University of Florida Thesis Or Dissertation

AN EXPLORATION OF FUSOBACTERIUM NECROPHORUM AND PORPHYROMONAS LEVII STRAINS ISOLATED FROM THE UTERI OF DAIRY COWS WITH METRITIS

AMYE MARIAH FRANCIS

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2019

To my Lord Jesus Christ, I dedicate this, the first fruits of my scientific research

ACKNOWLEDGMENTS

I would like to thank my co-advisors Dr. Tim Hackmann and Dr. Klibs Galvao for supporting me during the process of transitioning from one thesis project to another when things didn't work out well. I want to thank them for their guidance and for putting my needs and my future first throughout this project, it is because of them that I was still able to complete a project in time without having to go an extra semester to earn my degree.

I would also like to thank Federico Cunha and Soo Jin Jeon for giving me adivce throughout this project and to my other committee members, Dr. Lori Warren and Dr.

Antonio Faciola for their continued support throughout my projects and for the advise they offered.

Additional thanks goes to my friends and family who have been with me through the rough times and the exciting adventures I've experienced through the course of my studies.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 9

ABSTRACT ...... 12

CHAPTER

1 INTRODUCTION ...... 13

2 LITERATURE REVIEW ...... 16

The Uterus ...... 16 Puerperium ...... 16 Description and Diagnosisof Metritis ...... 17 Negative Effects of Metritis ...... 18 Risk Factors of Metritis ...... 21 Retained Placenta, Dystocia and Stillbirths ...... 23 Feeding, Nutrition and BCS ...... 24 Immunity and Immunosuppression ...... 27 Bacterial Infection of the Uterus ...... 29 Culture Dependent Studies ...... 32 Culture Independent Studies ...... 33 Virulence Factors ...... 37

3 FUSOBACTERIUM NECROPHORUM KG34 AND KG35 ...... 42

Methods ...... 42 Sample Collection and Culture ...... 42 16s rRNA Gene Amplification and Identification ...... 43 Illumina Whole Genome Sequencing ...... 43 Genome Trimming, Quality Control, Assembly and Annotation ...... 44 Virulence Factor Search ...... 45 Data Availability ...... 46 Results ...... 46 Statistics ...... 46 Virulence Factor Coding Genes ...... 46 Discussion ...... 47 Virulence Factors and Proteins of Interest...... 48 lktA Leukotoxin Family Filamentous Adhesin ...... 48

Hemolysin And Hemolysin Associated Proteins ...... 49 Filamentous Hemagglutinin ...... 49 Ecotin ...... 50 Hemin Receptors ...... 50 Omptin Proteases ...... 50 Clp Proteases and Associated Proteins ...... 51 Adhesins ...... 51 Virulence-Associated Protein E ...... 52 BrkB Proteins ...... 52 Closing Thoughts ...... 52

4 PORPHYROMONAS LEVII KG45 ...... 66

Methods ...... 66 Sample Collection ...... 66 Metritis Diagnosis ...... 66 Plating and Growing Samples ...... 67 Isolation of P. levii ...... 67 DNA Extraction ...... 68 Illumina Whole Genome Sequencing ...... 70 Quality Control, Assembly, and Annotation ...... 70 Virulence Factor Search ...... 71 Results ...... 72 Statistics ...... 72 Species Identification ...... 72 Virulence Factors ...... 73 Genome Comparison ...... 73 Discussion ...... 74 Proteases ...... 75 Aminopeptidases ...... 76 Phospholipase ...... 76 Nucleotidyl Transferase AbiEii/AbiGii Toxin Family Protein...... 77 Lipopolysaccharide ...... 77 Superoxide Dismutase ...... 77 Peroxidases ...... 78 Pilin Glycosylation Protein ...... 79 Virulence Protein E and YihY/Virulence Factor BrkB Family Protein ...... 79 Zinc-Dependent Metalloproteinase Lipoprotein ...... 79 Summary ...... 80

5 CONCLUSION ...... 107

LIST OF REFERENCES ...... 112

BIOGRAPHICAL SKETCH ...... 125

LIST OF TABLES

Table page

3-1 Results from the PGAP search of KG34 ...... 61

3-2 Results from the PGAP search of KG35 ...... 63

3-3 Virulence factors identified in the proteome of Fusobacterium necrophorum KG34 and KG35. Proteins of a potentially virulent nature were identified in previous research listed under "source" and searched for in the PGAP annotated proteomes of KG34 and KG35...... 65

4-1 Results from the PGAP search of sequence 1 ...... 93

4-2 Results from the PGAP search of sequence 2 ...... 98

4-3 Virulence factors identified in the proteome of sequence 1 (seq 1) and sequence 2 (seq 2). Proteins acting as virulence factors were identified in previous research listed under "source" and searched for in the PGAP annotated proteomes of sequence 1 and sequence 2 ...... 103

4-4 Virulence factors in the proteome of sequence 1 and sequence 2. and the genes identified with those proteins...... 104

4-5 A comparison of the virulence factors found in sequence 1 and other P. levii species through BLAST with their percent identities...... 105

4-6 A comparison of the virulence factors found in sequence 2 and other P. levii species through BLAST with their percent identities...... 106

LIST OF FIGURES

Figure page

2-1 Mean copy numbers of bacterial species in cows with and without metriti ...... 41

2-2 A comparison of copy numbers of F. necrophorum and P. levii in cows with and without metritis ...... 41

3-1 Sickle settings used for KG34 and 35 ...... 54

3-2 SPAdes settings for KG34 and KG35 ...... 55

3-3 Quast settings for KG34 and KG35 ...... 57

3-4 Quast custom job resource parameters, Account and QOS not shown ...... 58

3-5 Quast report summary for KG34 ...... 59

3-6 Quast report summary for KG35...... 60

4-1 P. levii KG 45 colonies on Brucella agar...... 82

4-2 Sickle settings for sequence 1 and sequence 2...... 83

4-3 SPAdes settings for sequence 1 and sequence 2 ...... 85

4-4 Quast settings for sequence 1 and sequence 2...... 89

4-5 Quast report summary of sequence 1 ...... 91

4-6 Quast report summary for sequence 2 ...... 92

LIST OF ABBREVIATIONS

AI Artificial insemination

API Analytical profile index

BCS Body condition score

BHBA Beta-hydroxybutyrate

BLAST Basic local alignment search tool

BoHV-4 Bovine herpesvirus 4

C Celsius

Ca Calcium

CDS Protein coding sequence within a gene

CL Corpus luteum

CO2 Carbon dioxide

Cu Copper ddPCR Droplet digital polymerase chain reaction

DGGE Denaturing gradient gel electrophoresis

DM Dry matter

DMI Dry matter intake

DNA Deoxyribonucleic acid

DPP Days post-partum

FHA Filamentous hemagglutinin adhesin

IE2 Immmediate-early protein 2

IgA Immunoglobulin A

IGF Insulin-like growth factor

IgG Immunoglobulin G

IgM Immunoglobulin M

LPS Lipopolysaccharide

Mg Magnesium min Minute ml Mililiter

Mo Molybdenum

NCBI National Center for Biotechnology Information

NEFA Non-esterified fatty acid ng Nanogram

O2 Oxygen

OTU Operational taxonomic unit

P Phosphorus

PAGE Polyacrylamide gel electrophoresis pBLAST Protein basic local alignment tool

PCR Polymerase chain reaction

PEG Pegbovigrastim

PGAP Prokaryotic Genome Annotation Pipeline

PGE2 Prostaglandin E2

PGF Prostaglandin F

PMN Polymorphonuclear cell

PMNL Polymorphonuclear leukocyte

PP Post-partum rbG-CSF Recombinant bovine granulocyte colony-stimulating factor

RNA Ribonucleic acid

ROS Reactive oxidative species rRNA Ribosomal RNA

Rt-PCR Real-time polymerase chain reaction

Se Selenium

SOD Superoxide dismutase tRNA Transfer ribonucleic acid ul Microliter

VD Vaginal discharge

VF Virulence factor x g Times gravity

Zn Zinc

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

AN EXPLORATION OF FUSOBACTERIUM NECROPHORUM AND PORPHYROMONAS LEVII STRAINS ISOLATED FROM THE UTERI OF DAIRY COWS WITH METRITIS

Amye Mariah Francis

August 2019

Chair: Timothy J. Hackmann Cochair: Klibs N. Galvao Major: Animal Sciences

Bacteria play an important role in the development of metritis, one of the more common diseases seen in dairy cows post-partum. Fusobacterium necrophorum and

Porphyromonas levii are two of the primary bacterial species thought to be responsible for the development and persistence of metritis. Virulence factors produced by these bacteria may play an important role in causing the disease.

In this thesis, two strains of F. necrophorum and one P. levii strain were isolated from dairy cows with metritis and their genomes were sequenced. The assembled genomes were then searched for potential virulence factors that may contribute to the organisms' pathogenesis. Virulence factors already identified in these species, but also new virulence factors, were observed in the strains isolated here.

CHAPTER 1 INTRODUCTION

Metritis is an inflammation of the uterus that is frequently see in dairy cows after calving. The incidence of metritis ranges from 8% to 40% (Chapinal et al., 2011; Drillich et al., 2001; Goshen & Shpigel, 2006). Several factors are thought to be responsible for causing metritis, but bacterial infection is thought to be the primary cause and probably the most studied area in the etiology of metritis.

Metritis is an important disease in the dairy industry as some of its negative effects include decreased fertility and pregnancy and increased time to ovulation after parturition (Giuliodori et al., 2013). Other negative effects that have been observed include a longer time to conception (Goshen & Shpigel, 2006; Opsomer et al., 2000) , a lower milk yield (Goshen & Shpigel, 2006; Wittrock et al., 2011) and decreased feed and water intake (Huzzey et al., 2007; Pérez-Báez et al., In press) All of these factors in turn not only negatively effect the health of the animal, but also the productivity and thus the profitability of the farm.

Many studies have investigated the risk factors of metritis in order to better understand how to treat and prevent it. Retained placenta was found to be one of the leading risk factors for metritis as described in Dubuc et al.'s (2010) study. Dubuc et al.

(2010) also observed that dystocia was a significant risk factor for the development of metritis. Like Dubuc et al. (2010), Dohmen et al. (2000) also suggested that retained placenta was an important player in the development of metritis. In a more recent study,

Vieira-Neto et al. (2016) reported an association between a vaginal laceration score of 2

(laceration > 2 cm at dorsal commissure of the vulva or at the lateral walls of the vulva/vagina, or both) with increased odds of developing metritis. Oxidative stress, body

condition score (BCS) and even nutrition have also been observed to affect the incidence of metritis in dairy cows (Abuelo et al., 2013; Dohmen et al., 2000; Dubuc et al., 2010; Hoedemaker et al., 2009) and it has been suggested that a negative energy balance and decreased dry matter intake (DMI) may suppress immune function as

Calcium and glucose may be important in allowing granulocytes to function (Weisdorf et al.,1982; Hoeben et al.,1997; Hammon et al., 2006; Kimura et al., 2006; Martinez et al.,

2012; Ster et al., 2012). Decreased DMI is also associated with an increase in NEFA and BHBA which may inhibit oxidative burst activity of polymorphonuclear neutrophils

(PMN), and attenuate inflammatory response through activation of niacin receptors

(Hoeben et al.,1997; Taggart et al., 2005; Hammon et al., 2006; Ster et al., 2012; Wu et al., 2010)

Many studies suggest however, that bacterial infection may be one of the most important causes of metritis. Dohmen et al. (2000) found that increased bacterial contamination and lipopolysacharrides (LPS) were associated with metritis. Cows may be particularly vulnerable to metritis immediately following parturition as they are not only immunosuppressed, but also contaminated with bacteria. All cows' uteri may be contaminated, but not all become infected (Jeon et al., 2015). Some of the most prominent species of bacteria observed in metritic cows in culture dependent studies include Escherichia coli, Truperella pyogenes and Fusobacterium necrophorum (Drillich et al., 2001; Huszenicza et al., 1999), while culture independent studies showed that the most prominent species were Truperella pyogenes, Fusobacterium necrophorum,

Prevotella melaninogenicus, Porphyromonas levii, and Helcococcus ovis (Cunha et al.,

2018; Jeon et al., 2015). Although several strains of F. necrophorum have been studied,

no strains have yet been isolated from the uterus of the dairy cow and even fewer strains of P. levii have been studied in detail.

Virulence factors (VF) are responsible for the pathogenesis of bacteria. Cows in which the bacterial VF fimH, lktA, and fimA were isolated from were more likely to develop metritis and experience decreased reproductive performance (Bicalho et al.,

2010). Bicalho et al. (2012) and Nagaraja et al. (2005) noticed a synergistic relationship between different bacterial species. This synergy was likely due to VF secreted by the bacteria. Several VF have been identified in pathogenic bacteria, including LPS, leukotoxins, endotoxins, hemolysins, hemagglutinins, proteases, and adhesins, many of which have previously been described in other strains of Fusobacterium necrophorum

(Nagaraja et al., 2005). Immunoglobulin A, M and G (IgA, IgM and IgG) proteases have been described in P. levii, but no other virulence factors have yet been identified in this species (Nagmoti, 2014).

In this project, we have isolated strains of F. necrophorum and P. levii from the uteri of metritic dairy cows in order to create a draft genome of each strain and identify virulence factors that may be responsible for the virulence of these bacteria and their contribution to metritis, and compare them to other strains. We beleive these particular strains from the uterus may have some unique properties responsible for the cause of metritis that may differ from strains isolated from other sites. Finding the virulence factors in the genomes of these bacteria will give us a better understanding of their pathogenic nature and allow us to find potential targets for drugs to treat or prevent metritis.

CHAPTER 2 LITERATURE REVIEW

The Uterus

The uterus of the dairy cow is bicornuate, meaning it has two uterine horns to which the ovaries are attached. The uterus is composed of three layers: the perimetrium, or outer layer, the myometrium, or middle layer, and the endometrium, or inner layer. The perimetrium, or serosa, consists of a layer of simple squamous epithelium and a thin layer of connective tissue. The myometrium is composed of layers of circular and longitudinal muscles directly beneath the serosa that aid in contractions important in fetal expulsion and clearance of contaminants. The endometrium is composed of the mucosa and submucosa and lies between the myometrium and the lumen of the uterus. The submucosa lies directly under the muscular layer of the myometrium and contains blood vessels, nerves and lymphatics. The mucosa is a layer of secretory epithelial cells below the submucosa and lines the lumen (Archbald et al.,

1972; Senger, 2012). The uterus is separated from the vagina and the vestibule by the cervix, which usually remains tightly closed. The cervix has a thick wall and acts as a barrier to prevent foreign material from entering the uterus and causing infection

(Senger, 2012). In cows and ewes, the cervix also secretes mucus that may help to flush out microbes and other outside particles (Senger, 2012).

Puerperium

Puerperium, or the puerperal period, is the time after parturition when the uterus returns back to its normal non-pregnant state (Archbald et al., 1972) and the period where metritis is observed. During this time, the uterus, and endometrial epithelium in particular, is involuted (repaired) and the myometrium contracts to expel tissue, fluid

and debris, constrict the vasculature to minimize the potential for hemorrhage, and reduce the size of the uterus (Archbald et al., 1972). A shorter puerperium is desired because conception cannot occur until the uterus has completely involuted.

Description and Diagnosisof Metritis

Metritis and endometritis are inflammatory processes caused by infection of the uterus that occurs after parturition, but the definitions of metritis and endometritis themselves can be rather broad.

Previously, the definitions of metritis and endometritis were not always consistent between researchers, veterinarians and farmers, which may have also played into some of the different results we’ve seen from research on metritis. Sheldon et al. (2006) discusses the definitions of metritis and has proposed standard guidelines for defining and diagnosing metritis. To understand what we mean when we talk about metritis, we must know which kind of metritis is being referred to and what its definition is.

Endometritis is simply inflammation of the endometrium whereas metritis is an inflammation of all layers of the uterine wall. Puerperal metritis is a uterine infection caused by invasion and establishment of pathogenic bacteria and is characterized by a fetid, reddish-brownish watery discharge from the uterus and is usually observed with pyrexia (Sheldon et al., 2006).

Sheldon et al. (2006) provides the following definition for puerperal metritis: abnormally enlarged uterus, fetid red-brown watery discharge, signs of systematic sickness, and a fever of > 39.5 °C.

Clinical metritis as defined by Sheldon et al. (2006) is simply an abnormally enlarged uterus and a purulent uterine discharge detectable in the vagina within 21 days after parturition without signs of illness.

The definition Sheldon et al. (2006) proposed for clinical endometritis includes purulent (>50% pus) uterine discharge after 21 days post partum (DPP) or more or mucopurulent (50% pus, 50% mucus) discharge at 26 DPP or more.

Subclinical endometritis was characterized as > 18% neutrophils in uterine cytology samples collected 21-33 DPP or >10% neutrophils at 34-47 DPP in the absence of clinical endometritis (Sheldon et al., 2006).

Finally, Sheldon et al. (2006) suggested that pyometra be defined as an accumulation of purulent material in the uterine lumen when a persistent corpus luteum

(CL) is present and the cervix is closed.

In their review, Sheldon et al. (2006) propose examining the vaginal discharge to diagnose clinical endometritis. This can be done through removal by hand, the use of a vaginoscope, or, more easily, a metricheck device (Metricheck, Simcro, New Zealand), which can be inserted into the vagina to retrieve the vaginal contents. Mucus is scored on a scale of 0-3, where 0 = clear or translucent mucus, 1 = flecks of white or off white pus, 2 = < 50% white or off white pus, and 3 = > 50% white or yellow pus (Sheldon et al., 2006). In this thesis, the focus will be entirely on metritis.

Negative Effects of Metritis

Metritis is one of the most prevalent diseases in dairy cows (Chapinal et al.,

2011; Martinez et al., 2012) and has negative effects on production and reproduction

(Vergara et al., 2014). The negative effects of metritis include decreased pregnancy rates, lower fertility, delayed ovulation, and in some cases, a lower milk yield. One study found that decreased pregnancy rates at 200 and 100 days post partum (DPP) were associated with puerperal metritis, but there was no significant difference in pregnancy rates between cows with clinical metritis and healthy cows (Giuliodori et al., 2013). In

this study, clinical metritis was defined as watery, purulent or brown-colored, and fetid discharge at 5-7 DPP and a rectal temperature of < 39.2 °C. Puerperal metritis was defined as watery, purulent or brown-colored, and fetid discharge at 5-7 DPP and a rectal temperature > 39.2 °C. Additionally Giuliodori et al. (2013) observed that cows with puerperal metritis had a longer calving to conception interval and that cows with metritis produced less milk in early lactation, but more in late lactation than healthy cows. Huszenicza et al. (1999) also concluded that bacterial infection of the uterus resulted in decreased fertility and a delay in ovarian cyclicity. Untreated cows with metritis produced less milk at 305 days post-partum than healthy cows and treated cows with metritis according to Goshen & Shpigel (2006). Wittrock et al. (2011) observed that multiparous cows with metritis produced less milk than healthy multiparous cows. Additionally, multiparous cows with metritis showed a reduction in dry matter intake (DMI) and were more likely to be culled than healthy multiparous cows.

When evaluating metritis in primiparous cows however, there was no significant difference between healthy and metritic cows. This contrasts with an older study in which primiparous cows showed a significant decrease in milk production in early metritis (metritis diagnosed within 28 days) but multiparous cows with metritis showed no difference in milk production compared to healthy cows (Rajala & Gröhn, 1998).

Galvão et al. (2010) observed that primiparous cows with metritis tended to have a lower milk yield than healthy cows in the 3rd and 4th weeks of lactation and less than cows with endometritis in the 2nd and 4th weeks of lactation. In multiparous cows, milk yield tended to be greater in metritic cows than healthy cows and cows with subclinical endometritis in the 5th and 6th weeks of lactation and the milk yields of healthy cows

also tended to be greater than that of endometritic cows on weeks 5 and 6 of lactation

(Galvão et al., 2010; Pérez-Báez et al., In Press).

Opsomer et al. (2000) reported a risk of a prolonged luteal phase in metritic cows compared to healthy cows and suggested metritis may play a role in delayed ovulation.

Goshen & Shpigel (2006) also noted a longer interval between artificial insemination

(AI) and pregnancy, a lower rate of conception, and a lower peak lactation in untreated metritic cows compared with healthy and treated metritic cows. In agreeance, a meta- analysis by Fourichon et al., (2000) showed that metritis was associated with more days to first AI service and a lower conception risk. Urton et al. (2005) observed that metritic

(mucopurulent or purulent and foul smelling, and fever within 3 days of observing clinical signs) and acutely metritic (putrid discharge and a fever within 3 days before observation of clinical signs) cows spent significantly less time feeding than healthy cows during post calving and acutely metritic cows also spent significantly less time feeding than healthy cows in the pre-partum period. This decrease in time spent at the feeding trough was also accompanied by a decrease in body condition score (BCS) as would be expected. Huzzey et al. (2007) also made similar observations. In their study, severely metritic cows (putrid discharge and a temperature of > 39.5o C) ate significantly less than healthy cows beginning at 2 weeks pre-partum until the end of the study 4 weeks later and also spent less time eating during the entirety of the study.

Mildly metritic cows (mucopurulent (≤50% pus present) or purulent (≥50% pus present) and foul smelling discharge with or without a fever) ate significantly less than healthy cows from 1 week pre- to 3 weeks post-partum and spent less time eating in the first week post-partum (Huzzey et al., 2007). Water intake by mildly metritic cows was also

significantly lower than that of healthy cows over the entire study and water intake by severely metritic cows decreased in weeks 1 – 3 post-partum compared to that of healthy cows (Huzzey et al., 2007).

Risk Factors of Metritis

In order to treat and prevent metritis it is important to identify the risk and causation factors. The main risk factors for metritis include placental retention (which appears to be particularly important), dystocia, stillbirths, immunosuppression, BCS, and diet - particularly an increase in pre-partum non-esterified fatty acid (NEFA) concentration in the blood (Dubuc et al., 2010; Földi et al., 2006; Giuliodori et al., 2013;

Hammon et al., 2006). Precalving blood NEFA concentration, parity, and even BCS and environmental factors may all be possible risk factors according to a study by Chapinal et al. (2011). Risk factors identified by Dubuc et al. (2010) included increased pre- partum blood NEFA, retained placenta, and dystocia.

Kaneene & Miller (1995) performed an analysis of the risk factors for metritis using data from previous studies and datasets. They performed analyses on both individual cows and herds. In the individual cow-based analysis, significant factors associated with a higher rate of metritis included retained placenta, ketosis and the use of health programs and pre-breeding examinations - which may have been due to more cows being observed and thus metritis was more likely to be detected. Decreased rates of metritis were associated with the number of times per year feed was tested - farms using feed testing may have been more attentive to the health of their herds - , adjusting feed to milk production, pasture calving, using veterinarians for investigating diseases, and (significantly) increasing pounds of dry hay fed to dry cows. When considering the herd-based model, factors significantly associated with an increased incidence of

metritis included increased herd size, retained placenta, abortion, routine post-partum exams, and increasing pounds of haylage fed to low producing cows. The significant factors associated with decreased rates of metritis included increasing pounds of dry hay fed to dry cows and increasing grain fed to cows producing 60 - 79 lbs of milk/day.

When the models for herd and individual cows were combined, the significant factors associated with an increased risk of metritis were retained placenta and abortion, customization of rations, and using individual calving facilities while the significant factor associated with decreased metritis incidence was increasing the amount of dry hay given to dry cows. Factors present in both models for determining metritis but only significant with either the herd-based or cow-based analysis included dystocia, number of times feeds were analyzed per year for increased rate of metritis, and using veterinarians to investigate disease for decreased incidence of metritis.

Higher serum NEFA and lower IGF-I may also be important factors in the development of metritis as discussed in greater detail later (Piechotta et al., 2012).

Oxidative stress is yet another possible factor involved in the development of metritis, as the amount of reactive oxidative species (ROS) rises through the pre-partum to post-partum stages and may also play a role in decreasing antioxidant potential

(Abuelo et al., 2013; Sordillo et al., 2007). Metabolic factors may play a role by contributing to oxidative stress and Castillo et al. (2005) suggested that supplementing dry cows with vitamins and minerals may help prevent oxidative stress. Oxidative stress may inhibit bacterial killing ability of neutrophiles as seen in Hogan et al.(1990)'s work in supplementing antioxidants.

In a recent study, vaginal laceration was also identified as a risk factor for metritis

(Vieira-Neto et al., 2016). Vaginal laceration was scored as 0 - 2 with 0 = no laceration,

1 = a lacteration < 2 cm at the dorsal commissure of the vulva or at the lateral walls of the vulva/vagina, or both, and 2 = laceration greater than 2 cm at dorsal commissure of the vulva or at the lateral walls of the vulva/vagina, or both. Cows with a laceration score of 2 showed a significant odds ratio of developing metritis and vaginal laceration was positively correlated with the incidence of metritis (Vieira-Neto et al., 2016)

In addition to various other determinants dependent on parity, Vergara et al.

(2014) suggested that multiparous cows with pre-partum lameness were more likely to develop metritis. They also concluded that abnormal birth was the most significant factor in increased odds of developing metritis in both primi- and multi-parous cows.

Retained Placenta, Dystocia and Stillbirths

Retained placenta has been identified as one of the most significant contributors to the risk of developing metritis (Dubuc et al., 2010; Huszenicza et al., 1999; Kaneene

& Miller, 1995). Dohmen et al. (2000) observed that cows with retained placenta had bacterial contamination more often and had higher numbers of bacteria than healthy cows or cows with dystocia. E. coli, gram negative anaerobes, and Clostridium species were particularly high. To parallel the higher numbers of these bacteria present, cows with retained placenta also had a higher concentration of the endotoxin lipopolysaccharide (LPS) present in lochia (Dohmen et al., 2000). The increased concentration of LPS was positively correlated with foul-smelling vaginal discharge, higher rectal temperature, and presence of E. coli, black colored anaerobes, and

Clostridium. Giuliodori et al. (2013) reported that a significantly higher number of cows with retained placenta, dystocia or stillbirths developed metritis compared to cows

undergoing normal calving. Dubuc et al. (2010) also recorded higher odds of developing metritis in cows that had dystocia or retained placenta.

Feeding, Nutrition and BCS

Nutrition may play a relevant part in the etiology of metritis. Many studies have focused on nutrition and metabolism and the effects they have on uterine disease and reproductive performance. In evaluating the effects of BCS and feeding time on metritis,

Urton et al. (2005) found that BCS decreased as the time spent feeding decreased which was in turn significantly associated with metritis. Over-conditioned cows were observed to eat less during the transition period than thinner cows and had a greater decline in feed intake, suggesting over-conditioning cows before parturition may not be helpful in preventing metritis. Kim & Suh (2003) likewise report that metritis was more prevalent in cows with a greater BCS loss from the dry to near-calving period (loss of 1 -

2 points) and that cows that lost more weight also took longer to breed. Hoedemaker et al. (2009) built on this conclusion in their study. Cows that lost BCS from 6 weeks antepartum to parturition showed a decreased reproductive performance and that at various points in time pre- and postpartum, a lower BCS was associated with an increased incidence of metritis (Hoedemaker et al., 2009). Similar to these results,

Huzzey et al. (2007) noted that metritic cows’ dry matter (DM) and water intake was significantly lower than that of healthy cows from 2 weeks pre- to 3 weeks postcalving and milk production was lower during the first 3 weeks of lactation in metritic cows.

During the few weeks before and after parturition, neutrophil function may be decreased. This decrease is associated with a decreased dry matter intake (DMI), higher serum NEFA concentration at 2 weeks pre-partum, and higher serum beta- hydroxybutyrate (BHBA) levels at 1-4 weeks post-partum (Hammon et al., 2006).

Hammon et al. (2006) also noted a significantly lower DMI in metritic cows at 1 week pre-partum compared to healthy cows. The authors suggested that the negative energy balance associated with increased NEFA and decreased DMI may suppress immune function, particularly the function of polymorphonuclear cells (PMN) and Hoeben et al.

(1997) and Ster et al.'s (2012) studies support this observation. Energy stored in fat is mobilized as NEFA and exported or stored in the liver. When there is a high concentration of NEFA, the liver cannot process it all, resulting in an increase in circulating triglycerides. When the liver removes excess NEFA - which can be converted to BHBA by incomplete oxidation - BHBA concentration will increase (Ospina et al.,

2010). The connection to increased BHBA and metritis may be in its impact on PMN through niacin receptors. Taggart et al. (2005) observed that BHBA acts as a ligan for the niacin receptors HM74A and HM74 which activate anti-inflammatory responses, decreasing chemotaxis and oxidative burst activity.

In their study, Ospina et al. (2010) identified both increased NEFA and increased

BHBA as risk factors for uterine disease. In their research however, Dubuc et al. (2010) observed that only increased pre-partum NEFA was associated with an increased incidence of metritis whereas post-partum NEFA and BHBA were not. In another study by Galvão et al. (2010), metritic cows only had a tendency to have a higher serum

NEFA concentration at calving and 35 DPP while cows with endometritis were significantly more likely to have high serum NEFA concentrations at the same time points, suggesting there may be differences in the etiology of endometritis and metritis.

Parity and time also affected the concentration of plasma NEFA. In particular, primiparous cows with metritis had higher plasma NEFA concentrations compared to

healthy cows. Like Hammon et al. (2006) and Ospina et al. (2010), Galvão et al. (2010) observed higher concentrations of serum BHBA in metritic cows at calving and cows with subclinical endometritis tended to have greater serum BHBA concentrations at 7 and 14 DPP.

Unlike the other studies on NEFA, Bicalho et al. (2014) saw contradictory results with a tendency for reduced serum NEFA in metritic cows and noted no difference in

BHBA concentration between healthy and metritic cows and no difference in serum

NEFA and BHBA in cows with endometritis. This suggests that further studies may be necessary to fully understand the exact role of NEFA, BHBA, and energy in metritis.

Other nutrients besides NEFA and BHBA may also be important to consider.

Trace mineral supplementation may help in preventing metritis by enhancing immune function as metritic cows had significantly less serum Ca, Mo, P, Se, and Zn compared to healthy cows as observed by Bicalho et al. (2014). These results support Martinez et al. (2012) who reported no difference in serum NEFA and BHBA between healthy and diseased cows but did see an association between a decreased serum Mg concentration and metritis. Martinez et al. (2012) also found that cows with subclinical hypocalcemia were at a greater risk of developing metritis and though metritic cows themselves did not have higher levels of NEFA and BHBA, cows with subclinical hypocalcemia did. Supplementing cows with trace minerals may decrease the abundance of pathogens such as Peptostreptococcus, Bacteroides, Porphyromonas, and Fusobacterium (V. Machado et al., 2012). It is possible that these trace minerals may have helped immune cells maintain their function, thus increasing host immunity, but more research is needed. Calcium may be of particular importance. Kimura et al.

(2006) observed that a decreased intracellular ionized calcium influx into peripheral blood mononuclear cells may inhibit their function. This decrease in calcium function is likely associated with the Ca depletion associated with lactation.

Immunity and Immunosuppression

All dairy cows will experience a contaminated uterus shortly after calving (Jeon et al., 2015) and some of these cows will develop metritis. Dairy cows become more at risk of developing disease as their immune function is reduced at calving (Kehrli, Nonnecke,

& Roth, 1989). During this time we also see some insulin resistance, decreased feed intake, negative energy balance, and weight loss (Kehrli et al., 1989).

Innate immunity is one of the primary factors observed in defense and diagnosis of metritis. Innate immunity is defined as a non-specific defense against microbes and includes physical barriers such as skin, mucosa, antimicrobial peptides, and both hematopoietic and non-hematopoietic cells that identify and target microbes.

Hematopoietic cells are undifferentiated cells that can become any kind of blood cell. In the immune system, hematopoietic cells include monocytes, macrophages, eosinophils, neutrophils, dendritic cells, and Natural Killer cells. Non-hematopoietic cells, cells which are already differentiated, include epithelial cells, stromal cells, and endothelial cells of the mucosa. Epithelial cells play a role in immunity by mediating innate immune responses and interacting with dendritic, B, and T cells to assist in adaptive immune responses (Turner et al., 2012). Contamination and establishment of bacteria, release of VF by bacteria, and inflammation of the uterus all depend on the strength of the immune system and the hormonal regulation of the individual animal (Földi et al., 2006;

Sheldon et al., 2009).

Immediately following parturition, cows become susceptible to uterine diseases partly because physical barriers are compromised, but also because the function of their immune cells may be suppressed during this stage of life.

Various factors such as IGF-I, NEFA and BHBA, VF, and eicosanoids, particularly PGF2a, may activate or inhibit the immune system and inflammation (Földi et al., 2006). Myometrial contractions defend the uterus from pathogens by flushing out debris and contaminants. Endotoxins may also activate histamines and other pro- inflammatory cytokines and eicosanoids and may have an effect on the function of ovaries, decreasing fertility (Földi et al., 2006). PGF2a in particular, is an eicosanoid that increases myometrial contraction but also has an immunosuppressive effect (Földi et al., 2006). IGF-I is important for stimulating PMN neutrophils which play a primary role in fighting off bacterial infections in the uterus. IGF-I is less available during early lactation which may make it an important factor (Földi et al., 2006). Through their role contrary to that of IGF-I, NEFA and BHBA are important as they may impair PMN function (Földi et al., 2006).

Hammon et al. (2006) observed a significantly lower amount of PMN myeloperoxidase and cytochrome c reduction around parturition. Overall phagocytic performance, as described and defined by Sander et al. (2011) to be the product of the phagocytic power (the product of phagocytic capacity and phagocytic activity) by the absolute number of PMN present in a given assay, increased sharply during 0-1 wk PP and dropped significantly from 1-2 wk PP. Hammon et al. (2006) also suggested that nutrient intake may have an effect on immunosuppression.

Kimura et al. (2014) studied the effects of a recombinant bovine granulocyte colony-stimulating factor (rbG-CSF) treatment on healthy cows from 5 days pre- to 13 days post-partum in an attempt to discover whether rbG-CSF could be used to enhance immunity during this time frame by increasing number and improving the function of

PMN. The authors found an increase in PMN numbers in the treated cows over the course of the study as well as an increase in extracellular killing. In a more recent study,

Zinicola et al. (2018) not only studied the effects of rbG-CSF—also known as pegbovigrastim (PEG)—but also its effects on health. Similar to Kimura et al. (2014),

Zinicola et al. (2018) found that PEG treatments increased the number of PMN but did not enhance immune function in both healthy cows and cows with diseases, one of those being metritis, over the course on one week pre- and two weeks post-partum.

Additionally, the authors evaluated the effects of PEG treatment on cows with metritis.

Their results demonstrated that cows with metritis had lower concentrations of PMN than healthy cows but that when treated with PEG, healthy and metritic cows had significantly higher numbers of PMN, particularly on day 3 PP. In the group of treated metritic cows, healthy cows still had a higher number of PMN compared to metritic cows. This study also revealed that the incidence of diseases was not decreased with treatment of PEG, suggesting that raising the numbers of PMN alone may not be helpful in combatting disease. Similar to Zinicola et al.'s (2018) results, Ruiz et al (2017) found that PEG not only failed to reduce the incidence of metritis, but increased the risk of metritis.

Bacterial Infection of the Uterus

Though many risk factors have been identified, bacterial infection is currently thought to be the primary cause of metritis in cows and puerperal sepsis in humans.

Mateus et al. (2002) and Cunha et al. (2018) observed that both aerobic and anaerobic bacterial density isolated fromt he uterus was greater in cows with metritis compared to healthy cows.

Immediately after parturition, during involution (the first 7 weeks post-partum), the uterus shifts through contamination and clearance of bacteria (Griffin et al., 1974;

Sheldon et al., 2006). Bacterial contamination is not the same as bacterial infection.

Bacterial contamination in puerperal animals is inevitable but does not always cause disease since bacteria are generally flushed out of the uterus before they can colonize and take over. Bacterial infection on the other hand involves the establishment of pathogens and their virulence factors such as endo- and exotoxins that can lead to inflammation (Sheldon et al., 2006). Generally, cows clear the bacterial contamination on their own but when pathogens are able to establish themselves, uterine infection can occur which may also be accompanied by depressed fertility (Griffin et al., 1974). The fact that some cows are not able to eliminate infection may be due to individual immune function or other risk factors discussed below. It has been suggested that some cows have an impaired neutrophil function, making them more susceptible to bacterial infection (Cai et al., 1994). Jeon et al. (2015) concluded that all dairy cows are contaminated with bacteria, though they do not all necessarily develop disease.

The uterus has often been considered sterile but a recent study focused on the microbiome of human placentas suggests that the reproductive tract may harbor a microbiome of its own (Aagaard et al., 2014). During parturition, the integrity of the cervix, vulva and vagina are compromised, allowing bacteria to invade (Sheldon &

Dobson, 2004). The interactions between these bacteria allow for the establishment of

opportunistic pathogens (Dohmen et al., 2000). The immune system, and neutrophils in particular, also play an important role in the defense of the uterus (Sheldon & Dobson,

2004). During the first days after parturition, the uterus may become contaminated or infected with bacteria. Often the uterus is cleared of bacteria but may become re- contaminated (Griffin et al., 1974). Cows have several ways to avoid contamination and infection and one of the first defenses is the cervix, which normally remains tightly closed. However, during parturition and immediately after, the cervix remains open, giving microbes a chance to enter the uterus (Sheldon & Dobson, 2004). In order to counteract this invasion, the cervix and vagina secrete mucus to flush out the bacteria and any other contaminants present and myometrial contractions further aid in the process. The blood, remaining fetal tissue, and healing tissue in the reproductive tract may provide a good nutrient source to the invading bacteria and necrotized tissue, particularly from dystocia and abnormal births, provides an entry for pathogens to invade the deeper tissue layers of the uterus (Sheldon et al., 2009). In order for anaerobes to colonize the uterus, the uterine environment must become anaerobic. This environment can be established through the metabolism of microbes and host utilization of O2 by PMN while fighting off infection during the first few DPP (El-Azab et al., 1988;

V. Machado & Bicalho, 2015). This creates a suitable environment for anaerobic pathogens to grow.

Bacteria Associated with Metritis

As discussed above, bacteria are responsible for the onset and persistence of metritis. Through culture dependent studies, the most prevelent bacteria observed in metritic cows were E. coli, Trueperella pyogenes, Fusobacterium necrophorum,

Porphyromonas levii and other Bacteroides spp. (Drillich et al., 2001; Huszenicza et al.,

1999). In culture independent studies, the most prevelent bacteria were E. coli,

Trueperella pyogenes, Fusobacterium necrophorum, Prevotella melaninogenicus,

Porphyromonas levii, and Helcococcus ovis, as well as other Fusobacterium and

Bacteroides species (Cunha et al., 2018; Jeon et al., 2015; Locatelli et al., 2013; Santos et al., 2011; Silva et al., 2009).

Culture Dependent Studies

In most culture studies, samples were typically collected via a sterile uterine swab inserted into the uterus (Dohmen et al., 2000; Huszenicza et al., 1999; Williams et al., 2005). Samples were withdrawn and stored in transport media while being transferred to the lab at 4° C (Dohmen et al., 2000). Samples were then suspended and plated onto petri plates, with blood agar being one of the most common choices, and then incubated at 37° C where growth was typically seen 2-7 days later (Dohmen et al.,

2000; Dohmen et al., 1995; Wagener et al., 2014). Often, black colonies were observed on anaerobic plates (Dohmen et al., 2000, 1995). Analytical profile index (API) strips were one of the preferred methods for characterizing and identifying species of isolates

(Dohmen et al., 2000; Wagener et al., 2014). Additionally, Wagener et al. (2014) used

16s rRNA amplification to identify cultured species.

Culture studies have identified T. pyogenes, E. coli, and F. necrophorum as the most abundant bacteria present in cows with metritis or signs of metritis (Drillich et al.,

2001; Huszenicza et al., 1999). When Huszenicza et al. (1999) cultured bacteria from the uterus of dairy cows, they found a significantly higher amount of T. pyogenes, E. coli, and gram negative obligate anaerobes in metritic cows at 10 DPP. Bacterial growth was also higher in cows with acute metritis (Huszenicza et al., 1999). After 20 DPP

however, the proportion of E. coli present did not differ between healthy and metritic cows, providing more evidence that E. coli are only important during the onset of disease (Huszenicza et al., 1999). By 35 DPP, only T. pyogenes were significantly higher in metritic cows (Huszenicza et al., 1999). Since then, several studies have used genetic sequencing to determine the virulence factors and interactions of these bacteria and the roles they play in the pathogenesis of metritis. In their study on Porphyromonas and Falsiporphyromonas, Wagener et al. (2014) used an oxoid Columbia sheep blood agar to anaerobically culture isolates. Using the 16s rRNA gene for identification,

Locatelli et al. (2013) isolated two Helcococcus spp. among a few others from cows with metritis.

Ruder et al. (1981) suggested there might be a synergy between F. necrophorum and T. pyogenes as cows infected with both organisms showed decreased fertility whereas cows affected with only one of the organisms did not. A more contemporary study supports Ruder et al. (1981) in their observations that F. necrophorum, T. pyogenes and possibly E. coli are synergistic (Bicalho et al., 2012).

Culture Independent Studies

Although culture studies can provide some insight into many characteristics of bacterial species such as morphologies, growing conditions, substrates, and products, they cannot accurately reflect a microbiome in vivo since many bacteria have very specific growth requirements or grow at lower abundances, thus making them difficult to grow and isolate. Because some bacteria grow better in in vitro conditions than others, the true microbiome of the uterus cannot be represented by culture alone. The use of genetic sequencing has allowed for more complete and accurate analyses of the diseased uterine microbiome as several studies have made more specific observations

and have discovered species involved in uterine disease that had not previously been described by culture dependent studies. Santos et al. (2011) performed a metagenomic study of the infected uterus using PCR of the 16s rRNA gene and DGGE. They found that the most predominant phylum in their uterine samples from metritic cows on 10

DPP was the Fusobacteria phylum. In addition to Fusobacteria which made up a substantial proportion of the metritic microbiome and was absent in healthy cows,

Bacteroidetes also had a weighted presence with the most distinct species being P. levii

(Santos et al., 2011). Both Santos & Bicalho (2012) and (Peng et al., 2013) performed metagenomic studies to observe shifts in the population of microbes living in the uterus of post-partum cows. Metritic cows had more chaotic, unstable changes compared to healthy and endometritic cows (Santos & Bicalho, 2012). Santos & Bicalho (2012) also noted that more operational taxonomic units (OTU) were identified in this study than in previous culture studies, which is to be expected as many bacteria are unculturable or difficult to culture when there is competition and their optimum growth requirements are unknown. Similar to Santos et al. (2011), Bicalho et al. (2012) and Machado et al.

(2012) also observed a strong association between F. necrophorum and metritic cows.

Illumina sequencing has also been used in metagenomic studies of the metritic uterus in dairy cows. Jeon et al. (2015) studied the microbiome of the uterus during the first 6 days post-partum using amplification of the V4 region of the 16s rRNA genes and illlumina sequencing and found differences between healthy cows and cows with metritis. Species richness, which is the mean number of different species present, was lower in cows with metritis compared to healthy cows. The Shannon index -- the measure of species diversity which includes richness and evenness -- was not

significantly different (Jeon et al., 2015). The primary phyla present in the uteri were

Bacteroidetes, Proteobacteria, Fusobacteria, Firmicutes, Tenericutes, Actinobacteria,

Spirochaetes, and Verrucomicrobia. Through use of discriminant analysis, Jeon et al.

(2015) determined that Bacteroides, Filifactor, Porphyromonas, Fusobacterium,

Helcococcus, Peptoniphilus, Peptostreptococcus, Campylobacter, and Prevotella may all play important roles in the etiology of metritis, though their individual contributions may not yet be known. As uterine discharge score (measure for metritis) increased, the abundance of Bacteroides and Fusobacterium increased while abundance of Sneathia,

Mycoplasma, Escherichia, Pedobacter, Serratia, Candidatus Blochmannia, Treponema,

Osciliospira, Pasturella, and Manmeimia decreased (Jeon et al., 2015). Additionally,

Jeon et al. (2015) were able to observe correlations and interactions between microbes which were previously nearly impossible to observe in culture studies. This study found that Fusobacterium was positively correlated with Bacteroides and Helcococcus as well as increasing uterine discharge score. Bacteroides was in turn positively correlated with

Filifactor and Fillifactor was positively correlated with Porphyromonas, Peptoniphilus,

Peptostreptococcus, and Campylobacter. Not only did this study reveal which bacteria were important in disease, it also revealed many new organisms not previously identified in culture studies and provided a good picture of the many interactions that go on between microbes and the advancement of disease and how they are all correlated and can affect one another.

Jeon et al. (2016) carried out a second study in which they used an Illumina

MiSeq platform to sequence amplicons of the V4 region of the 16s rRNA gene of uterine bacterial community samples and droplet digital PCR (ddPCR) to quantify microbes.

The bacterial communities of metritic cows with fever, metritic cows without fever, and healthy cows were analyzed and compared. In metritic cows, species richness was lower compared to healthy cows. There was also a significantly higher microbial diversity in healthy cows compared to metritic cows with fever, but no large difference between healthy cows and metritic cows with no fever. Overall, there was no significant difference between metritic cows with and without fever in either species richness or diversity, suggesting that fever is not related to either of these factors. When bacterial communities were analyzed, both metritis groups (fever and no fever) were similar to each other and both were different from that of the healthy group at the phylum level.

The Bacteroidetes phylum played a role in this difference. Within this phylum,

Bacteroides and Porphyromonas contributed most to the difference seen between the communities of healthy cows and metritic cows. Though they showed less diversity, metritic cows had a greater number of total bacteria as determined by ddPCR (Jeon et al., 2016).

Cunha et al. (2018) identified and quantified potential pathogens involved in the onset of metritis using ddPCR. In this study they found that metritic cows had significantly higher bacterial copy numbers compared to healthy cows and P. melaninogenica, F. necrophorum, P. levii, B. pyogenes, and H. ovis copy numbers were significantly higher in metritic cows compared to healthy cows. E. coli, T. pyogenes and

B. heparinolyticus were also quantified, but no significant difference in their copy numbers was observed between metritic and healthy cows (Cunha et al., 2018). Of particular interest in Cunha et al. (2018)'s study is the number of Porphyromonas and

Fusobacterium identified in Cunha et al.'s (2018) study. These same findings were

reported in greater detail in Cunha (2016). The results for total bacterial copy number and numbers of Fusobacterium necrophorum and Porphyromonas levii are summarized in Figure 2-1 and Figure 2-2.

Although the focus of the studies discussed above has been primarily on bacteria, Donofrio et al. (2008) has suggested that Bovine herpesvirus 4 (BoHV-4) may be involved in the development of metritis through co-infection with other bacteria though the virulence factors and eitiology remain unknown. More specifically, they noted that LPS was shown to activate the promoter of the Immmediate early protein 2 (IE2) gene in BoHV-4 which is assumed to be responsible for replication of the virus.

Prostaglandin E2 (PGE2) may also activate the IE2 promoter along with LPS (Donofrio et al., 2008). When Elad et al. (2004) and Blum et al. (2008) studied the interactions between bovine necrotic vulvovaginitis, BoHV-4, and bacteria, there was no significant evidence that the BoHV-4 played a role in the etiology of the disease however.

Virulence Factors

Virulence factors (VF) from bacteria are important in the etiology of metritis and endometritis and the type of disease that develops may be dependent on which VF are secreted. Bicalho et al. (2010) observed that the E. coli VF fimH, astA, cdt, kpsMII, ibeA and hlyA were associated with metritis and fimH had the strongest association. In a study published two years later, Bicalho et al. (2012) reported that cows with E. coli fimH at 1-3 DPP were more likely to develop metritis and cows with F. necrophorum Ikta at 8-10 DPP were more likely to have metritis at 8-10 DPP. Logistic regressions using the data collected from this study showed that cows with retained placenta -- a significant risk factor for metritis -- had 44.8 time higher odds of being contaminated with

E. coli fimH.

As with microbial species, even interactions, absence, and presence of certain

VF may have different effects on health. When fimH -- a gene involved in the coding of adhesin proteins -- was absent, the incidence of metritis in dairy cows was low even when other VF were present (Bicalho et al., 2010). Other VF were significantly associated with metritis, but even when they were absent and fimH was the only VF present, the incidence of metritis was equally high. The risk for metritis was highest when fimH was combined with astA, cdt, kpsII, ibeA, or hlyA (Bicalho et al., 2010).

Bicalho et al. (2010) also observed a lower reproductive performance in the presence of fimH, astA, cdt, kpsII, ibeA, and hlyA. Additionally, cows with fimH at 1-3 DPP were 2.1 times less likely to conceive, suggesting this VF may be a player in decreased fertility

(V. Machado et al., 2012).

E. coli most likely plays a role in establishing the disease by releasing VF such as LPS and fimH which may make the host more susceptible to invasion by other pathogenic bacteria. Silva et al. (2009) suggested that E. coli do not have an important role in metritis as they could find no VF that could significantly contribute to disease.

Contrary to Bicalho et al. (2010) and Sheldon et al. (2009), Silva et al. (2009) only isolated the VF hlyE, hlyAm iuc, and eaeA and observed no significant association between VF and uterine infection.

Other studies suggest LPS may provide for the establishment of T. pyogenes at

14 DPP (Dohmen et al., 2000) and E. coli secreted fimH at 1-3 DPP may help F. necrophorum establish itself at 8-10 DPP (Bicalho et al., 2012). Roberts (1967a, 1967b) first observed that F. necrophorum may promote the growth of T. pyogenes and T. pyogenes in turn supports the growth of F. necrophorum. T. pyogenes can cause

lesions by secreting cholesterol dependent cytolysin and pyolysin which can cause the death of animal tissue cells, cause inflammation, and may also affect immune cells

(Amos et al., 2014; Jost & Billington, 2005; Miller et al., 2007). Normally, host epithelium protects against pyolysin because it contains less cholesterol (Amos et al., 2014), suggesting that T. pyogenes is an opportunistic pathogen that takes advantage of damaged epithelium after parturition (Bicalho et al., 2012; Dohmen et al., 2000).

Fimbriases, neuraminidases, and extracellular matrix-binding proteins for adherence may also play a role in T. pyogenes’ virulence as well as the abilities to invade host epithelial cells, survive inside macrophages, and the ability to form biofilms (Jost &

Billington, 2005; V. Machado & Bicalho, 2014; Santos et al., 2010). Silva et al. (2009) in contrast, reported that there were no T. pyogenes sourced VF associated with metritis, but in addition to the other studies discussed above, Bicalho et al. (2012) and Santos et al. (2010) contradicted this observation when they noted an association between T. pyogenes sourced fimA and metritis.

It has also been suggested that F. necrophorum releases leukotoxin which acts against ruminant leukocytes (Nagaraja et al., 2005; Tan et al., 1996). This action protects other pathogens from phagocytosis by leukocytes and killing PMN (Nagaraja et al., 2005; Roberts, 1967a, 1967b; Tan et al., 1996). In addition to leukotoxins, F. necrophorum produces endotoxins, hemolysins, hemagglutinins, proteases, and adhesins such as FomA that can be harmful to the host's cells (Kumar et al., 2013;

Kumar et al., 2015; Nagaraja et al., 2005).

Although not yet studied in Porphyromonas levii, other species of

Porphyromonas are known to produce IgA, IgM and IgG proteases to protect themselves from the host immune system as discussed in a review by Nagmoti (2014).

Figure 2-1. Mean copy numbers of bacterial species in cows with and without metritis, the sum is the total of all bacterial strains analyzed (Cunha, 2016)

A B

Figure 2-2. A comparison of copy numbers of F. necrophorum A) and P. levii B) in cows with and without metritis (Cunha, 2016)

CHAPTER 3 FUSOBACTERIUM NECROPHORUM KG34 AND KG35

Here we explore the genome of two strains of Fusobacterium necrophorum, F. necrophorum KG34 and KG35, with a focus on potential virulence factors that may contribute to uterine diseases in dairy cows. We hypothesize that there are virulence factors expressed by this strain of bacteria that are found in other strains of the same species, but there may also be new virulence factors never observed in the genomes of previously studied Fusobacterium strains. In this study, the sequenced whole genomes of F. necrophorum KG34 and KG35 were assembled. The genomes and proteomes were annotated, and searched for potential virulence factors. We expected to find both previously described, and possibly new virulence factors in the strains isolated from metritic cows that may make them slightly different from other strains of Fusobacterium necrophorum. With this knowledge, we can have a better understanding of the contribution of these bacteria to uterine disease and how those diseases can be better treated and prevented.

Methods

Sample collection, culturing, DNA extraction, 16s rRNA gene amplification, species identification, and whole genome sequencing of Fusobacterium necrophorum

KG34 and KG35 were carried out prior to this project by Soo Jin Jeon and Federico

Cunha. These methods which are described below are outlined in Jeon et al. (2017) and obtained through personal communication.

Sample Collection and Culture

Swab samples were collected from the uteri of dairy cows suffering from metritis in June of 2016 from the University of Florida’s Dairy Research Unit in Hague, FL. The

samples were transported to the laboratory within two hours, and once in the laboratory, swabs were immediately streaked onto Wilkins-Chalgren Anaerobe Agar (5% Horse blood, Vitamin K, Hemin, G. N. Spore Anaerobic Supplement, and kanamycin) and incubated for 48 hours under anaerobic conditions in a BD GasPak system (ref #

260683) at 37°C. Isolates were again grown on Wilkins-Chalgren Anaerobe agar in pure culture, and taxonomic classification of purified bacteria was based the 16s rRNA gene using Sanger sequencing (Genewiz, South Plainfield, NJ, USA). Two isolates were confirmed as F. necrophorum, named KG34 and KG35, respectively.

16s rRNA Gene Amplification and Identification

Single isolates were grown on Wilkins-Chalgren Anaerobe agar and identified based on sequencing of the 16s rRNA gene and a pure culture was used for genomic

DNA extraction using a DNeasy blood and tissue kit (Qiagen, Valencia, CA, USA) according to manufacturer directions. Then, 16s rRNA gene was amplified and sequenced for identification using Sanger sequencing (Genewiz, USA). 16S rRNA sequences were entered into NCBI BLAST for identification, and both showed a 99% similarity with F. necrophorum. Confirmed isolates of F. necrophorum were labeled

KG34 and KG35.

Illumina Whole Genome Sequencing

Extraction of the genomic DNA was performed with a DNeasy blood and tissue kit (Quiagen, Valencia, CA, USA) according to manufacturer directions. An Illumina

MiSeq platform was used to carry out whole genome sequencing and a 2 × 250-bp 500- cycle cartridge was used. The total number of reads for KG34 was 932,174 and the total reads for KG35 were 1,093,012.

Genome Trimming, Quality Control, Assembly and Annotation

Forward and reverse reads from the Illumina sequencing were uploaded into

Galaxy through UF's Research Computing HiPerGator computer. Once in Galaxy, quality control and trimming of the forward and reverse reads for both strains KG34 and

KG35 was performed using Sickle (Joshi & Fass, 2011, version 1.33.1), the set up for

Sickle is shown in Figure 3-1. The paired-end (two separate files) option was chosen and the forward and reverse reads uploaded from the Illumina sequence were selected for "Paired-end forward strand FASTQ reads" and " Paired-end reverse strand FASTQ reads". The "Quality threshold" was set at 30 and the "Length threshold" was set to 50.

The option "No" was selected for "Don't do 5' trimming" and "Truncate sequences with

Ns at first N position” (default parameters). "Job Resource Parameters" was set to default and the job was run. The forward and reverse paired-end strand outputs from

Sickle were then assembled using SPAdes (Bankevich et al., 2012, version 3.11.1). The settings used for SPAdes can be seen in Figure 3-2. "Single-cell?", "Run only assembly? (without read error correction)", and "Careful correction?" were left at their default options ("No", "No" and "Yes" respectively). k-mers used were 21, 33, 55, 77,

99, and 127. "Coverage Cutoff" and "Libraries are IonTorrent reads?" were set at default settings ("Off" and "No"). The Library type was Paired-end/ Single reads and

"Orientation" was set to its default of "-> <- (fr)". The paired-end forward and reverse output files from Sickle were selected for "Forward reads" and "Reverse reads" respectively. "PacBio CLR reads", "Nanopore reads", "Sanger reads", "Trusted contigs" and "Untrusted contigs" remained untouched. "Output final assembly graph (contigs)?" and "Output final assembly graph with scaffolds?" both remained on the default option of "No" and the job was run using default parameters. Contigs were downloaded from

SPAdes and annotated in Patric (version 3.5.30); however, Patric yielded few and seemingly inaccurate results when virulence factors were searched and the results were not considered. It was later learned that Patric focuses more on Gram positive bacteria, particularly E. coli, and has a limited database for Gram negative bacteria such as F. necrophorum. This led to inaccurate results for the virulence factor search of the F. necrophorum genome so the NCBI Prokaryotic Genome Annotation Pipeline (PGAP)

(Tatusova et al., 2016) was used for annotation instead. Complete assembled genomes were further evaluated using Quast (version 4.6.3) through HiPerGator's Galaxy.

"SPAdes on data 4 and data 3: contigs (fasta)" was selected for "Contigs/scaffolds output file". "Type of data" was "Contig" and the size of the reference genome was

2110802 bp (NCBI RefSeq: NZ_AJSY00000000.1). No options were selected for

"Reference genome", "Genome annotations" or "Operon annotations". "Type of organism" was "Prokaryotes", "Lower Threshold" was set at the default of 500 and

"Thresholds" at the default of 0, 1000. These settings are summarized in Figure 3-3.

Because of issues with the HiPerGator account and the programs in Galaxy not running under default parameters, the "Job Resource Parameters" were set as follows:

"Processors": 1, "Memory": 12, "Time": 94, and the "Account" and "QOS" were set to the Galvao lab account. Figure 3-4 summarizes the settings for the custom job parameters.

Virulence Factor Search

The annotated PGAP genome was searched for known virulence factors of F. necrophorum listed in previous literature (Nagaraja et al., 2005; Wright, 2016). The keywords "leukotoxin", "endotoxin", "hemolysin", "hemagglutinin", "adhesin", "pili",

"dermonecrotic toxin", "toxin", "platelet aggregation", "aggregation", "protease",

"virulence" and "ecotin" were used to search the annotated PGAP proteomes for KG34 and KG35 . A complete list of all the results from this search was generated for both

KG34 and KG35, the lists can be seen in Table 3-1 and Table 3-2. The names of the proteins in the lists coupled with "fusobacterium necrophorum" or other terms related to pathogenesis and gram negative bacteria were then searched in google scholar and google searches in an attempt to find literature describing each of these proteins.

Data Availability

This Whole Genome Shotgun project has been deposited in

DDBJ/ENA/GenBank under the accession nos.SBAO00000000 (KG34) and

SBAP00000000 (KG35). The version described in this thesis is the first version,

SBAO00000000 (KG34) and SBAP00000000 (KG35). The accession number for this project is PRJNA513072.

Results

Statistics

The total number of reads for KG34 was 932174 and the total reads for KG35 were 1093012. KG34 had a total genome size of 2026495 bp, 37 contigs, a G + C content of 35.17%, and an N50 of 131753. The total genome size of KG35 was

2056859 bp with 50 contigs, a G + C content of 34.93%, and an N50 of 83523.

Additionally, KG34 had 2084 genes and 2009 CDS and KG35 had 2081 genes and

2008 CDS. The total number of tRNA genes for KG34 was 48 and 51 for KG35. The full

Quast reports for KG34 and KG35 are shown in Figure 3-5 and Figure 3-6 respectively.

Virulence Factor Coding Genes

The annotated genome from PGAP was searched for known virulence factors of

F. necrophorum. From the PGAP search, results were yielded from the keywords

"leukotoxin", "hemolysin", "hemagglutinin", "toxin", "protease", "ecotin" and "virulence".

The keyword "adhesin" only yielded results for KG34. The results of this search that were considered to be potentially virulent can be seen in Table 3-1. KG34 was found to have genes coding for leukotoxin, hemolysins, hemagglutinins, proteases, adhesin proteins, ecotin, hemin receptors, phage proteins, penicillin-binding protein, YihY virulence factor, and autotransporters. KG35 also had genes coding for leukotoxin, hemolysins, hemagglutinins, proteases, ecotin, hemin receptors, phage proteins, penicillin-binding proteins, YihY virulence factors and autotransporters. Additionally,

KG34 and KG35 coded for CRISPR and CRISPR associated proteins. A literature search on all the proteins found in the initial PGAP narrowed down the list to proteins that were described as being connected to the virulence of F. necrophorum or other pathogenic bacteria. These proteins are listed in Table 3-2 along with the sources that identified these virulence factors in F. necrophorum species.

Discussion

The genome lengths for KG34 and KG35 were 2026495 bp and 2056859 bp, both similar but slightly lower numbers than those of previously published genomes for

Fusobacterium necrophorum isolated from the human (DDBJ/EMBL/GenBank accession no. AJSY00000000.1) (2,110,802bp) and a Fusobacterium necrophorum strain isolated from a bovine liver abscess (DDBJ/EMBL/GenBank accession no.

AOJP00000000) (2,088,497bp).

Fusobacterium necrophorum is a gram negative, non-spore forming anaerobic rod that originates from the gastrointestinal tract (Nagaraja et al., 2005). F. necrophorum normally produces butyric acid as its main product of fermentation in the gastrointestinal tract but can become an opportunistic pathogen that causes necrotic

diseases. F. necrophorum is found in abundance in the uterus of the metritic cow

(Cunha et al., 2018; Jeon et al., 2015).

F. necrophorum has been observed to produce leukotoxins, endotoxins, hemolysins, hemagglutinins, proteases, and adhesins such as FomA (Kumar et al.,

2013, 2015; Nagaraja et al., 2005). In this study similarities between KG34 and KG35 and other F. necrophorum strains were observed but also some new and potentiallyvirulent proteins were discovered such as omptin proteases, clpP and clpX proteases, and BrkB proteins. Either these proteins are truly novel in the KG34 and

KG35 strains or they have not yet been identified in other strains.

Virulence Factors and Proteins of Interest

The search for virulence factors in PGAP yielded several proteins originating from unknown or previously undescribed genes. The proteins judged to be potential virulence factors based on literature included the lktA leukotoxin family filamentous adhesin protein, a hemolysin III family protein, ShlB/FhaC/HecB family hemolysin secretion/activation proteins, a Filamentous hemagglutinin N-terminal domain- containing protein, a hemin receptor, ecotin, virulence-associated protein E (only found in KG35), the YihY/virulence factor BrkB family protein, an adhesin (found in KG34 only), an omptin family outer membrane protease, a Clp protease ClpP, an ATP- dependent Clp protease proteolytic subunit, and an ATP-dependent Clp protease ATP- binding subunit ClpX. lktA Leukotoxin Family Filamentous Adhesin

To the author’s knowledge, no research has been conducted on the description or function of lktA leukotoxin family filamentous adhesin proteins specifically, though leukotoxins themselves have been identified in this species previously. F. necrophorum

is known to release leukotoxins which act against ruminant leukocytes (Nagaraja et al.,

2005; Tan et al., 1996). This action protects other pathogens from phagocytosis by leukocytes and killing PMN (Nagaraja et al., 2005; Roberts, 1967a, 1967b; Tan et al.,

1996). Though the lktA leukotoxin family filamentous adhesin itself has not been previously mentioned, it does show a 99% identity with the lktA operon (gene coding for leukotoxin) of Fusobacterium necrophorum as determined from a BLAST search of the protein’s CDS, indicating that it may be important for future investigation.

Hemolysin And Hemolysin Associated Proteins

The hemolysin III family protein and ShlB/FhaC/HecB family hemolysin secretion/activation protein may play virulent roles in causing hemolysis in infected cows. The hemolysin III protein has been observed to cause hemolysis in one study where it was first identified by Baida & Kuzmin (1995). Amoako et al. (1993, 1994) identified hemolysins and hemolytic activity in Fusobacterium necrophorum species, but no specific proteins. To the author’s knowledge, these are the first specific hemolysin proteins to be identified in F. necrophorum.

Filamentous Hemagglutinin

The Filamentous hemagglutinin N-terminal domain-containing protein is probably important in allowing the bacterium to alter the immune function of the host. Henderson et al. (2012) showed that FHA (filamentous hemagglutinin adhesin) deficient Bordetella bronchiseptica induced a greater inflammatory response and a stronger immune response from host cells. It is possible bacteria producing this protein are able to inhibit host immune and inflammatory responses, allowing the bacteria to survive longer.

Kanoe & Yamanaka (1989) also discovered that one strain of F. necrophorum caused aggregation of bovine platelets. Wright (2016) described filamentous hemagglutinins in

her work on Fusobacterium necrophorum as well but did not focus on the protein's functions.

Ecotin

Ecotin’s importance as a virulence factor may be in its inhibition of beneficial serine proteases such as chymotrypsin, trypsin, elastase and factor x. As a result of this inhibition, clotting time in the host may be increased (Ulmer et al., 1995). Increased clotting time, leading to longer blood flow, may play a role in the uterine necrosis that happens in metritic cows as healing could potentially be delayed. To the author’s knowledge, no studies have focused on Ecotin in F. necrophorum species other than it being mentioned by Wright (2016) who also identified the protein in the genomes that were searched.

Hemin Receptors

Hemin receptors could be potential virulence factor as they allow the bacteria to bind to hemin to extract iron which could allow them to better survive within the host

(Desai et al., 1995; Torres & Payne, 1997; Wright, 2016). Hemin receptors may be less of a concern than other virulence factors however, as the ability to extract iron from the host may or may not contribute significantly to the bacterium’s survival and pathogenicity (Wooldridge & Williams, 1993).

With the exception if Wright's (2016) study, the author is not aware of any other studies that have given attention to hemin receptors in Fusobacterium necrophorum species specifically.

Omptin Proteases

Omptin outer membrane proteases may be of particular importance in defending the bacterium against the host’s immune system. One study demonstrated that omptin

outer membrane proteases cleave cathelicidins, antimicrobials produced by the host immune cells (Brannon et al., 2015). This could be the first time omptin proteins are specifically mentioned in the proteome of Fusobacterium necrophorum as the author has no knowledge of other studies focused on F. necrophorum that have discussed omptin proteins.

Clp Proteases and Associated Proteins

Clp proteases have been observed to regulate the bacterium’s expression of virulence factors. These proteases may up-regulate some virulence factors, but down- regulate others (Michel et al., 2006). Of particular interest is the clpP gene’s up- regulation of the production of biofilms in bacteria (Michel et al., 2006) however,

Capestany et al. (2008) contradicted this result. The ATP-dependent Clp protease ATP- binding subunit ClpX was associated with the clpX gene when the nucleotide sequence was viewed in NCBI. CplX and ClpP may be important in promoting the transcription of other virulence factors such as proteases and hemolysins (Frees et al, 2003). Frees et al. (2003) also noted a reduced hemolytic activity in Staphylococcus aureus cells in which the clpX or clpP genes were deleted. As with omptin proteases, the author is unaware of any studies that describe or acknowledge clpP and clpX proteases in

Fusobacterium necrophorum species.

Adhesins

Adhesins may be critical virulence factors, especially in mucosal environments such as the uterus, as they allow bacteria to attach to host cells (Schembri et al., 2000).

Adhesins have been reported as important virulence factors present in Fusobacterium necrophorum by Kumar et al. (2013, 2015). In this study, only KG34 had the ability to produce adhesin proteins. It is possible through the search methods used, other

adhesin proteins may not have been uncovered or alternatively, KG35 may simply not produce adhesins.

Virulence-Associated Protein E

According to a BLAST search of the gene coding for the virulence-associated protein E, there were similar proteins that have been reported in other species of bacteria, but to the author’s knowledge there have been no studies that describe this protein or what its importance might be. Wright (2016) seemed to think this protein was a significant enough finding to report in her study, but did not perform any further investigations on it.

BrkB Proteins

YihY/virulence factor BrkB family proteins may also aid in the bacterium’s resistance to host immunity. Fernandez & Weiss (1994) demonstrated that BrkA and

BrkB proteins in Bordatella pertussis allowed the bacteria to survive human serum at a higher rate than the same bacterium when the brk genes were inactivated. We can anticipate that this virulence factor may also allow F. necrophorum to better survive the immune system of dairy cows, leading to its pathogenicity. To the author’s knowledge,

BrkB proteins have not been previously described in Fusobacterium species. This new protein warrants further investigation in future studies.

Closing Thoughts

These results support previous studies that concluded that Fusobacterium necrophorum is a pathogen and likely one of the more important bacterial species involved in the etiology of metritis and other uterine infections (Cunha et al., 2018;

Kumar et al., 2013, 2015). Future steps to take with this knowledge would be to study the structure and function of each protein, identify the genes that code for them,

whether or not the bacteria actually express these traits, and what conditions or drugs might cause expression or inhibition of the proteins’ coding genes, enabling us to better understand the eitiology behind bacterial induced metritis.

Figure 3-1. Sickle settings used for KG34 and 35

Figure 3-1. Continued

Figure 3-2. SPAdes settings for KG34 and KG35

Figure 3-2. Continued

Figure 3-3. Quast settings for KG34 and KG35

Figure 3-3. Continued

Figure 3-4. Quast custom job resource parameters, Account and QOS not shown

Figure 3-5. Quast report summary for KG34

Figure 3-6. Quast report summary for KG35.

Table 3-1. Results from the PGAP search of KG34 Keyword Gb accession Protein length Product name Accession no. leukotoxin RXZ29671.1 3243 leukotoxin LktA family filamentous adhesin SBAO01P000215 hemolysin RXZ29492.1 207 hemolysin III family protein SBAO01P000036 RXZ29672.1 549 ShlB/FhaC/HecB family hemolysin secretion/activation protein SBAO01P000216 RXZ27824.1 428 ShlB/FhaC/HecB family hemolysin secretion/activation protein SBAO01P001027 RXZ27039.1 589 ShlB/FhaC/HecB family hemolysin secretion/activation protein SBAO01P001379 hemagglutinin RXZ27825.1 1405 filamentous hemagglutinin N-terminal domain-containing protein SBAO01P001028 RXZ27040.1 6004 filamentous hemagglutinin N-terminal domain-containing protein SBAO01P001380 toxin RXZ29540.1 153 toxin-antitoxin system YwqK family antitoxin SBAO01P000084 RXZ29671.1 3243 leukotoxin LktA family filamentous adhesin SBAO01P000215 RXZ28869.1 63 toxin-antitoxin system YwqK family antitoxin SBAO01P000562 RXZ28181.1 89 type II toxin-antitoxin system RelE/ParE family toxin SBAO01P000765 RXZ28250.1 74 Antitoxin SBAO01P000834 RXZ27993.1 88 addiction module toxin RelE SBAO01P000890 RXZ27426.1 88 type II toxin-antitoxin system RelE/ParE family toxin SBAO01P001237 RXZ27111.1 177 toxin-antitoxin system YwqK family antitoxin SBAO01P001371 RXZ26697.1 248 toxin-antitoxin system YwqK family antitoxin SBAO01P001512 RXZ26675.1 79 type II toxin-antitoxin system RelE/ParE family toxin SBAO01P001596 RXZ26603.1 63 type II toxin-antitoxin system HicA family toxin SBAO01P001625 RXZ26604.1 141 type II toxin-antitoxin system HicB family antitoxin SBAO01P001626 RXZ26288.1 161 toxin-antitoxin system YwqK family antitoxin SBAO01P001731 RXZ26214.1 87 Txe/YoeB family addiction module toxin SBAO01P001799 RXZ26215.1 84 type II toxin-antitoxin system RelB/DinJ family antitoxin SBAO01P001800

Table 3-1. Continued Keyword Gb accession Protein length Product name Accession no. protease RXZ29536.1 729 ATP-dependent zinc metalloprotease FtsH SBAO01P000080 RXZ28242.1 357 omptin family outer membrane protease SBAO01P000826 RXZ27984.1 195 ATP-dependent Clp protease proteolytic subunit SBAO01P000881 RXZ27985.1 430 ATP-dependent Clp protease ATP-binding subunit ClpX SBAO01P000882 RXZ27845.1 198 YhfC family intramembrane metalloprotease SBAO01P001048 RXZ27577.1 746 serine protease SBAO01P001110 RXZ26682.1 111 ribosomal-processing cysteine protease Prp SBAO01P001603 RXZ26684.1 308 Protease SBAO01P001605 RXZ26613.1 359 Clp protease ClpP SBAO01P001635 RXZ26387.1 333 RIP metalloprotease RseP SBAO01P001708 RXZ26274.1 348 omptin family outer membrane protease SBAO01P001761 RXZ26275.1 243 CPBP family intramembrane metalloprotease SBAO01P001762 RXZ26276.1 304 CPBP family intramembrane metalloprotease SBAO01P001763 RXZ26094.1 272 CPBP family intramembrane metalloprotease SBAO01P001847 adhesin RXZ29671.1 3243 leukotoxin LktA family filamentous adhesin SBAO01P000215 RXZ28773.1 1011 Adhesin SBAO01P000466 virulence RXZ28045.1 394 YihY/virulence factor BrkB family protein SBAO01P000942 ecotin RXZ27415.1 159 Ecotin SBAO01P001226

Table 3-2. Results from the PGAP search of KG35 Keyword Gb accession Protein length Product name Accession no. leukotoxin RXZ69811.1 3243 leukotoxin LktA family filamentous adhesin SBAP01P001080 hemolysin RXZ71088.1 589 ShlB/FhaC/HecB family hemolysin secretion/activation protein SBAP01P000412 RXZ69879.1 207 hemolysin III family protein SBAP01P000987 RXZ69810.1 549 ShlB/FhaC/HecB family hemolysin secretion/activation protein SBAP01P001079 RXZ69520.1 430 ShlB/FhaC/HecB family hemolysin secretion/activation protein SBAP01P001212 hemagglutinin RXZ69519.1 1405 filamentous hemagglutinin N-terminal domain-containing SBAP01P001211 protein toxin RXZ71024.1 88 addiction module toxin RelE SBAP01P000348 RXZ70835.1 88 type II toxin-antitoxin system RelE/ParE family toxin SBAP01P000446 RXZ70674.1 185 type III toxin-antitoxin system ToxN/AbiQ family toxin SBAP01P000563 RXZ70549.1 248 toxin-antitoxin system YwqK family antitoxin SBAP01P000630 RXZ70073.1 89 type II toxin-antitoxin system RelE/ParE family toxin SBAP01P000934 RXZ70088.1 74 Antitoxin SBAP01P000949 RXZ69811.1 3243 leukotoxin LktA family filamentous adhesin SBAP01P001080 RXZ69336.1 177 toxin-antitoxin system YwqK family antitoxin SBAP01P001334 RXZ69183.1 182 toxin-antitoxin system YwqK family antitoxin SBAP01P001455 RXZ69053.1 153 toxin-antitoxin system YwqK family antitoxin SBAP01P001493 RXZ68411.1 88 type II toxin-antitoxin system RelE/ParE family toxin SBAP01P001873

Table 3-2. Continued Keyword Gb accession Protein length Product name Accession protease RXZ71105.1 272 CPBP family intramembrane metalloprotease SBAP01P000238 RXZ71188.1 746 serine protease SBAP01P000321 RXZ71031.1 430 ATP-dependent Clp protease ATP-binding subunit ClpX SBAP01P000355 RXZ71032.1 195 ATP-dependent Clp protease proteolytic subunit SBAP01P000356 RXZ70841.1 111 ribosomal-processing cysteine protease Prp SBAP01P000452 RXZ70843.1 308 Protease SBAP01P000454 RXZ70642.1 357 omptin family outer membrane protease SBAP01P000531 RXZ70519.1 333 RIP metalloprotease RseP SBAP01P000600 RXZ69499.1 198 YhfC family intramembrane metalloprotease SBAP01P001191 RXZ69344.1 304 CPBP family intramembrane metalloprotease SBAP01P001342 RXZ69345.1 243 CPBP family intramembrane metalloprotease SBAP01P001343 RXZ69346.1 348 omptin family outer membrane protease SBAP01P001344 RXZ68990.1 371 Clp protease ClpP SBAP01P001547 adhesin RXZ69811.1 3243 leukotoxin LktA family filamentous adhesin SBAP01P001080 ecotin RXZ70343.1 159 Ecotin SBAP01P000718 virulence RXZ71536.1 463 virulence-associated protein E SBAP01P000009 RXZ71614.1 394 YihY/virulence factor BrkB family protein SBAP01P000087

Table 3-3. Virulence factors identified in the proteome of Fusobacterium necrophorum KG34 and KG35. Proteins of a potentially virulent nature were identified in previous research listed under "source" and searched for in the PGAP annotated proteomes of KG34 and KG35. Gene product searched Source Gene product from PGAP search Present in Present for KG34 in KG35 Leukotoxin/Leukotoxin (Nagaraja et al., 2005; lktA leukotoxin family filamentous adhesin Yes Yes Operon Narayanan et al., 2001; Tan et al., 1996; Wright, 2016) Hemolysin (Amoako et al., 1993, Hemolysin III family protein Yes Yes 1994; Nagaraja et al., 2005; Wright, 2016) ShlB/FhaC/HecB family hemolysin Yes Yes secretion/activation protein Hemagglutinin (Forrester, Campbell, Filamentous hemagglutinin N-terminal domain- Yes Yes Berg, & Barrett, 1985; containing protein Kanoe & Yamanaka, 1989; Nagaraja et al., 2005; Wright, 2016) Hemin receptor (Wright, 2016) Hemin receptor Yes Yes Ecotin (Wright, 2016) Ecotin Yes Yes Virulence-associated (Wright, 2016) Virulence-associated protein E No Yes protein E YihY/virulence factor BrkB family protein Yes Yes Adhesin (Nagaraja et al., 2005) Adhesin Yes No Proteases (Nagaraja et al., 2005) Omptin family outer membrane protease Yes Yes Clp protease ClpP Yes Yes ATP-dependent Clp protease proteolytic subunit Yes Yes ATP-dependent Clp protease ATP-binding Yes Yes subunit ClpX

CHAPTER 4 PORPHYROMONAS LEVII KG45

In Chapter 4 we explore some of the virulence factors associated with

Porphyromonas levii KG45. This strain was isolated from the uterus of a dairy cow suffering from metritis. We believe this strain of bacteria shares some virulence factors in common with other strains of Porphyromonas levii and other Porphyromonas species, but may also be different. Here we explored the possible virulence factors present in the genome and proteome of KG45.

Methods

Sample Collection

Uterine samples for bacterial cultures were collected from lactating dairy cows with metritis at Alliance Dairy in Trenton, FL and the University of Florida Dairy

Research Unit in Hague, FL during the months of October 2018 through January 2019.

All cows sampled from showed signs of metritis and ranged from 1 to 10 DPP. Samples were collected from 20 cows. Cows with any other disease besides metritis were not considered for sample collection.

Metritis Diagnosis

At Alliance Dairy, during the months of October through mid November, as cows exited the milking parlor they were separated out and temporarily restrained while they were examined for signs of metritis and samples were collected. From mid November through the remainder of the collection period, diagnosis and sample collection was performed in the barns. Cows were provided with free stalls bedded with sand and were temporarily restrained in the stalls during diagnosis and collection. At the Hague location, diagnosis and sample collection occured when cows exited the milking parlor

for the duration of the study (October - January). Metritis diagnosis was performed by two trained investigators. Metritic cows were diagnosed as exhibiting a metricheck 5

(out of 5 point scale). The five point scale for the diagnosis of metritis was as follows: 1, discharge is normal, being clear, red or brown, viscous, and not fetid; 2, discharge is cloudy with flecks of pus; 3, discharge is mucopurulent and not fetid with < 50% pus; 4, discharge is mucopurulent, not fetid, with >50% pus; 5, discharge is fetid, watery, and red-brown in color (Cunha, 2016). Rectal temperatures were obtained at the time of sampling and all cows had a rectal temperature of greater than 38.3o C.

Plating and Growing Samples

Johnson & Holdeman (1983) noted that vitamin K and hemin are required for the growth of P. levii and thus only media supplemented with hemin and vitamin K were considered for this study. When samples arrived at the laboratory, they were plated onto either Columbia agar with 5% sheep’s blood (Remel, ref = R01215), Brucella agar with haemin and vitamin K (Hardy Diagnostics, ref = A30), or MacConkey agar (Hardy

Diagnostics, ref = G35) within 20 minutes to 4 hours of arrival. Samples that were not immediately plated were stored at 4o C until plating. Samples were plated in aerobic conditions under a bunsen burner and immediately placed in a sealed anaerobic

GasPak pouch with a CO2 generator (Becton, Dickinson, ref = 260683). Plates were incubated at 37o C and colonies began growing within 24 hours of inoculation and more growth persisted over the next five days.

Isolation of P. levii

Colonies were identified first based on color and morphology according to

Johnson & Holdeman (1983). A small, smooth, convex colony that started as a buff color and began to turn dark on Columbia and Brucella agar after about four days after

plating was picked and transferred to new plates for isolation. This colony originated from cow No. 29776 at the UF Dairy unit in Hague, Fl. Two Brucella agar plates with clones of this colony were submitted to Genewiz for amplification of the 16s rRNA gene and the resulting fasta files were uploaded to NCBI BLAST. A consensus sequence was created from the forward and reverse primers using methods described by the

Hackmann lab (personal communication) and uploaded to EZBioCloud identification services for additional identification. In summary, sequences were first trimmed in

Sequence Scanner 2 (Applied Biosystems, 2012). The trimmed sequences were aligned with Clustal Omega multiple sequence alignment (EMBL-EBI, 2019) for DNA, and outputted in the Pearson/FASTA format. Before uploading the sequences resulting from the reverse primers, the reverse compliment was generated at Bioinformatics.org

(2000). The reverse compliments were then uploaded with the forward sequences to

Clustal Omega. The FASTA files from Clustal Omega were then further inspected and edited as necessary and aligned in Mega 7 (Tamura, Kumar, & Stecher, version

7.0.26). A consensus sequence was then created in BioEdit (Hall, 1999).

The finished consensus sequences were uploaded to EZBioCloud's 16s rRNA identify service (ChunLab, Inc) and BLAST (https://blast.ncbi.nlm.nih.gov/). In both

BLAST and EZBioCloud, the sequences were within 99% similarity of Porphyromonas levii. Once identifiction of Porphyromonas levii was confirmed, the colony was transferred to Brucella agar and grown and maintained under anaerobic conditions at

37o C.

DNA Extraction

DNA was extracted from two samples (sequence 1 and sequence 2) of the KG45 strain. Colonies were scraped off the Brucella plates and extracted using a QIAamp

DNA Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s directions for isolation of genomic DNA from bacterial plate cultures. In greater detail, the lab bench was first wiped down with 70% ethanol. Bacteria were removed from the agar plate with an inoculation loop and suspended in 180 ul of a lysis buffer (Buffer ATL,

Quiagen). 20 ul of proteinase K were added and mixed through vortexing. The mixture was incubated at 56o C in a water bath for approximately 1 hour every 20 minutes. The mixture was then briefly centrifuged at 1500 x g. Next, 200 ul of lysis buffer (Buffer ATL,

Quiagen) were added, mixed by pulse vortexing for 15 s, and incubated at 70o C for 10 min and briefly centrifuged at 1500 x g. 200 ul of 96-100% ethanol were added and mixed by pulse vortexing for 15 s and the sample was again briefly centrifuged at 1500 x g. The mixture was then transferred to a spin column, centrifuged at 6000 x g for 1 min, and the filtrate was discarded. Five hundred microliters of Buffer AW1 were added to the column which was centrifuged at 6000 x g for 1 min and the flow-through was decanted. Next, 500 ul of Buffer AW2 were added, centrifuged at maximum speed

(18,000 x g) for 3 min, and the filtrate was removed. The wash step with Buffer AW2 was repeated a second time for 1 min. The spin column was transferred to a new 1.5-ml microcentrifuge tube, 200 ul of elution buffer were added. The mixture was allowed to incubate at room temperature for 1 min and centrifuged at 6000 x g for 1 min. The elution step was repeated in a new 1.5-ml centrifuge tube and the contents of the the centrifuge tubes from the 1st and 2nd elution runs were combined into one microcentrifuge tube. Approximately 400 ul of DNA and buffer were eluted. The concentrations of the eluted DNA samples were measured with a NanoDrop 2000

(Thermo Scientific). Three readings were taken with the Nanodrop for each sample

(sequence 1 and sequence 2 ) and the average concentration of the three readings was recorded. The final concentrations measured 12.6 ng/ul for sequence 1 and 13.4 ng/ul for sequence 2.

Illumina Whole Genome Sequencing

Whole genome sequences of the DNA samples were produced with an Illumina

MiSeq platform. A 2 × 250-bp 500-cycle cartridge was used and the total number of reads was 1,790,338 for sequence 1 and 1,453,088 for sequence 2.

Quality Control, Assembly, and Annotation

The forward and reverse reads obtained from the Illumina sequencing were uploaded into Galaxy based in UF's Research Computing HiPerGator computer. Sickle

(Joshi & Fass, 2011, version 1.33.1) was used for quality control and trimming of the forward and reverse reads for sequence 1 and sequence 2 once they were uploaded into Galaxy. Figure 4-2 shows the settings used in Sickle. The paired-end (two separate files) option was selected and the uploaded forward and reverse reads were selected for "Paired-end forward strand FASTQ reads" and " Paired-end reverse strand FASTQ reads respectively". The "Quality threshold" was set to 30 and the "Length threshold" to

50. "No" (default parameter) was selected for both "Don't do 5' trimming" and "Truncate sequences with Ns at first N position”. The job was run with "Specify job resource parameters" which from here on will refer to a "Processors" of 1, a "Memory" of 12, a

"Time" of 94, and "Account" and "QOS" set to the Galvao lab account. The genome was assembled with SPAdes (Bankevich et al., 2012, version 3.11.1) next. The settings for

SPAdes can be viewed in Figure 4-3. The questions "Single-cell?", "Run only assembly? (without read error correction)", and "Careful correction?" were left at their default options of "No", "No" and "Yes" respectively. The k-mers were 21, 33, 55, 77,

99, and 127. The "Coverage Cutoff" and "Libraries are IonTorrent reads?" were left at default settings ("Off" and "No"). The "Library type" was Paired-end/ Single reads and

"Orientation" was set to its default of "-> <- (fr)". The paired-end forward and reverse output files from Sickle were selected for "Forward reads" and "Reverse reads" respectively. "PacBio CLR reads", "Nanopore reads", "Sanger reads", "Trusted contigs" and "Untrusted contigs" were all left at the default parameters. "Output final assembly graph (contigs)?" and "Output final assembly graph with scaffolds?" both remained on

"No" (default option) and the job was run using the previously specified job parameters.

Once completed, the assembled contigs were uploaded to the NCBI Prokaryotic

Genome Annotation Pipeline (PGAP) (Tatusova et al., 2016) for annotation. The assembled contigs were evaluated using Quast (version 4.6.3) through HiPerGator's

Galaxy. "SPAdes on data 4 and data 3: contigs (fasta) was selected for

"Contigs/scaffolds output file". "Type of data" was set at "Contig". The size of the reference genome was 2514517bp (NCBI RefSeq: NZ_ARBX00000000.1). Nothing was selected for "Reference genome", "Genome annotations" or "Operon annotations".

"Type of organism" was set to "Prokaryotes", "Lower Threshold" was left at the default of 500 and "Thresholds" at the default of 0, 1000. This job was run under "Use default job resource parameters". The summary of these settings can be seen in Figure 4-4.

Virulence Factor Search

The annotated PGAP proteomes of sequence 1 and sequence 2 were searched for virulence factors of Porphyromonas species listed in reviews by Holt et al. (2000) and Nagmoti (2014). Additionally, the same keywords used in the search of the F. necrophorum KG34 and KG35 proteomes were used. The keywords included

"leukotoxin", "endotoxin", "hemolysin", "hemagglutinin", "adhesin", "pili", "dermonecrotic

toxin", "toxin", "platelet aggregation", "aggregation", "protease", "virulence", "ecotin",

"collagenase", "gelatinase", "aminopeptidase", "phospholipase", "alkaline phosphatase",

"phosphatase", "chodorintin", "sulfatase", "hyaluronidase", "keratinase", "heparinase",

"nuclease", "epitheliotoxin", "inhibitor", "lipopolysaccharide", "volatile", "indole",

"ammonia", fibrinolysin", "dismutase", "oxidase, "immunoglobulin" and "IgG". The names of the proteins in the list were then searched under "All Databases" at NCBI

(https://www.ncbi.nlm.nih.gov/), in Google Scholar, and in Google searches in an attempt to find literature describing each of these proteins to help determine which ones may be virulence factors.

Results

Statistics

The total number of reads was 1,790,338 for sequence 1 and 1,453,088 for sequence 2. The genome length for sequence 1 was 2400287 bp. Sequence 1 had 174 contigs, a G + C content of 45.59%, an N50 of 34652, 2259 genes, 2178 CDS , and 62 tRNA. The total genome size of sequence 2 was 2313236 bp with 340 contigs, a G + C content of 45.96%, and an N50 of 12250. Sequence 2 had 2109 genes, 2055 CDS and

43 tRNA. Figure 4-5 shows the Quast report for sequence 1 and Figure 4-6 shows the

Quast report for sequence 2.

Species Identification

Colonies growing on Brucella agar started as a buff color or colorless and turned dark around day 5 after inoculation. The colonies were small, convex, had entire margins, and grew in small clusters (Figure 4-1).

When the 16s rRNA sequences provided by Genewiz were entered in BLAST and EzBioCloud (ChunLab, 2010-2019), they resulted in a 99% identity with the species

Porphyromonas levii.

Virulence Factors

The keywords used the PGAP search and the results from the search are listed in Table 4-1 (sequence 1) and Table 4-2 (sequence 2). The following proteins from the virulence factor search were determined to be virulent or most likely virulent in some way based on the literature search. The list of these virulence factors for both sequence

1 and sequence 2 includes proteases, aminopeptidases, a phospholipase, a nucleotidyl tranferase AbiEii/AbiGii toxin family protein, LPS (Lipopolysaccharide), superoxide dismutase, peroxidases, virulence protein E, YihY/virulence factor BrkB family protein, and a zinc-dependent metalloproteinase lipoprotein. A Pilin glycosylation protein was present in the genome of sequence 2, but not in sequence 1. The complete results can be seen in Table 4-3. From the list of virulence factors in Table 4-3, each protein was viewed in the NCBI GenPept database. If the gene coding for each protein was named or identified, it was listed in Table 4-4 along with its protein. The majority of proteins however, did not have a gene name associated with them.

Genome Comparison

There were limited sources for annotated genomes of Porphyromonas levii available for use in identifying virulence factors as no other P. levii genomes have been published in NCBI's PGAP. Genomes were hard to find in other databases and even then, attempts at utilizing other methods to identify virulence factors did not prove to be accurate.

Each virulence factor protein in Table 4-3 was subject to a pBLAST

(https://blast.ncbi.nlm.nih.gov/) search. Similar sequences found in P. levii species resulting from the search and the percent identity between the searched protein and the resulting protein were recorded in Table 4-5 (seqience 1) and Table 4-6 (sequence 2).

The only proteins resulting for P. levii species in this search however were the current strains described in this thesis (sequence 1 and sequence 2) and proteins with accession numbers beginning in "WP_". "WP_" indicates that the sequence is from a non-redundant RefSeq genome, a genome put together from multiple strains of the same species and it is likely that this sequence is based only off of the sequences of sequence 1 and sequence 2.

Assembled contigs of sequence 1 and sequence 2 were processed in Prodigal in order to predict gene products. The resulting annotations from Prodigal were then compared to the type strain (P. levii) through Kegg but the results generated fewer virulence factors than what were observed in the PGAP search.

Discussion

The genome lengths of sequence 1 (2400287 bp) and sequence 2 (2313236 bp) after trimming were fairly close to that of the reference sequence NZ_ARBX00000000.1

(2514517 bp) and both maintained a coverage of 94.0% or greater ((strain's genome length)/(reference sequence length) x 100%).

Porphyromonas levii have been described as Gram negative, anaerobic bacteria that are 0.6 - 1.2 by 2 - 7 um that grow in short chains (Dohmen et al., 1995; Johnson &

Holdeman, 1983). When grown on plates, their colonies are described as dark or pigmented, minute, circular, and low convex with entire margins (Blum et al., 2008;

Dohmen et al., 1995; Johnson & Holdeman, 1983) and the P. levii strains in this study fit that same description.

Few virulence factors have been identified in the Porphyromonas levii species.

Although Nagmoti (2014) referenced some species of Porphyromonas as producing proteases and adhesion factors, P. levii was not in the list. Perhaps the only virulence factor known in P. levii is SOD activity as observed by Gregory et al. (1978).

Proteases

Both sequence 1 and sequence 2 posessed ATP-dependent Clp proteases ATP- binding subunit ClpX, an ATP-dependent Clp protease ATP-binding subunit, and a

BREX system Lon protease-like protein BrxL. Neither BREX, or Clp systems were observed in other P. levii strains through BLAST and literature searches and to the author's knowledge, have not been previously reported in Porphyromonas levii. Clp proteases may be important in the secretion of hemolysin and other exoproteins as

Frees et al. (2003) observed that protease activity and hemolysis were decreased in cultures lacking the clpP and clpX genes. Additionally, Capestany et al. (2008) studied the roles of the Clp system and suggested it may be important in heat tolerance, host invasion, and expression of fimbriae and serine phosphatases.

Although it may not allow the bacterium to directly affect the host, the BREX system could be important in protecting the bacterium against bacteriophages as BREX is thought to work specifically by preventing the replication of phage DNA (Goldfarb et al., 2015). This could give the bacterium expressing this gene an advantage over other species lacking the gene, giving it greater chances of survival.

Aminopeptidases

Both sequence 1 and sequence 2 coded for aminopeptidases. Aminopeptidases were not observed in any Porphyromonas levii strains other than the ones described in this thesis (sequence 1 and sequence 2). Cheng et al. (2015) studied metalloaminopeptidases and observed that 80% of mice infected with bacteria producing these proteins died whereas only 10% of mice infected with the same bacterial species in which the gene coding for the metalloaminopeptidases was lacking died. Bacteria possesing these aminopeptidases were also better able to invade human intestinal epithelial cells and inctracellular macrophages (Cheng et al., 2015). Luckett et al. (2012) found that aminopeptidases allowed pathogenic bacteria to better survive in mouse wound infections and caused a greater inflammatory response in the host compared to bacteria of the same strain that lacked the ability to produce the aminopeptidases. It is likely that P. levii KG45 is able to invade the uterine epithelium of dairy cows undergoing involution and colonize the tract where they can continue to cause further harm.

Phospholipase

Both sequences coded for phospholipases. As with proteases and aminopeptidases, the literature and BLAST searches did not result in phospholipases identified in any other strains of P. levii, meaning this is likely the first time phospholipases have been observed in a P. levii strain. In their review, Flores-díaz et al.

(2016) stated that phospholipases cleave glycerophospholipids, some of the major structural lipids in eukaryotic cell membranes. Istivan & Coloe (2006) also concluded that phospholipases degrade lipids and destroy membrane phospholipids. The

phospholipases of P. levii KG45 may be important virulence factors by allowing the bacterium to destroy the uterine tissue of the host, leading to metritis.

Nucleotidyl Transferase AbiEii/AbiGii Toxin Family Protein

Sequence 1 and sequence 2 coded for the nucleotidyl transferase AbiEii/BbiGii toxin family protein. Although this protein may not play a critical role in metritis as a virulence factor, it may be still worth mentioning. AbiEii toxins have been observed to cause the suicide of bacterial cells infected with phages (Dy et al., 2014). This may help prevent bacteriophages in the infected bacterial cell from replicating, saving more cells from death caused by the phages. The AbiEii/AbiGii system has not been reported in any other strain of Porphyromonas levii to the author's knowledge.

Lipopolysaccharide

The ability to produce lipopolysaccharide biosynthesis proteins was found in sequence 1 and sequence 2. The author was unable to find any other studies or PGAP annotations identifying LPS in other strains of P. levii however, with this being such a well studied protein it is possible that this information does exist somewhere and was simply not uncovered in this project.

LPS has been well observed in many other Gram negative bacteria. This protein is a harmful toxin that is found in the outer membrane of Gram negative bacteria not only protects the bacteria from the host, but can also cause harm to the host (King et al,

2009).

Superoxide Dismutase

The sequence 1 and sequence 2 proteomes also coded for superoxide dismutase. Some superoxide dismutase (SOD) activity has been identified in P. levii by

Gregory et al. (1978) and is probably the only factor in this discussion that was

previously known to be associated with P. levii, though it has not been studied any further in this species. It has however, been well discussed in Porphyromonas gingivalis.

SOD allows bacteria to survive in environments where oxidative species are present. This could explain why this strain was able to grow when it was inoculated under aerobic conditions. Lynch & Kuramitsu (1999) concluded that SOD is responsible for aerotolerance in Porphyromonas gingivalis as well as protection from oxidative DNA damage and superoxides. They did note however, that there was no difference in the effect of human PMN on P. gingivalis expressing SOD and P. gingivalis species unable to express SOD, though Poyart et al. (2001) reported that streptococci expressing SOD were better able to survive in macrophages than mutant strains. Additionally, mice infected with the wild-type strain expressing SOD became sick and died within 6 days wherease those infected with the mutant strains did not show signs of illness until 2 days post infection and only 2 of 10 mice died over a period of 15 days (Poyart et al.,

2001).

Peroxidases

Sequence 1 and sequence 2 coded for thioredoxin-dependent thiol peroxidase and thiol peroxidase. These proteins may not be significant as virulence factors, but as demonstrated in Pseudomonas aeruginosa, may also play a role in protection against hydrogen peroxide or possibly other oxidative species (Somprasong et al., 2012). As literature and BLAST results were negative for any peroxidases expressed by other P. levii strains, it is possible that this is the first time peroxidases are observed in the proteome of P. levii.

Pilin Glycosylation Protein

Of the two sequences explored in this study, only sequence 2 showed results for the keyword "pili". As was the case with the virulence factors discussed above, the pilin glycosylation protein was not identified in any literature on other P. levii strains or in any

P. levii strains through the BLAST search. This protein may be important in allowing the bacterium to adhere to surfaces of the host epithelium, an important contributing factor to pathogenesis and colonization. Power et al. (2000) studied the genes associated with pilin glycosylation, concluding that the genes and their protein product were important in producing pili in Neisseria meningitidis.

Virulence Protein E and YihY/Virulence Factor BrkB Family Protein

Both the sequence 1 and sequence 2 proteomes contained virulence factor E and YihY/virulence factor BrkB family proteins. In the PGAP search for virulence factor

E, the gene was unidentified, making it difficult to find any literature describing this protein and thus it's function in the pathogenesis of P. levii is unknown to the author.

This protein warrants further investigation in the future. BrkA and BrkB proteins may allow bacteria to survive better in host serum than bacteria lacking these proteins

(Fernandez & Weiss, 1994) which could give P. levii KG45 an advantage in establishing itself as a pathogen. As information on both these virulence factors was limiting, even more so was information identifing these proteins in other strains of P. levii meaning this is probably the first time either of these virulence factors have been noticed in P. levii specifically.

Zinc-Dependent Metalloproteinase Lipoprotein

Sequence 1 and sequence 2 both posessed a zinc-dependent metalloproteinase lipoprotein in their proteome. This may be the first time a zinc-dependent

metalloproteinase lipoprotein has been observed in P. levii as this protein has not been observed in any other strains to the author's knowledge. The gene for this protein was unidentified, making it more difficult to find literature describing the protein. A BLAST search yielded some similarities to zinc-dependent metalloproteinase lipoproteins in other Porphyromonas species, but not at a high level of identity. With these results it is difficult to say how these proteins may be involved in the pathogenicity of P. levii KG45, but Denkin & Nelson (2004) did determine that Vibrio anguillarum expressing metalloproteases, originating from the empA gene, were responsible for a high mortality rate in juvenille Atlantic Salmon.

Summary

Although in this study no virulence factors could be found in other P. levii strains, it is possible that there are other databases and sources available with this information that were unknown to the author. Alternatively, this could be the first time virulence factors have been described in a study on P. levii. To the author's knowledge, with the exception of superoxide dismutase, virulence factors have never been described in P. levii specifically, but have been identified in other Porphyromonas species. These other species are known to produce IgA, IgM and IgG proteases to protect themselves from the host immune system (Nagmoti, 2014). Addionally, Nagmoti mentioned other

Porphyromonas species produced proteases, proteinases, phospholipases, superoxide dismutase and LPS. The next step to take in this study would be to perform biochemical tests and use methods such as PAGE or Western blotting to identify and confirm these virulence factors and to perform tests to confirm expression of the genes associated with these virulence factors. Additionally, the geneome of these strains of bacteria could

be explored for potential drug targets, allowing us to have an idea of how to eliminate infection by these strains to ultimately improve the health of dairy cows.

Figure 4-1. P. levii KG 45 colonies on Brucella agar, photo courtesy of Amye Francis.

Figure 4-2. Sickle settings for sequence 1 and sequence 2

Figure 4-2. Continued

Figure 4-3. SPAdes settings for sequence 1 and sequence 2

Figure 4-3. Continued

Figure 4-4. Quast settings for sequence 1 and sequence 2

Figure 4-4. Continued

Figure 4-5. Quast report summary of sequence 1

Figure 4-6. Quast report summary for sequence 2

Table 4-1. Results from the PGAP search of sequence 1 Keyword Gb accession Protein length Product name Accession no. protease TFH97739.1 213 rhomboid family intramembrane serine protease SPNB01P000081 TFH97740.1 221 rhomboid family intramembrane serine protease SPNB01P000082 TFH97615.1 739 serine protease SPNB01P000165 TFH97065.1 439 RIP metalloprotease RseP SPNB01P000517 TFH96064.1 429 ATP-dependent Clp protease ATP-binding subunit ClpX SPNB01P001138 TFH95721.1 302 CPBP family intramembrane metalloprotease SPNB01P001347 TFH95379.1 480 BREX system Lon protease-like protein BrxL SPNB01P001529 TFH95335.1 716 ATP-dependent Clp protease ATP-binding subunit SPNB01P001549 TFH95292.1 158 CPBP family intramembrane metalloprotease SPNB01P001570 aminopeptidase TFH97482.1 397 Aminopeptidase SPNB01P000294 TFH96549.1 597 aminopeptidase P family protein SPNB01P000868 TFH96031.1 397 aminopeptidase P family protein SPNB01P001168 TFH95625.1 266 type I methionyl aminopeptidase SPNB01P001392 phospholipase TFH96581.1 260 Phospholipase SPNB01P000811 phosphatase TFH97689.1 231 phosphatase PAP2 family protein SPNB01P000031 TFH97620.1 356 endonuclease/exonuclease/phosphatase family protein SPNB01P000170 TFH97578.1 284 undecaprenyl-diphosphate phosphatase SPNB01P000248 TFH97475.1 411 alkaline phosphatase family protein SPNB01P000287 TFH97400.1 158 low molecular weight phosphotyrosine protein phosphatase SPNB01P000344 TFH97201.1 341 bifunctional oligoribonuclease/PAP phosphatase NrnA SPNB01P000422 TFH97074.1 206 HAD family phosphatase SPNB01P000526 TFH96998.1 114 Pyrophosphatase SPNB01P000552 TFH96928.1 184 histidine phosphatase family protein SPNB01P000584 TFH96941.1 576 bifunctional metallophosphatase/5'-nucleotidase SPNB01P000597 TFH96738.1 158 histidinol phosphate phosphatase SPNB01P000689

Table 4-1. Continued Keyword Gb accession Protein length Product name Accession no. phosphatase TFH96765.1 416 phosphoserine phosphatase SerB SPNB01P000716 TFH96412.1 176 alpha-ribazole phosphatase SPNB01P000936 TFH96282.1 174 3-deoxy-D-manno-octulosonate 8-phosphate phosphatase SPNB01P001015 TFH96242.1 242 2-phosphosulfolactate phosphatase SPNB01P001045 TFH96072.1 195 RdgB/HAM1 family non-canonical purine NTP pyrophosphatase SPNB01P001146 TFH96032.1 565 alkaline phosphatase SPNB01P001169 TFH96036.1 144 dUTP diphosphatase SPNB01P001173 TFH95871.1 644 alkaline phosphatase family protein SPNB01P001267 TFH95570.1 270 UDP-2,3-diacylglucosamine diphosphatase SPNB01P001438 TFH95380.1 798 BREX-4 system phosphatase PglZ SPNB01P001530 TFH95315.1 402 phosphatase PAP2 family protein SPNB01P001560 nuclease TFH97659.1 1238 restriction endonuclease subunit S SPNB01P000001 TFH97735.1 960 PD-(D/E)XK nuclease family protein SPNB01P000077 TFH97766.1 200 ribonuclease HII SPNB01P000108 TFH97599.1 131 ribonuclease P protein component SPNB01P000149 TFH97611.1 321 ribonuclease Z SPNB01P000161 TFH97620.1 356 endonuclease/exonuclease/phosphatase family protein SPNB01P000170 TFH97622.1 228 3'-5' exonuclease domain-containing protein 2 SPNB01P000172 TFH97632.1 219 endonuclease III SPNB01P000182 TFH97566.1 525 ribonuclease Y SPNB01P000236 TFH97474.1 185 crossover junction endodeoxyribonuclease RuvC SPNB01P000286 TFH97194.1 255 3'-5' exonuclease SPNB01P000415 TFH97201.1 341 bifunctional oligoribonuclease/PAP phosphatase NrnA SPNB01P000422 TFH97077.1 942 excinuclease ABC subunit A SPNB01P000529

Table 4-1. Continued Keyword Gb accession Protein length Product name Accession no. nuclease TFH96996.1 608 excinuclease ABC subunit C SPNB01P000550 TFH96414.1 273 TatD family deoxyribonuclease SPNB01P000938 TFH96354.1 262 ribonuclease III SPNB01P000955 TFH96362.1 711 excinuclease ABC subunit UvrB SPNB01P000963 TFH96366.1 322 HaeIII family restriction endonuclease SPNB01P000967 TFH96304.1 739 ribonuclease R SPNB01P000970 TFH96325.1 612 DNA mismatch repair endonuclease MutL SPNB01P000991 TFH96203.1 401 exonuclease subunit SbcD SPNB01P001067 TFH96066.1 947 excinuclease ABC subunit UvrA SPNB01P001140 TFH96070.1 72 exodeoxyribonuclease VII small subunit SPNB01P001144 TFH96071.1 476 exodeoxyribonuclease VII large subunit SPNB01P001145 TFH95794.1 339 restriction endonuclease SPNB01P001284 TFH95796.1 331 restriction endonuclease subunit M SPNB01P001286 TFH95806.1 955 type I restriction endonuclease subunit R SPNB01P001296 TFH95808.1 442 restriction endonuclease subunit S SPNB01P001298 TFH95765.1 340 CRISPR-associated endonuclease Cas1 SPNB01P001303 TFH95767.1 162 CRISPR-associated endonuclease Cas2 SPNB01P001305 TFH95719.1 282 deoxyribonuclease IV SPNB01P001345 TFH95599.1 553 Rne/Rng family ribonuclease SPNB01P001418 TFH95182.1 260 Eco47II family restriction endonuclease SPNB01P001633 TFH95161.1 366 restriction endonuclease subunit S SPNB01P001645 TFH95165.1 365 anticodon nuclease SPNB01P001649 TFH95166.1 999 type I restriction endonuclease subunit R SPNB01P001650 TFH95104.1 86 CRISPR-associated endonuclease Cas2 SPNB01P001681 TFH95105.1 336 type I-B CRISPR-associated endonuclease Cas1 SPNB01P001682

Table 4-1. Continued Keyword Gb accession Protein length Product name Accession no. nuclease TFH95111.1 223 CRISPR-associated endoribonuclease Cas6 SPNB01P001688 TFH95049.1 727 ATP-dependent endonuclease SPNB01P001711 TFH94945.1 584 single-stranded-DNA-specific exonuclease RecJ SPNB01P001766 TFH94852.1 249 3'-5' exonuclease SPNB01P001808 toxin TFH97674.1 501 toxin-antitoxin system YwqK family antitoxin SPNB01P000016 TFH96686.1 89 type II toxin-antitoxin system Phd/YefM family antitoxin SPNB01P000728 TFH96687.1 93 Txe/YoeB family addiction module toxin SPNB01P000729 TFH96688.1 92 type II toxin-antitoxin system YafQ family toxin SPNB01P000730 TFH96697.1 501 toxin-antitoxin system YwqK family antitoxin SPNB01P000739 TFH96698.1 381 toxin-antitoxin system YwqK family antitoxin SPNB01P000740 TFH96699.1 269 toxin-antitoxin system YwqK family antitoxin SPNB01P000741 TFH96714.1 145 toxin-antitoxin system YwqK family antitoxin SPNB01P000756 TFH95397.1 337 nucleotidyl transferase AbiEii/AbiGii toxin family protein SPNB01P001519 TFH94962.1 370 type II toxin-antitoxin system HipA family toxin SPNB01P001755 TFH94725.1 263 nucleotidyl transferase AbiEii/AbiGii toxin family protein SPNB01P001856 lipopolysaccharide TFH96230.1 499 lipopolysaccharide biosynthesis protein SPNB01P001033 dismutase TFH96661.1 194 Superoxide dismutase SPNB01P000798 oxidase TFH97401.1 166 thiol peroxidase SPNB01P000345 TFH97060.1 386 cytochrome d ubiquinol oxidase subunit II SPNB01P000512 TFH97061.1 528 cytochrome ubiquinol oxidase subunit I SPNB01P000513 TFH96739.1 468 protoporphyrinogen oxidase SPNB01P000690 TFH96656.1 216 pyridoxamine 5'-phosphate oxidase SPNB01P000793 TFH96577.1 533 L-aspartate oxidase SPNB01P000807 TFH96466.1 157 thioredoxin-dependent thiol peroxidase SPNB01P000890 TFH95904.1 454 oxygen-independent coproporphyrinogen III oxidase SPNB01P001249

Table 4-1. Continued Keyword Gb accession Protein length Product name Accession no. protease TFH97739.1 213 rhomboid family intramembrane serine protease SPNB01P000081 TFH97740.1 221 rhomboid family intramembrane serine protease SPNB01P000082 TFH97615.1 739 serine protease SPNB01P000165 TFH97065.1 439 RIP metalloprotease RseP SPNB01P000517 TFH96064.1 429 ATP-dependent Clp protease ATP-binding subunit ClpX SPNB01P001138 TFH95721.1 302 CPBP family intramembrane metalloprotease SPNB01P001347 TFH95379.1 480 BREX system Lon protease-like protein BrxL SPNB01P001529 TFH95335.1 716 ATP-dependent Clp protease ATP-binding subunit SPNB01P001549 TFH95292.1 158 CPBP family intramembrane metalloprotease SPNB01P001570 virulence TFH95766.1 339 virulence protein E SPNB01P001304 TFH94766.1 478 YihY/virulence factor BrkB family protein SPNB01P001844 proteinase TFH97284.1 503 zinc-dependent metalloproteinase lipoprotein SPNB01P000380

Table 4-2. Results from the PGAP search of sequence 2 Keyword Gb accession Protein length Product name Accession protease TFH97505.1 213 rhomboid family intramembrane serine protease SPNC01P000008 TFH97506.1 221 rhomboid family intramembrane serine protease SPNC01P000009 TFH96870.1 739 serine protease SPNC01P000304 TFH96436.1 429 ATP-dependent Clp protease ATP-binding subunit ClpX SPNC01P000457 TFH96421.1 302 CPBP family intramembrane metalloprotease SPNC01P000471 TFH95199.1 241 CPBP family intramembrane metalloprotease SPNC01P000975 TFH95009.1 439 RIP metalloprotease RseP SPNC01P001059 TFH94418.1 716 ATP-dependent Clp protease ATP-binding subunit SPNC01P001452 TFH94197.1 158 CPBP family intramembrane metalloprotease SPNC01P001638 TFH93982.1 387 BREX system Lon protease-like protein BrxL SPNC01P001796 aminopeptidase TFH96786.1 266 type I methionyl aminopeptidase SPNC01P000336 TFH95637.1 597 aminopeptidase P family protein SPNC01P000773 TFH95222.1 397 aminopeptidase P family protein SPNC01P000966 TFH94532.1 397 aminopeptidase SPNC01P001357 phospholipase TFH97427.1 260 phospholipase SPNC01P000053 phosphatase TFH97363.1 411 alkaline phosphatase family protein SPNC01P000101 TFH97175.1 284 undecaprenyl-diphosphate phosphatase SPNC01P000167 TFH96865.1 356 endonuclease/exonuclease/phosphatase family protein SPNC01P000299 TFH96669.1 114 pyrophosphatase SPNC01P000371 TFH96444.1 195 RdgB/HAM1 family non-canonical purine NTP SPNC01P000465 pyrophosphatase TFH96303.1 644 alkaline phosphatase family protein SPNC01P000516 TFH96169.1 231 phosphatase PAP2 family protein SPNC01P000548 TFH96086.1 341 bifunctional oligoribonuclease/PAP phosphatase NrnA SPNC01P000594 TFH95843.1 176 alpha-ribazole phosphatase SPNC01P000683

Table 4-2. Continued Keyword Gb accession Protein length Product name Accession no. phosphatase TFH95697.1 158 histidinol phosphate phosphatase SPNC01P000750 TFH95372.1 206 HAD family phosphatase SPNC01P000890 TFH95223.1 565 alkaline phosphatase SPNC01P000967 TFH95224.1 144 dUTP diphosphatase SPNC01P000968 TFH95115.1 416 phosphoserine phosphatase SerB SPNC01P001006 TFH95097.1 184 histidine phosphatase family protein SPNC01P001019 TFH95024.1 576 bifunctional metallophosphatase/5'-nucleotidase SPNC01P001051 TFH94832.1 270 UDP-2,3-diacylglucosamine diphosphatase SPNC01P001148 TFH94810.1 402 phosphatase PAP2 family protein SPNC01P001162 TFH94777.1 345 tyrosine-protein phosphatase SPNC01P001189 TFH94721.1 174 3-deoxy-D-manno-octulosonate 8-phosphate phosphatase SPNC01P001226 TFH94698.1 158 low molecular weight phosphotyrosine protein phosphatase SPNC01P001238 TFH94656.1 242 2-phosphosulfolactate phosphatase SPNC01P001265 TFH93983.1 308 BREX-4 system phosphatase PglZ SPNC01P001797 TFH93963.1 473 BREX-4 system phosphatase PglZ SPNC01P001811 nuclease TFH97501.1 960 PD-(D/E)XK nuclease family protein SPNC01P000004 TFH97532.1 200 ribonuclease HII SPNC01P000035 TFH97362.1 185 crossover junction endodeoxyribonuclease RuvC SPNC01P000100 TFH97333.1 401 exonuclease subunit SbcD SPNC01P000122 TFH97187.1 525 ribonuclease Y SPNC01P000179 TFH96853.1 219 endonuclease III SPNC01P000287 TFH96863.1 228 3'-5' exonuclease domain-containing protein 2 SPNC01P000297 TFH96865.1 356 endonuclease/exonuclease/phosphatase family protein SPNC01P000299 TFH96667.1 608 excinuclease ABC subunit C SPNC01P000369 TFH96496.1 162 CRISPR-associated endonuclease Cas2 SPNC01P000445

Table 4-2. Continued Keyword Gb accession Protein length Product name Accession no. nuclease TFH96498.1 340 CRISPR-associated endonuclease Cas1 SPNC01P000447 TFH96438.1 947 excinuclease ABC subunit UvrA SPNC01P000459 TFH96442.1 72 exodeoxyribonuclease VII small subunit SPNC01P000463 TFH96443.1 476 exodeoxyribonuclease VII large subunit SPNC01P000464 TFH96423.1 282 deoxyribonuclease IV SPNC01P000473 TFH96133.1 727 ATP-dependent endonuclease SPNC01P000579 TFH96086.1 341 bifunctional oligoribonuclease/PAP phosphatase NrnA SPNC01P000594 TFH95882.1 955 type I restriction endonuclease subunit R SPNC01P000676 TFH95884.1 370 restriction endonuclease subunit S SPNC01P000678 TFH95675.1 262 ribonuclease III SPNC01P000767 TFH95469.1 249 3'-5' exonuclease SPNC01P000853 TFH95375.1 942 excinuclease ABC subunit A SPNC01P000893 TFH95179.1 322 HaeIII family restriction endonuclease SPNC01P000984 TFH95074.1 739 ribonuclease R SPNC01P001024 TFH94687.1 339 restriction endonuclease SPNC01P001243 TFH94689.1 331 restriction endonuclease subunit M SPNC01P001245 TFH94669.1 131 ribonuclease P protein component SPNC01P001256 TFH94649.1 255 3'-5' exonuclease SPNC01P001274 TFH94493.1 32 restriction endonuclease SPNC01P001381 TFH94501.1 273 TatD family deoxyribonuclease SPNC01P001389 TFH94454.1 584 single-stranded-DNA-specific exonuclease RecJ SPNC01P001425 TFH94414.1 315 type I-B CRISPR-associated endonuclease Cas1 SPNC01P001461 TFH94415.1 209 CRISPR-associated endoribonuclease Cas6 SPNC01P001462 TFH94369.1 999 type I restriction endonuclease subunit R SPNC01P001494 TFH94370.1 365 anticodon nuclease SPNC01P001495

100

Table 4-2. Continued Keyword Gb accession Protein length Product name Accession no. nuclease TFH94357.1 612 DNA mismatch repair endonuclease MutL SPNC01P001504 TFH94226.1 711 excinuclease ABC subunit UvrB SPNC01P001613 TFH94209.1 321 ribonuclease Z SPNC01P001625 TFH94159.1 86 CRISPR-associated endonuclease Cas2 SPNC01P001665 TFH94078.1 133 restriction endonuclease subunit S SPNC01P001723 TFH94080.1 366 restriction endonuclease subunit S SPNC01P001725 TFH94045.1 553 Rne/Rng family ribonuclease SPNC01P001747 TFH93921.1 260 Eco47II family restriction endonuclease SPNC01P001836 TFH93916.1 92 restriction endonuclease subunit S SPNC01P001839 toxin TFH96125.1 263 nucleotidyl transferase AbiEii/AbiGii toxin family protein SPNC01P000571 TFH94756.1 370 type II toxin-antitoxin system HipA family toxin SPNC01P001190 TFH94607.1 152 nucleotidyl transferase AbiEii/AbiGii toxin family protein SPNC01P001296 TFH94282.1 255 toxin-antitoxin system YwqK family antitoxin SPNC01P001564 TFH94146.1 167 nucleotidyl transferase AbiEii/AbiGii toxin family protein SPNC01P001675 TFH94142.1 460 toxin-antitoxin system YwqK family antitoxin SPNC01P001679 TFH94095.1 347 toxin-antitoxin system YwqK family antitoxin SPNC01P001712 TFH94096.1 501 toxin-antitoxin system YwqK family antitoxin SPNC01P001713 TFH94057.1 145 toxin-antitoxin system YwqK family antitoxin SPNC01P001737 TFH93825.1 89 type II toxin-antitoxin system Phd/YefM family antitoxin SPNC01P001893 TFH93826.1 93 Txe/YoeB family addiction module toxin SPNC01P001894 TFH93803.1 92 type II toxin-antitoxin system YafQ family toxin SPNC01P001909 TFH93781.1 64 toxin-antitoxin system YwqK family antitoxin SPNC01P001920 lipopolysaccharide TFH94238.1 499 Lipopolysaccharide biosynthesis protein SPNC01P001601 dismutase TFH97050.1 194 superoxide dismutase SPNC01P000231 oxidase TFH97423.1 533 L-aspartate oxidase SPNC01P000049

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Table 4-2. Continued Keyword Gb accession Protein length Product name Accession no. oxidase TFH97029.1 216 pyridoxamine 5'-phosphate oxidase SPNC01P000210 TFH96255.1 386 cytochrome d ubiquinol oxidase subunit II SPNC01P000526 TFH96256.1 528 cytochrome ubiquinol oxidase subunit I SPNC01P000527 TFH95698.1 468 protoporphyrinogen oxidase SPNC01P000751 TFH95285.1 157 thioredoxin-dependent thiol peroxidase SPNC01P000939 TFH95243.1 454 oxygen-independent coproporphyrinogen III oxidase SPNC01P000960 TFH94699.1 166 thiol peroxidase SPNC01P001239 Pili TFH93805.1 108 pilin glycosylation protein SPNC01P001907 proteinase TFH96041.1 503 zinc-dependent metalloproteinase lipoprotein SPNC01P000602 virulence TFH96497.1 339 virulence protein E SPNC01P000446 TFH94289.1 478 YihY/virulence factor BrkB family protein SPNC01P001561

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Table 4-3. Virulence factors identified in the proteome of sequence 1 (seq 1) and sequence 2 (seq 2). Proteins acting as virulence factors were identified in previous research listed under "source" and searched for in the PGAP annotated proteomes of sequence 1 and sequence 2 Protein searched for Source Protein from PGAP search Present in Present in seq 1 seq 2 Protease (Holt et al., 2000; ATP-dependent Clp protease ATP-binding subunit ClpX Yes Yes Nagmoti, 2014) ATP-dependent Clp protease ATP-binding subunit Yes Yes BREX system Lon protease-like protein BrxL Yes Yes Aminopeptidase (Holt et al., 2000) Type 1 methionyl aminopeptidase Yes Yes

Aminopeptidase P family protein Yes Yes Aminopeptidase Yes Yes Phospholipase (Holt et al., 2000; Phospholipase Yes Yes Nagmoti, 2014) Toxin (Holt et al., 2000) Nucleotidyl transferase AbiEii/AbiGii toxin family protein Yes Yes Lipopolysaccharide (Holt et al., 2000; Lipopolysaccharide biosynthesis protein Yes Yes Nagmoti, 2014) Dismutase (Holt et al., 2000; Superoxide dismutase Yes Yes Nagmoti, 2014) Oxidase (Holt et al., 2000) Thioredoxin-dependent thiol peroxidase Yes Yes Thiol peroxidase Yes Yes Pili F. necrophorum study Pilin glycosylation protein No Yes above (CH 3) Virulence F. necrophorum study Virulence protein E Yes Yes above (CH 3) YihY/virulence factor BrkB family protein Yes Yes Proteinase (Holt et al., 2000; Zinc-dependent metalloproteinase lipoprotein Yes Yes Nagmoti, 2014)

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Table 4-4. Virulence factors in the proteome of sequence 1 and sequence 2. and the genes identified with those proteins. Protein Gene RIP metalloprotease RseP rseP ATP-dependent Clp protease ATP-binding clpX subunit ClpX BREX system Lon protease-like protein BrxL brxL Type 1 methionyl aminopeptidase map

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Table 4-5. A comparison of the virulence factors found in sequence 1 and other P. levii species through BLAST with their percent identities. Protein name (accession no.) Blast result protein name (accession no.) % identity Serine protease (TFH97615.1) Hypothetical protein (WP_018357413.1) 99.59% ATP-dependent Clp protease ATP-binding subunit ClpX None (TFH96064.1) ATP-dependent Clp protease ATP-binding subunit (TFH95335.1) ATP-dependent Clp protease ATP-binding subunit (WP_018357885.1) 99.58% BREX system Lon protease-like protein BrxL (TFH95379.1) None Type 1 methionyl aminopeptidase (TFH95625.1) Type 1 methionyl aminopeptidase (WP_018357766.1) 99.62% Aminopeptidase (TFH97482.1) Hypothetical protein (WP_018358358.1) 99.75% Aminopeptidase p family protein (TFH96031.1) Aminopeptidase p family protein (WP_018357644.1) 99.24% (TFH96549.1) Aminopeptidase p family protein (WP_134849393.1) Aminopeptidase 100% p family protein (WP_018358174.1) 99.83% Phospholipase (TFH96581.1) Hypothetical protein (WP_018357913.1) 99.62% Nucleotidyl transferase AbiEii/AbiGii toxin family protein Nucleotidyl transferase AbiEii/AbiGii toxin family protein 100% (TFH95397.1) (WP_018359201.1) (TFH94725.1) Nucleotidyl transferase AbiEii/AbiGii toxin family protein 100%; (WP_134850032.1); (WP_018358820.1) 96.20% Lipopolysaccharide biosynthesis protein (TFH96230.1) Hypothetical protein (WP_018358483.1) 99.20% Superoxide Dismutase (TFH96661.1) Same protein name (WP_026215535.1) 100.00% Thiol peroxidase (TFH97401.1) Same protein name (WP_018357979.1) 98.09% Thioredoxin-dependent thiol peroxidase (TFH96466.1) Same protein name (WP_018358205.1) 98.02% Virulence Protein E (TFH95766.1) Hypothetical protein (WP_018358423.1) 98.82% YihY/virulence factor BrkB family protein (TFH94766.1) YihY family inner membrane protein (WP_134850024.1); 100.00%; YihY/virulence factor BrkB family protein (WP_018359410.1) 99.58% Zinc-dependent metalloproteinase lipoprotein (TFH97284.1) Same protein name (WP_018358694.1) 99.80%

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Table 4-6. A comparison of the virulence factors found in sequence 2 and other P. levii species through BLAST with their percent identities. Protein name (accession no.) Blast result protein name (accession no.) % identity Serine protease (TFH96870.1) Hypothetical protein (WP_018357413.1) 99.59% ATP-dependent Clp protease ATP-binding subunit ClpX ATP-dependent Clp protease ATP-binding subunit ClpX 100.00% (TFH96436.1) (WP_018358808.1) ATP-dependent Clp protease ATP-binding subunit (TFH94418.1) ATP-dependent Clp protease ATP-binding subunit (WP_018357885.1) 99.58% BREX system Lon protease-like protein BrxL (TFH93982.1) None Type 1 methionyl aminopeptidase (TFH96786.1) Type 1 methionyl aminopeptidase (WP_018357766.1) 99.62% Aminopeptidase (TFH94532.1) Aminopeptidase (WP_134849013.1); 100.00%; Hypothetical protein (WP_018358358.1) 99.75% Aminopeptidase p family protein (TFH95637.1) Aminopeptidase p family protein (WP_018358174.1) 99.83% (TFH95222.1) Aminopeptidase p family protein (WP_018357644.1) 99.24% Phospholipase (TFH97427.1) Hypothetical protein (WP_018357913.1) 99.62% Nucleotidyl transferase AbiEii/AbiGii toxin family protein Nucleotidyl transferase AbiEii/AbiGii toxin family protein 96.20% (TFH96125.1) (WP_018358820.1) (TFH94607.1) Nucleotidyl transferase AbiEii/AbiGii toxin family protein 100.00% (WP_018359201.1) (TFH94146.1) Nucleotidyl transferase AbiEii/AbiGii toxin family protein 100.00% (WP_018359201.1) Lipopolysaccharide biosynthesis protein (TFH94238.1) Hypothetical protein (WP_018358483.1) 99.20% Superoxide Dismutase (TFH97050.1) Superoxide Dismutase (WP_026215535.1) 100.00% Thiol peroxidase (TFH94699.1) Thiol peroxidase (WP_018357979.1) 99.40% Thioredoxin-dependent thiol peroxidase (TFH95285.1) Thioredoxin-dependent thiol peroxidase (WP_018358205.1) 98.02% Pili (TFH93805.1) Hypothetical protein E4P48 02435 (TFH97124.1(KG45)) 100.00% Virulence Protein E (TFH96497.1) Hypothetical protein (WP_018358423.1) 98.82% YihY/virulence factor BrkB family protein (TFH94289.1) YihY family inner membrane protein (WP_134850024.1); YihY/virulence 100.00%; factor BrkB family protein (WP_018359410.1) 99.58% Zinc-dependent metalloproteinase lipoprotein (TFH96041.1) Zinc-dependent metalloproteinase lipoprotein (WP_018358694.1) 99.80%

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CHAPTER 5 CONCLUSION

Metritis is an important disease in the diary industry as it negatively impacts the health and production of dairy cows and affects anywhere from 6.5% to 47.3% of the population (Chapinal et al., 2011; Drillich et al., 2001; Hammon et al., 2006; Martinez et al., 2012). Metritis is known to decrease reproductive performance by increasing time to conception and ovulation and decreasing pregnancy rates (Giuliodori et al., 2013;

Goshen & Shpigel, 2006). Decreased DMI may also be observed which may lead to more negative impacts on health (Huzzey et al., 2007).

Several risk factors for metritis have been identified such as retained placenta

(perhaps one of the most significant factors), dystocia, immunosuppression, BCS and diet. Along with retained placenta, Dohmen et al. (2000) identified bacterial contamination and LPS accumulated in lochia as prominent risk factors.

Although there is evidence that bovine herpes virus may also infect the uterus in synergy with bacteria, bacterial infection is currently recognized as the main cause of metritis (Donofrio et al., 2008; Sheldon et al., 2009). Among some of the bacteria identified as important in the development of metritis are Fusobacterium necrophorum and Porphyromonas levii. (Cunha et al., 2018; Santos et al., 2011). These species of bacteria have been isolated and studied from several different sources, but no isolates of either of these species from the uterus of metritic dairy cows have yet been explored.

Virulence factors associated with these bacteria may play a critical role in their pathogenesis and thus studying them can provide some valuable information for treatment and prevention of the disease. This study was aimed at isolating

Fusobacterium necrophorum and Porphyromonas levii strains from the uteri of metritic

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dairy cows and sequencing their entire genome to gain a better understanding of characteristics that may make them virulent. Virulence factors in particular were explored and compared to those of other strains of the same species if data were available.

The results of this study show that these bacteria are pathogens with several mechanisms to both benefit themselves and cause harm to the host. With hemolysins, hemagglutinins, and heme receptors, bacteria are able to aggregate host blood cells, attach to and lyse them to aquire iron they need for their cellular functions. Adhesins and pili are crucial VF in that they allow the bacteria to attach to host cells which is important for colonization in an environment designed to flush them out (the uterus).

Many of the VF found allow the bacteria to evade the host immune system through various methods. The aminopeptidases, SOD, and peroxidases found in P. levii allow the bacterium to survive in the presence of ROS. Phagocytosing bacteria and exposing them to ROS is the mechanism by which macrophages in the host immune system destroy bacteria. As P. levii is able to survive exposure to ROS, they are able to survive inside macrophages, leaving this component of immunity essentialy useless against the bacterium. Another way P. levii can defend itself from host immunity is through the secretion of omptin proteases which cleave cathlecidins, antimocribials secreted by immune cells as discussed in Chapter 4. Leukotoxins found in F. necrophorum fight against host leukocytes and not only destroy them, but as a result, protect the bacteria from phagocytosis by leukocytes. Finally, both P. levii and F. necrophorum can protect themselves against other elements of the immune system found in host serum through the expression of BrkA and BrkB proteins which have been shown to allow bacteria to

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survive longer and grow in higher concentrations in host serum. These bacteria also have other mechanisms of survival in the host through the regulation of genes by clp proteases. Clp proteases may allow the bacteria to adjust to their enviroment by inducing or repressing other genes as well as VF. AbiEii/AbiGii toxin systems and BREX systems cause the suicide of bacterial cells infected with bacteriophages before the phage DNA can be replicated. These systems thus further aid F. necrophorum and P. levii in keeping the population of harmful bacteriophages to a minimum. Additionally, the bacteria are able to cause damage to the host tissue which may benefit them and other organisms that might grow better in an environment rich in blood and necrotized tissue.

Phospholipases cleave host cell membranes, and hemolysins cleave host blood cells.

Ecotin can then slow healing of the host tissue by increasing blood clotting time. These results clearly indicate that F. necrophorum and P. levii have been selected for survival and proliferation inside the host while having negative effects on the host. This demonstrates that these bacteria are pathogens.

Although each of these VF and bacteria on their own may not be able to cause a lot of harm, let alone kill the host, it is the interaction of all the many VF and bacterial species present that can have an effect great enough to cause disease and even death.

Though it is also possible that bacterial species found in low abundance may be responsible for much of the disease itself, knowing the contributions of the most abundent bacteria isolated during the disease may still play an important role in some way as indicated by their rise in numbers during the development of disease. If metritis is in fact caused primarily by bacteria found in a low abundance, those found in greater abundance may still aid in providing the right growth conditions for those pathogens of

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lower importance. Because bacterial communities are so dynamic however, it is unlikely that one or two species on their own can cause disease but rather, it is the make-up of the entire community interacting within itself that leads to the development of this disease. For this reason, it will be important to study other species of bacteria present in cows with metritis, including those found in both low and high abundance.

This study does have limitations in the search and sequencing methods used.

Although WGS can provide a good picture of the whole genome, because it works by sequencing fragments, it is possible for important genes to be left out of the sequence and thus the draft genome created from the final products. The searchmethod is also limited in that no comparisons were made with other genomes. This was because

PGAP annotations were not published for other genomes of P. levii and F. necrophorum. Furthermore, the search was only based on previously identified VF in F. necrophorum species and Porphyromonas species meaning that some novel VF may not have been detected if they were present. Because non-specific keywords were used, many non-virulent proteins were also detected and had to be sifted out through time-consuming literature searches. In the future, use of a better database and a list of all VF known to exist in pathogening bacteria should be considered.

Steps to take in the future with these findings should include testing F. necrophorum and P. levii for genetic and phenotypic expression of these VF identified and isolating these proteins through methods such as Western Blotting or PAGE. With this knowledge, we could attempt to develop vaccines against these bacteria in a similar manner to those developed by Machado et al. (2014). Developing antibiotics against these bacteria is another path that could be considered however, with rising concerns

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for antibiotic resistance, this may not be the ideal route. Phage treatment could be considered an alternative, assuming there are bacteriophages these F. necrophorum and P. levii strains have not developed resistance to. It is our hope that the findings in this study can be used to further research in better understanding these pathogenic bacteria, ultimately leading to better health of dairy cow herds.

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BIOGRAPHICAL SKETCH

Amye Francis ws born Gainesville, Florida and lived most of her life afterwards in

Trenton, Florida. During much of middle school and high school, Amye was very involved in 4-H where her interest and passion for agriculture and animal science developed. Amye was healvily involved in the equine and poultry projects but also actively took part in the rabbit, sewing, horticulture, and archery projects.

In 2016 she received her bachelor's in animal science from the University of

Florida with hopes of attending veterinary school. After taking a year off to work in a veterinary clinic, Amye decided that though she enjoyed much of her work, veterinary medicine wasn't the career for her. After one year, Amye returned to the University of

Florida to pursue a master's degree in animal sciences.

Some of Amye's interests and hobbies during her time as an undergraduate and graduate student at the university included designing dresses and sewing, swing dancing, and competing in horse judging and archery. Now, Amye's new goal is to find her place in the world while building a career in the scientific field and working on creative projects on the side.

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