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

ENVIRONMENTAL HEAT STRESS

By

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

© 2018 Bethany M. Dado Senn

To my family, the true dairy enthusiasts

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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

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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.

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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

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

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

LIST OF REFERENCES ............................................................................................... 93

BIOGRAPHICAL SKETCH .......................................................................................... 109

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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

9

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

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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

11

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

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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

13

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

By

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

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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.

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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-

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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

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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

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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

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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,

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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

21

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

22

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,

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

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

32

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

33

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,

34

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

35

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

36

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.

37

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.

38

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

39

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

40

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

41

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).

42

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.

43

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

44

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

45

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

46

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

47

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

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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

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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.

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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

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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,

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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