University of Florida Thesis Or Dissertation Formatting

RNA-SEQ REVEALS NOVEL GENES AND PATHWAYS INVOLVED IN BOVINE MAMMARY INVOLUTION DURING THE DRY PERIOD AND UNDER ENVIRONMENTAL HEAT STRESS

BETHANY M. DADO SENN

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

UNIVERSITY OF FLORIDA

2018

To my family, the true dairy enthusiasts

ACKNOWLEDGMENTS

To my advisor, mentor, and friend Dr. Jimena Laporta, I am humbled and grateful to have served as your graduate student as you provided invaluable advice and kindness throughout my projects. Your open-door policy has facilitated my growth both personally and professionally. Thank you for the opportunity to research lactation physiology, volunteer and teach, and pursue a degree at the University of Florida.

I extend my appreciation to my committee members Dr. Geoffrey Dahl and Dr.

Pete Hansen for utilizing their many years of experience to provide useful critiques and additional insight into my analysis and interpretations. Thank you to Dr. Hansen for the use of Ingenuity Pathway Analysis® and to Dr. Dahl for his heat stress expertise.

I thank the faculty and staff in the Department of Animal Sciences at the

University of Florida, especially Dr. Francisco Peñagaricano for his vital RNA- sequencing and statistical contribution to my thesis project. Further thanks to Dr. Corwin

Nelson, Dr. Stephanie Wohlgemuth, and Dr. John Bromfield for use of lab space and research support. Special appreciation goes to Joyce Hayen, Pam Krueger, and Renee

Parks-James and the UF Dairy Unit staff. I also express appreciation to the Animal

Molecular and Cellular Biology program, the Brélan E. Moritz family, and the National

Dairy Shrine for funding a portion of my education.

I am grateful for my supportive laboratory community for their assistance with projects and papers, not to mention the memories and laughter accumulated from long nights in the lab. Special thanks to Dr. Amy Skibiel for being an incredible role model and mentoring me through assays, presentations, and paper writing, Catalina Mejia

Bonilla for being my first UF friend and research confidante, Marcela Marérro-Perez and

Sena Field for bringing joy into research, Thiago Fabris for his guidance on-farm, and

Debora da Silva, Carolina Collazos, Fabiana Corra, and Therus Brown for their assistance with various aspects of my research projects including sample collection, analysis, and presentation practice.

Thanks to my undergraduate role models Dr. Laura Hernandez, Dr. Hasan

Khatib, Dr. Marina Danes, Dr. Michel Wattiaux, Ryan Pralle, Nicole Gross, and Patti

Hurtgen for helping me find academic direction and pointing me to UF. Thank you to my friends near and far—Jessi and Cody Getschel, Saager Paliwal, Eleanor Miller, Katey

Scholz, Mykayla Getschel, Alexus and Josh Berndt, Mackenzie Dickson, and the Flores,

Tyler, Sy, and Guernsey families—for listening to my crazy lab stories, offering solutions to my dilemmas, and being truly genuine friends throughout the journey.

I would like to give special thanks to my loving family. Thank you to my parents,

Rick and Gwen Dado, for serving as excellent examples of academics and dairy producers. To my siblings Ethan, Trent, and Meikah Dado, thank you for praying for me and setting the bar high for success. I thank my extended families, specially my

Grandma Thelma Betzold, Grandpa Gary Dado, and Grandma Arlene Dado, and my in- laws Jim, Deb, and Ted Senn and Jeremy and Tracy Keifenheim for the many phone calls inquiring about my research. And to my husband, Travis Senn: thank you for moving across the country for me, challenging me academically and spiritually, and providing for our beautiful future. I look forward to all our adventures to come.

Finally, I give thanks to my Heavenly Father who has granted me strength and patience for the journey and the talents and resources to serve others through this degree. To God be the Glory.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF OBJECTS ...... 10

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 13

CHAPTER

1 LITERATURE REVIEW ...... 15

The Bovine Mammary Gland Dry Period ...... 15 Physiology of the Dry Period ...... 16 Molecular Regulators of Mammary Involution and Redevelopment ...... 18 Heat Stress in Dairy Cattle ...... 21 Heat Stress During the Dry Period ...... 25 Mammary Gene Expression under Heat Stress ...... 27 RNA-Sequencing Technology ...... 30 Transcriptome Analysis Technology Comparisons ...... 32 RNA-Sequencing Application in Bovine Research ...... 34 Summary ...... 35

2 RNA-SEQ REVEALS NOVEL GENES AND PATHWAYS INVOLVED IN BOVINE MAMMARY INVOLUTION DURING THE DRY PERIOD AND UNDER ENVIRONMENTAL HEAT STRESS ...... 37

Abstract ...... 37 Introduction ...... 38 Materials and Methods...... 40 Animals, Treatments, and Experimental Design ...... 40 Mammary Tissue Collection and RNA Extraction ...... 40 Library Generation and RNA Sequencing ...... 41 Identification of Differentially Expressed Genes, Pathways, and Regulators ... 42 Results ...... 44 Physiological Parameters and Milk Yield ...... 44 Ingenuity® Pathways Analysis (IPA®) Regulator and Network Analysis...... 47 Differentially Expressed Genes and Regulators Impacted by Heat Stress ...... 48 Discussion ...... 49 Conclusions ...... 59

3 GENERAL DISCUSSION AND SUMMARY ...... 87

APPENDIX: TABLES IN LINKS ...... 92

LIST OF REFERENCES ...... 93

BIOGRAPHICAL SKETCH ...... 109

LIST OF TABLES

Table page

2-1 Primer sequences for genes utilized for quantitative real-time PCR (qRT- PCR) validation of RNA-Seq results in bovine mammary tissue...... 60

2-2 Top KEGG pathways and MeSH terms along with their corresponding DEGs in bovine mammary tissue during transition between lactation to involution...... 61

2-3 Top KEGG pathways and MeSH terms along with their corresponding DEGs inbovine mammary tissue during early involution...... 69

2-4 Differentially expressed genes (DEGs) in bovine mammary tissue during steady-state involution and redevelopment...... 71

2-5 Differentially expressed genes (DEGs) in bovine mammary tissue between heat-stressed and cooled cows during the dry period...... 73

LIST OF FIGURES

Figure page

2-1 Pictorial representation of experimental design...... 79

2-2 Volcano plot of DEGs in bovine mammary tissue during early involution (D3 vs. D-3 and D7 vs. D3)...... 80

2-3 Significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Medical Subject Headings (MeSH) terms in bovine mammary tissue during early involution (D3 vs. D-3 and D7 vs. D3)...... 81

2-4 Ingenuity® Pathway Analysis (IPA®) upstream regulators and summary network in bovine mammary tissue comparing D3 vs. D-3 relative to dry-off. .... 82

2-5 Ingenuity® Pathway Analysis (IPA®) upstream regulators and summary network in bovine mammary tissue comparing D7 vs. D3 relative to dry-off...... 83

2-6 Characterization of DEGs in bovine mammary tissue between heat-stressed (HT) and cooled (CL) dairy cattle during the dry period...... 84

2-7 Ingenuity® Pathway Analysis (IPA®) upstream regulators and summary network in bovine mammary tissue between heat-stressed (HT, n=6) and cooled (CL, n=6) dairy cattle during the dry period...... 85

2-8 Validation of RNA-Sequencing results by quantitative RT-PCR...... 86

LIST OF OBJECTS

Object page

A-1 Differentially expressed genes D3 vs. D-3...... 92

A-2 Differentially expressed genes D7 vs. D3...... 92

A-3 miRNAs and target genes impacted by heat stress...... 92

LIST OF ABBREVIATIONS

AKT Serine/threonine protein kinase B

BAX BCL2 Associated X

BHBA Beta-hydroxybutyrate

BMEC Bovine mammary epithelial cell bp Base-pair

C Celsius

CL Cooled

D or d Day

DEGs Differentially expressed genes

FasL Fas ligand

FC Fold change

FDR False-discovery rate

GO Gene Ontology

H Hour

HSP Heat shock protein

HSF1 Heat shock transcription factor 1

HT Heat stressed

IGF Insulin-like growth factor

IGFBP Insulin-like growth factor binding protein

IPA Ingenuity Pathway Analysis

KEGG Kyoto Encyclopedia of Genes and Genomes

LIF Leukemia inhibitory factor

LIFR Leukemia inhibitor factor receptor

lncRNA Long non-coding RNA

MEC Mammary epithelial cell

MeSH Medical Subject Headings

Min Minute miRNA microRNA

MMP Matrix metallopeptidase

NEFA Non-esterified fatty acid

NFκB Nuclear factor kappa-light-chain-enhancer of activated B cells qRT-PCR Quantitative real-time polymerase chain reaction

RNA-Seq RNA-Sequencing s seconds

STAT Signal transducer and activator of transcription

SNPs Single nucleotide polymorphisms

TGF Transforming growth factor

THI Temperature-humidity index

TNF Tumor necrosis factor

VDR Vitamin D receptor

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

RNA-SEQ REVEALS NOVEL GENES AND PATHWAYS INVOLVED IN BOVINE MAMMARY INVOLUTION DURING THE DRY PERIOD AND UNDER ENVIRONMENTAL HEAT STRESS

Bethany M. Dado Senn

May 2018

Chair: Jimena Laporta Major: Animal Molecular and Cellular Biology

The aim of this thesis was to characterize genes, pathways, and regulators involved in mammary involution and redevelopment during the bovine dry period and to determine how exposure to environmental heat stress impacts this dynamic process.

The objective of Chapter 1 is to review literature that uncovers physiological mechanisms controlling the bovine dry period, specifically involution and redevelopment, linking the impacts of heat stress on cellular turnover and subsequent milk production. It highlights histological characteristics and molecular factors of mammary involution and redevelopment. When undergoing these changes, the gland is sensitive to heat stress perturbation, thus the effect of heat stress both during lactation and the dry period on production, health, and gene expression was evaluated. Finally,

RNA-sequencing was discussed as a tool to uncover the transcriptome of the bovine mammary gland undergoing these alterations.

Chapter 2 describes the outcomes of an RNA-sequencing experiment conducted to determine mammary gene expression changes across the dry period and under heat stress insult. Mammary biopsies were collected before and during the dry period from

heat stressed or cooled late-lactation, pregnant cows under a 46-d dry-period. RNA-

Sequencing was conducted, and differentially expressed genes were analyzed under a false-discovery rate ≤ 5%. Changes in genes, pathways, and regulators during involution indicate downregulation of mammary metabolism, and upregulation of cell death and immune response. Compared to cooled cows, dry period heat-stressed cows had altered expression of genes and regulators involved in ductal branching, cell death, immune function, and stress protection, potentially impairing mammary development and function.

CHAPTER 1 LITERATURE REVIEW

The Bovine Mammary Gland Dry Period

The bovine dry period is a management practice consisting of six to eight-weeks of a non-lactating state initiated between two consecutive lactations. In a traditional dairy production setting, cows are dried-off through cessation of milking during late gestation. At this time, the cow has passed peak milk production of a typical lactation curve and has experienced a consistent decline in milk yield due to reduced number and activity of mammary epithelial cells (MEC), the cells responsible for milk synthesis.1

The old, senescent cells remaining do not secrete milk efficiently and have a reduced capacity for proliferation. Thus the dry period is critical as it allows for optimal milk yield in the subsequent lactation through the turnover of these worn, senescent MECs with new, active cells fully prepared for optimal milk synthesis.2

It is well-recognized that the dry period is essential to avoid significant reductions in milk production in the next lactation. If not allowed a dry period and continuously milked until calving, cows experience, on average, a 20% reduction in milk yield in the subsequent lactation and lower peak milk yield.3–6 Extensive research has been conducted to determine optimal duration of the dry period in commercial dairy herds to maximize production while minimizing negative energy balance. Dated retrospective analyses and experiments suggest that target dry period length should be between 40 to 60 d for maximal milk production, as nonlactating periods less than 40 d do not allow for enough MEC turnover and periods greater than 60 d are associated with higher feed costs with no return of increased milk production.7–9 However, a majority of these studies were uncontrolled observational studies and measured production from low-

yielding cattle with reduced genetic merit. Thus, dry period duration has more recently been re-examined through controlled experiments using today’s high-producing and genetically-superior cattle. More recent data illustrate that cows with a 30 d dry periods experience undergo lower levels of negative energy balance with non-significant reductions in subsequent milk yield compared to cows dried for 60 d in the next lactation.10–12 Further work is needed to refine the optimal dry period duration in today’s high-producing dairy cattle, accounting for the balance of cell turnover to postpartum energy demands and the complex environmental factors and management practices that impact production.13

Physiology of the Dry Period

Regardless of dry period length, the general physiological targets during the dry period remain the same. Upon cessation of milk removal, the accumulation of milk causes a cascade of events to initiate the first stages of the dry period. An increase in mammary pressure from the retained milk leads to a decrease in mammary blood flow, halting the exchange of nutrients and waste by-products from milk synthesis.14,15

Accumulated local factors within the mammary gland (e.g. serotonin, transforming growth factor β1) together with diminished prolactin concentration promote a decline in the rate of milk synthesis and secretion and initiation of programmed cell death such as apoptosis and autophagy.16–19 As expected, secretory volume and milk constituent (milk fat, protein, and lactose) concentrations decrease, except for inflammatory factors like lactoferrin.20

Histological and ultrastructural changes across the dry period reflect a secretory shift in the mammary gland rather than extensive tissue regression. Alveolar structure is generally maintained, and even though cell death is initiated, tissue and cellular

regression is not as dramatic as in other species such as rodents due to the concurrency of late gestation and the necessity for cellular proliferation for the next lactation.21 An inverse relationship between stromal and parenchymal tissue has been reported across a 60-d dry period.2 Luminal area decreases until about the middle of the dry period (25 d dry), but then increases 7 d prepartum due to colostrogenesis in preparation for the next lactation, whereas stromal area increases at 25 d dry and decreases as the cow reaches 7 d before calving.2 Other cytological changes include the appearance of large vacuoles through fusion of secretory vesicles in MECs, accumulation of lipid droplets, decrease of cellular organelles, microtubule disassembly, and increased tight junction permeability.21–23

Generally, the dry period is divided into three phases known as active involution, steady-state involution, and redevelopment. Involution is the natural process by which the mammary gland transitions from a lactating to a non-lactating state including a decrease in milk secretion and consequent rise in mammary pressure, apoptosis and autophagy of MECs, and inflammatory response.20,21,24,25 Involution continues for approximately 21 d, followed by redevelopment of the mammary gland until calving.26

Redevelopment consists of a higher rate of cell proliferation and, near parturition, an increase in secretion for colostrogenesis. However, there is some debate over the assignment of specific phases to the dry period of the pregnant, late-lactation cow.

Smith and Todhunter27 were the first to assign the three phases described above.

Others note that the short duration of the bovine dry period along with the concurrency of pregnancy indicates there is no time for a “steady-state” period of involution.2,20

Additionally, because there was no significant loss of mammary cells during the dry

period in Holstein cattle dried off in late-gestation, Capuco et al. believe that the term

“involution” was inappropriate to characterize the initial phase.2

Molecular Regulators of Mammary Involution and Redevelopment

Even though significant cell loss does not occur during the bovine dry period, the early stages of involution at the histological level are still complex, requiring initiation of epithelial cell death, tissue remodeling, and controlled influx of immune cells. Many factors involved have been well-established and described in mouse and bovine models using microarrays and quantitative real-time PCR (qRT-PCR). Time course and degree of mammary involution differs greatly between species, so caution must be taken when translating findings and specific molecular markers between the two models. Stein et al.

(2007)28 describes the main characteristics of cell death and immune signaling within the first 72 hours of involution in the mouse model. The first stage of mouse involution is reversible and is comparable to the active involution phase of dairy cattle. Accumulation of milk causes tight junction permeability and accumulation of local factors such as lactalbumin induce apoptosis, leading to upregulated pro-apoptotic factors including

Igfbp5, Stat3, Tgfb3, and FasL and caspases, and reduced survival factors such as Igf1,

Akt, and Stat5, to name a few.29 Within 12 hours of milk stasis, there is an increase of cell death-inducing ligands from these alternative cell death pathways; one of the most studied pathways is highlighted here.30 The protein LIF binds to LIFR, which activates the Jak/Stat pathway and phosphorylates the signal transducer STAT3.31 This transcription activator is highly proapoptotic, upregulating factors important for early apoptosis like C/EBPδ (activates an acute phase response) and IGFBP5

(downregulates IGF) and downregulating the major survival factor pAKT through induction of phosphoinositide 3-kinase.32–34 This 12-hour period also leads to an

increase in proinflammatory cytokines (such as interleukins IL-1a, IL-1b, and IL-13) and a neutrophil-attracting chemokine Cxc11.30 While mammary gland involution is not characterized by an inflammatory response, it does resemble a wound healing process with attraction of neutrophils and later macrophages to phagocytize apoptotic cell and debris. Genes such as p53, Tgfb3, Stat3, Igfbp5, C/ebpδ, and Vdr are landmarks of the first 12-hour phase.32,33,35–38 As involution progressed to 24 hours, Stein et al. (2004)39 found an increase in alternative cell death pathways involving the Vitamin D(3) receptor, prolonged expression of anti-inflammatory responses, an acute phase response, phagocytosis of apoptotic cells, and further activation of pro-apoptotic factors including

Tgfb3 and Bax.39

While cell death during involution is not nearly as extensive in the dairy cow, many of these cell death-inducing ligands and immune response factors are shared in the bovine model. Few studies in dairy cattle have utilized microarrays40 and qRT-

PCR25,26,41 to characterize the molecular events occurring in the bovine mammary gland. Indeed, only one study has used a model during a typical gradual involution of pregnant cows,26 whereas others have used different experimental models including forced involution of non-pregnant cows at peak lactation40,41 and gradual involution of non-pregnant cows at peak lactation.25 Singh et al.40,41 obtained tissues at short duration time points (e.g. within hours of one another), but slaughtered cows to collect this tissue. In contrast Sørenson et al.26 and Piantoni et al.25 utilized mammary biopsies to reduce variation in the model by using the same animal but needed to space out tissue collection to 3-d intervals or more. It was reported that there was an overall upregulation of genes and/or proteins related to apoptosis (e.g. STAT3P, LIF, SOCS1,

SOCS3, CASP1, CLU, MYC, and TGFB3), tissue remodeling (AKT1, IGF1, and MMP2), oxidative stress (e.g. SSAT, SOD2, and MT1A), and immune response (e.g. LTF, LBP,

SAA3, C3, and SPP1). There was also downregulation of cell survival signaling (e.g.

STAT5P) and biosynthesis of milk constituents including milk protein, fat, and lactose synthesis gene expression (e.g. CD36, ACACA, SCD, LALBA, FABP3, and FASN) during involution. Due to different physiological state at dry off, these different models present slightly varied patterns of gene expression. In non-pregnant cows under abrupt involution at maximal milk production, the mammary gland experiences extensive apoptosis and increases expression of molecular markers such as STAT3P, SOCS, and

IGF1, decreases in STAT5P, but no change in IGFBP5 and AKT.41 These are conflicting results compared to the pregnant, late-lactation dairy model that indicates that IGFBP5 and IGF1 expression increases if the cows are pregnant and dried off during late lactation.26

Research exploring the gene expression of the bovine mammary redevelopment period is scarce. The redevelopment phase is a proliferative, mammogenic period that occurs after the completion of involution and before calving. During this phase, upregulation of IGF1 and IGFBP326 promotes cell proliferation and turnover, leading to increased MEC number and secretory capacity in preparation for colostrogenesis and lactation.2 A shift in mammary gland gene expression occurs upon parturition as the cow transitions between redevelopment and early lactation (lactogenesis to galactopoiesis). When comparing gene expression between the late dry period (i.e. redevelopment/lactogenesis, 5 d prepartum) and early lactation (10 d postpartum, galactopoiesis) Finucane et al.42 found that genes upregulated during lactation were, as

expected, related to metabolic transport (e.g. amino acids, glucose, and ions), carbohydrate and lipid metabolism, and cell signaling factors, indicating an overall upregulation of milk synthesis upon calving. Meanwhile, genes downregulated during lactation (in other words, increased expression during the redevelopment phase prepartum) were associated with cellular proliferation and cell cycle (e.g. cyclins, cell division genes), microtubule assembly, chromosome organization, DNA replication, and

RNA and protein degradation (e.g. proteasome activity), further highlighting the importance of the redevelopment phase for tissue proliferation and regeneration of mammary gland microstructure necessary to initiate colostrum secretion.42 Because these shifts in gene expression and physiology both during the involution and redevelopment phases are so dynamic and time-specific, they are sensitive to environmental perturbations. One stressor that has been extensively studied and shown to have large negative impacts on both dairy cow and producer is heat stress.

Heat Stress in Dairy Cattle

Climate change is defined as the long-term variation from normal weather patterns including temperature, rainfall, and wind in a certain region.43 Rapid climate changes are unprecedented in Earth’s recent history and may be one of largest dilemmas facing life on the planet. Since 1880, global temperature has increased by an average of 0.85°C and 9 of the 10 warmest years since 1880 have occurred in the past

15 years.44 The Intergovernmental Panel on Climate Change (IPCC) predicts continual increases at unprecedented rates, with models indicating a 1.88°C to 4.08°C increase in global average surface temperature by 2100.45 Besides the biological impacts of rising temperatures on habitats, agricultural systems are suffering adverse consequences in terms of reduced crop and livestock productivity, health, and quality, which threaten

economies and global food security. In fact, it is estimated that in the United States alone, environmental heat stress in both lactating and dry cows costs the dairy industry nearly $2 billion in losses annually due to decreased cow performance and increased morbidity and mortality.46–48 Advances in heat abatement strategies that provide shade, move air (e.g. fans, cross-ventilated barns), soak the cow’s surface (e.g. sprinklers, soakers), and mist the cow in both the housing and milking facilities can maximize heat exchange and reduce production losses during hotter seasons.46 Therefore, southern and southeastern regions of the U.S. like Florida, Georgia, Texas, and Virginia that experience more than 140 d of heat stress per year and together have a population of nearly 1 million dairy cows48 should carefully consider providing heat stress abatement to their herd across the heat stress period to maximize animal performance.

Environmental heat stress causes behavioral and physiological adaptations in ruminant livestock that negatively impact productivity. As homeothermic animals, when cattle are in their thermoneutral zone (environmental temperature 5 to 25°C)49,50 minimal and constant energy is needed to maintain normal body temperature (38.0 to

39.3°C).51,52 Physiological heat stress occurs when an animal is pushed past the upper limit of the thermoneutral zone through increased environmental temperature or solar radiation, causing an increase in body temperature that increases total heat load

(environment plus heat internally produced) past equilibrium to total heat dissipation. To acclimate to this environmental strain, the animal adapts physiology and behaviors to reduce heat production and increase heat loss, primarily through respiratory and cutaneous evaporative heat loss.53 In dairy cattle, a livestock species especially susceptible to thermal stress due to high metabolic rates and high production demand,

heat stress response is initiated above skin-surface temperature of 35°C and acclimations occur at a temperature-humidity index (THI) as low as 68.54,55 Initial short- term acclimatory responses include homeostatic mechanisms such as increased water intake by approximately 30-35%, elevated sweating and respiration rates, decreased heart rate, reduced feed intake, and energy diversion from production (e.g. milk yield).52,56,57 If heat stress is prolonged, further alterations for long-term acclimation include alterations in the expression of specific genes and coordinated cellular responses to improve efficiency of signaling and metabolism, likely through the mediation of heat shock proteins (HSP)56,58 one of the hallmarks of heat stress response. Shifts in the endocrine system are also implicated in heat stress acclimation.

For example, decreased expression of growth hormone, glucocorticoids, and thyroid hormones thyroxine and triiodithryonine reduce basal metabolic rate to lower heat production,59–62 and increased expression of prolactin impacts sweat gland function and insensible (i.e. evaporative) heat loss.63,64

Physiological acclimations such as reduced feed intake, energy partitioning, and hormonal variation may ultimately adversely affect animal health and reproduction.65

Across species, heat stress directly causes illnesses like heat stroke, exhaustion, cramps, and eventual organ dysfunction that can lead to death.43,66,67 Further, thermal stress indirectly alters animal health by inducing lower feed intake, which leads to increased metabolic disorders like ketosis, liver lipidosis, and oxidative stress during the transition period.68–70 Rumen acidosis may also occur due to altered rumen pH from fewer buffering agents, reduced volatile fatty acid absorption, and increased respiration rates.71–74 Immune response is negatively impacted, as higher temperatures can alter

microbial populations in and around animals, improve survival and multiplication of bacteria in the animal, and decrease host resistance, all of which may increase mastitis and potentially other infections in dairy cattle.75–77 Furthermore, environmental exposure to heat stress impairs dairy cow reproductive performance. Dairy cows inseminated in the summer or heat-stressed in climate chambers experience altered estrous cycle hormone levels and lowered estrous expression, reduced conception rates, impaired embryo growth and survival, and inhibited fetal growth and maintenance, all leading to poor female fertility.63,78–80

One of the largest concerns for dairy producers is the impact of environmental heat stress on milk production. Lactating cows will reduce energy intake and divert remaining energy towards heat loss, leading to a negative energy balance and thus less energy available for lactation. Researchers estimate that for every increase in one THI unit above ~68-70, cows will experience a 0.23-0.50 kg/d drop in milk production.43,81–83

Stage of lactation and production demand factor into heat stress impact with mid- lactation, high-producing cows being most susceptible to heat stress perturbation due to their energetic demands.84,85 Traditionally, reduced feed intake has been cited as the cause for this drop in production.60,86 However, a pair-feeding study shows that the indirect action of reduced dry matter intake accounts for only approximately 35% of the heat stress induced lost yield in mid-lactation dairy cattle.57 Other contributing factors include direct downregulation of genes in MECs associated with milk synthesis,87 altered carbohydrate metabolism through greater glucose disposal, insulin-dependent glucose utilization, hepatic adaptations to thermal stress,88,89 and reduced mammary blood flow and secretory function.74

Heat Stress During the Dry Period

As previously discussed, the dry period is a critical window for extensive mammary growth and cell turnover required to maximize milk production in the next lactation. Because this period coincides with late gestation, the cow undergoes huge shifts in energy demands and will often experience negative energy balance, health and metabolic disorders, and immune dysfunction in the transition from late gestation to early lactation.90,91 To maximize milk production in the next lactation while minimizing risk of negative influences, it is crucial that the cow’s environment, including exposure to environmental heat stress, be well-managed to avoid further perturbations.

While dry cows generate less heat via metabolism86 and have a higher upper critical temperature to their thermoneutral zone than lactating cows,92 heat stress during the dry period can still negatively impact milk production. Compared to cows cooled with fans and soakers, cows heat-stressed during the dry period will have impaired milk yield in the next lactation, producing an average of 5-7.5 kg less milk per d for the entire duration of the next lactation even when all cows are provided active cooling after calving.93–95 Amount and duration of heat stress abatement will impact the effectiveness of cooling strategies; shade-only,61 mid-day soaking,96 and/or cooling for only the late dry-period97,98 will only partially rescue milk yield compared to more complex cooling systems with shades, fans, and soakers that are run for the duration of the dry period.95,99 Milk yield reduction has been partially attributed to altered cellular processes in the mammary gland during the dry period including reduced autophagy in the early dry period,100 decreased mammary cell proliferation during the late dry period,95 and altered tissue microstructure.101 Further explanations for loss of performance include reduced blood flow to the mammary gland that may impede mammary growth,102

altered endocrine signaling such as the inverse relationship between increased prolactin blood concentrations and decreased prolactin receptor expression,99,103 and induced

HSP expression that inhibits apoptosis in the early dry period.104

In contrast to lactating heat-stressed cows that experience negative energy balance due to reduced feed intake (30-35% reduction),57 cows under heat stress during the dry period do not undergo negative energy balance even with the combination of energy partitioned to the growing fetus and the energy lost to reduced dry matter intake of 10-15%.105,106 Furthermore, these cows do not experience altered concentrations or actions of glucose, insulin, beta-hydroxybutyrate (BHBA), or non- esterified fatty acids (NEFA).60,94,107,108 These differences in metabolism could be due to the different energetic needs between a high-producing, lactating cow and a dry cow in late gestation.109 The reduction in intake under dry period heat stress does, however, lead to reduction in body weight gain in late gestation.99 After calving, dry matter intake between dry period heat-stressed and cooled cows is similar.95,110

Late-gestation heat stress will negatively impact cow performance outside of milk production by influencing health, immune function, and reproduction during the transition period. As part of a large-scale commercial farm analysis (n=2613),

Thompson and Dahl (2012)112 report increased incidence of mastitis, respiratory disorders, and retained fetal membranes by 60 d postpartum in cows that were dried off in the summer months, suggesting that compromised immune function due to dry-period heat stress may be playing a role in these transition cow health disorders.104 Studies also suggest that dry period heat stress alters both innate and acquired immunity by impairing neutrophil function in early lactation,99 reducing peripheral blood mononuclear

cell proliferation,103,113 and increasing TNFA and IL8 gene expression in peripheral blood mononuclear cells in late gestation and early lactation, respectively.114 Further, reproduction is compromised in heat-stressed dry cows, as shown by cows dried off in the summer months having increased number of breedings, days to first breeding, and days to pregnancy after 150 d postpartum compared to cows dried in the cooler winter months.112 However, these results should be considered with caution, as data was confounded with seasonal effects during lactation, and other commercial (n=341) and controlled studies (n=38) found conflicting results with no difference in reproductive performance between heat-stressed and cooled dry cows.96,97

Mammary Gene Expression under Heat Stress

While physiology, endocrine status, and histology have been well-studied in bovine heat stress models both during lactation and the dry period, relatively little research has been conducted on heat stress acclimation via altered cellular gene expression and accompanying molecular events, particularly within the mammary gland.

However, extrapolations from other models may be made, as the ability to survive and adapt to thermal stress is a requirement for cellular life, demonstrated by the ubiquitous stress responses among eukaryotes and prokaryotes and high conservation of heat shock proteins across species, including the bovine.115–117 Sonna et al. (2002)118 established that thermal stress in animal models triggers anomalies in cellular function, including inhibition of protein synthesis through altered transcription, translation, and cell cycle progression, defects in protein structure and function, cytoskeletal disruption and morphological changes, metabolic shifts, changes in membrane permeability, and decreased cellular proliferation. These alterations invoke large changes in gene transcription and protein synthesis in a heat stress response, causing activation of heat

shock transcription factor 1 (HSF1) and increased expression of HSP, increased glucose and amino acid oxidation and reduced fatty acid utilization, stress-induced endocrine activation, and immune response activated by heat shock proteins.115,117

Timing and activation of these pathways is critical for successful acclimation and ultimately cell survival. HSF1 and HSP serve as the first line of defense against acute cellular heat stress. Heat shock factors are transcription factors that regulate HSP by binding to specific DNA sequences called heat shock elements in HSP promoters. Of the three mammalian heat shock factors, HSF1 is known for its involvement in acute response to heat stress.119 HSF1 is activated by the hydrophobic regions of extracellular denatured proteins (a consequence of heat shock) then binds to heat shock elements to increase HSP gene expression during elevated temperatures.120 HSF1 gene is mapped to chromosome 14 in cattle,121 but bovine studies are limited in HSF1 regulation and function despite importance for heat stress response initiation.

HSP are a group of highly conserved proteins induced by a variety of cellular stresses, but originally identified in response to heat shock.115 Several HSPs are expressed under thermoneutral, unstressed conditions and play roles in normal physiological functions. However, HSP increases expression under heat stress response for a short period of time, beginning within minutes of exposure and peaking up to 3 hours later.118 These proteins possess three fundamental biochemical activities include: 1) chaperone activity to prevent misaggregation of denatured proteins and refolding denatured proteins into original conformation; 2) regulation of cellular redox state; and 3) regulation of protein turnover by marking proteins for proteasome degradation.116,122,123 HSP requires further investigation in livestock models, but few

studies in ruminants report possible associations of single nucleotide polymorphisms

(SNPs) in the HSP70 genes with weight gain, pregnancy, and mastitis124–126 and directly with heat stress response in vitro.87,127

Outside of HSP, additional transcription factors and genes experience expression changes under cellular heat stress in a variety of species and tissues (e.g. downregulated: Myc, Bcl2, TnfA; upregulated: Vegf, TgfB, p53, Nfκb, C/ebpB) that are likely to alter the physiological cellular stress response through roles in apoptosis, cell growth, differentiation, and division.118 These genes may act in a tissue specific manner to modulate cellular responses and are of interest in dry cows due to their additional roles in mammary gland involution and redevelopment.

To capture genetic alterations related to BMEC development and function under early, acute heat shock response, Collier et al. (2006)87 conducted a microarray analysis of in vitro bovine mammary epithelial cells (BMEC) exposed to acute hyperthermia at 42°C vs. control thermoneutral cells at 37°C with RNA collected at 1, 2,

4, and 8 h after initiation of heat shock. Overall, there were 340 genes responsive to thermal stress with the majority downregulated. These heat-stressed cells experienced downregulation of genes related to ductal branching and microtubule assembly. That observation was supported by phallodin-stained BMEC collagen whole mounts that showed a dramatic reduction of ductal structures compared with thermoneutral cultures.

Cell growth was reduced through downregulation of genes related to cell cycle, cell- specific biosynthesis, metabolism, and structural proteins. Concurrently, there was an upregulation of genes involved in stress responses, protein repair, and apoptosis.

Further, HSP70 was upregulated in the heat-stressed cells through 1, 2, and 4 h (with

peak expression occurring at 4 h) before expression declined to basal levels at 8 h of acute exposure accompanied by increased apoptotic gene expression, indicating that the cells lose thermotolerance after 8 h of exposure and undergo cell death.87 Together, these results indicate a shutdown of cellular growth and development and an increase in cell survival in response to heat stress until the thermal load becomes too great and cells die.

While the effect of acute heat stress on primary cellular processes and in vitro

BMEC gene expression has been determined, the impact of both acute and long-term heat stress on whole genome expression of the mammary gland in vivo has yet to be elucidated for the bovine. As genomic and transcriptomic analytic tools continue to advance, scientists can discover even more genes associated in the heat stress response and elicit the complex pathways that lead to thermotolerance.

RNA-Sequencing Technology

RNA-Sequencing (RNA-Seq) is a technology that emerged just over a decade ago and has revolutionized biotechnology, specifically transcriptomics, in the 21st century.128 The transcriptome contains the full set of RNA transcripts in a cell and their relative quantities under different physiological conditions. Because RNA is a baseline indicator of cell identity and function, assessing animal cellular transcriptomics can be utilized for determining phenotype. Therefore, the development of this high-throughput

RNA-Seq tool has provided avenues for detailed exploration of entire transcriptomes.

The term “RNA-sequencing” was first mentioned in literature in 2008 according to the

ISI Web of Knowledge, and to date over 16,000 articles containing this keyword have been published (as of a February 2018 search), indicating an explosion of research in this field in only ten years. It has been utilized in transcriptome analysis of many model

organisms such as mice,129,130 yeast,131–133 Drosophila,134 Arabidopsis,135 and humans136–138 to name a few and can be utilized to explore non-model organisms such as lesser known plant, insect, and mammalian species to gain further insight into their physiology.

The basis of RNA-Seq technology is a “sequencing-by-synthesis” approach using deep-sequencing technologies.139 It is used for two major types of analyses: discovering novel sequences or quantifying current transcripts by comparing samples from wild- types vs. mutants, different treatments, or even different tissues within the same organism. Any RNA sample extracted with high enough quality and purity to be reverse- transcribed can be analyzed through RNA-Seq. Illumina IG,129,131,132 Applied

Biosystems SOLiD,130 and Roche 454 Life Science140–142 sequencing systems have been utilized in published RNA-Seq research. The following brief description of library preparation and sequencing is based on the method used in this research: Illumina

(Illumina®, New England Biolabs, USA).

After tissue collection and RNA extraction, library preparation occurs starting with

RNA fragmentation to the necessary base pair (bp) length (~30-400 bp). RNA fragmentation allows for cleaner reads at the core of the transcript whereas fragmentation further in the process after reverse transcription, DNA fragmentation, leads to improved recognition at the 3’ ends of fragments.139 The population of fragmented RNA is converted to a library of cDNA transcripts with adaptors added to one or both ends. These adaptors allow for the fragments to be recognized by the sequencing machine and make it possible to sequence multiple barcoded samples at one time, saving time and resources. DNA fragments are PCR amplified via bridge

amplification and quality control checked for concentration and length.128 Next, the fragments are fixed to a glass surface in a grid and this flow cell is inserted into the sequencing machine. In the machine, a new DNA strand is synthesized alongside the immobilized transcripts as immunofluorescent probes color-coded to the four nucleotides affix themselves to each fragment one nucleotide at a time. After each probe addition, a highly sensitive camera system records the fluorescent colors at that nucleotide level for each fragment in the flow cell, then the color is washed away for the addition of the next probe at the next nucleotide level,128 repeating until the full sequence has been read. The Illumina HiSeq instrument, as an example, is capable of generating up to 5 billion reads, allowing for a high number of reads for a large number of samples (e.g. assuming 10 million reads is sufficient for a high level of coverage, 500

RNA-Seq reactions are possible). Thus, this incredibly high-throughput capacity of the

Illumina system has made it the preferred method for RNA-Sequencing.128 Following sequencing, the reads are aligned to a reference genome for eventual quantification or assembled without genomic sequence to generate data of the transcriptional structure and gene expression to later unravel or compare differentially expressed genes between treatments, specimen, or tissues.

Transcriptome Analysis Technology Comparisons

While the RNA-Seq technology is still advancing, its current features have far superseded previous transcriptomic analysis technologies under the hybridization approach (e.g. microarrays) or technologies utilizing Sanger sequencing (e.g. serial- analysis of gene expression, cap-analysis of gene expression, and massively parallel signature sequencing).139 In fact, authors that correlate RNA-Seq results to previous

microarray work conclude that this new technology will soon replace the previous methods because of numerous advantages described below.135,142

First, RNA-Seq is not limited to detecting changes in transcripts from known sequences as it is not dependent on existing knowledge of the genome; whereas microarrays, for example, require prior information from genome sequencing or expressed-sequence tags to draw conclusions.139 This independence from sequence comparison allows simultaneous sequence discovery and quantification. RNA-Seq can determine transcription boundaries, exon connections, and sequence variations in the transcriptome. Again, this makes RNA-Seq a vital tool for transcriptomics in non-model organisms and complex transcriptomes.

Next, microarrays measure relative fluorescent intensity, so they generate high background noise due to cross-contamination and saturation of signals, making it difficult to detect a broad range of expression especially reads with relatively very low or high expression.143,144 Unlike microarrays, RNA-Seq has little to no background signal as sequences are mapped unambiguously to unique genomic regions.139 Thus RNA-

Seq directly measures RNA abundance and does not have an upper limit in quantification, allowing for at least a two orders of magnitude broader range in expression when compared with mircoarrays.128 In fact, studies report estimated dynamic ranges of greater than 9,000-fold in Saccharomyces cerevisiae131 and spanning 5 orders of magnitude in mice.129 This specificity also allows for high levels of accuracy, confirmed through qRT-PCR and spike-in RNA controls, and improved replicability of RNA-Seq studies between labs.133,145 Finally, as previously mentioned,

this technology utilizes small amounts of RNA and is high-throughput with relatively low costs (especially compared to Sanger sequencing) that are dropping every year.128

RNA-Seq is not without its challenges, however. Library construction introduces several manipulation steps that can complicate identification of both large and small transcripts, introduce bias into the reads, and hinder statistical analysis.139 Further, the large number of reads generated upon sequencing proves a bioinformatics challenge, as a huge amount of storage space and computer capacity is needed to analyze and store RNA-Seq data. Finally, researchers must consider coverage versus cost when running their data. Higher read numbers will lead to fuller coverage of the transcriptome; for example, in the study of the S. cerevisiae transcriptome, 4 million reads covered

80% of the transcriptome whereas 35 million reads covered >90%.131 Large and complex transcriptomes will also require more sequencing depth for satisfactory coverage. However, higher read numbers lead to added expense, and one must weigh the moderate increase in level of coverage against the sizable increase in reads.

RNA-Sequencing Application in Bovine Research

Transcriptomics is now being widely utilized in bovine research. Studies using

RNA-Seq have characterized the transcriptome of the mammary gland and milk secretions to determine production phenotypes,146 characterized the bovine milk transcriptome,147 determined expression profiles of microRNAs (miRNAs) related to lactation and the dry period,148 revealed candidate genes for extreme milk protein and fat concentration,149,150 and even analyzed the optimal RNA source for determining transcriptional activity during lactation.151 RNA-Seq has been extensively applied to study reproduction and metabolism in the bovine. Huang and Khatib (2010)152 surveyed the bovine embryo transcriptome, citing it as the first application of RNA-Seq

in cattle, while further research uncovered embryo genome activation153 and effect of methionine supplementation on the embryo.154 RNA extracted from bovine blastocysts has been analyzed in RNA-Seq to characterize the blastocyst transcriptome155 and determine transcriptomic differences between in vivo and in vitro models.156 The bovine liver transcriptome has been studied to determine the impact of negative energy balance, particularly on expression of miRNAs.157,158

With bovine RNA-Seq research exploding in the past five to eight years, further questions continue to be asked about the physiology of the many organs that coordinate responses to milk production, metabolism, reproduction, and stresses. To my knowledge, this research is the first RNA-Seq analysis of the bovine mammary gland transcriptome both across the dry period and under environmental heat stress.

Summary

Further research is needed in the bovine model to characterize the late-lactation, late-gestation dry period mammary transcriptome through both involution and redevelopment. Additionally, there are no in vivo models that have studied the impact of chronic heat stress and heat stress acclimation on the dry period mammary transcriptome. Previous research, mainly from the University of Florida, has highlighted the importance of heat stress abatement during the dry period to improve production in the next lactation, but there are still questions as to how heat stress impacts the mammary gland long-term at the cellular level and how to develop complementary methods to active cooling that could rescue production loss. I was motivated to utilize

RNA-Seq to investigate the landscape of the mammary transcriptome both across the dry period and under heat stress in order to answer some of these questions and to provide a direction for future research in this area. The objective of this thesis was to

characterize novel genes, pathways, and upstream regulators involved in bovine mammary gland involution and redevelopment during the dry period and to determine how heat stress affects this dynamic process. I hypothesize that, relative to cooled cows, cows exposed to heat stress will experience alterations in expression of key genes and pathways required for normal involution and redevelopment, compromising mammary function and milk production in the subsequent lactation. This thesis will not only contribute to the knowledge in mammary gland and lactation physiology but will also provide candidate genes and highlight entire pathways and transcription factors involved in this processes that can be used for further investigation to manipulate the dry period and to determine mitigation strategies against heat stress.

CHAPTER 2 RNA-SEQ REVEALS NOVEL GENES AND PATHWAYS INVOLVED IN BOVINE MAMMARY INVOLUTION DURING THE DRY PERIOD AND UNDER ENVIRONMENTAL HEAT STRESS

Abstract

The bovine dry period is a dynamic non-lactating phase wherein the mammary gland undergoes extensive tissue remodeling. Utilizing RNA-Sequencing, I characterized novel genes and pathways involved in this process and determined the impact of dry period heat stress. Mammary tissue was collected before and during the dry period (-3, 3, 7, 14, and 25 d relative to dry-off i.e. D0) from heat-stressed (HT, n=6) or cooled (CL, n=6) pregnant Holstein cows. RNA-Seq identified 3,315 differentially expressed genes between late lactation and early involution, and 880 genes later in the involution process. Differentially expressed genes, pathways, and upstream regulators during early involution highlight the downregulation of functions such as anabolism and milk component synthesis, and upregulation of cell death, cytoskeleton degradation, and immune response. Environmental heat stress affected genes, pathways, and upstream regulators involved in processes such as ductal branching, metabolism, cell death, immune function, and protection against tissue stress. This research advances the understanding of the mammary gland transcriptome during the dry period, particularly under heat stress insult. Individual genes, pathways, and upstream regulators highlighted in this study point towards potential targets for dry period manipulation and mitigation of the negative consequences of heat stress on mammary function.

Introduction

In dairy cows, the dry period is a six to eight-week non-lactating state initiated between lactations that allows for optimal milk yield in the subsequent lactation through the turnover of worn, senescent mammary epithelial cells (MEC) with new, active cells.2

It consists of three phases known as active involution, steady state involution, and redevelopment. Involution is the natural process whereby the mammary gland transitions from a lactating to a non-lactating state. It begins after the cessation of milk removal and is characterized by a decrease in milk secretion and rise in mammary pressure, apoptosis and autophagy of MEC, and immune response.20,21,24,25 Involution continues for approximately 21 d, followed by redevelopment of the mammary gland until calving.26

The onset of involution triggers the expression of genes and pathways that function to increase cell death and immune signals. Downregulated pathways during involution include prolactin signaling (via the inactivation of signal transducer and activator of transcription [STAT]5, a cell proliferation and differentiation regulator)159,160 and insulin-like growth factor (IGF; via the upregulation of IGF-binding protein [IGFBP]5, a regulator of cell apoptosis and tissue remodeling).161 The redevelopment phase is a mammogenic period where upregulation of genes, such as IGF1 and IGFBP3, promote cell proliferation and turnover to increase MEC number and secretory capacity in preparation for colostrogenesis and lactation.2,26 Key candidate genes of involution have been well characterized in rodent models. In dairy cattle, limited studies have been done utilizing microarrays and quantitative real-time PCR (qRT-PCR) evaluate the molecular events occurring in the mammary gland during a typical dry period of pregnant cows,26 during forced involution of non-pregnant cows at peak lactation,40,41

and during gradual involution of non-pregnant cows at peak lactation.25 These studies report an overall upregulation of genes related to cell turnover, oxidative stress, tissue remodeling, and inflammation and downregulation of cell survival signaling and biosynthesis of milk constituents during involution and upregulation of cellular proliferation later during redevelopment. However, a more thorough characterization of the entire bovine mammary transcriptome through in vivo dry period models is lacking.

Perturbations, such as impaired nutrition and poor management, during the dry period may alter the involution process and affect cow performance. Indeed, exposure of dairy cows to environmental heat stress during the dry period decreases milk production in the subsequent lactation.94,95 This phenomenon has been partially attributed to reduced autophagy in the early dry period,100 decreased cell proliferation in the late dry period,95 and altered alveolar microstructure.101 Bovine MEC exposed to acute heat stress in vitro downregulate genes related to cell cycle, focal adhesion and cytoskeleton activity, cell biosynthesis and metabolism, ductal branching, and morphogenesis and upregulate genes involved in stress response and protein repair.87,127 Whereas the effect of heat stress on cellular processes and in vitro gene expression has been studied, its impact on the mammary gland transcriptome through in vivo models has yet to be elucidated for the bovine.

The aim of this study was to discover and characterize novel genes, pathways, and upstream regulators involved in mammary gland involution and redevelopment during the dry period and to determine how heat stress affects this dynamic process in the dairy cow by utilizing RNA-Seq. I hypothesize that, relative to cooled cows, cows exposed to environmental heat stress will experience alterations in expression of key

genes and pathways required for normal involution and redevelopment, compromising mammary function and milk production in the subsequent lactation.

Materials and Methods

Animals, Treatments, and Experimental Design

This study was conducted at the University of Florida Dairy Unit (Hague, FL;

29.7938° N, 82.4944° W) during the summer of 2015. The University of Florida

Institutional Animal Care and Use Committee approved all treatments and procedures.

Twelve multiparous Holstein cows selected based on mature equivalent milk production and parity were dried off at ~46 d before expected calving. Cows were randomly assigned to two treatments for the duration of the dry period: heat-stressed (Figure 2-

1A, HT, n=6; access to shade in a sand-bedded free-stall pen) or cooled (CL, n=6; access to shade, fans and soakers in a separate pen). Fans (J&D Manufacturing, Eau

Claire, WI) ran continuously and soakers (Rain Bird Manufacturing, Glendale, CA) were activated when ambient temperature reached 21.1°C, running for 1.5 min in 6 min intervals. After calving, cows were treated identically with access to shade, fans, and soakers. Details of the total mixed ration diet, dry matter intake, rectal temperature and respiration rates during the dry period, and milk production during lactation are reported in Fabris et al. (2017).106

Mammary Tissue Collection and RNA Extraction

For all cows, mammary biopsies were collected at day (D) -3 (before dry-off during late lactation) and at D3, 7, 14, and 25 relative to dry-off (which was considered

D0) based on the method described by Farr et al. (1996)162 with slight modifications95

(Figure 2-1B). Time points for mammary biopsy collection were chosen to capture the three phases of the dry period: D-3 represents late lactation, D3 and D7 represents

active involution, D14 represents the steady-state phase, and D25 captures the beginning of the redevelopment phase. Mammary tissue biopsies were washed in sterile saline, trimmed of visible fat, placed in RNALater (ThermoFisher, Invitrogen,

Grand Island, NY), and stored at -80° C until RNA isolation. Total RNA was extracted using the RNeasy Mini Kit (catalog #74104, Qiagen, Valencia, CA) according to the manufacturer’s instructions. RNA concentration was determined on Qubit® 2.0

Fluorometer (ThermoFisher, Invitrogen, Grand Island, NY), and RNA quality was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.). Total RNA with 28S/18S > 1 and RNA integrity number ≥ 7 were used for library construction.

Library Generation and RNA Sequencing

RNA-Sequencing (RNA-Seq) library was constructed using NEBNext® Ultra™

RNA Library Prep Kit for Illumina® (New England Biolabs, USA) following manufacturer’s recommendations. Briefly, 500 ng of total RNA was used for mRNA isolation using NEBNext Poly(A) mRNA Magnetic Isolation module (catalog #E7490) then followed by RNA library construction with NEBNext Ultra RNA Library Prep Kit for

Illumina (catalog #E7530) according to the manufacturer's user guide. Sixty barcoded libraries (n=12 cows at 5 different time points D-3, 3, 7, 14, 25) were sized on the

Bioanalyzer, quantitated by QUBIT and quantitative PCR using the KAPA library quantification kit (Kapa Biosystems, catalog #KK4824). Finally, the 60 individual libraries were pooled equimolarly and sequenced by Illumina NextSeq 500 for 5 runs

(Illumina Inc., CA) which generated 150 base-pair single-ended reads.

Mapping, Assembly, and Normalization of RNA-Seq Data

The quality of the sequencing reads was evaluated using FastQC software, and if necessary, sequencing reads were trimmed using the software Trim Galore (v0.4.1).

Sequence reads were mapped to the bovine reference genome (bosTau7) using the software package Tophat (v2.0.13).163,164 Two rounds of alignment were performed to maximize sensitivity to splice junction discovery, allowing for full utilization of novel splice junctions. Novel splice junctions were first determined in each sample individually, then combined with the known ENSEMBL annotated splice junctions and entered in Tophat for a second alignment.154,165 Read alignments were discarded if they had greater than two mismatches or were equally mapped to more than 40 genomic locations. The subsequent alignments were used to reconstruct transcript models using the software package Cufflinks (v2.2.1).166 The Cuffmerge tool was used to merge each assembly to the bovine annotation file, combining novel transcripts with known annotated transcripts to maximize quality of the final assembly. The number of reads that mapped to each gene in each sample was calculated using the tool htseq-count.167

Identification of Differentially Expressed Genes, Pathways, and Regulators

Differentially expressed genes were detected using the R package edgeR

(v.3.4.2).168 This package combines the use of the trimmed mean of M-values as the normalization method of the count data, an empirical Bayes approach for estimating tagwise negative binomial dispersion values, and finally, generalized linear models and quasi-likelihood F-test for detecting differentially expressed genes (DEGs). The following comparisons over time were made: D3 vs. D-3, D7 vs. D3, D14 vs. D7, and

D25 vs. D14 to highlight differences in gene expression as the cow transitions between dry period phases, focusing on the active involution phase. Additionally, due to the lack of a significant interaction between time and treatment, HT vs. CL were compared for each time point independently.

Genes that were differentially expressed over time or between treatments were analyzed using Fisher’s exact test to determine significant enrichment of Gene Set

Enrichment Analysis Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes

(KEGG) pathways and Medical Subject Headings (MeSH) terms.169 For all comparisons, genes that had an ENSEMBL annotation and a false-discovery rate (FDR)

≤ 5% were tested against the background set containing all expressed genes with

ENSEMBL annotation. The GO, KEGG and MeSH enrichment analyses were performed in R software using goseq170 and meshr171 packages respectively. Functional categories with a nominal p <0.05 were considered significantly enriched by DEGs.

Additionally, DEGs were explored using Ingenuity® Pathway Analysis (IPA®,

Ingenuity Systems, Qiagen, Valencia, CA) to determine upstream regulators. For each comparison, lists of DEGs with ENSEMBL annotation were uploaded into IPA and compared to the background annotated bovine genome (24,616 unique ENSEMBL IDs).

Both up- and downregulated genes were analyzed together. The IPA feature Upstream

Analysis was used to determine significant upstream regulators within the dataset. IPA broadly describes upstream regulators as any molecule that can affect the expression of other molecules. The impact of upstream regulators was calculated using overlapping p- value to identify regulators that explained observed gene expression changes and activation z score to estimate the activation state of predicted regulators. From this list of upstream regulators, IPA generates a molecular network of upstream regulators, downstream target genes, and biological functions that are impacted by expression changes in these molecules.

Validation of RNA-Seq Results with qRT-PCR

Ten DEGs were chosen for validation of RNA-Seq results, five DEGs downregulated at D3 (α-lactalbumin, LALBA; β-casein, CSN2; casein-αS1; CSN1S1; casein-αS2, CSN1S2; solute carrier family 7 member 5, SLC7A5) and five upregulated genes at D3 (matrix-remodeling-associated protein 5, MXRA5; lipopolysaccharide binding protein, LBP; lysyl oxidase like 4, LOXL4; angiopoietin like 4, ANGPTL4; solute carrier family 7 member 8, SLC7A8). Validation was performed using qRT-PCR conducted with the CFX96 Touch Real-Time PCR Detection System (Bio-Rad). A total of 1 μg RNA from each sample was used to synthesize cDNA using the iScript cDNA synthesis kit (Bio-Rad Laboratories, CA) and diluted 1:5 in dH2O. Reaction mixtures were performed as previously described172 and cycling conditions were as follows: 1 cycle for 3 min at 95°C then 50 cycles of 10 s at 95°C and 30 s at 60°C followed by melt curve measurement from 65°C to 95°C in 0.5° increments for 5 s. Positive and negative controls were added to each PCR plate. Each sample was assessed in duplicate and the %CV between the duplicates was < 2%. Primer sequences for the validated genes were obtained from the literature or specifically designed to span exon-exon junctions to minimize the potential of amplifying genomic DNA using Primer3 software (Table 2-1).

173,174 The geometric mean between two housekeeping genes (ribosomal protein S9,

RPS9 and ubiquitously expressed prefoldin-like chaperone, UXT) was used to calculate the relative gene expression using the method 2-ΔΔCt with D3 as the reference group.175

Results

Physiological Parameters and Milk Yield

Physiological parameters and production data of the cows used in this study are reported in Fabris et al. (2017).106 Briefly, heat-stressed and cooled pens had similar

temperature humidity index (THI) which was never lower than 68 at any time during the experimental period. Cows provided with active cooling during the dry period had a tendency toward higher feed intake (11.0 vs. 10.3 ± 0.46 kg/d, p = 0.10; CL vs. HT respectively), had lower rectal temperature (38.92 vs. 39.31 ± 0.05°C, p < 0.01), and had reduced respiration rates (45.2 vs. 77.2 ± 1.59 breaths/min, p < 0.01) compared with heat-stressed cows. Thus, heat stress was effective in inducing physiological changes. On average, cows provided with active cooling during the dry period produced

4.8 kg more milk over 9 weeks compared to heat-stressed cows (40.7 vs. 35.9 ± 1.6 kg/d, p = 0.09).

Mapping Statistic Summary

RNA-Seq technology was used to analyze genome-wide gene expression of mammary samples collected on D-3, 3, 7, 14, and 25 relative to dry-off (D0) for cows under HT or CL conditions. Through Illumina sequencing, roughly 34 million single- ended reads per sample were acquired. Approximately 81% of the reads were successfully mapped to the bovine genome. Among these aligned reads, 98% were mapped to unique genomic regions. Only uniquely mapped reads were considered in the analysis. Sequencing data can be accessed through NCBI GEO with accession number GSE108840.

Differentially Expressed Genes and Pathways Across the Dry Period

The main effect of time relative to dry-off on the mammary gland transcriptome was analyzed, comparing D3 vs. D-3, D7 vs. D3, D14 vs. D7, and D25 vs. D14. When comparing D3 (initiation of involution) vs. D-3 (late lactation) 3,315 genes were differentially expressed, of which 1,311 were upregulated, and 2,004 were downregulated at D3 relative to D-3 (FDR ≤ 5%, Figure 2-2A, Object 2-1). These DEGs

were associated with 44 KEGG pathways and 51 MeSH terms (p ≤ 0.01, Figure 2-3A,

Table 2-2). KEGG pathways with a high percentage of DEGs upregulated at D3 were related to cytoskeleton and cellular degradation and immune response, whereas pathways with a greater ratio of downregulated DEGs were associated with anabolism and amino acid biosynthesis and metabolism. Similarly, MeSH terms related to cytoskeletal proteins and cellular differentiation and movement had a high proportion of

DEGs upregulated at D3, whereas terms with a greater number of downregulated DEGs at D3 were associated with lactation, milk proteins, and amino acids.

There were fewer DEGs when comparing D7 vs. D3, which captures the first week of involution, with 880 DEGs between these time points, 292 of which were upregulated and 588 of which were downregulated at D7 (FDR ≤ 5%, Figure 2-2B;

Object 2-2). These DEGs were grouped into 11 enriched KEGG pathways and 14

MeSH terms (p ≤ 0.01, Figure 2-3B; Table 2-3). Only one KEGG pathway, cell cycle, had a high proportion of DEGs that were upregulated at D7. The other ten pathways had a greater ratio of DEGs that were downregulated, and these were associated with cytoskeleton degradation and immunity. DEGs in MeSH terms related to cyclin were exclusively upregulated at D7, while the majority of DEGs in MeSH terms such as actin and kinases were downregulated at D7. Interestingly, the majority of KEGG pathways and MeSH terms had a higher percentage of downregulated DEGs at D7 compared with

D3, and 6 out of these 11 KEGG pathways were simultaneously enriched in the D3 vs.

D-3 comparison (e.g. regulation of actin cytoskeleton, focal adhesion, adherens junction, p53 signaling pathway, bacterial invasion of epithelial cells, and leukocyte

transendothelial migration) indicating a common pattern of regulation during the first week of involution.

As involution progressed to steady state and D14 vs. D7 was compared, there were no DEGs at a FDR ≤ 5%. Using a nominal p ≤ 0.005 and log2 fold change ≥ |0.5|,

10 DEGs with 9 upregulated and 1 downregulated genes at D14 were identified, most of which were unknown or uncharacterized (Table 2-4). As involution concluded and redevelopment of the mammary tissue initiated, a slight increase in the number of DEGs was detected when comparing D25 to D14. Twenty-six DEGs were identified, 4 of which were upregulated and 22 downregulated at D25 (nominal p ≤ 0.005 and log2 fold change ≥ |0.5|; Table 2-4). These DEGs were related to cell death and proliferation, immune function, and metabolism. No pathways, terms, or upstream regulators were determined for these comparisons.

Ingenuity® Pathways Analysis (IPA®) Regulator and Network Analysis

Upstream regulators and summary networks for D3 vs. D-3 and D7 vs. D3 were assessed utilizing IPA. The list of 2,816 mapped DEGs for D3 vs. D-3 generated a catalog of 179 predicted biological upstream regulators through IPA. After restricting the analysis to those differentially expressed within the dataset with log2 fold change ≥ |1.0|,

41 significant upstream regulators were revealed (Figure 2-4A). The network analysis of upstream regulators and corresponding downstream genes relative to D3 revealed the participation in functions related to involution and metabolism of lipids, carbohydrates, and proteins (Figure 2-4B).

As involution progressed (D7 vs. D3 comparison), there were fewer upstream regulators expressed. From 748 mapped DEGs, a list of 556 predicted biological upstream regulators was obtained through IPA. After restricting the analysis to those

differentially expressed within the dataset with log2 fold change ≥ |1.0|, 11 were significantly different and the majority was upregulated at D7 (Figure 2-5A). The network analysis of these 11 upstream regulators and corresponding downstream genes relative to D7 indicates that these regulators play a role in involution, cell division, and transcription and translation (Figure 2-5B).

Differentially Expressed Genes and Regulators Impacted by Heat Stress

Differentially expressed genes between dry period HT and CL cows at each specific time point (e.g. D3, 7, 14, and 25 d relative to dry-off) were evaluated. When using a FDR ≤ 5%, the only significant DEG was a non-annotated gene at D25 (log2FC

= -3.95 and q < 0.0001). The UCSC Genome Browser and NCBI identified this non- annotated gene as a long non-coding RNA (lncRNA) at position chr7: 61592484-

61595879. The Sequence-Structure Motif Base Pre-miRNA Prediction Webserver was used to discern pre-microRNAs (miRNA), corresponding mature miRNA seed regions, and the miRNA secondary structures within the lncRNA sequence.176,177 The program utilizes PriMir filtration and Mirident software to screen and confirm candidate pre- miRNA sequences by score matrix based on features in sequence or structure of known pre-miRNAs. The program revealed 7 mature miRNA seed regions and their secondary structures. According to the bioinformatics program TargetScan utilizing the human database,178 seed regions regulate 1,159 downstream target genes (Object 2-3).

Using a less stringent approach (p ≤ 0.005 and log2 fold change ≥ |0.5|), a total of

180 DEGs were detected when comparing HT to CL with 9, 115, 27 and 29 DEGs at

D3, 7, 14 and 25, respectively (Figure 2-6A; Table 2-5). Additionally, from D7 to D25, 11 genes were consistently upregulated and 7 consistently downregulated in HT cows

(Figure 2-6B). Upstream regulators and their resultant networks for HT vs. CL cows at

D7 were determined using IPA, where a catalog of 504 upstream regulators was predicted. The network analysis of 11 significant upstream regulators (Figure 2-7A; restricting the cut-off to differential expression within the dataset and log2 fold change ≥

|1.0|) and their corresponding downstream genes indicate these influence functions related to cell death, immunity, lipid synthesis, and development (Figure 2-7B).

Validation of RNA-Seq Results with qRT-PCR

Ten DEGs of D3 vs. D-3 (D3 downregulated: LALBA, CSN2, CSN1S2, CSN1S1,

SLC7A5; D3 upregulated: MXRA5, SLC7A8, LBP, ANGPTL4, LOXL4) were selected to validate RNA-Seq results followed the same direction of expression under qRT-PCR and had comparable log2 fold change (Figure 2-8A). Expression levels calculated via

RNA-Seq were significantly positively correlated to expression levels determined via qRT-PCR (Figure 2-8B; R2= 0.9386, p < 0.0001).

Discussion

The dry period is characterized by dynamic shifts in mammary gland cellular metabolism, cell turnover, immune signaling, and tissue remodeling. Any perturbation

(e.g. exposure to heat stress) of these cellular processes and developmental events could severely reduce the mammary gland’s ability to effectively involute and redevelop, negatively affecting milk production in the next lactation.95,108 The present study confirms the involvement of metabolic, cell death, and immune-related genes and pathways in the bovine mammary gland during the dry period and reveals others not previously reported. These findings provide insights into the landscape of the bovine mammary transcriptome undergoing involution when exposed to environmental heat stress, highlighting changes in cell death, branching morphogenesis and cell response to stress.

Cessation of milking induces the recruitment of immune cells and local factors, such as pro-apoptotic signaling factors, and increases mammary pressure. This leads to a dramatic decline in milk synthesis and metabolic processes and protects against inflammation.20,40 More than 3,000 DEGs between late lactation and early involution and more than 800 DEGs during the first week of involution were discovered. After seven d of milk stasis, the mammary gland approaches the end of the active involution phase.

Interestingly, there were no DEGs under FDR ≤ 5% during the steady state and redevelopment time-point comparisons (D14 vs. D7 and D25 vs. D14). Possible explanations include failure to capture peak gene expression associated with redevelopment, inability to identify post-transcriptional modifications through RNA-Seq, and subtle physiological alterations not captured under the stringent statistical analysis.

To better understand the physiology of these two phases, statistical analysis was relaxed to a nominal p ≤ 0.005 and log2 fold change ≥ |0.5| and uncovered 10 DEGs during steady-state involution and 26 DEGs during redevelopment.

The most significant pathways downregulated during early involution were related to synthesis and metabolism of lipids, proteins, and carbohydrates. These findings are consistent with previous research where, in general, concentrations of milk- specific constituents decline as galactopoietic activity halts in the involuting mammary gland.4,20 Pathways and terms related to lipid metabolism (e.g. steroid biosynthesis, synthesis and degradation of ketone bodies, fatty acid degradation, saturated and unsaturated fatty acids) expressed a higher number of downregulated genes, indicating reduced lipid synthesis and metabolism at D3 of involution. Pathways related to biosynthesis, degradation, and transport of amino acid and terms related to milk

proteins (e.g. lactalbumin, caseins, and lactoglobulins) had a higher number of downregulated genes at D3 of involution, which is consistent with downregulation of milk protein gene expression and decreased concentrations of milk-specific proteins upon milk stasis.40,179 Fifteen out of 17 DEGs in the valine, leucine, and isoleucine degradation pathway were also downregulated. Interestingly, some of those genes (e.g.

IVD, DBT, BCAT2) are involved in catabolism of the branched-chain amino acids for eventual milk protein synthesis.180,181 Production of the milk-specific carbohydrate lactose declines rapidly upon milk stasis, accompanied by decreased lactose synthetase activity.25,111 Six (UGP2, PFKM, LALBA, GANC, HK2, and B4GALT1) of the

11 DEGs in the galactose metabolism pathway, related to lactose synthesis and lactose synthetase formation, were downregulated after 3 d of milk stasis.

Cell death is one of the molecular landmarks of involution. Pathways and genes involved in different cell death mechanisms are well described in mouse and bovine models of involution using microarrays and qRT-PCR and are confirmed in the present study utilizing RNA-Seq. However, some discrepancies between animal models are apparent. Accumulation of milk in a mouse model causes local factors to induce apoptosis as soon as 12-hours after milk cessation. For example, LIF phosphorylates the signal transducer STAT3,31 which downregulates a major survival factor pAk through induction of PI3-kinase and downregulates IGF1 through upregulation of

IGFBP5.30,161,182 Cell death during involution is not as extensive in the dairy cow, and while many of these factors discussed above were present in this study, their temporal expression pattern was different. In this study, pro-apoptotic factors such as LIF,

STAT3, IGFBP5, CASP9, BAX, and SOCS3 were all upregulated at D3 of involution,

while the survival-signaling factor AKT1S1 was downregulated. Similarly, elevated levels of apoptosis during the early dry period in Holstein cows are evidenced by upregulation of histological markers and pro-apoptotic genes (e.g. CASP3 and IGFBP5) at D4 of involution.26 These authors also reported a simultaneous increase in mammary expression of proliferative genes (e.g. IGF1 and IGF1R) during the early involution (D4) and redevelopment (D36) phases of the dry period. In the present study, not IGF-1 but

IGF1-R, IGFBP2 and IGFBP4 were upregulated in the mammary gland at D3 of involution compared with late lactation. Abruptly drying-off non-pregnant dairy cows at peak lactation increased apoptosis of the mammary gland (D3 to D8 after milk stasis), indicated by increased STAT3 and SOCS3 and decreased STAT5 gene expression.41

However, IGF1 expression increased and IGFBP5, AKT and AKTP protein concentrations did not change.41 Non-pregnant cows gradually dried-off had increased mammary apoptosis from D5 to D14 of involution evidenced by upregulation of STAT3 and downregulation of AKT1, but no changes in IGFBP5 were reported.25 Additionally, in this study, autophagy-promoting genes (e.g. ATG9, DRAM1, and EPG5) were upregulated in the mammary gland of cows at D3 of involution, corroborating the participation of autophagic cell death in the involuting bovine mammary gland.100,183,184

Discrepancies between the present model and other mouse and bovine models may be attributed to the stage of lactation at dry-off, state of concurrent pregnancy, and reduced extent of MEC turnover during involution. Pro-apoptotic and pro-proliferative molecules may be co-expressed in the mammary gland of pregnant cows that requires both cell death and proliferation during the dry period.

Other molecular landmarks of involution include disruption of cell tight junctions, immune cell signaling, and cytoskeleton and extracellular matrix degradation. Not surprisingly, mammary cell tight junction permeability was impacted by milk stasis.15

Herein, 15 out of 16 DEGs in the tight junction pathway were downregulated during the first week of involution. Immune cell signaling is activated in response to milk stasis to protect against mammary inflammation and remove debris through phagocytosis.185 In the present study, the influx of immune factors was indicated by the upregulation of bacterial invasion of epithelial cells and leukocyte transendothelial migration pathways and upregulation of immune-related genes (e.g. LBP, TMSB4X, ANXA1, and STAT3) after D3 of initiated involution. In addition, genes upregulated in the lysosome, phagosome, and peroxisome pathways (e.g. SOD, LAMP1, SORT1, and COMP) indicate clearing of apoptotic cell bodies after D3 of involution. Phagocytosis of apoptotic cells is not pro-inflammatory and acts in a wound-healing manner39 by inducing expression of inflammatory factors that were upregulated in this dataset (e.g.

IL34, IL27RA, IL6R, IL10RB, and IL1R1). Neutrophil-attracting chemokines (e.g.

CXCL12, CXCL13, and CXCL17) were upregulated at D3, in accordance with the proinflammatory molecules reported in a mouse model of involution.30 The observed downregulation of genes involved chemotaxis at D7 of involution is consistent with the reported presence of immune factors in a non-pregnant bovine model at 36 h after milk stasis.41 Pathways and terms associated with cytoskeleton degradation (e.g. adherens junction, focal adhesion, regulation of actin cytoskeleton, and actins) had a greater number of genes upregulated D3 of involution. This was accompanied by upregulation of genes (e.g. RHOA) involved in the reorganization of the actin cytoskeleton. As

involution progressed to D7, adherens junction and actin cytoskeleton pathways were downregulated while the stromal matrix metallopeptidase 27 (MMP27) was upregulated indicating promotion of extracellular matrix breakdown.185

The present study revealed novel upstream regulators in the mammary gland during early involution. Two upstream regulators that play central roles in energy metabolism, PPARGC1A and INSIG1, were downregulated in the mammary gland of dairy cows during early involution, supporting a rapid and coordinated decrease of overall cellular metabolism upon milk stasis. Upstream regulators of lipid synthesis that coordinate downstream target networks were downregulated at D3 of involution, consistent with a previous bovine involution study.25 The regulator ACACA and its lipogenic downstream target genes (FASN and GPAM) were downregulated. Similarly,

SCD, a key upstream regulator in oleic acid biosynthesis that interacts with and regulates other upstream regulators (such as ACACA, LPL, and SREBF1) was downregulated. The upstream regulator ALOX15, which acts on polyunsaturated fatty acids to generate bioactive lipid mediators that regulate inflammation and immunity, was also downregulated. Three pro-apoptotic factors IGFBP5, PTGES, and BACH2 are examples of upstream regulators related to cell death that were upregulated at D3 of involution relative to late lactation in the bovine model. As involution progressed to D7, the number of upstream regulators dropped but the majority were upregulated and related to the cell cycle. Specific functions of these factors include mitotic regulation

(NEK2), chromosome segregation through the spindle checkpoint (BUB1B), cyclin dependent kinases (CKS2), and regulation of cyclin expression (FOXM1). There were three downregulated upstream regulators: NUPR1, involved in combating micro-

environmental cellular stress, EFNA1, modulating developmental events in the vascular system, and RET, a cell proliferation and growth signaling molecule. The upstream regulator NUPR1 is not only a negative regulator of cell cycle but also targets downstream genes that assist stress signaling to fortify cells against perturbations like reactive oxygen species and defective DNA repair, all critical components of immune response and tissue remodeling.

When analyzing gene expression changes further in the dry period, a less stringent statistical analysis of steady-state involution (D14 vs. D7) and redevelopment

(D25 vs. D14) revealed interesting DEG patterns. There were 10 DEGs during the steady-state involution (D14 vs. D7), 9 of which were upregulated relative to D14. This was expected, as this phase does not express dynamic changes.27 The few genes encoding known proteins play roles in heme metabolism (e.g. HMOX1 and

LOC100850059/hemoglobin subunit beta) and inhibition of immune response signaling

(e.g. CD300A), pointing towards vascular development and reduced need for an immune response. The single downregulated DEG was the transcription factor GATA5 that regulates smooth muscle cell diversity. The redevelopment phase comparison

(D25 vs. D14) uncovered 26 DEGs with the majority downregulated at D25. While downregulation may seem counterintuitive for a phase that promotes cellular proliferation,26 these genes point toward inhibition of involution markers and decreased expression of proliferation inhibitors. For example, genes downregulated include those related to immune function and oxidative stress (e.g. LEP, C6, and GPX3), cell death

(e.g. FOSL2, TEAD4, and NFIL3), inhibition of metabolism (e.g. CH25H and GFPT2), and inhibition of proliferation (e.g. CXCL2, TEAD4, and DACT2), all of which indicate a

shift away from cell death towards cellular proliferation. Across each time-point comparison, this study revealed novel genes, pathways, upstream regulators and transcription factors that could be targets of future studies to promote more rapid and efficient mammary gland involution.

Interestingly, a non-annotated lncRNA was downregulated in the mammary glands of heat-stressed dry cows compared with cooled cows at D25 relative to dry-off.

Long non-coding RNAs are involved in gene regulation through a variety of mechanisms like binding to complementary RNA to affect RNA processing, turnover, or localization or serving as precursors for smaller regulatory RNAs such as microRNAs or piwiRNAs.186 I identified seven miRNA seed regions within the lncRNA sequence that impact 1,159 downstream target genes, including known markers of involution (e.g.

SOCS3, IGF1R, IGFR, AKTIP) and upstream regulators that are significantly up- or downregulated during involution or in heat-stressed dry cows (e.g. PPARGC1A,

ACACA, VEGFA, ERBB2). Recent studies have identified miRNAs differentially expressed between lactating and non-lactating ruminants.148,187,188 For example, target genes for miRNAs (e.g. miRNA-148 and miR-145) expressed during the dry period promote cell death by downregulating STAT5188 and play a role in mammary metabolism by targeting INSIG1 for lipogenesis. Inhibition of miR-145 in goat MEC led to increased methylation levels of FASN, SCD1, PPARG, and SREBF1.189,190 Thus, the downregulation of the lncRNA by heat stress might affect the regulation of miRNAs, resulting in altered expression of proapoptotic and metabolic genes and key transcription factors involved in mammary gland cell turnover and metabolism. Further

investigation is needed to determine how important these miRNAs are in regulating downstream target gene expression in the mammary gland of heat-stressed cows.

Under a less stringent statistical analysis, this study identified genes impacted by heat stress that play a role in key processes such as ductal branching, mammary metabolism, cell death, immune function, and cell stress protection. Branching morphogenesis of the mammary ductal network was inhibited in cows exposed to environmental heat stress. Ductal branching during mammary development is coordinated by epithelial cell cilia under the influence of signaling pathways, such as

Wnt and Hedgehog.191 In the present study, Wnt pathway inhibitor (WIF1) and genes involved in ciliary function (e.g. LCA5, TEKT3, ACTL8, and MYO3B) were downregulated at D7 of involution in cows exposed to heat stress relative to cooled cows. In addition, upstream regulators and target genes involved in branching morphogenesis were impacted by heat stress (e.g. PTHLH, MFGE8, and FGF2). These results support previous reports of aberrant ductal branching of bovine MEC exposed to very high temperatures in vitro.87 Furthermore, emerging results from related studies suggest compromised mammary alveolar microstructure during lactation in cows exposed to heat stress during the dry period.16

Genes related to fatty acid metabolism (e.g. FABP3, ACSM1), amino acid transport (e.g. SLC38A3, SLC27A6, SLC39A8, and SLC31A2) and key upstream metabolic regulators (e.g. INSIG, ALOX15, HSD11B1, PPARGC1A, and SCD) were upregulated in the mammary gland of heat-stressed cows at D7 of involution. It is possible that cows exposed to heat stress compensate for increased cellular stress by promoting cellular metabolism. This possibility is supported by an in vitro study that

identified upregulated pathways related to functions necessary for cells undergoing proliferation, such as cell biogenesis, in bovine MECs exposed to high temperatures.127

However, these findings are contradictory to another in vitro study that reported a downregulation of metabolic genes in bovine MECs, leading to reduced cell growth,87 and to in vivo bovine models that reported increased utilization of glucose and amino acids but reduced fatty acid metabolism in attempt to thermoregulate.88 It is possible that these discrepancies could be due to the differences between in vivo and in vitro models, the alternative metabolic needs between lactating and non-lactating cows, the lack of negative energy balance in dry cows, and the cow’s ability to acclimate to heat stress over the course of a few days.88,104

Changes in expression of autophagy proteins in the mammary tissue of heat- stressed dairy cows have been reported,100 however, no genes related to autophagy were impacted by heat stress in this study. Two other key cellular processes for a successful involution of the mammary gland, apoptosis and immune response, were impacted by heat stress. Genes playing a role in phagocytosis of apoptotic cells (e.g.

MFGE8), induction of STAT3 expression (e.g. IL20RB) and upstream regulators of apoptotic promotion were upregulated in the mammary gland of heat-stressed cows during early involution. Meanwhile, genes related to breakdown of the extracellular matrix (e.g. MMP7 and MMP16), direct induction of apoptosis (e.g. FASLG), and lysogenic activity (e.g. LYG2, GZMK) were downregulated. The discrepancy between apoptosis promotion and inhibition could be due to the alternative roles of apoptosis, that is, death of heat stress injured cells vs. sloughing mammary epithelial cells during involution. Genes related to immune function (e.g. FCAMR, GP2, CRTAC1) and

inflammation (e.g. ILR20B, KLK7) were upregulated at D7 in heat-stressed cows.

Immune signaling was upregulated to combat heat stress in an in vitro rat model, where immune response activation occurred via extracellular secretions of heat-shock proteins.192 Herein, the heat shock protein Family (Hsp40) Member C12 (DNAJC12) was upregulated in the mammary gland of heat-stressed cows at D7. Similarly, previous literature has reported overexpression of other heat-shock proteins in the mammary gland of rat and bovine models to protect cells against hyperthermia.117,127,193,194

Conclusions

This is the first in vivo study to characterize the bovine mammary gland transcriptome during the dry period and under environmental heat stress utilizing RNA-

Seq. The findings reveal genes, pathways, and upstream regulators involved in the dynamic process of mammary gland involution and point towards key genes and pathways impacted by dry period heat stress. This work serves as the basis for more exhaustive research to investigate candidate genes and pathways to combat the negative effects of heat stress and promote successful cell turnover and tissue restoration with the goal of improving synthetic capacity for the subsequent lactation.

Table 2-1. Primer sequences for genes utilized for quantitative real-time PCR (qRT- PCR) validation of RNA-Seq results in bovine mammary tissue. Gene Name and Symbol Accession 5’ Primer Sequence Source Number >3’ α-lactalbumin (LALBA) BC102173.1 F AAAGACTTGAAGGGCTACGGA 195 R AGATGTTGCTTGAGTGAGGGTT

β-casein (CSN2) BC111172.1 F AGTGAGGAACAGCAGCAAACAG 195 R AGCAGAGGCAGAGGAAGGTG casein-αS1 (CSN1S1) BC109618.1 F TACCTGTCTTGTGGCTGTTGC 195 R CCTTTTGAATGTGCTTCTGCTC casein-αS2 (CSN1S2) BC114773.1 F GCCTGGACTACTTGTCTTCCTTTTA 195 R TCCTCTTCATTTGCGTTCCTTAC solute carrier family 7 BC126651 F GGGTGACGTAGCCAATCTGG 196 member R ATCCCCCATAGGCAAAGAGG 5 (SLC7A5) matrix-remodeling- XP_001254410.3 F CGCTGGGATCTCTCCACAT 173,174 associated protein 5 R GAGCTCCAGCTTCGTCAGTC (MXRA5) lipopolysaccharide binding NM_001038674 F TGGAGGTGCACATATCAGGA 173,174 protein (LBP) R CTTGCTCTCCAAGACCCTTC lysyl oxidase like 4 NM_174384 F CCAGCTTCTGCCTAGAGGAC 173,174 (LOXL4) R TAGGTATCCCAGCAGCCAAC angiopoietin like 4 NM_001046043 F GAAGAGGCTGCCCAAGATG 173,174 (ANGPTL4) R CCCTCTTCAAACAGCTCCTG solute carrier family 7 NM_001192889.2 F TCAAGGCTCCTTTGCCTATG 173,174 member R CAAATGTGACCAGTGGGATG 8 (SLC7A8) ubiquitously expressed XM_004022128.3 F TGTGGCCCTTGGATATGGTT 197 prefoldin-like chaperone R GGTTGTCGCTGAGCTCTGTG (UXT) 172 ribosomal protein S9 NM_001101152 F GGAGACCCTTCGAGAAGTCC (RPS9) R CTTTCTCATCCAGCGTCAGC

Table 2-2. Top KEGG pathways and MeSH terms along with their corresponding DEGs in bovine mammary tissue during transition between lactation to involution. KEGG pathways and MeSH terms with DEGs when contrasting D3 vs. D-3 (n=12, early involution vs. late lactation), relative to D3. D0 indicates dry-off (~46 d relative to expected calving). Cut-off criteria for DEG significance was FDR ≤ 5% and pathway/term significance was set at p ≤ 0.01 (Fisher’s exact test). KEGG Pathway p-value Genes Upregulated at D3 Genes Upregulated at D-3 NFKBIB, PTPN11, ACSL1, PRKAB1, CAMKK1, TNFRSF1A, ACSL5, IKBKB, Adipocytokine signaling MAPK8, PPARA, ADIPOR2, 1.70E-6 SOCS3, CPT1C, F1MVS.1, pathway SLC2A1, CAMKK2, PCK2, CPT1B, AKT3, NFKB1, PPARGC1A, CD36, PRKAA2 STAT3, CPT1A, TNFRSF1B FUCA2, HEXA, GBA, GLB1, Other glycan degradation 0.00024 MAN2C1, HEXDC HEXB, NEU3, FUCA1 DLD, HAGHL, ACYP2, MDH2, ALDH7A1, DLAT, PCK2, Pyruvate metabolism 0.00025 ACAT2, LDHA, ALDH3A2 ACSS2, ACACA, MDH1, PC, PDHA1, HAGH, PDHB BDH1, AACS, ACADS, HMGCS1, L2HGDH, ECHS1, Butanoate metabolism 0.0003 ACAT2 EHHAD, PDHA1, PDHB, OXCT1, ACSM5 PTPRF, RHOA, ACTG1, SRC, RAC1, ACTN4, EGFR, SMAD3, INSR, CTNNA1, ACTN2, NLK, PTPRB, Adherens junction 0.00032 NECTIN2, MAPK3, CTNNB1, TCF7L2 NECTIN4, ACTN1, WASF2, SSX2IP, IGF1R, RAC3, ACTB PFKM, DLD, ALDH7A1, ENO3, LDHA, BPGM, HK1, Glycolysis/ DLAT, PCK2, ALDOC, HK2, 0.00046 ALDH3B1, GAPDH, PGM1, Gluconeogenesis ACSS2, PDHA1, GALM, TPI1, ALDH3A2 PDHB ACO1, DLD, ACO2, DLST, SUCLA2, MDH2, DLAT, Citrate cycle (TCA Cycle) 0.0006 IDH3A PCK2, MDH1, PC, PDHA1, IDH1, PDHB RHOG, ILK, GAB1, LOC531038, RHOA, ARPC3, Bacterial invasion of CTTN, ACTG1, CBL, FN1, CD2AP, PTK2, MAD2L2, 0.0011 epithelial cells SRC, RAC1, ARPC2, CAV2 CTNNA1, CTNNB1, WASF2, ACTB, DOCK1, ARPC5 IARS, DARS2, LARS, CARS, CARS2, FARSB, TARS, Aminoacyl-tRNA 0.0012 WARS2, TARSL2 RARS2, YARS, MARS, biosynthesis GARS, NARS, AARS, FARSA, EARS2 HIBADH, DLD, IVD, DBT, MCCC1, ACADS, BCAT2, Valine, leucine, and 0.0012 ACAT2, ALDH3A2 ALDH7A1, HMGCS1, MUT, isoleucine degradation PCCB, BCKDHA, ECHS1, EHHAD, OXCT1

Table 2-2. Cont. KEGG Pathway p-value Genes Upregulated at D3 Genes Upregulated at D-3 YKT6, BNIP1, BET1, STX16, SNARE interactions in VAMP2, GOSR1, SNAP23, 0.0019 STX7, VAMP5, STX6 vesicular transport SEC22B, STX3, VAMP1, STX10, SNAP29 CYP51A1, MSMO1, DHCR24, SQLE, HSD17B7, NSDHL, Steroid biosynthesis 0.002 N/A DHCR7, LSS, SC5D

Glycosaminoglycan IDUA, HEXA, GLB1, SGSH, 0.002 NA, HPSE, HGSNAT degradation HEXB, GNS PPARG, SCP2, ACSL1, ILK, ANGPTL4, OLR1, PPARA, Glycerol kinase, PPAR signaling pathway 0.0022 ACSL5, CPT1C, CPT1B, PCK2, LPL, FABP3, CD36, SLC27A1, CPT1A EHHAD, FABP4, Acyl-CoA desaturase B4GALT2, HK1, GLB1, UGP2, PFKM, LALBA, GANC, Galactose metabolism 0.0023 PGM1, GLA HK2, B4GALT1 Glyoxylate and ACO1, ACO2, MDH2, PGP, 0.0032 ACAT2 dicarboxylate metabolism MUT, PCCB, MDH1 Amino sugar and GNPDA1, HK1, HEXA, UGP2, GNE, PMM2, nucleotide sugar 0.0054 UGDH, HEXB, PGM1, GNPNAT1, UXS1, UAP1, metabolism CYB5R1, RENBP HK2, GFPT1, TSTA3 GALC, AP4M1, DNASE2, IDUA, ABCB9, SORT1, LAMP1, ASAH1, CTSB, CTNS, GGA2, AP4B1, HEXA, GBA, GLB1, SGSH, ATP6V0A2, AP1M1, AP1S1, Lysosome 0.0055 HEXB, ARSA, CTSL2, GGA3, GNPTAB, AP3M1, AP4S1, GNS, M6PR, CLNP5, HGSNAT ATP6V0B, GLA, GM2A, SUMF1 MSN, RHOA, PFN1, ARPC3, TMSB4, ACTG1, PDGFRA, FN1, ITGB6, RAC1, EZR, ARPC2, LIMK2, PFN2, ACTN4, MYL9, EGFR, PIKFYVE, PIP4K2B, CYFIP2, Regulation of actin PDGFA, MYLK, MYLK2, PTK2, ACTN2, PIP5K1B, 0.0061 cytoskeleton CFL2, MYL12A, MAPK3, FGF12, GNA12, MAP2K2, CYFIP1, ARHGEF6, ITGB4, SSH3 ACTN1, WASF2, ITGA2, NCKAP1L, ITGAV, CFL1, RAC3, ACTB, DOCK1, TMSB4Y, ARPC5 ILK, PARVA, LAMC2, THBS1, RHOA, COMP, ACTG1, PDGFRA, PARVG, FN1, SRC ITGB6, RAC1, ACTN4, MYL9, EGFR, CAPN2, PDGFA, KDR, TLN2, NA, MAPK8, Focal adhesion 0.0078 MYLK, MYLK2, MYL12A, PTEN, PTK2, ACTN2, MAPK3, CTNNB1, AKT3, VEGFC, FLT1, CAV2 ZYX, ITGB4, ACTN1, THBS2, ITGA2, BCL2, VASP, ITGAV, IGF1R, RAC3, TLN1, ACTB, DOCK1

Table 2-2. Cont. KEGG Pathway p-value Genes Upregulated at D3 Genes Upregulated at D-3 Terpenoid backbone FDPS, IDI1, MVK, HMGCR, 0.009 ACAT2 biosynthesis HMGCS1, MVD PEX16, SCP2, ACSL1, MVK, SLC25A17, PECR, PEX26, SOD, ACSL5, HSD17B4, XDH, ABCD4, ECH1, PAOX, Peroxisome 0.0095 GSTK1, ACOT8 SOD1, PEX11G, EHHAD, IDH1, ABCD1, NUDT19, PEX1, NUDT12 THBS1, DYNC12, TUBB2B, NOS1, PIKFYVE, ATP6V0A2, OLR1, COMP, ACTG1, NCF4, COLEC12, SEC22B, MPO, C1R, RAC1, LAMP1, RAB7B, ATP6V1G2, DYNC1L1, CD36, Phagosome 0.01 DYNC1H1, CTSL2, STX7, TUBB1, FCGR2B, TUBA3E, M6PR, THBS2, ATP6V0B, TFRC, FCGR2A, ITGA2, ITGAV, CYBB, ACTB, LOC100295712, RAB5A TUBB2A Starch and sucrose PYGB, HK1, UGDH, PGM1, 0.012 UGP2, UXS1, GANC, HK2 metabolism ENPP1, GYS1 ALG14, RPN1, ALG3, STTA3, ALG11, ALG5, B4GALT1, N-Glycan biosynthesis 0.015 MGAT4B, B4GALT2, MGAT5 GANAB, DOLPP1, MAN2A2, DAD1, ALG10 CSF1, TNFRSF1A, STAT2, IL1R1, IRF9, NFKB2, FOSL1, MAP3K14, NCF4, IKBKB, PPARG, JAK1, MAPK8, SOCS3, RAC1, JUNB, Osteoclast differentiation 0.105 MAPK12, FCGR2B, TRAF6, IFNGR1, IFNAR2, SQSTM1, FCGR2A MAPK3, AKT3, BTK, CYBB, NFKB1, SPI1, IFNAR1, FOSL2, RELB DCTN6, VAMP2, ADCY6, Vasopressin-regulated DYNC12, DCTN1, DCTN2, CREB3L1, AQP3, DYNC1L1, 0.018 water reabsorption PRKX, DYNC1H1 CREB3L2, DYNLL2, RAB5A

PFKM, PMM2, PFKFB2, Fructose and mannose PFKFB4, KHK, ALDOC, HK2, 0.018 PFKFB3, HK1, TPI1 metabolism MTMR2, TSTA3

CBS, DLD, GAMT, GCAT, Glycine, serine and 0.02 N/A ALDH7A1, CHDH, PSPH, threonine metabolism PSAT1, CTH, AOC2, PHGDH SUCLA2, ALDH7A1, ACSS2, Propanoate metabolism 0.02 ACAT2, LDHA, ALDH3A2 MUT, PCCB, ACACA, ECHS1, EHHAD PLPP2, GALC, SPHK1, Sphingolipid metabolism 0.023 SGPL1, ASAH1, GBA, GLB1, SPTLC3 ARSA, ACER3, GLA, NEU3 Valine, leucine and IARS, LARS, BCAT2, PDHA1, 0.024 N/A isoleucine biosynthesis PDHB

Pentose and glucuronate 0.024 UGDH, ALDH3A2 UGP2, DCXR, XYLB, RP3E interconversions

Table 2-2. Cont. KEGG Pathway p-value Genes Upregulated at D3 Genes Upregulated at D-3 CLDN15, PTPN11, MSN, RHOA, PTK2B, ACTG1, NCF4, RAC1, ICAM1, EZR, Leukocyte transendothelial CXCL12, RAPGEF3, PTK2, 0.026 RASSF5, ACTN4, MYL9, migration ACTN2, MAPK12 GNAI3, CTNNA1, MYL12A, CTNNB1, F11R, ACTN1, VASP, CYBB, ACTB, CLDN4

IMMP1L, SPCS1, SRP19, Protein export 0.03 SEC11A, OXA1L SRP54, SRP9, SEC62

Alanine, aspartate and GPT2, ASNS, GPT, PPAT, 0.031 GLUD1, GLUL glutamate metabolism ADSSL1, GFPT1, CAD, ASS1

TOB1, CNOT9, C1D, EXOSC9, DHX36, LSM4, ENO3, PATL1, PABPC1L, MPHOSPH6, HSPA9, RNA degradation 0.034 PABPC4, PABPC1 HSPD1, LSM8, CNOT3, EXOSC5, CNOT7, BTG3, ZCCHC7

NOL6, RPP40, POP5, NOP58, NHP2, PWP2, LSG1, Ribosome biogenesis in MPHOSPH10, RIOK2, IMP3, 0.035 EIF6, UTP14A, POP7 eukaryotes BMS1, DKC1, GTPBP4, NMD3, RPP25L, RRP7A, NOB1, TAF9

Glycosphingolipid ST3GAL1, ST6GALNAC5, biosynthesis – 0.037 HEXA, GLB1, HEXB SLC33A ganglio series ACSL1, ACADS, ALDH7A1, ACAT2, ACSL5, CPT1C, Fatty acid degradation 0.038 ECI1, ECHS1, EHHAD CPT1B, CPT1A, ALDH3A2

Synthesis and degradation BDH1, HMGCS1, OXCT1 0.039 ACAT2 of ketone bodies

SESN2, CASP9, STEAP3, GADD45G, ATM, CCNE2, p53 signaling pathway 0.046 SFN, FAS, BAX, SERPINE1, PTEN, MDM2, BID, PIDD1, PERP, RFWD2 CYCS, CD82

CRLS1, PCYT2, CHPT1, AGPAT1, GPAT4, ETNK1, Glycerophospholipid PLPP2, PISD, PTDSS1, 0.04 GPD1L, APT2, GPAM, metabolism CHKB, PCYT1B, MBOAT1 PLA2G3, PLA2G12A, GPD1, PEMT, GPAT3

RDH11, CYP2B6, CYP3A5, Retinol metabolism 0.047 N/A ALDH1A1, LRAT, RETSAT, LOC540707

Table 2-2. Cont. MeSH Term p-value Genes Upregulated at D3 Genes Upregulated at D-3

PYCR1, TMEM39A, TOB1, LZTFL1, RBM47, NARFL, CSN2, TINF2, MFGE8, ECHDC1, CDCA7L, SQLE, SNAP23, PLIN2, LALBA, EIF2S2, SPRTN, RSRC2, SLC25A17, NHP2, STT3A, CLDN15, TAGLN2, HBP1, TPD52, ERLIN1, FKBP4, IGFBP2, ACSL5, SLC38A7, RFTN2, CSN1S1, SREBF1, GNPDA1, PON2, C1R, ALDH1A1, TMEM97, AQP3, ANXA2, PPP2R3C, CNN1, TMEM171, GID4, MDH2, TMX1, ACOT9, HSPB1, STAT5A, ALDH7A1, FAN1, SNX6, GNAI3, THOC5, DDX1, EMC2, STAT5B, CLIC1, SPNS1, FMOD, UAP1, TALDO1, OCIAD1, TBCB, DSTN, SQSTM1, ABT1, FASTK, HSPA9, OGN, Milk Proteins 1.50E-6 ANXA1, MYL12A, CTNNB1, ELF5, XDH, LPO, PAH, LPL, RALY, CSNK1G1, OSER1, ALDOC, PAEP, LRWD1, CTSL2, EMP3, M6PR, CTH, BSG, FABP3, NFE2L2, CD151, TPI1, PPARGC1A, MMACHC, POR, PPP2R1A, ITGAV, ORMDL2, KITLG, ABCG2, ECHS1, CFL1, JAML, RNF41, RSU1, VPS72, DAD1, RNASE4, OSMR, PGM5, DUSP11, AUP1, C6orf62, PARK7, PFN1, CLU, SOD, SQRDL, ARHGEF16, MAT2A, SET, MYL9, UTP14A P2RY14, FOLR1, PDHB, C25H16orf59, AOC2, ALG10, RBM4, C8orf4, FABP4, MLX, CSN1S2, APT2, HSPA8, DNAJA1, BS1A1, FOLR3, ANG, ACAD, LOC785756, ANG2

CSN2, CSN1S1, CSN1S2, Pepsin A 1.60E-5 COMP, TIMP1, TIMP2, CTSB ACYP2, PAEP, SOD1, HBB, H4, TROPT

AGPAT1, NDUFAB1, SREBF1, PPARA, PTEN, CAST, SOCS3, CPT1B, NFYA, MSTN, LDLR, LPL, Fatty acids 7.10E-5 CYB561 PAEP, FASN, FABP3, PC, FABP4, ACACA, SOD1, ACAD

ILK, MYO1C, MKL1, SYNJ1, ACTR3, RHOA, ARPC3, ACTG1, TAGLN, SRC, RAC1, ANXA2, ACTR2, MYH9, DCTN6, PTEN, CLIC5, PFN2, CD44, HSPB1, MYLK, UACA, PRKG1, MYO10, Actins 3.00E-5 S100A10, MAP4, MYL12A, PDCL, CAV2, NA, ANG, CTNNB1, HSPB6, ARRB1, TROPT CFL1, ACTB, TPM1, ARPC5, CCDC53, PFN1, ARPC2, ACTA2

Table 2-2. Cont. MeSH Term p-value Genes Upregulated at D3 Genes Upregulated at D-3 MFAP5, FBN1, SYNJ1, ACTR3, CAPZB, ARPC3, CAPG, TAGLN, SRC, ACTR2, FBN3, HPSE, CLIC5, UACA, Microfilament Proteins 0.00018 PFN2, CD44, LTBP1, LTBP2, PRKG1, MYO10, PDHB ACTB, DNAJC6, CCDC53, PFN1, GAPDH ACTR3, RHOA, GLUD1, PTEN, CAMKK2, SLC25A4, Adenosine Diphosphate 0.00018 ACTR2, ACTB, TPM1, ATP5B, MYO10, HSPA8 GAPDH, ARPC2 UQCRB, PFKFB2, CSN2, CRYAB, RHOA, TIMP1, MFGE8, LALBA, ACO2, IDH3A, GLUD1, ANXA2, CSN1S1, RNASEH2A, SERPINF1, TIMP2, CTSB, SPADH1, PDE1A, CYC1, Trypsin 0.00041 AGER, UGDH, LGALS1, AK3, MRPS23, LPO, LPL, ATP5B, F5, M6PR, CYB561, CNP, PAEP, MMACHC, PEBP1, PFN1, GAPDH, HNRNPA1, PRKG1, COX6B1, HBB, CLU, MT2A NME2, ANG, TROPT, CSN1S2, ACYP2, CYCS, H4 THBS1, GAB1, RHOA, OLR1, KDR, GRK2, MAPK8, TIMP1, FN1, SRC, RAC1, Phosphatidyl-inositol CAMKK2, ANGPT2, PTGS2, 0.00084 EZR, EGFR, INSR, 3-Kinases FLT1, PPARGC1A, UPK1A, SERPINE1, MAPK3, ITGA2, NA, SCDCT1, BCL2, ITGAV, IGF1R ANXA2, TIMP2, CTSB, Oxidants 0.001 PPARG, PTK2, XDH, SOD1 MAPK3 TPD52, PRDX3, PTGS2, PAEP, FABP3, ATF4, MFAP5, FBN1, TIMP2, Neoplasm Proteins 0.0014 ABCG2, NR3C1, FABP4, AHNAK, TBCB, SOD SPAM1, ABCB1, HSPA8, H4, ANG ILK, KIF3B, ACTR3, TIMP1, ARPC3, EHD1, ACTR2, EZR, NA, DMD, CLIC5, FRZB, Cytoskeletal Proteins 0.0018 S100A10, DSC3, CTNNB1, RAPGEF2, MYOC NFE2L2, MICALL1, DSC2, ARPC5, NA, ARPC2 Fatty Acids, SREBF1, MSTN, PAEP, 0.0019 N/A Monounsaturate LRAT, FABP4, ACAD PFN1, SRC, TIMP2, BCL2, NA, NDUFA6, NDUFS8, XDH, Peroxynitrous Acid 0.002 STAT3 SLC8A1, NDUFA1, SOD1 UQCRB, SLC34A2, CNGA1, GGA2, CSN2, MFGE8, SCP2, RBP4, GLTP, ACTR3, VAMP2, SLC18A2, PLIN2, TNFRSF1A, DCTN1, TIMP1, SLC7A5, BCL2L2-PABPN1, IGFBP2, IGFBP5, RTN2, CUL2, FKBP4, AZIN1, AAK1, IGFBP4, RAC1, ACTR2, SND1, MYBPC1, OMD, DCTN2, MAP1LC3B, RAPGEF2, NAPA, SLC8A1, Carrier Proteins 0.0028 GABARAPL1, AGER, ISG15, PAEP, ATG4B, GGA3, VCAN, FMOD, LGALS1, CLINT1, FABP3, MMACHC, CTNNB1, LBP, CYFIP1, USO1, LRAT, PEBP1, TSPO, M6PR, SLC6A15, PRKG1, PIBF1, APLN, SLC1A1, LTBP2, IGFALS, MYO10, FOLR1, TGFBR3, RAET1G, DPDS4 PDCL, FABP4, TUBG1, BDA20, FOLR3, SCGB1D

Table 2-2. Cont. MeSH Term p-value Genes Upregulated at D3 Genes Upregulated at D-3 CASP9, LAMP1, FAS, BAX, Fas Ligand Protein 0.0021 VAMP2, DYSF CYBB, GAPDH Clusterin 0.0022 CLU CSN1S2, LALBA, CSN1S1 Myxovirus Resistance 0.0022 MX2, ISG15, Mx1, OAS1X NA Proteins SREBF1, PAEP, SOD1, Carbon 0.0023 FN1 ACAD Growth Inhibitors 0.0023 LIF, EGFR ARG2, AMH, FABP3 PPARG, PPARA, PTGS2, Fatty Acids, Unsaturated 0.0029 MAPK3 FASN, FABP3, PPARGC1A, FABP4, SOD1, ACAD Glucose-phosphate 0.0032 GLA CD36 Dehydrogenase CSN2, LALBA, CSN1S1, Lactalbumin 0.0032 CRYAB, CLU B4GALT1, CSN1S2 RUSC1, BCL2L2-PABPN1, MSTN, B4GALT1, SLC7A1, CHRNA3, ICAM1, DDAH2, Arginine 0.0033 MYOC, ASS1, NCAPG, CPT1B HSPA8, SOD1, HIST1H2AC, H4, ACAD PTK2B, SRC, NOX5, RAC1, NOS1, VAMP2, DYSF, NADPH Oxidase 0.0042 LAMP1, NOX4, CYBB, STAT3 PRKG1, NR3C2, SOD1 CNGA1, ACO2, SLC16A1, Sulfhydryl Reagents 0.0047 F5, CYB561 PRKG1 PPARG, LALBA, PAEP, Oleic Acid 0.0047 N/A FASN, SOD1, ACAD THBS1, RHOA, COMP, FN1, Thrombospondins 0.0047 NA THBS2 OLR1, ICAM1, TIMP2, NOX4, KDR, PRDX3, SLC8A1, FLT1, Antioxidants 0.0058 BCL2, NFKB1, SOD NA, GSTM1, SOD1 CLU, FN1, FMOD, F5, FBD, OMD, BSG, POR, MYO10, Blood Proteins 0.0063 DEFB5, NA ANG2, LOC540707 CRYAB, TUBB2B, IGFBP2, CSN2, LALBA, LARS, Caseins 0.0065 SOCS3, SERPINE1, FMOD, LMAN1, CSN1S1, STAT5A, BCL2 PTEN, PAEP, CSN1S2 THBS1, KCNMB1, NOX5, RAC1, LAMP1, FAS, CD44, ROM1, MFGE8, SLC18A2, CD9, LBP, MOG, THBS2, PLIN2, SLC2A8, ANGPT2, Membrane Glycoproteins 0.0064 ITGAV, CYBB, UPK3B, LPL, B4GALT1, BSG, USO1, STAT3, ADGRE5, ENPP1, UPK1A, DMD, CD36, SOD1, SELPLG, CLU, GAPDH, BS1A1 DEFB5, LOC100298356 Acetylgluco-aminidase 0.0067 GNG5, FMOD HSPA8, HIST1H2AC, H4 Ascorbic Acid 0.0067 OLR1, CYB561 HAPLN1, PTGS2, SOD1 Lactoglobulins 0.0067 RBP4 CSN2, CSN1S1, PAEP, NA KDR, RAPGEF3, FLT1, NA, Epoprostenol 0.0067 N/A ANG ILK, TMSB10, ACTG1, Thymosin 0.0067 N/A TMSB4, TMSB4X

Table 2-2. Cont. MeSH Term p-value Genes Upregulated at D3 Genes Upregulated at D-3 Glycosylation End 0.0067 OLR1, AGER, NFE2L2 NA, HBB Products, Advanced Actin-Related Protein 2-3 ACTR3, ARPC3, ACTR2, 0.0067 N/A Complex ARPC5, ARPC2 LALBA, SLC7A5, PAEP, GLTP, ACTR3, GLUD1, Tryptophan 0.0067 B4GALT1, YARS, PRKG1, ACTR2 HBB, SOD1 BDH1, NQO2, DLD, XDH, NAD 0.0073 IDH3A, GLUD1, GAPDH IDH1, NDUFV1, NDUFS1 SLC34A2, CSN2, ATP5B, Alanine 0.0073 OLR1, CYB561 SLC1A5, FLT1, PRKAA2, FOLR1 RHOA, OLR1, COMP, FN1, ANXA2, SERPINF1, PRG4, JAK1, ANGPT2, MSTN, OMD, Proteoglycans 0.0088 VCAN, FMOD, CTNNB1, HAPLN1, FLT1, TGFBR3, NA IL6R, STAT3 Papain 0.009 CTSB CSN2, CSN1S1, FABP3 Profilins 0.009 SYNJ1, PFN2, ACTB, PFN1 N/A OAT, KLF15, ADRA2B, Retinol-Binding Proteins 0.0095 RBP4 PAEP, BDA20 Wiskott-Aldrich Syndrome CCDC53, ACTR3, ARPC3, 0.0095 N/A Protein Family ACTR2, ARPC5, ARPC2

Table 2-3. Top KEGG pathways and MeSH terms along with their corresponding DEGs in bovine mammary tissue during early involution. KEGG pathways and MeSH terms with DEGs when contrasting D7 vs. D3 (n=12, first week of involution), relative to D7. D0 indicates dry-off (~46 d relative to expected calving). Cut-off criteria for DEG significance was FDR ≤ 5% and pathway/term significance was set at p ≤ 0.01 (Fisher’s exact test). KEGG Pathway p-value Genes Upregulated at D7 Genes Upregulated at D3 CLDN15, MYH14, RHOA, ACTG1, MYH2, YBX3, Tight junction 0.00018 MYH7 ACTN4, MYL9, RRAS2, CTNNA1, MYL12A, VAPA, F11R, CLDN3 HRAS RHOA, TMSB4, ACTG1, RAC1, EZR, ACTN4, MYL9, Regulation of actin RRAS2, PPP1CB, PDGFA, 0.00057 PIK3R2, IQGAP3 cytoskeleton MYLK, MYLK2, MYL12A, MAPK3, RAC3, TMSB4Y, GNA13, HRAS ILK, LAMC2, THBS1, RHOA, ACTG1, RAC1, ACTN4, MYL9, PPP1CB, CAPN2, Focal adhesion 0.00067 PIK3R2, LAMB1 PDGFA, MYLK, MYLK2, MYL12A, MAPK3, ZYX, RAC3, HRAS CLDN15, CTNND1, RHOA, Leukocyte transendothelial ACTG1, RAC1, EZR, 0.00071 PIK3R2, CXCL12 migration ACTN4, MYL9, CTNNA1, MYL12A, F11R, CLDN3 CTNND1, RHOA, ACTG1, RAC1, ACTN4, INSR, Adherens junction 0.0013 N/A CTNNA1, MAPK3, SSX2IP, RAC3 ITPR2, KCNMB1, RHOA, Vascular smooth muscle MYL9, PPP1CB, KCNMA1, 0.0024 PLA2G3, MYL6B contraction MYLK, ACTA2, MYLK2, MAPK3, GNA13 ILK, ARHGAP10, GAB1, Bacterial invasion of 0.0052 PIK3R2 RHOA, ACTG1, RAC1, epithelial cells CLTB, CTNNA1 RHOA, RAC1, PLXNB2, UNC5C, EPHB3, ROBO1, Axon guidance 0.0072 MAPK3, PPP3CC, EFNA1, EPHA4 SEMA6A, RAC3, HRAS RAC1, HSPB1, MAPK3, VEGF signaling pathway 0.016 PIK3R2, PLA2G3 PPP3CC, RAC3, HRAS CCNE1, GTSE1, CCNB1, p53 signaling pathway 0.042 STEAP3, FAS, RFWD2 PIDD1

Type II diabetes mellitus 0.044 PIK3R2, SOCS1, ADIPOQ INSR, MAPK3 CDC45, CCNE1, CCNA2, Cell cycle 0.046 BUB1B, ESPL1, CDC20, CDC26, CDKN1B CDKN2C, CCNB1, BUB1

Table 2-3. Cont. MeSH Term p-value Genes Upregulated at D3 Genes Upregulated at D-3 ILK, TMSB4, ACTG1, Thymosin 0.00032 N/A TMSB4Y Cyclin A 0.0007 CCNA2, MYBL2, CCNB1 N/A Cyclin-Dependent Kinase 0.0032 N/A RAC1, MAPK3, CDKN1B Inhibitor p27 Elastin 0.0035 CCNA2, MYBL2, FBLN5 TMEM43 ILK, MYO1C, RHOA, ACTG1, TAGLN, RAC1, Actins 0.0058 MYH7 CLTB, WASHC1, HSPB1, MYLK, ACTA2, MAP4, MYL12A, ARRB1 Deoxyadenosines 0.0083 CCNA2, CCNB1 N/A Ornithine Decarboxylase 0.0093 ODC1 SAT1 GTP-Binding Protein alpha 0.0093 N/A THBS1 RHOA Subunit, Gi2 Insulin-Like Growth Factor THBS1 IGFBP2 IGFBP5 0.0093 N/A Binding Protein 3 INSR CLDN15, EEF2, HBP1, IGFBP2, KRT17, TNS4, PRKCSH, PON2, PPP2R3C, TPT1, CNN1, ACOT9, ALDH1A1, FAN1, LAMB1, MYL9, HSPB1, MAF1, OGN, ARSE, POR, LRRC17, Milk proteins 0.0015 UTP14A, EIF2S3, DSTN, C7H8orf4, PCLAF, SQSTM1, MYL12A, OSER1, FAM118B, CTSV, NFE2L2, CD151, ORMDL2, JAML, RNF114, RSU1, PGM5, DUSP11, BICD2 Mitogen-Activated Protein THBS1, GAB1, EEF2, LIF, 0.0037 N/A Kinases HSPB1, MAPK3, HRAS Calreticulin 0.0032 N/A THBS1, RHOA, INSR Cholera Toxin 0.001 N/A ARF2, SAT1 rhoB GTP-Binding Protein 0.0007 N/A RHOA, RHOB Myosin Type II 0.0032 N/A RHOA, MYL12A Cyclin-Dependent Kinase 2 0.0093 CCNA2, MSTN N/A

Table 2-4. Differentially expressed genes (DEGs) in bovine mammary tissue during steady-state involution and redevelopment. All DEGs between D14 vs. D7 (n=12, steady-state involution vs. active involution), relative to D14, and DEGs between D25 vs. D14 (n=12, redevelopment vs. steady-state), relative to D25. D0 indicates dry-off (~46 d to expected calving). Cut-off criteria for DEG significance was nominal p ≤ 0.005 and log2 fold change (FC) ≥ |0.5|. D14 vs. 7

-Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value ENSBTAG00000038748 hemoglobin subunit beta LOC100850059 2.61 2.74 ankyrin repeat, SAM and basic leucine zipper domain ENSBTAG00000000169 containing 1 ASZ1 1.84 2.47 ENSBTAG00000015582 heme oxygenase 1 HMOX1 1.31 2.31 antimicrobial peptide NK-lysin- ENSBTAG00000047449 like LOC104968634 1.29 2.89 ENSBTAG00000031828 uncharacterized LOC616323 LOC616323 1.29 2.89 ENSBTAG00000047816 Uncharacterized protein NA 1.29 2.89 XLOC_017734 NA NA 0.95 2.48 ENSBTAG00000000812 CD300a molecule CD300A 0.5 2.42 XLOC_019509 NA NA 0.5 2.38 ENSBTAG00000047944 GATA binding protein 5 GATA5 -1.26 3.23 D25 vs. D14

XLOC_008198 NA NA 1.47 2.72 XLOC_020169 NA NA 0.97 2.35 ENSBTAG00000025448 IZUMO family member 4 IZUMO4 0.93 2.89 ENSBTAG00000029982 microRNA 142 MIR142 0.72 2.33 ENSBTAG00000019929 integrin subunit alpha V ITGAV -0.5 2.40 ENSBTAG00000021483 olfactomedin like 1 OLFML1 -0.51 2.29 solute carrier family 16, member 2 (thyroid hormone ENSBTAG00000002585 transporter) SLC16A2 -0.57 2.66 nuclear factor, interleukin 3 ENSBTAG00000017763 regulated NFIL3 -0.59 2.36 ENSBTAG00000019788 TEA domain family member 4 TEAD4 -0.67 2.41 microtubule associated protein ENSBTAG00000001961 1B MAP1B -0.69 2.48 ENSBTAG00000011115 cholesterol 25-hydroxylase CH25H -0.69 2.29 ENSBTAG00000023929 FOS like antigen 2 FOSL2 -0.71 2.80 ENSBTAG00000007101 tissue factor LOC101909187 -0.72 2.31 ENSBTAG00000046328 Xg blood group XG -0.72 2.31 ENSBTAG00000000442 retinol binding protein 4 RBP4 -0.93 2.33

Table 2-4. Cont. –Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value family with sequence similarity ENSBTAG00000002027 167-member B FAM167B -1.04 2.54 periostin, osteoblast specific ENSBTAG00000012409 factor POSTN -1.06 2.29 glutamine-fructose-6- ENSBTAG00000002215 phosphate transaminase 2 GFPT2 -1.19 2.52 ENSBTAG00000020647 RAS-like family 11-member B RASL11B -1.2 2.52 ENSBTAG00000043553 glutathione peroxidase 3 GPX3 -1.41 2.82 ENSBTAG00000014177 complement component 6 C6 -1.41 2.72 ENSBTAG00000007344 FERM domain containing 7 FRMD7 -1.44 2.54 chemokine (C-X-C motif) ENSBTAG00000027513 ligand 2 CXCL2 -1.65 2.70 leucine-rich alpha-2- ENSBTAG00000031647 glycoprotein 1 LRG1 -1.68 3.03 disheveled-binding antagonist ENSBTAG00000031532 of beta-catenin 2 DACT2 -1.81 2.72 ENSBTAG00000014911 leptin LEP -2.84 2.64

Table 2-5. Differentially expressed genes (DEGs) in bovine mammary tissue between heat-stressed and cooled cows during the dry period. All DEGs between heat stress (HT, n=6) and cooled (CL, n=6) cows at D3, 7, 14, and 25 relative to dry-off (D0, ~46 d relative to expecting calving). Expression is relative to HT cows. Cut-off criteria for DEG significance was nominal p ≤ 0.005 and log2 fold change (FC) ≥ |0.5|. HT vs. CL D3

–Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value XLOC_017640 NA NA 2.57 3.35 XLOC_014942 NA NA 2.08 2.77 ENSBTAG00000019588 MHC cell surface glycoprotein LA-DQB 1.69 2.46 ENSBTAG00000012668 Uncharacterized protein NA 1.39 3.00 XLOC_002535 NA NA 1.27 2.32 ENSBTAG00000001154 diacylglycerol O-acyltransferase 2 DGAT2 1.21 2.74 ENSBTAG00000013249 spalt-like transcription factor 2 SALL2 1.11 2.77 FBJ murine osteosarcoma viral ENSBTAG00000008182 oncogene homolog B FOSB -1.48 3.00 feline leukemia virus subgroup C ENSBTAG00000011985 receptor-related protein 2 LOC509034 -2.13 2.42 HT vs. CL D7 patatin-like phospholipase ENSBTAG00000045746 domain-containing protein 3 LOC786474 3.18 3.48 ENSBTAG00000003367 Uncharacterized protein NA 3.18 3.48 ENSBTAG00000019799 Fc receptor, IgA, IgM, high affinity FCAMR 2.86 2.96 ENSBTAG00000008102 cartilage acidic protein 1 CRTAC1 2.64 2.64 ENSBTAG00000016819 fatty acid binding protein 3 FABP3 2.53 3.00 acyl-CoA synthetase medium- ENSBTAG00000001417 chain family member 1 ACSM1 2.5 4.07 XLOC_017865 NA NA 2.46 2.30 ENSBTAG00000038461 Uncharacterized protein NA 2.41 4.82 ENSBTAG00000032819 mucin 20, cell surface associated MUC20 2.26 2.77 ENSBTAG00000019588 MHC cell surface glycoprotein LA-DQB 2.24 3.47 ENSBTAG00000008509 solute carrier family 38 member 3 SLC38A3 2.18 2.46 ENSBTAG00000018481 major allergen Equ c 1 LOC513329 2.1 2.41 ENSBTAG00000006263 glycoprotein 2 GP2 2.1 2.34 ENSBTAG00000045728 acyl-CoA desaturase NA 1.97 2.28 ENSBTAG00000012393 angiotensinogen AGT 1.88 2.30 DnaJ heat shock protein family ENSBTAG00000010932 (Hsp40) member C12 DNAJC12 1.85 2.60 ENSBTAG00000039787 casein kappa CSN3 1.77 2.40 3-hydroxybutyrate ENSBTAG00000000448 dehydrogenase, type 1 BDH1 1.72 3.51 ENSBTAG00000030483 kallikrein related peptidase 7 KLK7 1.67 4.15

ENSBTAG00000015493 Uncharacterized protein NA 1.64 2.51

Table 2-5. Cont. –Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value solute carrier family 27 (fatty acid ENSBTAG00000004860 transporter), member 6 SLC27A6 1.64 2.28 ENSBTAG00000046391 GRAM domain containing 2 GRAMD2 1.62 2.36 solute carrier family 39 (zinc ENSBTAG00000005668 transporter), member 8 SLC39A8 1.52 2.40 potassium channel, voltage gated ENSBTAG00000007553 shaker related subfamily A, member 5 KCNA5 1.43 2.96 ENSBTAG00000007694 kinesin family member 25 KIF25 1.42 4.57 ENSBTAG00000008493 aquaporin 3 (Gill blood group) AQP3 1.42 2.34 solute carrier family 31 (copper ENSBTAG00000005115 transporter), member 2 SLC31A2 1.4 3.19 XLOC_012445 NA NA 1.29 2.36 intestine-specific transcript 1 ENSBTAG00000033886 protein CIST1 1.26 3.32 ENSBTAG00000008434 glycine C-acetyltransferase GCAT 1.26 3.19 ENSBTAG00000020665 GDNF family receptor alpha 2 GFRA2 1.2 2.66 solute carrier family 22 member ENSBTAG00000012742 18 SLC22A18 1.15 2.42 ENSBTAG00000011034 angiopoietin 2 ANGPT2 1.1 5.17 XLOC_024514 NA NA 1.09 2.34 ENSBTAG00000048017 proline rich 16 PRR16 1.05 2.59 XLOC_004900 NA NA 1.04 2.49 ENSBTAG00000006738 G protein-coupled receptor 68 GPR68 1.03 3.00 ENSBTAG00000034848 F2R like trypsin receptor 1 F2RL1 1.01 2.35 ENSBTAG00000010361 delta-like 4 (Drosophila) DLL4 0.97 3.00 ENSBTAG00000021082 transmembrane protein 125 TMEM125 0.96 2.59 ENSBTAG00000004356 roundabout guidance receptor 4 ROBO4 0.91 3.24 potassium channel, voltage gated ENSBTAG00000007352 Shaw related subfamily C, member 4 KCNC4 0.91 2.37 ENSBTAG00000000496 solute carrier family 12, member 8 SLC12A8 0.88 2.39 ENSBTAG00000006835 melanoma cell adhesion molecule MCAM 0.85 2.30 6-phosphofructo-2- ENSBTAG00000006752 kinase/fructose-2,6- biphosphatase 4 PFKFB4 0.82 2.39 RUN and SH3 domain containing ENSBTAG00000011403 2 RUSC2 0.82 2.38 purinergic receptor P2Y, G- ENSBTAG00000020990 protein coupled, 14 P2RY14 0.81 2.34 ENSBTAG00000004207 CD93 molecule CD93 0.8 2.70 ENSBTAG00000005871 MDS1 and EVI1 complex locus MECOM 0.79 2.89 ENSBTAG00000047144 Uncharacterized protein NA 0.79 2.89 ENSBTAG00000001803 four and a half LIM domains 5 FHL5 0.79 2.48

Table 2-5. Cont. –Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value sterol regulatory element binding ENSBTAG00000007884 transcription factor 1 SREBF1 0.76 2.60 ENSBTAG00000006894 nitric oxide synthase 2 NOS2 0.76 2.37 ENSBTAG00000015794 nestin NES 0.74 2.68 ENSBTAG00000006434 synaptopodin 2 SYNPO2 0.73 3.62 amine oxidase, copper containing ENSBTAG00000030333 3 AOC3 0.73 3.37 v-ets avian erythroblastosis virus ENSBTAG00000011001 E26 oncogene homolog ERG 0.73 3.15 ENSBTAG00000010366 hypocretin (orexin) receptor 1 HCRTR1 0.73 2.48 ENSBTAG00000046047 Uncharacterized protein NA 0.72 4.10 ENSBTAG00000022837 zinc finger protein 75D ZNF75D 0.72 4.10 ENSBTAG00000034373 cadherin 13 CDH13 0.69 3.55 family with sequence similarity ENSBTAG00000038700 124 member B FAM124B 0.67 3.00 ENSBTAG00000003238 mesenchyme homeobox 2 MEOX2 0.66 2.96 ENSBTAG00000003711 endothelial PAS domain protein 1 EPAS1 0.66 2.38 adhesion G protein-coupled ENSBTAG00000004347 receptor F5 ADGRF5 0.64 3.08 ENSBTAG00000031252 CD82 molecule CD82 0.64 3.00 murine retrovirus integration site 1 ENSBTAG00000007129 homolog MRVI1 0.64 2.36 carbohydrate (N- ENSBTAG00000003312 acetylgalactosamine 4-sulfate 6- O) sulfotransferase 15 CHST15 0.63 3.21 ENSBTAG00000017869 caveolin 1 CAV1 0.62 2.82 dehydrogenase/reductase (SDR ENSBTAG00000010297 family) member 11 DHRS11 0.62 2.29 ENSBTAG00000000053 filamin A interacting protein 1 FILIP1 0.61 3.77 platelet/endothelial cell adhesion ENSBTAG00000012066 molecule 1 PECAM1 0.6 2.60 solute carrier family 2 (facilitated ENSBTAG00000009617 glucose transporter), member 1 SLC2A1 0.56 2.72 ENSBTAG00000037508 early B-cell factor 1 EBF1 0.54 2.43 ENSBTAG00000005077 chemokine (CXC motif) ligand 12 CXCL12 0.54 2.42 protein tyrosine phosphatase, ENSBTAG00000012119 receptor type Z1 PTPRZ1 -0.55 2.31 poly(ADP-ribose) polymerase ENSBTAG00000004066 family member 8 PARP8 -0.57 2.34 ENSBTAG00000021469 cortactin binding protein 2 CTTNBP2 -0.61 3.40 ENSBTAG00000046343 cyclin J like CCNJL -0.74 2.44 ENSBTAG00000037510 GTPase, IMAP family member 1 GIMAP1 -0.76 2.34 ENSBTAG00000039847 Uncharacterized protein NA -0.78 3.16

ENSBTAG00000039380 Uncharacterized protein NA -0.78 3.16

Table 2-5. Cont. –Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value v-myc avian myelocytomatosis ENSBTAG00000006338 viral oncogene lung carcinoma derived homolog MYCL -0.79 2.85 ENSBTAG00000044087 KIAA1328 KIAA1328 -0.8 3.27 ENSBTAG00000046744 paralemmin 3 PALM3 -0.8 2.35 ENSBTAG00000003802 tektin 3 TEKT3 -0.83 2.80 neuralized E3 ubiquitin protein ENSBTAG00000000961 ligase 2 NEURL2 -0.84 2.64 ENSBTAG00000005122 kininogen 1 KNG1 -0.85 2.55 SID1 transmembrane family ENSBTAG00000013292 member 1 SIDT1 -0.85 2.37 ENSBTAG00000006742 paired box 1 PAX1 -0.88 2.34 ENSBTAG00000011854 solute carrier family 38 member 5 SLC38A5 -0.93 2.62 killer cell lectin-like receptor ENSBTAG00000046389 subfamily D, member 1 KLRD1 -0.94 2.59 ENSBTAG00000047625 protocadherin Fat 2 precursor NA -0.96 2.80 protein tyrosine phosphatase, ENSBTAG00000010178 receptor type D PTPRD -0.97 2.37 ENSBTAG00000009460 zinc finger protein 550 ZNF550 -0.99 2.52 solute carrier family 29 ENSBTAG00000007772 (equilibrative nucleoside transporter), member 4 SLC29A4 -0.99 2.48 ENSBTAG00000008550 GTPase, IMAP family member 7 GIMAP7 -1.01 2.38 EGF like repeats and discoidin ENSBTAG00000044033 domains 3 EDIL3 -1.12 3.06 ENSBTAG00000046648 carboxypeptidase A4 CPA4 -1.15 2.62 ENSBTAG00000013476 carboxypeptidase A5 CPA5 -1.15 2.62 XLOC_023625 NA NA -1.15 2.62 StAR related lipid transfer domain ENSBTAG00000008168 containing 6 STARD6 -1.17 2.34 family with sequence similarity ENSBTAG00000015467 184 member A FAM184A -1.19 2.51 beta-1,3-N- ENSBTAG00000046104 acetylgalactosaminyltransferase 1 B3GALNT1 -1.26 3.09 ENSBTAG00000006438 actin like 8 ACTL8 -1.3 2.30 ENSBTAG00000006877 matrix metallopeptidase 16 MMP16 -1.42 3.09 ENSBTAG00000003626 myosin IIIB MYO3B -1.54 3.02 ENSBTAG00000031348 chemokine (C-C motif) receptor 9 CCR9 -1.55 2.62 ENSBTAG00000043950 Leber congenital amaurosis 5 LCA5 -1.61 2.96 XLOC_006989 NA NA -1.62 2.55 ENSBTAG00000002582 lysozyme g2 LYG2 -1.78 2.48 ENSBTAG00000019460 monooxygenase, DBH-like 1 MOXD1 -1.81 2.80

ENSBTAG00000011985 feline leukemia virus subgroup C receptor-related protein 2 LOC509034 -1.96 2.42

Table 2-5. Cont. Log2 Fold –Log10 ENSEMBL Gene ID Gene Name Symbol Change p-value ENSBTAG00000039425 keratin 6A KRT6A -2.2 2.96 ENSBTAG00000014758 WNT inhibitory factor 1 WIF1 -3.18 3.08 HT vs. CL D14 ENSBTAG00000011666 thyroid hormone responsive THRSP 4.71 3.11 ENSBTAG00000031802 spermatogenesis associated 16 SPATA16 2.95 4.77 acyl-CoA synthetase medium- ENSBTAG00000001417 chain family member 1 ACSM1 2.93 4.17 ENSBTAG00000019588 MHC cell surface glycoprotein LA-DQB 2.73 3.64 ENSBTAG00000030483 kallikrein related peptidase 7 KLK7 2.02 2.54 ENSBTAG00000012393 angiotensinogen AGT 1.93 2.27 ENSBTAG00000000908 hydroxycarboxylic acid receptor 1 HCAR1 1.78 2.43 ENSBTAG00000017677 secretogranin III SCG3 1.32 3.30 ENSBTAG00000023659 metallothionein 2A MT2A 1.17 3.01 ENSBTAG00000046583 transmembrane protein 61 TMEM61 1.14 2.68 receptor (G protein-coupled) ENSBTAG00000020704 activity modifying protein 3 RAMP3 1.05 2.55 adhesion G protein-coupled ENSBTAG00000013848 receptor D1 ADGRD1 0.7 2.49 kynurenine-oxoglutarate ENSBTAG00000036101 transaminase 1 LOC781863 0.65 3.16 bone morphogenetic protein ENSBTAG00000002081 receptor type IB BMPR1B -1.93 2.43 ENSBTAG00000001835 gap junction protein alpha 1 GJA1 -2.44 2.33 XLOC_021776 NA NA -2.66 2.34 C1q and tumor necrosis factor ENSBTAG00000008074 related protein 6 C1QTNF6 -2.79 2.42 ENSBTAG00000007344 FERM domain containing 7 FRMD7 -2.89 2.64 ENSBTAG00000018765 semaphorin 5B SEMA5B -2.91 2.74 prolyl 4-hydroxylase, alpha ENSBTAG00000006579 polypeptide III P4HA3 -3.23 2.47 potassium channel, two pore ENSBTAG00000017500 domain subfamily K, member 12 KCNK12 -3.44 2.54 ENSBTAG00000004206 leucine rich repeat containing 55 LRRC55 -4 2.89 C1q and tumor necrosis factor ENSBTAG00000017071 related protein 3 C1QTNF3 -4.13 2.64 ENSBTAG00000026708 protease, serine 35 PRSS35 -4.13 2.62 cell migration inducing protein, ENSBTAG00000007431 hyaluronan binding CEMIP -4.39 2.85 ENSBTAG00000021217 collagen type XI alpha 1 COL11A1 -4.45 2.77 fibronectin type III domain- ENSBTAG00000003938 containing protein 1 LOC783891 -4.64 2.92 HT vs. CL D25 ENSBTAG00000005005 casein alpha-S2 CSN1S2 2.94 2.33 ENSBTAG00000045514 Uncharacterized protein NA 2.91 2.46

Table 2-5. Cont. –Log10 ENSEMBL Gene ID Gene Name Symbol Log2 FC p-value ENSBTAG00000031802 spermatogenesis associated 16 SPATA16 2.7 3.82 ENSBTAG00000019588 MHC cell surface glycoprotein LA-DQB 2.19 2.70 ENSBTAG00000023270 cadherin 19, type 2 CDH19 1.95 2.62 ENSBTAG00000010880 troponin I type 2 (skeletal, fast) TNNI2 1.58 2.89 ENSBTAG00000040103 synaptotagmin 8 SYT8 1.58 2.89 ENSBTAG00000013854 calmodulin-like 5 CALML5 1.49 2.74 patatin-like phospholipase ENSBTAG00000045746 domain-containing protein 3 LOC786474 1.48 2.31 ENSBTAG00000003367 Uncharacterized protein NA 1.48 2.31 potassium channel, voltage gated ENSBTAG00000016646 shaker related subfamily A, member 1 KCNA1 1.21 2.96 ENSBTAG00000030483 kallikrein related peptidase 7 KLK7 1.2 2.49 phosphatase and actin regulator ENSBTAG00000015080 3 PHACTR3 1.15 2.46 ENSBTAG00000012768 HMG box domain containing 3 HMGXB3 0.73 3.92 solute carrier family 26 (anion ENSBTAG00000014615 exchanger), member 2 SLC26A2 0.73 3.92 ENSBTAG00000007102 G2 and S-phase expressed 1 GTSE1 0.69 2.36 ENSBTAG00000037510 GTPase, IMAP family member 1 GIMAP1 -0.62 2.34 XLOC_008612 NA NA -0.69 2.44 ENSBTAG00000006452 CD3d molecule CD3D -0.7 2.29 SID1 transmembrane family ENSBTAG00000013292 member 1 SIDT1 -0.7 2.54 ENSBTAG00000013476 carboxypeptidase A5 CPA5 -0.94 2.60 ENSBTAG00000046648 carboxypeptidase A4 CPA4 -0.94 2.60 ENSBTAG00000003802 tektin 3 TEKT3 -0.95 3.02 XLOC_003026 NA NA -1.05 2.31 ENSBTAG00000010103 tripartite motif containing 9 TRIM9 -1.57 2.77 ENSBTAG00000004145 anoctamin 4 ANO4 -1.71 2.89 XLOC_001738 NA NA -1.97 2.55 ENSBTAG00000014758 WNT inhibitory factor 1 WIF1 -2.63 2.74 XLOC_021690 NA NA -3.95 9.44

Figure 2-1. Pictorial representation of experimental design. (A) Design of treatment group pens. Freestall pens were divided in two, with the left pen under cooled (CL, n=6) treatment with access to shade, fans, and soakers on a timer and the right pen under heat-stressed (HT, n=6) treatment with access only to shade. (B) Timeline and protocol of mammary gland biopsy collections that occurred at D-3, 3, 7, 14, and 25, relative to dry-off (D0). Pictures display location of biopsy collection in the rear quarters of the udder and approximate size of tissue collected.

Figure 2-2. Volcano plot of DEGs in bovine mammary tissue during early involution (D3 vs. D-3 and D7 vs. D3). Differential gene expression in the bovine mammary gland contrasting (A) D3 vs. D-3 (n=12, early involution vs. late lactation) and (B) D7 vs. D3 (n=12, first week of involution). D0 indicates dry-off (~46 d relative to expected calving). Cut-off criteria for DEG significance was FDR ≤ 5%. The y-axis displays the -log10 q-value for each gene, while the x-axis displays the log2 fold change for that gene relative to D3 (A) or D7 (B). Red dots indicate upregulation, green dots indicate downregulation, and black dots indicate non-significance relative to (A) D3 or (B) D7.

Figure 2-3. Significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Medical Subject Headings (MeSH) terms in bovine mammary tissue during early involution (D3 vs. D-3 and D7 vs. D3). Enriched KEGG pathways and MeSH terms among DEGs in the bovine mammary gland contrasting (A) D3 vs. D-3 (n=12, early involution vs. late lactation) and (B) D7 vs. D3 (n=12, first week of involution). D0 indicates dry-off (~ 46 d relative to expecting calving). DEG significance was set at FDR ≤ 5%, and pathway/term significance was set at p ≤ 0.01 (Fisher’s exact test). The y-axis displays the names and the total number of genes of each pathway/term. The x-axis displays the total significance of enrichment (–log10 p-value) and the number of DEGs within each pathway/term with expression relative to (A) D3 or (B) D7. Red and blue bars indicate proportion of upregulated DEGs while green and orange bars indicate proportion of downregulated DEGs.

Figure 2-4. Ingenuity® Pathway Analysis (IPA®) upstream regulators and summary network in bovine mammary tissue comparing D3 vs. D-3 relative to dry-off. Significant upstream regulators and network in the bovine mammary gland contrasting early involution vs. late lactation (D3 vs. D-3, n=12). D0 indicates dry-off (~ 46 d relative to expecting calving). The DEG significance was set at FDR ≤ 5%, and the upstream regulator significance of enrichment at p ≤ 0.05 with log2 fold change ≥|1.0|. (A) Upstream regulators are grouped by functional categories with log2 FC (equivalent to expression log ratio) in blue bars, Z-score (activated: >2, inhibited: <-2) in orange bars, and significance of enrichment (–log10 p-value) in gray dots. (B) The summary network depicts the interactions between upstream regulators, downstream genes, and physiological functions. Red and green molecules indicate upregulated and downregulated genes, respectively, relative to D3. Figure legend displays molecules and function symbol types and colors.

Figure 2-5. Ingenuity® Pathway Analysis (IPA®) upstream regulators and summary network in bovine mammary tissue comparing D7 vs. D3 relative to dry-off. Significant upstream regulators and network in the bovine mammary gland during the first week of involution (D7 vs. D3, n=12). D0 indicates dry-off (~46 d relative to expecting calving). The DEG significance was set at FDR ≤ 5%, and upstream regulator significance of enrichment at p ≤ 0.05 with log2 fold change ≥|1.0|. (A) Upstream regulators are grouped by functional categories with log2 fold change (equivalent to expression log ratio) in blue bars, Z-score (activated: >2, inhibited: <-2) in orange bars, and significance of enrichment (–log10 P-value) in gray dots. (B) The summary network depicts the interactions between upstream regulators, downstream genes, and physiological functions. Red and green molecules indicate upregulated and downregulated genes, respectively, relative to D7. Figure legend displays molecules and function symbol types and colors.

Figure 2-6. Characterization of DEGs in bovine mammary tissue between heat-stressed (HT) and cooled (CL) dairy cattle during the dry period. (A) Number and direction of DEGs (nominal p ≤ 0.005, absolute log2 fold change ≥ 0.5) in the bovine mammary between HT (n=6) and CL (n=6) dairy cattle at D3, 7, 14, and 25 relative to dry-off (D0, ~46 d relative to expecting calving). Expression is relative to HT cows. Red indicates upregulation, green indicates downregulation, and grey is the total number of DEGs. (B) DEGs that are consistently up- or downregulated over time (D7, 14, and 25) relative to HT cows. The y-axis displays the -log2 fold change of each DEG and the x-axis lists the gene name.

Figure 2-7. Ingenuity® Pathway Analysis (IPA®) upstream regulators and summary network in bovine mammary tissue between heat-stressed (HT, n=6) and cooled (CL, n=6) dairy cattle during the dry period. Significant upstream regulators and network in the bovine mammary gland in HT vs. CL cows (relative to HT) at D7 relative to dry-off (D0). The DEG significance was set at FDR ≤ 5%, and upstream regulator significance of enrichment at p ≤ 0.05 with log2 fold change ≥|1.0|. (A) Upstream regulators are grouped by functional categories with log2 fold change (equivalent to expression log ratio) in blue bars, Z-score (activated: >2, inhibited: <-2) in orange bars, and significance of enrichment (–log10 P-value) in gray dots. (B) The summary network depicts the interactions between upstream regulators, downstream genes, and physiological functions impacted by heat stress. Red and green molecules indicate upregulated and downregulated genes, respectively, relative to HT at D7. Figure legend displays molecules and function symbol types and colors.

Figure 2-8. Validation of RNA-Sequencing results by quantitative RT-PCR. (A) Log2 fold change comparison of RNA-Seq (dark blue bars) and quantitative real-time PCR (qRT-PCR, light blue bars) for five differentially expressed genes downregulated at D3 (LABLA, CSN2, CSN1S2, CSN1S1, SLC7A5; n=12) and five genes upregulated at D3 (MXRA5, SLC7A8, LBP, ANGPTL4, LOXL4; n=12) when comparing D3 vs. D-3 relative to dry-off (D0, ~ 46 d relative to calving). (B) Correlation between RNA-Seq and RT-PCR gene expression (R2 = 0.9386, p < 0.0001).

CHAPTER 3 GENERAL DISCUSSION AND SUMMARY

The importance of providing a dry period to dairy cows between consecutive lactations is well-recognized, allowing for involution of the mammary gland characterized by decreased milk secretion and increased cell death and immune response24,26 followed by redevelopment through increased cell proliferation and eventually colostrogenesis prior to calving.20 Recent research has shown that exposure of dairy cows to environmental heat stress during the dry period can negatively alter these important cellular processes and impact milk production.94,95,117 Thus, strategies must be implemented to rescue mammary function if thermal stress occurs.

Recent literature has identified key factors involved in mammary involution in mouse and bovine models,25,39,40 but none have collected multiple tissues from the same animal across the full duration of the late-lactation and late-gestation dry period to capture gene expression changes through involution and redevelopment. This thesis research is the first to utilize RNA-Sequencing to deep-sequence the dry period mammary transcriptome, providing insight into genes, pathways, and upstream regulators that influence metabolism, cell turnover, and immune function. This study also highlights the negative impact of chronic dry period heat stress on in vivo mammary development and function at the transcriptomic level, particularly in ductal development, metabolism, and stress response.

From the differentially expressed genes, pathways and upstream regulators over time or under heat stress, RNA-Seq uncovered target genes that will undergo further investigations and validation that will eventually serve to, for example, improve the dry period cellular turnover during early involution and/or combat the negative alterations

caused by heat stress. From these, upstream regulators, including some transcription factors, are ideal candidates with the potential to alter expression of key downstream genes. Regulators central to metabolism, such as PPARGC1A and INSIG, were significantly downregulated during early involution as milk component synthesis decreased. Manipulation of these factors immediately prior to milk stasis may more quickly downregulate their expression upon dry-off and allow the gland to produce less milk after stasis, leading to reduced mammary pressure and potential for decreased mammary infections (e.g. residual milk can serve as a nutrient source for bacteria).

Interestingly, both PPARGC1A and INSIG had increased expression in heat-stressed cows (relative to cooled cows) at D7 of involution, and I speculate that this change in gene expression is a positive physiological acclimation to heat stress to promote thermotolerance. Other candidates for manipulation include pro-apoptotic regulators such as IGFBP5, PTGES, and BACH2 that were upregulated during involution and could be promoted in mammary tissue to accelerate cell death during involution, potentially shortening the dry period to lend more days to milk production, particularly in high producing cows to increase revenue.

Genes were identified with altered expression under dry period heat stress that may also serve as targets to improve mammary development under stress and increase milk production in the next lactation. Candidate genes include those in the Wnt pathway and involved in ciliary formation that have been shown to play a role in ductal formation during mammary development.191 Decreasing pathway inhibitors (WIF1) and increasing expression of ciliary function genes such as LCA5 and MYO3B, all of which were downregulated in heat stressed cows at D7, might promote mammary redevelopment

during the dry period. Other candidate genes include those involved in cellular death and extracellular matrix breakdown. The gene IL20RB promotes mammary involution and immune function by inducing STAT3 expression and was consistently upregulated in heat-stressed cows, potentially as a mechanism of cell death for damaged cells. The upstream regulator MMP7, involved in breakdown of the extracellular matrix as a necessary step in completing the involution process, was downregulated at D7 in heat- stressed cows. Manipulation of these target genes for down or upregulation, respectively, may impact thermotolerance, promoting faster involution and removal of damaged cells, both of which may improve milk production in the next lactation.

Another target area for exploration is long non-coding RNAs (lncRNA) and microRNAs (miRNA) that regulate gene and protein expression through a variety of modifications and are listed as non-annotated genes within this dataset. In particular,

RNA-Seq characterized one lncRNA with seed regions for seven miRNAs that was downregulated in heat-stressed cows. These miRNAs were shown to target several key downstream genes that play important roles in metabolism and involution. Targeting these miRNAs for increased or decreased expression might influence downstream gene expression to promote thermotolerance and mammary development.

While speculation can be made about targeting individual genes, non-coding

RNA or even entire pathways, caution must be taken that other aspects of the dry-cow physiology like body maintenance and the developing fetus are not negatively affected by manipulation of any of these factors, as many participate in functions outside of mammary metabolism, apoptosis, and development. For example, IGFBP5 upregulation

leads to the downregulation of IGF1, a growth factor necessary for cellular proliferation during the end of the dry period and an important factor for fetal development.

It is challenging to draw finite conclusions from RNA-Seq discoveries as this tool generates a large amount of data by opening the “black box” that is the mammary transcriptome. To narrow analysis and provide statistically sound data, I analyzed only large statistical differences over time or between treatments but recognize that employing strict cut-offs limits the expression analysis as some crucial biological functions may have subtle statistical changes in time or treatment but can alter mammary form and function. Another limitation from this study is the inability to fully capture important changes in expression due to necessary duration between biopsies within the same animal, with the added uncertainty of calving date to capture the specific phases (e.g. redevelopment). I also recognize that biopsied mammary tissue is a homogeneous tissue containing both parenchymal and stromal cells; cell-sorting may be conducted in the future to address this matter. Another important consideration is that the bioinformatics tools used in this experiment are based on human and/or mouse literature (e.g. IPA® and TargetScan). I acknowledge that the findings from this work are exploratory and warrant additional research and validation in the bovine model.

Even with these limitations, RNA-Seq proves to be a valuable tool for transcriptomic discovery as a basis for more exhaustive research. By analyzing the full landscape of the mammary transcriptome, I uncovered interesting and novel aspects to both the dry period and heat stress that I will study in future experiments. In the future, I plan to utilize in vitro culture systems to manipulate the previously discussed target genes in bovine MECs. I hope to develop in vitro systems that represent a more chronic

heat-stressed environment versus the acute heat shock induced in previous literature87 by exposing cells to a lower temperature across days instead of hours. After in vitro confirmation, I will move to in vivo models to determine the effect and feasibility of manipulating candidate genes in a whole animal model. In the near future, I aim to propose manipulations that can advance the efficiency of the dry period and impact thermotolerance of the mammary gland to improve milk yield in the next lactation. As global climate continues to pressure the dairy industry, solutions such as these will prove to be vital complements of active cooling to rescue milk production.

APPENDIX TABLES IN LINKS

Object A-1. Differentially expressed genes D3 vs. D-3. (.xlsx file 288 KB)

Object A-2. Differentially expressed genes D7 vs. D3 (.xlsx file 84 KB)

Object A-3. miRNAs and target genes impacted by heat stress (.xlsx file 64 KB)

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BIOGRAPHICAL SKETCH

Bethany M. Dado Senn was born in Lansing, Michigan, USA in 1993 to Rick and

Gwen Dado. In 2000, her family relocated to their family dairy farm, Four Hands

Holsteins, in Amery, Wisconsin, USA where she received her childhood education. She attended the University of Wisconsin-Madison from August 2012 to May 2016, earning a

Bachelor of Science degree in dairy science and genetics (double major). In July 2016 she moved to Gainesville, Florida, USA to study for the Master of Science degree at the

University of Florida under the supervision of Dr. Jimena Laporta. She studied animal molecular and cellular biology in the Department of Animal Molecular and Cellular

Biology and earned her degree in Spring 2018.

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