The Ability of Oat Beta-Glucan and Stress to Modulate Equine Immune Function

THE ABILITY OF OAT BETA-GLUCAN AND STRESS TO MODULATE EQUINE IMMUNE FUNCTION

JILL BOBEL

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

To my parents- it’s plain and simple; this was not possible without you

ACKNOWLEDGMENTS

I would like to once again thank my amazing advisor, Dr. Lori Warren, for inspiring and believing in me and hiring me with no prior lab experience. Her mentorship over the last nine years has been priceless and I continue to learn from her on a daily basis. It makes me wish this saga of my life was not coming to an end. Her dedication to her students and her research has always amazed me. I know she hopes to graduate students that will advance our field of study, and I hope I do not disappoint.

Thank you to all my committee members, who have individually added vital components to my PhD program. Dr. Jeffrey Abbott hired me as his biological scientist in 2012 and the position has far exceeded my expectations. The knowledge and skills I have gained under his guidance are priceless, and will no doubt play a crucial role in my future career. Over the years, the work load in his lab has waned but I’m forever grateful that he continued my employment and allowed me to use his lab as my own. Similar to my master’s program, there were several late nights and long discussions and I am grateful for all his advice and support. Unfortunately, he has set the bar extremely high for my next employer.

Dr. Maureen Long was a late but necessary addition to my committee. She taught me the nasopharyngeal flush procedure; however, her assistance did not stop there.

She became a vital member of our team during the POGA oat study and was frequently in the field with us collecting samples. Hopefully when she reminisces on that experience, she’ll remember the weight she lost and not all the snot, blood, feces and sweat in the 90°F heat with 100% humidity! Upon completion of that study, it was clear to me that Dr. Long needed to be a permanent part of my PhD program. Her knowledge continues to astound me, and I’m grateful for everything she has taught me. Apart from

research, she was also kind enough to loan me a round pen, and therefore played an important part in my horse’s recovery. I will be forever thankful for her support during that emotional time of my life.

Dr. Cynda Crawford provided important counsel and guidance during the planning of the POGA oat study. Her knowledge and assistance with the neutrophil function assay, and analysis of IgA samples were greatly beneficial. Additionally, her qualifying exam encouraged me to look at my data in a different light and I have since applied that new thinking to all of my data. In part, the length of my dissertation reflects my new way of thinking about and analyzing data.

When grant approval came through for the POGA oat study, I knew exactly which professor could add the missing functional fiber component to my committee. Dr.

Wendy Dahl has a unique passion for fiber and her enthusiastic teaching style made an impression on me as a master’s student. Her classes are among my favorites and I wish she taught more! Her research in humans is impressive and I continually reference her papers.

It took many years of graduate school, but I was finally able to fit Dr. Joel

Brendemuhl’s nutrition class into my schedule and now I realized what all the hype was about! He is an excellent instructor and it was my favorite general nutrition class. Never mind that he is a recommended part of all committees within animal sciences, his attendance on mine has vastly enhanced my knowledge. He provided tough yet applicable questions during my qualifying exam, much of which I used in this dissertation. This year in particular, Dr. B has been instrumental in finding hidden rules within the graduate catalog that ultimately led to my graduation. Thank you.

Many thanks to the numerous individuals who made these studies and subsequent laboratory work possible; Jessie Weir, Leigh Ann Skurupey, Victoria Robbins, Justin

Callaham, Clare Page, crews of the Equine Science Center and the Horse Teaching

Unit, all the undergraduate volunteers and of course, all of the research horses.

Special thanks to Megan Di-Lernia. I was lucky to find this “diamond in the rough” of undergraduates that usually only volunteer to build their resumes. Both of my research studies would not have been possible without her help. I’m forever grateful and have made a friend for life.

Extra thanks to Tayler Hansen for agreeing to drive twenty-four consecutive hours and sample horses while they remained on the trailer. It was a little crazy but I couldn’t have asked for a better partner.

It is with a heavy heart that I acknowledge the passing of Jan Kivipelto earlier this year. She was an instrumental part of my acceptance into a master’s program, and graduate school has since defined my future. When I look back on my life as a student, I think of her and realize I would not be here if not for her. You will be missed.

And last but certainly not least, I would like to thank my parents for their unyielding monetary and emotional support of my ambitious goals in life. As an undergraduate student, my parents told me they would happily pay for my riding hobby as long as I was pursuing my education. They certainly did not expect I would be in school this long!

They have been my number one support system during this long and bumpy road. In many ways, my parents have also earned a graduate degree. There really are no words to express my gratitude and I hope they realize my directions in life will always be influenced by the foundation they provided me. I hope to always make them proud.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 11

LIST OF FIGURES ...... 13

LIST OF ABBREVIATIONS ...... 18

ABSTRACT ...... 21

CHAPTER

1 LITERATURE REVIEW ...... 23

Overview of Stress on Equine Population and Economic Impact ...... 23 Mucosal Immunology ...... 25 Mucosal Immune Responses ...... 25 Equine Respiratory Tract Anatomy ...... 27 Respiratory Lymphoid Tissues ...... 29 Immunoglobulin A ...... 29 Equine IgA ...... 31 Immunoglobulin G ...... 33 Equine IgG ...... 34 Cellular Respiratory Defenses ...... 35 Stress Physiology ...... 36 Stress Hormones and Cortisol ...... 37 Enhancement of Immunity After Stress ...... 43 Enhancement of Immunity After Exercise...... 44 Stress-Induced Immunosuppression ...... 45 Exercise-Induced Immunosuppression ...... 46 Other Equine Stressors ...... 49 Methods to Reduce Stress-Induced Immunosuppression...... 54 Dietary Interventions ...... 54 Beta-Glucan Introduction ...... 57 Beta-Glucan Structure ...... 58 Beta-Glucan Receptors ...... 59 Immune Effects of Cereal Beta-Glucan ...... 61 Beta-Glucan Immune Affects Following a Challenge ...... 62 Effects of Beta-Glucan on Immune Function in Horses ...... 69 Beta-Glucan Fermentation ...... 71 Beta-Glucan Fermentation in Equine Foregut ...... 73 Beta-Glucan Fermentation in Equine Hindgut ...... 75 Beta-Glucan as a Prebiotic ...... 76 Beta-Glucan Uptake in GIT ...... 79

Form of Dietary Beta-Glucan ...... 82 Specific Aims and Hypotheses ...... 83

2 CAN OAT BETA-GLUCAN IMPROVE IMMUNE RESPONSES IN HORSES FOLLOWING STRESS-INDUCED IMMUNOSUPPRESSION? ...... 87

Introduction ...... 87 Materials and Methods...... 89 Horses ...... 89 Dietary Treatments ...... 90 Feed Sample Collection and Analysis ...... 91 Experimental Design ...... 91 Stress Induction ...... 92 Sample Collection ...... 93 Nasopharyngeal Flush ...... 93 Saliva Collection ...... 95 Fecal Collection ...... 95 Blood Collection ...... 96 PBMC Isolation ...... 96 Lymphocyte Proliferation ...... 98 Lymphocyte Subsets ...... 100 Neutrophil Function ...... 101 IgA ELISA ...... 103 Total Protein ...... 104 Cortisol EIA ...... 104 Fecal Dry Matter Analysis ...... 105 Statistical Analysis ...... 105 Results ...... 111 Dietary Consumption ...... 111 Stress Induction ...... 111 Cortisol ...... 111 Nasopharyngeal Mucus ...... 111 Leukocyte Populations ...... 112 Nasopharyngeal leukocytes- phase 1 ...... 112 Nasopharyngeal leukocytes- phase 2 ...... 113 Whole blood leukocytes- phase 1 ...... 114 Whole blood leukocytes- phase 2 ...... 116 Lymphocyte Subsets ...... 117 Nasopharyngeal lymphocytes- phase 1 ...... 117 Nasopharyngeal lymphocytes- phase 2 ...... 117 Whole blood lymphocytes- phase 1 ...... 119 Whole blood lymphocytes- phase 2 ...... 119 Lymphocyte Proliferation- Phase 1 ...... 120 Lymphocyte Proliferation- Phase 2 ...... 120 Lymphocyte Proliferation- Period differences ...... 122 Nasopharyngeal Neutrophil Function ...... 122 Whole Blood Neutrophil Function- Phase 1 ...... 123

Whole Blood Neutrophil Function- Phase 2 ...... 124 Immunoglobulin A Concentration ...... 127 Nasopharyngeal IgA- phase 1 ...... 127 Nasopharyngeal IgA- phase 2 ...... 127 Salivary IgA- phase 1 ...... 128 Salivary IgA- phase 2 ...... 128 Fecal liquid IgA- phase 1...... 129 Fecal liquid IgA- phase 2...... 129 Serum IgA ...... 130 IgA and total protein correlations ...... 130 Fecal Measurements ...... 131 Discussion ...... 132

3 IMMUNOLOGICAL CHANGES OCCUR EARLY DURING ROAD TRANSPORTATION ...... 224

Introduction ...... 224 Methods and Materials...... 225 Horses ...... 225 Experimental Design ...... 226 Transportation ...... 226 Sample Collection ...... 227 Nasopharyngeal Flush ...... 227 Nasal Swabs ...... 229 Saliva Collection ...... 229 Cecal Collection ...... 230 Fecal Collection ...... 231 Blood Collection ...... 231 PBMC Isolation ...... 232 Lymphocyte Proliferation ...... 233 IgA ELISA ...... 235 Total Protein ...... 236 Cortisol EIA ...... 237 Dry Matter Analysis ...... 237 Statistical Analysis ...... 238 Results ...... 238 Transportation ...... 238 Body Weight ...... 240 Cortisol ...... 240 Nasopharyngeal Flush Mucus Scores ...... 240 Nasopharyngeal Leukocyte Populations ...... 240 Systemic Leukocyte Populations ...... 241 Lymphocyte Proliferation ...... 242 Immunoglobulin A ...... 243 Salivary ...... 243 Nasopharyngeal IgA ...... 244 Nasal swab IgA ...... 244

Serum ...... 245 Cecal ...... 245 Fecal liquid ...... 245 Dry Matter and pH ...... 246 Discussion ...... 247

4 CONCLUDING REMARKS AND FUTURE PERSPECTIVES ...... 290

Overall Conclusions ...... 290 Future Perspectives ...... 293

APPENDIX

A OVERNIGHT SHIFT PROTOCOL FOR HEAD ELEVATION STRESS INDUCTION ...... 296

B FIELD COLLECTION AND LAB PROTOCOL FOR NASOPHARYNGEAL FLUSH ...... 298

C FIELD COLLECTION AND LAB PROTOCOL FOR SALIVA ...... 301

D FIELD AND LAB PROCOTOL FOR FECAL LIQUID ...... 303

E PERIPHERAL BLOOD MONONUCLEAR CELLS ISOLATION ...... 305

F LYMPHOCYTE PROLIFERATION ASSAY ...... 311

G PROTOCOL FOR ANTIBODY LYMPHOCYTE SUBSET DETERMINATION ...... 321

H EQUINE NEUTROPHIL FUNCTION ASSAY ...... 326

I SAMPLE PREPARATION FOR HORSE IgA ELISA ...... 338

J PASTURE SAMPLE COLLECTION AND DRY MATTER PROTOCOLS ...... 339

K FIELD COLLECTION PROTOCOL FOR NASAL SWABS ...... 342

L FIELD COLLECTION AND LAB PROTOCOL FOR CECAL CONTENTS ...... 343

LIST OF REFERENCES ...... 345

BIOGRAPHICAL SKETCH ...... 375

LIST OF TABLES

Table page

2-1 Nutrient composition and beta-glucan content provided by each feed ingredient ...... 108

2-2 Beta-glucan quantity provided by each diet and average quantity of concentrate fed per day ...... 109

2-3 Serum and saliva cortisol concentrations before and after head elevation ...... 163

2-4 Nasopharyngeal flush mucus scores before and after head elevation ...... 163

2-5 Differences in leukocyte population of nasopharyngeal flush and whole blood by study period ...... 170

2-6 Leukocyte populations in nasopharyngeal flush before and after head elevation ...... 171

2-7 Percentage of leukocyte populations in nasopharyngeal flush before and after head elevation ...... 172

2-8 Leukocyte populations in whole blood before and after head elevation...... 177

2-9 Percentage of leukocyte populations in whole blood before and after head elevation ...... 178

2-10 Correlation between white blood cell populations and serum cortisol at 0 h Post-stress...... 179

2-11 PBMC proliferation and stimulation index in response to Con A before and after head elevation ...... 191

2-12 Differences in PBMC proliferation by study period...... 192

2-13 Differences in whole blood neutrophil function by study period ...... 197

2-14 Correlation between phagocytosis index or phagocytosis-induced oxidative burst index and serum cortisol at 0 h Post-stress ...... 206

2-15 Differences in IgA concentrations by study period ...... 212

2-16 Correlation between IgA concentrations measured in biological samples during the study ...... 219

2-17 Differences in fecal variables by study period...... 223

3-1 Samples collected and measurements performed during the study...... 265

3-2 Hay and water intake and fecal excretion during 24 h transportation...... 266

3-3 Body weight before, during and after transit ...... 268

3-4 Body temperature before, during and after transit ...... 268

3-5 Serum and saliva cortisol concentrations before, during and after transit ...... 269

3-6 Nasopharyngeal flush mucus scores before, during and after transit ...... 271

3-7 Leukocyte populations in nasopharyngeal flush before, during and after transit ...... 272

3-8 Leukocyte populations in whole blood before, during and after transit ...... 273

3-9 Correlations between IgA in biological samples during the entire study ...... 288

3-10 Dry matter and pH of cecal contents and fecal liquid before, during and after transit ...... 289

LIST OF FIGURES

Figure page

1-1 Example of structural differences between cereal and yeast/fungi beta-glucan . 86

2-1 Example of treatment period, sample collection and wash-out timeline...... 110

2-2 Example of head elevation and neck angle during 12 h stress induction...... 162

2-3 Total leukocytes in nasopharyngeal flush before and after dietary treatment ... 164

2-4 Number of neutrophils in nasopharyngeal flush before and after dietary treatment ...... 165

2-5 Percentage of neutrophils in nasopharyngeal flush before and after dietary treatment ...... 166

2-6 Number of lymphocytes in nasopharyngeal flush before and after dietary treatment ...... 167

2-7 Number of monocytes in nasopharyngeal flush before and after dietary treatment ...... 168

2-8 Percentage of eosinophils in nasopharyngeal flush before and after dietary treatment ...... 169

2-9 Total leukocytes in whole blood before and after dietary treatment ...... 173

2-10 Total lymphocytes in whole blood before and after dietary treatment ...... 174

2-11 Percentage of monocytes in whole blood before and after dietary treatment ... 175

2-12 Percentage of eosinophils in whole blood before and after dietary treatment .. 176

2-13 Number of CD4+ lymphocytes in nasopharyngeal flush before and after dietary treatment ...... 180

2-14 Percentage of CD4+ lymphocytes in nasopharyngeal flush before and after dietary treatment ...... 181

2-15 Ratio of CD4+ to CD8+ lymphocytes in nasopharyngeal flush before and after dietary treatment ...... 182

2-16 Number of CD4+ lymphocytes in whole blood before and after dietary treatment ...... 183

2-17 Whole blood CD4+ lymphocytes as a percentage of total lymphocytes before and after dietary treatment during phase 2 ...... 184

2-18 Background PBMC proliferation without mitogen stimulation before and after dietary treatment ...... 185

2-19 Background PBMC proliferation without mitogen stimulation before and after head elevation...... 186

2-20 PBMC proliferation in response to LPS before and after head elevation ...... 187

2-21 Stimulation index of PBMC in response to LPS before and after head elevation ...... 188

2-22 PBMC proliferation in response to PWM before and after head elevation ...... 189

2-23 Stimulation index of PBMC in response to PWM before and after head elevation ...... 190

2-24 Percentage of phagocytosis by whole blood neutrophils before and after dietary treatment ...... 193

2-25 Mean fluorescence intensity of propidium iodide of whole blood neutrophils before and after dietary treatment ...... 194

2-26 Differences in mean fluorescence intensity of propidium iodide and phagocytosis index by whole blood neutrophils by gender ...... 195

2-27 Phagocytosis index of whole blood neutrophils before and after dietary treatment ...... 196

2-28 Differences in phagocytosis index of whole blood neutrophils by age ...... 198

2-29 Mean fluorescence intensity of dihydrorhodamine of whole blood neutrophils before and after dietary treatment ...... 199

2-30 Phagocytosis-induced oxidative burst index of whole blood neutrophils before and after dietary treatment ...... 200

2-31 Percentage of whole blood neutrophils that did not react to Streptococcus equi before and after dietary treatment ...... 201

2-32 Percentage of phagocytosis and phagocytosis-induced oxidative burst by whole blood neutrophils before and after head elevation ...... 202

2-33 Mean fluorescence intensity of propidium iodide and phagocytosis index of whole blood neutrophils before and after head elevation ...... 203

2-34 Mean fluorescence intensity of propidium iodide and phagocytosis index of whole blood neutrophils by diet during phase 2 ...... 204

2-35 Phagocytosis index of whole blood neutrophils by diet and time during phase 2 ...... 205

2-36 Percentage of phagocytosis and phagocytosis-induced oxidative burst of whole blood neutrophils by diet during phase 2 ...... 207

2-37 Mean fluorescence intensity of dihydrorhodamine and phagocytosis-induced oxidative burst index of whole blood neutrophils before and after head elevation ...... 208

2-38 Phagocytosis-induced oxidative burst index of whole blood neutrophils by diet during phase 2 ...... 209

2-39 Percentage of whole blood neutrophils that did not respond to Streptococcus equi before and after head elevation ...... 210

2-40 Whole blood neutrophils that did not respond to Streptococcus equi by diet during phase 2 ...... 211

2-41 Total IgA and secretory index in nasopharyngeal flush before and after head elevation ...... 213

2-42 Total IgA in saliva before and after dietary treatment ...... 214

2-43 Secretory index of IgA in saliva before and after dietary treatment ...... 215

2-44 Total IgA and IgA secretory index in saliva before and after head elevation .... 216

2-45 Total IgA and IgA secretory index in fecal liquid before and after head elevation ...... 217

2-46 Total IgA in serum before head and after elevation ...... 218

2-47 Percentage of fecal dry matter before and after dietary treatment ...... 220

2-48 Percentage of fecal dry matter before and after head elevation ...... 221

2-49 Percentage of fecal dry matter by diet during phase 2 ...... 222

3-1 Diagram of sampling timeline ...... 263

3-2 Diagram of how horses were oriented in the 6-horse stock trailer during the 24 h trip...... 264

3-3 Salivary flow rate before, during, and after transit...... 267

3-4 Morning and evening serum and salivary cortisol levels before, during and after transit...... 270

3-5 Background PBMC proliferation without mitogen stimulation before, during and after transit ...... 274

3-6 PBMC proliferation in response to Con A mitogen before, during and after transit ...... 275

3-7 Stimulation index of PBMC in response to Con A mitogen before, during and after transit...... 276

3-8 PBMC proliferation in response to PWM before, during and after transit ...... 277

3-9 Stimulation index of PBMC in response to PWM before, during and after transit ...... 278

3-10 PBMC proliferation in response to LPS before, during and after transit ...... 279

3-11 Stimulation index of PBMC in response to LPS before, during and after transit ...... 280

3-12 Total IgA and IgA secretory index in saliva before, during and after transit ...... 281

3-13 Total IgA, IgA secretory index and IgA secretion rate in saliva before, during and after transit ...... 282

3-14 Total IgA and IgA secretory index in nasopharyngeal flush before, during and after transit...... 283

3-15 Total IgA and IgA secretory index of nasal swabs before, during and after transit ...... 284

3-16 Total IgA in serum before, during and after transit ...... 285

3-17 Total IgA and IgA secretory index in cecal fluid before, during and after transit ...... 286

3-18 Total IgA and IgA secretory index in fecal liquid before, during and after transit ...... 287

F-1 PBMC stimulation with varying concentrations of PWM...... 319

F-2 PBMC stimulation with varying concentrations of LPS ...... 319

F-3 PBMC stimulation with varying concentrations of Con A ...... 320

G-1 Example of CD4 and CD8 lymphocytes gated by quadrants ...... 324

G-2 Example of B lymphocytes gated by forward scatter and B antibody marker. .. 325

H-1 Bacteria to neutrophil ratio optimization using Streptococcus equi equi...... 332

H-2 Incubation time optimization using bacteria to neutrophil ratio of 40:1...... 332

H-3 FACS plot of whole blood sample gated by leukocyte populations with forward scatter on the x-axis and side scatter on the y-axis...... 333

H-4 Example of negative sample from neutrophil function assay ...... 334

H-5 Example of positive sample from neutrophil function assay ...... 335

H-6 FACS plot of granulocyte population first loaded with non-fluorescent DHR and then stimulation with PI-labeled Streptococcus equi...... 336

LIST OF ABBREVIATIONS

ACTH Adrenocorticotropic hormone

BAL Bronchoalveolar lavages

BALT Bronchial-associated lymphoid tissues

BG Beta-glucan

BW Body weight

CBC Complete blood count

CD Cluster of differentiation

CFU Colony forming units

Con A Concanavalin A

CPM Counts per minute

CV Coefficient of variation

DE Digestible energy

DHR Dihydrorhodamine

DM Dry matter

DMSO Dimethyl sulfoxide

EDTA Ethylenediaminetetraacetic acid

EHV Equine herpes virus

EIA Enzyme immunoassay

EIV Equine influenza virus

ELISA Enzyme-linked immunosorbent assay

Fab Fragment antigen binding

FACS Fluorescence-activated cell sorting

FBS Fetal bovine serum

FcR Fragment crystallizable region

FcRn Fragment crystallizable region neonatal

FOS Fructo-oligiosaccharides

GALT Gut-associated lymphoid tissues

GIT Gastrointestinal tract

GR Glucocorticoid receptors

GRE Glucocorticoid response elements

HPA Hypothalamic pituitary adrenal

Ig Immunoglobulin

IgA Immunoglobulin A

IL Interleukin

INF Interferon

IURD Infectious upper respiratory disease

KHL Keyhole limpet hemocyanin

LPS Lipopolysaccharide

LSM Lymphocyte separation medium

M cells Microfold cells

MALT Mucosal-associated lymphoid tissues

MBL Mannose-binding lectin

MFI Mean fluorescence intensity

NALT Nasal-associated lymphoid tissues

NF-κB Nuclear factor-kappa B

NPF Nasopharyngeal flush

OD Optical density

PAMP Pathogen-associated molecular patterns

PBMC Peripheral blood mononuclear cells

PBS Phosphate buffered saline

PHA Phytohemmaglutinin

PI Propidium iodide

PIM Pulmonary intravascular macrophages

PRR Pattern recognition receptors

PWM Pokeweed mitogen

RPMI Roswell Park Memorial Institute-1640 medium

SAM Sympathetic adrenal medullary

SI Stimulation index sIgA Secretory immunoglobulin A

TGF Transforming growth factor

Th T helper

VFA Volatile fatty acid

WBC White blood cells

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE ABILITY OF OAT BETA-GLUCAN AND STRESS TO MODULATE EQUINE IMMUNE FUNCTION

Jill Bobel

May 2018

Chair: Lori Warren Major: Animal Sciences

Stress is a known immunosuppressant and equine athletes experience many types of unavoidable stress. Rigorous training schedules and traveling to competitions are just a few stressors that collectively contribute to stress-induced immunosuppression and increased risk of respiratory disease. Previous research has illuminated some aspects of equine immune function that are affected by these stressors, but a further understanding of the dysfunction is required. Vaccination is not completely effective and the equine industry would benefit from complementary methods of protection which may ameliorate the side effects of stress.

The aims of this dissertation were to characterize changes to immunity following a stressor, investigate inclusion of dietary oat beta-glucan (BG) to reduce stress- induced immunosuppression, and explore the use of IgA as an indicator of mucosal immune status.

In the first study, horses were fed diets containing varying levels of oat BG for 18 days and then tethered with their heads elevated for 12 hours, preventing natural drainage of the upper respiratory tract. The head elevation model was used to mimic transportation stress, which was confirmed by increased cortisol levels, altered systemic

and nasopharyngeal leukocyte populations, and decreased in vitro leukocyte function and mucosal immunoglobulin A (IgA) concentrations for up to 72 hours after head elevation. Although oat BG did alter some immune variables prior to simulated transportation, it did not alleviate stress-induced immune dysfunction that followed.

In the second study, horses were tethered with their heads elevated and transported by trailer for 24 consecutive hours. Systemic changes to leukocyte populations occurred within 6 hours of transit and in vitro lymphocyte function was decreased for 24 hours after transit. Mucosal IgA concentrations were elevated during transit and took at least 24 hours to normalize.

Mucosal and innate immunity were the most affected by head elevation and both studies observed decreased leukocyte function following stress. Although dietary oat

BG did not improve immune function, other dietary interventions remain viable options considering 70% of immune tissue resides within the gastrointestinal tract. This research highlighted how quickly the immune system reacted to a common stressor, such as transportation, which can help improve management practices.

CHAPTER 1 LITERATURE REVIEW

Overview of Stress on Equine Population and Economic Impact

Horses experience many types of unavoidable stressors throughout their lives.

The majority of the 9.2 million horses in the U.S. are used for recreation or competition

(Kilby, 2007). Stress is a known immunosuppressant in many species including horses.

Research has indicated physical exertion of competition and crowded stabling areas combined with transportation and training leading up to competitions can cause an altered state of physiological well-being and immunosuppression (Nieman, 1997; Allen et al., 2008). The exact mechanisms remain unknown and probably vary based on the type and duration of stress; however, the end result is the same: increased risk of disease, more specifically infectious upper respiratory disease (IURD). The most effective treatment for common respiratory viruses is merely supportive, including rest in a low-stress environment with good ventilation. Veterinarians advise against forced exercise such as training or competing until the horse has fully recovered, which could take several weeks (Myers and Wilson, 2006). Missing weeks of training or showing due to an IURD can cause a large financial deficit for owners and riders from non-refundable competition fees and absent earnings. Some vaccines exist for common respiratory viruses, but results from epidemiological and clinical studies suggest that vaccination does not provide adequate protection and alternative methods of protection should be investigated (Myers and Wilson, 2006).

IURD is a common occurrence in race and sport horses and can be caused by several viruses and bacteria, such as Equine Influenza Virus (EIV), Equine Herpes

Virus (EHV), and Streptococcus equi (Pusterla et al., 2011). These viruses are endemic

and poor vaccination methods have led them to be clinically and economically the most relevant equine pathogens in the performance horse industry, costing millions of dollars to eradicate one outbreak (Garner et al., 2011). Early studies on the prevalence of EHV-

1 and -4 in Australia’s Thoroughbred population, showed that up to 30% of horses tested positive for type 1 specific antibodies and 100% tested positive for type 4 (Crabb et al., 1995). Similar results have been seen in the Netherlands and the U.S. There is a well-established connection between EHV and respiratory disease, which is the second most common cause of lost training days in racing stables (Burrell et al., 1996). In 2011, an EHV outbreak in Utah resulted in 90 confirmed cases reported in 10 states and was responsible for the cancellation of several major horseshows (USDA, 2011). Over the past 40-50 years, very few countries with significant horse populations have avoided an

EIV outbreak. In many cases, the international shipment of breeding or showing horses has inadvertently introduced EIV into countries previously free of the virus. Equine influenza is caused by two virus types, A-equi-1 and A-equi-2, the latter causing more severe disease and pneumonia (Timoney, 1996). The disease is most prevalent in young horses in training; however, horses of all ages are susceptible if not previously exposed to or vaccinated against the virus. Like EHV, horses are susceptible to influenza all year round but it most commonly occurs during cooler months when stressful events, such as weaning and initiation of training take place. Although extensive use of vaccines has reduced the morbidity rate, major influenza and herpes outbreaks still occur. The 2007 outbreak of EIV in Australia, a country previously free of the disease, took 4 months to eradicate and estimated to cost the industry more than $1 billion (Garner et al., 2011). Mucosal responses to these viruses and bacteria are key

contributors to developing infection or immunity. Prevention of future outbreaks will require a better understanding of causes, mucosal responses and risk factors within both equine physiology and management.

Mucosal Immunology

Mucosal immunity is a unique system that evolved to protect epithelial surfaces from the harsh outside environment to which they are exposed. Mucosal surfaces are composed of mucus, antimicrobial peptides and a natural host microbiota, with additional protection provided by both innate and adaptive immunity. Microbiota composition varies depending on the mucosal tissue and the host where they reside.

Microbes living in the gastrointestinal tract (GIT) fulfill a special niche and, therefore are different than the microbes living in the respiratory tract. Typically, microbial invaders are recognized by the host immune system and destroyed. However, in the case of the

GIT microbiota, the host develops natural immunity and tolerance towards the microbes, allowing a symbiotic relationship to occur. Many mammals, including horses, rely on microbial fermentation to enzymatically breakdown indigestible components of their diets to produce energy and other useful byproducts, which can be used by the host.

This symbiotic relationship allows microbes to live within an environment that would innately eliminate them, while the host benefits from dietary energy that was otherwise inaccessible.

Mucosal Immune Responses

Mucosal immunity is accomplished by the cooperation of inductive and effector sites, and innate immunity. Immune cells migrate from inductive sites to effector tissues via the lymphatic system. Inductive sites are comprised of specialized mucosal- associated lymphoid tissues (MALT) including the gut-associated lymphoid tissues

(GALT), nasopharyngeal-associated lymphoid tissues (NALT) and bronchial-associated lymphoid tissue (BALT). Peyer’s patches, mesenteric lymph nodes and isolated lymphoid follicles make up the GALT inductive sites, whereas tonsils/adenoids, inducible bronchus-associated lymphoid tissues and other lymph nodes make up the

NALT (McGhee and Fujihashi, 2012). Specialized microfold cells (M cells) line the

MALT, phagocytose luminal or nasal antigens and transport them to underlying dendritic cells to initiate an immune response (McGhee and Fujihashi, 2012). The antigen is carried to inductive sites of the Peyer’s patches or mesenteric lymph nodes via the lymphatic system for initiation of mucosal T and B cell responses. Interaction with dendritic cells at inductive sites enhances homing receptors on activated T and B cells for migration through the lymphatics and bloodstream to the MALT effector sites to launch an immune response (McGhee and Fujihashi, 2012).

The effector sites of mucosal immunity include the lamina propria within the GIT, upper respiratory tract and female reproductive tract, as well as secretory glandular tissues. These effector sites contain antigen-specific mucosal effector cells such as memory T and B cells and immunoglobulin (Ig) A-producing plasma cells. Adaptive mucosal immune responses require help from cluster of differentiation (CD) 4+ T cells to develop IgA-producing plasma cells of the secretory tissues. Mucosal epithelial cells play a vital role in mucosal immunity by providing a physical barrier, producing mucus and antimicrobial peptides, and participating in peristalsis and immune responses.

Mucosal epithelial cells also produce polymeric Ig receptors which bind polymeric IgA and IgM for secretion into the mucosal space. Secretory IgA (sIgA) antibodies are produced against antigens that were encountered in inductive sites. Unlike traditional

immune responses, IgA B cells within the MALT can arise independent of T cell help by interaction with dendritic cells in the mesenteric lymph nodes. Mucosal immunity is largely dependent on a continuous supply of antibodies, and therefore B cells. Epithelial cells play a critical role in B cell maturation, proliferation and IgA secretion by secreting transforming growth factor (TGF) –β and interleukin (IL) -6, -2 and -10 (McGhee and

Fujihashi, 2012). TGF-β and IL-10 also act as anti-inflammatory cytokines to keep the otherwise overly stimulated immune response in check. Intraepithelial lymphocytes, consisting of T helper (Th) 1, Th2, Th17, and T regulatory subsets, are the major resident immune cells within mucosal effector sites, but also act to maintain homeostasis and control inflammation. Paneth cells in the GIT are key effectors in innate mucosal immunity. They produce several antimicrobial peptides (i.e.α-defensins, lysozyme, secretory phospholipase A2) in response to bacterial stimulation (McGhee and Fujihashi, 2012). By these methods of antigen delivery and subsequent immune response and control, both mucosal and systemic immunity is achieved.

Equine Respiratory Tract Anatomy

Equine respiratory anatomy and physiology differs from other species, which gives horses a few unique aspects to their immune function. The upper respiratory tract of a horse is comprised of the nasal cavity, sinuses, guttural pouches, the larynx and the pharynx. The lower respiratory tract consists of the trachea, bronchi and lungs.

Horses are obligate nasal breathers, which may predispose them to respiratory tract infections. During breathing, the epiglottis rests above the soft palate allowing air to pass into the trachea, while arytenoid cartilages block the esophagus. In order to swallow, caudal movement of the epiglottis and adduction of the arytenoid cartilages forms an airtight seal over the trachea. The soft palate also elevates to allow food to

pass over the epiglottis and into the esophagus. This unique anatomy prevents them from breathing orally, but allows them to smell their environment and not their food while chewing, which is a beneficial adaptation for prey species (Rush and Mair, 2008).

However, after prolonged head elevation or pharynx fatigue caused by intense exercise, bacteria originating in the oral cavity can contaminate the upper airway and lead to lower airway infection (Raidal et al., 1997a).

Compared to other domestic species, horses have a large mucosal surface area within their respiratory tract that includes areas void of cilia in the nasal cavity and terminal bronchioles (Pirie et al., 1990a; Pirie et al., 1990b). An essential non-specific defense mechanism within the respiratory tract is the mucociliary escalator, which consists of a double layer of mucus produced by goblet cells and ciliated epithelial cells

(Mair et al., 1987). The mucus is a rich source of non-specific soluble host defense molecules including lysozyme, defensins and IgA (Davis et al., 2014). The ciliated epithelium of the escalator extends from the pharynx to the bronchioles and continually beats to move mucus and accumulated debris from the bronchioles to the trachea.

Mucus at the rostral end of the escalator is swallowed and neutralized by digestive enzymes in the stomach or alternatively expelled through the nostrils by gravity flow.

Debris and small particles that bypass the escalator can be phagocytosed by alveolar macrophages. Due to anatomy and common management practices among horse owners, the escalator is uniquely exposed to several factors that will cause a reduction in efficacy, making horses more susceptible to respiratory infections. Because of their long neck, the mucociliary escalator must work hard to overcome gravity when horses are unable to lower their heads, such as during tying or trailering. Additionally,

dehydration will increase viscosity of the mucus layer and thereby impair mucus clearance. High ammonia levels created by poor airflow and sanitation in barns and trailers, can also depress ciliary motility. Smoke inhalation and herpes and influenza viruses will damage the ciliated epithelium, which requires 21 d to regenerate (Rush and

Mair, 2008). Fortunately, the equine respiratory tract has many other means of immune defense.

Respiratory Lymphoid Tissues

The NALT of the upper respiratory tract possesses well-developed isolated and concentrated lymphoid sections, where antigen-specific responses stimulate both humoral and cell-mediated defenses. In contrast to humans, the equine lower tract contains BALT located within the submucosa of the bronchi and terminal bronchioles

(Randall, 2010). These lymphoid tissues contain B and T lymphocytes, plasma and memory cells, and macrophages. Immunoglobulin production and functions of plasma cells differ between the upper and lower tracts. The predominant immunoglobulin class secreted by upper respiratory plasma cells is IgA compared to IgG predominating in the lower tract (Tizard, 2004). The primary function of IgA is to block pathogens from adhering to the epithelium in the upper respiratory tract, whereas IgG exists to opsonize antigens for uptake and destruction in the lower tract (Russell et al., 2015).

Immunoglobulin A

Monomeric IgA is produced by plasma cells in the spleen and lymph nodes whereas the IgA dimers, linked by a joining chain, are produced locally within mucosal associated lymphoid tissues. After activation of naïve B cells in Peyer’s patches or lymph nodes, B cells class switch to IgA and express homing integrins or chemokine receptors which direct the B cells to a specific mucosal tissue. Epithelial cells lining

mucosal tissues express type 1 transmembrane glycoproteins called poly-Ig receptors, which have a high affinity for the joining chain that links together monomers of IgA or

IgM molecules (Lewis et al., 2010). Dimeric IgA secreted into the subepithelial space binds the poly-Ig receptors and is transported by transcytosis into the luminal space. IgA with the poly-Ig receptor still bound is cleaved and released into the lumen and the complex is now known as sIgA. The receptor that makes up the secretory component enhances the protective role of sIgA in two ways; it allows N-glycan mediated anchoring to the mucosal surface and provides enhanced resistance to proteases, both bacterial or host derived (Phalipon and Corthesy, 2003). A long half-life allows sIgA retained near the epithelial surface to prevent adherence of bacteria and neutralize their toxins for 3-6 d (Koh and Koh, 2007).

In absence of its ligand, the secretory component can be released into secretions and bind bacteria and toxins. It also acts as a regulatory molecule in the respiratory tract by sequestering soluble IL-8 (Lewis et al., 2010). This may be a feedback mechanism to attenuate neutrophil recruitment and an inappropriate inflammatory response (Marshall et al., 2001). Free secretory component can be measured in equine milk, which suggests constitutive release into other secretions as well (McGuire and Crawford,

1972).

Human poly-Ig receptors can also bind and transport IgM, and recent cloning of the equine poly-Ig receptor and joining chain suggests horses possess this ability as well (Lewis et al., 2010). Studies have confirmed widespread epithelial distribution of poly-Ig receptors in mucosal tissues, as well as less obvious tissues such as renal tubules, salivary and sweat glands, and gall bladder (Baker et al., 2015). Although the

magnitude of expression varies between tissues and is influenced by their corresponding cytokines and hormones, the gut mucosa and upper respiratory tract have consistently high expression. Upregulation of poly-Ig receptor mRNA can be induced by interferon (INF) -γ, tumor necrosis factor and IL-4 (Johansen and

Brandtzaeg, 2004).

Alternative to direct secretion of sIgA by epithelial cells that line mucosal tissues, serum-derived IgA from the portal vein can be bound by poly-Ig receptors on hepatocytes, transcytosed and secreted into bile (Baker et al., 2015). This hepatobiliary transport of IgA exists in humans, rats, rabbits, chickens and hamsters, but has not yet been investigated in horses.

Although humans on average secrete about 5 g (66 mg/kg BW/d) of IgA into mucosal tissues on a daily basis, it can also function before secretion (Koh and Koh,

2007). IgA located inside epithelial cells can bind and neutralize lipopolysaccharide

(LPS) that penetrated the epithelial cells. IgA is believed to have limited ability to activate complement or to act as opsonin, and therefore does not induce inflammation like other Ig classes, which makes it an ideal molecule for highly challenged mucosal surfaces, such as the GIT (Russell et al., 2015) . Additionally, complement proteins would not typically reside at mucosal surfaces unless the epithelial barrier was breached or inflamed.

Equine IgA

IgA is the principal immunoglobulin within the equine upper respiratory tract, tears, milk and is also present at high concentrations in serum (Lewis et al., 2010).

Equine serum IgA is predominantly dimeric with some monomers, trimers and tetramers, which is in contrast to humans, where IgA monomers predominant in

circulation. The molecular mass of equine serum IgA is about 350 kDa, although it ranges from 150-700, with the secretory component adding about 80 kDa (Tizard,

2004). In common with many other mammals, but different from humans, horses have a single IgA heavy chain constant region (Wagner et al., 2003).

In general, effector functions of IgA are well characterized; however, differences between equids and other species are not yet known. The fragment antigen binding

(Fab) arms of IgA will bind antigens and the IgA fragment crystallizable region can induce uptake by binding to a receptor (FcR) expressed on phagocytes. The Fab arms of serum IgA may activate the alternative complement pathway and an unknown portion of the molecule has also been shown to activate the mannose-binding lectin (MBL) pathway in humans (Gorter et al., 1989; Roos et al., 2001). The MBL protein consists of a carbohydrate recognition domain at the head and collagen-like tails that can interact with MBL-associated serine proteases -1, -2 and -3. Ligands of the carbohydrate recognition domain include several different saccharides and IgA. Calcium-dependent binding of these ligands activates the MBL-associated serine proteases to cleave C2,

C3 and C4, thereby activating the complement pathway. Equine IgA, in serum and nasal flushes, exhibits strong opsonization activity against Streptococcus equi and can mediate killing through the FcR on phagocytes or by complement activation (Sheoran et al., 1997). Homologous to the human IgA FcR, equine CD89 can bind both serum IgA and sIgA (Morton et al., 2005). High expression of equine CD89 has been confirmed on neutrophils; however, expression on other leukocytes has yet to be determined (Morton et al., 2005).

Immunoglobulin G

Mucosal lymphoid tissues throughout the body primarily rely on IgA-producing B cells for immune defenses. Although IgA is present in the lower respiratory tract under a variety of conditions, IgG-producing B cells typically predominate in this region. IgG is actively transported across the mucosal epithelium in a pH-dependent manner by the

IgG-specific neonatal FcR (FcRn) (Baker et al., 2009). Contrary to the name, FcRn is functionally expressed in humans throughout adult life. Tissue distribution of this specific receptor varies across species, but is commonly expressed in the intestinal tract, pulmonary and mammary epithelial cells, and endothelial cells, as well as on most hematopoietic immune cells (Baker et al., 2009). During the neonatal period, this receptor facilitates passive transfer of maternal IgG to offspring. Throughout the adult life of several species, FcRn also bi-directionally shuttles antigen-bound IgG between different body compartments, which facilitates an efficient immune response towards the opsonized antigen (Baker et al., 2009). It also regulates IgG and albumin homeostasis in serum by efficient recycling and transcytosis. In contrast to IgA secretion, FcRn-mediated transcytosis of IgG does not release a secretory component

(Baker et al., 2009). This allows IgG to be bound by another FcRn on the apical surface of epithelial cells and be returned to the basolateral side. Therefore, IgG acts as an immunological sensor for mucosal surfaces by binding antigen within the lumen and transporting them to local or systemic immune compartments. Expression of FcRn in the human, non-human primate, mouse and bovine lower respiratory tract is high

(Spiekermann et al., 2002; Mayer et al., 2004); however, expression within the equine lung, although likely, has yet to be investigated. Unless the epithelial barrier is compromised or broken, passive diffusion of this molecule is unlikely given the

molecular weight (150 kDa) and the low rate of secretion into colonic fluid (Baker et al.,

2010). Once secreted, IgG functions within respiratory tract and other mucosal tissues to opsonize antigens for uptake and destruction. IgG-producing B cells generated in the

BALT are long-lived memory plasma cells that maintain virus-neutralizing antibodies in serum and bronchial lavage fluid (Moyron-Quiroz et al., 2004). During a secondary infectious challenge, these antibodies have the ability to completely prevent infection making IgG a vital mucosal defense mechanism.

Equine IgG

Among mammals, horses have the highest number of IgG constant region genes giving rise to seven IgG subclasses which are all expressed in vivo (Lewis et al., 2008).

This antibody is the most abundant in equine serum, colostrum, urinary tract, lower respiratory tract, and lungs. Subclasses IgG1 and IgG4, previously IgGa and IgGb, are the only subclasses found in nasal wash samples (Sheoran et al., 2000). A small proportion of IgG4 molecules lack disulfide bonds connecting the heavy chains and instead, may be stabilized by non-covalent interactions (Lewis et al., 2008). The ability of IgG to activate the classical complement pathway or bind to Fc receptors on leukocytes differs between subclasses. Lewis and colleagues found that five of seven subclasses, including IgG1 and 4, can elicit strong respiratory burst from equine peripheral blood leukocytes suggesting interaction with the FcR (Lewis et al., 2008).

Interestingly, two subclasses, IgG2 and 6 elicited little or no response from the leukocytes (Lewis et al., 2008). They also reported the most potent activator of complement was IgG3 followed by 1, 4, and 7. In contrast to other mucosal tissues which locally produce IgG, salivary IgG is thought to be mostly serum-derived

(Brandtzaeg, 2007). Palm and colleagues reported low individual variation in total

salivary IgG compared to IgA from 51 healthy horses, suggesting passive diffusion of

IgG from serum in contrast to active secretion of IgG by FcRn at other mucosal sites

(Palm et al., 2016). The actual quantity and function of IgG within the equine respiratory tract has yet to be investigated.

Cellular Respiratory Defenses

Cellular responses within the equine respiratory tract play critical roles in host defense, because the mucociliary escalator and BALT do not encompass the respiratory zone. Alveolar macrophages bridge the gap between innate and adaptive immunity in the respiratory tract, since they phagocytose non-specifically, but also present antigen to T cells to initiate an adaptive response (Davis et al., 2014). Alveolar macrophages are responsible for elimination of inhaled particles and recruitment of other immune cells. These macrophages can travel to the pharynx via the escalator, where they are swallowed and destroyed or they can leave the alveolar space, enter general circulation and subsequently be cleared by the lymphatic system. Strenuous exercise, long distance travel and viruses can impair or destroy these cells (Raidal et al., 1997b). During exercise or prolonged head elevation, the amount of inhaled debris increases and phagocytosis of these non-opsonized particles may reduce the subsequent phagocytic capacity of alveolar macrophages. Additionally, exercise- induced pulmonary hemorrhage, a common occurrence in race horses, burdens mucociliary clearance and provides nutrients that can accelerate bacterial growth.

Alveolar macrophages have been blamed for causing the pathophysiology of equine obstructive airway disease; however, a recent study showed that in vivo depletion of pulmonary intravascular macrophages (PIM) with gadolinium chloride attenuates the development of airway pathology (Aharonson-Raz et al., 2012). In

contrast to some other species, horses have a resident population of PIM that phagocytose bacteria and endotoxins from general circulation upon first pass through the lungs. PIM are activated by intravascular LPS and express pro-inflammatory cytokines that enhance neutrophil migration into the lungs (Parbhakar et al., 2005). For this reason, horses have an extreme sensitivity to endotoxins and can quickly undergo endotoxin-induced cardio-pulmonary shock and death. The precise mechanism of this is still unclear but it is likely due to PIM toll-like receptor 4 binding LPS, followed by release of pro-inflammatory cytokines and upregulation of vascular adhesion molecules.

Next, neutrophils are recruited and they release reactive oxygen species and proteases, which causes lung vascular and tissue damage (Aharonson-Raz and Singh, 2010).

Newer studies suggest that inhaled endotoxins, or subsequent inflammatory mediators from inhaled particles, can be transported across the blood-air barrier and cause PIM activation (Aharonson-Raz et al., 2012). Consequently, horses may be more susceptible to airway inflammation caused by long periods of head elevation and increased dust particle inhalation, such as during transportation.

Stress Physiology

It is generally well accepted that chronic stress can induce immunosuppression in many species including humans and horses. A meta-analysis of research conducted in humans reported that acute stress enhances humoral immunity, whereas chronic stress suppresses cellular and humoral immunity while increasing susceptibility to infections (Segerstrom and Miller, 2004). Stress can be broken down into three phases: it begins with a stimulus, which triggers a reaction in the brain, which activates physiological support for a reaction. Upon perception of a stressor by the hypothalamus, the sympathetic nervous system immediately increases heart and breathing rate, dilates

pupils, and increases blood pressure and blood glucose in preparation for a fight or flight reaction. Following this immediate sympathetic response, epinephrine is released from the medulla of the adrenal glands. This pathway is known as the sympathetic adrenal medullary (SAM) system and is activated within the first ten seconds of a stressful event (Sapolsky et al., 2000; Koh and Koh, 2007). Along with their rapid secretion, physical effects of hormones released by the SAM pathway occur within minutes. The release of epinephrine stops once the perceived danger has passed and homeostasis is achieved within minutes. Stressful events that extend for a longer period of time are also perceived by the hypothalamus in the brain, but will activate the hypothalamic pituitary adrenal (HPA) axis (Cranston, 2014). In this pathway, the hypothalamus releases corticotropin-releasing hormone and vasopressin, which travel a short distance through the blood to reach the pituitary gland (Cranston, 2014). The main function of these peptide hormones during a stress response is to stimulate the anterior pituitary to synthesize and release adrenocorticotropic hormone (ACTH). ACTH travels through the blood to the adrenal glands and stimulates the cortex to release corticosteroid hormones into the blood. Although these hormones are secreted within minutes, their physical effects will occur about an hour after onset of the stressor

(Sapolsky et al., 2000). Neurotransmitters and hormones released during this response are a double edged sword as they can be helpful in the short run, but damaging in the long run. Additionally, behavioral changes in response to stress, such as anorexia or lack of sleep, can also deleteriously affect immune function.

Stress Hormones and Cortisol

Recognition of stress by the brain can regulate immune function through sympathetic nerve fibers, which innervate primary and secondary lymphoid tissues, and

by release of adrenal stress hormones. Catecholamines, epinephrine and norepinephrine released during acute stress lasting only minutes, can aid in preparing the immune system for potential challenges imposed by the stressor (Ader et al., 2001).

In contrast, chronic stress lasting hours or days will activate the HPA-axis, which suppresses immune function through the release of adrenal glucocorticoids and mineralocorticoids. Receptors for these stress hormones exist on most leukocytes and ligand binding can regulate their distribution and function (Anstead et al., 1998). The anti-inflammatory properties of glucocorticoids are well documented and supported by widespread therapeutic use for inflammatory disorders, such as autoimmune diseases and allergies (De Bosscher et al., 2000). During an immune response, physiological concentrations of glucocorticoids inhibit cytokine gene expression and adhesion molecules, and redirect lymphocyte traffic, which concurrently controls the inflammatory response so it does not become detrimental to the host (Cato and Wade, 1996).

Chronic stress and the resulting stress hormones can also shift the patterns of cytokine secretion, causing simultaneous enhancement of Th-2 humoral immunity and suppression of Th-1 cellular immunity (Chiappelli et al., 1994). This cascade of events is thought to occur mainly through the action of cortisol.

Cortisol has widespread metabolic effects including, but not limited to, increasing blood pressure, gluconeogenesis, glycogenolysis and proteolysis, regulating memory and electrolyte balance, and maintenance of normal sleep/wake patterns (Sapolsky et al., 2000). It also functions to suppress the immune system, thereby diverting energy elsewhere and preventing the inflammatory response from overshooting and causing damage to the host.

Serum cortisol exists in two forms: either bound to corticoid binding globulin in whole blood or unbound, which is the biologically active form. Cortisol can enter saliva by passive diffusion or means independent of active transport, and therefore is unaffected by salivary flow rate (Kirschbaum and Hellhammer, 1994). Acinar cells lining the salivary glands prevent protein-bound molecules from entering saliva, so salivary cortisol is unbound and active, which reflects the unbound form found in serum.

Because HPA-axis activation in response to stress is not immediate, both serum and salivary cortisol are considered accurate measurements of chronic stress lasting several minutes to hours (Valera et al., 2012). Measuring salivary cortisol is often chosen over serum cortisol to avoid the stress of venipuncture; however, there is some controversy on whether free salivary cortisol accurately correlates to free serum cortisol. Previous studies report correlations in stallions, but a less clear relationship when investigated in foals (Lebelt et al., 1996; Moons et al., 2002). Although salivary flow rate does not appear to influence salivary cortisol levels, poor sampling techniques and inexperienced young animals probably explains the lack of correlation in foals (Scott et al., 1990).

Additionally, cortisol is considered a diurnal molecule in horses and other species, which complicates analysis of cortisol level depending on the time of sampling.

Characteristics of this diurnal pattern are low levels during deep nocturnal sleep, a steady increase during late sleep, and peak levels just after waking followed by a sustained, gradual decrease throughout the day (Koh and Koh, 2007). Once released in response to stress, cortisol feeds back to inhibit the HPA-axis and restore homeostasis.

However, this negative feedback loop can be interrupted by intensely distressing or prolonged stressful events. Continuous secretion of cortisol will have sustained anti-

inflammatory effects and downregulate glucocorticoid receptors (GR) in the brain, which will flatten the diurnal curve (Uno et al., 1994; Cranston, 2014). A hallmark trait of the deleterious effects of stress is a disruption in the normal circadian rhythm (Dhabhar,

2000).

Although the exact mechanisms are still controversial, it is generally accepted that physiological concentrations of cortisol have initial immune stimulatory effects, followed by immune suppressing effects. In the short term, cortisol seems to cause leukocyte redistribution to tissues that are likely to endure injury from a stressor, like the skin (Dhabhar, 2000). General suppressive effects of cortisol on immune function are suppression of inflammatory cytokines, inhibition of leukocyte migration, alterations to systemic leukocyte numbers and interference with leukocyte, fibroblast and endothelial cell functions (De Bosscher et al., 2000). Free cortisol will pass through the cell membrane and bind to the GR, causing a conformational change and nuclear translocation of the GR complex. Many genes contain glucocorticoid response elements

(GRE) in their promoter regions and activated GR can bind GRE to inhibit or enhance transcription of pro-inflammatory or anti-inflammatory genes, respectively (De Bosscher et al., 2000). Within the nucleus, GR can also bind and inhibit transcription factors nuclear factor-kappa beta (NF-κB) and activator protein-1, which would normally promote pro-inflammatory gene transcription (De Bosscher et al., 2000). NF-κB activates genes coding for adhesion molecules, chemotactic proteins and cytokines, which are all vital during immune responses. Post-transcriptionally, GR has recently been shown to directly bind to mRNA and recruit decapping proteins, which cause rapid mRNA degradation (Park et al., 2016). It is also suggested that anti-inflammatory

proteins, synthesized by GR-GRE binding, disrupt polyadenylation thereby decreasing mRNA stability (Newton, 2000). Other post-transcriptional and translation methods of regulation certainly exist, but the mechanisms have yet to be elucidated.

Neutrophils have long been recognized as a direct target of both exogenous and endogenous glucocorticoids (Roth and Kaeberle, 1981; Weber et al., 2004). These phagocytes are crucially important as a first line of innate defense in all tissues, especially in the lungs. While fighting an infection, neutrophils rapidly degranulate and release harmful proteolytic enzymes and reactive oxygen species into the surrounding tissues. Excessive damage to surrounding healthy tissue will exacerbate inflammation unless it is properly controlled. In an attempt to control neutrophil activities, glucocorticoids can alter gene expression that regulates apoptosis, adhesion and inflammation. Cortisol-induced impairment of adhesion molecules has direct effects on neutrophil migration and indirect effects on neutrophil function.

Endogenous and exogenous glucocorticoids down regulate surface expression of adhesion molecule L-selectin on peripheral neutrophils (Buckham Sporer et al., 2007).

L-selection expression allows neutrophils to slowly roll on endothelial cells and, in response to chemoattractants, undergo diapedesis into infected or inflamed tissues.

Temporary down regulation of L-selectin prevents neutrophils from leaving circulation, and is thereby responsible for the neutrophilia and possibly the increased risk of infection commonly associated with stress (Murata et al., 1987; Gupta et al., 2007;

Buckham Sporer et al., 2007). Rebound L-selection expression is thought to occur immediately following peak stress levels as immature neutrophils are released from the bone marrow (Van Eeden et al., 1995). These immature neutrophils express high levels

of L-selectin and one study in rabbits showed the immature neutrophils are preferentially sequestered in the lungs (van Eeden et al., 1997). The excessive immature neutrophil accumulation in the lungs could play a role in respiratory disease from exacerbated inflammation and inefficient immune responses. An important inflammatory control mechanism for neutrophils is programmed cell death, which prevents tissue bystander damage. Pro-apoptotic Fas proteins in bovine were downregulated within 4.5 h of transportation stress, which could extend neutrophils survival and unnecessarily prolong an inflammatory response (Buckham Sporer et al.,

2007). Downregulated Fas proteins have also been observed during high levels of serum cortisol and in glucocorticoid treated steers (Chang et al., 2004).

Along with neutrophilia, cortisol is also responsible for lymphocytopenia and monocytopenia. Continuous circulation of leukocytes from the blood into various body compartments and back into the blood is essential for an effective immune defense system. In rats, leukocyte alterations can occur within 30 min of applying a stressor, but can return to normal within 3 h after the cessation of stress (Dhabhar et al., 1995). This phenomenon was significantly reduced in adrenalectomized animals and then induced by corticosterone administration, suggesting cortisol was the major mediator of these stress-induced changes. Rapid recovery of circulating leukocytes and static levels of plasma lactate dehydrogenase, a marker of cell damage, suggests a redistribution of cells verses cell loss (Dhabhar et al., 1995). Stress hormone-induced redistribution of circulating leukocytes has been reported in many species, including horses and humans, and may be an important evolutionary adaptation to stress (Snow et al., 1983;

Dhabhar et al., 1995). Lymphocytes have been shown to accumulate in lymphatic

tissues and mucosal sites following stress or corticosteroid injection (Walzer et al.,

1984; Toft et al., 1993). Redistributing leukocytes to vital organs may allow for a more prompt immune response, should one be needed.

The effect of cortisol on leukocyte function is still controversial. Many studies do not correlate increased cortisol levels to neutrophil dysfunction, although it is always suggested and assumed. In vitro, high doses glucocorticoids inhibit phagocytic function, but it is unclear whether this is physiologically relevant (Jones et al., 1983). Cortisol suppressed in vitro superoxide production in human granulocytes, but the mechanism is not fully elucidated (Békési et al., 2000). During transportation stress, upregulation of a key neutrophil anti-bacterial gene strongly correlated with peak serum cortisol levels in steers (Buckham Sporer et al., 2007). Functional capability was not assessed, but those data suggest enhanced neutrophil function driven by cortisol. Countless studies have investigated the effect of the stress on immune function, but the mechanisms are likely multifactorial and cannot be attributed to cortisol alone.

Enhancement of Immunity After Stress

Enhancement of immune function following acute stress may be an evolutionary adaptation to prepare the body for potential immunological challenges presented by the stressor. There is a delay before cortisol-dependent immunological changes are observed, suggesting a different mechanism of immune enhancement following acute stress (Benham et al., 2009). A meta-analysis of human studies reported the most robust effects of acute stress, such as public speaking or mental arithmetic, were increased peripheral natural killer cells and granulocytes (Segerstrom and Miller, 2004).

These results support the evolutionary view that leukocytes quickly redistribute to compartments where they can effectively defend against an invader. Cytotoxicity of

natural killer cells also appeared to be enhanced, but this was an artifact of the increased number and was eliminated when examined on a per-cell basis. However, an increase in general cytotoxic potential of the periphery is still advantageous. Circulating lymphocytes largely remained unchanged, but proliferative responses to B and T cell mitogens were decreased in response to acute stressors. The same meta-analysis also reported salivary sIgA, IL-6 and INF-γ increased in response to acute stress. Given the short time frame of these stressors, the synthesis of new antibody is improbable and the marked increase is more likely due to release of stored antibody. Increased pro- inflammatory cytokines IL-6 and INF-γ could stimulate macrophages, natural killer cells and T cells and enhance their functional properties. Taken together, these results suggest certain acute stressors upregulate innate immunity, while down regulating adaptive immunity, which is analogous to the effects of certain types of exercise.

Enhancement of Immunity After Exercise

Several positive changes in immune function have been linked to moderate intensity exercise. In humans, moderate exercise tends to increase phagocyte toxicity and natural killer cell activity, and has been reported in several studies comparing athletes to sedentary individuals (Kappel et al., 1991; Nieman, 1997; Nieman, 2007).

Both acute and chronic, moderate intensity exercise has been shown to improve antibody response to an influenza vaccine in humans (Edwards et al., 2006; Woods et al., 2009). The incidence of upper respiratory tract infections in humans has also been shown to decrease with moderate intensity physical activity (Nieman et al., 2008). Since respiratory diseases are so prevalent in the professional sport horse industry, it would be beneficial to know if progressive training could improve, verses suppress certain immune functions.

In 4 to 5 year old colts, progressive training induced an increase in chemotaxis and cytotoxic capacity of neutrophils compared to untrained controls (Escribano et al.,

2002). The percentage of neutrophils that underwent phagocytosis was significantly higher in trained colts. These results were confirmed in another study that showed trained horses at rest, and immediately after exercise, had increased neutrophil oxidative metabolism and greater phagocytic response during recovery compared to untrained horses (Escribano et al., 2005). These data indicate non-specific immune responses are improved with training and moderate intensity exercise. However, improvements to immune function seen with acute stress or moderate exercise are only maintained under these temperate circumstances and can be reversed by more extreme situations.

Stress-Induced Immunosuppression

Many human studies have investigated the immune effects following various types of chronic stress including intense exercise, sequential academic examinations, insomnia, unemployment, and bereavement. Compared to acute stress, which primarily affects enumerative immune parameters, longer term stressors tend to have greater effects on functional parameters. The same meta-analysis as mentioned above concluded the general effects of chronic stress in humans are cytokine shifts toward Th-

2 responses, decreased lymphocyte proliferation and natural killer cell cytotoxicity, and increased antibody production to latent viruses (Segerstrom and Miller, 2004).

Undergraduate students with higher anxiety scores exhibited lower in vitro lymphocyte proliferation to mitogens (Gonzalez-Quijano et al., 1998). Prolonged unemployment over the course of four months lowered in vitro natural killer cell cytotoxicity, which was attenuated after the participants became employed (Cohen et al., 2007). A common

limitation of human studies is the inability to challenge the immune system in vivo following stress, although most results suggest an ineffective response would occur.

One study did experimentally infect human subjects with respiratory viruses and concluded infection rate increased in a dose-dependent manner with increased degree of physiological stress (Cohen et al., 1991). Other limitations include classifying and comparing real-life human stressors between studies, and accounting for individual perceptions of stressors and coping mechanisms. Exercise, on the other hand, is a popular and reproducible model to induce immunosuppression in many species.

Exercise-Induced Immunosuppression

In general, the body undergoes several changes in response to strenuous exercise. Many of these changes cause important modulatory effects on immune function, which have been well documented in humans and several animal species, including horses. Exercise research in horses has been a focus of our scientific community for several decades. Although the results of these studies have been somewhat diverse, it generally can be agreed that exercise does influence the immune system similar to that observed in humans. A common theory, known as the ‘open window theory’, proposes that prolonged exercise leads to altered immunity that can last between 3-72 h (Lakier Smith, 2003). During this time, it is possible for bacteria or viruses to gain a foothold, thereby increasing the risk of infection.

In response to heavy exertion, the phagocytic and oxidative burst capacity of neutrophils and monocytes has been shown to decrease (Nieman et al., 1990). The enhanced function seen after moderate exercise may be part of the inflammatory response to repair damaged muscle cells, whereas suppression after intense activity may be due to overloading or stress on the cells. Compared to non-exercised controls,

the capacity of phagocytes to ingest Escherichia coli was significantly reduced in human athletes for 3 d following a 20-km road race (Muns, 1994). Prolonged suppression of the innate immune system was also seen in horses following an 80-km endurance race

(Robson et al., 2003). Immediately post-race, monocyte and neutrophil counts were elevated and lymphocyte counts were depressed compared to pre-race values.

Oxidative burst activity of both neutrophils and monocytes decreased after the race and remained below pre-race values after 3 d of rest, but the correlation to increased serum cortisol was not determined.

Both human and animal studies have shown decreased lymphoproliferative response to the T cell mitogens phytohemmaglutinin (PHA) and concanavalin A (Con A) up to several hours after intensive or prolonged exercise (Nieman, 1997; Pedersen and

Hoffman-Goetz, 2000; Bobel et al., 2012). Intensely exercised Quarter horses showed a significant decrease in lymphocyte proliferation in response to T cell mitogens Con A and PHA (Kurcz et al., 1988). The proliferative response was suppressed at 30 min post-exercise, but returned to baseline by 24 h post-exercise. The lymphocyte suppression coincided with an elevation in cortisol levels, but a correlation analysis was not performed. Following intense acute exercise, Thoroughbred horses showed a significant decrease in influenza virus and pokeweed mitogen (PWM) stimulated lymphocyte proliferation (Keadle et al., 1993). On the contrary, cytotoxic T cell activity had an early post-exercise enhancement, but returned to baseline 2 h post-exercise.

This enhancement was suggested as a possible evolutionary adaptation in horses due to their fitness requirement in the wild.

Thoroughbreds participating in 1-3 races of ≥ 1,600 m had significantly decreased lymphoproliferative responses and increased white blood cell (WBC) counts

12-16 h after the race (Nesse et al., 2002). Lymphocytes isolated from competing horses 12-16 h after the race had a suppressed reaction to mitogens PHA, Con A and

PWM compared to their pre-race values and their unraced counterparts. A strenuous 5- d exercise program resulted in significant suppression of lymphoproliferative responses and INF-γ mRNA production by PBMC, and increased susceptibility to influenza in unconditioned ponies (Folsom et al., 2001). The ponies were vaccinated against and then challenged with the influenza virus. Three of the four exercised ponies exhibited clinical signs of influenza infection following the challenge. No significant differences were seen in the neutralizing antibody titers of the exercised ponies compared to the resting controls, suggesting that exercise-induced changes in antibodies were not contributing to disease susceptibility. The possible explanation instead is the decrease in INF-γ mRNA, since this cytokine is produced by Th1 and natural killer cells, and plays a role in immunity to viruses. There was also no noted change in IL-2 mRNA production by PBMC, even after adding recombinant equine IL-2 to the cultures. This suggested that the exercise stressed ponies were producing IL-2, but cells were less responsive.

More recently, our lab has shown that prolonged submaximal exercise in unconditioned horses resulted in an increase in circulating neutrophils and decreases in lymphocytes and eosinophils, which persisted through 24 h post-exercise (Bobel et al.,

2012). Additionally, suppressed lymphoproliferative responses to Con A, PHA and PWM were noted at 6 and 24 h post-exercise. Lymphocyte cell viability following in vitro hydrogen peroxide exposure was also decreased at 24 h post-exercise. These data

indicate lymphocytes may be more vulnerable to oxidative damage and not function properly during recovery from exercise.

Collectively, these findings indicate strenuous exercise (either high intensity or prolonged duration) can result in impaired cell mediated immunity, and therefore greater susceptibility to viruses. This is especially important to the sport horse industry because of the marked susceptibility of these athletes to IURD. Concurrent to exercise stress, horses frequently endure other types of stress, which could further compromise immune defenses.

Other Equine Stressors

Many previous studies in horses have shown immune dysregulation in response to different types of stressors. Horses anaesthetized for a castration procedure had decreased lymphocyte proliferation 24 h after the procedure (Strasser et al., 2012).

Foals that underwent weaning had lower in vitro and in vivo production of tumor necrosis factor-α, INF-γ and IL-10, indicating lower cell mediated immunity, although it was not directly analyzed (Adams and Horohov, 2013). As with humans studies, comparing different stressors across different populations of horses is challenging.

It is well documented that prolonged transportation is stressful for horses and causes both metabolic and immune dysfunction leading to poor performance and increased risk of infection (Stull and Rodiek, 2000; Oikawa et al., 2005). A recent cross- sectional online survey of people from any equine discipline, who aided in the transportation of horses at least monthly over the past two years, reported that respiratory problems (33.7%), GIT problems (23.8%) and injuries were the most commonly reported issues after transportation (Padalino et al., 2017b). Findings from this survey supported previous studies that journey duration is positively associated with

development of serious health problems, and journeys longer than 24 h pose the greatest risk. Compared to travel lasting <8 h, the likelihood of respiratory diseases was

15 times greater on journeys of 8-24 h, and 100 times greater on journeys of more than

24 h. The presence of hay and dust in an enclosed space and ineffective postural drainage during transport, because horses are unable to lower their heads, results in a heavy bacterial load in the respiratory tract.

Early studies pointed to prolonged head elevation as the main culprit for many of the negative effects associated with transportation and increased risk of IURD. Without using transportation, researchers showed as little as 6 h of head elevation could cause reduced mucociliary transport rates and accumulation of lower respiratory tract secretions and bacteria (Racklyeft and Love, 1990; Raidal et al., 1996). Researchers further showed prophylactic antibiotic usage did not reliably attenuate these effects

(Raidal et al., 1997c). The additional act of trailering horses, increased transtracheal mucus and bacteria, and decreased neutrophil phagocytosis and lymphocyte proliferation, which took several days to recover (Raidal et al., 1997a; Padalino et al.,

2017a). Dehydration due to water deprivation during transport is also associated with reduced mucociliary clearance, making the assistance of gravity even more important for drainage.

It been hypothesized that offering periods of rest or transporting horses untied, would attenuate respiratory inflammation; however, studies have reported mixed results.

Researchers from Japan investigated several important aspects of transportation in two separate consecutive experiments (Oikawa et al., 2005). During the first experiment, three groups of four Thoroughbred horses were transported for an average of 42 h.

Group 1 and 2 were loaded onto the trailer forward facing or rear facing, respectively, and given a short 30 min period of rest for every 4 h driven. Group 3 was loaded forward facing, but given a long 2 h period of rest for every 4 h driven. Each group had ad libitum hay suspended in nets and water in buckets throughout the journey. During transport and rest, all horses were untethered but only able to lower their heads over a

1 m high chest bar. Jugular blood samples were taken before departure, 23 h of transit, upon arrival, and 24 h after arrival. Endoscopic examinations were performed before and after transport. Vigilant measurements of the interior trailer environment were taken during transport, including temperature, humidity and concentrations of aerial dust, ammonia, endotoxins, and pathogens. Increased systemic neutrophil counts and plasma fibrinogen were all associated with transportation and highest for forward facing horses given short rest, and lowest in forward facing horse given long rest. Serum cortisol was highest at 23 h of transit, but did not differ between groups. All blood parameters were at or below baseline by 24 h after transport. Results of the first experiment indicated that body position had no effect on physiological parameters; however, periods of longer rest resulted in lower markers of stress. It is unclear whether this is biologically relevant or associated with lower disease incidence.

In the second experiment, two groups of horses were transported forward facing with flake hay or pelleted hay, rested 1 h for every 5 h driven and after clinical examination, humanely euthanized upon completion of the trip (Oikawa et al., 2005).

Hay dust and ammonia concentrations are often blamed for transportation effects, so the group with pelleted hay also had urine and feces removed during each rest stop.

The sampling timeline remained the same as experiment one, except tracheal washes

were performed before and after transit and an additional blood sample was taken at h

5 of transit. As expected, gaseous ammonia, airborne dust, pathogens and endotoxins were all higher in the group with flake verses pelleted hay. Systemic neutrophils in both groups were elevated during transport, but statistically higher in the group with flake hay compared to pelleted hay. Unexpectedly, serum cortisol was only above baseline in the group with flake hay at h 5 of transit, but returned to baseline by h 23. Plasma fibrinogen also increased above baseline in the group with flake hay. Tracheal wash endotoxin concentrations and serum equine pulmonary surfactant protein A, an indicator of pulmonary damage, were not affected by transport or group conditions. Plasma endotoxin was elevated after transport, but not different between groups. The results of the second experiment indicated the act of transport, and not the environment, was more associated with endogenous translocation of endotoxin and general measures of systemic stress.

Horses tend to maintain an upright head and neck posture to maintain balance in a moving trailer. However, some studies reported horses traveling loose within the trailer compartment have less indices of stress compared to horses that are tied (Stull,

1999; Stull and Rodiek, 2002). A cross-over designed study compared the effects of horses traveling loose or cross-tied during 24 h of road transportation (Stull and Rodiek,

2002). Compared to loose horses, cross-tied horses had higher WBC counts, neutrophil to lymphocyte ratios, and glucose and serum cortisol concentrations after transportation and during recovery. Muscle fatigue, as indicated by serum lactate concentration, was elevated above baseline in both groups during transportation and recovery, suggesting cross-tie restriction did not increase muscle fatigue. Activities of creatine kinase and

aminotransferase, and lactate concentrations indicate all horses experienced only minimal muscular insult, similar to that of moderate exercise.

Less frequently investigated are the effects of short distance transportation. A recent study hypothesized that short distance travel would cause changes typically associated with chronic non-infectious respiratory conditions, and lead to the misdiagnosis of such (Allano et al., 2016). Eight healthy Standardbred horses were transported with hay for 2.5 h, then given 10 d of rest and transported again, this time without hay. Tracheal washes and bronchoalveolar lavages (BAL) were performed before and at 1 and 2.5 h after transport. There was no transportation effect on tracheal mucus, cytology or bacterial counts. Horses without hay had higher percentages and counts of BAL neutrophils compared to before transport and horses transported with hay. This was controversial finding since hay is typically associated with increased dust and contaminates, and has previously been shown to increase airway cytology and inflammation during transportation (Oikawa et al., 2005). The authors suggest that low hanging hay nets in the trailer, actually helped mucociliary clearance whereas horses without hay were more prone to keeping their heads elevated. Another recent study reported increased serum ACTH and cortisol after only 45 min of transportation, but no immune parameters were measured (Fazio et al., 2016). Although this study revealed even horses accustomed to transportation can have rapid increases in stress hormones, the amount of time it takes transportation stress to cause physiological changes remains unknown. The vast majority of published studies compare measurements taken before transportation to measures taken after, with little to no evaluation during transport. The equine industry would greatly benefit from knowing how

long horses can travel before there is an increased probability of adverse health outcomes.

Methods to Reduce Stress-Induced Immunosuppression

Transportation is an integral part of the horse industry and although efforts have been made to improve this practice, engineering limitations and willingness of drivers to change management, remains a problem. Orientation in the trailer (forward facing, rear facing or diagonal) does not affect a horses’ ability to maintain balance nor does it reliably reduce stress indicators (Oikawa et al., 2005; Padalino, 2015). To date, there is no published research comparing air suspension systems, typically on large commercial trailers, to common leaf-spring or torsion bar systems on smaller trailers. Previous work showed leaf-spring suspension with low-pressure tires produced a significantly smoother ride than torsion-bar suspension with normal pressure; however this was not biologically relevant (Smith B.L. et al., 1996; Smith et al., 1996). Perhaps allowing horses intermittent periods of rest, off the trailer or at least untied and able to lower their heads, could alleviate stress and respiratory load, but this an unreasonable request for commercial hauling companies who typically employ two drivers to haul up to 15 unfamiliar horses at once. Considering that horses are transported more frequently than any other type of livestock, with an estimated $3 billion spent on transportation annually in the U.S., methods of reducing the associated stress are warranted (American Horse

Council, 2005).

Dietary Interventions

Few studies have investigated dietary methods to attenuate transportation- induced immunosuppression. A placebo controlled study supplemented horses with ethanol extracts of herbs (Siberian ginseng, schizandra chinensis, rhodiola rosea and

Asian devil’s club) previously shown to affect the HPA-axis in rats (Udintsev et al., 1991;

Stull et al., 2004). Once daily, the treatment or placebo group received 8 mL of supplemental herb extract or ethanol, respectively, top-dressed on chopped alfalfa and molasses. After 30 d, six horses from each group were transported for 24 h while the remaining horses were kept in stalls. Horses were transported in a commercial van and cross-tied while in-route, which prevented lowering of their heads. Immunological variables evaluated before and after transportation were unaffected by the herbal supplement. Transportation provoked elevations in cortisol, total WBC, and neutrophils, and decreased CD3+, CD4+, CD8+ and CD21+ lymphocyte counts and lymphocyte proliferation to Con A. These variables returned to normal by 24 h post-transport. The authors suggested transport-associated metabolic fluctuations and/or direct effects of stress-induced soluble mediators caused the short-lived immunological impairment.

However, additional stressors that may be experienced upon arrival at a destination, like exercise or new surroundings, could prolong the impairment.

Gastrointestinal problems in horses were the second most commonly reported aliment associated with travel (Padalino et al., 2017b). Changes to intestinal microbiota as a result of transportation have previously been reported and may be associated with the gastrointestinal distress and colic horses frequently experience (Schoster et al.,

2016). Dietary strategies to prevent microbial disturbances during transport have been investigated with mixed results. Supplementation of live Saccharomyces cerevisiae yeast at a rate of 4.87 x 109 CFU/kg BW/d was fed to two horses for 19 d (Faubladier et al., 2013). Control horses were fed the basal diet of hay, pelleted fed and barley without added yeast. On d 16, horses were transported for 2 h followed by blood and fecal

collections for 3 d. Horses were fed a washout basal diet for 23 d before treatments were switched and the study protocol repeated. Due to the small number of horses, fecal samples within a treatment were pooled to test the global effects of transportation.

Horses experienced an increase in total WBC and the ratio of neutrophils to lymphocytes immediately after transport, but the yeast group had a smaller increase in total WBC. Altered fecal microbial profiles and fermentation end products were seen 3 d later; however, the percentage of similarity was greater in the yeast group before transport compared to after. Fecal cellulolytic and lactate-utilizing bacteria were greater in the yeast group and unaffected by transport. Lower fecal acetate and butyrate/propionate ratio was also observed 3 d after transport and may reflect a negative impact of transportation on cellulolytic bacteria activity, which could trigger dysbiosis. Yeast supplementation also prevented the decrease in fecal pH seen in control horses after transport. Live yeast cells were never detected in the feces of control horses, but were detected during supplementation suggesting the yeast were able to reach and survive in the equine large intestine. Although this study had a small number of horses, the results indicate that 2 h of transport disturbed the fecal microbiota and live yeast supplementation may mitigate the changes. Yeast supplementation in this study and previously has been shown to increase microbial diversity, which is positively associated with health benefits (Rodriguez et al., 2015). Moreover, yeast supplementation did attenuate the increase in total WBC and may indicate these horses were less stressed, but more research is needed to examine this relationship.

Bidirectional communication exists between the GIT microbiota, and the brain to influence behavior, brain activity, microbial content and even disease (Collins and

Bercik, 2009). During homeostasis, the GIT provides a stable habitat for commensal bacteria, which help maintain GIT structural and functional integrity. GIT disturbances, such as stress, can destabilize the environment, shift the microbial population and cause unregulated inflammation. Norepinephrine increased the uptake of pathogenic bacteria into porcine jejunal lymphoid follicles and was prevented by adrenergic agonists (Green et al., 2003). In vitro, catecholamines have also been shown to stimulate growth of pathogenic and non-pathogenic Escherichia coli and influence their adherence to the mucosa (Chen et al., 2003). Experimental physiological and physical stress can decrease intestinal barrier function, mucus secretion and gut motility, all contributing to a cycle of dysbiosis and potentially disease (Groot et al., 2000). These data suggest the gut-brain axis plays an integral, yet unclear, role in the physiology of stress and targeting the gut to mitigate the effects of stress is well founded.

Beta-Glucan Introduction

One method of enhancing immune function and preventing IURD would be the inclusion of oat beta-glucan (BG) at higher rates than in current equine diets. Oats have been the most popular feed ingredient for horses for centuries. With a higher percentage of protein and fat, and lower non-structural carbohydrates, oats are a better feed choice compared to other common ingredients such as corn or barley. Cereal BG have well documented effects on serum lipid profiles in humans, and the Food and Drug

Administration issued a health claim recommending 3 g BG/d to maintain healthy serum lipid profiles (FDA, 1997; Jenkins et al., 2002). There is also a U.S. patent for a β1-3, 1-

6 BG product that can be used orally to treat and prevent inflammatory disease of the gut (Johansen et al., 2009). Because BG are indigestible by mammals, they have prebiotic potential, and therefore could cause some digestive upset, like gas and

bloating; however, no studies in humans or animals have reported side effects of consuming these BG. Biological activity of BG depends on characteristics of the molecule and vary depending on the source.

Beta-Glucan Structure

BG is naturally occurring polysaccharides found in many plants, yeast, fungi, and oat and barley cereal grains. There are many different forms of BG with varying molecular mass, length, tertiary structure and degree of branching. Oat BG and barley

BG have very similar molecule structures with a straight chain of repeating units of D- glucose rings, linked by glycosidic β1-3 and β1-4 bonds (Figure 1-1). Fungal and yeast

BG are β1-3 linked glucose molecules with β1-6 branch points (Figure 1-1), making them more complex (Colleoni-Sirghie et al., 2003). The β refers to the spatial orientation of the etheric oxygen linkage (Sikora et al., 2013). Specifically β means a glucose molecule is attached to the anomeric carbon above the ring. The glycosidic bond between two D-glucose rings connects the number 1 carbon of the first ring, to the number 3 carbon of the second ring. The 1-3 linkage is also what makes BG soluble.

Although they both contain the same β linked glucose molecules, there can be great diversity in length, ratio of 1-4:1-3 linkages, molecular weight and solubility of cereal BG.

Barley tends to have a higher linkage ratio, which may make them more biologically active. Chain length and frequency of branch points seems to determine yeast BG bioactivity (Raa, 2015). Both oat and barley BG are located throughout the endosperm cell wall of the grain. Likewise, yeast BG is located in the inner cell wall attached to a surface layer of complex proteoglycans and chitin, to provide mechanical strength.

Barley and oats can contain up to 11% and 8% BG respectively, whereas yeast can contain up to 30% BG on a dry matter (DM) basis (Rieder and Samuelsen, 2012; Stier

et al., 2014). Other cereal grains (e.g. rye, wheat, sorghum, rice, corn) have the same

BG structure as oats and barley, but contain a much smaller quantity. Oats and barley

BG also have higher molecular weight compared to wheat and rye.

Although this polysaccharide is indigestible, certain immune cells possess BG specific receptors (Drummond and Brown, 2011). The binding of BG to receptors primes immune cells and stimulates innate immunity, which can increase resistance to infections (Vetvicka, 2011). Phagocytes, which possess BG receptors, respond to viral infections such as those that could occur following stress. Rodent studies have reported oat BG supplementation augmented phagocyte function, and therefore decreased the risk of infections after exercise stress (Davis et al., 2004b). Fungal and yeast BG have well documented effects on immune function; however, less is known about the immune modulating properties of oat BG (Ramakers et al., 2007; Raa, 2015).

Beta-Glucan Receptors

Recognition of non-self-structures, such as pathogens, occurs by membrane bound or secreted pathogen recognition receptors (PRR) located throughout the body

(Nieman et al., 2008). These receptors non-specifically bind two types of ligands: highly conserved regions of microorganisms known as pathogen-associated molecular patterns (PAMP) and damage-associated molecular patterns, which are cell components released during cell damage or death (Murphy et al., 2007). PAMP include, but are not limited to bacterial peptides, bacterial or viral nucleic acids, bacterial carbohydrates, peptidoglycans, chitin and lipoproteins (Murphy et al., 2007). Binding of these ligands activate immune cells and causes an appropriate immune response. BG is among the PAMP that bind PRR and are theorized to “prime” immune cells for faster responses to evading organisms. Eight receptors have been identified for fungal BG

and two of them, dectin-1 and complement receptor 3, also bind cereal BG (Rieder and

Samuelsen, 2012). Receptors for BG have been identified on most immune cells and new evidence reports these receptors are located on some epithelial and endothelial cells (Nieman et al., 2008). Through these cellular receptors, BG may directly activate phagocytic, cytotoxic and antimicrobial activities of macrophages and neutrophils.

Currently, four BG PPR have been identified as activators of the alternative complement pathway, which amplifies the innate immune response: complement receptor 3, lactosylceramide, dectin-1, and selected scavenger receptors (Akramiene et al., 2007).

The complement system is an elementary defense mechanism involved in both innate and adaptive responses to invading microorganisms. Complement receptor 3 is a transmembrane glycoprotein that is highly expressed on neutrophils, monocytes and natural killer cells. Lactosylceramide is a glycosphingolipid found in plasma membranes of many cells. Binding of the BG ligand is thought to induce macrophage inflammatory protein secretion and activation of NF-κB, and enhance neutrophil oxidative burst activity and antimicrobial functions, although the mechanisms are still unknown

(Akramiene et al., 2007). Dectin-1 has been identified as a specific receptor for 1-3 and/or 1-6 β-linked glucans and is expressed on almost all immune cells (Rieder and

Samuelsen, 2012). The binding of BG structures to dectin-1 mediates phagocytic activity and increases pro-inflammatory cytokine production, which will trigger an adaptive immune response and the production of antibodies. Although dectin-1 is more specific for fungal and yeast BG structures, there is evidence for cross-binding cereal

BG and promise that more specific cereal BG receptors exist. For example, barley BG was shown to activate transcription factor NF-κB via dectin-1 signaling pathway (Tada

et al., 2009). In both human and animal models, BG has been shown to stimulate immunity and increase resistance to many infections (Nieman et al., 2008).

Immune Effects of Cereal Beta-Glucan

BG has received much attention due to their immune-modulating properties. In other countries, BG isolated from yeast and fungi have long been considered a biological response modifier and prescribed for the treatment of cancer and infectious diseases (Mizuno et al., 1999). In the 1980s, a large number of glucans were screened for their ability to activate macrophages in vitro (Seljelid et al., 1981). The most active was β1-3, 1-6 glucans prepared from baker’s yeast. Seven years after the discovery, a feed additive named Macrogard® was created and quickly gained worldwide attention after it enhanced salmon resistance to infectious diseases (Robertsen et al., 1990).

Later the product was shown to have the same effects in chickens, pigs, calves, horses and companion animals (Krakowski et al., 1999; Brown and Gordon, 2003; Stuyven et al., 2009; Stuyven et al., 2010; Raa, 2015). Although the immune-modulating properties of yeast and fungi BG are more universally recognized, many studies report that cereal

BG are also able to enhance immune cell function.

Barley, oat and rye BG have all been shown to increase cytokine production from various murine and human immune cells in vitro (Estrada et al., 1999; Roubroeks et al.,

2000; Tada et al., 2009; Rieder and Samuelsen, 2012). Oat BG can additionally increase phagocytic ability of macrophages and neutrophils (Yun et al., 2003). Barley

BG were unable stimulate T lymphocyte proliferation, but were able to increase natural killer cell cytotoxicity and activate complement (Di Renzo et al., 1991; Vetvicka et al.,

1996). Oral administration of oat or barley BG decreased the spread of lung tumors and enhanced activity of anti-tumor antibodies in mice, respectively (Cheung et al., 2002;

Murphy et al., 2004). According to published research, barley BG seems to have a greater effect against cancer, whereas oat BG has a greater effect against infections, specifically during immuno-compromised states, which makes oat BG a substance of interest for equine athletes.

Beta-Glucan Immune Affects Following a Challenge

Several different types of animal models, including fish, have been used to demonstrate the immune enhancing properties of BG after an infectious or physical challenge. In mice, oat BG treatment resulted in enhanced in vitro phagocytic activity and protection against an in vivo challenge with Staphylococcus aureus (Yun et al.,

2003). These researchers reported injections of oat BG either 3 mg intragastrically or

500 µg intraperitoneally administered for 10 d, induced proliferation and phagocytosis in peritoneal macrophages, and enhanced resistance against coccidiosis by inducing high levels of antigen specific antibodies to Eimeria vermiformis. Intraperitoneal injections containing 5-15 mg/kg BW of barley BG, which contains the same linkages as oat BG, enhanced several immune variables and decreased mortality in fish that were challenged with two separate pathogens (Misra et al., 2006). The injections were administered four times in 2 wk intervals. Interestingly after only a few injections, fish receiving 10 mg/kg BW had the most significant increase in superoxide anion production, phagocytic activity, lymphokine production index, serum bactericidal activity and antibody titers to the pathogens. In vitro, soluble yeast BG (1.6-100 µg/mL) increased INF-γ producing cells in an antigen independent fashion (Xiao et al., 2004).

Immune cells were isolated from piglets challenged with porcine reproductive and respiratory syndrome virus, and the in vitro results suggest an enhancement of innate anti-viral immunity. Another study in piglets showed a similarly protective effect of the

same oral yeast BG against enterotoxigenic Escherichia coli (Stuyven et al., 2009). In this study, piglets were fed Macrogard® (500 g/ton feed) for 2 wk; however, individual consumption rate and body weights were not reported. Young chickens orally fed purified yeast BG were protected against Salmonella enteric infection and had increased phagocytosis, bactericidal killing and oxidative burst, compared to a control group (Lowry et al., 2005). Unfortunately, the dose of yeast BG fed was not reported.

Based on distinct cytokine profiles, yeast derived BG (0.1% of diet) fed to young chickens supported a Th-1 cell response during a challenge with Eimeria acervulina,

Eimeria maxima, and Eimeria tenella (Cox et al., 2010).

Human studies have also shown beneficial effects of BG after different types of physical stressors. Oral (0.1-20 mg BG/kg BW/d) administration of yeast BG were shown to increase antibody production to mucosal antigens and cancer, enhance defense against infections, reduce bacterial endotoxin toxicity, increase wound healing and regeneration of damaged tissues, and reduce gut infection and inflammation

(Akramiene et al., 2007; Samuelsen et al., 2014). In high risk human patients undergoing abdominal or thoracic surgery, intravenous yeast BG (0.5 mg/kg BW) 12-24 h before surgery significantly reduced infectious complications, decreased intravenous antibiotic requirement, and shortened intensive care unit length of stay (Babineau et al.,

1994). Similarly, high risk patients who received one preoperative and three postoperative doses of intravenous yeast BG (0.5-1.0 mg/kg BW) had a 39% decrease in gastrointestinal postoperative infections and death (Dellinger et al., 1999).

More relevant to the equine industry, a group of researchers from the University of South Carolina have conducted several well controlled studies using orally

supplemented oat BG, exercise stress and viral challenges. Male mice were given a

68% soluble oat BG solution dissolved in their drinking water at a concentration of 0.6 mg/mL for 10 d (Davis et al., 2004b). The study was controlled using 4 groups; exercise

+ oat BG, exercise + tap water, control + oat BG and control + tap water. Fluid consumption was unaffected by the dissolved BG or exercise, as there was no difference in daily fluid consumption between the groups. Body weight remained constant across groups and mice consumed approximately 6.2 mL fluid/d providing 3.7 mg oat BG/d (estimated 230 mg/kg BW). After a treadmill acclimation period (20 min/d for 3 d prior to exercise protocol), the mice were exercised until voluntary fatigue on 3 consecutive days. This exercise protocol was designed to mimic short bouts of heavy training, such as those experienced by military personnel or athletes. To control for extraneous stressors associated with exercise, cages with control mice were housed in the treadmill room during the exercise bouts, and deprived of food and water. Control mice were also exposed to similar handling and noise stressors. Fifteen minutes after the final bout of exercise, all mice were intranasally inoculated with herpes simplex virus

1, which causes respiratory infections in mice. Macrophages and natural killer cells are two main immune cell types that respond to viral infections, such as those that could occur following exercise-induced stress. In vitro anti-viral activity of peritoneal macrophages and cytotoxicity of natural killer cells were measured by infecting cells harvested from another population of unexercised and exercised mice. The exercise stress and viral challenge were associated with an increase in morbidity and a non- significant increase in mortality in the both control groups. Oat BG consumed before the infection counteracted the morbidity and mortality following exercise stress. Additionally,

exhaustive exercise decreased macrophage anti-viral resistance, which was ameliorated by the oat BG. However, neither exercise nor oat BG had any effect on natural killer cell cytotoxicity.

Following the ability of oat BG to ameliorate the effects of exhaustive exercise, researchers investigated a possible additive effect of sucrose (Murphy et al., 2009). The research design was similar to the previous studies with minor adjustments. Treatment groups included plain tap water, 6% sucrose water, oat BG water, or sucrose plus oat

BG. Mice were given a 50% soluble oat BG solution dissolved in their drinking water at a concentration of 0.8 mg/mL for 10 d. The mice were housed five per cage and each cage was only allowed to consume a maximum of 40 mL of treated or tap water/d. The dosage per mouse was 6.4 mg/d (estimated 400 mg/kg BW), but the authors did not report intake or BW data. After a treadmill acclimation period (20 min/d for 3 d prior to exercise protocol), the mice were exercised until voluntary fatigue on 3 consecutive days. Herpes simplex virus 1 inoculation and in vitro evaluation of anti-viral activity of peritoneal macrophages followed the same protocols as previously stated. All treatment groups, except the control, had lowered morbidity and increased macrophage anti-viral resistance after exercise, but only the oat BG plus sucrose treatment lowered mortality.

Sucrose and the combined treatment also reduced morbidity and mortality in the resting controls following the herpes challenge, and showed a trend towards increased macrophage anti-viral resistance. However, there was no significant additive effect of sucrose plus oat BG in resting controls or exercised animals. These data confirmed the ability of oat BG to decrease susceptibility to respiratory infections and increase macrophage anti-viral activity as previously shown by the same authors.

Although BG receptors dectin-1 and complement receptor 3 are present on macrophages, a direct test of their role in this mechanism needed to be explored.

Macrophages have both intrinsic and extrinsic roles in anti-viral resistance following stress. Intrinsically they inhibit viral growth within themselves and extrinsically they inactivate extracellular virus, suppress virus replication in adjacent cells and destroy other infected cells. Alveolar macrophages are an important first line of defense for respiratory infections, and therefore a good target for reducing immune suppression following exercise. Researchers from the same lab performed a follow up study with the same design and exercise protocol, but depleted mice of macrophages using clodronate-filled liposome-mediated apoptosis (Murphy et al., 2008). This in vivo technique has previously been used to selectively eliminate macrophages in various tissues (Van Rooijen and Sanders, 1994). There were 8 treatment groups: exercise + water + intact macrophages, exercise + water + depleted macrophages, exercise + oat

BG + intact macrophages, exercise + oat BG + depleted macrophages, control + water

+ intact macrophages, control + water + depleted macrophages, control + oat BG + intact macrophages, control + oat BG + depleted macrophages. The daily dose of oat

BG in this study was about 2.5 mg/mouse (estimated 156 mg/kg BW), which was lower than the previous studies. Depletion of lung macrophages negated the beneficial effects of oral oat BG, suggesting that macrophages play a direct role in the actions of oat BG.

The precise upregulated activities of these lung macrophages were not studied.

However, resting mice that were macrophage depleted and consumed oat BG were less susceptible to infection compared to resting control mice that drank plain water,

suggesting other immune cells are also stimulated by oat BG. This is not surprising, considering the many types of cells that possess BG receptors.

To address the theory that moderate exercise enhances resistance to infections, the same group of researchers investigated the possible combined effects of oat BG and moderate exercise (Murphy et al., 2004; Davis et al., 2004a). Both studies had 4 treatments groups: exercise + water, exercise + oat BG, control + water, control + oat

BG. Mice were given a 68% soluble oat BG solution dissolved in their drinking water at a concentration of 0.6 mg/mL for 10 d. The mice were housed 4 per cage and each cage had ad libitum access to BG water or tap water. These mice were exercised for 1 h/d for 6 consecutive d. This exercise protocol elicited approximately 68-78% of VO2max.

Simultaneous to macrophage anti-viral activity and natural killer cell cytotoxicity, metastatic spread of infected tumor cells and macrophage anti-tumor activity was also investigated (Murphy et al., 2004). During this study, mice consumed 3.4 mg oat BG/d

(estimated 212 mg/kg BW) and there were no BW or fluid consumption differences reported. Although not additive in their effects, moderate exercise and oat BG increased macrophage anti-viral resistance and anti-tumor activity, and decreased lung tumor foci.

Once again, natural killer cell function was not affected by oat BG, but was enhanced by moderate exercise. Morbidity and mortality were decreased using this exercise protocol and mortality was further decreased by oat BG, but it was not statistically significant.

These data suggest positive effects of both moderate exercise and oat BG on immune function, but only moderate exercise significantly reduced the risk of upper respiratory tract infections.

After the success of the previous research, a double-blind placebo controlled study in trained cyclists was conducted to determine the effects of oat BG on resting immunity, exercise-induced changes in immune function and IURD incidence (Nieman et al., 2008). Extrapolating the dose used in rodent studies, the investigators hypothesized that 5.6 g/d (estimated 80 mg/kg BW) of oat BG would augment immune function during normal training, counteract immune changes after intense exercise and decrease IURD incidence rates compared to the placebo controls. Forty trained male cyclists were recruited for the 31 d trial. The 54% oat BG concentrate was dissolved into

600 mL of Gatorade and the placebo Gatorade beverage contained an equal quantity of cornstarch instead of oat BG. The cyclists drank the supplements in two 300 mL doses each day, on an empty stomach, before their first and last meals. The beverages were given for a total of 18 d, during which time the cyclists underwent a 3 d period of exercise. The exercise protocol involved cycling for 3 h at approximately 57% maximal watts for 3 consecutive days, which represented a 70% increase in duration compared to a normal training bout for these cyclists. Compared to controls, results showed no significant differences in natural killer cell activity, PHA-stimulated lymphocyte proliferation, polymorphonuclear cell oxidative burst activity, or number of sick days.

This lack of effect could be because BG may exert stronger effects in response to an overwhelming direct viral challenge verses low dose environmental pathogens such as those cyclists were exposed. Previous research showed that oat BG has a primary influence on macrophages, but function of these cells is difficult to measure in humans and was not assessed in this study.

Effects of Beta-Glucan on Immune Function in Horses

Direct BG supplementation and possible effects on immunity are largely unstudied among the horse population. Many studies have investigated effects of dietary yeast on various health parameters including gastric ulceration, digestion, fermentation, microbial populations, behavior, sand clearance, and exercise recovery

(Landes et al., 2008; Mackenthun et al., 2013; Sykes et al., 2014; Kerbyson et al., 2016;

Powell et al., 2017; Lindinger et al., 2017). Results of these studies are mixed but overall suggest feeding yeast is safe and may be beneficial under certain circumstances. Yeast has also been shown to stimulate equine immune function in vitro and in vivo. Colostrum from mares injected with yeast BG had higher IgG and IgM content, and foals from these mares had a higher level of immunity based on an increase in phagocytic index and destructive ability of their neutrophils (Krakowski et al.,

1999). Over the course of 3 weeks, 6 pregnant mares received 3 intramuscular injections, each providing 85 mg of BG (estimated 0.17 mg/kg BW/d) derived from yeast. The injections were timed for 4-6 wk before expected delivery. Blood was collected immediately upon birth and at 7 d intervals until 56 d of age. Interestingly, the increased activity of polymorphonuclear cells in reducing nitrobluetetrazolium, occurred before the foals consumed the first colostrum, suggesting passive transfer of BG primed immune cells, although this has not been investigated in horses. These foals were also free of clinical signs of disease during the neonatal and postnatal period of life, compared to their control counterparts.

Dietary Saccharomyces cerevisiae (22 mg/kg BW/d) fed to mares for 88 d did not influence IgG-specific antibody responses following a tetanus vaccination (Koke et al.,

2013). A much shorter study reported only 15 d of dietary Saccharomyces cerevisiae

fed at either low (1 mg/kg BW/d), medium (2 mg/kg BW/d) or high (4 mg/kg BW/d) doses ameliorated the effects of glucocorticoid administration (Jacobs et al., 2017).

Regardless of dose, blood samples taken 8 h post-injection had higher CD8+ T cells compared to control horses. The low and high yeast doses also increased monocyte and lymphocyte adhesion molecules compared to control horses. At 24 h post-injection, blood parameters had returned to baseline levels. Although BG is the most abundant cell wall polysaccharide in yeast (Fesel and Zuccaro, 2016), results of the previous studies cannot be fully attributed to BG since whole yeast cells were utilized.

Research in senior horses (>20 years old) fed a prebiotic supplement has also produced encouraging results. The proprietary prebiotic supplement is derived from a multi-stage fermentation process and contains vitamins, minerals, amino acids, antioxidants and BG, although the source is undisclosed. They reported horses that were fed the prebiotic for 161 d had lower basal production of inflammatory cytokines compared to the control group (Adams et al., 2015). In response to vaccination with equine influenza or a novel antigen (ovalbumin), the dietary effects were no longer present. The follow up study fed the prebiotic for 84 d to determine the appropriate dose

(Adams et al., 2017). These horses were also challenged with equine influenza and a novel antigen, keyhole limpet hemocyanin (KHL). A seasonal rise in inflammatory cytokines was expected and occurred in all groups, but was only statistically higher than baseline in the control group. There were no effects of diet on KHL-specific vaccination response, but the prebiotic did increase influenza titers. The inherently large biological variation in these studies makes the results difficult to interpret.

Oral administration of oat BG has yet to be studied in horses. Based on research in other animal species using oat BG and the results of yeast studies in horses, BG may be able modulate immune function and combat the stress-induced increased risk of

IURD in performance horses. However, means by which dietary BG could come in contact with leukocytes and initiate a response are still controversial, and have not been investigated in horses. Understanding what happens to BG within the equine digestive tract precedes speculating how BG could come in contact with immune cells and alter immune function.

Beta-Glucan Fermentation

Repeating D-glucose rings that make up cereal BG are linked by β1-3 and β1-4 glycosidic bonds, and therefore require non-mammalian cellulase enzymes to break the

β-linkages (Colleoni-Sirghie et al., 2003). Microbes secrete extracellular enzymes into their environment to degrade large complex polymers into lower molecular weight nutrients that can be transported into bacterial cells. Microbes within the GIT utilize endoglucanases followed by cellobiohydrolases and β-glucosidases to break down BG, leaving glucose molecules for absorption by the host or fermentation by bacterial cells

(Hemsworth et al., 2016). Several types of bacteria have these enzymes, and thus the ability to ferment BG to various degrees. The most commonly studied are bifidobacteria, lactobacilli and Enterobacteriaceae because these are considered probiotic species in humans (Lam and Chi-Keung Cheung, 2013). Complete breakdown of a BG molecule requires the enzyme family of endo-β-glucanases or lichenases (Hughes et al., 2008).

Several species of bacteria have these enzymes, including many Bacillus, Prevotella and Bacteroides species, Ruminococcus flavefaciens, Clostridium thermocellum,

Fibrobacter succinogens and Streptococcus bovis (Planas, 2000). Most types of

bacteria can ferment many different carbohydrates and participate in cross-feeding, whereby one species initiates the breakdown of the BG and another species may finish it while all microbes in the area benefit from the liberation of glucose (Russell, 2002).

Fibrobacter and Ruminococcus are usually considered cellulolytic and hemicellulolytic and their main end products are succinate and acetate, respectively. Streptococci are glycolytic and amylolytic, producing large amounts of lactate or acetate if glucose supply is low. Lactobacilli are amylolytic and pectinolytic and likewise produce a large amount of lactate. Prevotella species have long been known to utilize BG as well as other structural and non-structural carbohydrates to produce large amounts of succinate

(Russell, 2002). Bifidobacteria are inulinolytic and Enterobacteriaceae are pectinolytic, but their fermentation activities both produce lactate. True bacterial substrate preference is unknown and while many species have endo-β-glucanases, it does not mean they would selectively ferment BG in vivo. In fact, species often possess enzymes that allow them to breakdown the food matrix to reach their substrate of choice (Russell,

2002). Culture based techniques create bias and limit our understanding of each microbe’s niche. In reality, the microbiota works together in ways not yet understood.

Researchers demonstrated that isolated Bacteroides and Clostridia species were able to grow in vitro with barley BG supplemented media, but lactobacilli, bifidobacteria,

Enterococcus and Escherichia coli were not as proliferative (Crittenden et al., 2002).

Although these bacteria have BG degrading enzymes, they were unable to proliferate with barley BG, suggesting they may only use BG during cross-feeding with other bacteria present. Since the complete degradation of BG leads to glucose molecules, any species of bacteria in the area could technically benefit.

Beta-Glucan Fermentation in Equine Foregut

Research of the equine microbiota is in its infancy and often can only report the phylum, class or order of microbes present. However, BG relevant species, including

Clostridium, Streptococcus, Lactobacillus, Ruminococcus, Prevotella and Fibrobacter have been found in varying quantities (Costa et al., 2015). The equine GIT microbiota varies greatly by GIT compartment, with less variability between adjacent segments.

The Firmicutes phylum, which includes Lactobacillus, Streptococcus and lactate-utilizing species, appears to dominate in the equine stomach and proximal small intestine (Costa et al., 2015). The microbiota of the distal small intestine seems to be highly conserved between humans and other animals, including horses (Hayashi et al., 2005; Dougal et al., 2012). The class of Gammaproteobacteria within the Proteobacteria phylum account for approximately 35% of this microbiota, with Firmicutes representing greater than

55%.

Absorption of glucose mainly occurs within the small intestine of the horse; therefore, complete degradation BG would have to occur within the stomach or small intestine in order for BG to be absorbed as glucose. A few relevant BG degrading species, such as lactobacilli and streptococci, do exist in the equine stomach and small intestine, although fermentation of this substrate in horses has not been investigated in vivo. In contrast to previously reported in vitro data (Crittenden et al., 2002), oat starch promoted lactobacilli proliferation in equine fecal bacterial cell suspensions (Harlow et al., 2015). The discrepancy is likely because lactobacilli can participate in cross-feeding and the previous study used pure cultures. Since Harlow and colleagues used fecal bacterial populations, it is unlikely gastric lactobacilli would be able to ferment BG without help from their hindgut counterparts. However, short chain fructo-

oligosaccharides (FOS) were undetectable in gastric samples 2 h after supplementation, suggesting utilization of FOS or rapid passage (Respondek et al.,

2007). FOS is another indigestible soluble fiber with chemical bonds similar to BG, although they contain fructose monomers and a shorter molecule chain. The authors reported an increase in streptococci bacteria in gastric samples of the supplemented horses compared to the control horses, suggesting FOS provided a suitable substrate for these species. Streptococci are one of the fastest growing bacteria and can double their population in as little as 24 min (Russell, 2002). Previous work has shown that in vitro utilization of FOS by streptococci depends on chain length, and has not been examined with equine streptococci (Culurgioni et al., 2016). Quantity of microbes within the equine stomach can vary by region and feeding state from 102-108 CFU/mL (Varloud et al., 2007). Postprandially, the high number of gastric bacteria mainly produces acetate and lactate end products. Disappearance of BG in the stomach could occur if enterocytes or other bacteria used the fermentation end products. Lactate-utilizing bacteria could further ferment lactate to propionate or butyrate (Russell, 2002). Butyrate seems to be the preferred energy source for enterocytes and the remaining VFA would be metabolized by the liver (Reynolds and Maltby, 1994). Gastric emptying in horses can occur within 1-4 h (Nadeau et al., 2000) and even considering the high number of bacteria, it is unlikely that BG are fully degraded, but the process could be initiated.

Depending on the food matrix and rate of passage, BG could be degraded to glucose in the small intestine and potentially absorbed. The number of BG relevant bacteria is lower in the proximal small intestine (total anaerobes; 2.9 x 106 CFU/mL) but digesta spends 4-7 h, depending on diet composition and quantity (Mackie and Wilkins,

1988; de Fombelle et al., 2004). Given the limit of glucose uptake in the equine small intestine, enzymatically digested food could saturate uptake before bacteria could liberate glucose from BG (Woodward et al., 2013). If purified or partially degraded BG entered the small intestine, the microbes might have time to break apart glucose molecule and allow some to be absorbed. The total volatile fatty acid (VFA) concentration in the small intestine is higher than the stomach (de Fombelle et al.,

2003), and it is more likely microbes would ferment the BG to useful VFA. However, most of the substrate utilization data is generated in vitro with limited nutrient options available in the media, and true substrate preference in vivo could vary considerably.

Likely, only a small amount of BG is fermented or absorbed in the small intestine and the rest enters the hindgut as partially degraded molecules.

Beta-Glucan Fermentation in Equine Hindgut

Once BG enters the hindgut, it would be completely degraded and fermented to acetate or propionate. The equine hindgut is dominated by cellulolytics in the Firmicutes phylum with some Bacteroidetes (Costa et al., 2015). Total anaerobe concentration in the cecum is about 107 CFU/mL. These microbes utilize all types of substrates; however, the cellulolytics appear to be very active given the high concentration of VFA produced. Soluble carbohydrates can by-pass the cecum and flow into the colon where higher populations of streptococci, lactobacilli and lactate-utilizing bacteria reside. Thus,

BG might have a limited impact on cecal microflora, but could stimulate colonic microbes. Glucose liberated from dietary components will either be absorbed in the small intestine or pass into the hindgut for fermentation. It has been recently reported that horses have limited abundance of high capacity/low affinity glucose transporter genes for GLUT2 and GLUT5 within the large colon (Taylor et al., 2012). The same

study also reported unusually high transcript abundance of GLUT1 within the large colon. It is not known whether these proteins are functional and/or apically expressed.

Woodward and colleagues reported a lack of measurable glucose uptake within the large colon, suggesting that the glucose transporter transcripts are not functionally expressed on the apical side (Woodward et al., 2013). Therefore, glucose liberated from

BG degradation in the hindgut would most likely be fermented by bacteria, and not absorbed by the horse. Glucose released from BG degradation may cause proliferation of bacterial communities and result in a positive health outcome, either immune or otherwise.

Beta-Glucan as a Prebiotic

The proportion of BG fermented to VFA versus absorbed as smaller BG molecules or glucose, would depend on several factors: relevant bacterial species present, site of digestion, substrate availability, and retention time. To be considered a prebiotic in human nutrition, the substance must make it into the large intestine and selectively stimulate “good” bacteria, thus providing a benefit to the host (Gibson, 2004;

Lam and Chi-Keung Cheung, 2013). A recent update to the definition by the

International Scientific Association for Probiotics and Prebiotics, proposed a prebiotic is an indigestible substance that is selectively utilized by host microorganisms conferring a health benefit (Gibson et al., 2017). The abundance of BG relevant bacteria in the human stomach and small intestine is much lower than the horse, so it is unlikely BG fermentation would occur prior to the human large intestine. Research has shown cereal

BG does have prebiotic properties in humans, as they can pass undigested through the

GIT and can act as a substrate for microbial fermentation (Gibson, 2004). However there is still controversy on whether they provide a true prebiotic benefit. Humans

evolved as omnivores; therefore, our large intestine has a different and lower population of cellulolytic microbes compared to horses. The human colon is dominated by

Bacteroides, Peptostreptococci and Eubacterium (Orrhage and Nord, 2000). Strict cellulolytic bacteria are needed to initiate the breakdown of cell walls and allow access to the BG, unless purified BG is initially delivered to the GIT. Although some relevant

BG fermenting bacteria exist in the human colon, strict cellulolytics are not abundant suggesting little BG fermentation occurs. Research into BG as a potential prebiotic in humans is currently inconclusive, although based on positive in vitro results, research continues.

A limited number of studies have investigated the effects of prebiotics on equine gut health and the results have been variable. The most commonly studied prebiotics in horses are short-chain FOS, galacto-oligosaccharides, Saccharomyces cerevisiae yeast fermentation products, and non-viable Lactobacillus. The prebiotic potential of BG has not directly been investigated in horses. Studies report short-chain FOS supplementation (20-75 mg/kg BW/d) improves colonic health, decreases pathogenic

Escherichia coli, increases energy production in the hindgut, and prevents microbial population changes in response to abrupt diet changes (Berg et al., 2005a; Respondek et al., 2007; Respondek et al., 2008). Galacto-oligosaccharide supplementation (30 g/d; estimated 171 mg/kg BW/d) in foals has been demonstrated to reduce pro-inflammatory responses, and authors suggest this could potentially reduce development of inflammatory diseases later in life (Vendrig et al., 2014). However, suppression of immune responses in foals that are not yet fully immuno-competent (<1 year old) could be undesirable (Perkins and Wagner, 2015). Horses had higher apparent digestibility of

DM, crude protein and neutral detergent fiber when fed low quality forage plus

Saccharomyces cerevisiae yeast fermentation products (Morgan et al., 2007). In contrast, non-viable Lactobacillus preparations fed to cannulated geldings had no effect on microbial populations, VFA concentrations or apparent digestibility (Booth et al.,

2001a; Booth et al., 2001b). Unfortunately, it is hard to draw applicable conclusions from current equine prebiotic research. Studies differ in the horses or prebiotics used, supplementation periods, and outcomes measured.

The selective bacterial stimulation and primary end products that BG yield largely depends on the host microbiota and has not been investigated in hindgut fermenters, like the horse. Oat BG as the main carbon source in cultures inoculated with cecum and colon digesta from pigs, produced lactate as the primary end product followed by acetate and propionate (Lin et al., 2011). Firmicutes and Bacteroidetes dominated these cultures and oat BG exerted low selection pressure on the microbial populations as evident by low similarities between subcultures. These results suggest BG fermentation may produce a large quantity of lactate, which could be detrimental. Lactate-utilizing bacteria will ferment lactate, but if production exceeds utilization, the intestinal pH will drop and digestive upset may follow. Another in vitro study showed oat BG selectively stimulated Lactobacillus and Bifidobacterium strains from rat fecal inoculations, where acetate was the primary VFA produced; however, they did not measure lactate production (Dong et al., 2017). Using equine fecal inoculates, Harlow and colleagues found that oat starch caused proliferation of lactate-utilizers and lactobacilli and the main end products were lactate and acetate (Harlow et al., 2015). The results of this

experiment cannot be attributed solely to BG since whole oat starch was used. For now, the potential prebiotic effect of BG in horses remains unknown.

Beta-Glucan Uptake in GIT

Alternative to complete degradation to glucose and providing a prebiotic effect, oat BG could be partially degraded by bacteria, thereby increasing the surface area and making the β1-3 linkage more available for binding to its major receptor, dectin-1

(Sahasrabudhe et al., 2016). Uptake by GIT cells would potentially allow BG to pass from the gut lumen into systemic circulation, where it could exert immune modulating effects. Several studies provide evidence that BG in the small intestine is bound by receptors and internalized by intestinal cells, resulting in systemic immune responses

(Hong et al., 2004; Rice et al., 2005). BG receptors are widely distributed on many cell types including leukocytes, M cells, epithelial and endothelial cells. Rice and colleagues showed that mice intestinal epithelial cells, possibly M cells, were able to actively take up several different types of fluorescently-labeled BG (Rice et al., 2005). Three water soluble glucans were orally dosed at 1 mg/kg BW: glucan phosphate (β1-3 linkages), laminarin (β1-3, β1-6 linkages) and scleroglucan (β1-3, β1-6 linkages). The method of uptake remains to be elucidated because the researchers were unable to identify dectin-1 receptors. Several other receptors exist for BG, but the previous study only investigated one type. Glucan phosphate, which is similar to oat BG, had a tenfold lower bioavailability compared to the other soluble BG, as evident by only one peak in plasma level 4 h after administration and a gradual decline for over 24 h. The other soluble BG had biphasic responses with two peak plasma concentrations. The researchers also showed that water-insoluble particulate glucans were not present in plasma.

Although the uptake mechanism is unclear, oral administration of fluorescently- labeled barley BG were phagocytosed by GIT macrophages and transported to the spleen, lymph nodes and bone marrow of mice (Hong et al., 2004). Macrophages degraded the BG into smaller β1-3 fragments, which were then bound by complement receptor-3 on granulocytes. Amazingly, only granulocytes with complement receptor-3 bound β1-3 fragments destroyed opsonized tumor cells. Although these results are striking, the fluorescent label could have altered the BG structure and/or affinity for receptors.

Fungal BG are thought to be pinocytosed by M cells of the Peyer’s patches and are detectable in the GALT, which is a secondary lymphoid tissue responsible for launching an appropriate immune response when required (Rieder and Samuelsen,

2012). Due to the structural similarities between fungal and cereal BG, it is probable that they share a common uptake mechanism. However, during their passage through the

GIT, BG directly interacts with many host and microbial cells and may exert immune modulation, which would negate the need for substantial uptake.

Intestinal epithelial cells and leukocytes showed increased activation of NF-κB following oral administration of oat BG in mice, suggesting BG directly interact with these cells on the intestinal surface (Rieder and Samuelsen, 2012). Likewise, dendritic cells with BG receptors extend processes through the intestinal epithelium to sample the surroundings and may directly interact with luminal BG in this manner (Rieder and

Samuelsen, 2012). Partially degraded BG may also directly interact with GIT epithelial cells affecting intestinal expression of tight junction proteins and disrupting barrier function allowing diffusion across the apical lumen. Volman and colleagues measured

lower expression small intestine tight junction proteins in mice given oral doses of pure oat BG (Volman et al., 2011). Loss of epithelial integrity produces a “leaky” gut and could result in translocations of pathogens, which are undesirable, yet the researchers reported no side effects or elevation of pro-inflammatory cytokines. Potentially a follow- up of only 3.5 d was not long enough to see detrimental effects, but other longer term studies also report no side effects. In this mechanism, smaller BG fragments could cross the apical membrane and directly interact with immune cells.

Another possibility is normal absorptive enterocytes in the GIT may absorb BG.

Although these cells are not specialized for the transcytosis of antigens, previous work has shown that they are capable of taking up soluble protein and lipid antigens, and nanoparticles (Florence, 1997; Zeissig et al., 2015). Absorptive enterocytes are highly abundant in the GIT, but human enterocytes do not apically express the major BG receptor dectin-1 (Volman et al., 2010). However, researchers did confirm dectin-1 mRNA and intracellular protein in both the small and large intestine. Dectin-1 inhibitors did not counteract the immune stimulation by BG, suggesting the effects are dectin-1 independent and either another BG receptor is involved, or paracellular passive diffusion occurs. Particulate uptake by enterocytes is affected by surface charge, particle size, hydrophobicity and the presence or absence of surface ligands (Florence,

1997). For example, greater systemic uptake occurs for particles with bound lectin

(Florence, 1997). The intestinal epithelium is constantly exposed to antigens and perhaps functional dectin-1 receptors would lead to over stimulation and inflammation.

Human research has yet to show that oral delivery of BG can pass through the epithelial basolateral membrane and enter systemic circulation. Leentjens and

colleagues gave healthy human volunteers 1000 mg of yeast BG (estimated 14 mg/kg

BW/d) for 7 d, but found no detectable level in serum (Leentjens et al., 2014). They also report the yeast BG did not modulate in vitro innate immune function or cytokine production. Unfortunately, the study was very short and the immune measurements were arguably irrelevant. Oral delivery of BG into systemic circulation requires the BG molecules to be degraded to a more applicable size for absorption or uptake. Different forms of BG, purified verses entire grain, and specific microbiotas will determine if BG undergo complete or partial breakdown as well as the location this occurs in the GIT.

Form of Dietary Beta-Glucan

Although previous work in humans has shown oat BG to promote favorable health benefits, there is conflicting evidence as to which form is most biologically active

(Charlton et al., 2012). In 1997, the US Food and Drug Administration approved health claims for the ability of soluble oat BG, from either oat bran or rolled oats, to lower total serum cholesterol and LDL-cholesterol by 5-10% (FDA, 1997; Jenkins et al., 2002). A recent study found no difference in serum cholesterol lowering ability of oat porridge, oat based cereal bars and ready-to-eat oat flakes in mildly hypercholesterolemic men and women (Charlton et al., 2012). Presently, immune function related studies involving oat BG have only demonstrated the effects of the soluble concentrated form. Potentially the void is just due to ease of application (e.g. concentrated BG supplied in drinks), but given of the complex food matrix of oats, it is unclear whether oat BG can enhance immune function when delivered in whole oat form. Being that horses will most likely consume whole oats with little to no processing, it is important to determine if this form is able produce immune modulation.

Specific Aims and Hypotheses

Stress-induced immunosuppression contributing to the high prevalence of IURD in performance horses, coupled with ineffective vaccines, highlights the need to explore supplementary methods for prevention of disease, such as dietary interventions or management changes. Oat BG fiber has been previously show to enhance immune function in many species, specifically during immunocompromised states (Estrada et al.,

1999; Davis et al., 2004b; Murphy et al., 2007). To date, no studies have investigated the effects of dietary oat BG as an immunomodulator in horses. A key stressor for many horses is transportation, and alterations in immune status and heightened risk for respiratory disease following transportation are well documented in horses (Raidal et al., 1997a; Oikawa et al., 2005; Padalino et al., 2017a). However, it remains unclear how quickly immune defenses become compromised during transport. Additionally, performance of the equine mucosal immune system has not yet been characterized in response to transport or other stressors, but remains an important first line of defense against infection.

The first specific aim of this dissertation research was to characterize changes in systemic and upper respiratory immunity in response to a stressor lasting several hours.

The hypothesis was that stress would result in cellular and mucosal alterations to both systemic and upper respiratory tract immunity that could put the horse at risk for infection. This hypothesis was examined in two studies. In the first study, the stress model involved tethering horses with their head and neck elevated for 12 hours, which prevented normal drainage of respiratory secretions through the nostrils. This stressor has been previously shown to induce short-term upper respiratory and systemic distress in horses (Raidal et al., 1996). Nasopharyngeal and systemic immune responses were

characterized before and immediately after head elevation, and for 3 days of recovery from head elevation. These data will be described in Chapter 2. In the second study, the stress model involved transporting horses by trailer for 24 consecutive hours while they were simultaneously tethered with their heads elevated. Nasopharyngeal and systemic immune responses were characterized for 2 days before transport, every 6 hours during transport, immediately after transport, and for 5 days during recovery from the trip.

These investigations will be described in Chapter 3.

The second specific aim of this dissertation research was to investigate inclusion of oat BG in the diet as a method to reduce stress-induced immunosuppression. The hypothesis was that elevated intake of oat BG would improve innate immunity and/or mitigate stress-induced immunosuppression. To test this hypothesis horses were fed

170 mg of oat BG/kg BW/d either from whole oats or in the form of a powder concentrate for 22 days. Following 18 days of dietary treatment, horses underwent 12 h of head elevation stress and immune responses were examined for 3 days following cessation of the stress. These data will be described in Chapter 2.

The third specific aim of this dissertation research was to explore the use of IgA as a potential indicator of mucosal immune status. The hypothesis was mucosal IgA would increase quickly in response to a stressor and, compared to cortisol, IgA fluctuations would more accurately represent physiological stress. The approach to test this hypothesis was to obtain samples from various mucosal secretions, including nasopharyngeal flush, nasal swabs, cecal contents, fecal liquid, and saliva and evaluate the changes in IgA in response to stress induced by head elevation or transportation.

Mucosal IgA responses were compared to other known indicators of stress, including

cortisol and shifts in leukocyte populations. These investigations will be described in

Chapters 2 and 3.

The equine industry supports 1.4 million jobs and contributes approximately $39 billion to the U.S. economy each year (American Horse Council, 2005). One IURD outbreak is estimated to cost millions of dollars to eradicate and large financial deficits for owners and riders are incurred when horses miss weeks of training or showing

(Garner et al., 2011). The ultimate goal of this research would be to diminish or prevent stress-induced immunosuppression often experienced by horses used for recreation, exhibition and competition. The economic effect of horses having less “sick” days could be global.

Figure 1-1. Example of structural differences between A) cereal beta-glucan (BG) with β1-3 and β1-4 glycosidic bonds between glucose molecules and B) yeast/fungi BG with β1-3 glycosidic bonds between glucose molecules and β1-6 branch points (Waszkiewicz-Robak, 2013).

CHAPTER 2 CAN OAT BETA-GLUCAN IMPROVE IMMUNE RESPONSES IN HORSES FOLLOWING STRESS-INDUCED IMMUNOSUPPRESSION?

Introduction

Many health benefits have been ascribed to the inclusion of oats in the human diet, including improvements to cardiovascular and immune system health. The characteristic of oats responsible for many of these health benefits is their relatively high fiber content and, more specifically, the BG fiber (Charlton et al., 2012). Although oats are considered a traditional feed for horses and have been fed for generations, research on their use in equine rations has been primarily limited to the caloric value of oats compared to other cereal grains such as corn (Lawrence, 2011; NRC, 2007). To date, research evaluating the potential benefits of oat BG in horses does not exist. Because few horses suffer from cardiovascular disease, oat BG is likely to show the biggest advantage towards maintaining proper immune system function during periods of stress. Investigating the potential benefits of oat BG to immunity in horses can build on the momentum and notoriety of previous BG research in humans, and ultimately help revitalize the equine feed oat market.

IURD is a common occurrence in racehorses and sport horses and can be caused by several viruses and bacteria, such as EIV, EHV, and Streptococcus equi

(Pusterla et al., 2011). Infected horses usually develop fever, cough and nasal discharge, resulting in poor performance, delays in training, and lower earnings. The

2007 outbreak of EIV in Australia, a country previously free of the disease, took 4 months to eradicate and was estimated to cost the industry more than $1 billion (Garner et al., 2011). In 2011, an EHV outbreak in Utah resulted in 90 confirmed cases reported

in 10 states and was responsible for the cancellation of several major horseshows in the

U.S. and Canada (USDA-APHIS, 2011).

Strenuous exercise has been shown to suppress the immune system, thereby increasing susceptibility to IURD (Murphy et al., 2008). In addition, the stress response associated with rigorous training schedules, traveling to competitions, and crowded stabling areas further contributes to immunosuppression in equine athletes (Allen et al.,

2008). Results from epidemiological and clinical studies suggest that vaccination does not provide adequate protection and alternative methods of protection should be investigated (Myers & Wilson, 2006).

Research has shown that the BG in oats directly interacts with immune cells and can strengthen response to pathogens. Although this polysaccharide is indigestible, certain immune cells (e.g., phagocytes, dendritic cells) possess BG-specific receptors

(Drummond & Brown, 2011). The binding of BG to these receptors “primes” immune cells, which stimulates innate and adaptive immunity. Previous research in rodents and humans has shown that oat BG enhances neutrophil function, increases lymphocyte proliferation and strengthens mucosal immunity (via IgA) (Reider & Samuelsen, 2012).

Oat BG was also shown to augment phagocyte function and decrease risk of infection in mice following strenuous exercise (Davis et al., 2004a). Oats have been the most popular feed ingredient for horses for centuries, and high-BG oat varieties may confer immunomodulatory benefits that have thus far remained uninvestigated.

Although previous work in humans has shown oat BG promotes favorable health benefits, there is conflicting evidence on which form is most biologically active (Charlton et al., 2012). When oat BG has been evaluated in regards to its immunomodulating

effects, studies have predominantly supplied BG as a soluble concentrate rather than as part of an intact oat matrix. From these studies, it is unclear whether a concentrated source of oat BG is more readily available to immune cells, or whether this method of

BG delivery (usually in water) is more convenient for test subjects to ingest. In equine diets, oats serve as a key calorie source; thus, increasing supply of BG would be more easily accomplished through provision of the entire oat. Ultimately, differences in BG bioavailability between BG concentrates and whole oats need to be evaluated to determine the effective form of BG administration in horses.

Immunosuppression is a natural response to stress, but it can leave the horse susceptible to infection by opportunistic pathogens. Given its identified mechanisms of action, oat BG may help to mitigate compromised innate immune responses, as well as bolster adaptive immunity, including the mucosal barrier. The objective of this research was to investigate the influence of oat BG on immune cell function following a period of physiological stress, and to evaluate potential differences in the bioavailability of BG from intact oats verses a soluble concentrate of oat BG. We hypothesize that BG bioavailability will be similar between whole oats and BG concentrate, and that elevated intake of oat BG will improve innate and mucosal immunity, and mitigate stress-induced immunosuppression.

Materials and Methods

Horses

Twelve mature horses (mean ± SEM, 552 ± 10 kg; 11.5 ± 1.4 yr) were used in a

4 X 4 Latin square design study. Horses were Thoroughbred geldings (n = 4) or Quarter horses (geldings n = 4, mares n = 4) with no prior history of chronic respiratory inflammation or infection. Horses were housed in two separate groups with ad libitum

access to bahiagrass pasture at the Institute of Food and Agricultural Sciences (IFAS)

Equine Sciences Center in Ocala, FL. During stress induction, horses were individually housed indoors in 3.7 m x 3.7 m stalls. All horses received routine healthcare, including vaccination, anthelmintic treatment and hoof care established in the standard operating procedures for the IFAS Equine Sciences Center. All animal protocols and procedures were reviewed and approved by the IFAS Animal Research Committee.

Dietary Treatments

Horses were fed 4 diets differing in amount and form of BG (Table 2-1; Table 2-

2): cracked corn (CORN), regular whole feed oats (REG; Avena sativa L. ‘AC Morgan’, grown in Saskatchewan, Canada), high BG oat cultivar (HBG; Avena sativa L. ‘Hi-Fi’; grown in Saskatchewan, Canada), and corn top-dressed with a concentrated oat BG powder (SOLBG; B-Can™, Garuda International, Inc., Exeter, CA). High BG oats and oat BG powder were fed in quantities to deliver 170 mg BG/kg BW/d. Previous research in mice has shown oat BG fed at this level for 10 d enhances neutrophil function and decreases susceptibility to infection following exercise stress (Murphy et al., 2007).

Approximately 250 mL of water and 14 mL of pancake syrup (Great Value™ Original

Syrup, Walmart Corporation, St. Bentonville, AK) were mixed with corn and oat BG powder in the SOLBG treatment to promote intake. Diets were formulated to be isocaloric and contain similar trace mineral and vitamin composition. Caloric intakes for all diets were based on calculated digestible energy of the high-BG oats (NRC, 2007). A pelleted vitamin/mineral supplement (Pacesetter Ration Balancer, Lakeland Animal

Nutrition, Lakeland, FL) was added (0.05% BW) to all diets to ensure minimum micronutrient requirements of horses at average maintenance were met (NRC, 2007).

Daily rations were split into two equal sized feedings and horses were individually fed in

3 m x 3 m outdoor pens at 0700 and 1600 h. Before and after each period, BW of horses was determined using a livestock scale accurate to ± 0.5 kg (MP800, Tru-Test,

Inc., Mineral Wells, TX). Feeding regimes were adjusted for each period, based on changes in BW. Horses also had unlimited to bahiagrass pasture, which was mowed approximately every 4 weeks to prevent seed head formation and BG accumulation.

Feed Sample Collection and Analysis

Nutrient analysis was performed on all feeds prior to the start of the study.

Additionally, pasture grass samples were collected once during each period from known grazing areas within each pasture. Feeds and pasture samples were analyzed for BG by Medallion Labs (Minneapolis, MN) using a streamlined enzymatic method (995.16) outlined by AOAC (2000). Absorbance was determined at 510 nm by spectrophotometer. All other nutrient analyses were performed by Dairy One (Ithaca,

NY) using standard wet chemistry analytical methods. The nutrient composition of all dietary ingredients and daily BG intake are presented in Table 2-1 and Table 2-2, respectively.

Experimental Design

This study was conducted from May 2014 to September 2014. The 4 experimental diets were fed to 12 horses according to a replicated 4 x 4 Latin square design. In each period, 3 horses were randomly assigned to each of the 4 diets. At the conclusion of each period, dietary treatments were switched until all horses received all diets, giving 12 observations for each experimental diet. Diets were assigned based on a logical progression of BG content, where the HBG treatment always followed the REG treatment and the SOLBG always followed the CORN treatment.

Horses received their experimental diet for 22 d. On d 18, horses underwent a

12-h period of physiological stress induced by prolonged head elevation. Following the cessation of the head elevation, horses resumed their assigned dietary treatments through d 22. At the completion of each 22-d period, horses underwent a 14-d dietary washout where they were maintained on bahiagrass pasture, corn, and the pelleted vitamin mineral supplement to ensure continual acclimation to daily grain feeding.

Previous research has reported that oat BG supplementation induced pro-inflammatory cytokine and adhesion molecule expression by immune cells and these immunological changes were eliminated within 1 week of the cessation of BG intake (Ramakers et al.,

2007). Before treatments were reallocated, each horse was examined by a licensed veterinarian for signs of respiratory disease and subsequently approved to continue on the study. A detailed experimental timeline is presented in Figure 2-1.

Stress Induction

A physiological stressor was used to challenge the immune system in order to evaluate potential improvements conferred by BG. Horses were tethered for 12 h with their heads elevated at a height of approximately 1.5 m (Figure 2-2), which has been previously shown to humanely induce physiological stress and upper airway inflammation (Raidal et al., 1997a). The tethering apparatus consisted of a metal D-ring mounted on the stall wall and an adjustable 1-inch wide nylon trailer tie. The bull snap of the tie was attached to the D-ring and the quick release strap attached to the horse’s halter. During the period of head elevation, horses had ad libitum access to Coastal bermudagrass hay provided in nets and were checked every 30 min for signs of discomfort. Without untethering the horses, water was offered via bucket every 2 h.

Individual behaviors such as coughing, sneezing or pawing were recorded. Total fecal

excretion during the 12 h stress was also measured. Head elevation began on d 18 of each period at 2000 h and ended on d 19 at 0800 h. Mean ambient temperature during head elevation was 22.7 ± 1.9°C with 81.0 ± 0.07% relative humidity. Horses were returned to pasture turnout following the head elevation. Previous reports indicate clinical, hematological and tracheal wash cytology of horses returned to normal within

72 h following 24 h of head elevation (Racklyeft and Love, 1990). Thus, the current study design will allow adequate time (14 d) for recovery before the next period begins.

Sample Collection

Biological samples collected were chosen based on the ability to indicate local and systemic immune function as well as specific innate and adaptive immunity. During each period, all samples were obtained before initiation of dietary treatment on d 0, before head elevation on d 18 (Pre-stress), and immediately after (0 h Post-stress), and

12, 24, and 72 h post-head elevation, which was the last day of dietary treatment

(Figure 2-1).

Nasopharyngeal Flush

Nasopharyngeal flush samples were collected from horses after sedation with detomidine hydrochloride (Dormosedan®, Zoetis; 0.2-0.4 mL/100 kg BW). A sterile 56 cm x 8 French-gauge catheter was passed through the nasal cavity into the pharyngeal region. Sterile phosphate buffered saline (PBS; 120 mL) was pushed through the catheter and nasal drainage was caught using a funnel collection cup system consisting of a funnel attached to a 90 mL sterile urine collection cup held below the nostrils

(UrinAssist®, Express Diagnostics International Inc., Blue Earth, MN). Correct catheter placement in the nasopharyngeal region was determined by drainage exiting both nostrils simultaneously. The catheter was removed and the procedure was repeated in

the opposite nasal cavity, passing a total of 240 mL of sterile PBS. Total flush recovery

(mean ± SEM) was 188 ± 12 mL in 3-4 collection cups per horse. Flush cups were placed on ice and transported to the laboratory where subjective evaluation of turbidity and volume of each cup was recorded. The same investigator assigned a score of 1-3 to each flush collection cup based on visible mucus quantity and turbidity. A score of 0 represented an absence of mucus and turbidity in the cup and a score of 3 represented a large quantity of mucus was present and the flush was very turbid. To account for elevated cell numbers in the flush, scores were increased by 1 if the sample was absent of mucus but was still turbid. Individual cup scores were then averaged for each horse.

The contents of each cup were filtered through a 15 x 15 cm square of 80 µm sterile nylon mesh (Component Supply Company, Fort Meade, FL) to remove mucus and nasal debris. Contents of the cups were then combined for each horse, and filtered through a 40 µm cell strainer into 50 mL conical tubes to remove large cells. Collection cups were rinsed with an additional 5-10 mL of PBS and the rinseate was poured into a separate conical tube. The samples and rinseates were centrifuged for 7 minutes at 300 x g at 4°C to obtain a cellular pellet. Subsamples of nasal flush supernatant were taken from all but the conical tubes containing rinseate and subsequently stored in polypropylene vials in 1.5 mL aliquots at -80°C until analysis of IgA was performed.

Remaining supernatant was decanted and discarded. Cell pellets in each conical tube were re-suspended in 1-2 mL PBS and combined into one conical tube. Empty conical tubes were rinsed with an additional 3-5 mL PBS to obtain any cells that remained. Conical tubes were re-centrifuged as described above and decanted.

Depending on size, cell pellets were re-suspended in 250 µL-1.3 mL PBS and placed

into 1.3 mL vials (Microtainer®, Becton Dickinson, Franklin Lakes, NJ). Total volume in each micro vial was determined and recorded. Leukocyte populations were determined using an IDEXX Procyte DX® Hematology Analyzer (Westbrook, ME), which was previously validated for nasopharyngeal flush samples (Appendix B). The machine provided cell number and percentage of total WBC, neutrophils, lymphocytes, monocytes, eosinophils and basophils. After population analysis, lymphocyte subsets were determined on remaining nasopharyngeal flush samples, as described below.

Saliva Collection

Saliva was collected by attaching two synthetic swabs (Salivette® Cortisol,

Sarstedt Inc., Newton, NC) to a 13 cm brass or stainless steel chifney bit. Each swab was tightly attached by crisscrossing two 10 cm plastic cable ties. The bit was inserted into the horse’s mouth and attached to each side of the halter with chifney snaps. Each horse was encouraged to chew on the bit for at least 5 min. Once swabs were saturated, they were detached from the bit, placed into the tube supplied by the manufacturer, and placed on ice until processing. Tubes containing swabs were centrifuged at 1700 x g for 10 minutes at 4°C to extract saliva (approximately 2 mL per horse per sampling time). Samples were stored in polypropylene vials in 0.1-1.0 mL aliquots at -80°C until analysis of cortisol and IgA could be performed.

Fecal Collection

Freshly voided feces were collected in a plastic bag, sealed and stored on ice until processing. All processing occurred within 2 h of collection. Feces were homogenized and a composite 200-g sample was placed in a plastic bag (Whirl-Pak®,

Nasco, Fort Atkinson, WI) and stored at -80°C until analyzed for DM content.

An additional 200 g of feces was manually squeezed through two layers of cheesecloth (Electron Microscopy Sciences, Hatfield, PA) to obtain fecal liquid. The volume recovered was recorded and the fecal liquid was stored in polypropylene vials in

2-mL aliquots at -80°C until analyzed for IgA content.

Blood Collection

Whole blood and serum were collected via jugular venipuncture using evacuated tubes (Vacutainer®, Becton Dickinson Co., Franklin Lakes, NJ). Whole blood (16 mL) was collected in tubes containing no anticoagulant for harvesting of serum. Serum samples were allowed to clot for approximately 1-2 h before centrifugation at 3300 x g for 15 min at 4 ˚C. Serum was harvested and stored in polypropylene vials in 1-2 mL aliquots at -80˚C until analysis of cortisol and IgA were performed. Whole blood (32 mL) was collected in tubes containing sodium heparin for harvesting of PBMC. Blood collected in sodium heparin tubes was continuously mixed on a tube rotator at room temperature until PBMC isolation. Whole blood (2 mL) was collected in tubes containing tri-potassium ethylenediaminetetraacetic acid (VetCollect™, IDEXX Laboratories,

Westbrook, ME) and placed on ice until complete blood count analysis was performed

(Procyte DX® Hematology Analyzer, IDEXX Laboratories, Westbrook, ME). The machine provided cell number and percentage of total WBC, neutrophils, lymphocytes, monocytes, eosinophils and basophils.

PBMC Isolation

Reagents for PBMC isolation included PBS (MediaTech Inc, Manassas, VA), lymphocyte separation medium (LSM, MP Biomedicals, Solon, OH), trypan blue (0.4%,

Sigma-Aldrich, St Louis, MO), dimethyl sulfoxide (DMSO, MediaTech Inc, Manassas,

VA), and fetal bovine serum (FBS, Atlanta Biologicals, Lawrenceville, GA).

Blood samples collected for PBMC isolation were transported to the laboratory and processed within 3 h of collection. During transport and until processing, blood tubes were continually mixed by gentle inversion at room temperature. Using sterile technique, sodium heparin tubes of whole blood were transferred into a 50-mL conical tube and centrifuged at 1200 x g for 30 min at 18°C. After centrifugation, plasma was aspirated off to within 0.5 cm above the red blood cells and discarded. The layer of

WBC that formed on top of the red blood cells was pipetted off and placed in a new 50 mL conical tube. The volume was brought up to 35 mL using PBS and the mixture was gently inverted. The diluted white blood cell mixture was slowly layered over 15 mL of

LSM while maintaining a sharp interface. Vials were centrifuged at 400 x g for 25 min at

18°C. Plasma was aspirated off to within 0.5 cm above the PBMC buffy coat and discarded. The buffy coat (containing PBMC) and all the LSM (approximately 15 mL) was removed and placed in a separate conical tube. The tubes were brought up to a volume of 45 mL with PBS and gently inverted. Tubes were then centrifuged at 200 x g for 10 min at 25°C (room temperature). The supernatant was suctioned off and discarded, leaving the PBMC pellet undisturbed. The PBMC pellet was then broken up and rinsed with 40-45 mL of PBS. The vials were centrifuged again for 10 min at 200 x g. The supernatant was suctioned off and discarded and the PBMC pellet was re- suspended in 1 mL of PBS. Using trypan blue exclusion, live cells were counted using light microscopy at 40x magnification by loading a hemacytometer with 10 µL of a mixture containing 90 µL trypan blue and 10 µL PBMC. A maximum of 9 x 106 live cells per vial were added to freezing media and frozen in 1-mL aliquots in cryogenic vials.

Freezing media consisted of 10% DMSO and 90% FBS. Cryogenic vials were placed in

a Nalgene® Mr. Frosty Cryo 1°C Freezing Container and initially placed in a -80°C freezer for 24 h. The vials were then removed and stored in liquid nitrogen until lymphocyte proliferation analyses were performed.

Lymphocyte Proliferation

Reagents for lymphocyte proliferation included Roswell Park Memorial Institute-

1640 medium (RPMI, Hyclone Laboratories Inc, Logan UT), fetal bovine serum (FBS,

Atlanta Biologicals, Lawrenceville, GA), 2-mercaptoethanol (Fisher Scientific, Fairlawn,

NJ), gentamycin (MediaTech Inc, Manassas, VA), GlutaMax 100x (Gibco Invitrogen cell culture, Grand Island, NY), hydroxyethyl piperazineethanesulfonic acid (HEPES,

MediaTech Inc, Manassas, VA), trypan blue (0.4% Sigma-Aldrich, St Louis, MO), PBS

(MediaTech Inc, Manassas, VA), Concanavalin A (Con A, Sigma-Aldrich, St Louis, MO), lipopolysaccharide (LPS, Sigma-Aldrich, St Louis, MO) and pokeweed mitogen (PWM,

Sigma-Aldrich, St Louis, MO).

Each mitogen for this assay was chosen based on the ability to stimulate a different population of immune cells. Con A predominately stimulates the T cell population, while PWM and LPS predominately stimulate B cells (Bell et al., 2001). In addition, two concentrations of Con A were evaluated to show a possible titration effect.

The tritiated [3H] thymidine incorporation method was used to assess lymphoproliferative responses. To maintain cell viability, three samples were removed from liquid nitrogen at a time and partially thawed in a 56°C water bath for 1-2 min. The ice chunk created from partial thawing was immediately emptied and dissolved into 13 mL of lymphocyte culture medium in an effort to limit PBMC exposure to the DMSO in the freezing medium. Lymphocyte culture medium consisted of 86% RPMI-1640 medium, 10% FBS, 0.1% 2-mercaptoethanol (50 mM), 0.1% gentamycin (50 mg/mL),

1% L-glutamine (200 mM), and 2.5% HEPES (25 mM). The same batch of culture medium and mitogen preparations were used for all study samples. Vials with thawed

PBMC and medium were centrifuged for 10 min at 250 x g at 10°C. Supernatant was removed and the cell pellet was re-suspended in an amount of culture medium appropriate for cell pellet size (approximately 300-500 µL). Using trypan blue exclusion, live cells were counted using light microscopy at 40x magnification by loading a hemacytometer with 10 µL of a mixture of a 90 µL trypan blue and 10 µL PBMC.

Viability of cells varied from 52-89%. Aliquots of 50 µL of the cell suspension (2 x 105 live cells/well) were pipetted into 96-well clear, round-bottom plates (Corning Inc.,

Corning, NY). Most samples were analyzed in triplicate for each mitogen concentration; however, a small number of samples were analyzed in duplicate. PBMC samples obtained from a donor horse that was not part of this study were included on each plate to serve as an interassay control. Cells in separate wells were stimulated with 50 µL of either 1 µg/mL Con A, 2 µg/mL Con A, 10 µg/mL LPS, 1 µg/mL PWM, or culture medium (no mitogen-control). Optimal concentrations of cells, mitogen concentrations and incubation times were determined prior to the start of this study (Appendix F). The cells were incubated at 37ºC for 78 h in 6% CO2. Sixty hours into the incubation period,

25 µL of [3H] thymidine (0.25 µCi/well; PerkinElmer, Boston, MA) was added to each well and then the plate was returned to the incubator. Eighteen hours after the [3H] thymidine was added, wells were harvested using a FilterMate™ Harvester

(PerkinElmer, Turku, Finland) onto printed Filtermat A glass fiber filter paper (size 90 x

120 mm; Wallac Oy, Waltham, MA) and dried in a 900 W household microwave oven

(Westing House, Lake Forest, IL) for 90 sec. The filter paper was then sealed in a

plastic bag with 3.5 mL of scintillation fluid (Betaplate Scint, PerkinElmer Life Sciences,

Waltham, MA). The scintillation fluid was evenly distributed over the filter paper to ensure saturation. [3H]-thymidine incorporation in PBMC DNA was measured using a liquid scintillation and luminescence counter (MicroBeta® Jet 1450, PerkinElmer

Precisely, Turku, Finland) using standard parameters for tritium. Inter-assay variation for stimulated cells and non-stimulated cells was 8.2% and 17.4%, respectively. Data were analyzed as counts per minute (CPM) and as a stimulation index (SI) which is the ratio of stimulated culture CPM to non-stimulated culture CPM.

Lymphocyte Subsets

Specific lymphocyte subsets present in whole blood and nasopharyngeal flush were measured using monoclonal anti-equine surface cell markers (AbD Serotec®) for

CD4 (clone CVS4; labeled with R-phycoerythrin), CD8 (clone CVS8; purified) and B lymphocytes (clone CVS36; labeled with fluorescein isothiocyanate isomer 1) and enumerated by flow cytometry (FACSort™, Becton Dickinson, San Jose, CA) and analyzed using fluorescence-activated cell sorting (FACS) computer software (FlowJo®

LLC, version 10, Ashland, OR). Purified CD8 antibody was labeled with a reactive dye labeling kit in the lab (Alexa Fluor™ 647, Invitrogen™, Carlsbad, Ca). Greatest separation of lymphocyte populations on the FACS plot was achieved with undiluted

CD4 antibody, 1:32 and 1:10 dilution of the CD8 and B cell antibodies, respectively

(data not shown). Propidium iodide (PI) was included in each sample to distinguish between live and dead cells. FACS plots were first gated for live cells only, then of those live cells, lymphocytes were gated and within that lymphocyte population, the specific markers were determined. Each sample was gated using a quadrant gate which divides the FACS plot into quadrants displaying positive cells for two chosen markers on the x

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and y-axis, respectively (Appendix G, Figure G-1). Samples were additionally gated by each specific marker (y-axis) and by forward scatter (x-axis; Appendix G, Figure G-2).

For each cell marker, the percentage of positive cells in quadrant gates and single gates were averaged.

Neutrophil Function

To measure whole blood neutrophil function, Streptococcus equi equi (strain

9528™, ATCC®, Manassas, VA) was grown by the University of Florida Clinical

Microbiology Laboratory. Bacteria were inoculated into thioglycollate broth and incubated at 37°C for 48 h. To determine the concentration, bacteria were plated onto

Columbia blood agar and counted. Approximately, 7 vials of live bacteria at a final volume and concentration of 60 mL and 1.5 x 108 cells/mL, respectively, were provided at the beginning of the study. To prevent contamination, upon arrival sealed vials containing live bacteria were immediately heat killed by placing in a 56°C water bath for

30 min. Dead bacteria were harvested by centrifugation at 900 x g for 15 min and pooled to create a homogenous stock. For convenience, the stock bacteria were stored in 9 mL aliquots at 4°C until labeled with PI. Due to the prolonged storage required for this study, optical density (OD) of each vial was determined using a spectrophotometer

(Genesys™ 20, Thermo Scientific™, Waltham, MA) at the beginning of every period to track potential changes or inconsistences to the dead bacteria population. Vials maintained similar OD values throughout the study. To avoid decay of PI fluorescence, the quantity of bacteria required for one period was labeled at the beginning of each period. PI-labeled bacteria were stored in the dark at 4°C.

Whole blood neutrophil function was measured using dual color flow cytometry, which simultaneously measures phagocytosis and oxidative burst. A previous study

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optimized dihydrorhodamine (DHR) loading dose in equine neutrophils; however, bacteria (Streptococcus aureus) to neutrophil ratio and incubation time to induce maximum neutrophil phagocytosis, were optimized for the current study using

Streptococcus equi equi (Vineyard, 2008). Briefly, neutrophils were loaded with 5 μM of non-fluorescent DHR and incubated at 37°C for 10 minutes with constant rotation. PI- labeled Streptococcus equi equi was added to create a bacteria: neutrophil ratio of 40:1 and the samples were incubated for an additional 30 minutes at 37°C. After incubation, samples were processed for flow cytometry using an Immunoprep reagent system

(Coulter Corporation, Miami, FL) and an automated Q-Prep Epics immunology workstation (Coulter Corporation, Miami, FL). Neutrophils which are PI positive have phagocytosed the labeled bacteria (Appendix H, Figure H-6). Neutrophils that have undergone phagocytosis-induced oxidative burst, will cause DHR to fluoresce and will subsequently fluoresce both PI and DHR positive (Appendix H, Figure H-6). Neutrophils that did not respond to the bacteria will have little or no fluorescent signal (Appendix H,

Figure H-4). The percentage of neutrophils that underwent phagocytosis and phagocytosis-induced oxidative burst was determined from the acquisition of >10,000 events/sample using flow cytometry (FACSort™, Becton Dickinson, San Jose, CA) and analyzed using FACS computer software (FlowJo® LLC, version 10, Ashland, OR).

Function of neutrophils recovered in the nasopharyngeal flush was determined using slight modification of the last step of the whole blood neutrophil function assay.

After incubation with bacteria, 800 µL of PBS was added to each sample to increase the total volume. Neutrophils were fixed by adding 100 µL of 1% paraformaldehyde solution

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to each sample and analyzed using flow cytometry (FACSort™,Becton Dickinson, San

Jose, CA) and FACS computer software (FlowJo® LLC, version 10, Ashland, OR).

IgA ELISA

IgA concentration was measured in nasopharyngeal flush, saliva, fecal liquid, and serum using an equine-specific enzyme-linked immunosorbent assay (ELISA,

Immunology Consultants Laboratory, Inc., Portland, OR) according to the manufacturer’s instructions. Absorbance was determined at 450 nm by microplate reader (Synergy HT™, Bio-Tek® Instruments, Inc., Winooski, VT) and concentrations were calculated using a 4-parameter logistic curve. Each ELISA plate included an inter- assay control sample collected from a horse not participating in the current study.

Dilution of saliva samples varied from 1:1,000 up to 1:40,000. Inter- and intra-assay coefficients of variation (CV) were 5.7% and 3.1%, respectively. Dilution of nasal flush samples varied from 1:70 up to 1:2,500. Inter- and intra-assay CV were 3.0% and 2.7%, respectively. Dilution of fecal liquid samples varied from 1:2 up to 1:100. Inter- and intra- assay CV were 3.7% and 3.3%, respectively. Serum samples were diluted 1:5,000 or

1:10,000. Inter- and intra-assay CV were 4.1% and 3.0%, respectively. Although specific to equine-IgA, this ELISA measures total IgA and not just sIgA. Using the IgA concentrations, a secretory index was calculated to account for possible leakage of serum IgA into mucosal samples. The secretory index was calculated by:

(mucosal IgA / serum IgA) / (mucosal total protein / serum total protein)

A ratio of >1 indicates active secretion of sIgA by the mucosal tissue in addition to serum IgA leaking into the mucosal space (Mathews, 1981).

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Total Protein

Total protein in samples was determined using the Pierce™ Coomassie Plus

Protein Assay kit (ThermoFisher™ Scientific, Waltham, MA) per manufacturer’s instructions for using microplates. Briefly, 10 µL of standard or unknown sample was pipetted into 96-well clear, flat bottom plates. Approximately 300 µL of the Coomassie

Plus Reagent (warmed to room temperature) was added to each well and mixed on a plate shaker for 30 seconds. The plate was incubated on a level surface for approximately 12 min. The absorbance was determined at 595 nm by microplate reader

(Synergy HT™, Bio-Tek® Instruments, Inc., Winooski, VT) and concentrations were calculated using a 4-parameter logistic curve. Standards can be kept at 4°C for several days.

Cortisol EIA

Serum and saliva cortisol were evaluated before head elevation (Pre-stress) through 24 h post-stress. Cortisol concentrations were measured using an enzyme immunoassay (EIA) kit (DetectX®, Arbor assays®, Ann Arbor, MI) with some alterations to the manufacturer’s instructions. After consulting with the manufacturer’s technical services, the following procedure was determined. Thawed saliva samples were centrifuged at 9,300 x g for 10 minutes at 4°C. Half of kit standard number 6 (250 µL;

100 pg/mL) was used to make an additional standard (number 7) with a final concentration of 50 pg/mL. The final standard curve only included standards 2-7 (range of detection 1,600 – 50 pg/mL). The volume of sample and standard added to the plates was doubled to 100 µL. Therefore, the quantity of assay buffer was increased to 125 µL and 100 µL in the non-specific binding wells and maximum binding wells, respectively.

The conjugate and antibody were still added at the recommended volume (25 µL),

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resulting in total volume of 150 µL in each well. Additionally, the plates were shaken at room temperature for an additional hour (2 h total). The absorbance was determined at

450 nm by microplate reader (Synergy HT™, Bio-Tek® Instruments, Inc., Winooski, VT) and concentrations were calculated using a 4-parameter logistic curve. Each EIA plate included an inter-assay control sample collected from a horse not participating in the current study. All serum samples were diluted 1:100 with inter- and intra-assay CV of

8.7% and 6.1%, respectively. Saliva sample dilution varied considerably (1:2 up to

1:100) with inter- and intra-assay CV of 11.8% and 7.4%, respectively.

Fecal Dry Matter Analysis

Fecal samples were thawed at 4°C and approximately 100 g of thawed feces was placed into 14.6 x 12.4 cm aluminum foil tins. Samples were dried in a forced air oven at 60°C for at least 3 d or until sample dry weight was stable. Percentage of DM was determined by difference between wet and dry sample weight.

Statistical Analysis

All statistical analyses were conducted using a mixed model ANOVA with repeated measures using SAS statistical software (Proc mixed; version 9.3, SAS

Institute Inc., Cary, NC). For testing purposes, the 12 horses were divided into 4 groups, each group including 1 mare and 1 Thoroughbred. Each period had 3 horses on each diet and at the end of 4 periods there were a total of 12 observations per diet. The statistical model included fixed effects of period, group, diet, time, and the diet*time interaction. The random effect of horse within each group was also included in the model. For some variables, gender and age were included in the model. Repeated measurements of time were analyzed using a compound symmetry covariance

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structure. Residuals were examined for normality and were log10 transformed prior to analysis if non-normally distributed.

To determine if oat BG had an effect on immune variables before horses were exposed to the stressor, data were analyzed in two separate phases. Phase one (diet phase) included data collected on d 0 and d 18. Phase two (stress phase) included data collected on d 18 (pre-head elevation) through 72 hours post-head elevation. Where necessary, d 0 samples were used as a covariate based on a positive covariance between d 0 samples and diets. Relationships between variables were determined by calculating Pearson correlation coefficients, and simple linear regression was analyzed using the Proc Corr and Proc Reg procedures in SAS, respectively (Version 9.3, SAS

Institute Inc., Cary, NC). Effects were considered significant when P ≤ 0.05 and trends were identified at P < 0.10. When treatment effects were absent, data were pooled across treatments and presented by time. Data were presented as means ± SEM.

One horse was diagnosed with early onset pasture-associated asthma at the beginning of period 4 while on the SOLBG treatment. He was excluded from participating in the remainder of the study; however, his diagnosis was unrelated to his previous involvement in the study. Data collected from this horse during period 4 was omitted from the statistical analysis. A small number of nasopharyngeal and saliva samples were excluded from analysis if known to be contaminated with blood or saliva.

During period 2 of the study, the antibodies for lymphocyte subset determination became unavailable from manufacturer. Therefore, several samples during periods 2 and 3 did not contain all 3 antibody markers for exact numerical determination of CD4+,

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CD8+ and B lymphocytes. When applicable, lymphocyte subsets in samples that did not contain all 3 markers were determined by difference.

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Table 2-1. Nutrient composition and beta-glucan (BG) content provided by each feed ingredient. High BG Oat BG Regular Ration Nutrient1 Corn Pasture forage3 oats powder feed oats Balancer2 DM, % 90.1 93.9 90.8 88.8 90.9 93.0 DE, Mcal/lb 1.37 1.78 1.39 1.80 1.52 1.23 CP, % 13.80 2.00 11.20 7.10 41.30 16.10 ADF, % 15.90 0.20 16.60 3.80 8.00 31.00 NDF, % 35.00 0.40 32.10 8.00 12.00 61.90 WSC, % 3.50 5.50 2.00 4.20 12.40 8.20 ESC, % 2.00 2.70 1.30 2.70 10.30 5.20 Starch, % 38.50 1.40 35.90 70.40 2.20 2.70 NFC, % 42.60 92.50 49.10 79.60 39.30 11.30 Crude fat, % 4.30 0.50 3.10 3.70 2.90 2.80 Ca, % 0.07 0.07 0.08 0.20 2.30 0.39 P, % 0.51 0.41 0.27 0.24 0.670 0.31 BG, % 4.00 79.40 2.85 <0.60 <0.60 1.19 1With the exception of DM, all values presented on 100% DM basis. 2Pelleted vitamin-mineral supplement added to each diet. 3Average of bahiagrass pasture samples collected 4 times during the study.

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Table 2-2. Beta-glucan (BG) quantity provided by each diet and average quantity of concentrate fed per day.

mg BG/kg BW Total mg BG/kg BW Daily amount of concentrate Diet /d /d1 ,kg DM2

HBG 170 369 2.16 ± 0.04 SOLBG3 190 389 1.66 ± 0.04 REG 112 311 2.10 ± 0.04 CORN4 <20 219 1.66 ± 0.04 1Includes BG provided from the dietary treatment, pasture intake (estimated 1.5% BW) and pelleted vitamin-mineral supplement. 2Means ± SEM; quantities are on 100% DM basis and include treatment diet and pelleted vitamin-mineral supplement. 3Average of 120 g/d of concentrated oat BG powder top-dressed on corn. 4BG content of corn was below detectable range of laboratory analysis.

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Repeated for each of 4 dietary treatments

22-day dietary treatment 14-day diet wash-out Stress induction by head elevation

day 0 1… 18 19 20 21 22 23… 37

Post-Head Elevation Stress Phase 1

Pre- 0 h 12 h 24 h 72 h stress

Phase 2

Figure 2-1. Example of treatment period, sample collection and wash-out timeline.

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Results

Dietary Consumption

All horses readily consumed all diets with few instances of feed refusal.

Stress Induction

The head elevation procedure was very well tolerated throughout the entire study as none of the horses exhibited signs of extreme discomfort during the 12 h stress

(Figure 2-2). Coughing was the most common symptom noted by the volunteers monitoring the horses.

Cortisol

Cortisol levels in serum and saliva were unaffected by dietary treatment during phase 1 and 2. As expected, serum and salivary cortisol were both higher at 0 h post- head elevation compared to before head elevation (P < 0.05; Table 2-3). Serum cortisol decreased by 12 h post and was lower than before head elevation (P < 0.0001). At 24 h post, it had returned to the level observed before head elevation. Salivary cortisol measured at 12 and 24 h post-head elevation were both lower than 0 h post (P < 0.05) and neither differed from pre-head elevation. Serum and salivary cortisol were not correlated during this study (R2 = 0.002, P = 0.49).

Nasopharyngeal Mucus

The mucus score was higher at 0 h post-head elevation but returned to normal

12 h post-head elevation (Table 2-4). Although statistically significant, scoring the flushes from 1 to 3 produced unimpressive mean scores. Looking at these scores another way, before head elevation only 26% of horses had a mucus score of ≥ 2 but at

0 h post-head elevation, 41% of horses scored ≥ 2. Mucus scores may have normalized

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quickly because the horses were returned to pasture following the head elevation, allowing natural postural drainage to occur.

Leukocyte Populations

Nasopharyngeal leukocytes- phase 1

Total leukocytes and neutrophils in nasopharyngeal flush were not affected by diet or time during phase 1, but a diet*time interaction was detected for both (P = 0.04,

P = 0.01, respectively). There was no difference in number or percentage of total leukocytes (Figure 2-3) or neutrophils (Figure 2-4) between diets on d 0. When horses received the REG diet, total leukocytes (P = 0.01) and neutrophils (P = 0.006) in the nasopharyngeal flush were lower on d 18 than d 0. When horses received HBG or

SOLBG, total leukocytes in nasopharyngeal flush were greater than CORN (P = 0.01, P

= 0.003, respectively) and REG (P = 0.07, P = 0.02, respectively) on d 18 (Figure 2-3).

Nasopharyngeal neutrophils were higher when horses received HBG than CORN (P =

0.03) and REG (P = 0.0002) on d18 (Figure 2-4). The percentage of neutrophils in nasopharyngeal flush was higher when horses received HBG compared to REG (P =

0.007) and SOLBG (P = 0.02) on d 18 (Figure 2-5).

The number of lymphocytes and monocytes in the nasopharyngeal flush were not affected by period or time during phase 1, but lymphocytes were affected by diet (P

= 0.08) and both exhibited a diet*time interaction (P = 0.05, P = 0.06, respectively).

There was no difference in lymphocytes (Figure 2-6) or monocytes Figure 2-7) between the diets on d 0. From d 0 to d 18, lymphocytes (P = 0.02) and monocytes (P = 0.007) decreased in horses receiving REG. Lymphocytes tended to increase from d 0 to 18 when horses were fed SOLBG (P = 0.07). On d 18, a higher number of lymphocytes

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and monocytes were observed in horses fed SOLBG compared to REG (P = 0.0005, P

= 0.001, respectively) and CORN (P = 0.01, P = 0.08, respectively).

The number of eosinophils were unaffected by diet, time and diet*time during phase 1 (data not shown), although the percentage had a tendency to be affected by diet (P = 0.08) and diet*time (P = 0.09). Overall, a higher percentage of eosinophils was observed when horses were fed REG compared to HBG (P = 0.02) or CORN (P = 0.06;

Figure 2-8). Percentage of eosinophils in nasopharyngeal flush did not differ between diets on d 0, but was higher on d 18 when horses received REG compared to the other three diets (P < 0.05).

Some nasopharyngeal leukocyte populations fluctuated by periods during both phases of this study. To characterize this effect, all data (d 0-22) were pooled across all diets and times and analyzed by period. The number and percentage of nasopharyngeal eosinophils was higher during periods 1 and 2 compared to periods 3 and 4 (P < 0.05; Table 2-5). In contrast, the percentage of nasopharyngeal neutrophils was lower during periods 1 and 2 compared to 3 and 4 (P < 0.05; Table 2-5). The percentage of nasopharyngeal lymphocytes tended to be higher during period 2 compared to period 1 (P = 0.06; Table 2-5).

Nasopharyngeal leukocytes- phase 2

During phase 2, total leukocytes and neutrophils were unaffected by period, diet or the diet*time interaction but were affected by 12 h of head elevation (P < 0.0001).

The number of total leukocytes and neutrophils in nasopharyngeal flush increased (P <

0.0001) at 0 h post-head elevation, but returned to pre-head elevation values by 12 h post (Table 2-6). However at 72 h post, these cell populations were lower than that observed before head elevation (P < 0.05).

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The number of nasopharyngeal lymphocytes, monocytes and eosinophils were unaffected by period, diet and diet*time, but were affected by head elevation (P <

0.0001). During phase 2, the number of nasopharyngeal lymphocytes, monocytes and eosinophils were higher (P < 0.05) following head elevation at 0 h post, but then decreased and were lower (P < 0.05) at 72 h post compared to before head elevation

(Table 2-6).

The percentage of nasopharyngeal leukocytes is based on the percentage of total mononuclear cells recovered in the flush. The percentage of neutrophils was the only cell population to increase at 0 h post-head elevation (P < 0.0001; Table 2-7), whereas lymphocytes, monocytes and eosinophils decreased (P < 0.05); lymphocytes weren’t statistically lower until 12 h post (P = 0.008). The percentage of neutrophils and monocytes remained elevated or reduced, respectively, through 24 h post (P ≤ 0.06), but were normal at 72 h post-head elevation. The percentage of lymphocytes recovered by 24 h post and remained stable at 72 h post. The percentage of eosinophils recovered by 12 h post-head elevation and remained there through 72 h post.

Whole blood leukocytes- phase 1

During phase 1, total leukocytes in whole blood were not affected by period, time or diet*time, but tended to differ by diet (P = 0.09). There was no difference in total leukocytes between diets on d 0 (Figure 2-9). Eighteen days after dietary treatments began, total leukocytes in whole blood were higher when horses were fed HBG compared to the three other diets (P < 0.01). The increase in total leukocytes reflects a numerical increase in neutrophils when horses received HBG; however, their population did not differ between diets (P = 0.14). Before dietary treatments began, there was no

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difference in neutrophils, but on d 18 horses on the HBG diet had more neutrophils compared to the other three diets (P < 0.06).

The number of circulating lymphocytes was unaffected by diet and the diet*time interaction but did decrease over time (P = 0.02). Although lymphocytes decreased from d 0 to d 18 in all diets, statistical significance was only reached when horses received

REG (P = 0.05; Figure 2-10).

The percentage of monocytes and eosinophils were unaffected by diet and time but both exhibited a diet*time interaction. On d 0 of phase 1, no differences existed between diets for either cell population (Figure 2-11; Figure 2-12). On d 18, horses fed

CORN tended to have a higher percentage of monocytes (P = 0.09; Figure 2-11) and a lower percentage of eosinophils (P = 0.07; Figure 2-12). Horses fed SOLBG had a lower percentage of monocytes (P = 0.06) and horses fed HBG had a higher percentage of eosinophils (P = 0.05; Figure 2-15) compared to d 0. Percentage of neutrophils and lymphocytes, and the number of monocytes and eosinophils were all unaffected by period, time, diet and the diet*time interaction during phase 1.

The number and percentage of whole blood lymphocytes was lower during periods 1 and 2 compared to 3 and 4 (P < 0.05; Table 2-5). In contrast, the number and percentage of whole blood eosinophils was higher during periods 1 and 2 compared to periods 3 and 4 (P < 0.05; Table 2-5). Interestingly, nasopharyngeal eosinophils followed the same pattern as whole blood. During period 3, the percentage of monocytes was higher compared all other periods (P < 0.05; Table 2-5). The neutrophil to lymphocyte ratio was highest during period 2 compared to period 1 (P = 0.02) and 3

(P = 0.004) but similar to period 4 (Table 2-5).

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Whole blood leukocytes- phase 2

Circulating lymphocytes had an immediate response to head elevation with a modest but significant decrease in number at 0 h post (P = 0.008; Table 2-8). However by 12 h post-head elevation, total circulating WBC, neutrophils, lymphocytes and monocytes increased (P < 0.0001) to values much higher than before head elevation

(Table 2-8). The ratio of neutrophils to lymphocytes increased (P = 0.01) at 0 h post- head elevation and returned to normal at 24 h post (Table 2-8). At 72 h post, only total leukocytes and lymphocytes remained elevated above pre-head elevation (P < 0.05).

The number of circulating eosinophils were unaffected by head elevation (P = 0.63;

Table 2-8). Percentage of circulating neutrophils was higher at 0 h post-head elevation

(P =0.03) and crested above pre-head elevation at 12 h post (P = 0.0001; Table 2-9) before returning to normal at 24 h post-head elevation. The percentage of circulating lymphocytes followed the opposite pattern, where they were lower at 0 h post-head elevation (P = 0.04; Table 2-9), and subsequently lower than pre-head elevation at 12 h post (P = 0.002) before returning to normal at 24 h post-head elevation. Monocytes behaved similar to lymphocytes; however, they continued to decrease below pre-head elevation at 24 h post (P = 0.0003; Table 2-9) before recovering at 72 h post-head elevation. Eosinophils also decreased after head elevation reaching significance at 12 h post (P = 0.02; Table 2-9), but recovered by 24 h post.

Serum cortisol level at 0 h post-head elevation was not correlated to the number of neutrophils or lymphocytes in whole blood at 0 h, 12 h, 24 h or 72 h post (Table 2-

10).

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Lymphocyte Subsets

Nasopharyngeal lymphocytes- phase 1

The number and percentage of B and CD8+ lymphocytes present in nasopharyngeal flush were not affected by period, diet, time or diet*time during phase 1

(data not shown).

The number of CD4+ lymphocytes was unaltered by period and time, but tended to differ by diet (P = 0.07) and diet*time (P = 0.09). Before dietary treatments began, the number of CD4+ lymphocytes was similar between diets (Figure 2-13). On d 18 of treatment, horses receiving REG had less CD4+ cells compared to d 0 (P = 0.008) and compared to all other diets on d 18 (P ≤ 0.01).

The percentage of CD4+ lymphocytes was unaltered by period, diet and diet*time, but did change over time (P = 0.03) during phase 1 (Figure 2-14). Although the percentage of CD4+ lymphocytes appeared to decrease from d 0 to 18 in all treatments, a trend for a lower percent of this population was only detected when horses received the SOLBG diet (P = 0.06).

The stability of CD8+ cells, coupled with the change in the CD4+ population tended to change in the ratio of CD4+ to CD8+ from d 0 to d 18 (P = 0.08; Figure 2-15).

When horses were fed REG, the ratio decreased from d 0 to 18 (P = 0.03). On d 18,

REG differed from CORN (P = 0.03) and HBG (P = 0.03). The ratio of CD4+ to CD8+ were not affected by period, diet or the diet*time interaction.

Nasopharyngeal lymphocytes- phase 2

The number and percentage of CD4+ lymphocytes in nasopharyngeal flush were unaltered by diet or diet*time, but were altered by head elevation (P < 0.05). The number and percentage of CD4+ cells in nasopharyngeal flush increased (P < 0.01) at 0

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h post-head elevation and the percentage remained elevated through 72 h post (P =

0.02; Table 2-6, Table 2-7).

The number and percentage of CD8+ lymphocytes in the nasopharyngeal flush were unaltered by diet or diet*time, but were altered by head elevation (P = 0.0004, P =

0.08, respectively). CD8+ cells increased after head elevation (P = 0.01) and returned to pre-head elevation at 12 h post (Table 2-6). The percentage of nasopharyngeal CD8+ cells decreased at 0 h post (P = 0.006) but recovered by 12 h post and remained stable at 24 and 72 h post (Table 2-7).

The number and percentage of B lymphocytes in nasopharyngeal flush were unaltered diet or diet*time, but tended to change following head elevation (P = 0.14, P =

0.07, respectively). The number of B cells decreased after head elevation (P = 0.03) and remained lower until 24 h post (Table 2-6). A further decrease in B lymphocytes was observed at 72 h post-head elevation (P = 0.05). The percentage nasopharyngeal

B cells decreased at 0 h post (P = 0.004) but recovered by 12 h post and remained stable at 24 and 72 h post (Table 2-7).

The ratio of the number of CD4+ to CD8+ in nasopharyngeal flush increased at 0 h post-head elevation (P = 0.008) and remained elevated at 72 h post (P = 0.007; Table

2-6). The percentage ratio of CD4+ to CD8+ cells in nasopharyngeal flush, increased at

0 h post (P = 0.007) and remained elevated, then returned to normal by 72 h post-head elevation (Table 2-7). The percentage ratio to T to B cells in nasopharyngeal flush was unchanged by head elevation until 72 h post when it decreased below that observed before head elevation was initiated (P = 0.004; Table 2-7).

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Whole blood lymphocytes- phase 1

The number and percentage of CD4+ lymphocytes in whole blood were unchanged by diet and diet*time, but numbers did decrease from d 0 to d 18 (P =

0.0006; Figure 2-16), whereas the percentage remained unchanged over time. The number of CD4+ cells appeared to decrease in all diets from d 0 to 18; however, lower

CD4+ cells were only detected on d 18 when horses were fed REG (P = 0.009) and

HBG (P = 0.05).

The number and percentage of CD8+ and B lymphocytes and the ratio of CD4+ to CD8+ and T to B lymphocytes were unchanged by diet, time and diet*time during phase 1 (data not shown).

Whole blood lymphocytes- phase 2

The percentage of CD4+ lymphocytes in whole blood was unchanged by head elevation and diet*time, but was affected by diet (P = 0.04) where it was lower in horses fed CORN compared to all other diets (P < 0.05; Figure 2-17). The percentage of CD8+ lymphocytes was unchanged by diet and diet*time, but was affected by head elevation

(P = 0.0004). Initially, the percentage of CD8+ cells increased above pre-head elevation

(P = 0.02), but returned to normal at 12 h post (Table 2-9). The percentage of CD8+ cells remained stable at 24 h post but then dropped below pre-head elevation at 72 h post (P = 0.07). The percentage of B cells in whole blood was unchanged by diet and diet*time, but was affected by head elevation (P < 0.0001). B cells had a delayed response to head elevation and were higher than pre-head elevation at 12 h post (P <

0.0001; Table 2-9), but subsequently decreased by 24 h post and remained stable through 72 h post.

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The number of whole blood CD4+ lymphocytes also exhibited a delayed response to head elevation and were higher than pre-head elevation at 12 h post (P <

0.0001) and remained elevated at 72 h post (P = 0.003; Table 2-8). Similarly, the number of whole blood CD8+ lymphocytes were higher at 12 h post (P = 0.04), but were comparable to pre-head elevation at 72 h post (Table 2-8). Circulating B lymphocytes decreased at 0 h post (P = 0.01), but then increased at 12 h post (P < 0.0001) above the pre-head elevation values (Table 2-8). The number of B lymphocytes recovered by

24 h post-head elevation. The ratio of T to B lymphocytes in whole blood was unchanged at 0 h post-head elevation but then decreased at 12 h post (P = 0.005) and recovered by 24 h post (Table 2-8). The ratio of CD4+ to CD8+ lymphocytes in whole blood were lower at 0 h post-head elevation (P = 0.003), but returned to pre-head elevation by 12 h post (Table 2-8).

Lymphocyte Proliferation- Phase 1

Background lymphocyte proliferation was not affected by diet or diet*time, but did increase from d 0 to d 18 during phase 1 (P = 0.04; Figure 2-18). An increased proliferative response was only observed when horses were fed HBG (P = 0.05).

Lymphocyte stimulation by PWM, LPS and Con A, as well as their stimulation indices were unaltered by diet, time and diet*time during phase 1 (data not shown).

Lymphocyte Proliferation- Phase 2

Background lymphocyte proliferation was unaffected by diet or diet*time, but was affected by head elevation (P < 0.0001). Lymphocyte proliferation without mitogen stimulation was depressed at 0 h post-head elevation (P < 0.0001; Figure 2-19).

Proliferation returned to normal at 12 h post, but was again lower at 24 h post (P <

0.0001) and remained lower at 72 h (P = 0.05) compared to before head elevation.

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Stimulation by LPS and the stimulation index were not affected by diet or diet*time, but were affected by head elevation (P = 0.04, P < 0.0001, respectively).

Lymphocyte stimulation with LPS was higher at 12 h post-head elevation (P = 0.01), but was similar to pre-head elevation response at 24 h post and 72 h post (Figure 2-20).

The LPS stimulation index was higher 0 h post-head elevation (P = 0.001; Figure 2-21) and remained elevated at 72 h post (P = 0.0002). This occurred due to a simultaneous increase in LPS stimulated proliferation and decrease in background proliferation.

Stimulation by PWM and the stimulation index were not affected by diet or diet*time, but were affected by head elevation (P = 0.03, P < 0.0001, respectively).

PWM stimulation of lymphocytes was unchanged at the conclusion of head elevation, but slightly increased at 12 h post (P = 0.05; Figure 2-22) compared to before head elevation. At 24 h post-head elevation, proliferation was similar to before head elevation, but then increased again at 72 h post (P = 0.02). The stimulation index of

PWM was higher (P < 0.0001) at 0 h post-head elevation and remained higher through

72 h post-head elevation (P < 0.0001; Figure 2-23). The initial increase after head elevation was caused by decreased in background proliferation while PWM stimulation remained unchanged. The index fluctuated but remained above pre-head elevation as by simultaneous increases in PWM stimulation and decreases in background proliferation.

Stimulation by Con A and the stimulation indices were not affected by diet or diet*time. Lymphocyte stimulation with either 1 or 2 µg/mL of Con A was lower at the conclusion of head elevation (P < 0.05), but recovered by 12 h post (Table 2-11). The stimulation indices for both concentrations of Con A were higher at 0 h post-head

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elevation compared to pre-head elevation (P < 0.01) and remained elevated above through 72 h post (P < 0.05; Table 2-9). This increase was caused by a simultaneous decrease in background and Con A stimulated proliferation after head elevation.

Lymphocyte Proliferation- Period differences

Interestingly, an effect of period was detected for many proliferative measures during both phases of this study. To characterize this effect, all data (d 0-22) were pooled across all diets and times and analyzed by period. The general pattern of proliferation was a lower response in period 4, although the statistical differences varied by mitogen.

Background lymphocyte proliferation and stimulation with PWM or LPS was lower during period 4 compared to all other periods (P < 0.05; Table 2-12). LPS stimulation during period 2 was also lower than period 1 (P = 0.04) and period 4 (P =

0.002).

In response to either concentration of Con A, proliferation was lowest during period 4 compared to all other periods (P < 0.05; Table 2-12). Additionally, in response to 1 µg/mL Con A, proliferation was also lower in period 3 compared to period 1 and 2

(P < 0.05; Table 2-12).

Nasopharyngeal Neutrophil Function

Only a small number (55 out of possible 288) of nasopharyngeal flush samples had enough neutrophils to assess neutrophil function. Most commonly, the only samples with sufficient neutrophil counts were samples obtained at 0 and 12 h post- head elevation. Because of this limited sample size and few pre-head elevation samples, results are not comparable to pre-head elevation, but possibly still comparable with limited interpretation.

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Whole Blood Neutrophil Function- Phase 1

The percentage of whole blood neutrophils that phagocytosed Streptococcus equi was unaltered by diet or diet*time, but did increase from d 0 to d 18 (P = 0.03;

Figure 2-24). Neutrophil phagocytosis on d 18 was higher in horses fed CORN (P =

0.01) and tended to be higher in horses fed HBG compared to their respective measurements on d 0. As a result, neutrophil phagocytosis was higher on d 18 in horses fed CORN (P < 0.05) and HBG (P < 0.05) compared to REG and SOLBG.

Additionally, the percentage of phagocytosis was greater during period 1 compared to all other periods (P < 0.05; Table 2-13).

The quantity of bacteria consumed by the neutrophils was measured by the mean fluorescence intensity (MFI) of PI. There was no overall effect of diet or diet*time

(Figure 2-25), but MFI was affected by time (P = 0.002). MFI increased from d 0 to 18 when horses were fed REG or HBG. On d 18, MFI was higher when horses received

HBG compared to CORN (P = 0.03) or SOLBG (P = 0.01). During phase 1, the MFI was higher during period 1 compared to the other periods (P < 0.05; Table 2-13). Neutrophils from mares tended to consume more bacteria than cells from geldings (P = 0.08; Figure

2-26).

Phagocytosis activation index was calculated by multiplying the MFI by the percent of neutrophils that phagocytosed, and dividing the product by 100. During phase 1, the phagocytosis index increased over time in all diets (P = 0.003), but was only statistically different when horses received REG (P = 0.09) or HBG (P = 0.003;

Figure 2-27). Phagocytosis index was higher during period 1 compared to all other periods (P < 0.05; Table 2-13). Neutrophils from mares and neutrophils from horses

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older than 13, had higher indices compared to cells from geldings (P = 0.05; Figure 2-

26) or cells from younger horses (P = 0.09; Figure 2-28), respectively.

The percentage of neutrophils that underwent phagocytosis-induced oxidative burst (15.40 ± 2.20 %; data not shown) was unaltered by period, diet, time or diet*time.

The MFI of DHR was also unaffected by period, diet, or diet*time. The DHR MFI increased over time in all diets (P = 0.01), but was only statistically different when horses received REG (P = 0.06) or HBG (P = 0.09; Figure 2-29).

Oxidative burst index was calculated by multiplying the MFI by the percent of neutrophils that underwent phagocytosis-induced oxidative burst, and dividing the product by 100. The oxidative burst indices were the same across diets on d 0, but were higher on d 18 (P = 0.02; Figure 2-30); however, this increase was only statistically significant when horses were fed REG (P = 0.008). On d 18, oxidative burst index tended to differ between REG and SOLBG (P = 0.08).

Percent of neutrophils that were unresponsive to the Streptococcus equi decreased from d 0 to d 18 when horses were fed CORN (P = 0.04), but increased when horses were fed SOLBG (P = 0.05; Figure 2-31). This increase made the SOLBG diet different from all other diets at the end of phase 1 (P < 0.10).

Whole Blood Neutrophil Function- Phase 2

The percentage of whole blood neutrophils able to phagocytose bacteria was unaffected by diet and diet*time, but did respond to head elevation (P < 0.0001).

Phagocytosis fluctuated up and down following head elevation and was statistically lower than pre-head elevation at 0, 24 and 72 h post (P < 0.0001; Figure 2-32).

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The quantity of bacteria phagocytosed by the neutrophils was measured by MFI of PI and differed by time (P < 0.0001) and diet (P = 0.06), although there was no diet*time interaction. The PI MFI gradually decreased following head elevation, falling below pre-head elevation at 24 h post (P = 0.01) and continued to decline at 72 h post

(P < 0.0001; Figure 2-33). Overall, the PI MFI was lower when horses were fed HBG compared to SOLBG (P = 0.02) and CORN (P = 0.04; Figure 2-34).

The phagocytosis index differed by time (P < 0.0001), diet (P = 0.05) and diet*time (P = 0.07). The index was decreased at 24 (P = 0.007) and 72 h post (P <

0.0001; Figure 2-33) compared to pre-head elevation. During phase 2, horses fed HBG had a lower index compared to horses fed CORN (P = 0.05) and SOLBG (P = 0.01;

Figure 2-34). The phagocytosis index was also lower in horses fed REG compared to

SOLBG (P = 0.05), but did not differ from horses fed CORN. Before head elevation, horses fed HBG and SOLBG had the highest and lowest indices, respectively, making them different from each other (P = 0.03; Figure 2-34), but similar to the other diets. The phagocytosis index from horses fed CORN gradually decreased following head elevation and was lower than pre-head elevation at 72 h post (P = 0.001; Figure 2-35).

The phagocytosis index of neutrophils from horses fed HBG was lower at 0 h post compared to pre-head elevation (P = 0.002) and remained lower at 12 h (P = 0.006), 24 h (P = 0.001) and 72 h post (P < 0.0001; Figure 2-35). Horses fed SOLBG had a slightly opposite response; the index was higher than pre-head elevation at 12 h post (P =

0.08), but then returned to normal at 24 h post and remained there at 72 h post (Figure

2-35).

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Overall, the phagocytosis index was not correlated with serum cortisol levels (P =

0.36, R2= 0.006; data not shown) during this study. Although cortisol level at 0 h post- head elevation was slightly correlated to the phagocytosis index at 12 and 24 h post, the relationship is poor and not biologically relevant (Table 2-14).

The percentage of neutrophils able to undergo phagocytosis-induced oxidative burst was affected by diet (P = 0.01) and time (P = 0.0002), but there was no diet*time interaction. This ability was slightly enhanced at 12 h post (P = 0.002), but it normalized by 24 h post and then declined making it lower than before head elevation at 72 h post

(P = 0.0008; Figure 2-32). During phase 2, horses fed REG had a lower percentage of oxidative burst compared to horses fed CORN (P = 0.02; Figure 2-36), and SOLBG (P =

0.04). DHR MFI was affected by head elevation (P < 0.0001), but not diet or diet*time

(Figure 2-37). DHR MFI was lower at 0 h post-head elevation (P = 0.003), but recovered by 12 h post. At 24 h post, the DHR MFI was again lower than pre-head elevation (P =

0.04) and remained lower at 72 h post (P < 0.0001). The induced oxidative burst index differed by time (P < 0.0001) and diet (P = 0.05) but there was no interaction (Figure 2-

37). The index increased above pre-head elevation at 12 h post (P = 0.009), returned to normal at 24 h post but then further decreased at 72 h post (P = 0.008). During phase 2, the induced oxidative burst index was highest when horses received CORN compared to the other diets (P < 0.10; Figure 2-38).

Although cortisol level at 0 h post-head elevation was weakly correlated to the oxidative burst index at each time point, the relationship is poor and not biologically relevant (Table 2-14).

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The percentage of neutrophils that were not activated by the bacteria, differed by time (P < 0.0001) and diet (P = 0.04), but there was no diet*time interaction. This percentage declined and was lower than pre-head elevation at 12 h post-head elevation

(P = 0.02; Figure 2-39). The percentage was higher than pre-head elevation at 24 (P =

0.05) and 72 h post (P = 0.001). Overall, the percentage of un-activated neutrophils was lowest when horses were fed SOLBG compared to REG (P = 0.009) and HBG (P =

0.07), but was similar to CORN (Figure 2-40).

Immunoglobulin A Concentration

Nasopharyngeal IgA- phase 1

During phase 1, nasopharyngeal IgA concentration was 79.66 ± 19.30 µg/mL.

IgA concentration in the nasopharyngeal flushes was not affected by diet, time or their interaction during phase 1. Nasopharyngeal IgA tended to differ by period (P = 0.07) where flushes from period 2 had higher IgA concentrations compared to all other periods (P < 0.05; Table 2-15)

During phase 1, secretory index was 20.99 ± 4.37. Secretory index of nasopharyngeal IgA were unaffected by period, diet, time, or diet*time (data not shown).

Nasopharyngeal IgA- phase 2

During phase 2, nasopharyngeal IgA concentration was 67.11 ± 17.68 µg/mL.

Nasopharyngeal IgA concentration was unaffected by diet or the diet*time interaction but was affected by head elevation (P < 0.0001; Figure 2-41). Twelve hours after head elevation ceased, nasopharyngeal IgA was lower than pre-head elevation (P < 0.0001), but normalized by 24 h post and remained constant at 72 h post-head elevation (Figure

2-41).

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During phase 2, secretory index of nasopharyngeal IgA was 16.42 ± 2.61.

Secretory index of nasopharyngeal IgA were unaffected by diet, period or diet*time, but was affected by head elevation (P < 0.0001; Figure 2-41). The index was lower at 0 h post-elevation (P = 0.009), continued to decrease below pre-head elevation at 12 h post

(P < 0.0001), but recovered by 24 h post and remained normal at 72 h post.

Salivary IgA- phase 1

During phase 1, salivary IgA concentration was 1.02 ± 0.37 mg/mL. IgA concentration in saliva was not affected by diet or diet*time, but was affected by time (P

= 0.0007) during phase 1 (Figure 2-42). Across treatments, salivary IgA increased from d 0 to d 18. Saliva from period 4 had a higher concentration of IgA compared to all other periods (P ≤ 0.05; Table 2-15).

During phase 1, secretory index of salivary IgA was 20.14 ± 3.57. Salivary IgA secretory index was not affected by period, diet or time but there was a diet*time interaction (P = 0.03) during phase 1 (Figure 2-43). Secretory indices were similar between diets on d 0; however, on d 18 the indices were higher in horses fed CORN (P

= 0.04) and tended to be lower in horses fed SOLBG (P = 0.06), compared to d 0. On d

18, horses fed CORN had higher salivary IgA compared to horses fed HBG (P = 0.03) and SOLBG (P = 0.0006) and horses fed SOLBG had lower salivary IgA compared to horses fed REG (P = 0.009).

Salivary IgA- phase 2

During phase 2, salivary IgA concentration was 1.13 ± 0.45 mg/mL. Salivary IgA was unaffected by diet or diet*time, but was affected by head elevation (P < 0.0001) during phase 2 (Figure 2-44). Salivary IgA concentration increased at 0 h post-head

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elevation (P = 0.002), then decreased below pre-head elevation at 12 h post (P <

0.0001) and remained lower through 72 h post (P = 0.007; Figure 2-44).

During phase 2, salivary IgA secretory index was 17.17 ± 3.96. The salivary IgA secretory index was unaffected by diet, period or diet*time, but was affected by head elevation (P < 0.0001; Figure 2-44) during phase 2. Similar to total IgA, the index tended to increase at 0 h post-head elevation (P = 0.07), then decreased below pre-head elevation at 12 h post (P < 0.0001) and tended to remain lower through 72 h post (P =

0.06).

Fecal liquid IgA- phase 1

During phase 1, fecal liquid IgA concentration and secretory index was 1.92 ±

0.79 µg/mL and 0.03 ± 0.01, respectively. IgA concentration in fecal liquid and the secretory index were unaffected by diet, time and diet*time, but did differ by period (P =

0.04, P = 0.003, respectively; Table 2-15) during phase 1. To characterize the effect of period, all data (d 0 - 22) were pooled across all diets and times and analyzed by period. Total IgA and the secretory index were higher during period 1 compared to all other periods (P < 0.05).

Fecal liquid IgA- phase 2

Fecal liquid IgA concentration was 3.07 ± 1.77 µg/mL. Fecal liquid IgA was unaffected by diet and diet*time, but was affected by head elevation (P < 0.0001; Figure

2-45). Fecal liquid IgA was higher at 0 h post (P = 0.01) compared to pre-head elevation and remained higher through 12 h post (P = 0.04). At 24 h post-head elevation, fecal liquid IgA was lower than pre-head elevation (P = 0.005), but recovered at 72 h post.

Secretory index of fecal liquid IgA was 0.05 ± 0.03. Similar to total IgA concentration, the fecal liquid secretory index was unaffected by diet and diet*time, but

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was affected by head elevation (P < 0.0001; Figure 2-45). The pattern of secretion after head elevation was the same as total IgA concentration; increased at 0 h post (P =

0.006), remained higher at 12 h post (P = 0.0009), then decreased below pre-head elevation at 24 h post-head elevation (P = 0.01), but recovered at 72 h post (Figure 2-

45).

Serum IgA

Serum IgA concentration was significantly affected by period of this study during both phases (P < 0.0001; Table 2-15). To characterize the effect of period, all data (d 0 -

22) were pooled across all diets and times, and analyzed by period. Serum IgA was highest during periods 2 and comparatively slightly lower during period 3 (P = 0.005).

Concentrations were similar between periods 1 and 4, but both significantly lower than periods 2 and 3 (P < 0.0001). Serum IgA concentration for period 1 and 4 was 97.01 ±

3.65 mg/dL compared to 125.43 ± 3.61 mg/dL for periods 2 and 3.

Dietary treatment and the interaction of diet*time did not affect serum IgA concentrations during either phase 1 or phase 2 of this study (data not shown).

During phase 2, serum IgA was affected by head elevation (P = 0.002; Figure 2-

46). Following head elevation, serum IgA remained unchanged until 72 h post-head elevation where it increased above pre-head elevation concentrations (P = 0.0002).

IgA and total protein correlations

Correlations between IgA and total protein measurements were determined

(Table 2-16). In general, all IgA variables were related based on significant P values, but only had weak positive correlations. The strongest correlations were between serum and nasopharyngeal IgA, salivary and nasopharyngeal IgA, and salivary and serum IgA.

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Total protein concentrations in different biological samples were related, but there were no strong correlations.

Fecal Measurements

Before head elevation stress, fecal DM percentage was affected by time (P =

0.02), but there was no effect of diet. Across treatments, fecal DM increased from d 0 to

18; however, within treatments this increase was only observed when horses were fed

REG (P = 0.03; Figure 2-47).

During phase 2 of this study, fecal DM percentage differed by diet (P = 0.02) and sampling time (P < 0.0001), but there was no interaction (Figure 2-48, Figure 2-49).

Overall, fecal DM was higher when horses consumed REG (P = 0.002) and HBG (P =

0.04) compared to CORN. The DM percentage was increased at 12 h post-head elevation compared to before head elevation (P < 0.0001). At 24 h post, DM remained higher (P < 0.0001), but at 72 h post it had returned to pre-head elevation values.

Fecal DM percentage also differed by period (P = 0.01; Table 2-17). DM was higher during periods 1 and 2 compared to periods 3 and 4.

Fecal liquid obtained by manually squeezing 200 g of feces was 59.0 ± 5.6 mL.

Fecal liquid obtained per gram of feces was lower 12 h post-head elevation compared to before head elevation (P < 0.0001; data not shown) and corresponds with the highest measured DM percentage (Figure 2-48).

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Discussion

The objective of this study was to determine if dietary oat BG, fed from two different sources, would affect immune function following physiologically induced stress.

Elevated intake of oat BG changed leukocyte populations and somewhat enhanced neutrophil function before head elevation stress. Prolonged head elevation for 12 h caused a short-term, reproducible immune response with no long-term side effects.

Horses did have immune dysfunction for up to 3 d following head elevation, but oat BG did not alleviate the stress-induced immunosuppression.

In general, the high BG diets (HBG and SOLBG) induced elevated numbers of leukocytes both in the local nasopharyngeal region and systemically. The high BG diets raised nasopharyngeal lymphocytes, specifically CD4+ lymphocytes, which are required for T-cell dependent activation of B cells and are thus important for any mucosal immune response. Following a parasitic challenge, mice treated with oat BG had more intestinal CD4+ and CD8+ lymphocytes compared to non-BG treated mice (Yun et al.,

2003). Another study reported that yeast BG modulated lymphocyte populations and the

Th1/Th2 balance within the small intestine of chickens (Cox et al., 2010). Cytokine gene expression, induced by yeast BG, supported an upregulation of Th1 responses and downregulation of Th2 responses during an intracellular pathogen challenge. The current study also showed an increase in nasopharyngeal neutrophils when horses consumed HBG, which agrees with a systemic increase in neutrophils in mice consuming oat BG dissolved in drinking water (Murphy et al., 2007). BG is likely changing leukocyte populations by binding to receptors in the gut and altering the cytokine environment. Alternatively, small BG particles may be absorbed into the vascular or lymphatic systems and directly interact with immune cells that possess BG

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receptors (Rice et al., 2005). However, a persistent increase in leukocytes may be detrimental to the host. Immune cells by nature are inflammatory, and over stimulation may cause bystander damage and/or autoimmunity. BG are theorized to “prime” the immune system for a faster response and one study did show peak immune responses at d 14 and then a decline at d 21 suggesting BG do not result in chronic inflammation

(Cox et al., 2010). Following head elevation stress in the current study, only whole blood

CD4+ lymphocytes remained altered by the high BG diets, which also suggests BG do not cause chronic inflammation. There was a statistical difference between diets, albeit small and ultimately diet did not confer any benefit to horses following head elevation.

Elimination of dietary differences following stress could be because the change was too small and the stressor was too larger for the diet to overcome.

Consumption of regular feed oats (REG) often resulted in a decrease in immune cells from d 0-18. Potentially the higher starch content of the CORN diet and the higher

BG of the HBG and SOLBG diets may have stimulated immune cells during dietary treatment. The lower BG and more moderate level of starch in regular feed oats (REG) may not have stimulated leukocytes, resulting in a decrease over time during dietary treatment. During the wash-out periods, horses were fed corn and a pelleted vitamin- mineral supplement to maintain grain feeding and thereby avoid abrupt diet changes at the start of each period. Higher starch content of corn fed during the wash-out periods may have stimulated immune cell numbers thus reversing the effect of regular feed oats. Excess starch can escape digestion in the small intestine and enter the hindgut where it is rapidly fermented to lactic acid. The accumulation lactic acid can decrease pH and has previously been shown to damage bovine intestinal epithelia and allow

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bacterial by-product translocation into the blood stream (Dong et al., 2011). The increase in systemic LPS can trigger a low-grade inflammatory state and increase pro- inflammatory cytokines and immune cells. However, the quantity of starch fed during the current study did not exceed 2 g/kg BW/meal and would need to be greater than 4 g/kg

BW/meal to escape small intestine digestion and enter the hindgut (Hoffman, 2013).

During phase 1, there were no true diet effects on neutrophil function, but all measures of function increased from d 0-18. These results cannot be attributed to either oats or oat BG, because neutrophil function also increased when horses were fed

CORN. The only main difference between the wash-out diet and the treatment diets was starch. In general, the treatment diets contained less starch and the horses tended to gain weight over the course of the study. As mentioned above, lactic acid from fermentation of excess starch in the hindgut could damage intestinal epithelia and increase systemic pro-inflammatory cytokines, which could pre-activate neutrophils.

However, the quantity of starch fed did not exceed 2 g/kg BW/meal and was generally lower during the treatment periods. Research in horses suggests a maximum of 2 g of starch/kg BW/meal to prevent escape starch from entering the hindgut and < 1 g of starch/kg BW/meal to avoid gastric ulcers (Luthersson et al., 2009). Compared to our study, a much higher dose of starch is probably needed to induce systemic inflammation. One study showed 15 g/kg BW induced a systemic increase of LPS and laminitis (Sprouse et al., 1987). If an inflammatory response was occurring during the higher starch wash-out diet, we should expect to see the opposite results; decrease in neutrophil function from d 0-18 of treatment diets. Horses did tend to gain weight over the course of the study (mean ± SEM; 28 kg ± 4.30) which was likely because of grain

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feeding in combination with ad-libitum pasture. Adipose tissue is now recognized as an inflammatory organ and contributes to a low-grade inflammatory state. If horses had an increase in adipose tissue, we may expect to see a general increase in inflammatory responses; however, neutrophil function actually decreased from period 1 to 4. Although the horses gained weight during the study, only 2 had a body condition score >7 and were considered overweight.

To further understand what caused function to increase from d 0 - 18, data were combined to represent a common dietary element (data not shown). Data were grouped to represent a BG diet (HBG + REG + SOLBG compared to CORN), an OAT diet (HBG

+ REG compared to CORN or SOLBG) or a high BG diet (HBG + SOLBG compared to

CORN or REG) and neutrophil function was reanalyzed. Analysis of these new diets did not provide any additional insight; neutrophil function still increased from d 0 -18 and there was no dietary influence.

Upon further analysis of the original data set, the period by day interaction revealed the most likely culprit. On d 18 of period 1, neutrophil function was greatly increased compared to d 0 of period 1 and d 18 of periods 2-4. In an attempt to reduce variability of neutrophil measures, we had planned to source and pool the bacteria needed throughout the entire study. Due to bacteria culturing restraints, the total quantity of bacteria needed for the entire 19-wk study, was not available for the first sampling interval (period 1, d 0). To complete those measures, we had to use an older batch of bacteria which we had been working with to optimize the neutrophil function assay prior to starting the study. Once the bacteria required for this study became available, all cultures were pooled and the measurements normalized. To confirm that

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this laboratory issue was the problem, removal of d 18 from period 1, eliminates time effects of all measures of neutrophil function during phase 1 (P > 0.10; data not shown).

Without data from d 18 of period 1, both the phagocytosis index and PI MFI had a diet*time effect (P = 0.06, P = 0.04, respectively). Horses fed HBG and SOLBG had higher phagocytosis index on d 18 compared to d 0 (P = 0.06, P = 0.05, respectively; data not shown). Additionally, horses fed HBG had higher PI MFI on d 18 compared to d

0 (P = 0.03; data not shown). However these data should be interpreted carefully as it is not a full data set. This enhanced phagocytic ability of neutrophils did not remain after head elevation stress.

During phase 2, diet did have some impact on neutrophil function. Horses fed

CORN and SOLBG tended to have the most active neutrophils that were able to phagocytose and kill more bacteria compared to cells from horses fed the other diets.

Neutrophils from horses fed REG had the highest percentage of un-activated neutrophils and, of the active neutrophils, the lowest percentage of phagocytosis and induced oxidative burst. Neutrophils from horses fed HBG mirrored the effects of REG but out performed on percentage of induced oxidative burst. BG is proposed to activate immune cells by binding to BG specific receptors on their membranes initiating a cascade of immune defenses (Brown and Gordon, 2003). In the current study, the only diet containing BG that enhanced neutrophil function was a soluble concentrated BG powder top-dressed on corn (SOLBG). Neutrophils from horses on HBG had a comparable amount of induced oxidative burst, but unimpressive phagocytosis. Overall, these findings suggest the CORN and SOLBG diets were more inflammatory and may have resulted in pre-activation of neutrophils in circulation, thereby enhancing in vitro

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neutrophil function. These results alone do not necessarily mean neutrophils were better able to counteract an in vivo immune challenge. In place of a direct immune challenge, we used head elevation. Interestingly, neutrophils from horses fed SOLBG were the only ones with enhanced function following head elevation and, after returning to baseline, were the only ones that did not have suppressed phagocytosis at 72 h post.

This pattern existed for several other measures of neutrophil function, but did not reach statistical significance. All other diets resulted in suppressed function following head elevation, which continued to decline further during recovery. Potentially the soluble BG powder, without interference from the whole oat matrix, was better able to interact with immune cells to cause a systemic change. Previous studies which have shown enhanced immune cell function all used a soluble concentrated oat BG powder and not whole oats (Murphy et al., 2007; Murphy et al., 2008). This may have been due to ease of application in the animal models studied, but the current study seems to support use of a concentrate over a high BG oat. The BG from concentrated oat BG powder may have come in contact with more immune cell throughout the GIT compared to BG from whole oats that may only be exposed following fermentation in the distal small intestine or hindgut. An oat BG supplement may enhance neutrophil function, although more research in horses is required to confirm these results.

Before head elevation stress, horses fed HBG had higher non-mitogen stimulated background lymphocyte proliferation on d 18 compared to d 0. If BG were to contact lymphocytes in the gut mucosa or in circulation, they could have bound receptors and stimulated lymphocytes. However, there was no overall effect of diet and proliferation numerically increased in all diets from d 0 to 18, so this may be an artifact

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of diet change from wash-out to treatments. During the wash-out periods, horses were fed corn and a pelleted vitamin mineral supplement to maintain grain feeding and thereby avoid abrupt diet changes at the start of each period. During the dietary treatment periods, the CORN diet had the lowest numerical proliferation increase, indicating increases seen in the other diets may be due to diet change from wash-out to treatments. Typical lifespan of lymphocytes is 1-2 weeks, but some can survive several months or years depending on tissue location and function (Kindt et al., 2007). Likely, both wash-out and phase 1 of treatment periods were long enough to initially affect lymphocyte populations; however, spontaneous background proliferation in vitro is not necessarily biologically relevant. Lymphocytes in culture can respond to proteins released by cell necrosis during culture or even the type of serum in the culture medium

(Orlik and Splitter, 1996). In some cases, spontaneous background proliferation in vitro is indicative of certain diseases in vivo (Prince et al., 1994; Orlik and Splitter, 1996). In the current study, proliferative changes were not seen in mitogen stimulated lymphocytes and increased spontaneous background proliferation did it carry over into phase 2, indicating this response may have been a short-term artifact of diet change, but not biologically significant.

Common mucosal immunity means that a response generated at one mucosal site, will subsequently provide immunity at all mucosal sites. Considering 70% of the immune system is located in the GIT (Kindt et al., 2007), oral delivery of immune enhancing dietary components seems practical. Secretory IgA is a first line of defense for mucosal surfaces, and therefore an ideal target for immune enhancement. IgA can be easily measured in many mucosal samples and may be a good indicator of mucosal

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immune status, an indicator which is currently lacking in clinical veterinary practice. BG has been previously shown to alter immunoglobulin concentrations (Krakowski et al.,

1999; Stuyven et al., 2010); however, in the current study, there were inconsistent effects of diet on IgA measures. Salivary IgA and the secretory index increased during phase 1 but only the index exhibited a diet*time interaction. The salivary IgA index was similar between diets on d 0, but on d 18 it was highest in horses fed CORN and lowest in SOLBG. These data were opposite of what might be expected from high BG diets based on the immune stimulatory ability reported in other studies (Davis et al., 2004b;

Murphy et al., 2007). However, few studies have investigated the effects of oat BG on immunoglobulin levels and the results are inconclusive. Following immunosuppression with dexamethasone and then an infectious challenge, mice given intragastric or subcutaneous oat BG had higher serum levels of IgG and IgM compared to control mice

(Yun et al., 1997). Although numerically increased in the BG treated groups, serum and intestinal IgA were not statistically different compared to non-BG groups. Another study orally supplemented dogs with yeast BG and found lower IgA concentrations in saliva and tears after 14 d (Stuyven et al., 2010). Systemic increase of IgM and decrease of

IgA did not occur until d 21 and 28 respectively, suggesting BG initially prompt changes at the mucosal level. The authors suggest BG do not directly affect B cells but target gastrointestinal macrophages and dendritic cells, which shift the cytokine environment toward Th1 and away from the IgA-facilitating Th2 environment. Shortly after supplementation ceased, IgA concentrations began to rise, indicating IgA+ B cells were still present. Conversely, healthy human volunteers had increased salivary IgA concentrations following oral administration of yeast BG for 4 d but there was no change

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in systemic IgA and no systemic absorption of the yeast BG (Lehne et al., 2006).

Colostrum from mares stimulated with yeast BG via weekly IM injections, had higher

IgG and IgM but no change in IgA (Krakowski et al., 1999). Results from the current study are not precisely in line with previous reports, but comparisons are made difficult because BG source, dosage, linkages, branching, and molecular weight of BG all affect immune responses (Mikkelsen et al., 2014). Possibly the decrease in the secretory index and total salivary IgA from horses on the BG diets was because of a mucosal Th1 shift. Unfortunately, these results are not desirable as salivary IgA acts as a barrier from oral colonization of microbes and low levels are associated with an increased risk of respiratory disease (Neville et al., 2008).

Prolonged head elevation proved to be a reproducible metabolic stressor for the horses in the current study, based on the increases in serum and salivary cortisol.

There is a well-documented relationship between stress and cortisol level in many species, including horses (Henry et al., 2012). Serum cortisol exists in two forms, bound to corticoid binding globulin in whole blood and unbound, which is the biologically active form. Salivary cortisol is a measure of unbound active cortisol, which reflects the unbound form found in serum (Koh and Koh, 2007). Both are considered accurate measurements of chronic stress lasting several minutes to hours (Valera et al., 2012).

Acute stress, lasting only a few minutes, is associated with the activation of the SAM system whereas chronic stress activates the HPA system (Koh and Koh, 2007).

Measuring salivary cortisol is often chosen over serum cortisol to avoid the stress of venipuncture; however, there is some disagreement on whether salivary free cortisol accurately correlates with serum free cortisol (Peeters et al., 2011). In the current study,

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serum and salivary cortisol were not correlated. Additionally, cortisol is considered a diurnal molecule in horses, which complicates analysis of cortisol in response to stress depending on the time of sampling (Aurich et al., 2015). The current study showed serum and salivary cortisol were lowest twelve hours after head elevation ceased, which was also the only evening sampling time (2000 h). Interestingly, the lowest serum cortisol level we observed in the evening was two times higher than previously reported evening cortisol levels in horses. Previous studies in horses have reported daily peak cortisol concentrations in the morning of approximately 65 ng/mL, which agrees with data from morning samples obtained in the current study, and evening cortisol levels of approximately 25 ng/mL (Stull and Rodiek, 1988). In the current study, mean serum cortisol concentration from the evening sampling was 45 ng/mL, which corresponds with the higher values for the salivary cortisol measured at the same evening sampling.

Because the half-life of serum and salivary cortisol is about one hour, the higher nocturnal level of cortisol observed in the evening was likely unrelated to the initial cortisol response to head elevation (Koh and Koh, 2007). One of the many functions of cortisol is to suppress the immune system; thereby, diverting energy elsewhere and preventing the inflammatory response from overshooting (Sapolsky et al., 2000). Higher evening cortisol levels observed in the current study may be a response to ongoing inflammation from head elevation, and the body’s attempt to restore homeostasis.

Supportively, systemic WBC were also elevated at this time after head elevation (12 h post). However, only one sample was taken in the evening and higher nocturnal cortisol level could be normal for these horses.

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Twelve hours of head elevation caused evident and significant changes to local leukocyte populations in the nasopharyngeal region, as well as systemically. Previous studies in horses using prolonged head elevation have shown it to cause mucus and bacteria accumulation in the lower respiratory tract (Raidal et al., 1996). The abnormal amount of mucus and bacteria in this region likely causes inflammation and damage to the mucosal epithelium. These cells are equipped to signal for help by secreting chemokines, which will attract leukocytes. First responders of the immune system are typically resident tissue macrophages and systemic neutrophils which, upon pro- inflammatory cytokines or bacterial endotoxins signaling by the damaged mucosa, will migrate through the damaged mucosa and begin killing bacteria in the mucosal space.

As the inflammatory response continues, more leukocytes arrive and their enzymatic processes of killing bacteria inadvertently cause more damage to the mucosa, prolonging the inflammation. In support of this theory, we showed an increase in all leukocyte populations in the nasopharyngeal region after 12 h of head elevation, specifically CD4+ cells. Most cell populations returned to a normal level by 12 h post- head elevation, most likely because of the frequent interval of nasopharyngeal flushes performed rather than cessation of the inflammatory response. However, CD4+ cells remained elevated through 72 hours post, suggesting that there was still a need for this type of cell. CD4+ cells are identified as T helper cells for their ability to activate B cells and thereby assist with antibody mediated clearance of extracellular agents during an acquired immune response (Crotty, 2015). This acquired immune response can take up to two weeks when the immune system is faced with a novel microbial agent. However, upon exposure to the same microbial agent, the secondary acquired response can

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occur within 3 d (Kindt et al., 2007). The head elevation in this study did not pose a novel antigen challenge to the immune system. Instead, it prevented natural postural drainage, and therefore increased exposure time to environmental contaminants, which horses encounter on a regular basis. The responding cells were already in place in the mucosal tissue and able to quickly mount an acquired response. Our data indicate that

CD4+ cells may be important for mounting an acquired immune response in the nasopharyngeal region.

All other leukocyte populations were lower at 72 hours post than they were before head elevation, which may reflect local immunosuppression and may contribute to the higher risk of pneumonia following transportation with prolonged head elevation, as seen in other studies (Stull et al., 2004). The interval between the last two nasopharyngeal flush samples was 48 h, so it is unlikely leukocytes were lower because they were washed out during prior flushes. Additionally, this response was consistent across the 4 periods. Results from our subsequent study (Chapter 3) also support that this was not a wash-out effect because those horses had normal or elevated nasopharyngeal leukocytes 72 h after transportation stress using a similar sampling schedule.

Immediately following head elevation, a systemic pattern of WBC known as a stress leukogram was observed, indicating the horses were experiencing stress

(Latimer, 2011). The equine leukogram has two distinct profiles which can provide insight into the physical state of the animal. Leukocytosis involves changes in circulating

WBC and is associated with the activation of the SAM pathway prompted by fear or excitement. During this response, splenic contraction, increased blood flow and reduced

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adherence capacity of cells mobilizes the marginal pool of neutrophils and/or lymphocytes causing neutrophilia and/or lymphocytosis. Eosinophilia and monocytosis are also possible. However, this is a short lived systemic profile as the marginal pool of neutrophils and lymphocyte counts can be reestablished within one hour (Satue et al.,

2014). In contrast, stress leukocytosis is under the influence of cortisol, which induces mature neutrophilia, lymphopenia and eosinopenia 2-4 hours after its release (Satue et al., 2014). Cortisol causes mature neutrophilia by mobilizing the marginal pool, reducing their ability to move from circulation into tissues and mobilizing the bone marrow reserve population. Lymphocyte sequestration in lymphoid tissues and eosinophil marginalization with decreased release from bone marrow cause the reduction in circulating lymphocytes and eosinophils. These values can return to normal 24 h after the initial stressor, assuming it is eliminated (Satue et al., 2014). It is often hard to differentiate between a stress and inflammatory leukogram based solely on white blood cell counts. An inflammatory leukogram can cause the same changes, but is often accompanied by a neutrophilic left shift and other clinical pathologic signs like hypoferremia and hypoalbuminemia (Satue et al., 2014). In the current study, neutrophils were numerically higher and lymphocytes statistically lower following head elevation. The stress leukogram was short lived after the head elevation was terminated, and it appears that an inflammatory response followed. At 12 h post-head elevation, all circulating leukocytes were increased above values measured before head elevation. The leukocytosis was transient for most populations but lymphocytes, specifically CD4+ helper cells remained elevated at 72 h post. These data agree with the response observed in nasopharyngeal flush, where there was also a progressive

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increase in CD4+ helper cells, although the systemic response was slightly delayed.

Systemic effector lymphocytes CD8+ and B cells also had a delayed response, but did increase 12 h after head elevation, probably due to systemic inflammation. The decreased ratio of circulating T to B cells indicates that there was greater mobilization of

B cells compared to T cells. B cells function to provide humoral immunity against extracellular pathogens, such as bacteria. Mobilization of B cells in the current study indicates the body was preparing for a humoral immune response. Additionally, the continued elevation of CD4+ cells also observed supports a humoral response since they are required to activate B cells for antibody production (Crotty, 2015).

Prolonged head elevation also affected lymphocyte function, which has not previously been shown without transportation. It is well documented that prolonged transportation is stressful for horses and causes both metabolic and immune dysfunction leading to poor performance and increased risk of infection (Stull and

Rodiek, 2000; Oikawa et al., 2005). It is hypothesized that transport-associated metabolic fluctuations and/or direct effects of stress-induced soluble mediators cause immunological impairment. In the current study, head elevation resulted in an initial suppression of both mitogen stimulated and non-stimulated lymphocyte proliferation, which was most likely stress-induced. Non-mitogen stimulated (background) lymphocyte proliferation was evaluated from the negative control wells in the lymphocyte proliferation assay and is reflective of the activity of lymphocytes at the time of sampling. Previous studies in horses have shown suppressed proliferation in response to exercise and other stressful events (Strasser et al., 2012; Bobel et al., 2012).

Lymphocyte function is sensitive to cortisol levels and the depressed function observed

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immediately after head elevation corresponds to the highest level of cortisol.

Background proliferation experienced a second wave of suppression at 24 h post-head elevation, which also corresponds with another serum cortisol peak. Corticosteroids interfere with leukocyte migration by inhibiting expression of cell surface adhesion molecules, induce death by apoptosis, and alter cytokine gene transcription (Sapolsky et al., 2000). In the current study, serum cortisol levels in were not correlated with lymphocyte function (data not shown), indicating factors other than cortisol influence lymphocyte proliferation. Suppression of lymphocyte function may produce a window of opportunity for pathogens while lymphocytes cannot mount a sufficient response; however, in the current study, this was a small window as proliferation had recovered 12 h after the cessation of head elevation.

Lymphocyte stimulation in response to LPS and PWM was enhanced 12 h after head elevation. Mitogens non-specifically stimulate lymphocytes to undergo mitosis by activating the mitogen activating protein kinases pathway and thereby create a polyclonal population (Ashraf and Khan, 2003). PWM and LPS predominately stimulate

B lymphocytes and their enhanced function corresponds with the highest circulating number and percentage of B cells observed following head elevation in the current study. One explanation could be that head elevation and leaked endotoxins activated appropriate effector cells for this mucosal challenge. Alternatively, the freeze-thaw process involved in handling cell samples has known effects on the proliferative ability of lymphocytes and we cannot predict which cells will survive (Garcia-Pineres et al.,

2006). If more B cells survived the freeze-thaw cycle, the enhanced function may be an artifact of freeze-thaw bias. By comparison, proliferative responses to Con A, a

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predominately T cell mitogen, were suppressed following head elevation but recovered

12 h later. Stimulation indices, which are the ratio of mitogen stimulated proliferation to non-stimulated proliferation, normalizes data and permits the comparison of lymphocyte activity between animals that possess different levels of non-mitogen stimulated proliferation (Kindt et al., 2007). Therefore, the stimulation index is a better indicator of true stimulatory ability. The PWM and Con A stimulation indices were enhanced following head elevation and remained enhanced 3 d later. LPS stimulation indices were less affected by the same stressor and only enhanced for a short time. Although background proliferation seemed to be suppressed by head elevation, the activation ability of cells was modestly enhanced. This is in contrast to many studies which show decreased or unchanged proliferation following stress. It is generally accepted that following intense or prolonged exercise stress, horses have decreased proliferative responses (Keadle et al., 1993; Nesse et al., 2002; Bobel et al., 2012). The discrepancy between these studies and the current work is probably because head elevation is stationary and induces a localized response with systemic changes that were easily reversed once head elevation ceased. By comparison, physical exercise is a system- wide assault, inducing changes to all body systems and may require a longer recovery.

Transportation stress is more closely related to head elevation stress, yet studies conducted in transported horses have reported inconsistent lymphocyte responses.

Following 24 h of road transportation, lymphocyte proliferation in response to Con A was depressed; however responsiveness to PHA and PWM was unaffected (Stull et al.,

2004). Conversely, responsiveness to PWM was enhanced following 36 h of transportation possibly due to increased bacterial antigens within the respiratory tract

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and an observed increase in systemic cytokines (Maeda et al., 2011). The current study did not measure systemic cytokines but based on cellular data, it is likely an elevation of cytokines followed head elevation and could explain the enhanced lymphocyte function.

Although modestly enhanced, the greater proliferative response to Con A and PWM persisted for 3 d, which may or may not help reduce the risk of infection following this type of stress. Early during a stress response, epinephrine and glucocorticoids cause immune enhancement by activating cells that can quickly respond and relocating cells to locations where they could be needed (Dhabhar et al., 1995). Glucocorticoids accomplish this indirectly by culling the older leukocyte populations and sequestering lymphocytes from circulation (Sapolsky, 1994). Upregulated immune function is energetically costly and increases the risk of autoimmune disorders, so transient enhancement is followed by suppression to bring immune function back to baseline

(Sapolsky et al., 2000). The enhancement observed in the current study, may be a failure of glucocorticoids to return immune function back to normal. Longer duration stressors may cause glucocorticoids to over-shoot, causing a decrease below baseline and continued suppression as observed with neutrophil function.

Whole blood neutrophil phagocytosis and killing capacity was initially depressed immediately following head elevation, but this was not correlated with circulating cortisol

(data not shown). Although all neutrophil functional measurements were either numerically or statistically depressed after the cessation of head elevation, the MFI of induced oxidative burst was the most affected. As with lymphocyte proliferation, neutrophil activation indices take into account cell percentage and activity level, and therefore a better indicator of true function (Kindt et al., 2007). In the present study,

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oxidative burst index was numerically lower following head elevation, but the decrease was modest and may not be biologically relevant. The depression was short lived and by the next sample, neutrophils had recovered or were able to consume and kill more bacteria than observed before stress initiation. The most likely explanation, although not measured in this study, was that endotoxins and pro-inflammatory cytokines released during the mucosal inflammatory response, entered systemic circulation through the leaky mucosa and pre-activated the neutrophils (Moore et al., 1995; Sabroe et al.,

2005). Increased plasma endotoxins have been previously measured following transportation stress in horses (Oikawa et al., 2005). The oxidative burst ability of neutrophils had returned to normal at 24 h after head elevation, indicating that systemic inflammation may have been reduced, but phagocytosis was depressed once again.

Functionality continued to decrease, and in accordance with other studies, all neutrophil function was suppressed at 72 h after head elevation, signifying a possible stress- induced immunosuppression following prolonged head elevation (Raidal et al., 1997a).

The relationship between equine cellular function and corticosteroids is still unclear.

According to one study, horses are somewhat steroid resistant based on relatively slow stress leukocyte responses that take several hours following intravenous cortisol

(Burguez et al., 1983). Perhaps, equine neutrophils had a delayed response to prolonged stress and the suppressive actions of glucocorticoids exceeded homeostasis causing unintentional reduced function. Neutrophils circulate for approximately 12 h and then enter tissues where they can survive for 1-2 d or longer during inflammatory reactions (Drifte et al., 2013). Given the timeline of our study, the population of circulating neutrophils immediately following stress and then 72 h later, are likely not the

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same. Stress can induce the release of immature or band neutrophils from bone marrow which are less efficient killers compared to their mature counterparts and could be the reason for our noted decreased function (Pillay et al., 2010). Band neutrophils were observed in the current study although statistical analysis was not performed on these data.

Neutrophil function was also mildly affected by age and gender. It is generally accepted that elderly humans >65 years old experience an overall decrease in immune function known as immunosenescence (Hansen et al., 2015). Changes to immune function with aging involve both innate and adaptive responses and are usually associated with a more inflammatory profile. Inflamm-aging was the term coined to describe the propensity of older individuals to exhibit increased or over-exaggerated inflammatory responses. Studies reported that neutrophil phagocytosis and reactive oxygen species generation decreased with age (Wenisch et al., 2000). There is sufficient evidence that geriatric horses >20 years old also experience immunosenescence and inflamm-aging (Hansen et al., 2015). The oldest horses in current study were 18 years old and horses that were ≥13 years, tended to have better phagocytosis indices compared to horses between 4-12 years old. There is no age related neutrophil function data available in horses, but given that older age is associated with decreased neutrophil function in other species, it is likely our results are bias due to sample size and not biologically relevant. Additionally, the age groups were not evenly divided (n=7 in 4-12 age group verses n=5 in >13 age group) and there were no longer age differences after head elevation.

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Gender differences in immune responses have also been previously reported.

One study reported that neutrophils from male participants had higher responsiveness to LPS and INF-γ stimulation compared to women (Aomatsu et al., 2013), whereas another study reported neutrophils from male participates had lower myeloperoxidase content compared to women (Nikulshin et al., 2015). Neutrophils from female rats were resistant to anesthesia and surgery suppression and had better phagocytic responses to an endotoxin challenge compared to male rats (Spitzer and Zhang, 1996). The current study measured greater phagocytosis indices in mares. However, there were only 4 mares compared to 8 geldings and differences between sexes did not hold true after horses had been stressed.

Salivary IgA has recently been suggested as a sensitive and accurate marker of acute stress because of local storage and the ability to be released quickly (Escribano et al., 2015). Pigs had a significant increase in salivary IgA within the first ten minutes of being physically restrained and it remained elevated for the duration of restraint (Muneta et al., 2010). This biomarker returned to a baseline level ten minutes after the pigs were released. In contrast, chronic exercise stress in humans causes a decrease in salivary

IgA levels, which was associated with higher risk for infectious upper respiratory disease (Shimizu et al., 2012; Moreira et al., 2014). Our study found a statistical or numerical increase in salivary, fecal, nasal and serum IgA after head elevation, which may indicate a stress response and is in agreeance with the literature (Bundgaard et al.,

2012). Following the increase in IgA, nasal and salivary IgA fell below baseline at 12 h post and fecal IgA at 24 h post-head elevation. Secretory IgA is considered a diurnal molecule in several species, although the cyclic activity tends to vary by species

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(Kikkawa et al., 2003). Data from the current study may support cyclic activity in horses, as the 12 h post-head elevation sample had the lowest nasal and salivary IgA concentration and was the only sample taken in the evening (2000 h). In contrast, serum IgA did not exhibit a diurnal pattern. The lowest concentration of fecal IgA did not occur in the evening, which may be due to gut transit time whereby a circadian reduction in fecal IgA could be more delayed compared to the other mucosal samples.

Diurnal activity of immune variables, including IgA, has not yet been investigated in horses.

IgA concentrations in nasopharyngeal flush and fecal liquid recovered to a baseline level by 72 h post; however, salivary IgA remained lower than baseline. This is most likely due to a suppressed function of the plasma cells in the salivary glands rather than depletion of the stored molecule. Human can secrete >3 g of sIgA/d into the intestinal lumen, although there is no estimate for other mucosal tissues. Studies in rats have shown that sIgA is not only stored but continually synthesized by plasma cells at a rapid rate (Carpenter et al., 1998). Basal salivary sIgA secretion in rats was 1.7

µg/g/min, which could increase to 10.3 µg/g/min upon sympathetic stimulation of their salivary glands (Carpenter et al., 1998). The findings of this study suggest that IgA secretion into saliva is mediated by the transcytosis ability of epithelial cells and not limited by IgA synthesis. Therefore, the lower concentration of salivary IgA observed at

72 h post in the current study was likely due to suppressed secretion and not depletion of the molecule.

Serum IgA was higher than baseline at 72 h post-head elevation. Although this was not associated with an increased number or percentage of circulating B

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lymphocytes, it may indicate the beginning of a systemic challenge. Additionally, CD4+ lymphocytes were still elevated at 72 h post-head elevation, and in combination with circulating cytokines (not measured), they may have helped B lymphocytes class switch and begin producing IgA to combat an invader.

Although the IgA ELISA used in the current study was horse specific, it was not specific for sIgA nor a particular antigen and only provides a measure of total IgA in the sample. An increase in total IgA measured in a fluid could indicate more sIgA was actively secreted or locally produced, or serum derived IgA leaked through compromised tissues. The secretory index may be a better indicator of actively secreted

IgA compared to IgA leaked from serum (Mathews, 1981). Albumin is used as a reference protein because albumin can cross the mucosal barrier, depending on barrier integrity, but cannot be locally produced. A secretory index of greater than 1 indicates the mucosal tissue is actively secreting IgA in addition to the transudation of serum IgA across the mucosal membrane. The main function of IgA secreted into mucosal spaces is to bind soluble or particulate antigens, a function otherwise known as immune exclusion (Russell et al., 2015). Whether increased IgA is due to receptor mediated transcytosis of sIgA or paracellular diffusion because of compromised epithelial barriers, more IgA within the mucosal space should be beneficial. However, serum derived leaked IgA will not contain the secretory component and can be degraded by bacterial proteases much faster than sIgA which has a half-life of 3-6 d (Woof and Mestecky,

2015).

With the exception of the nasopharyngeal flush IgA secretory index, fecal and salivary indices followed the same pattern as the corresponding total IgA, suggesting

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the secretory index of the nasopharyngeal flush may not be an accurate indicator. The nasopharyngeal flush IgA secretory index decreased after head elevation and continued to decline before returning to baseline at 24 h post. Previous reports suggest normalization of respiratory IgA with total protein in the sample is inaccurate, because sampling technique, frequency and fluid recovery are so varied (McGorum et al., 1993;

Schnabel et al., 2017). In the current study, IgA and protein (data not shown) concentrations in nasopharyngeal flush were higher after horses had a 24 h interval between flushes, causing a lower secretory index; however, accumulated nasal IgA and protein was not likely the reason. Subsequent flushes were within the same time interval and concentrations had returned to baseline and not below. Until a standardized method for measuring equine sIgA becomes available, it seems most accurate to report total IgA concentrations in respiratory secretions along with detailed methods and dilutions (Schnabel et al., 2017).

The volume of fecal liquid obtained during the study was lowest (data not shown)

12 h post stress, which corresponds with the highest fecal DM and relates to temporary dehydration from induced head elevation. Horses were offered hay ad libitum and water in buckets every 2 h during head elevation, but the stress and decreased intake likely prevented them from drinking a sufficient amount. Once horses were returned to pasture, fecal DM slowly rebounded and returned to normal by 72 h post-head elevation. More feces (wet basis) were excreted during our 12 h stress from horses consuming HBG and SOLBG (data not shown). BG is a soluble fiber which absorbs water in the GIT and increases stool bulk allowing for easier excretion (Slavin, 2013).

Both HBG and SOLBG diets had the highest amount of soluble fiber so these excretion

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results are not surprising; however total fecal excretion was only measured during the

12 h head elevation and may not represent a true effect of diet.

Fecal DM differed by period of our study which is likely due to weather and seasonal differences in moisture content of pasture grass. The current study was conducted from May to September and as daily temperatures and precipitation increased (data not shown), horses probably drank more water and pasture grass was more saturated as the rainy season progressed. The first period of the current study was took place during May and June, which correspond to the highest percentage of

DM and lowest average of fecal liquid obtained. Fecal DM and water continued to decrease or increase, respectively, as the study progressed through the summer months of Florida. The hottest months of the current study were August and September, which agrees with lowest percentage of DM measured during periods 3 and 4. There was also a diet effect on fecal DM; horses consuming CORN had a lower fecal DM percentage (19.1%) compared to horses on REG (20.2%) and HBG (19.9%). A recent study also reported horses consuming corn (0.4% BW; 2 g starch/kg BW) had lower fecal DM compared horses consuming oats fed at the same rate and providing the same quantity of starch (Harlow et al., 2016). Lactate production from hindgut microbes that ferment starch that escapes SI digestion, will decrease pH and can cause digestive upset and diarrhea. Although CORN from the current study had higher starch content compared to the other diets, the feeding rate of starch was still low and probably fully digested in the SI. The overall differences between diets were minor and did not indicate abnormal GIT function and no horses experienced symptoms of colic.

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Non-mitogen and mitogen stimulated lymphocyte proliferation and neutrophil phagocytosis progressively decreased during the study and were lowest during period

4. Many immune parameters have circadian and seasonal rhythms based on external signals, like day length and internal signals like circulating hormones. Yet differences between species make it difficult to broadly describe these seasonal changes. Monkeys have previously been shown to have lower lymphocyte proliferation during the summer and higher during winter (Mann et al., 2000). Conversely, dogs and mice are reported to have higher proliferation during the summer and lower during the winter (Garsd and

Shifrine, 1982; Planelles et al., 1994). Piglets exposed to artificially long days with 23 h of light had increased phagocytosis, while hamsters exposed to artificially short days with 8 h of light had reduced granulocytic function compared to when they were exposed to 16 h of light (Yellon et al., 1999; Lessard et al., 2012). Several species are also reported to have elevated circulating leukocytes during short photoperiods (Haldar and Ahmad, 2010). In the current study, nasopharyngeal neutrophils and lymphocytes, and systemic lymphocytes and monocytes increased from period 1 to 4. Horses also had a general decrease in nasopharyngeal and whole blood eosinophils from period 1 to 4. Although there is no literature to support this theory, a decrease in eosinophils may be an evolutionary adaptation to reduce immune system energy wasting. Eosinophils function against extracellular parasites and may decrease during times of low parasite loads, such during the shorter cooler winter days or the hottest summer months in

Florida. Immunoglobulins in some animal species have also been reported to be higher during short photoperiods, compared to long photoperiods (Demas and Nelson, 1998).

On the contrary, healthy teenagers had no change in immunoglobulin concentrations

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during all four seasons but CD4+ and total lymphocytes were increased during the spring (Afoke et al., 1993). The current study also detected period differences in IgA.

Overall, salivary IgA increased from period 1 to 4 and fecal liquid IgA decreased. Serum and nasopharyngeal IgA were highest during period 2 but no real seasonal pattern was apparent. Changes in photoperiod induce alterations in melatonin, prolactin, sex hormones and glucocorticoid secretion which directly or indirectly affect immune cells by binding to their receptors or shifting cytokines, respectively (Haldar and Ahmad, 2010).

This has not been investigated in horses and remains as a possible explanation for the differences between study periods. Photoimmunomodulation is usually related to large changes in daylight and day length in Florida differs from ~14 h during the summer to

~11 h during the cooler months. From May to September in the current study, daylight did decrease by 2 h and may have changed immune status.

An alternative explanation for our period differences may be the increase of ambient temperature over the course of the study. Heat stress is well studied in cows and shown to have detrimental effects on immune function. Most studies indicate that heat stress from temperatures and durations similar to those experienced during the current study impairs lymphocyte function (Lacetera et al., 2005; do Amaral et al.,

2010). Heat stressed chickens and cows were also reported to have reduced phagocytic ability of granulocytes (Bartlett and Smith, 2003; do Amaral et al., 2011). The mechanisms remain unknown but often glucocorticoids are blamed. Salivary cortisol increased over the course of the current study, and was highest during period 4 compared to all other periods (P < 0.08; data not shown); however serum cortisol did not differ between periods 1, 2 or 4 but was lowest during period 3 (P < 0.05; data not

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shown). In the present study, temperature increased by only 2°C but humidity increased by 18% from May to September. High humidity prevents sweat from evaporating and the body has a harder time cooling itself. Although the temperature change was not drastic, the change in humidity and corresponding heat index may have negatively affected immune function.

Many challenges exist when conducting longitudinal studies. The current study spanned from May to September which encompassed spring and summer months in

Florida. Exposure to changing environmental allergens, individual variation, diurnal or seasonal rhythms, and human error likely all contributed to the period differences observed.

Dietary oat BG did not consistently affect immune responses seen in the horses.

The target dosage of BG (170 mg/kg BW) was extrapolated from previous studies where positive immune benefits were measured in mice, suggesting that our dose should have been appropriate (Davis et al., 2004a). After accounting for BG consumed from all dietary components, including that intentionally supplemented in oats and soluble BG powder, total BG intake was almost double the targeted dosage (>340 mg/kg BW for HBG and SOLBG; Table 2-2). Based on estimated intake, pasture grass alone provided 178.5 mg BG/kg BW, without the addition of extra dietary components.

This study was originally designed to provide low BG (REG) and no BG control (CORN) diets; however, the quantity of BG in pasture grass negated the ability to do this and lack of a true control diet could be one reason there were only modest immune effects.

The present study fed dietary oat BG for 12 d longer than other published research using oral oat BG (Davis et al., 2004b). However, most studies used

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exhaustive exercise or a chemical compound to induce immunosuppression which was followed by an infectious challenge (Estrada et al., 1999; Murphy et al., 2009). It is possible that prolonged head elevation was not enough of a challenge to the immune system to show benefits from oat BG. Robust immune modulating effects of oat BG seems to only be in response to a direct and overwhelming infectious challenge.

Laboratory mice are the most common animal model used in oat BG research making it hard to extrapolate results to other species. As in the horse, laboratory rodents do not have an established fiber requirement and most laboratory rodent diets contain less than 6% crude fiber and a minor amount of oats, if any (NRC, 1995).

Considering horses often consume higher quantities of BG and in many different forms, it could be that BG does not provoke an immune response due to lack of novelty of this dietary component. As hindgut fermenters, horses also have a more complex microbial fermentation process which may be another reason we did not see systemic immune enhancement. Soluble fibers are rapidly fermented to short chain fatty acids once they reach the hindgut (Harlow et al., 2015). BG could have a prebiotic effect within the hindgut of the horse, causing a change to the local microbiota; however, this was not the focus of the present study. Although still heavily debated, a common definition of a prebiotic is “a non-digestible food ingredient that beneficially affects the host by selectively stimulating the growth and/or activity of one or a limited number of bacteria in the colon” (Florowska et al., 2016). Although the BG could have changed the hindgut microbiota, we were unable to see any benefit with the immune measurements we investigated.

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Based on previous research, oat BG may have a primary influence on macrophages (Murphy et al., 2008; Murphy et al., 2009). Unfortunately, the activity of this immune cell is not easy to measure in horses. A few studies have shown in vivo neutrophil modulation in response to oat BG, but this would depend on the oat BG reaching systemic circulation (Murphy et al., 2007). Oral BG are hypothesized to have both indirect and direct effects on immune responses (Nieman et al., 2008). Indirect stimulation of immune responses occurs when BG are pinocytosed by M cells in the gut, which release cytokines and chemokines thereby changing the microenvironment and the subsequent immune response. Direct stimulation could occur if small BG particles are absorbed, enter the lymphatics or the vascular systems and then bind to BG- specific receptors located on many leukocytes. The binding of BG is thought to prime the cell for enhanced activity, which could include more effective killing or cytokine production. Oat BG may cause an indirect stimulation of local immune function within the gut of horses, but this would be a difficult response to measure. Although receptors for BG are known to exist in the horse genome, their distribution is still unknown and recently researchers were unable to identify dectin-1 and MBL on equine esophageal tissues (Nina Hornickel et al., 2011). The researchers explain that equine liver tissues were positive for MBL but perhaps there is not enough antigenic pressure in the esophageal region to warrant expression. Although we did not measure serum BG, our results suggest oral oat BG did not reached systemic circulation.

The potential to show immune benefits from an established equine feed commodity would have been advantageous for all parties involved. Unfortunately, the immune changes measured in this study do not provide sound evidence for further

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research regarding the potential for immune benefits from oat BG. The current study did however promote a reproducible model for upper respiratory and systemic distress, and introduce a less invasive way to measure the response. Transtracheal wash (TTW) or bronchoalveolar lavage (BAL) are standard methods used to characterize cytology and diagnose respiratory disease. TTW and BAL are invasive, require heavy sedation, and cannot be repeated at frequent intervals, thereby limiting progressive evaluation of the horse’s immunological response. Nasopharyngeal flushes are less invasive and require less sedation than other respiratory sampling techniques, which can allow more frequent sampling to track the progression of upper airway distress or proactive disease diagnosis. Further, the stress model from the current study proved useful as a short- term stressor with no long term clinical complications and may help to better understand the stress induced by transportation without having to transport horses.

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Figure 2-2. Example of head elevation and neck angle during 12 h stress induction. Photo courtesy of Jill Bobel.

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Table 2-3. Serum and saliva cortisol concentrations before head elevation (Pre-stress), immediately after (0 h Post-stress), and at 12, and 24 hours after head elevation. Due to lack of diet effect, data were pooled across treatments. Pre- 0 h Post- 12 h Post- 24 h Post- SEM P value stress stress stress stress Serum ng/mL 64.05b 77.18a 46.10c 64.49b 4.37 < 0.0001 Salivary pg/mL1 2.32b 2.64a 2.15c 2.31b 0.06 < 0.0001 1 Values are log10 transformed. a,b,cMeans in the same row with different superscripts differ (P ≤ 0.05).

Table 2-4. Nasopharyngeal flush mucus scores (range 1-3) before head elevation (Pre- stress), and immediately after (0 h Post-stress), 12, and 24 hours after head elevation. Due to lack of diet effect, data were pooled across treatments. Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- P value stress stress stress stress stress Mean score 1.3a 1.7b 1.3a 1.5a 1.3a 0.03 Mucus score ≥ 2 26% 41% 13% 27% 21% a,b,cMeans in the same row with different superscripts differ (P < 0.05).

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0.60 A A 0.50 a

0.40 /µL

3 CORN REG

x x 10 0.30

10 B HBG

Log 0.20 SOLBG bB 0.10

0.00 Day 0 Day 18

Figure 2-3. Total leukocytes in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.23), diet (P = 0.20) and diet*time (P = 0.04). a,bWithin a diet, bars with different letters differ between days (P < 0.05). A,BWithin a day, bars with different letters differ between diets (P < 0.10).

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3.50

A 3.30 a AB 3.10 BC 2.90 CORN

/ 100 µL / 100 bC REG 10 10 2.70 HBG

Log SOLBG 2.50

2.30

2.10 Day 0 Day 18

Figure 2-4. Number of neutrophils in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.48), diet (P = 0.12) and diet*time (P = 0.01). a,bWithin a diet, bars with different letters differ between days (P < 0.05). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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60 aA

AB 50 b B 45 B CORN REG 40 HBG SOLBG

35 % of Total Mononuclear Cells Mononuclear Total % of 30

25 Day 0 Day 18

Figure 2-5. Percentage of neutrophils in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.75), diet (P = 0.45) and diet*time (P = 0.04). a,bWithin a diet, bars with different letters differ between days (P < 0.05). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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3.30

3.10 bA AB 2.90 a a 2.70 BC CORN 2.50 bC

/ 100µL / REG

10 2.30 HBG

Log 2.10 SOLBG

1.90

1.70

1.50 Day 0 Day 18

Figure 2-6. Number of lymphocytes in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.47), diet (P = 0.09) and diet*time (P = 0.03). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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2.90

A 2.70 a AB

BC 2.50

bC CORN / 100µL /

2.30 REG 10

HBG Log 2.10 SOLBG

1.90

1.70 Day 0 Day 18

Figure 2-7. Number of monocytes in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.16), diet (P = 0.15) and diet*time (P = 0.06). a,bWithin a diet, bars with different letters differ between days (P < 0.05). A,BWithin a day, bars with different letters differ between diets (P < 0.10).

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1.40

1.30

1.20 B B 1.10 b CORN B REG 1.00 HBG SOLBG

% of Total Mononuclear Cells Mononuclear Total % of 0.90

Log 0.80

0.70 Day 0 Day 18

Figure 2-8. Percentage of eosinophils in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.16), diet (P = 0.08) and diet*time (P = 0.09). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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Table 2-5. Differences in leukocyte population of nasopharyngeal flush and whole blood by study period. Data were pooled across dietary treatments and include samples taken from both phases (d 0-22). Period 1 Period 2 Period 3 Period 4 SEM P value Nasopharyngeal # / µL flush Eosinophil 0.91a 0.81a 0.60b 0.54b 0.11 < 0.0001 %1 Neutrophils 46.76b 47.13b 51.85a 52.53a 1.59 0.005 Lymphocytes 19.37y 22.31x 20.57xy 21.61xy 1.23 0.06 Eosinophils 1.12a 1.07a 0.93b 0.86b 0.05 < 0.0001 Whole blood # x 103 / µL Lymphocytes 3.56b 3.50b 3.82a 3.72a 0.09 < 0.0001 Eosinophils2 2.49a 2.41ab 2.32bc 2.37c 0.04 0.0003 N:L ratio3 1.12b 1.26a 1.07b 1.14ab 0.09 0.03 %4 Neutrophils 1.65ab 1.67a 1.64b 1.67a 0.02 0.03 Lymphocytes 1.64a 1.61b 1.64a 1.63a 0.02 0.03 Eosinophils 0.45a 0.41ab 0.31bc 0.35c 0.04 0.0005 Monocytes 0.72b 0.74b 0.77a 0.74b 0.02 0.0006 1Percentage of total mononuclear cells in flush. 2 Log10 transformation of # /µL whole blood. 3N:L=neutrophil to lymphocyte ratio. 4 Percentage of total leukocytes. Values are log10 transformed. a,bMeans in the same row with different letters differ between periods (P ≤ 0.05). x,yMeans in the same row with different letters differ between periods (P < 0.10).

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Table 2-6. Leukocyte populations in nasopharyngeal flush before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24 and 72 hours after head elevation. Populations are represented by the number of cells per 100 µL of recovered flush. Due to lack of diet effect, data were pooled across treatments. Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- SEM P value stress stress stress stress stress x 103/100 µL Total WBC 3.79b 16.06a 3.75bc 6.98b 3.09c 2.95 <0.0001 Neutrophils 1.51b 9.92a 2.24bc 4.44b 1.82c 1.98 <0.0001 Lymphocytes 0.93bc 4.19a 0.53cd 1.33bd 0.58d 0.76 <0.0001 Monocytes 0.51bc 1.35a 0.28cd 0.52bd 0.33d 0.28 <0.0001 Eosinophils1 -0.63b -0.45a -0.75bc -0.71b -0.89c 0.13 <0.0001 # /100 µL CD4+1 2.22b 2.73a 2.15b 2.33b 2.14b 0.21 0.01 CD8+ 129b 443a 82b 290ab 90b 112 0.02 B Cells1 0.53a 0.25b 0.26b 0.39ab 0.28b 0.11 0.14 CD4+:CD8+1 0.20b 0.47a 0.42a 0.39a 0.41a 0.08 0.01 1 Values are log10 transformed. a,b,c,dMeans in the same row with different letters differ between time points (P < 0.05).

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Table 2-7. Percentage of leukocyte populations in nasopharyngeal flush before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24 and 72 hours after head elevation. Populations represent the percentage of total mononuclear cells in recovered flush. Due to lack of diet effect, data were pooled across treatments. Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- SEM P value stress stress stress stress stress % Neutrophils 45.3b 56.7a 52.9ac 51.0cd 46.9bd 2.1 <0.0001 Lymphocytes 21.7a 21.4a 16.5b 20.4a 21.7a 1.6 0.03 Monocytes 17.1a 11.6b 13.8bc 14.4c 18.2a 1.3 <0.0001 Eosinophils 15.1ac 9.5b 15.9a 13.5ac 12.6c 1.5 0.0003 CD4+ 26.8b 36.3a 35.2a 36.7a 36.2a 4.4 0.04 CD8+ 14.7a 9.8b 12.6ab 15.1a 12.0ab 1.7 0.02 B cells 8.2a 1.9b 4.7ab 5.3ab 5.6a 1.7 0.07 CD4+:CD8+ 2.6b 4.6a 3.7a 3.3a 3.2ab 0.8 0.06 T:B cells 33.9a 42.5a 39.7a 42.0a 12.0b 7.4 0.0009 a,b,c,dMeans in the same row with different letters differ between time points (P ≤ 0.05).

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0.96 aA 0.94 b

0.92 /µL 3 B B CORN

x 10 x 0.90 B 10 REG

Log Log 0.88 HBG SOLBG 0.86

0.84

0.82 Day 0 Day 18

Figure 2-9. Total leukocytes in whole blood before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.44), diet (P = 0.09) and diet*time (P = 0.16). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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0.59

0.58 a 0.57

0.56 / µL /

3 0.55 b CORN

x 10 x REG 0.54 10 HBG 0.53 Log Log SOLBG 0.52

0.51

0.50

0.49 Day 0 Day 18

Figure 2-10. Total lymphocytes in whole blood before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.02), diet (P = 0.95) and diet*time (P = 0.77). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05).

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0.84

0.82 bA

A 0.80 a AB 0.78 a

0.76 CORN bB

0.74 REG

% of Total WBC Total % of

10 HBG 0.72

Log SOLBG 0.70

0.68

0.66

0.64 Day 0 Day 18

Figure 2-11. Percentage of monocytes in whole blood before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.60), diet (P = 0.33) and diet*time (P = 0.07). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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0.70

bA 0.60

a 0.50 a B B

0.40 bB CORN

REG % of Total WBC Total % of

0.30 HBG 10

SOLBG Log 0.20

0.10

0.00 Day 0 Day 18

Figure 2-12. Percentage of eosinophils in whole blood before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.74), diet (P = 0.26) and diet*time (P = 0.06). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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Table 2-8. Leukocyte populations in whole blood before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24 and 72 hours after head elevation. Due to lack of diet effect, data were pooled across treatments. Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- SEM P value stress stress stress stress stress x 103/ µL Total WBC 7.95d 8.08cd 10.05a 8.80b 8.35c 0.38 <0.0001 Neutrophils 3.59c 3.96bc 5.20a 4.21b 3.79c 0.32 <0.0001 Lymphocytes 3.51c 3.32d 3.94a 3.78b 3.68b 0.12 <0.0001 Monocytes 0.48bc 0.45b 0.54a 0.46bc 0.49c 0.04 <0.0001 Eosinophils 0.27 0.23 0.25 0.24 0.27 0.03 0.63 CD4+ 2.76c 2.62c 3.17a 3.02b 2.95b 0.08 <0.0001 CD8+ 0.18b 0.18ab 0.19a 0.20a 0.17b 0.01 0.04 B cells 0.60b 0.52c 0.78a 0.65b 0.63b 0.07 <0.0001 CD4+:CD8+ 20a 18b 21a 20a 21a 2.1 0.02 T:B cells 6a 7a 5b 6a 6a 0.5 0.0002 N:L Ratio1 1.0:1b 1.3:1a 1.4:1a 1.1:1ab 1.1:1b 0.1 0.0005 1N:L=neutrophil to lymphocyte ratio. a,b,c,dMeans in the same row with different letter differ between time points (P < 0.05).

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Table 2-9. Percentage of leukocyte populations in whole blood before head elevation (Pre- stress), immediately after (0 h Post-stress), and 12, 24 and 72 hours after head elevation. Due to lack of diet effect, data were pooled across treatments. 0 h 12 h 24 h 72 h Pre- Post- Post- Post- Post- SEM P value stress stress stress stress stress % of total leukocytes Neutrophils 44.44c 47.81b 50.42a 46.62cb 44.33c 1.58 0.0003 Lymphocytes 44.92a 42.32bc 40.61b 44.17ac 45.23a 1.72 0.008 Monocytes 5.92a 5.56b 5.33b 5.29b 5.86a 0.32 0.0002 Eosinophils 3.09a 2.73ab 2.40b 2.64ab 3.13a 0.30 0.06 CD4+ 79.67 81.02 80.54 79.72 80.07 1.22 0.47 CD8+1 0.65y 0.71x 0.64yz 0.67y 0.61z 0.03 0.0004 B cells 16.96b 15.87b 19.81a 16.93b 17.04b 1.53 <0.0001 1 Values represent the log10 transformation of the mean. a,b,c,dMeans in the same row with different letters differ between time points (P < 0.05). x,y,zMeans in the same row with different letters differ between time points (P < 0.10).

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Table 2-10. Correlation between white blood cell populations and serum cortisol at 0 h Post-stress during phase 2 (d 18-22). Due to lack of diet effect, data were pooled across treatments. 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress Lymphocytes x 103 / µL P value 0.57 0.74 0.20 0.83 R2 0.007 0.003 0.04 0.001 % of total leukocytes P value 0.17 0.71 0.82 0.34 R2 0.04 0.003 0.001 0.02 Neutrophils x 103 / µL P value 0.18 0.46 0.99 0.15 R2 0.04 0.01 0.00 0.05 % of total leukocytes P value 0.16 0.71 0.81 0.19 R2 0.04 0.003 0.001 0.04

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2.40 A A a A 2.20

2.00

CORN

1.80 / 100µL /

bB REG 10 HBG

Log 1.60 SOLBG 1.40

1.20

1.00 Day 0 Day 18

Figure 2-13. Number of CD4+ lymphocytes in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.15), diet (P = 0.07) and diet*time (P = 0.09). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

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1.70

a 1.60

1.50

CORN 1.40 b REG

1.30 HBG SOLBG

% of Total Mononuclear Cells Mononuclear Total % of 1.20

Log 1.10

1.00 Day 0 Day 18

Figure 2-14. Percentage of CD4+ lymphocytes in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.03), diet (P = 0.72) and diet*time (P = 0.50). a,bWithin a diet, bars with different letters differ between days (P = 0.06).

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5.0 a A A

4.5

4.0

3.5 AB CORN 3.0 REG 2.5 bB HBG SOLBG

Ratio of CD4+ CD4+ CD8+ oftoRatio 2.0

1.5

1.0

0.5 Day 0 Day 18

Figure 2-15. Ratio of CD4+ to CD8+ lymphocytes in nasopharyngeal flush before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.08), diet (P = 0.14) and diet*time (P = 0.15). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

182

0.49 a 0.48 a

0.47

0.46

/µL

3 b CORN

0.45 x x 10

b REG 10

0.44 HBG Log 0.43 SOLBG

0.42

0.41

0.40 Day 0 Day 18

Figure 2-16. Number of CD4+ lymphocytes in whole blood before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.0006), diet (P = 0.87) and diet*time (P = 0.78). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05).

183

a a

82 a

81 b 80

% of Total Lymphocytes Total % of 78

76 CORN REG HBG SOLBG

Figure 2-17. Whole blood CD4+ lymphocytes as a percentage of total lymphocytes in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Data represent the overall mean ± SEM of samples evaluated in phase 2 of the study (d 18-22). Overall effect of time (P = 0.47), diet (P = 0.04) and diet*time (P = 0.99). a,bBars with different letters differ between diets (P ≤ 0.05).

184

2.98

a 2.94

2.90

CORN CPM b 10 2.86 REG

Log HBG

2.82 SOLBG

2.78

2.74 Day 0 Day 18

Figure 2-18. Background PBMC proliferation without mitogen stimulation before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). PBMC proliferation was measured in CPM (counts per minute). Overall effect of time (P = 0.04), diet (P = 0.77) and diet*time (P = 0.69). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05).

185

2.95 a 2.90 a 2.85 2.80

2.75 CPM 2.70 10 c bc

Log 2.65 2.60 2.55 2.50 2.45 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-19. Background PBMC proliferation without mitogen stimulation before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. PBMC proliferation was measured in CPM (counts per minute). Due to lack of diet effect, data were pooled across diets. Overall effect of time (P < 0.0001), diet (P = 0.49) time*diet (P = 0.91).a,b,c,dBars with different letters differ over time (P ≤ 0.05).

186

1100

1000 a ab 900 abc 800 bc c 700

CPM 600

500

400

300

200 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-20. PBMC proliferation in response to 10 ng/mL LPS before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. PBMC proliferation was measured in CPM (counts per minute). Due to lack of diet effect, data were pooled across diets. Overall effects of time (P = 0.04), diet (P = 0.98) and time*diet (P = 0.67). a,b,c,dBars with different letters differ over time (P ≤ 0.05).

187

2.00 a* 1.80 a 1.60 a a*

1.40 1.20 1.00 b 0.80

Stimulation IndexStimulation 0.60 0.40 0.20 0.00 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-21. Stimulation index of PBMC in response to LPS before head elevation (Pre- stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. Overall effect of time (P < 0.0001), diet (P = 0.86) and time*diet (P = 0.58). a,bBars with different letters differ over time (P < 0.05). *Differ from each other (P = 0.08).

188

10500

x 10000 xz

9500 yz

y y

9000 CPM

8500

8000

7500 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-22. PBMC proliferation in response to 1 µg/mL PWM before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. PBMC proliferation was measured in CPM (counts per minute). Due to lack of diet effect, data were pooled across diets. Overall effect of time (P = 0.04) and diet (P = 0.18). x,y,zBars with different letters differ over time (P < 0.10).

189

1.60

a 1.40 d ad c 1.20 b

1.00

Stimulation IndexStimulation

0.80 Log

0.60

0.40 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-23. Stimulation index of PBMC in response to PWM before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. Overall effect of time (P < 0.0001), diet (P = 0.95) and time*diet (P = 0.74). a,b,c,dBars with different letters differ over time (P < 0.05).

190

Table 2-11. PBMC proliferation1 and stimulation index in response to Con A before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. P Values 0 h Post- 12 h Post- 24 h Post- 72 h Post- Diet* Pre-stress SEM Time Diet stress stress stress stress Time Con A2 1 µg/mL 4,386a 3,712b 4,674a 4,381a 4,578a 455 0.01 0.74 0.78 2 µg/mL 7,064a 6,357b 7,386a 6,929ab 7,281a 634 0.07 0.85 0.49 SI3 1 µg/mL 0.72b 0.92a 0.82c 0.93a 0.92a 0.07 <0.0001 0.65 0.87 2 µg/mL 0.94b 1.14a 1.03c 1.14a 1.13a 0.07 <0.0001 0.29 0.77 1Proliferation measured in counts per minute (CPM). 2Con A=concanavalin A. 3 SI=Log10 of stimulation index, calculated as stimulated CPM/background CPM. a,bMeans in the same row with different superscripts differ over time (P ≤ 0.05).

191

Table 2-12. Differences in PBMC proliferation1 by study period. Data were pooled across dietary treatments and include samples taken from both phases (d 0-22). Period 1 Period 2 Period 3 Period 4 SEM P value Background2 2.78a 2.78a 2.77a 2.62b 0.03 0.0006 Mitogen3 PWM 3.92a 3.96a 3.93a 3.81b 0.03 < 0.0001 LPS 2.80a 2.67b 2.72ab 2.52c 0.08 0.0003 Con A 1 µg/mL 3.83a 3.73a 3.41b 3.07c 0.10 0.0007 Con A 2 µg/mL 3.81a 3.81a 3.84a 3.65b 0.04 < 0.0001 1 Proliferation measured in counts per minute (CPM). Values represent the log10 transformation of the mean. 2Background CPM without mitogen stimulation. 3PWM=pokeweed mitogen; LPS=lipopolysaccharide mitogen; Con A=concanavalin A. a,b,cMeans in the same row with different superscripts differ over time (P ≤ 0.05).

192

50 aA aA AB 45

b b B CORN 40 REG HBG

%Phagocytosis 35 SOLBG

25 Day 0 Day 18

Figure 2-24. Percentage of phagocytosis of Streptococcus equi by whole blood neutrophils before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.03), diet (P = 0.42) and diet*time (P = 0.16). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

193

180000 aB 160000 aAB 140000 A A 120000 b b CORN 100000 REG PI MFI PI HBG 80000 SOLBG

60000

40000

20000 Day 0 Day 18

Figure 2-25. Mean fluorescence intensity (MFI) of propidium iodide (PI) indicating quantity of Streptococcus equi phagocytosed by whole blood neutrophils before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.002), diet (P = 0.36) and diet*time (P = 0.19). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

194

PI MFI Phagocytosis index

150000 65000 x a 60000 130000 y 55000 110000 b 50000

PI MFI PI 90000 45000

70000 Index Phagocytosis 40000

50000 35000

30000 30000 Geldings Mares

Figure 2-26. Differences in mean fluorescence intensity (MFI) of propidium iodide (PI) and phagocytosis index by whole blood neutrophils by gender during phase 1 (d 0-18). x,yBars with different letters differ within a variable (P = 0.08). a,bBars with different letters differ within a variable (P = 0.05).

195

bA 80000

70000 AB bAB

60000 CORN a B a REG 50000 HBG

Phagocytosis Index Phagocytosis SOLBG 40000

30000

20000 Day 0 Day 18

Figure 2-27. Phagocytosis index of whole blood neutrophils before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.003), diet (P = 0.39) and diet*time (P = 0.17). a,bWithin a diet, bars with different letters differ between days (P < 0.10). A,BWithin a day, bars with different letters differ between diets (P ≤ 0.05).

196

Table 2-13. Differences in whole blood neutrophil function by study period. Data were pooled across dietary treatments and include samples taken from both phases (d 0-22). Period 1 Period 2 Period 3 Period 4 SEM P value % Phagocytosis 45.74a 42.27b 40.57b 40.95b 1.22 0.02 PI MFI1 150463a 114527b 100029b 103006b 7933 0.0005 Phagocytosis index2 69323a 51673b 44836b 45240b 4357 0.001 1PI MFI=propidium iodide mean fluorescence intensity. 2Phagocytosis index calculated by (PI MFI x % phagocytosis)/100. a,bMeans in the same row with different superscripts differ (P ≤ 0.05).

197

60000 x

55000

50000

45000

40000 Phagocytosis Index Phagocytosis

35000

30000 Age 4- 12 years Age ≥ 13 years

Figure 2-28. Differences in phagocytosis index of whole blood neutrophils by age during phase 1 (d 0-18). x,yBars with different letters differ (P = 0.09).

198

4.90 a 4.80 a

4.70

4.60 CORN

4.50 DHR MFI DHR

b b REG 10

4.40 HBG Log 4.30 SOLBG

4.20

4.10

4.00 Day 0 Day 18

Figure 2-29. Mean fluorescence intensity (MFI) of dihydrorhodamine 123 (DHR) of whole blood neutrophils before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.01), diet (P = 0.99) and diet*time (P = 0.69). a,bBars with different letters differ between diets (P < 0.10).

199

4.30

aA 4.10

AB 3.90 AB AB CORN

3.70 REG Induced OB IndexOBInduced

b HBG 10 SOLBG

Log 3.50

3.30

3.10 Day 0 Day 18

Figure 2-30. Phagocytosis-induced oxidative burst index of whole blood neutrophils before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.02), diet (P = 0.88) and diet*time (P = 0.25). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P < 0.10).

200

1.72

1.70 aB

1.68 a 1.66 b A A 1.64 CORN

1.62 bA REG HBG

1.60 % Inactive Neutrophils % Inactive

SOLBG 10

1.58 Log 1.56

1.54

1.52 Day 0 Day 18

Figure 2-31. Percentage of whole blood neutrophils that did not react to Streptococcus equi before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effects of time (P = 0.61), diet (P = 0.41) and diet*time (P = 0.04). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P < 0.10).

201

Phagocytosis Induced OB 50 35 a a 45 30 b b

40 25 a c 35 20 b b b 30 15

c OB % Induced % % Phagocytosis 25 10

20 5

15 0 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-32. Percentage of phagocytosis and phagocytosis-induced oxidative burst (OB) by whole blood neutrophils before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. Overall phagocytosis effect of time (P < 0.0001), diet (P = 0.16) and diet*time (P = 0.28). Overall induced OB effect of time (P = 0.0002), diet (P = 0.008) and diet*time (P = 0.73). a,b,cWithin a variable, different letters differ between times (P < 0.05).

202

PI MFI Phagocytosis index 160000 70000 a ab 60000 140000 ab

b 50000

120000 a c 40000 ab PI MFI PI ab 100000

b 30000 Phagocytosis Index Phagocytosis

80000 c 20000

60000 10000 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-33. Mean fluorescence intensity (MFI) of propidium iodide (PI) and phagocytosis index of whole blood neutrophils before head elevation (Pre- stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. Overall PI MFI effect of time (P < 0.0001), diet (P = 0.06) and diet*time (P = 0.11). Overall phagocytosis index effect of time (P < 0.0001), diet (P = 0.05) and diet*time (P = 0.07). a,b,cWithin a variable, different letters differ between times (P < 0.05).

203

PI MFI Phagocytosis Index 150000 65000

a a a 130000 60000 ab ab b 55000

110000 bc c 50000

PI MIF PI 90000 45000 70000 40000 Index Phagocytosis

50000 35000

30000 30000 CORN REG HBG SOLBG

Figure 2-34. Mean fluorescence intensity (MFI) of propidium iodide (PI) and phagocytosis index of whole blood neutrophils in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Data represent the overall mean ± SEM of samples evaluated in phase 2 of the study (d 18-22). Overall PI MFI effect of time (P < 0.0001), diet (P = 0.06) and diet*time (P = 0.11). Overall phagocytosis index effect of time (P < 0.0001), diet (P = 0.05) and diet*time (P = 0.07). a,b,cWithin a variable, bars with different letters differ between diets (P ≤ 0.05).

204

CORN REG HBG SOLBG 80000 †

70000

60000 * 50000 * *

Phagocytosis Index Phagocytosis 40000 * * 30000

20000 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-35. Phagocytosis index of whole blood neutrophils in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG) before head elevation (Pre-stress), immediately after (0 h Post- stress), and 12, 24, and 72 hours after head elevation. Overall phagocytosis index effect of time (P < 0.0001), diet (P = 0.05) and diet*time (P = 0.07). *Within a diet, time points differ from Pre-stress (P < 0.05). †Within a diet, time points differ from Pre-stress (P < 0.10).

205

Table 2-14. Correlation between phagocytosis index or phagocytosis-induced oxidative burst index and serum cortisol at 0 h Post-stress. Due to lack of diet effect, data were pooled across treatments. 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress Phagocytosis Index P value 0.74 0.03 0.02 0.65 R2 0.002 0.10 0.10 0.005 Oxidative

Burst Index1 P value 0.55 0.09 0.05 0.01 R2 0.008 0.07 0.08 0.13 1 Phagocytosis-induced oxidative burst.

206

Phagocytosis Induced OB 46 1.30

45 a 1.25 a a

a 1.20

43 ab 1.15 42 b b b 1.10

41 % Induced OB % Induced 1.05

40 10 % % Phagocytosis

1.00 Log 39

38 0.95

37 0.90 CORN REG HBG SOLBG

Figure 2-36. Percentage of phagocytosis and phagocytosis-induced oxidative burst (OB) of whole blood neutrophils in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Data represent the overall mean ± SEM of samples evaluated in phase 2 of the study (d 18-22). Overall phagocytosis effect of time (P < 0.0001), diet (P = 0.16) and diet*time (P = 0.28). Overall OB effect of time (P = 0.0002), diet (P = 0.008) and diet*time (P = 0.73).a,bWithin a variable, bars with different letters differ between diets (P ≤ 0.05).

207

DHR MFI Induced OB Index 4.70 a a 13000 4.60 11000

b bc 4.50 c 9000

a DHR MFI DHR 4.40

10 7000 Log

4.30 IndexOBInduced b 5000

bc 4.20 bc 3000

c 4.10 1000 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-37. Mean fluorescence intensity (MFI) of dihydrorhodamine 123 (DHR) and phagocytosis-induced oxidative burst (OB) index of whole blood neutrophils before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Overall DHR MFI effect of time (P < 0.0001), diet (P = 0.84) and diet*time (P = 0.87). Overall OB index effect of time (P < 0.0001), diet (P = 0.05) and diet*time (P = 0.57). a,b,cWithin a variable, different letters differ between times (P < 0.05).

208

10000 a

9000

8000 ab b 7000 b

6000

Induced OB IndexOBInduced 5000

4000

3000 CORN REG HBG SOLBG

Figure 2-38. Phagocytosis-induced oxidative burst (OB) index of whole blood neutrophils in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Data represent the overall mean ± SEM of samples evaluated in phase 2 of the study (d 18-22). Overall effect of time (P < 0.0001), diet (P = 0.05) and diet*time (P = 0.57). a,bBars with different letters differ between diets (P ≤ 0.05).

209

1.74 a 1.72 ab 1.70 bc

1.68 c 1.66 1.64 d 1.62 b b

% Inactive Cells Inactive % b

10 1.60 c

Log 1.58 1.56 1.54 1.52 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-39. Percentage of whole blood neutrophils that did not respond to Streptococcus equi before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. Overall effect of time (P < 0.0001), diet (P = 0.04) and diet*time (P = 0.33). a,b,c,dBars with different letters differ over time (P ≤ 0.05).

210

1.72

1.70 a

a 1.68 ab

1.66 b

% Inactive Cells Cells % Inactive 1.64

Log 1.62

1.60

1.58 CORN REG HBG SOLBG

Figure 2-40. Whole blood neutrophils that did not respond to Streptococcus equi in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Data represent the overall mean ± SEM of samples evaluated in phase 2 of the study (d 18-22). Overall effect of time (P < 0.0001), diet (P = 0.04) and diet*time (P = 0.33). a,bBars with different letters differ between diets (P < 0.10).

211

Table 2-15. Differences in IgA concentrations by study period. Data were pooled across dietary treatments and include samples taken from both phases (d 0- 22).1 Period 1 Period 2 Period 3 Period 4 SEM P value IgA NPF (µg/mL) 1.67b 1.76a 1.69b 1.69b 0.05 0.06 Saliva (µg/mL) 2.63b 2.66b 2.68b 2.77a 0.08 0.07 Serum (mg/dL) 1.85b 1.97a 1.93c 1.85b 0.01 <0.0001 Fecal liquid (ng/mL) 3.06a 2.94b 2.88b 2.88b 0.07 0.01 IgA Secretory Index Fecal liquid -1.55a -1.67b -1.77b -1.71b 0.05 0.001 1 Values represent log10 transformation of the mean. a,b,cMeans in the same row with different letters differ between time points (P ≤ 0.05).

212

NPF IgA NPF SI 1.90 a 1.60 a a a 1.50 1.70 b

1.40

1.50 1.30

1.20 SI IgA

10 10 µg IgA /mL IgA µg

1.30 a 10

ac 1.10 Log

bc ac Log 1.10 d 1.00

0.90 0.90 0.80

0.70 0.70 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-41. Total IgA (µg/mL) and secretory index (SI) in nasopharyngeal flush (NPF) before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. IgA overall effect of time (P < 0.0001), diet (P = 0.55) and diet*time (P = 0.54). SI overall effect of time (P < 0.0001), diet (P = 0.98) and diet*time (P = 0.48). a,b,c,d Within a variable, different letters differ between times (P ≤ 0.05).

213

2.95 aA 2.90 aAB 2.85

aAB 2.80 B

2.75 CORN

µg IgA /mL IgA µg b b 2.70 b REG 10 HBG 2.65 Log SOLBG 2.60

2.55

2.50

2.45 Day 0 Day 18

Figure 2-42. Total IgA (µg/mL) in saliva before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.007), diet (P = 0.60) and diet*time (P = 0.53). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P < 0.10).

214

30 aB AB 25 a b AC 20 bC CORN 15 REG HBG SOLBG

IgA Secretory Index Secretory IgA 10

0 Day 0 Day 18

Figure 2-43. Secretory index of IgA in saliva before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.57), diet (P = 0.15) and diet*time (P = 0.03). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,B,CWithin a day, bars with different letters differ between diets (P < 0.10).

215

Salivary IgA Salivary SI 3.20 a 1.60 3.00 b 1.50 d 2.80 d

1.40

2.60 c 1.30 2.40

a* 1.20 SI IgA

µg IgA /mL IgA µg 10 10 2.20 10 a 1.10

2.00 Log Log a* a* 1.00 1.80 1.60 0.90 1.40 b 0.80 1.20 0.70 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-44. Total IgA (µg/mL) and IgA secretory index (SI) in saliva before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. IgA overall effect of time (P < 0.0001), diet (P = 0.55) and diet*time (P = 0.54). SI overall effect of time (P < 0.0001), diet (P = 0.98) and diet*time (P = 0.48). a,b,c,dWithin a variable, different letters differ between times (P ≤ 0.05). *Differ from Pre-stress (P < 0.10).

216

Fecal IgA Fecal IgA SI 0.90 3.20 a a ab 0.80 b 3.00

0.70

a c 0.60 2.80 a

0.50 SI IgA

10 10 ng IgA /mL IgA ng

2.60 10

b b 0.40 Log Log 2.40 0.30 c 0.20 2.20 0.10

2.00 0.00 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-45. Total IgA (ng/mL) and IgA secretory index (SI) in fecal liquid before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. IgA overall effect of time (P < 0.0001), diet (P = 0.72) and diet*time (P = 0.97). SI overall effect of time (P < 0.0001), diet (P = 0.82) and diet*time (P = 0.59). a,b,c,dWithin a variable, different letters differ between times (P ≤ 0.05).

217

2.00 a 1.98

1.96

1.94 b 1.92 b b

mg IgA /dL IgA mg b

10 1.90

Log 1.88

1.86

1.84

1.82 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- Stress stress stress stress stress

Figure 2-46. Total IgA (mg/dL) in serum before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. IgA overall effect of time (P = 0.002), diet (P = 0.31) and diet*time (P = 0.93). a,bDifferent letters differ between time points (P ≤ 0.05).

218

Table 2-16. Correlation between IgA concentrations measured in biological samples during the entire study (d 0-22, periods 1-4). NPF Serum Fecal liquid Salivary P value < 0.0001 < 0.0001 < 0.0001 R2 0.52 0.43 0.26 NPF1 P value < 0.0001 < 0. 0001 R2 0.54 0.23 Serum P value < 0.0001 R2 0.36 1NPF=nasopharyngeal flush.

219

20.0 aA 19.5 AB AB 19.0 B CORN b 18.5 REG

HBG % Fecal DM % Fecal 18.0 SOLBG

17.5

17.0 Day 0 Day 18

Figure 2-47. Percentage of fecal dry matter (DM) before (d 0) and after (d 18) dietary treatment with CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Overall effect of time (P = 0.02), diet (P = 0.56) and time*diet (P = 0.73). a,bWithin a diet, bars with different letters differ between days (P ≤ 0.05). A,BWithin a day, bars with different letters differ between diets (P < 0.10).

220

23.0 a 22.0 c

21.0

20.0 b bd 19.0 d

% Fecal DM % Fecal 18.0

17.0

16.0

15.0 Pre- 0 h Post- 12 h Post- 24 h Post- 72 h Post- stress stress stress stress stress

Figure 2-48. Percentage of fecal dry matter (DM) before head elevation (Pre-stress), immediately after (0 h Post-stress), and 12, 24, and 72 hours after head elevation. Due to lack of diet effect, data were pooled across diets. Overall effect of time (P < 0.0001), diet (P = 0.02) and time*diet (P = 0.78). a,b,c,dBars with different letters differ between times (P < 0.05).

221

21.0 a 20.5 a ab

20.0

19.5 % % DM

19.0

18.5

18.0 CORN REG HBG SOLBG

Figure 2-49. Percentage of fecal dry matter in horses fed CORN, regular feed oats (REG), high BG oats (HBG) or concentrated BG powder (SOLBG). Data represent the overall mean ± SEM of samples evaluated in phase 2 of the study (d 18-22). Overall effect of time (P < 0.0001), diet (P = 0.02) and time*diet (P = 0.78). a,bBars with different letters differ between diets (P < 0.05).

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Table 2-17. Differences in fecal variables by study period. Data were pooled across dietary treatments and include samples taken from both phases (d 0-22). Period 1 Period 2 Period 3 Period 4 SEM P value % Dry matter 19.88ab 19.79a 19.04b 19.05b 0.36 0.01 Fecal liquid 48.15c 61.07b 58.51b 68.12a 2.62 < 0.0001 1Total mL of fecal liquid per 200 g feces squeezed. a,bMeans in the same row with different letters differ between periods (P ≤ 0.05).

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CHAPTER 3 IMMUNOLOGICAL CHANGES OCCUR EARLY DURING ROAD TRANSPORTATION

Introduction

Alterations in physiological and immune status and heightened risk for respiratory disease following transportation are well documented in horses (Smith et al., 1996;

Raidal et al., 1997a; Oikawa et al., 2005; Padalino et al., 2017a). However, information on mucosal immune responses and how quickly immunological changes occur during transit are lacking.

Only a small number of studies have investigated the effects of short distance travel between 2-4 hours, and the results have been inconsistent. Studies have shown no change in tracheal cytology, bacterial counts or systemic WBC, but one study did report an increased neutrophil to lymphocyte ratio, suggesting the effects of transportation may occur early on (Casella et al., 2012; Faubladier et al., 2013; Allano et al., 2016). There has been a significant amount of research into transportation lasting

24 h or longer and these studies consistently show horses have increased airway cytology and systemic WBC, and decreased immune cell function, which can last for several days after the travel and leave horses at risk for respiratory infections (Raidal et al., 1996; Stull et al., 2004; Oikawa et al., 2005). Most research on transportation only compared measurements taken before transit to measurements taken after, so it is uncertain when changes to immune status begin to occur.

It is also unclear how transportation affects mucosal immunity, but given the elevated risk of respiratory infection after transportation, it appears to play an important role. Horses rely on gravity flow to clear respiratory accumulations, and the inability to lower their head during transit causes an increased pathogen load and requires an

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efficient mucosal response to prevent respiratory infection. In our previous study, salivary, nasal and fecal IgA were elevated following 12 h of head elevation, which simulated the posture held during transportation (Chapter 2). All IgA markers dropped below baseline at some point during recovery and, in the case of salivary IgA, it did not return to normal 3 d after stress concluded. The mucosal IgA response to head elevation prompted us to investigate this further. During the previous study, we also observed a possible diurnal pattern of IgA secretion. Diurnal fluctuation of immune components is reported in other species (Haldar and Ahmad, 2010) and humans (Keller et al., 2009), but results in horses are inconsistent (Schnabel et al., 2015).

Salivary IgA has recently been suggested as a sensitive and accurate marker of acute stress because of the local storage and ability to be released quickly (Escribano et al., 2015). Our previous study indicated that mucosal IgA may respond to stress, but this has not been investigated in horses.

The aim of this preliminary study was to determine the onset of physiological changes and the mucosal immune response to long distance road transportation stress lasting 24 h. Based on our previous work, we hypothesized that physiological changes would begin within the first 12 h of road transportation and last up to 3 d during recovery. Upon noticing a possible diurnal pattern of salivary IgA during the previous study, this study included some evening samples to investigate this further.

Methods and Materials

Horses

Three cecally cannulated geldings (mean ± SEM, 589 ± 10 kg; 17.7 ± 1.8 y) were tethered with their heads elevated and transported for 24 consecutive hours. The

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Quarter horse geldings and had no prior history of chronic respiratory inflammation or infection. Before transportation, horses were housed with ad libitum access to bermudagrass pasture at the Texas A&M College of Veterinary Medicine and

Biomedical Sciences in College Station, Texas. During recovery, horses were individually housed indoors in 3.7 m x 3.7 m stalls at the IFAS Horse Teaching Unit in

Gainesville, Florida. Upon arrival in Florida, they were provided ad libitum access to

Coastal bermudagrass hay and water and were fed a pelleted vitamin-mineral supplement twice daily (Equalizer, Seminole Feed®, Ocala, FL). All animal protocols and procedures were reviewed and approved by the University of Florida, Institutional

Animal Care and Use Committee.

Experimental Design

This study began in College Station, Texas on December 15 and ended in

Gainesville, Florida on December 22, 2016. Horses were studied in their home environment for 2 d before traveling for 24 consecutive hours to Florida. Samples were obtained from horses before leaving Texas, during transportation, and for up to 120 h after arrival in Florida (Figure 3-1).

Transportation

Horses were continuously transported for 24 h in a six-horse stock trailer owned by the University of Florida. The horse compartment of the trailer measured 6.1 m long,

2.1 m wide and 2.3 m high for a total area of 6.36 km2. The horses were equally spaced within the trailer giving each horse approximately 2.11 km2 of space (Figure 3-2). The horses were very familiar with the loading and unloading process and had been previously transported on a trailer, although not in the past 4 weeks. Horses were fitted with a halter and lead rope and voluntarily followed a human onto the trailer without

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incident. During transportation, the horses were restrained (tethered) with their heads elevated approximately 1.5 m above trailer floor. The restraint equipment included a halter and a trailer tie with a quick release snap anchored to the trailer. Horses had ad libitum access to Coastal bermudagrass hay provided in nets and the trailer was stopped at least every 4 h to offer them water in buckets. Approximately half way through the 24-h trip, horses were off loaded from the trailer and hand walked by halter and lead rope for about 30 min. During walking and water breaks, horses remained tied or were prevented from lower their heads.

Sample Collection

Three separate pre-transit samples were obtained while horses remained in their home environment. Two days prior to transportation, one morning (0800 h; -48 h) and one evening (2000 h; -36 h) sample were collected and one day before transportation, another morning sample (0800 h; -24 h) was collected (Figure 3-1). Sample collection occurred in the field where the horses were group-housed. During transit, the trailer was stopped to collect samples at 6, 12, 18 and 24 h of transit. Horses remained tied in the trailer for sample collection. Based on previous assessments conducted before transporting the horses, there was adequate room for investigators to safely sample from the horses while they remained on the trailer. All samples were kept on ice during transit. After transit, the horses were housed in individual stalls for recovery and samples were collected at 12, 24, 72 and 120 h post-transit.

Nasopharyngeal Flush

Because mild sedation was required, nasopharyngeal flush (NPF) samples were only collected before and after transportation. Nasopharyngeal flush samples were collected from horses after sedation with detomidine hydrochloride (Dormosedan®,

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Zoetis; 0.2-0.4 mL/100 kg BW). A sterile 56 cm x 8 French-gauge catheter was passed through the nasal cavity into the pharyngeal region. Sterile phosphate buffered saline

(PBS; 120 mL) was pushed through the catheter and nasal drainage was caught using a funnel collection cup system consisting of a funnel attached to a 90 mL sterile urine collection cup held below the nostrils (UrinAssist®, Express Diagnostics International

Inc., Blue Earth, MN). Correct catheter placement in the nasopharyngeal region was determined by drainage exiting both nostrils simultaneously. The catheter was removed and the procedure was repeated in the opposite nasal cavity, passing a total of 240 mL of sterile PBS. Total flush recovery was (mean ± SEM) 191 ± 28 mL in 3-4 collection cups per horse. Flush cups were placed on ice and transported to the laboratory where subjective evaluation of turbidity and volume of each cup was recorded. The same investigator assigned a score of 1-5 to each flush collection cup based on visible mucus quantity and turbidity. A score of 0 represented an absence of mucus and turbidity in the cup and a score of 5 represented a large quantity of mucus was present and the flush was very turbid. To account for elevated cell numbers in the flush, scores were increased by 1 if the sample was absent of mucus but was still turbid. Individual cup scores were then averaged for each horse. The contents of each cup were filtered through a 15 x 15 cm square of 80 µm sterile nylon mesh (Component Supply

Company, Fort Meade, FL) to remove mucus and nasal debris. Contents of the cups were then combined for each horse, and filtered through a 40 µm cell strainer into 50 mL conical tubes to remove large cells. Collection cups were rinsed with an additional 5-

10 mL of PBS and the rinseate was poured into a separate conical tube. The samples and rinseates were centrifuged for 7 minutes at 300 x g at 4°C to obtain a cellular pellet.

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Subsamples of nasal flush supernatant were taken from all but the conical tubes containing rinseate and subsequently stored in polypropylene vials in 1.5 mL aliquots at

-80°C until analysis of IgA was performed.

Depending on size, cell pellets were re-suspended in 250 µL–1.3 mL PBS and placed into 1.3 mL vials (Microtainer®, Becton Dickinson, Franklin Lakes, NJ). Total volume in each micro vial was determined and recorded. Leukocyte populations were determined using an IDEXX Procyte DX® Hematology Analyzer (Westbrook, ME), which was previously validated for nasopharyngeal flush samples (Appendix B). The machine provided cell number and percentage of total WBC, neutrophils, lymphocytes, monocytes, eosinophils and basophils.

Nasal Swabs

Nasal swabs were obtained by inserting a nylon swab (FLOQSwabs™, Copan

Diagnostics Inc., Murrieta, CA) approximately 10 cm into the nostril and rubbing it against the nasal wall for 10 seconds. The procedure was conducted once in each nostril at every sampling interval. Each swab was placed in a polypropylene vial containing 500 µL of PBS and vials with swabs were frozen at -80°C until analysis of

IgA.

Saliva Collection

At least 10 min before collection of saliva began, the horse’s mouth was rinsed with 60 mL of tap water. Saliva was collected by attaching two synthetic swabs

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(Salivette® Cortisol, Sarstedt Inc., Newton, NC) to a 13 cm brass or stainless steel chifney bit. Each swab was tightly attached by crisscrossing two 10 cm plastic cable ties. The bit was inserted into the horse’s mouth and attached to each side of the halter with chifney snaps. Each horse was encouraged to chew on the bit for at least 10 min and the total collection time was recorded. Once swabs were saturated, they were detached from the bit, placed into the tube supplied by the manufacturer, and placed on ice until processing. Tubes containing swabs were centrifuged at 1700 x g for 10 minutes at 4°C to extract saliva (approximately 2 mL/horse). Samples were stored in polypropylene vials in 0.1-1.0 mL aliquots at -80°C until analysis of cortisol and IgA could be performed.

Cecal Collection

Cecal samples were obtained before and after transit, and only once during transit (at 12 h). Cannula plugs were removed and cecal digesta was collected in a bucket by gravity flow or grab sample using a long handled small plastic spoon. Cecal contents were thoroughly mixed before a 200-g subsample was poured into a plastic bag (Whirl-Pak®, Nasco, Fort Atkinson, WI) and stored at -80°C until DM analysis.

Remaining contents were filtered through two layers of cheese cloth and fluid was stored in polypropylene vials in 4-mL aliquots at -80°C until analysis of IgA. Once the remaining cecal fluid reached room temperature, pH was determined using an automated pH meter (Orion Star A221, Thermo Scientific™, Waltham, MA). Sample temperature (mean ± SEM) was 24.4°C ± 1.3 and average calibration of the pH meter was 95.4%.

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Fecal Collection

Freshly voided feces were collected in a plastic bag and stored on ice until processing. All processing occurred within 2 h of collection. Feces were homogenized and a composite 200-g sample was placed in a plastic bag (Whirl-Pak®, Nasco, Fort

Atkinson, WI) and stored at -80°C until analyzed for DM content. An additional 100 g of feces was manually squeezed through two layers of cheesecloth (Electron Microscopy

Sciences, Hatfield, PA) to obtain fecal liquid. The fecal liquid was stored in polypropylene vials in 2-mL aliquots at -80°C until analyzed for IgA content. Once the remaining fecal liquid reached room temperature, pH was determined using an automated pH meter (Orion Star A221, Thermo Scientific ™, Waltham, MA). Sample temperature (mean ± SEM) was 25.1 ± 0.8°C and average calibration of the pH meter was 95.4%. If horses did not defecate during a sampling interval, feces were obtained by rectal palpation. The investigator wore a lubricated (OB Lube, AgriLabs®, St.

Joseph, MO) polyethylene fiber palpation sleeve and inserted their hand into the rectum to obtain feces. In this situation, 300 g of feces was only obtained if the rectum contained an adequate amount. Preliminary analysis in the laboratory confirmed lubrication had no effect on fecal liquid IgA measurement (data not shown).

Blood Collection

Whole blood was collected via jugular venipuncture using evacuated tubes

(Vacutainer®, Becton Dickinson Co., Franklin Lakes, NJ). Whole blood (16 mL) was collected in tubes containing no anticoagulant for harvesting of serum. Serum samples were allowed to clot for approximately 1-2 h before centrifugation at 3300 x g for 15 min at 4 ˚C. Serum was harvested and stored in polypropylene vials in 1-2 mL aliquots at

-80˚C until analysis of cortisol and IgA were performed. Whole blood (32 mL) was

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collected in tubes containing sodium heparin for harvesting of PBMC. Blood collected in sodium heparin tubes was continuously mixed on a tube rotator at room temperature until PBMC isolation. Whole blood (2 mL) was collected in tubes containing tri- potassium ethylenediaminetetraacetic acid (VetCollect™, IDEXX Laboratories,

Westbrook, ME) and placed on ice until complete blood count analysis was performed

(Procyte DX® Hematology Analyzer, IDEXX Laboratories, Westbrook, ME). The machine provided cell number and percentage of total WBC, neutrophils, lymphocytes, monocytes, eosinophils and basophils.

PBMC Isolation

Reagents for PBMC isolation included PBS (MediaTech Inc, Manassas, VA), lymphocyte separation medium (LSM, MP Biomedicals, Solon, OH), trypan blue (0.4%,

Sigma-Aldrich, St Louis, MO), dimethyl sulfoxide (DMSO, MediaTech Inc, Manassas,

VA), and fetal bovine serum (FBS, Atlanta Biologicals, Lawrenceville, GA).

LSM while maintaining a sharp interface. Vials were centrifuged at 400 x g for 25 min at

18°C. Plasma was aspirated off to within 0.5 cm above the PBMC buffy coat and

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discarded. The buffy coat (containing PBMC) and all the LSM (approximately 15 mL) was removed and placed in a separate conical tube. The tubes were brought up to a volume of 45 mL with PBS and gently inverted. Tubes were then centrifuged at 200 x g for 10 min at 25°C (room temperature). The supernatant was suctioned off and discarded, leaving the PBMC pellet undisturbed. The PBMC pellet was then broken up and rinsed with 40-45 mL of PBS. The vials were centrifuged again for 10 min at 200 x g. The supernatant was suctioned off and discarded and the PBMC pellet was re- suspended in 1 mL of PBS. Using trypan blue exclusion, live cells were counted using light microscopy at 40x magnification by loading a hemacytometer with 10 µL of a mixture containing 90 µL trypan blue and 10 µL PBMC. A maximum of 9x106 live cells per vial were added to freezing media and frozen in 1-mL aliquots in cryogenic vials.

Freezing media consisted of 10% DMSO and 90% FBS. Cryogenic vials were placed in a Nalgene® Mr. Frosty Cryo 1°C Freezing Container and initially placed in a -80°C freezer for 24 h. The vials were then removed and stored in liquid nitrogen until lymphocyte proliferation analyses were performed.

Lymphocyte Proliferation

Reagents for lymphocyte proliferation included Roswell Park Memorial Institute-

1640 medium (RPMI, Hyclone Laboratories Inc, Logan UT), fetal bovine serum (FBS,

Atlanta Biologicals, Lawrenceville, GA), 2-mercaptoethanol (Fisher Scientific, Fairlawn,

NJ), gentamycin (MediaTech Inc, Manassas, VA), GlutaMax 100x (Gibco Invitrogen cell culture, Grand Island, NY), hydroxyethyl piperazineethanesulfonic acid (HEPES,

MediaTech Inc, Manassas, VA), trypan blue (0.4% Sigma-Aldrich, St Louis, MO), PBS

(MediaTech Inc, Manassas, VA), Concanavalin A (Con A, Sigma-Aldrich, St Louis, MO),

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lipopolysaccharide (LPS, Sigma-Aldrich, St Louis, MO) and pokeweed mitogen (PWM,

Sigma-Aldrich, St Louis, MO).

1% L-glutamine (200 mM), and 2.5% HEPES (25 mM). The same batch of culture medium and mitogen preparations were used for all study samples. Vials with thawed

Viability of cells varied from 36-80%. Aliquots of 50 µL of the cell suspension (2x105 live cells/well) were pipetted into 96-well clear, round-bottom plates (Corning Inc., Corning,

NY). Most samples were analyzed in triplicate for each mitogen concentration; however,

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a small number of samples were analyzed in duplicate. PBMC samples obtained from a donor horse that was not part of this study were included on each plate to serve as an interassay control. Cells in separate wells were stimulated with 50 µL of either 1 µg/mL

Con A, 2 µg/mL Con A, 10 µg/mL LPS, 1 µg/mL PWM, or culture medium (no mitogen- control). Optimal concentrations of cells, mitogen concentrations and incubation times were determined prior to the start of this study (Appendix F). The cells were incubated

3 at 37ºC for 78 h in 6% CO2. Sixty hours into the incubation period, 25 µL of [ H] thymidine (0.25 µCi/well; PerkinElmer, Boston, MA) was added to each well and then the plate was returned to the incubator. Eighteen hours after the [3H] thymidine was added, wells were harvested using a FilterMate™ Harvester (PerkinElmer, Turku,

Finland) onto printed Filtermat A glass fiber filter paper (size 90 x 120 mm; Wallac Oy,

Waltham, MA) and dried in a 900 W household microwave oven (Westing House, Lake

Forest, IL) for 90 sec. The filter paper was then sealed in a plastic bag with 3.5 mL of scintillation fluid (Betaplate Scint, PerkinElmer Life Sciences, Waltham, MA). The scintillation fluid was evenly distributed over the filter paper to ensure saturation. [3H]- thymidine incorporation in PBMC DNA was measured using a liquid scintillation and luminescence counter (MicroBeta® Jet 1450, PerkinElmer Precisely, Turku, Finland) using standard parameters for tritium. Inter-assay variation for stimulated cells and non- stimulated cells was 35% and 73%, respectively. Data were analyzed as counts per minute (CPM) and as a stimulation index (SI) which is the ratio of stimulated culture

CPM to non-stimulated culture CPM.

IgA ELISA

IgA concentration was measured in NPF, nasal swabs, saliva, cecal fluid, fecal liquid, and serum using an equine-specific enzyme-linked immunosorbent assay

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(ELISA; Immunology Consultants Laboratory, Inc., Portland, OR) according to the manufacturer’s instructions. Absorbance was determined at 450 nm by microplate reader (Synergy HT™, Bio-Tek® Instruments, Inc., Winooski, VT) and concentrations were calculated using a 4-parameter logistic curve. Each ELISA plate included an inter- assay control sample collected from a horse not participating in the current study.

Dilution of saliva samples varied from 1:5,000 up to 1:50,000. Dilution of nasal flush and nasal swabs samples varied from 1:100 up to 1:5,000. Dilution of fecal liquid samples varied from 1:5 up to 1:1000. Serum samples were diluted 1:5,000 up to 1:50,000. Inter- and intra-assay CV were < 5 and < 3%, respectively. Although specific to equine-IgA, this ELISA measures total IgA and not just sIgA. Using the IgA concentrations, a secretory index was calculated to account for possible leakage of serum IgA into mucosal samples. The secretory index was calculated by:

(mucosal IgA / serum IgA) / (mucosal total protein / serum total protein)

A ratio of >1 indicates active secretion of sIgA by the mucosal tissue in addition to serum IgA leaking into the mucosal space (Mathews, 1981).

Total Protein

Total protein in samples was determined using the Pierce™ Coomassie Plus

Protein Assay kit (ThermoFisher Scientific™, Waltham, MA) per manufacturer’s instructions for using microplates. Briefly, 10 µL of standard or unknown sample was pipetted into 96-well clear, flat bottom plates. Approximately 300 µL of the Coomassie

(Synergy HT™, Bio-Tek® Instruments, Inc., Winooski, VT) and concentrations were

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calculated using a 4-parameter logistic curve. Standards can be kept at 4°C for several days.

Cortisol EIA

Cortisol concentrations were measured using an enzyme immunoassay (EIA) kit

(DetectX®, Arbor assays®, Ann Arbor, MI) with some alterations to the manufacturer’s instructions. After consulting with the manufacturer’s technical services, the following procedure was determined. Thawed saliva samples were centrifuged at 9,300 x g for 10 minutes at 4°C. Half of kit standard number 6 (250 µL; 100 pg/mL) was used to make an additional standard (number 7) with a final concentration of 50 pg/mL. The final standard curve only included standards 2-7 (range of detection 1,600-50 pg/mL). The volume of sample and standard added to the plates was doubled to 100 µL. Therefore, the quantity of assay buffer was increased to 125 µL and 100 µL in the non-specific binding wells and maximum binding wells, respectively. The conjugate and antibody were still added at the recommended volume (25 µL), resulting in total volume of 150 µL in each well. Additionally, the plates were shaken at room temperature for an additional hour (2 h total). The absorbance was determined at 450 nm by microplate reader

(Synergy HT™, Bio-Tek® Instruments, Inc., Winooski, VT) and concentrations were calculated using a 4-parameter logistic curve. Each EIA plate included an inter-assay control sample collected from a horse not participating in the current study. All serum samples were diluted 1:50 or 1:100; however, saliva sample dilution varied considerably

(1:2 up to 1:10). Inter-and intra-assay CV were < 5 and < 8%, respectively.

Dry Matter Analysis

Cecal and fecal samples were thawed at 4°C. Approximately 100 g of thawed feces was placed into 14.6 x 12.4 cm aluminum foil pan. Thawed cecal samples were

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transferred into a large beaker and thoroughly mixed before pouring approximately 100 g into a foil pan. Samples were dried in a forced air oven at 60°C for at least 3-5 d or until sample dry weight was stable. Percentage of DM was determined by difference between wet and dry sample weight.

Statistical Analysis

Data residuals were checked for normality and were log10 transformed if necessary and compared using mixed model ANOVA with two different models. The data were first analyzed by phase (pre-transit, during transit and post-transit) where individual samples during each phase were averaged. The second model investigated differences between individual time points compared to the average of pre-transit measurements. Presence of diurnal variation was also investigated using all individual time points. All models used horse as a random effect. Relationships between variables were determined by calculating Pearson correlation coefficients using Proc Corr and simple linear regression was analyzed using the Proc Reg procedures in SAS (Version

9.3, SAS Institute Inc., Cary, NC). Effects were considered significant when P ≤ 0.05 and trends were identified at P < 0.10. Data are presented as means ± SEM.

At h 18 of transit, horses did not defecate and although feces were collected from the trailer floor for DM analysis, no feces were available via rectal palpation for fecal liquid extraction. At h 24 of recovery, one horse’s cecum was empty; thus, no cecal sample could be obtained.

Results

Transportation

Horses loaded onto the trailer with ease and showed no signs of discomfort

(physical agitation, fever or decreased gut motility) during the 24 h trip. However, by

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hour 18, the horses were noticeably lethargic and there were no feces available for collection. The horses were transported total of 1574 km at an average speed of 108 km/h. An attempt to measure temperature and humidity inside the trailer was made

(HOBO® Pro v2, Onset®, Bourne, MA), but the data could not be recovered from the device. Average daytime temperature and humidity outside the trailer was 23.6°C and

84.3%, respectively (Weather underground mobile phone application, version 5.9.2, San

Francisco, CA). Average nighttime temperature and humidity outside the trailer was

19.3°C and 94.2%, respectively.

Over the 24 h journey, DM and water intake was 2.64 ± 0.29 kg and 11.86 ± 2.47 kg, respectively (Table 3-2). Salivary flow rate (mL/minute) was lower during transit (P =

0.03) and the recovery phase (P = 0.02), compared to before transportation (Figure 3-

3). Flow rate tended to be lower beginning at h 6 of transit (P = 0.08). Although it slightly recovered at h 12, flow rate was again lower at h 18 (P = 0.05) and h 24 of transit (P =

0.08). During recovery, flow rate was lower at 24 h (P = 0.04) and 72 h post (P = 0.04) but fully recovered by 120 h post. Fecal excretion over the 24 h period was 7.32 ± 0.85 kg (Table 3-2). Transportation caused an overall increase (P = 0.01) in rectal temperature of the horses (Table 3-4). Beginning at h 6 hour of transport, rectal temperature was elevated over pre-transit (P = 0.006) and stayed elevated for the remainder of the trip. Upon arrival in Florida, rectal temperature was not statistically elevated over pre-transit, likely because arrival occurred in the cool morning hours.

However, rectal temperatures increased once again 12 h after arrival (P = 0.001). At 24 h post, rectal temperature was back to normal and remained there at 72 h post.

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Body Weight

Road transportation for 24 h resulted in significant loss of body weight for all horses (P = 0.0001; Table 3-3). Despite access to hay and water while in transit, the weight loss averaged 35 ± 1.75 kg, and was not regained by 120 h post transport (P =

0.009).

Cortisol

Both serum and salivary cortisol increased (P = 0.01, P = 0.03 respectively; Table

3-5) above pre-transit values, during the transportation phase of the study. Serum cortisol returned to normal after transport whereas salivary cortisol remained elevated

(P < 0.05). Although salivary cortisol fluctuated during the study, there was no apparent diurnal pattern to salivary or serum cortisol levels (Figure 3-4).

Nasopharyngeal Flush Mucus Scores

As expected, the nasopharyngeal flush mucus scores were affected by the transport (P = 0.02); however, the only mucus score that differed from pre-transit was the sample obtained at 24 h of transport (P = 0.006; Table 3-6).

Nasopharyngeal Leukocyte Populations

Total leukocytes in NPF were elevated above pre-transit from 24 h transit (P =

0.001) to 24 h post transport (P = 0.03), but had returned to pre-transit by 72 h post

(Table 3-7). Neutrophils were also elevated following transport (P = 0.004); however, they recovered by 12 h post (Table 3-7). At 24 h post, neutrophils tended to be elevated above pre-transit again (P = 0.09), but recovered at 72 h post. Lymphocytes and eosinophils were elevated after transportation (P < 0.0001) and remained elevated through 72 h post (P < 0.01; Table 3-7). Although monocytes were numerically elevated

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following transportation, it was not statistical higher than before transportation (Table 3-

7).

The percentage of neutrophils in the NPF was only numerically elevated after transportation and during recovery (Table 3-7). The percentage of lymphocytes (P =

0.05) and eosinophils (P = 0.02) were increased after transport, and both remained elevated through 72 h post (P = 0.05; Table 3-7). The percentage of monocytes was lower after transit compared to before (P = 0.03; Table 3-7), but recovered at 12 h post and remained stable for the rest of recovery.

Systemic Leukocyte Populations

There was no overall effect of transportation on total circulating leukocytes or neutrophils; however, values slowly increase during transport and both tended to be higher than pre-transit at h 24 of transit (P < 0.10; Table 3-8). Neutrophils tended to remain elevated (P = 0.09) at 12 h post-transit, but returned to normal at 24 h post.

Systemic neutrophils (as % of WBC) were elevated from h 6 of transit through 24 h after transport (P < 0.10; Table 3-8) and had returned to pre-transit by 72 h post transport.

A decline in the number (P < 0.05) and percentage (P < 0.05) of systemic lymphocytes occurred from h 6 of transit to 0 h post, and from h 18 of transit to 24 h post transport, respectively (Table 3-8). Interestingly, systemic lymphocytes continued to increase in number and were greater than pre-transit at 120 h post transport (P =

0.02), while the percentage remained relatively stable. The neutrophil to lymphocyte ratio began to rise during transportation and was statistically elevated above pre-transit at h 18 (P = 0.03; Table 3-8). The ratio tended to remain elevated through 24 h post (P

= 0.09), but returned to pre-transit values by 72 h post.

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The number and percentage of circulating monocytes was unaffected by transportation, but the percentage began falling during transportation and was statistically lower than pre-transit at h 18 (P = 0.03; Table 3-8). The percentage did not recover until 120 h post-transport.

The number and percentage of whole blood eosinophils gradually declined during transportation reaching significance below pre-transit at h 12 (P = 0.01, P =

0.009 Table 3-8). Eosinophils remained lower through 24 h post-transit (P < 0.05), but both number and percentage were back to normal at 72 post-transit.

Lymphocyte Proliferation

Background lymphocyte proliferation, without mitogen stimulation, was affected by transportation (P = 0.06; Figure 3-5). Proliferation tended to increase and was higher at

24 (P = 0.04) and 72 h post (P = 0.01) compared to proliferation before transit.

Although the pattern of stimulation between Con A concentrations was the same, transportation only significantly affected lymphocyte proliferation in response to 1 µg/mL

(P = 0.02) and not 2 µg/mL of Con A (Figure 3-6). The proliferative response was depressed at h 24 of transit (P = 0.01) and 24 h post-transit (P = 0.03). The response to

2 µg/mL also tended to be depressed in at 24 h post (P = 0.09); however, at 72 h post proliferation was restored to pre-transit values.

Alternatively, the stimulation indices from both concentrations of Con A were affected by transport (P < 0.05; Figure 3-7). At h 24 of transport, the indices were depressed (P < 0.05) and continued to decreased during recovery. At 72 h post, the stimulation indices from both concentrations of Con A were still lower than pre-transit values (P < 0.05).

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Overall, transportation did not affect lymphocyte proliferation to PWM; however, proliferation was increased at 72 h post compared to before transit (P = 0.05; Figure 3-

8). The stimulation index for PWM was affected by transport (P = 0.05). It was lower at

24 (P = 0.04) compared to pre-transit and did not recovered by 72 h post (P = 0.01;

Figure 3-9).

Lymphocyte proliferation in response to 10 ng/mL of LPS was not affected by transport (Figure 3-10), but the stimulation index tended to differ (P = 0.09; Figure 3-11).

The LPS stimulation index tended to be lower at h 24 of transit (P = 0.07) compared to before transport. At 24 h post, the stimulation index had decreased further below pre- transit values (P = 0.03) and remained lower at 72 h post (P = 0.03).

Immunoglobulin A

Salivary

Salivary IgA concentration varied considerably during the current study (3.76 ±

4.01 mg/mL). In general, salivary IgA was not affected by transportation, but it was greater at h 18 of transit (P = 0.02; Figure 3-12) compared to pre-transit. At h 24 of transport, IgA had returned to a normal concentration. IgA was numerically elevated again 24 h after transportation; however, the concentration was not statistically higher than before transport. By 72 h post, IgA had returned to pre-transit values and remained there for the rest of recovery.

Overall, the secretory index of salivary IgA was also not affected by transport; however, at h 24 of transit salivary IgA secretory index was lower than pre-transit (P =

0.02; Figure 3-12). The index recovered at 12 h post-transit and remained there throughout recovery from transport. Secretory index of salivary IgA was 16.8 ± 6.8 and ranged from 0.24-52.9. Salivary IgA secretion rate (µg/minute) was calculated and

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followed the same pattern as the secretory index (Figure 3-13). The secretion rate decreased at h 24 of transit (P = 0.02) compared to before transportation, but was restored by the next sample. At 72 h post transportation, the secretion rate tended to be lower than pre-transit once again (P = 0.09).

Nasopharyngeal IgA

NPF IgA concentration varied considerably during the current study (86.0 ± 60.4

µg/mL). Overall, transport altered total IgA concentration in the NPF (P = 0.005; Figure

3-14). NPF IgA was elevated at h 24 of transport (P = 0.0006) and at 24 h post transport

(P = 0.02), but returned to a normal level in between and following those samples.

The NPF IgA secretory index was not affected by transportation, but the index numerically decreased at h 24 of transport (Figure 3-14). Secretory index was 10.1 ±

2.2 and ranged from 5.2-18.7.

Nasal swab IgA

The IgA concentration recovered from nasal swabs was affected by transportation

(P = 0.006; Figure 3-15). Swab IgA was higher than pre-transit concentrations beginning at h 6 of transportation (P = 0.009) and remained elevated above pre-transit at 72 h post

(P = 0.05). Nasal swab IgA concentration varied considerably during the current study

(426.7 ± 320.1 µg/mL).

The IgA secretory index of the nasal swabs was affected by transport (P <

0.0001; Figure 3-15). The IgA index was lower than pre-transit from 6-18 h of transit (P

< 0.05). By the end of transport, the index recovered to pre-transit values, but continued to increase and tended to be higher than pre-transit at 24 (P = 0.06) and 72 h post (P =

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0.06). The secretory index was 1923 ± 578 and ranged from 762-5969. Nasal swab and

NPF IgA were also correlated (R2 = 0.62, P < 0.0001; Table 3-9).

Serum

Total systemic IgA was higher than pre-transit concentration at h 24 of transit (P =

0.001) and 12 h after transit (P = 0.02; Figure 3-16). Serum IgA tended to remain higher at 24 h post (P = 0.09), but recovered by 72 h post-transportation. Serum IgA concentration was 216 ± 131 mg/dL.

Serum IgA was correlated with most other measures of IgA (Table 3-9), but only had strong relationships with nasal swab and salivary IgA. Less defined relationships existed with cecal, NPF, and fecal liquid IgA.

Cecal

In general, cecal IgA and the IgA secretory index were unaffected by transport, but both were elevated at h 12 of transport (P = 0.09, P = 0.05; Figure 3-17). At the termination of transport, cecal IgA was back to pre-transit values and remained stable during the entire recovery phase, whereas the secretory index tended to be higher than pre-transit at 72 h post (P = 0.08). Cecal IgA concentration was 1.45 ± 1.42 µg/mL.

Secretory index was 0.05 ± 0.36 and ranged from 0.002 – 0.22.

Fecal liquid

Fecal liquid IgA and the secretory index were unaffected by transport, but both were elevated at h 6 of transport (P = 0.04, P = 0.03; Figure 3-18). Total IgA concentration returned to normal at h 12 of transport and remained stable for the rest transport and during recovery. Fecal liquid IgA secretory index did not recover until h 24 of transit, but then remained stable during recovery. Fecal liquid IgA concentration was

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46.7 ± 93.5 µg/mL. Secretory index was 0.58 ± 0.45 and ranged from 0.01-2.8. Total IgA measured in fecal liquid and cecal fluid were correlated (Table 3-9).

Dry Matter and pH

Cecal and fecal DM ranged from 2-11% and 14-24%, respectively, and pH from

7.0-8.2 and 5.8-7.6, respectively. Cecal DM was lower (P = 0.07) during the transportation phase, compared to before transportation (Table 3-10). Fecal DM was lower (P = 0.02) during the recovery phase, compared to before transportation (Table 3-

10). Fecal pH tended to be lower during transportation (P = 0.08), whereas cecal pH was higher (P = 0.02) during the recovery phase following transportation (Table 3-10).

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Discussion

The first objective of this study was to determine the onset of physiological changes and the mucosal immune response to long distance road transportation.

Systemic immunological changes occurred within 6 h of road transportation, but mucosal responses exhibited varied and inconsistent patterns. For the most part, immunological recovery occurred within 3 d following transportation. The second objective of this study was to investigate a possible diurnal pattern of salivary IgA and other immune variables; however, none of immune components evaluated displayed a circadian rhythm. The third objective was to determine if mucosal IgA responses, compared to cortisol, would more accurately indicate stress. IgA did not respond to road transportation stress as expected, and results from these 3 horses do not support mucosal IgA as a sensitive marker of stress.

As expected, the nasopharyngeal leukocytes were all elevated following transportation. Total WBC in nasopharyngeal flush increased 31-fold over pre-transit at h 24 of transportation, which indicates changes in immune status started several hours before. This large change in populations waned quickly once the horses were untied and off the trailer, but remained 2-4 fold above pre-transit through 72 h of recovery from transport. Changes to systemic immune cell populations did begin to occur within 6 h of transportation; however, many of these changes did not reach statistical significance until later in the trip. For many measurements during this study, the variability between the 3 horses was very high making it hard to detect statistical differences. In general, the percentage of systemic leukocytes responded faster than the numbers, indicating early population shifts in response to stress. In support of this theory, the numerical increase in neutrophils and decrease in lymphocytes indicates a stress leukogram

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(Satue et al., 2014) by h 6 of transport, which continued into the recovery phase.

Coincidentally, like our previous study, the decrease in lymphocytes reached statistical significance before the increase in neutrophils. The equine leukogram has two distinct profiles which can provide insight into the physical state of the animal (Satue et al.,

2014). Leukocytosis involves changes in circulating WBC that is associated with the activation of the SAM pathway prompted by fear or excitement (Satue et al., 2014).

During this response, splenic contraction, increased blood flow and reduced adherence capacity of cells, mobilizes the marginal pool of neutrophils and/or lymphocytes causing neutrophilia and/or lymphocytosis (Satue et al., 2014). Eosinophilia and monocytosis are also possible. However, this is a short lived systemic profile as the marginal pool of neutrophils and lymphocyte counts can be reestablished within one hour (Satue et al.,

2014). In contrast, stress leukocytosis is under the influence of cortisol, which induces mature neutrophilia, lymphopenia and eosinopenia, 2-4 h after its release (Satue et al.,

2014). Cortisol causes mature neutrophilia by mobilizing the marginal pool, reducing their ability to move from circulation into tissues and mobilizing the bone marrow reserve population. Lymphocyte sequestration in lymphoid tissues and eosinophil marginalization with decreased release from bone marrow, cause the reductions in circulating lymphocytes and eosinophils. These values can return to normal 24 h after the initial stressor, assuming it is eliminated. It is often hard to differentiate between stress and inflammatory leukograms based solely on WBC counts. An inflammatory leukogram can cause the same changes, but is often accompanied by a neutrophilic left shift and other clinical pathologic signs, like hypoferremia and hypoalbuminemia (Satue et al., 2014). The data presented in Chapter 3 displayed a stress leukogram pattern

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within 6 h of transit, although not all changes were statistically different from pre-transit values, and most normalized 24 h after transport.

Although the nasopharyngeal response was still ongoing 3 d after transport, systemic equilibrium was reached within the recovery period for most cell populations.

Systemic lymphocytes regained normal circulating numbers once serum cortisol subsided, but then continued to increase above pre-transit, which may have been fueling the sustained nasopharyngeal response. Lymphocytes in the nasopharyngeal region had the largest fold change following transportation and throughout recovery, and were still higher at the end of the recovery period.

Non-mitogen stimulated background lymphocyte proliferation was evaluated from the negative control wells in the lymphocyte proliferation assay, and is reflective of the activity of lymphocytes at the time of sampling. Background proliferation increased during recovery from the 24-h trip, which was an unexpected response. Previous studies, including the study presented in Chapter 2, have shown proliferation is typically depressed following a stressor and the depression may continue for several days during recovery (Strasser et al., 2012; Bobel et al., 2012). In the current study, background proliferation before transportation was low, indicating that either the lymphocytes did not possess sufficient activity before transportation, human error in the isolation of cells, or improper freezing and storage after isolation. From previous experience, background proliferation is quite variable between horses, ranging from 700-900 CPM with a standard deviation of 300-500 CPM. The pre-transit background average from the present study was only 281 CPM with a standard deviation of 326. PBMC from two of the three horses hardly proliferated in vitro, indicating a potential problem during

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isolation or maybe the freezing media was unknowingly expired. Other problems could be improper temperature of isolated cells during shipment from Texas to Florida, or perhaps problems within the assay plate itself. The inter-assay variation of the control

PBMC on each plate for stimulated cells and non-stimulated cells was 11% and 21%, respectively, suggesting the problem did not lie within the experiment. The viability of cells isolated in Texas ranged from 80-85%, which seems to suggest the problem lay within storage or shipment. Upon thawing and counting the cells under the microscope, viability ranged from 47-60%. Although the pre-transit samples had poor background proliferation, the lymphocytes seemed to respond to mitogen stimulation within the normal range. There was also a noticeable titration effect of the two concentrations of

Con A for most samples. Immediately following transportation, Con A and LPS stimulated lymphocytes and stimulation indices were numerically or statistically lower than pre-transit values and, in most cases, this response continued to be depressed until 72 h after transport. Previous studies conducted in transported horses have reported inconsistent lymphocyte responses. For example, following 24 h of road transportation, lymphocyte proliferation in response to Con A was depressed; however, responsiveness to PHA and PWM was unaffected (Stull et al., 2004). Conversely, responsiveness to PWM was enhanced following 36 h of transportation, possibly due to increased bacterial antigens within the respiratory tract and the observed increase in systemic cytokines (Maeda et al., 2011). In the current study, a slight enhancement of proliferation in response to PWM was seen 72 h after transport. Given the compromised pre-transit proliferation, the delayed enhancement seems to be an artifact and not

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biologically relevant. In support of this, the stimulation index, indicating true stimulatory capability over background proliferation, was lower during recovery from transport.

All stimulation indices were lower after transport and had not recovered by 72 h after transport, which contradicts some previous studies that report lymphocytes recover within 24 h after transportation (Stull et al., 2004; Maeda et al., 2011). However, other studies reported immune depression persisted for days following head elevation, exercise or transportation (Raidal et al., 1997a; Bobel et al., 2012). Reduced neutrophil phagocytosis was reported 36 h after a transportation bout lasting 12 h and authors suggest this was due to alterations in circulating corticosteroids or hormones, although they were not measured (Raidal et al., 1997a). In the current study, serum cortisol was not correlated with stimulated or non-stimulated proliferation (data not shown); however, circulating cytokines that may affect lymphocyte function were not measured. Our lab has shown that prolonged submaximal exercise suppressed lymphoproliferative responses to Con A, PHA and PWM, and decreased viability following in vitro hydrogen peroxide exposure, which persisted through 24 h post-exercise (Bobel et al., 2012).

Data from that study indicated lymphocytes may be more vulnerable to oxidative damage from exercise stress and may not function properly during recovery.

Comparison of cell viability before and after transit, and following the freeze thaw cycle, is one way to assess cell vulnerability to oxidative damage and durability, but due to the poor viability of the pre-transit samples, this comparison is negligible in the current study.

The tendency for mucosal IgA to be elevated during and after transport was consistent across biological samples, although significance was reached at difference

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times. Nasal swabs and fecal liquid were the first samples to be elevated at h 6 of transport. Based on transit time within the GIT, it was unexpected for fecal liquid IgA to be elevated so soon after transport commenced. However, these horses had not been on a trailer in a long time and all horses exhibited nervous defecation during the first 6 h of transport, which may have increased rate of passage and could cause the elevation of IgA. Even though the subsequent fecal liquid IgA concentrations during transit and recovery were not statistically different from pre-transit values, they did remaine numerically higher.

Although the IgA ELISA used in the current study was horse specific, it was not specific for sIgA nor a particular antigen and only provides a measure of total IgA in the sample. An increase in total IgA measured in a fluid could indicate more sIgA was actively secreted or locally produced, or serum derived IgA leaked through compromised tissues (e.g. from inflammation). The secretory index may be a better indicator of actively secreted IgA compared to IgA leaked from serum (Mathews, 1981).

Albumin is used as a reference protein because albumin can cross the mucosal barrier, depending on barrier integrity, but cannot be locally produced. A secretory index of greater than 1 indicates the mucosal tissue is actively secreting IgA in addition to the transudation of serum IgA across the mucosal membrane. In the current study, fecal liquid secretory index followed the same pattern as fecal liquid IgA; it was elevated at h

6 and 12 of transit and remaining numerically higher for subsequent samples.

Nasopharyngeal flush samples require mild sedation and were therefore not collected during transit. In an attempt to measure nasal IgA during transit, nasal swabs were obtained. Previous work in our lab with nasal swabbing suggested they provided a

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consistent measurement of local IgA concentration, no matter which researcher obtained the sample or which nostril was swabbed. Even though the nasal swabs only collected IgA at the surface of the nasal mucosa and only 10 cm into the nostrils, swab

IgA and nasopharyngeal flush IgA were in fact correlated (R2 = 0.62, P < 0.0001), suggesting the swab IgA was a good indicator of the nasal environment. Nasal swab

IgA was elevated beginning at h 6 of transit and remained elevated for the rest of the study; however, the secretory index followed the opposite pattern, where it was decreased during transit and then elevated towards the end of recovery. Potentially this is because swab and nasopharyngeal flush total protein appear to have no linear relationship (R2 = 0.07, P = 0.23). Nasopharyngeal flush total protein followed the same pattern as nasopharyngeal flush IgA, but swab total protein followed the opposite pattern compared to swab IgA. During transit, the nasal mucosal membranes are probably less moist because of the head elevation and limited water intake, which may have concentrated nasal IgA at the mucosal level. However, all swabs were eluded in the same volume of solution and the IgA concentration of swabs did not return to normal even after the horses were rehydrated and nasal gravity flow resumed during recovery in stalls. Similar to the study presented in Chapter 2, nasopharyngeal flush and swab total IgA did not follow the same pattern as the calculated secretory index suggesting the secretory index for these samples may not be a useful indicator. Previous reports suggest normalization of respiratory IgA with total protein in the sample is inaccurate because sampling technique, frequency and fluid recovery are so varied (McGorum et al., 1993; Schnabel et al., 2017). Until a standardized method for measuring equine

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sIgA becomes available, it seems most accurate to report total IgA concentrations in respiratory secretions along with detailed methods and dilutions (Schnabel et al., 2017).

Nasopharyngeal flush and swab IgA were higher at the conclusion of transport, and then decreased slightly at 12 h post, but both increased again 24 h after transport.

By 72 h post, nasopharyngeal flush IgA had recovered and swab IgA remained elevated. Even though nasal swabs were collected before flushing the nasopharyngeal region, a wash-out effect remains possible. Both IgA concentrations peaked after horses had a 24 h interval between flushes (h 24 of transit) and were then lower when horses were flushed 12 h later (12 h post-transit). However, the subsequent flush (24 h post-transit) had the same time interval and both IgA concentrations peaked again.

These data, along with the nasopharyngeal flush cellular data, clearly signify a mucosal immune response to transportation was ongoing and lasted for days following transportation. The main function of sIgA is to bind soluble or particulate antigens, a function otherwise known as immune exclusion (Russell et al., 2015). Whether the prolonged increase in nasal swab IgA was due to receptor mediated transcytosis of sIgA or leaked serum-derived IgA because of compromised epithelial barriers, more IgA within the nasal cavity should be beneficial. Leaked serum-derived IgA will not contain the secretory component and will be degraded by bacterial proteases much faster than sIgA, which has a half-life of 3-6 d (Woof and Mestecky, 2015).

In response to transportation, salivary IgA and the secretory index were largely unaffected. Salivary IgA had two modest transient peaks at h 18 of transport and 24 h post and the index dropped below pre-transit at h 24 of transit, but recovered by 12 h after transport. This is in contrast to our previous study (Chapter 2) where salivary IgA

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and secretory index were elevated after 12 h of stress, then dropped below pre-stress values and did not recover in 3 d. Unfortunately, salivary IgA was extremely variable between the 3 horses in the current study, and drawing conclusions from this small sample size would be erroneous. Salivary IgA tends to be variable and can be impacted by sample collection, protein loss during sample handling, flow rate, stress, and serum

IgA leakage (Brandtzaeg, 2007). Besides creating a secretory index, saliva IgA secretion rate can also help normalize samples (Koh and Koh, 2007). Interestingly, the secretion rates of the current study followed the same pattern as the secretory index, which suggests creating a secretory index could replace the need to measure actual secretion rate. However, neither method provides a true quantity of sIgA. Lower salivary

IgA has been previously associated with increased risk of IURD (Shimizu et al., 2012), but the temporal reduction of saliva IgA in the current study was likely too short lived to cause any problems.

A side objective of this study was to determine possible diurnal variation of salivary IgA and its potential as a sensitive marker of stress. Our previous study

(Chapter 2) and other studies in humans indicate salivary IgA may be higher during the morning hours and lower in the evening (Dimitriou et al., 2002; Li and Gleeson, 2004).

Although one horse did show a diurnal pattern during the pre-transit sampling in Texas, the other two horses exhibited the opposite; higher IgA during the evening sample and lower in the morning (data not shown). The calculation of IgA secretion rate eliminated the slight diurnal pattern of the one horse, but supports the opposite pattern in the other two horses (data not shown). Stress can interrupt the normal diurnal rhythm of cortisol and it remains possible that the stress of initial sample collection disrupted any pattern

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of IgA secretion. Although sample collection occurred in their home pasture, the horses had not been handled regularly or sampled for a year. Saliva collection also involved a chifney bit, which was potentially stressful in itself since these horses had not had a bit in their mouth for many years. No other mucosal IgA samples displayed any diurnal pattern, individually among horses or otherwise.

To my knowledge, this is the first time cecal IgA has been measured in horses.

IgA within the gut lumen acts as first line of defense against dietary antigens and bacteria, and facilitates intraepithelial neutralization of pathogens and pro-inflammatory microbial products (Snoeck et al., 2006). Not surprisingly, cecal IgA was low and often required the same dilution as fecal liquid. In these horses, cecal IgA was much less variable ranging from 0.30-3.8 µg/mL compared to 0.24-800 µg/mL for fecal liquid.

Cecal IgA or IgA+ lymphocytes have been previously measured in rodents and chickens for various reasons (Peterson et al., 2007; Bobikova et al., 2015; Genda et al., 2017).

These studies report mice have a range of 1-200 µg IgA/mL cecal contents. The discrepancies between concentrations measured in the current study and previous reports are probably due to methodology more than species (Morita et al., 2004). The rodent studies also measured total IgA, but do not comment on the difference between total and secretory. Expansion of IgA-secreting plasma cells can occur by T cell independent and dependent pathways (Cerutti, 2008). Studies in rodents have provided evidence that oral FOS (60 g/kg diet) causes mild dietary inflammation and IgA plasma cell expansion ensues independent of T cell help (Genda et al., 2017). This is relevant for the current study because plasma cell expansion and increased IgA secretion

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independent of T cell help occurs much faster than expansion requiring T cells (Cerutti,

2008).

In response to transportation, total IgA and the IgA secretory index in cecal fluid both increased at h 12 of transport, but were similar to pre-transit values at the end of

24 h of transport. Both measures fluctuated above pre-transit values for the remainder of the study, but only the secretory index was statistically higher when the study ended.

Given the presence of a cecal stoma does not occur naturally, the elevated IgA measured in cecal fluid may be an immune response to the opening and closing of the cannulas, and not related to transportation. Two of the three cannulas were very difficult to open and did not fit snuggly within the cecal stoma. Within the first 2 d of the study, there was inflamed tissue and some blood around the stoma opening. Care was taken to collect cecal samples from deeper inside the cecum to avoid blood contamination and was successful based on the cecal IgA dilutions required for the ELISA. Because of the difficulty opening the cannulas, cecal samples were only taken once during transit. The intra-horse variation between the 3 pre-transit samples was fairly low and suggests that if an immune response was prompted from opening the cannula, it was delayed since cecal IgA was first elevated 3 d after the initial opening. Given the timeline of the study, it would be difficult to separate a response to cannula opening from a response to transportation because this increase coincided with h 12 of transportation. Additionally, decreased feed and water intake during transit would slow rate of passage, and may further delay the ability to detect an immune response to cannula opening or transportation. Although there may have been a response to cannula opening and closing, likely there was also a true reaction to the stress of traveling in a trailer for 24 h.

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Transportation has previously been shown to alter intestinal microbiota and cause GIT disturbances (Faubladier et al., 2013). In rats, antigenic molecules were able to permeate the colonic barrier following 2 h of restraint stress (Santos et al., 1999). The mechanisms behind this are still being investigated, but there is strong evidence that nerve fibers which innervate the GIT release corticotrophin-releasing hormone and activate mast cells (Soderholm and Perdue, 2001). These cells can release proteases that degrade the barrier and increase transcellular and paracellular pathways of permeability. Research has also shown that mast cells in the GIT regulate an initial surge of mucin secretion in response to acute stress, leaving goblet cells depleted and unable to respond to new or ongoing threats (Castagliuolo et al., 1998). Although not investigated in horses, the GIT disturbances following transportation likely involve similar mechanisms. If a “leaky gut” occurs during transport stress, bacteria could breach the mucosal barrier and cause an inflammatory response and increased production of IgA.

Systemic changes to the IgA pool were not necessarily expected, yet serum IgA was higher at h 24 of transit, until h 24 of recovery. The previous study (Chapter 2) proved that a head elevation stress model would cause a reproducible local mucosal response, but the addition of transportation stress along with the increased length of head elevation, may have caused a more pronounced mucosal assault. In contrast to the anti-inflammatory role of sIgA as a first line of defense on mucosal surfaces, serum

IgA functions as second line of defense by binding to FcR for IgA on immune cells to cause inflammatory responses (Snoeck et al., 2006). The stress of road transportation may have gastrointestinal disturbances and allowed bacteria to breach the mucosal

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barrier, causing systemic inflammation and promoting B cell class switching to IgA.

Ligand binding to the FcR can trigger phagocytosis, oxidative burst, antibody- dependent-cell-mediated cytotoxicity, and degranulation and facilitate antigen presentation (Snoeck et al., 2006). Although indicative of an immune response, increased levels of serum IgA are necessary to aid in the resolution (Snoeck et al.,

2006).

Previous studies indicate that cecal pH of cannulated horses on hay diets can range from 6.7-6.9 (Kristoffersen et al., 2016). Cecal pH depends on diet and physiological state, but a healthy cecum maintains a pH close to neutral. Cellulolytic bacteria within the cecum function most efficiently at a pH of 6.0-6.8; however, amylolytics can function at a lower pH to withstand the environment their end products produce (Merritt and Julliand, 2013). VFA from fermentation must be ionized to be eligible for transport through the cecum wall and this happens rapidly at a pH of 6.5

(Argenzio et al., 1974). The cecal pH from this study was quite high, ranging from 7.0-

8.2, the upper range being too basic for the cecal microorganisms. The contradiction seems to lie within the method of obtaining pH. For this study, cecal fluid was allowed to adjust to room temperature (~23°C) before measuring pH. Methodologies from other studies specifically state that pH was measured immediately after collection, at body temperature (~37°C) (Kristoffersen et al., 2016). Lower temperatures can favor the formation of hydrogen bonds which will decrease the number of free hydrogen ions and increase pH (Ashton et al., 2011). Although an accurate measure of cecal pH would have been interesting, cannulation likely disrupts the normal microbial environment in the cecum, and a potential change of pH over time was more relevant to the current

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study. Therefore, an attempt was made to control for temperature in the current study.

Unfortunately cecal fluid temperatures ranged from 20.4-27.1°C, which may have eliminated the ability to detect a difference.

Previous studies report equine fecal pH can range from 6.1-7.1 (Zeyner et al.,

2004; Berg et al., 2005b), which is comparable with measurements from the current study (5.8-7.6). Again the method of obtaining fecal pH used in the current study differed from most publications. Other researchers measured fecal pH immediately after defecation by mixing the feces with equal amounts of deionized water (Zeyner et al.,

2004; Berg et al., 2005b). In the current study, feces were manually squeezed to obtain fecal liquid and pH was determined at room temperature (~23°C). Recently, fecal pH was used to develop a predictive equation for determining cecal pH and monitoring GIT health (Douthit et al., 2014). Using their equation to predict cecal pH from fecal pH in the current study, corrects the cecal pH values within range of previous reports.

However, the authors concluded that fecal pH has limited usefulness in predicting cecal pH. Additionally, statistical analysis of the new predicted values for the current study, does not change the results and there is no correlation between the observed values of cecal pH and fecal pH (data not shown).

Although cecal fluid and fecal DM, and pH did not differ from pre-transit values, they both differed by phase of the study. Cecal pH was higher during recovery from the transportation, whereas fecal pH tended to be lower during transit. Another study reported fecal pH was lower following transport, but horses only traveled for 2 h

(Faubladier et al., 2013). Because pH is largely dependent on postprandial status and diet, self-imposed fasting during transit should cause an increase of pH. Limited

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substrate availability in the hindgut will decrease fermentation end products thereby increasing pH. During recovery and re-feeding, substrates and fermentation are re- initiated thereby decreasing pH. Horses in the current study had free-choice access to hay during transit; however, they only consumed an average of 2.5 kg DM, which is far lower than typical daily consumption, but not unexpected during transit (Smith et al.,

1996). Although intake during transit was low, potentially the free-choice access to hay prevented any biologically relevant changes to pH. In theory, long distance transportation would also cause an increase in cecal and fecal DM because of restricted water intake and potential body water loss from evaporation. Cecal DM from this study was lower during transit and fecal DM was lower during recovery, compared to before transportation. Gastrointestinal problems are commonly associated with transportation; however, results from this study do not fully support that statement since none of the horses showed signs of colic. Both pH and DM measurements tend to support a healthy

GIT environment that was not adversely affected by transit. Given the small number of horses, these results should be interpreted with caution, as they may not be representative of the horse population. Moreover, the collection method for cecal contents made it difficult to obtain a representative sample of cecal digesta and fluid.

Two of the cannula openings were very small (opening size = 6.35 cm), permitting only a long small plastic spoon to be used. Depending on the location of the contents within the cecum, the cannula opening and utensil made sample collection very difficult.

Potentially these challenges resulted in the variable cecal DM results and not transportation.

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The first immunological changes to occur following the onset of transportation were nasal and fecal IgA, and systemic neutrophils and lymphocytes. At h 12 of transport, cecal IgA and systemic eosinophils had changed followed by salivary IgA and more systemic WBC populations at h 18. At the end of 24 h of road transport, serum

IgA, lymphoproliferative responses, circulating WBC and nasopharyngeal leukocytes were all different than pre-transit values. All IgA concentrations had returned to normal within 24 h of recovery, and most systemic and nasopharyngeal cell populations had recovered by 72 h after transport. However, functional capability of lymphocytes and a few cell populations did not recover during the recovery period. Together these data indicate that even short bouts of transport (<6 h) may induce immunological changes.

Length of transit stress has been previously correlated with the increased risk of negative health outcomes (Padalino et al., 2017b). Changes that may occur early during journeys of short duration are modest and will be able to normalize quickly after cessation of transport. This study showed changes can begin early and are additive as the trip continues. The consequences of these changes, and specifically the length of time required for recovery, may be a predisposing factor for respiratory disease.

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Pre-Transit Post-Transit (Texas) During-Transit (Florida)

-48 h -36 h -24 h 6 h 12 h 18 h 24 h 12 h 24 h 72 h 120 h AM PM AM AM PM AM AM Post Post Post Post PM AM AM AM

Horses remained in home Horses remained on trailer Horses recovered in stalls pasture for collections for collections after transit

Figure 3-1. Diagram of sampling timeline. Sampling began 2 days before transport (Pre-Transit), every 6 h during transport while horses were on the trailer, and for up to 120 h after transit (Post-Transit).

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Figure 3-2. Diagram of how horses were oriented in the 6-horse stock trailer during the 24 h trip. Horses were tied in this manner to facilitate fecal collections. Each horse also had ab libitum access to hay in nets.

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Table 3-1. Samples collected and measurements performed during the study. Nasal Fecal Cecal AM/ WB2 NPF3 Serum Saliva NPF Fecal Cecal swab liquid fluid Cortisol4 PM1 CBC CBC IgA IgA IgA DM/pH DM/pH IgA IgA IgA Pre-Transit -48 h AM X5 X X X X X X X X X X -36 h PM X X X X X X X X X X X -24 h AM X X X X X X X X X X X During Transit 6 h AM X X X X X X X 12 h PM X X X X X X X X X 18 h AM X X X X X X 24 h AM X X X X X X X Post-Transit 12 h PM X X X X X X X X X X X 24 h AM X X X X X X X X X X X 72 h AM X X X X X X X X X X X 120 h AM X X X X X X 1AM samples collected between 0600-1200 h and PM samples collected between 2000-2400 h. 2WB=whole blood. 3NPF=nasopharyngeal flush. 4Serum and salivary. 5X indicates sample obtained.

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Table 3-2. Hay and water intake and fecal excretion during 24 h transportation. Horse Hay DM intake, kg Water intake, kg Feces excreted1, kg 1 2.33 13.07 8.94 2 2.80 7.11 6.96 3 2.77 15.39 6.07 Mean 2.64 11.86 7.32 Standard deviation 0.26 4.27 1.47 1As-excreted basis.

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0.09

0.08

0.07

0.06 † † 0.05 * * *

0.04 mL/minute 0.03

0.02

0.01

0.00 Pre- 6 h 12 h 18 h 24 h 12 h 24 h 72 h 120 h Transit Post Post Post Post

Transit Recovery

Figure 3-3. Salivary flow rate before (Pre-transit), during the 24 h trip (6-24 h Transit) and during recovery from transit (12-120 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall effect of time (P = 0.40) and phase (P = 0.002). *Differs from Pre-Transit (P ≤ 0.05); †Differs from Pre-Transit (P < 0.10).

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Table 3-3. Body weight before transit (Pre-Transit), immediately after the 24 h trip (24 h) and during recovery from transit (24, 72, 120 h Post). Data were pooled across horses. 24 h 72 h 120 h Pre-Transit 24 h SEM P value Post Post Post Body weight, 589 554* 549* 560* 574* 25 0.0001 kg Weight loss1, -35 -40 -29 -15 1.8 kg 1Weight loss difference from Pre-transit. *Means in the same row differ from Pre-Transit (P ≤ 0.05).

Table 3-4. Body temperature before transit (Pre-Transit), during the 24 h trip (6, 12, 18 24 h) and during recovery from transit (12, 24, 72 h Post). Data were pooled across horses. Pre- 12 h 24 h 72 h P 6 h 12 h 18 h 24 h SEM Transit Post Post Post value Rectal temperature, 36.6 37.7* 37.8* 38.0* 37.2 37.9* 37.2 37.2 0.43 0.01 °C *Differ from Pre-Transit (P ≤ 0.05).

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Table 3-5. Serum and saliva cortisol concentrations before transit (Pre-Transit), during the 24 h trip (6, 12, 18, 24 h) and during recovery from transit (12, 24, 72,120 h Post). Data were pooled across horses and presented as log10 transformed values. Pre- 12 h 24 h 72 h 120 h 6 h 12 h 18 h 24 h SEM P value Transit Post Post Post Post Serum, ng/mL 1.49 1.65 1.73† 1.91* 1.78* 1.66 1.65 1.47 1.65 0.13 0.06 Salivary, pg/mL 2.63 3.13† 2.95 3.26* 3.05 3.20* 3.04 3.06 3.06 0.20 0.41 *Means in the same row differ from Pre-Transit (P ≤ 0.05). †Means in the same row differ from Pre-Transit (P < 0.10).

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Serum Saliva 2.6 3.4 2.4 3.0

2.2

2.6

2.0

pg/mL

ng/mL

10 10 1.8 2.2

1.6

1.8 Saliva log Saliva Serum log Serum 1.4 1.4 1.2

1.0 1.0 -48 h -36 h -24 h 6 h 12 h 18 h 24 h 12 h- 24 h- 72 h- 120 h- AM PM AM AM PM AM AM post post post post PM AM AM AM

Pre-Transit Recovery Transit

Figure 3-4. Serum and salivary cortisol levels before transit (-48, -36, -24 h Pre-Transit), during the 24 h trip (6, 12, 18, 24 h Transit), and during recovery after transit (12, 24, 72, 120 h post). Data were pooled across horses and represent the overall mean ± SEM. Salivary cortisol overall effect of time (P = 0.20) and phase (P = 0.01). Serum cortisol overall effect of time (P = 0.02) and phase (P = 0.009). AM samples collected between 0600- 1200 h and PM samples collected between 2000-2400 h.

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Table 3-6. Nasopharyngeal flush mucus scores before transit (Pre-Transit), during the 24 h trip (24 h) and during recovery from transit (12, 24, 72 h Post).Data were pooled across horses. 12 h 24 h 72 h Pre-Transit 24 h P value Post Post Post Mean score1 0.80 3.43* 1.25 1.00 0.56 0.02 1Mucus score ranges from 1-5, 0=absence of mucus and turbidity, 5=large quantity of mucus and very turbid. To account for elevated cell numbers, scores were increased by 1 if the sample was absent of mucus but was still turbid. *Means in the same row differ from Pre-Transit (P < 0.05).

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Table 3-7. Leukocyte populations in nasopharyngeal flush before transit (Pre-Transit), during the 24 h trip (24 h) and during recovery from transit (12, 24, 72 h Post). Populations are represented by the number of cells per 100 µL of recovered flush or percentage total mononuclear cells in recovered nasopharyngeal flush. Data were pooled across horses. 12 h 24 h 72 h Pre-Transit 24 h SEM P value Post Post Post x / 100 µL1 Total WBC 3.04 4.29* 3.57† 3.69* 3.50 0.20 0.01 Neutrophils 2.84 4.04* 3.30 3.42† 3.13 0.26 0.03 Lymphocytes 1.58 3.42* 2.72* 2.89* 2.78* 0.18 0.001 Monocytes 2.50 2.95 2.64 2.82 2.74 0.25 0.76 Eosinophils 0.04 3.18* 2.64* 2.73* 2.64* 0.32 0.0001 %

Neutrophils 63.11 58.97 56.97 55.07 44.20 11.62 0.75 Lymphocytes1 0.53 1.13* 1.14* 1.20* 1.28* 0.19 0.12 Monocytes1 1.48 0.66* 1.07 1.13 1.24 0.22 0.22 Eosinophils 0.10 11.53* 13.07* 11.40* 13.70* 3.42 0.04 1 Values are log10 transformed. *Differs from Pre-Transit (P < 0.05). †Differs from Pre-transit (P < 0.10).

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Table 3-8. Leukocyte populations in whole blood before transit (Pre-Transit), during the 24h trip (6, 12, 18, 24 h) and during recovery from transit (12, 24, 72, 120 h Post). Data were pooled across horses.

Pre- 12 h 24 h 72 h 120 h 6 h 12 h 18 h 24 h SEM P value Transit Post Post Post Post x 103/ µL Total WBC 8.30 8.21 9.12 11.11 13.94† 13.40 11.94 9.18 7.82 3.27 0.36 Neutrophils 5.50 6.05 6.74 9.10 11.66† 10.83† 9.39 6.33 4.69 3.12 0.28 Lymphocytes 2.10 1.60* 1.87 1.58* 1.78† 1.97 1.93 2.24 2.51* 0.17 0.0006 Monocytes 0.50 0.38 0.38 0.37 0.43 0.52 0.47 0.37 0.36 0.09 0.48 Eosinophils 0.20 0.14 0.09* 0.04* 0.03* 0.05* 0.12* 0.21 0.24 0.04 0.0002 N:L1 0.40 0.56 0.54 0.71* 0.76* 0.66† 0.64† 0.44 0.27 0.10 0.04 % Neutrophils2 1.80 1.86† 1.86† 1.90* 1.91* 1.88* 1.88* 1.84 1.78 0.02 0.006 Lymphocytes 27.23 20.70 21.47 16.23* 15.13* 18.50† 18.40† 24.70 32.27 3.15 0.03 Monocytes 5.77 4.70 4.27 3.53* 3.27* 4.00† 3.90* 3.97† 4.57 0.64 0.30 Eosinophils 2.57 1.83 1.13* 0.60* 0.40* 0.67* 1.50* 2.50 3.20 0.56 0.0002 1 N:L=neutrophil to lymphocyte ratio. Values are log10 transformed. 2 Values are log10 transformed. *Means in the same row differ from Pre-Transit (P < 0.05). †Means in the same row differ from Pre-Transit (P < 0.10).

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3.50 * * 3.00

2.50

CPM 2.00 10 10

Log 1.50

1.00

0.50

0.00 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-5. Background PBMC proliferation without mitogen stimulation before (Pre- Transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall effect of time (P = 0.06) and phase (P = 0.02). *Differs from Pre-Transit (P ≤ 0.05).

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Con A 1 µg/mL Con A 2 µg/mL 8000

7000

6000

†

5000 CPM 4000 * 3000 *

2000

1000

0 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-6. PBMC proliferation in response to Con A mitogen (1 or 2 µg/mL) before (Pre-Transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall Con A (1 µg/mL) effect of time (P = 0.02) and phase (P = 0.20). Overall Con A (2 µg/mL) effect of time (P = 0.15) and phase (P = 0.35).*Within a variable, bars differ from Pre-Transit (P ≤ 0.05).†Within a variable, bars differ from Pre-Transit (P < 0.10).

275

Con A 1 µg/mL Con A 2 µg/mL 2.00

1.80

1.60

1.40 † 1.20

1.00 * * *

0.80 * Stimulation IndexStimulation

10 0.60

Log 0.40

0.20

0.00 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-7. Stimulation index of PBMC in response to Con A mitogen (1 or 2 µg/mL) before (Pre-transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall Con A (1 µg/mL) effect of time (P = 0.01) and phase (P = 0.003). Overall Con A (2 µg/mL) effect of time (P = 0.03) and phase (P = 0.007). *Within a variable, bars differ from Pre-Transit (P ≤ 0.05). †Within a variable, bars differ from Pre-Transit (P < 0.10).

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8000 * 7000

6000

5000 CPM 4000

3000

2000

1000

0 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-8. PBMC proliferation in response to 1 µg/mL PWM before (Pre-transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall effect of time (P = 0.22) and phase (P = 0.23).*Differs from Pre-Transit (P ≤ 0.05).

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1.80

1.60

1.40

1.20 * 1.00 *

0.80 Stimulation IndexStimulation

0.60 10

Log 0.40

0.20

0.00 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-9. Stimulation index of PBMC in response to PWM before (Pre-Transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall effect of time (P = 0.05) and phase (P = 0.02). *Differs from Pre-Transit (P ≤ 0.05).

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1400

1200

1000

CPM 800

600

400

200

0 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-10. PBMC proliferation in response to 10 ng/mL of LPS before (Pre-Transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall effect of time (P = 0.71) and phase (P = 0.72).

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6.00

5.00

4.00

3.00 †

Stimulation IndexStimulation 2.00 * *

1.00

0.00 Pre- 24 h 24 h 72 h Transit Post Post

Transit Recovery

Figure 3-11. Stimulation index of PBMC in response to LPS before (Pre-Transit), during (24 h), and at 24 and 72 hours of recovery from transport. PBMC proliferation was measured in CPM (counts per minute). Data were pooled across horses and represent the overall mean ± SEM. Overall effect of time (P = 0.09) and phase (P = 0.03).*Differs from Pre-Transit (P ≤ 0.05). †Differ from Pre-Transit (P < 0.10).

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Saliva IgA IgA SI

4.5 2.3

4.0 * 2.0

3.5 1.8

3.0 1.5

2.5 1.3

IgA SI IgA

µg IgA /mL IgA µg

2.0 1.0 10

10 Log

Log 1.5 0.8

1.0 0.5

0.5 0.3 * 0.0 0.0 Pre- 6 h 12 h 18 h 24 h 12 h 24 h 72 h 120 h Transit Post Post Post Post

Transit Recovery

Figure 3-12. Total IgA (µg/mL) and IgA secretory index (SI) in saliva before (Pre- Transit), during transit (6-24 h Transit), and during recovery from transit (12- 120 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.14) and phase (P = 0.36). Overall SI effect of time (P = 0.22) and phase (P = 0.86). *Within a variable, time points differ from Pre-Transit (P ≤ 0.05).

281

Saliva IgA Saliva IgA SI Saliva IgA rate

4.5 3.5 4.0 *

3.0

3.5

µg/mL

3.0 2.5 10 2.5 δ 2.0 * 2.0 /min IgA µg

1.5 10

1.5 Log IgA and SI log andSI IgA 1.0 1.0 * 0.5 0.5

0.0 0.0 Pre- 6 h 12 h 18 h 24 h 12 h 24 h 72 h 120 h Transit Post Post Post Post

Transit Recovery

Figure 3-13. Total IgA (µg/mL), IgA secretory index (SI) and IgA secretion rate (µg/minute) in saliva before (Pre-Transit), during transit (6-24 h Transit), and during recovery from transit (12-120 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.14) and phase (P = 0.36). Overall SI effect of time (P = 0.22) and phase (P = 0.86). Overall secretion rate effect of time (P = 0.16) and phase (P = 0.30). *Within a variable, time points differ from Pre-Transit (P ≤ 0.05). δSecretion rate at 72 h post, differs from Pre-Transit (P < 0.10).

282

NPF IgA NPF IgA SI 5.8 2.3

5.5 * 2.0 5.3 1.8 5.0 *

1.5

4.8

4.5 1.3 IgA

ng IgA /mL IgA ng 4.3 1.0

10 10

10 4.0 0.8 Log Log 3.8 0.5 3.5 3.3 0.3 3.0 0.0 Pre- 24 h 12 h 24 h 72 h Transit Post Post Post

Transit Recovery

Figure 3-14. Total IgA (ng/mL) and IgA secretory index (SI) in nasopharyngeal flush (NPF) before (Pre-Transit), during transit (24 h Transit), and during recovery from transit (12-72 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.005) and phase (P = 0.001). Overall SI effect of time (P = 0.32) and phase (P = 0.08). *Within a variable, time points differ from Pre-Transit (P ≤ 0.05).

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Swab IgA Swab IgA SI 6.1 4.7 5.9 * * * * 4.4 5.6 * * * 4.1 5.4 3.8

5.1 IgA SI IgA

† 3.5 10

ng IgA /mL IgA ng 4.9 †

3.2 Log

4.6 Log 4.4 * 2.9 * * 4.1 2.6

3.9 2.3 Pre- 6 h 12 h 18 h 24 h 12 h 24 h 72 h Transit Post Post Post

Transit Recovery

Figure 3-15. Total IgA (ng/mL) and IgA secretory index (SI) of nasal swabs before (Pre- Transit), during transit (6-24 h Transit), and during recovery from transit (12- 72 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.006) and phase (P = 0.005). Overall SI effect of time (P < 0.0001) and phase (P = 0.43). *Within a variable, time points differ from Pre-Transit (P ≤ 0.05). †Within a variable, time points differ from Pre-Transit (P < 0.10).

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3.20 * 3.15 * †

3.10

3.05

3.00

µg IgA /mL IgA µg

2.95 Log 2.90

2.85

2.80 Pre- 6 h 12 h 18 h 24 h 12 h 24 h 72 h 120 h Transit post post post post

Transit Recovery

Figure 3-16. Total IgA (µg/mL) in serum before (Pre-Transit), during transit (6-24 h Transit), and during recovery from transit (12-120 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.02) and phase (P = 0.30). *Differ from Pre-Transit (P ≤ 0.05). †Differs from Pre-Transit (P < 0.10).

285

Cecal IgA Cecal IgA SI 4.0 4.0 † 3.5 3.5

3.0 3.0

2.5 2.5

ng IgA /mL IgA ng

IgA SI IgA

2.0 2.0

10 10 Log 1.5 1.5 Log * † 1.0 1.0

0.5 0.5

0.0 0.0 Pre- 12 h 24 h 12 h 24 h 72 h Transit Post Post Post

Transit Recovery

Figure 3-17. Total IgA (ng/mL) and IgA secretory index (SI) in cecal fluid before (Pre- Transit), during transit (12-24 h Transit), and during recovery from transit (12- 72 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.42) and phase (P = 0.27). Overall SI effect of time (P = 0.28) and phase (P = 0.20). *Within a variable, time points differ from Pre-Transit (P ≤ 0.05). †Within a variable, time points differ from Pre-Transit (P < 0.10).

286

Fecal IgA Fecal IgA SI 5.5 4.0 * 5.0 3.5 4.5 3.0

4.0

2.5

3.5

2.0

IgA SI IgA

ng IgA /mL IgA ng 3.0

10 1.5 2.5 * Log Log * 1.0 2.0

1.5 0.5

1.0 0.0 Pre- 6 h 12 h 24 h 12 h 24 h 72 h 120 h Transit Post Post Post Post

Transit Recovery

Figure 3-18. Total IgA (ng/mL) and IgA secretory index (SI) in fecal liquid before (Pre- Transit), during transit (6-24 h Transit), and during recovery from transit (12- 120 h Post). Data were pooled across horses and represent the overall mean ± SEM. Overall IgA effect of time (P = 0.40) and phase (P = 0.10). Overall SI effect of time (P = 0.24) and phase (P = 0.06). *Within a variable, time points differ from Pre-Transit (P ≤ 0.05).

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Table 3-9. Correlations between IgA in biological samples during the entire study. Correlations were performed on log10 transformed means. Salivary Serum Nasal swab NPF Fecal liquid Salivary P value < 0.0001 < 0.0001 0.02 0.001 R2 0.73 0.68 0.35 0.38 Serum P value 0.001 R2 0.39 Nasal swab P value < 0.0001 0.001 R2 0.79 0.43 NPF1 P value 0.007 < 0.0001 0.001 R2 0.44 0.62 0.41 Cecal fluid P value 0.03 0.003 0.007 0.09 0.0001 R2 0.29 0.46 0.54 0.22 0.63 1NPF=nasopharyngeal flush.

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Table 3-10. Dry matter (DM) and pH of cecal contents and fecal liquid before (Pre- Transit), during and after transit (Post-Transit). Data for individual time points were pooled within each phase.

Pre-Transit During Transit Post-Transit SEM P value

DM Cecal 6.53 4.65† 5.81 1.35 0.19 Fecal 22.26 21.49 19.32* 1.12 0.04 pH Cecal 7.39 7.64 7.73* 0.12 0.07 Fecal 6.61 6.32† 6.66 0.17 0.08 *Means in the same row differ from Pre-Transit (P < 0.05). †Means in the same row differ from Pre-Transit (P < 0.10).

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CHAPTER 4 CONCLUDING REMARKS AND FUTURE PERSPECTIVES

Overall Conclusions

Stress affects all horse populations to varying degrees. The stress accompanying transportation, training programs, competitions, weaning, and new environments can cause metabolic and immune dysfunction leading to poor performance and increased risk of infection. The mechanisms behind stress-associated illness are complex and some remain elusive. Transport stress is not uniform but caused by a variety of simultaneous stressors from many different sources; physical restraint from normal activity, abnormal head carriage, close proximity to other horses, isolation from herdmates, novel surroundings, exposure to new pathogens, extreme temperatures, water and/or feed deprivation, and dust, particulate matter and ammonia tainted breathing air. This complex stressor triggers a physiological response that can produce undesirable health outcomes. A recent online survey indicated that respiratory problems

(33.7%), gastrointestinal problems (23.8%) and injuries (16.3%) were the most frequently reported complications following transportation (Padalino et al., 2017b). This survey and previous studies indicate that journey duration is positively associated with development of serious health problems, and journeys longer than 24 h pose the greatest risk. Research has shown horses may require 10 transport episodes to become familiarized with the process (Schmidt et al., 2010). Although it seems horses can acclimate to transportation and be less psychologically affected, the physical stressors remain and do not end when transportation concludes. Stressors at their destination such as training programs and competitions, or new surroundings and horses, are additive to the stress experienced during transport. Although we cannot

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eliminate stressors, the research performed as part of this dissertation, combined with studies over the past decade, has revealed some ways to mitigate them.

For safety reasons, a common management practice is to tether horses with a halter and lead during transport. Some disciplines also tie their horses for extended periods of time for training purposes. Horses rely heavily on the ability to lower their head and drain respiratory accumulations through their nose. Normally, inhaled dust and allergens from feed being eaten at ground level would be easily eliminated by this method of gravity flow. This dissertation research has shown that the inability to lower their head causes a systemic immune response in horses. The obvious solution to this problem is to not tie them; however, loose horses stand with their heads elevated in order to maintain balance while the trailer is in motion. Head elevation during transportation cannot be totally eliminated, but loose horses benefit from the ability to lower their heads during rest stops. Once off the trailer, encouraging horses to lower their head by providing turnout and feeding at ground level, will encourage nasal drainage and may lessen the subsequent immune response.

Providing hay during transport is beneficial for ulcer prevention, promoting GIT motility and alleviating boredom, but it also increases the amount of inhaled dust and allergens. Most studies agree that inhaled particles are undesirable, but one study showed horses that had hay during transport, actually inhaled less particles compared to horses without hay (Allano et al., 2016). The researchers suggested low hanging hay nets in the trailer encouraged horses to lower their heads, thereby increasing nasal drainage. One solution to minimize particle inhalation is to feed damp or wet hay, which will also help keep the horses hydrated and prevent choke. However, in hot humid

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climates caution must be taken when offering wet hay since mold growth and bacterial proliferation can occur quickly (Moore-Colyer et al., 2014).

In many cases, duration of the stressor is positively associated with the risk of negative health outcomes. Transporting for >8 h without rest can hinder performance, but journeys of any length endured immediately before competition can also reduce performance, particularly if the horses are less experienced travelers (Slade, 1987;

Covalesky et al., 1992). Based on the transportation induced elevation of muscle enzymes, a recovery period of 2 h for every 3 h of travel has been suggested (Tateo et al., 2012). Although rest is less important if there is no physical exertion required upon arrival, transportation stress can still cause many unwelcome side effects, and rest should be considered for all horses being transported.

This dissertation research attempted to elucidate when the immune system recognizes transportation stress and which part of the immune system is most affected.

We did find that this occurs within 6 h; however, it remains unclear whether this increases the risk of a negative health outcome. Based on previous research, it is likely that 6 h of transport or head elevation would only contribute to disease if there were other stressors involved. Mucosal and innate immunity were the most affected by head elevation and took the longest to recover; however, the transportation study (Chapter 3) failed to support these data. We also wanted to help horses mitigate the stress by altering dietary intake. The inclusion of oat BG from two distinct sources did enhance some immune functions, but did not alleviate stress-induced immunosuppression caused by prolonged head elevation.

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Future Perspectives

Horses experience many types of unavoidable stressors throughout their lives which can increase the risk of disease. Vaccination does not provide adequate protection and certain management practices are unavoidable, so alternative methods of protection should be investigated. Oat BG did not mitigate stress under the conditions evaluated, but under direct infectious challenges the results may be different. Prior to conducting another in vivo study, certain in vitro experiments would help discern whether BG can activate equine immune cells and the type of response generated.

Previous researchers have used fungal BG to stimulate equine BAL cells in vitro and this would also be possible with the soluble oat BG powder used during the study presented in Chapter 2 (Ainsworth et al., 2007). The stimulatory ability and cytokines produced would provide valuable information; however, it is still unknown whether BG can reach equine immune cells in vivo. Measuring BG in serum or feces may help elucidate what happens to oat BG within the equine GIT. Commercially available kits can measure fungal BG and should cross-react with oat BG to some extent. Better perhaps would be to develop a detection method for oat BG.

Fiber is the largest component of an equine diet and yet their daily fiber requirement remains unknown. Research shows that minimal daily quantities of dietary fiber can lead to gastric ulcers, colic, hindgut acidosis, stereotypies and behavioral problems (NRC, 2007). In order to better understand how to enhance health and possibly immune function, we first must know the minimum level required to maintain health. Additionally, there is little or no information available on how nutrient requirements change during periods of stress.

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During my program, I was partially able create a nasopharyngeal leukocyte stimulation protocol; however, I was often limited by cell numbers and other assays took precedence. This protocol could be better optimized and provide a non-invasive way to measure respiratory cell function. Of note, horses had highly individual responses to the in vitro protocol and this may be due to underlying, undiagnosed respiratory problems.

Most of the nasopharyngeal cells that did not respond to stimulation also displayed a different, but common between them, FACS plot pattern which made it difficult to differentiate cell populations. Towards the end of the study presented in Chapter 2, one of the horses with this consistently unusual FACS plot was diagnosed with early onset pasture-associated asthma and removed from the study. The biological sister of this horse also regularly had this same FACS plot pattern and pasture-associated asthma has a proposed genetic component (Hansen et al., 2015). Although she did not displays signs of the disease during the study, she could be diagnosed later in life considering both horses share a mother that also had the disease (not on the study). These results hint towards an untapped diagnostic tool that may exist with nasopharyngeal flushes and unusual FACS plots.

The mucosal immune system of horses is largely uncharted. Measurements of immunoglobulins in various biological samples and accurate reference ranges would provide valuable information on immune status, and may be good diagnostic tools for stress or disease risk. Molecular characterization of the entire equine sIgA molecule has occurred (Lewis et al., 2010) and a method to measure secretory immunoglobulins needs to be established.

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Research has started to shed light on diurnal rhythms which exist in most species (Haldar and Ahmad, 2010) and assuming this occurs in horses, researchers should consider these patterns when designing studies. In order to do that, diurnal rhythms of immune parameters need to be investigated in horses.

Many types of stress cause immune dysfunction and duration of stress is often positively associated with risk for negative health outcomes. The equine population would greatly benefit from understanding exactly when stressors begin to increase disease risk.

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APPENDIX A OVERNIGHT SHIFT PROTOCOL FOR HEAD ELEVATION STRESS INDUCTION

**Please stay in the barn area so you can hear any problems that may occur** Short visits to lab down the hill are fine Prop door open to the barn lab, if you would like to sit in there

Horses can be groomed if it will not upset them Some horse may be more agitated with human attention (better left alone), whereas some horses may be the opposite Keep the barn area as quiet as possible Re-fill hay nets as necessary

General checks of tied horses occur every 30 min Place check mark on check list sheet hanging outside each stall, along with any important notes about the horse (defecation, urination, agitation) Fecal collection Pick up feces with pitchfork or hands Remove as many shavings as possible while collecting ALL feces Place feces into appropriate muck bucket outside each horse stall Watering of horses Use water bucket outside the stalls Fill it about half way and hold up to tied horse Offer water for about 5 min to each horse, unless they immediately drink Re-fill bucket if necessary Record approximate volume each horse drank (1/2 bucket…)

**Do not untie horses unless they are in critical condition** Think of your safety first! IF a horse is pulling back on the tie and thrashing, they are probably too dangerous to be around so DO NOT enter the stall unless you believe you can comfortably intervene (make forward suggesting noises like kissing or clicking)

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Horses are OK to whinny, move around and paw, without requiring human intervention IF a horse will NOT settle down and you are comfortable removing them from the tie and the stall, the horse can be walked down the aisle of the far side of the barn (away from other tied horses) or outside to try and calm them down. Try to keep their head elevated during this walk session Attempt to tie them again once they have calmed

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APPENDIX B FIELD COLLECTION AND LAB PROTOCOL FOR NASOPHARYNGEAL FLUSH

1. Wear gloves (new gloves for every horse)

2. Pre-load 4 syringes with 60 cc sterile 1X PBS (2 for each side of nose)

3. Open orange top fluid cup and attach funnel

4. Investigator inserts catheter into nasal cavity and flushes PBS through catheter

5. Catch expelled nasal fluid in labeled orange top fluid cup + funnel

6. If collected flush exceeds volume of cup, pour excess flush into a new fluid cup

7. Place fluid cup on flat surface and while holding fluid cup, gently remove funnel

8. Close fluid cup tightly, label cups in numerical order as they are removed from

funnel (usually 1-4)

9. Place on ice until processing

10. Place funnel on a fresh fluid cup (labeled by horse)

11. Repeat steps 4-9 until flush is complete (two 60 cc syringes per nostril)

Lab Protocol for Processing Nasopharyngeal Flushes

Non-Sterile procedure

1. Wear gloves (new gloves for every horse)

2. Record volume and turbidity of each nasal cup for each horse

3. Cut 6 x 6 squares of 80 µM nylon mesh

4. Using 25 mL serological, filter nasal wash through nylon mesh into 250 mL

plastic bottle

a. If measuring neutrophil function, glass will activate the cells

b. Can squeeze nylon mesh with fingers to encourage liquid

c. If nylon gets too clogged, you can use a new piece

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d. Save nasal cups for rinse

5. Label appropriate number of 50 mL conicals for volume in plastic bottle

6. Place 40 µM cell strainer in 50 mL conical

7. Use 25 mL serological pipette or pour nasal wash through cell strainer

a. If strainer gets clogged, slowly pick up strainer to encourage liquid through

the side and then put strainer back down into conical, repeat as necessary

b. If strainer is too clogged, you can use a new one

8. Label new conical with horse’s name and “rinse”

9. Add a few mL (~10) to each nasal cup and to plastic bottle

10. Swirl PBS in each cup and bottle to collect any cells that are stuck to side

11. Filter rinseate through 40 µM cell strainer into conical labeled “rinse”

12. Spin conicals at 1500 rpm (~300 x g) for 7 min at 4°C

13. Remove 1 mL x 3 of supernatant (from each or any conical) and place into

labeled nasal wash microvials

a. Do not take from “rinse” conical

14. Store microvials at -80°C in correct cardboard box

15. Decant using serological pipette and discard the remaining supernatant in sink

16. Resuspend cell pellets in ~1 mL PBS

17. Combined cell pellets into one conical

18. Rinse each conical with a 3-5 mL PBS

19. Pour into combined conical with cells

20. Spin as above

21. Decant using serological pipette and discard the remaining supernatant in sink

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22. Resuspend in ~250 µL PBS

23. Remove cell volume and place into micro red top vial for count on Procyte Dx or

any white blood cell counting machine

Validation of Procyte Dx for nasopharyngeal flush samples

Validation for use of the IDEXX Procyte DX® Hematology Analyzer (Westbrook,

ME) for measurement of leukocyte populations in the nasopharyngeal flush was determined by two different methods. Flush samples from the same horse were submitted to the Clinical Pathology lab at the University of Florida for manual counting of leukocyte populations. Samples were also run on the IDEXX Procyte DX®

Hematology Analyzer as two different sample types; whole blood and other fluid which was validated by the company for abdominal and thoracic fluids. The whole blood setting provides 26 parameters (i.e. WBC, number and percentage neutrophils, lymphocytes, monocytes, eosinophils, basophils and band neutrophils). The other fluid setting provides total nucleated and red blood cell counts, and number and percentage of agranulocytes and granulocytes. Overall, major leukocyte populations were similar between those manually counted by the clinical pathology lab and those reported by the

Procyte machine when the flush was run as a whole blood or other fluid. There was some discrepancy between monocytes/macrophages reported by the clinical pathology lab and the Procyte machine which could be because the lab was not given a concentrated flush sample and often reported a small quantity of countable cells which may not accurately represent the true population.

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APPENDIX C FIELD COLLECTION AND LAB PROTOCOL FOR SALIVA

**Wear gloves; new gloves for every horse**

1. Rinse horse’s mouth with 60 cc of water

2. Wait at least 10 minutes before collecting saliva

3. Zip tie two salivette swabs onto chifney bit, at an angle (preloaded for each

collection)

4. Insert chifney into horse’s mouth

5. Clip to both sides of halter

6. Start horse’s timer (make sure horse is actively chewing, shake chifney if

necessary) *horse cannot eat or drink while bit is in mouth*

7. Unclip, remove chifney and stop timer (at least 10 minutes in mouth)

8. Record time bit was in mouth

9. Cut zip ties and place salivette swabs into correct salivette tubes

10. Place on ice until processing

Lab Protocol for Saliva Processing

1. Wear gloves (new gloves for every horse)

2. Spin salivette at 3000 rpm (1730 x g), 10 min at 4°C

3. Locate micro tube rack with labeled micro vials

4. Remove and dispose of blue cap and plastic piece containing swab

5. Using transfer pipette, aliquot 0.1 mL x 3 microvials per horse for salivary IgA

a. Saliva can come from either one of the two saliva collection tubes

b. Do not disturb sediment at very bottom of tube

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6. Using transfer pipette, aliquot remaining saliva into 3 microvials per horse for

salivary cortisol (equal volume per microvial)

7. Record total volume of saliva recovered based on volume marks on microvials

(transfer pipettes leave ~0.1 mL of saliva at bottom of salivette, so record this

amount with total volume recovered)

8. Store both sets of vials at -80°C in appropriate cardboard boxes

9. Dispose of saliva collection tube

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APPENDIX D FIELD AND LAB PROCOTOL FOR FECAL LIQUID

Everyone needs to be on “feces watch” during our collections and collect when defecation occurs

1. Wear gloves (new gloves for every horse)

2. Collect fecal pile into correctly labeled bag immediately after defecation (pinch off

ANY fecal matter contaminated with dirt, sand, other fecal matter or shavings)

3. Close bag and place on ice (process for fecal liquid within 2 hours of fecal

collection)

Lab Protocol for Obtaining Fecal Liquid

1. Wear gloves (new gloves for every horse)

2. While fecal bag is closed, homogenize feces (mix together until all blended

evenly)

3. Place correctly labeled whirl pak bag on scale

4. Zero scale by pressing tare

5. Weigh ~200 g of feces into whirl pak bag, taking from different parts of the fecal

pile *if units on scale 200 g = 0.2 Kg*

6. Place in labeled whirl pak with horse’s name

7. Store whirl pak at -80°C

8. Wipe off scale

9. Place piece of cheese cloth on scale

10. Zero scale by pressing tare

11. Weigh 100 g of feces taking from different parts of the fecal pile

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12. Enclose feces in cheese cloth and squeeze out fecal liquid into labeled plastic

container with horse’s name

13. Swirl fecal liquid in container to mix thoroughly

14. Using transfer pipette, aliquot 1.5 mL x 3 micro vials per horse (new pipette for

each horse)

15. Place in correct cardboard box and store at -80°C

16. With remaining fecal liquid, measure and record pH of fecal liquid (must be after

IgA aliquot and at room temp 21-23°C)

17. Wipe scale clean with PBS gauze after every horse

18. Dispose of left over fecal matter in trash

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APPENDIX E PERIPHERAL BLOOD MONONUCLEAR CELLS ISOLATION

NOTE: rinse pipette with PBS before drawing cells into clean pipette (cells are sticky).

Use sterile technique; spray all unsterilized instruments, counter top, & gloves with 70% Ethanol

50 mL conical labels: whole blood, WBC (white blood cells), LSM (lymphocyte separation media), and PBMC (peripheral blood mononuclear cells)

1. From vaccutainer tubes, transfer whole blood into 50 mL conical vial (Labeled

whole blood)

a. About 4 vaccutainer tubes per 50 mL conical

b. Dump vaccutainer or use serological pipette

c. Optional- add HBSS or PBS EDTA 2mM to help with clumping (0.5M

EDTA→ 4ml/1L PBS), not usually a problem with equine cells

2. Centrifuge 1200 x g (2500 rpm), 30 min @ 18°C (program 9)

a. Centrifuge in Abbott’s lab, press RCL, 9, press enter, check program is

correct, press start.

3. Pipette 15 mL LSM into newly label 50 mL conical (labeled LSM)

4. Remove and discard most of the plasma from whole blood conical

a. 25 mL serological

5. Collect white blood cell layer that forms on top of the red blood cells, include

some plasma and RBC, place into new 50 mL conical (labeled WBC)

a. Use 5 mL serological pipette (sucks fast!), angle conical so you can see

WBC

b. ~6 mL from each whole blood conical into WBC conical

6. Bring WBCs volume up to ~35 mL with PBS (can have EDTA)

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7. Invert gently

8. Slowly layer 35 mL diluted WBC on top of 15 mL LSM, **Maintain sharp

interface**

a. Use 25 mL serological pipette

b. To layer, tilt conical vial and slowly dribble diluted WBC along edge of

conical vial

c. Once a few mL of diluted WBC has been added, you can go a bit faster,

slow down if you see turbulence

d. Don’t disturb the layering when finished (don’t invert!)

9. Centrifuge 900 x g (2100 rpm), 30 min @ 18°C (program 8)

10. Aspirate and discard plasma to within 1 cm above PBMC buffy coat

a. Use 25 mL pipette

b. Don’t need clean pipette for each horse as long as you don’t touch the

PBMC

11. Remove ALL LSM, place in a new 50 mL conical (labeled PBMC)

a. Use 5 or 10 mL serological

b. PBMC are fuzzy interface between plasma and clear LSM, but some cells

may still be in LSM

c. Tilt conical vial slightly and suction in a circular motion at the fuzzy layer

d. See diagram

12. Bring conical vials to a volume of 45 mL with PBS, cap and mix by gentle

inversion

13. Centrifuge at 200 x g for 10 min at 25°C (program 14)

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a. If successful, should see a small cell pellet at the bottom of the vial

14. Suction off and discard supernatant, being careful not to disturb cell pellet

a. Use 25 mL pipette ← PLASMA b. Can suction off until ~1/2 cm above pellet ← PBMC c. Cells will dissolve a bit so don’t delay too long ← LSM

d. Dumping supernatant results in about 250 µL left ← RBC

15. Bring volume up to 45 mL with PBS, mix by gentle inversion

a. Add PBS fast to help breakup/dissolve cell pellet, or flick tube with finger

16. Centrifuge at 200 x g for 10 min at 25°C (program 14)

17. Suction off and discard supernatant as close to cell pellet as you can get without

disturbing it

18. Resuspend cells in 1 mL PBS

a. If cell pellet is large, can resuspend in more PBS, as long as volume is

known

b. If cell pellet is small, can resuspend in less PBS, as long as volume is

known

19. Swirl or re-pipette cell pellet to resuspend

20. Place 90 µL trypan blue into microvial

21. Add 10 µL cells to 90 µL trypan blue, lightly vortex micro vial on ~5.5 setting

(1:10)

a. If cell pellet was large, you can do a second trypan blue dilution by taking

10 µL from 1:10 dilution and putting it in another vial with 90 µL trypan

blue (1:100)

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22. Place stock PBMC conical on ICE while counting

23. Load hemacytometer with 10 µL cells/trypan blue mix

a. To load, touch tip of pipette to bottom of hemacytometer (don’t touch

coverslip), pipette mixture in slowly

24. Count live cells in quadrants A, B, C, D under 40X magnification (see diagram)

a. Live cells will be clear, but do not need to be perfectly round as long as

they do not absorb any dye

b. Record live cell number before moving on to another quadrant

25. Average the quadrant counts

26. IF you counted the 1:100 dilution, the average count is number of million PBMC

per mL (AVG x 106 PBMC/mL)

27. IF you counted the 1:10 dilution, you will need to move the decimal place of the

average count 1 place to the left and that will equal millions of PBMC/mL

a. Example, counts 102, 109, 112, 100: average equals 105 = 10.5 x 106

PBMC/mL

28. Remove cover slip and rinse hemacytometer with DI water, dry with kimwipes

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Cells lying on edge COUNTED A D Not

C ounted

COUNTED

Cells Cells on lying edge

B C

Not Counted

29. Centrifuge at 200 x g for 10 min at 25°C (program 14)

30. Suck or pour off supernatant

31. Add appropriate amount of freezing media to cell pellet

a. Maximum number of cells per 1 mL freezing media is 10 x 106 (30 x 106

cells total), if cell total is higher than this, will need to freeze back more

than 3 vials

b. If total cell count is under 7 x 106, add 2 mL and freeze in 2 cryo vials

c. If total cell count is 8-30 x 106, add 3 mL and freeze in 3 cryo vials

d. If total cell count is 30-40 x 106, add 4 mL and freeze in 4 cryo vials

e. If total cell count is over 40 x 106 , add 5 mL and freeze in 5 cryo vials

32. Flick/re-pipet to suspend cells

33. Add 1 mL of freezing media cells suspension to each cryo vial

a. Freezing media recipe is 10% DMSO and 90% heat-inactivated FBS

b. Sterile filtered

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34. Immediately place cryo vials into Mr. Freezy (filled with 70% ethanol or stock

isopropanol) and in to -80°C freezer for 12-24 hours

35. Remove cryo vials from Mr. Freezy and place into liquid nitrogen for long term

storage

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APPENDIX F LYMPHOCYTE PROLIFERATION ASSAY

Protocol 3H Working Solution

1. Place tray with absorbent lab paper in sterile hood

2. Put media into conical before removing 3H from freezer

3. Unscrew blue stock 3H container, flip over blue top and use to open red top

container with 3H solution

a. Change gloves after opening 3H stock solution

4. Add 3H to media, don’t touch container

a. Change gloves

5. Close 3H stock solution

a. Change gloves

6. Perform swipes

a. Swipe the following: 1- outside of blue 3H stock container, 2-conical

containing working 3H solution, 3- pipette tip box, 4- pipette, 5- Tube rack,

6- blank

7. If swipe ok, 3H stock solution can be returned to freezer

8. Label 3H working solution with radioactive stickers and return to fridge

9. Place pipette tips in the center of the lab paper and fold paper into a package

10. Hold waste package in gloved hand and remove glove over package to secure

radioactive pipette tips inside; repeat with the other gloved hand

11. Dispose of radioactive package appropriately

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Complete RPMI-1640 Media Recipe

1. Sterilize and place all necessary components under sterile hood

2. Attach (screws on like a cap) 0.2 µm filter sterilization unit to bottle which final

complete media will be stored in

a. Orange tip attaches to filter unit, big opening

b. Hook up stiff plastic end of tubing unit to vent inside hood

c. Hook up soft plastic end of tubing unit to filter unit

3. Add RPMI 1640 media into filter unit (from fridge)

4. Add heat denatured fetal bovine serum into filter unit

a. Must be thawed in incubator/water bath

b. Mix by pipetting up and down, until all one color

5. Add 2-Mercaptoethanol (50mM) (dilution in rack next to hood, room temp)

a. From Fisher, bottle is 14.3M, must make dilution

6. Add gentamycin (50mg/mL) (on shelf next to hood, room temp)

7. Add L-glutamine (200mM)

a. Frozen, thaw with hands, once thawed mix by inversion until becomes

clear

8. Add HEPES (1M) (on shelf, next to hood, room temp)

9. Turn vent in hood on to filter media through into bottle (half way turn)

10. Mix thoroughly, label and store in fridge. Good for several weeks

To make 500ml of media:

431.5ml RPMI 1640 50ml FBS 0.5ml 2-ME (50mM) 0.5ml Gentamycin (50mg/ml)

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5ml L-Glutamine (200mM) 12.5ml HEPES (1M)

Lymphocyte Proliferation Assay Protocol

**STERILE PROCEDURE**

Thawing Protocol for PBMC from Liquid Nitrogen

1. Remove medium from fridge, sterilize outside of bottle and place under sterile hood;

a. Medium should remain cold until use

2. Pour ~11-13 mL of medium into sterile 15ml conical vial; Do not touch vial edge to

medium container as you are pouring

3. Remove cryo vial from liquid nitrogen

4. Rub vial between hands to quickly thaw the outside of the vial until the ice chunk in

vial is barely mobile and able to be poured

5. Pour ice chunk into 15 mL conical vial with medium

6. Rinse cryo vial twice with ~1-2 mL of medium and pour into 15 mL conical vial

7. Look to make sure ice chunk dissolved in medium before spinning vial

8. Centrifuge 15 mL vial of stock cell solution with program 4 or 5 on Dr. Abbott’s

centrifuge.

a. Use beige tube holders in centrifuge

b. Make sure to balance

c. Program 5→ 300g rpm, 5 mins, 10°C

d. Program 4→ 250g rpm, 10mins, 10°C

e. To start centrifuge program, press Rcl, #5 enter, start

9. While waiting for centrifuge, pipette 90 µL of trypan blue in two separate 1 mL

micro vials; does not need to be under hood

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a. 1:10 1st dilution

b. 1:100 2nd dilution (optional)

10. Use serological pipette to remove most of the medium, Do not disturb cell pellet

11. Re-suspend cells in appropriate amount of medium depending on pellet size

a. 200-1000 µL media

12. Mix cells by gently drawing liquid up into pipette a few times, try not to get

bubbles

13. Use what is left over in pipette after mixing stock cells or pipette 10 µL from stock

cell suspension into 1st dilution of the trypan blue

14. Option for large cell pellet- With same pipette tip, gently mix in trypan blue

a. Pipette 10 µL from 1st trypan blue dilution into 2nd trypan blue dilution

15. Place stock cell solution on ice while counting live cells

16. Follow cell counting procedure

17. Fill out plate map

18. Calculate/make working cell solution and working mitogen solution; see below

19. Plate 50 µL of working cell solution on a 96-well plate; start with lowest

concentration of cells. Only touch pipette tip to bottom of well if necessary.

20. Plate 50 µL of working mitogen solution, start with lowest concentration of

mitogen. Only touch pipette tip to upper sidewall of well if necessary. Total

volume per well is 100 µL.

21. Put cover on plate and place in incubator for specified time (~72 hr total)

Calculations for plate: 1. Calculate working solution of cells + medium

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a. (live count x 106 PBMC/mL) (x mL) = (2x desired working solution/mL)(µL

total volume of working solution needed per run)

b. X= µL of cells needed from stock solution

c. Total volume of working solution needed per run - x = µL of medium to

add to working solution of cells

2. Calculate working solution of mitogens

a. (µg stock solution of mitogen/mL) (x µL) = (µg 2x desired working

concentration of mitogen/mL) (Total volume of working solution needed

per run mL or µL)

b. X= µL needed from stock solution of mitogen

c. Total volume of working solution needed per run – x = µL or mL of

medium to add to working solution of mitogen

Addition of Tritiated Thymidine (3H)

22. Sterilize and place beige tray, with lab paper, under hood

a. Wear lab coat

23. 60 hr into incubation, remove plate from incubator and place under hood

24. Remove working solution of 3H Thymidine from fridge and place under hood

25. Use digital auto pipette to dispense 25ul/well 3H Thymidine

a. Use auto pipette tip AND additional regular pipette tip

b. When drawing up the radioactive liquid, do not touch side of vial

26. Dispense first 2 aliquots back into working solution of 3H Thymidine

27. Dispense 3H Thymidine into wells

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a. Only touch pipette to side of wells, opposite of side mitogen pipette

touched

28. Put extra 3H Thymidine back into working solution

a. Leave pipettes under hood until swiped

29. Place radioactive warning stickers on plate’s cover

30. Return plate to incubator

31. Wrap up pipette tips in lab paper and pull gloves over to create a package

32. Dispose of radioactive package in dry radioactive waste container

33. Perform swipes, use correct swipe sheet for locations to be swiped (9 areas)

34. Place each swipe in correct scintillation vial, add 2ml of scintillation fluid to each

vial

a. Cap vials with lids

35. Place divider top on plate holding vials

36. Place plate in Microbeta machine below stop plate, close door

a. Open Microbeta program on computer

b. Follow instructions on side of Microbeta to set up swipe program

c. Name file “preharvest swipes, initials, date”

d. Make sure it is set to Excel 4

e. Green light on program means you can run the swipes

f. Good to let vials sit because touching swipes with gloves causes static

that could read as radioactivity

37. Fill out swipe sheet with CPM numbers from machine, DPM numbers x2

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a. If any numbers above 100, area correlating to that swipe must be

decontaminated with Fantastik, re-swiped and re-run

b. If clean, fill out swipe sheet as Net CPM <50, DPM <100

38. Throw scintillation vials in radioactive dry waste container

Cell Harvest

39. Remove plate from incubator after ~72 hours total (~12-18 with 3H)

40. Turn on power strip and then compressor

41. Label filter paper with pencil and place into harvest machine

42. Wet filter paper using blank plate by pushing the wash/vacuum buttons

a. Black handle does lock into place, push slowly to lock

b. Don’t wash more than 2x, filter paper will break

43. Remove blank plate and put harvest plate in machine

44. Vacuum out wells of plate, wash wells 2x

45. Carefully remove filter paper, place on paper towel, place in the microwave to

~90 sec

46. Turn off harvest machine, make sure vacuum in locked ON

47. Place dry filter paper in plastic bag

48. Seal bag very close to top edges of paper

49. Cut corner of sealed bag opened and add ~3ml of scintillation fluid into bag

50. Use roller to evenly distribute fluid completely over filter paper

51. Re-seal opened corner of bag

a. Use sealer with no heat to press air bubble to edge of bag and double seal

b. Cut in between double seal to remove air bubble

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52. Place filter paper into reader plate; line up wells to wells, makes sure you can’t

see any black when you put top on reader plate

53. Place reader plate into Microbeta machine below stop plate

54. Select protocols, general then 3H filter map on computer program; select

program, output, file 1, name file

55. Name file (note 3 day difference between plate set up and harvest date)

56. Dispose of filter paper in dry radioactive container

57. Empty liquid radioactive waste container from harvest machine

58. Perform swipes (19 areas)

Optimization of mitogen concentrations

To determine concentration of mitogens for maximum lymphocyte stimulation, frozen PBMC were thawed and stimulated with PWM (1, 2, 4, 8, 16, and 35 µg/mL),

LPS (1, 2.5, 5, 10, 50, and 100 µg/mL) and Con A (0.5, 1, 2, 4, and 8 µg/mL).

Stimulation induced by PWM was similar for all concentrations and did not show a titration effect, nor did high concentrations induce cell death (Figure F-1). Therefore, the lowest concentration (1 µg/mL) was chosen. Lymphocyte stimulation with LPS increased with the concentration until 50 µg/mL at which point lymphocyte stimulation decreased (Figure F-2). Maximum stimulation was achieved at 10 µg/mL. Con A stimulation of lymphocytes also increased with the concentration until 8 µg/mL at which point lymphocyte stimulation decreased (Figure F-3). Although a nice titration effect was since with 0.5 and 1 µg/mL, 0.5 was not chosen because overall CPM was too low.

Instead, 1 and 2 µg/mL were used for the experiment.

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18000

16000

14000

12000 CPM 10000

8000

6000

4000 1 µg 2 µg 4 µg 8 µg 16 µg 35 µg

Figure F-1. PBMC stimulation with varying concentrations (µg/mL) of PWM measured in CPM (counts per minute).

1200 1100 1000 900

800

CPM 700 600 500 400 300 200 1 µg 2.5 µg 5 µg 10 µg 50 µg 100 µg

Figure F-2. PBMC stimulation with varying concentrations (µg/mL) of LPS measured in CPM (counts per minute).

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17500

15500

13500

11500 CPM 9500

7500

5500

3500 0.5 µg 1 µg 2 µg 4 µg 8 µg

Figure F-3. PBMC stimulation with varying concentrations (µg/mL) of Con A measured in CPM (counts per minute).

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APPENDIX G PROTOCOL FOR ANTIBODY LYMPHOCYTE SUBSET DETERMINATION

*Procedure NOT sterile, unless using sterile reagents (Antibodies, PBS or Gey’s)

Gey’s A B C Solution Recipe (use sterile procedure) Gey’s Solution A

70 g NH4Cl - Ammonium Chloride 3.7 g KCl - Potassium Chloride 3.0 g Na2HPO4 .12 H2O Sodium Phosphate, dibasic dodecahydrate (or 1.2 g anhydrous) 0.24 g KH2PO4 - Potassium Phosphate 10 g Dextrose 0.10 g Phenol Red In 2 L DDH20 Autoclave

Gey’s Solution B

2.1 g MgCl2.6H20 – Magnesium Chloride (or 0.35 g anhydrous) 0.7 g MgSO4.7H20 – Magnesium Sulfate (or 0.34 g anhydrous) 2.25 g CaCl2.2H20 – Calcium Chloride (or 1.7 g anhydrous) In 500 mL DDH20

Gey’s Solution C

11.25 g NaHCO3 – Sodium Bicarbonate In 500 mL DDH20

Gey’s Working Solution Recipe (sterile)

4 mL Gey’s needed per horse x 6 horses = 24 mL geys + ~5 mL extra 7 mL x 3 = 21 mL sterile H20 2 mL x 3 = 6 mL Gey’s A 0.5 mL x 3 = 1.5 mL Gey’s B 0.5 mL x 3 = 1.5 mL Gey’s C 10 mL total x 3 = 30 mL total Vortex or invert to mix

Calculation for CD8 + B Antibody Working Solution (Sterile)

First, calculate quantity of antibodies required:

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Number of tubes _12_ + 0.5 tube extra = __12.5__ (1 tube/horse for blood, 1 tube for naso)

CD 8 1:32 dilution (*STERILE*)

1/32 = x/10 µL = 0.313 µL stock x # of tubes __12.5__=__3.9 µL CD 8 stock

B cells 1:10 dilution (*STERILE*)

1/10 = x/10 µL = 1 µL stock for 1 tube x # of tubes _12.5_ = _12.5 µL B cell stock

# of tubes_12.5_ x 10 µL total in each tube = _125_ µL total mixture needed

_3.9_uL CD 8 stock + __12.5_ µL B cell stock = __16.4_ µL total AB

_125_ µL total mixture needed - __16.4_ µL total Ab = _108.6_uL PBS needed

In micro vial, combine µL PBS needed + µL CD 8 stock + µL B cell stock = antibody working solution

CD4 neat 10 µL in each tube (*STERILE*)

# of tubes 12.5 x 10 µL per tube = 125 µL total CD4 needed (place into microvial; do not use directly from stock CD4)

Antibody Staining Procedure

1. Label flow glass tubes for each horse

a. AB Positive blood and AB Positive naso flush

2. Add 2 mL PBS into each tube labeled “blood”

3. Add 200 µL whole blood into each tube labeled “blood”

4. Vortex tubes (setting 8)

5. Spin @ 1500 rpm (400 x g) 5 min at 4°C (program # 18)

6. Aspirate plasma

7. Add 2 mL Gey’s solution to each tube

8. Vortex tubes (setting 8)

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9. Put tubes in fridge (4°C) for 5 min

10. Spin as above

11. Aspirate supernatant (cell pellet is hard to see so leave ~300 µL in bottom of

tube)

12. Repeat Gey’s treatment steps 7-11 (total 2x)

13. Add 2 mL PBS to each tube to wash cell pellet

14. Vortex tubes (setting 8)

15. Spin as above

16. Pour off supernatant and shake tube once

17. Resuspend cell pellet in left over volume (~150-170 µL) by raking tube on tube

rack

18. Add ~150 µL of naso cells suspension to each tube labeled “naso”

19. Add 10 µL blocking agent (IgG; optional)

a. Vortex lightly

b. Incubate for 10 min at RT

20. Add 10 µL of Ab working solution to each tube

21. Add 10 µL of CD 4 stock solution to each tube

22. Vortex lightly (setting 6)

23. Incubate 30 min at 4°C

24. Add 2 mL PBS to each tube

25. Spin as above

26. Pour off supernatant

27. Re-suspend cell pellets in 300 µL PBS

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28. Add 10 µL PI to each tube (may interfere with CD4 labeled RPE)

29. Determine lymphocyte subsets by flow cytometry

Figure G-1. Example of CD4 and CD8 lymphocytes gated by quadrants. Quadrant (Q) 1 contains lymphocytes positive for CD4 (RPE; y-axis) and Q3 are positive for CD8 (A647; x-axis). Q4 are lymphocytes negative for both CD4 and CD8. Q2 are lymphocytes positive for both CD4 and CD8.

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Figure G-2. Example of B lymphocytes gated by forward scatter (FSC; x-axis) and B antibody marker (FITC; y-axis). The rectangle gate contains lymphocytes positive for the B cell marker.

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APPENDIX H EQUINE NEUTROPHIL FUNCTION ASSAY

Protocol for Propidium Iodide-Labeling of Bacteria

1. Obtain a known concentration of bacteria

a. 5 x 108/mL in Feb 2014

b. 1 x 108/mL in 10 mL x 3 vials on 5-15-14

2. Heat kill the bacteria by heating broth in 56°C for 30 minutes

3. Spin bacteria at 2200 rpm (~900 x g) for 30 minutes (do not spin in glass culture

tubes)

4. Decant and resuspend in 10 mL sterile PBS

a. Supernatant should be clear after each spin

b. For Strep Equi, leave gel layer above cell pellet after all spins

5. Vortex

6. Spin as above

7. Decant and resuspend in 5 mL of 1 mg/mL PI solution

8. Cover tube in aluminum foil and mix by continuous rotation at 22°C for 1 hour

9. Spin as above

10. Decant (cell pellet and supernatant should be pinkish or red)

11. Resuspend cell pellet in 10 mL sterile PBS

12. Spin as above

13. Decant and resuspend in appropriate volume of sterile PBS for desired bacterial

concentration

a. Suspension will be pinkish

b. 5 mL in Feb 2014 for 1 x 109/mL

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c. 1 mL on 5-15-14 for 1 x 109/ mL

14. Cover vial in tin foil and store at 4°C in the dark

Equine Whole Blood Neutrophil Function Assay

Materials:

1- Vacutainer tubes containing sodium heparin anticoagulant

2- DHR (dihydrorhodamine 123).

a. Reconstitute 10 mg DHR 123 in 57.5 mL DMSO, = 500 µM solution (stock

solution).

b. Store aliquots at -20°C in dark (~7 mL each)

c. OR 2 mg of DHR 123 to 11.5 ml of DMSO

3- PMA (phorbol 12-myristate, 13-acetate) approx 99% TLC P8139 – 1 mg from

Sigma-Aldrich / catalog # p8139-1mg).

a. UPON ARRIVAL, KEEP IN FREEZER.

b. Add 1 mL of DMSO to 1 mg PMA (1 mg/mL)

c. Store aliquots at -20°C in dark (100 µL each)

4- Bacteria strain (Dr. Crawford has the procedure to label the bacteria with

propidium iodide)

5- Trypan blue 0.4 % (Sigma T8154 – CAS 72-57-1)

6- Propidium Iodide 10 mg (Sigma – P4170-10mg)

Prepare the following:

50 µM working solution of DHR 123 from 500 µM stock solution

100 µL DHR 123 stock + 900 µL PBS = 1000 µL of 50 µM DHR 123

Store working solution on ice, in the dark

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5 µg/mL working solution of PMA from 1 mg/mL stock solution

5 µL PMA stock + 995 µL PBS = 1000 µL of 5 µg/mL PMA working solution

Store working solution on ice, in the dark

Calculation for bacteria:

Bacteria concentration in stock = 1 x 109/mL = 1 x 106/µL

Example ratio 40:1, 40 bacteria to every 1 neutrophil

Multiply total granulocytes in flow tube x desired ratio of bacteria = # of bacteria needed in tube

4 x 105 grans x 40 = 1.6 x 107 bacteria needed

Divide bacteria needed by # of bacteria per µL in stock = µL of bacteria stock to add to flow tube

1 x 107 / 1 x 106 = 10 µL of bacteria stock

**Do not put blood tubes on ice after collection**

1. Label 4 plastic falcon 5 mL round bottom tubes per horse : Negative control

(DHR 123 only), positive control (DHR 123 + PMA), SE-1 and SE-2 (DHR 123 +

Strep Equi) for duplicates

2. Add 100 µL whole blood to each flow tube

3. Add 10 µL of 50 µM DHR 123 working solution into ALL flow tubes

a. Final DHR 123 concentration/tube = 5 µM

4. Vortex tubes briefly/lightly

5. Incubate tubes at 37°C for 10 minutes with constant rotation (to load DHR 123

into neutrophils)

a. Place rotating tube rack in incubator and close door best you can

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b. Turn off CO2

c. Calculate amount of bacteria needed for each tube based on neutrophil

count from Procyte, 40:1 bacteria to neutrophil ratio

6. Add 10 µL PMA working solution to POSITIVE CONTROL tube ONLY

a. Final concentration of PMA per tube = 50 ng

7. Add appropriate amount of bacteria to SE TUBES ONLY

8. Incubate all tubes at 37°C for 30 minutes in the dark with constant rotation

9. Immediately place tubes on ice to stop phagocytosis and oxidative burst activity

10. Once samples are cold, vortex lightly

11. Add 600 µL of reagent A

a. Formic Acid (98%) 1.2 mL in 1000 mL DI water

b. Store at RT

12. Vortex lightly for about 10 seconds

13. Add 265 µL of reagent B

a. Sodium carbonate (CAS 497-19-8) 6.0 g

b. Sodium chloride (CAS 7647-14-5) 14.5 g

c. Sodium sulfate (CAS 7757-82-6) 31.3 g

d. Into 1000 mL DI water

e. Store at RT

14. Vortex lightly for about 10 seconds

15. Add 100 µL of reagent C

a. 1% paraformaldehyde 10 g

b. 1000 mL PBS

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c. Heat at 56°C to dissolve

d. Store in the dark at RT

16. Vortex lightly for about 10 seconds

17. Add 10 µL of 0.4% trypan blue to each tube to quench extracellular fluorescence

18. Keep tubes on ice and process on Flow cytometer immediately

Optimization with Streptococcus equi equi

To determine optimum assay bacteria to neutrophil ratio, whole blood neutrophil function was examined at ratios of 5, 10, 20, 30, 40, 60 and 80:1. Incubation times of

30, 45 and 60 minutes were also tested. Appropriate neutrophil loading dose of DHR

123 (5 µM) and PMA concentration (5 µg/mL) for neutrophil stimulation were previously validated and used for our optimization with Streptococcus equi equi (Vineyard, 2008).

Whole blood was obtained via jugular venipuncture into heparinized tubes from mature quarter horses. Percentage of neutrophils that underwent phagocytosis and phagocytosis induced oxidative burst, plateaued around a bacteria to neutrophils ratio of

40:1 (Figure H-1). Although percentage of neutrophils activation increased with incubation time, 30 minutes was chosen based on time constraints during the study

(Figure H-2).

Phagocytosis and induced oxidative burst were determined using quadrant gates on the FACS plots. The samples were first gated by granulocytes (Figure H-3) and then by DHR 123 (x-axis) and PI (y-axis; Figure H-4). Neutrophil function assay consists of 4 tubes for every sample; negative, positive and duplicate tubes with PI-labeled bacteria.

The negative control contained neutrophils loaded with non-fluorescent DHR 123 but no

PI-labeled bacteria (Figure H-4). Neutrophil were un-stimulated and remained double

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negative for DHR 123 and PI (lower left quadrant). The positive control was neutrophils loaded with non-fluorescent DHR 123 and then stimulated with PMA to undergo artificial oxidative burst. The reactive oxygen species generated converts non-fluorescent DHR

123 to rhodamine 123 which now fluoresces positive on the DHR spectrum (Figure H-

5). This tube contained no PI-labeled bacteria so neutrophils were only positive for DHR

(lower right quadrant). Within the duplicate tubes containing PI-labeled bacteria, neutrophils that phagocytosed bacteria fluoresces PI+ (upper left and right quadrants) and neutrophils that underwent phagocytosis-induced oxidative burst were double positive for PI and DHR (upper right quadrant; Figure H-6).

331

Phagocytosis Induced-OB 100 90

80 70 60 50 40

% of neutrophilsof % 30 20 10 0 5:1 10:1 20:1 30:1 40:1 60:1 80:1 Bacteria:neutrophil

Figure H-1. Bacteria to neutrophil ratio optimization using Streptococcus equi equi.

Phagocytosis Induced OB 100 90

80 70 60 50 40 30

20 % of neutrophils % of 10 0 30 45 60 Minutes

Figure H-2. Incubation time optimization using bacteria to neutrophil ratio of 40:1.

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Granulocytes

Lymphocytes

Monocytes

Figure H-3. FACS plot of whole blood sample gated by leukocyte populations with forward scatter (FSC) on the x-axis and side scatter on the y-axis.

333

Figure H-4. Example of negative sample from neutrophil function assay. FACS plot of granulocyte population loaded with non-fluorescent DHR (x-axis) but no PI (y- axis) labeled Streptococcus equi. Neutrophils are negative for DHR and PI and remain in lower left quadrant.

334

Figure H-5. Example of positive sample from neutrophil function assay. FACS plot of granulocyte population first loaded with non-fluorescent DHR (x-axis) and then stimulated with PMA which causes DHR to fluoresce. DHR+ neutrophils are in lower right quadrant. Sample contains no PI (y-axis) labeled Streptococcus equi.

335

Figure H-6. FACS plot of granulocyte population first loaded with non-fluorescent DHR (x-axis) and then stimulation with PI (y-axis)-labeled Streptococcus equi. Neutrophils that phagocytosed bacteria are PI+ in the upper left and right quadrants. Neutrophils that underwent phagocytosis-induced oxidative burst are double positive for PI and DHR in the upper right quadrant. Unresponsive neutrophils remain in the lower left quadrant.

336

Equine Nasopharyngeal Neutrophil Function Protocol

1. Add appropriate volume of naso wash to obtain desired number of neutrophils

2. Bring volume in flow tube up to 100 µL (if it is not already)

3. Add 10 µL DHR to each tube (recipe on whole blood protocol)

4. Vortex tubes lightly

5. Incubate tubes at 37C in the shaking incubator (~125 RPM) for 10 min

6. Calculate quantity of bacteria required to have 40:1 ratio (leave Negative tubes in

incubator and turn off shaker)

7. Add appropriate amount of bacteria to SE tubes

8. Add 10 µL PMA (recipe on whole blood protocol) to POSITIVE tubes ONLY

9. Vortex tubes lightly

10. Incubate as above for 30 minutes

11. Place tubes on ice

12. Once cool, add 800 µL PBS to each tube

13. Add 100 µL of reagent C from Q prep to each tube (1% paraformaldehyde)

14. Lightly vortex for 10 seconds

15. Add 20 µL of 0.4% trypan blue to each tube

16. Lightly vortex to mix

17. Analyze by flow cytometry

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APPENDIX I SAMPLE PREPARATION FOR HORSE IgA ELISA

Fecal liquid centrifuge protocol 10,000 RPM (9,500 x g), 10 minutes, 4°C

Serum, saliva and naso flushes do not need to be centrifuged, just thaw and vortex before making dilutions Horse IgA ELISA Protocol- Follow manufacturer instructions (Immunology Consultants Laboratory, Inc.)

Helpful suggestions: Follow standard diluting instructions provided in each kit (will vary based on kit) Auto-pipetters are very accurate and best for plating samples/standards Using a plate washer for rinses, provides more accurate results

338

APPENDIX J PASTURE SAMPLE COLLECTION AND DRY MATTER PROTOCOLS

Pasture Samples and Dry Matter Protocol

1. Collection ½ ice bag of grass (~3 kg wet weight) for each pasture from areas

where horses are known or show signs of grazing. Try not to collect roots or

dead grass, although these can be picked out later.

2. Double bag two paper bags and label bag with study name, pasture sample,

date, bag weight, bag + wet grass weight and dry grass + bag weight

3. Weigh bags and record weight on bags

4. Split up the pasture sample into 4 separate double bagged paper bags

5. Weigh bags + grass and record weight on bags

6. Tip bags on their side and distribute grass evenly in the bag to allow as much air

circulation as possible so grass dries completely

7. Fold the top of the bag over and staple twice to close

8. Place bags in 60°C oven for 3 days

9. Remove bags after 3 days and record weight of dry grass + bag

10. Subtract bag weight from dry grass + bag weight

11. Subtract bag weight from wet grass + bag weight

12. Divide dry grass weight by wet grass weight = moisture * 100 = % moisture

13. Subtract 100 - % moisture = DM value

14. If DM is 85% or higher, grass does not need to dry any more

15. Combined all dried grass samples and aliquot into 3 separate whirl pak bags. Do

not close bags if the grass is still warm

16. Place in walk in cooler for storage

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Fecal and Cecal Dry Matter Protocol

1. Thaw sample to be analyzed

a. Thaw at 4C unless sample will be used immediately

b. ~200 g fecal/cecal sample takes 24-28 hours at 4°C

c. Cecal whirl pack bags cannot go into -80, they will leak when thawed

2. Weigh tin/paper bag, record weight

a. Analyze samples in duplicate, so label tin/paper bags appropriately

3. Zero scale with tin

4. Homogenize cecal/feces in sample collection bag

a. Quickly pour cecal contents into beaker

b. Allow cecal contents to settle then some fluid back into sample collection

bag

c. Hold sample collection bag closed and shake fluid in bag to remove

particles that were missed

d. Quickly pour remaining cecal sample back into beaker

5. Place ~100 g of feces into, record weight

a. Swirl or use stirring rod to homogenize cecal contents in beaker

b. Pour fast into tin to avoid separation, do not use spout, pour from side of

beaker

c. If total sample weighs less than 200 g, use half the sample for each

duplicate tin

6. Place tin on tray

7. Cover tray with cheese cloth and secure cloth with tape

340

8. Place tray in forced air oven at 60C for 2-3 days (cecal sample can take 5-7

days)

9. Remove tray and tins and re-weigh samples, record weight

341

APPENDIX K FIELD COLLECTION PROTOCOL FOR NASAL SWABS

1. Have horse handler cover eyes of horse to be swabbed

2. Insert swab as far as possible into left nasal cavity

3. Gently rub swab against ventral nasal cavity for 10 seconds

4. Remove swab

5. Place swab into appropriately labeled microvial (Left or Right; has 0.5 mL

PBS)

6. Cut handle of swab so vial will close

7. Place on ice

8. Repeat procedure in opposite nostril with new swab

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APPENDIX L FIELD COLLECTION AND LAB PROTOCOL FOR CECAL CONTENTS

Field Collection of Cecal Contents

1. Wear gloves or palpation sleeves and coveralls, if desired (new gloves for every

horse)

2. Remove cannula/plug (usually requires pliers and long flat head screw driver)

3. Once cannula/plug is removed, cecal contents may flow out by gravity or use

spoon/hand to scoop out digesta into pitcher or bucket

a. Do not stand in front of cannula as contents may spew out

b. Keep mouth closed 

4. Replace cannula/plug by folding it in half and inserting folded side into cecum

5. Manipulate cannula into place so it is flush with body

6. Measure pH immediately for accurate cecum pH

Lab Protocol for Cecal Contents

1. Wear gloves (new gloves for every horse)

2. Place some fluid separate tube to measure pH (fluid is not re-usable)

3. Homogenize cecal sample using spoon

4. If cecal sample was collected in a bucket, pour sample from bucket into pitcher

5. Place correctly labeled cecal whirl pak bag on scale

6. Zero scale by pressing tare

7. Pour ~200 g (1/8 to ¼ of bag) of cecal digesta from pitcher into whirl pak bag *if

units on scale 200 g = 0.2 Kg*

8. Close whirl pak and store at -80°C

9. Place a piece of cheese cloth over cecal fluid collection cup

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10. Hold cheese cloth and pour pitcher with cecal digesta through cloth to collect

fluid (only small volume required)

11. Squeeze cecal digesta to expel remaining fluid

12. Using transfer pipette, aliquot 5 mL x 3 micro vials per horse (new pipette for

each horse)

13. Place in correct cardboard box and store at -80°C

14. With remaining cecal fluid, measure temperature and pH once sample reaches

room temperature (21-23°C) ***this is incorrect; pH should be measured

immediately after collection for accurate pH of cecum*

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BIOGRAPHICAL SKETCH

Jill Bobel is an Ohio native but spent her developing years in Chicago, Illinois.

Following in her mother’s passion, she was put on a horse before she could walk and seems to have never come down. Throughout middle school and high school, she worked at stables and took lessons regularly. She started three-day eventing at the age of 16 and has since been on the list of top ten riders in the nation five times. While completing her master’s degree, she termed her current horse “a horse of a lifetime” and he has certainly lived up to his title. In 2014, he suffered a career-ending and potentially life-ending injury, and vets suggested he be humanely euthanized while in surgery. Instead, she gave him a winning shot at recovery and, after a year of intense rehab, they were back riding. This year they placed 3rd in the regional championships and 4th in the national championship at the preliminary level of three-day eventing. They plan to advance to the intermediate level of eventing this fall.

Growing up, Jill had always wanted to be a large animal veterinarian. However, during high school, she got her first taste of animal nutrition and was captivated. After high school, Jill moved to Florida and attended the University of Florida. In 2008, she earned her Bachelor of Science in Animal Sciences with an emphasis on animal industry-equine and a minor in Management and Sales in Agribusiness and was proud to be on the Dean’s list throughout her college career. She was fortunate enough to meet an amazing professor of equine nutrition, Dr. Lori Warren. From here, her path in life became clear: she wanted to get her PhD in Equine Nutrition. In the summer of

2008, she received a paid internship by the Florida Agriculture Experiment Station to work in Dr. Warren’s laboratory, and it was here that she discovered her love of research and lab work. After her internship ended, she remained employed by Dr.

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Warren until August 2009. In 2011, she earned her Master of Science in Animal

Sciences with an emphasis equine nutrition and immune function from the University of

Florida. Immediately following her master’s defense, a member of her committee, Dr.

Jeffrey Abbott, asked if she would be interested in working in his lab. She has been working as his biological scientist since 2012 and dually participated in the employee education program to complete her doctorate.

Upon completion of her PhD program, Jill hopes to make the field of equine/animal nutritional research a career.

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