RNA-SEQ REVEALS NOVEL GENES AND PATHWAYS INVOLVED IN...
Transcript of RNA-SEQ REVEALS NOVEL GENES AND PATHWAYS INVOLVED IN...
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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
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© 2018 Bethany M. Dado Senn
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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
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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
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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
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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
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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
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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,
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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,
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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
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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
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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.
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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.
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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
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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
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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
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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).
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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.
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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
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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
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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
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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
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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