterial communities from bulk soil and chicken manure collected in May and September, respectively.

166

Indices Means by Sampling Month and Treatment Sample Origin and July September Indices Chicken Sample Pasture Sample Chicken Sample Pasture Sample

Grazed Size Only Size Grazed Size Only Size Rhizosphere Soil Chao1 Species Richness 5404.69 5166.66 3952.09 5336.60 Shannon's Diversity Index 6.77 9 7.07 10 5.70 8 6.99 9 Faith's Phylogenetic Diversity 162.23 166.72 100.77 120.41

Root Chao1 Species Richness 2131.27 1955.61 750.40 1347.76 Shannon's Diversity Index 4.67 10 5.31 9 3.13 8 3.08 8 167 Faith's Phylogenetic Diversity 68.32 69.40 35.93 43.34

Stem Chao1 Species Richness 1700.66 1682.45 191.66 1614.77 Shannon's Diversity Index 4.76 8 4.46 8 3.20 7 4.37 7 Faith's Phylogenetic Diversity 17.36 13.23 53.79 88.65

Leaf Chao1 Species Richness 3217.36 510.84 3531.28 1614.77 Shannon's Diversity Index 4.99 8 3.16 8 4.58 4 4.37 2 Faith's Phylogenetic Diversity 63.30 15.60 144.40 88.65

Table 3.11. Variations in bacterial community richness, evenness and diversity in samples obtained from chicken-grazed pasture plots and pasture only plots. Alpha diversity metrics (Chao1 species richness, Shannon’s diversity index and Faith’s Phylogenetic diversity) of bacterial communities from rhizosphere soil, root, stem and leaf of tomato plants sampled in June and Septem

167

Indices Means by Treatment Sample Origin and Indices1 Chicken Grazed Pasture Only Tomato Flesh Chao1 Species Richness 130.85 495.74 Shannon's Diversity Index 2.13 3.46 Faith's Phylogenetic Diversity 10.48 26.43

Tomato Seed Chao1 Species Richness 271.03 190.72 Shannon's Diversity Index 3.45 2.15 Faith's Phylogenetic Diversity 0.92 0.71

1There were four samples for both sample origins per treatment. Tomato flesh and seed was collected in September.

Table 3.12. Variations in bacterial community richness, evenness and diversity in tomato fruit and seed samples obtained from chicken-grazed pasture plots and pasture only plots. Alpha diversity metrics (Chao1 species richness, Shannon’s diversity index and Faith’s Phylogenetic diversity) of bacterial communities from tomato flesh and seed sampled in September.

168

Indices Means by Sampling Month and Treatment July Sample Origin and Chicken Grazed Pasture Only P Indices Value1 Sample Sample Sample Sample 201 301 201 301 Size size Size Size Rhizosphere soil Chao1 Species Richness 7450.49 3768.05 6547.41 3095.54 <0.001 Shannon's Diversity 7.73 6.01 7.60 6.28 <0.0004 Index 4 5 6 3 Faith's Phylogenetic 195.48 135.63 145.90 197.93 <0.002 Diversity 169

Leaf Chao1 Species Richness 95.26 6339.46 136.23 1178.61 <0.008 Shannon's Diversity 2.45 7.54 2.67 4.50 <0.003 Index 4 4 4 4 Faith's Phylogenetic 6.30 120.31 7.57 30.01 Diversity <0.007 Continued

1Kruskal Wallis test was used to determine significant differences between plots between sampling months. P value is considered significant at 0.1.

169

Table 3.13 Continued

Indices means by Sampling Month and Treatment September P Sample Origin and Indices Chicken Grazed Pasture Only Value1 Sample Sample Sample Sample 201 301 201 301 Size Size Size Size Rhizosphere soil Chao1 Species Richness 5869.80 2034.37 5945.03 4849.86 <0.001

170 Shannon's Diversity Index 7.61 4 3.79 4 3.79 4 6.54 5 <0.0004

Faith's Phylogenetic Diversity 91.07 110.47 110.47 162.68 <0.002

Leaf Chao1 Species Richness 29.88 29.88 25.25 <0.008 Shannon's Diversity Index 1.43 2 1.43 2 1.39 2 <0.003 Faith's Phylogenetic Diversity 139.62 139.62 7.74 <0.007

1Kruskal Wallis test was used to determine significant differences between plots between sampling months. P value is considered significant at 0.1.

170

Sample Number of Number of Origin Phylum and Corresponding Differentially ASVs (Sampling Families Abundant Considered Month) ASVs Rhizosphere Soil (July) 16,192 Acidobacteria 3 NA 2 Thermoanaerobaculaceae 1 Actinobacteria 16 67-14 4 Demequinaceae 1 Gaiellaceae 1 Kineosporiaceae 1 Microbacteriaceae 2 Mycobacteriaceae 2 NA 1 Promicromonosporaceae 1 Solirubrobacteraceae 3 Bacteroidetes 3 Chitinophagaceae 2 Saprospiraceae 1 Chloroflexi 1 Roseiflexaceae 1 Firmicutes 8 Alicyclobacillaceae 7 Family_XII 1 Gemmatimonadetes 1 Gemmatimonadaceae 1 Nitrospirae 2 Nitrospiraceae 2 Proteobacteria 18 A21b 2 Blfdi19 1 Continued Table 3.14. Differentially abundant taxa (p<0.1) between chicken grazed pasture plots and pasture only plots within sampling months. All taxa shown, except (indicate which) were more abundant in chicken grazed plots.

171

Table 3.14 Continued Sample Number of Number of Origin Phylum and Corresponding Differentially ASVs (Sampling Families Abundant Considered Month) ASVs Rhizosphere Soil (July) 16,192 Burkholderiaceae 1 Dongiaceae 1 Hyphomicrobiaceae 1 Micropepsaceae 1 Nitrosomonadaceae 1 Rhizobiaceae 1 Rhizobiales_Incertae_Sedis 1 Rhodomicrobiaceae 1 Sphingomonadaceae 2 TRA3-20 1 Unknown_Family 1 Xanthobacteraceae 2 Xanthomonadaceae 1 Verrucomicrobia 1 Pedosphaeraceae 1

Total 53

Stem (July) 1,615 Actinobacteria 2 Kineosporiaceae 2 Deinococcus-Thermus 1 Deinococcaceae 1 Proteobacteria 12 Acetobacteraceae 1 Beijerinckiaceae 1 Burkholderiaceae 6 Rhizobiaceae 1 Sphingomonadaceae 3

Total 15 Continued 172

Table 3.14 Continued

Sample Number of Number of Origin Phylum and Corresponding Differentially ASVs (Sampling Families Abundant Considered Month) ASVs Root (September) 9,397 Proteobacteria 1 Burkholderiaceae 1

Total 1

173

Figures

174

Figure 3.1. Overview of the field trial conducted in the Summer of 2019 at Mellinger Research Farm. Plots grazed by chickens in August 2017 and 2018 were used as vegetable plots in 2019. Tomato plants were grown in soils with differing chicken grazing history.

174

175

176

177

Figure 3.5. Amplicon sequence data processing and analysis pipeline adapted from Huo, 2020.

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18 Bacillus 1 (AVC.025) 16 Bacillus 10 (AVC.146) Bacillus 2 (AVC.038) 14 Bacillus 3 (AVC.051) Bacillus 4 (AVC.054) Chryseobacterium 1 (AVC.135) 12 Comamonas 1 (AVC.114) Curtobacterium 1 (AVC.177) 10 Enterobacteriaceae 1 (AVC.057) Enterobacter 1 (AVC.156) 8 Massilia 1 (AVC.234) Microbacterium 1 (AVC.022) Recovered Microbacterium 5 (AVC.066) 6 Paenibacillus 4 (AVC.172) Pantoea 2 (AVC.218) 4 Pseudomonas 1 (AVC.041) Pseudomonas 2 (AVC.116) Number of Bacterial Number of Endophytes 2 Pseudomonas 3 (AVC.154) Pseudomonas 4 (AVC.160) 0 Pseudomonas 5 (AVC.169) Chicken Grazed Pasture Only Chicken Grazed Pasture Only Chicken Grazed Pasture Only Chicken Grazed Pasture Only Rhizobium 1 (AVC.221) Pasture Pasture Pasture Pasture Sphingobacterium 1 (AVC.136) Plot 201 . Plot 301 . Plot 403 . Plot 105 Sphingomonas 1 (AVC.153) 179 Plots and Treatments

179

10 9 8 7 Bacillus 1 (AVC.025) 6 Bacillus 2 (AVC.038) Bacillus 3 (AVC.051) 5 Bacillus 4 (AVC.054) 4 Bacillus 10 (AVC.146)

Recovered Microbacterium 1 (AVC.022) 3 Pantoea 1 (AVC.130) 2 Pseudomonas 1 (AVC.041) Sphingomonas 2 (AVC.157) 1 Sphingomonas 3 (AVC.203)

Number of Bacterial Number of Endophytes 0 Chicken Grazed Pasture Only Chicken Grazed Pasture Only Chicken Grazed Pasture Only Chicken Grazed Pasture Only Pasture Pasture Pasture Pasture Plot 201 . Plot 301 . Plot 403 . Plot 105 Plos and Treatments

Figure 3.7. Endophytic bacterial isolates recovered from tomato flesh of tomatoes harvested from plants grown in plots with or

180 without history of chicken grazing. Different colors indicate the taxa to which each isolate belongs.

180

Figure 3.8. Sequencing depth for each sample origin displayed using rarefaction curves. Each sample is divided by treatment. Legend represents sampling months.

181

182

Figure 3.9. The relative abundance of major phyla in bulk soil samples collected in May. Each bar represents a sample within a treatment.

182

183

Figure 3.10. The relative abundance of major phyla in tomato root samples. Each bar represents a sample within a treatment in a sampling month.

183

184

Figure 3.11. The relative abundance of major phyla in tomato stem samples. Each bar represents a sample within a treatment in a sampling month.

184

185

Figure 3.12. The relative abundance of major phyla in tomato leaf samples. Each bar represents a sample within a treatment in a sampling month.

185

Figure 3.13. The relative abundance of major phyla in tomato flesh samples. Each bar represents a sample within a treatment in a sampling month.

186

Figure 3.14. The relative abundance of major phyla in tomato seed samples. Each bar represents a sample within a treatment in a sampling month.

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Figure 3.15. The relative abundance of major phyla in tomato rhizosphere soil samples. Each bar represents a sample within a treatment in a sampling month.

188

Figure 3.16. The relative abundance of major phyla in chicken manure samples. Each bar represents a sample within a plot.

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190

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Figure 3.18. The top five phyla (Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria) were distributed among all samples with the exception of Acidobacteria being the most clustered with rhizosphere samples. Principal coordinate analysis of bacterial communities of rhizosphere soil, root and stem samples and top five phyla. A: Distribution of bacterial communities by treatment, sample origin and sampling month. B: Distribution of top five phyla across samples. Samples that cluster close together share a greater similarity in composition.

191

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Appendix A. Supplementary Table

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Table A.1. Identification of differentially abundant taxa (p<0.1) by comparing chicken grazed pasture plots with pasture only plots.

Sample Origin ASV P Value Phylum Class Order Family Genus (Sampling Month) Rhizosphere Soil (July) 317 2.1x10-6 Actinobacteria Thermoleophilia Solirubrobacterales 67-14 NA 1159 7.5x10-6 Actinobacteria Thermoleophilia Solirubrobacterales 67-14 NA 1371 8.6x10-5 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus 2130 9.7x10-5 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus 1719 1.4x10-4 Actinobacteria Thermoleophilia Solirubrobacterales Solirubrobacteraceae NA 2024 1.4x10-4 Actinobacteria Thermoleophilia Solirubrobacterales 67-14 NA 2325 1.7x10-4 Proteobacteria Gammaproteobacteria Betaproteobacteriales TRA3-20 NA 1806 1.8x10-4 Actinobacteria Thermoleophilia Solirubrobacterales 67-14 NA 226 1790 2.3x10-4 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus

647 2.8x10-4 Nitrospirae Nitrospira Nitrospirales Nitrospiraceae Nitrospira 954 4.7x10-4 Proteobacteria Gammaproteobacteria Betaproteobacteriales Nitrosomonadaceae mle1-7 1217 4.8x10-4 Proteobacteria Gammaproteobacteria Betaproteobacteriales A21b NA 76 4.9x10-4 Proteobacteria Gammaproteobacteria Xanthomonadales Xanthomonadaceae Stenotrophomonas 758 5.5x10-4 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas Rhizobiales_Incertae_S

1922 5.6x10-4 Proteobacteria Alphaproteobacteria Rhizobiales edis Alsobacter Allorhizobium-Neorhizobium-

1875 5.6x10-4 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Pararhizobium-Rhizobium 2260 6.0x10-4 Chloroflexi Chloroflexia Chloroflexales Roseiflexaceae NA 689 6.1x10-4 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus 789 6.3x10-4 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae Hyphomicrobium 298 6.3x10-4 Proteobacteria Alphaproteobacteria Rhizobiales Xanthobacteraceae Bradyrhizobium Continued 226

Table A.1. Continued

Sample Origin ASV P Value Phylum Class Order Family Genus (Sampling Month) Rhizosphere Soil (July) 2554 6.7x10-4 Actinobacteria Actinobacteria Micrococcales Demequinaceae Demequina 1711 7.2x10-4 Proteobacteria Gammaproteobacteria Betaproteobacteriales A21b NA 378 7.9x10-4 Proteobacteria Alphaproteobacteria Rhizobiales Rhodomicrobiaceae Rhodomicrobium 155 8.8x10-4 Actinobacteria Actinobacteria Micrococcales Microbacteriaceae Microbacterium 2807 9.5x10-4 Verrucomicrobia Verrucomicrobiae Pedosphaerales Pedosphaeraceae ADurb.Bin063-1 478 9.6x10-4 Proteobacteria Alphaproteobacteria Dongiales Dongiaceae Dongia 119 9.9x10-4 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus 1035 1.1x10-3 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus 1065 1.2x10-3 Acidobacteria Subgroup_5 NA NA NA 612 1.3x10-3 Proteobacteria Alphaproteobacteria Micropepsales Micropepsaceae NA 227 656 1.3x10-3 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas

1105 1.3x10-3 Actinobacteria Actinobacteria Micrococcales Microbacteriaceae Microbacterium 1038 1.4x10-3 Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae NA 1874 1.5x10-3 Actinobacteria Thermoleophilia Gaiellales NA NA 1931 1.6x10-3 Proteobacteria Gammaproteobacteria Gammaproteobacteria_Incertae_Sedis Unknown_Family Acidibacter 2005 1.6x10-3 Bacteroidetes Bacteroidia Chitinophagales Chitinophagaceae NA 205 1.6x10-3 Actinobacteria Actinobacteria Corynebacteriales Mycobacteriaceae Mycobacterium 1299 1.6x10-3 Actinobacteria Actinobacteria Kineosporiales Kineosporiaceae Angustibacter 1943 1.8x10-3 Acidobacteria Acidobacteriia Acidobacteriales NA NA

Continued

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Table A.1. Continued Sample Origin ASV P Value Phylum Class Order Family Genus (Sampling Month) Rhizosphere Soil (July) 574 1.9x10-3 Actinobacteria Actinobacteria Corynebacteriales Mycobacteriaceae Mycobacterium 2556 2.0x10-3 Proteobacteria Alphaproteobacteria Rhizobiales Xanthobacteraceae Rhodoplanes 1691 2.0x10-3 Acidobacteria Thermoanaerobaculia Thermoanaerobaculales Thermoanaerobaculaceae Subgroup_10 1770 2.2x10-3 Proteobacteria Deltaproteobacteria Myxococcales Blfdi19 NA 2210 2.3x10-3 Actinobacteria Actinobacteria Micrococcales Promicromonosporaceae Cellulosimicrobium 374 2.4x10-3 Firmicutes Bacilli Bacillales Alicyclobacillaceae Tumebacillus 159 2.4x10-3 Firmicutes Bacilli Bacillales Family_XII Exiguobacterium 2346 2.4x10-3 Actinobacteria Thermoleophilia Solirubrobacterales Solirubrobacteraceae Conexibacter 3454 2.4x10-3 Gemmatimonadetes Gemmatimonadetes Gemmatimonadales Gemmatimonadaceae NA 1419 2.4x10-3 Bacteroidetes Bacteroidia Chitinophagales Chitinophagaceae Ferruginibacter 52 2.5x10-3 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Pseudoduganella 1426 2.5x10-3 Actinobacteria Thermoleophilia Solirubrobacterales Solirubrobacteraceae Solirubrobacter 228 364 2.5x10-3 Actinobacteria Thermoleophilia Gaiellales Gaiellaceae Gaiella

397 2.6x10-3 Nitrospirae Nitrospira Nitrospirales Nitrospiraceae Nitrospira

Continued

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Table A.1. Continued Sample Origin ASV P Value Phylum Class Order Family Genus (Sampling Month) Allorhizobium- Stem (July) Neorhizobium- 30 6.9x10-13 Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Pararhizobium-Rhizobium 701 9.4x10-5 Actinobacteria Actinobacteria Kineosporiales Kineosporiaceae Kineococcus 226 3.3x10-4 Deinococcus-Thermus Deinococci Deinococcales Deinococcaceae Deinococcus 1121 1.3x10-3 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Variovorax 468 1.5x10-3 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas 103 1.8x10-3 Proteobacteria Alphaproteobacteria Rhizobiales Beijerinckiaceae Methylobacterium 140 1.9x10-3 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas 678 2.3x10-3 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Xenophilus -3

229 91 2.5x10 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Variovorax 352 3.6x10-3 Actinobacteria Actinobacteria Kineosporiales Kineosporiaceae Kineococcus

469 6.0x10-3 Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae Sphingomonas 252 7.9x10-3 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia 336 8.0x10-3 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia

Root (September) 141 1.0x10-6 Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae Massilia

229