Faculty of Science

ASSESMENT OF THE SLICK PHENOTYPE FOR THERMOTOLERANCE IN CATTLE
THROUGH HSP70 GENE EXPRESSION ANALYSIS
2024 | SOPHIE ANNE TEUFELE

B.Sc. HONOURS THESIS – BIOLOGY

ASSESMENT OF THE SLICK PHENOTYPE FOR THERMOTOLERANCE
IN CATTLE THROUGH HSP70 GENE EXPRESSION ANALYSIS
by
SOPHIE ANNE TEUFELE

A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
BACHELOR OF SCIENCE (HONS.)
in the
DEPARTMENT OF BIOLOGICAL SCIENCES
(Cellular, Molecular and Microbiology)

This thesis has been accepted as conforming to the required standards by:
Joanna Urban (Ph.D.), Thesis Supervisor, Dept. Biological Sciences
John Church (Ph.D.), Co-supervisor, Dept. Natural Resource Sciences
Emily Studd (Ph.D.), External examiner, Dept. Natural Resource Sciences

Dated this 22nd day of October 2024, in Kamloops, British Columbia, Canada

© Sophie Anne Teufele, 2024

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ABSTRACT
Climate change poses a significant challenge for agricultural production, especially
livestock. As cattle are unable to adapt as fast as global temperatures are rising, heat stress has
become of growing concern. Heat stress in cattle often causes a decrease in appetite and weight
gain. In addition to underweight cattle, the adverse effects of heat stress also include a decreased
reproduction rate, increased susceptibility to disease and poor-quality meat and dairy products.
With the growing demand for high quality meat and dairy products, mitigating the effects of heat
stress in cattle has become a priority. A promising solution to this problem is the introduction of
the SLICK phenotype for cattle production, which has been associated with a shorter haircoat to
enhance heat dissipation. This study aimed to evaluate the effectiveness of the SLICK phenotype
in alleviating heat stress by comparing Heat Shock Protein 70 (HSP70) gene expression levels in
Red Angus and Angus-Senepol hybrid (SLICK) cattle. In addition, haircoat properties, including
length and diameter were compared to determine the difference in haircoat morphology between
the two groups. RNA was extracted from hair follicles during different environmental conditions
to generate cDNA for quantitative PCR (qPCR) analysis of HSP70 gene expression. Results
confirmed that SLICK cattle exhibited significantly shorter haircoats than Red Angus cattle,
suggesting an increased ability for thermoregulation. HSP70 gene expression analysis did not show
significant differences between the two groups after experiencing a maximum temperature
humidity index (THI) of 76.4. Although gene expression results were inconclusive, the findings
from this study highlight the potential for selecting heat tolerant traits to improve heat stress
resistance. This research supports the idea that the SLICK phenotype offers a promising strategy
for enhancing the sustainability of cattle production in temperate climates.
Thesis Supervisor: Associate Professor Joanna Urban

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ACKNOWLEDGMENTS
I would like to express my deepest appreciation for my thesis supervisor, Dr. Joanna Urban
for her support, encouragement and patience throughout the course of my research. I want to thank
her for the insight and mentorship she provided me during this project that I am extremely grateful
for and can apply to my future experiences outside of Thompson Rivers University. I would also
like to thank my co-supervisor, Dr. John Church for making this project possible. I am very
thankful for his expertise that was necessary for completion this project, and the opportunities he
provided. I would like to thank both my supervisors for the resources and access to laboratories
and animals they provided and acknowledge the time they have dedicated to my research.
I want to extend my appreciation to Joanne Niklas, for her generosity with her time,
property and animals. I would also like to thank Matthew Francis for his commitment to helping
handle the animals, collect samples, and providing lab advice. I would like to express my
thankfulness and gratitude to Robert J. Wester, MSc for sharing his assay designs, guidance, and
the time he dedicated to helping me complete this project. I am also very appreciative for the time
that Dr. Jay Singh spent to help me finish this project. I am extremely thankful for his knowledge,
mentorship and generosity. Last but not least, I would like to acknowledge and thank Kathy
Baethke for all of her help and guidance and confidence she provided me in the lab.

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TABLE OF CONTENTS
ABSTRACT .................................................................................................................................. ii
ACKNOWLEDGMENTS............................................................................................................. iii
LIST OF FIGURES ...................................................................................................................... vi
LIST OF TABLES........................................................................................................................ vii
LIST OF ABBREVIATIONS ………………………………………………………...………. viii
1. INTRODUCTION ………………………………………………………………...………… 1
1.1

The Economic Importance of Cattle in Canada …………………...………………… 1

1.2

Climate Change Impacts on Cattle Production ……………………….….…………...2

1.3

Identifying Heat Stress ………………………………………………………….…… 3
1.3.1

Heat Stress in Cattle …………………………………………….…………….…. 3

1.3.2

The Temperature Humidity Index ………………………….…………………… 4

1.4

Characteristics of Heat Stress in Cattle ……………………………………………….6

1.5

The Significance of the SLICK Phenotype ……………………………..…………… 8
1.5.1

Hair Coat Morphology and Evaporative Heat Loss ………………………………8

1.5.2

The SLICK Phenotype ……………………………...…………………………… 8

1.6

Heat Shock Proteins …………………………………………………………………. 9

1.7

Objective …………………………………………………………………………… 11

2. MATERIALS AND METHODS ……………………………………………...…………… 11
2.1

Ethical Approval and Biosafety …………………………….……………………… 11

2.2

Herd Access and Animal Population ………...……..……………………………… 12

2.3

Genotyping …………………………………………………………………………. 12
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2.4

Hair Measurement with Image J ……………………………………………….……13

2.5

HSP70 Gene Expression Analysis ………………………….…...….……………… 15

2.6

2.5.1

Collection of Hair Samples ……………………………...….…………..……… 15

2.5.2

THI Measurements ……………………………………………………………... 15

2.5.3

RNA Extraction …………………………………………..……………………. 16

2.5.4

cDNA Generation ……………………………………..…………….…………. 18

2.5.5

Quantitative PCR ……………………………………….……………………… 19
Statistical Analysis ………………………………………………………..……. 21

3. RESULTS ………………………………………………………….………………………. 21
3.1

ImageJ Hair Measurements ………………………………………………………… 21

3.2

Genotypes ……………………………………………………...………...………… 22

3.3

RNA Extractions ……………………………...……………………………………. 23

3.4

cDNA Generations ………………………………………………………...……….. 24

3.5

HSP70 Gene Expression Analysis …………………………………………………. 24

4. DISCUSSION ………………………………………...……………………………………. 27
4.1

Hair Coat Measurements …………………………………………………...………. 28

4.2

Genotypes ………………………………………………………..………………… 29

4.3

RNA Extractions and cDNA Generations …………………………...…………….. 29

4.4

HSP70 Gene Expression Analysis …………………………………………...…….. 30

5. CONCLUSION AND FUTURE WORK …………………………………..……………… 34
5.1

Limitations ……………………………...………………………………………….. 35

6. LITERATURE CITED ……………………..……………………………………………… 36

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LIST OF FIGURES
Figure 1. Temperature Humidity Index chart used by farmers to assess heat stress thresholds for
cattle (University of Georgia, 2023) ……………………………………………………..……… 5
Figure 2. Representation of the adverse effects caused by heat stress in cattle (BioRender, 2024)
...…………………………………………………………………………………………….……. 7
Figure 3. Schematic representation of the HSF1 and HSP activation-attenuation cycle that
mediates the expression of HSP genes (Pessa et al., 2023) ……………………………….…… 10
Figure 4. Image taken of a hair follicle underneath a dissecting microscope and used for
measurement with ImageJ-2 …………………………………………………………………… 14
Figure 5. Gel electrophoresis of 10 RNA extractions used to visualize integrity and quantity of
samples …………………………………………..……………………………………………... 23
Figure 6. Reverse transcription products run alongside respective RNA sample run without
MultiScribe Reverse Transcriptase ……………………………………………………..……… 24
Figure 7. Bar graph of the median HSP70 gene expression values (2-DCt) for each cattle group
during different sample collection dates …………………………………..…………………… 25
Figure 8a. Hourly THI for the 72 hours prior to sample collection on June 13th, 2023 ….…… 26
Figure 8b. Hourly THI for the 72 hours prior to sample collection on August 21st , 2023 …… 27

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LIST OF TABLES
Table 1. Environmental conditions for each sample collection ………………………………. 16
Table 2. Primer sequences used for gene expression analysis ………………………………… 20
Table 3. Measurements for hair characteristics pulled from Angus and SLICK cattle …...…... 21
Table 4. Animals used in the assessment of the SLICK Phenotype with corresponding genotypes
to determine the presence of the SLICK1 mutation …………………………………….……… 22
Table 5. Difference in cycle threshold values between HSP70 and endogenous controls (DCt). 26

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LIST OF ABBREVIATIONS
Abbreviation

Meaning

GDP

Gross Domestic Product

CBT

Core Body Temperature

THI

Temperature Humidity Index

DMI

Dry Matter Intake

LH

Luteinizing Hormone

PRLR

Prolactin Receptor

EVHL

Evaporative Heat Loss

HSP

Heat Shock Proteins

HSP70

Heat Shock Protein 70

HSF1

Heat Shock Factor 1

ACC

Animal Care Committee

cDNA

Complementary Deoxyribonucleic Acid

RT-PCR

Reverse Transcription Polymerase Chain Reaction

qPCR

Quantitative Polymerase Chain Reaction

Ct

Cycle Threshold

mL

Milliliter

µL

Microliter

ng

Nanogram

µg

Microgram

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1 INTRODUCTION
1.1 The Economic Importance of Cattle
The Canadian economy relies heavily upon agriculture for revenue and stability (AAFC,
2023). Cattle are essential to the economy as the Canadian beef sector contributes approximately
$24 billion annually (2020-2022) to Canada’s gross domestic product (GDP) (Canfax Research
Services, 2023). Canadian cattle farms and beef processing provides a reliable supply of meet for
Canada, which produced 3.61 billion pounds of beef in 2021 (Canfax Research Services, 2023).
In 2022 Canadians consumed 967,166 metric tons of Canadian beef, a 3% increase from 2021,
with continuous increases projected for the coming years (Statistics Canada, 2023). Not only does
the beef sector provide for Canadians, but approximately half of productions are exported to over
half a dozen countries, with 49% of total beef products exported in 2022 (Statistics Canada, 2023;
AAFC, 2023). Canada is projected to be the 8th largest beef exporter in the world as exported
products were valued at $4.68 billion in 2022, a 5.1% increase from 2021 (Statistics Canada, 2023).
Canadian beef is also recognized as a sustainable food source that contributes to both domestic
and international food security (CAA, n.d). However, the global demand for Canadian meat and
dairy is expected to increase by 57% and 48% in 2050, respectively, due to the foreseeable increase
in the global population and urbanization (Ominski et al. 2021). Overall, maintaining the health
and productivity of cattle is essential to meet consumer demand, ensure a secure food supply, and
sustain its role as a major contributor to the Canadian Economy.

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1.2 Climate Change Impacts on Cattle Production
Climate change has become an increasing concern to multiple areas of Canadian
agriculture. As the average temperature continues to increase, and situations of extreme heat such
as the 2021 B.C. heat wave are expected more often (Beugin, 2023), the agri-food sector will be
challenged. Global warming and climate variability not only affect feed and water resources, but
animal health and production (Godde et al. 2021). These changes can significantly impact cattle
farming which is critical to both the Canadian economy and food security. This creates another
major pressure on the Canadian agri-food sector as the demand for more livestock products is met
with less land, water, and feed for production (Ominski et al., 2021).
Specifically, rising temperatures can have a detrimental impact on the beef production
sector due to the onset of heat stress and the associated health affects it causes for cattle (Godde et
al., 2017; Cheng et al., 2022). Heat stress is a major concern in the livestock sector, particularly
for beef cattle, as they are more susceptible than other livestock species. This is largely due to
being raised primarily outdoors, providing them with little environmental protection, in addition
to their reduced water retention capacity (Archana et al., 2017; Islam et al., 2023). Heat stress
affects meat production for all commercial livestock (Gonzalez-Rivas et al., 2020) as animal body
size is compromised and can lower meat quality (Summer et al., 2019; Cheng et al., 2022). In the
early 2000’s the estimated economic loss in the United States due to heat stress in cattle production
was estimated at $1.7 billion annually (St-Pierre et al., 2003). In the absence of current data, it is
assumed the costs of heat stress for the cattle industry are likely greater, especially considering
climate change has become more prevalent. On a global scale, it is predicted that cattle production
could lose up to $40 billion annually due to increases in temperature extremes (Thornton et al.,

2

2022). The physiological challenges brought on by heat stress can lead to significant economic
losses in addition to threatening food security.
Multiple reviews have stated that finding ways for farmers and ranchers to combat climate
change affects will become increasingly necessary to ensure animal welfare and to keep up with
economic demands (Osei-Amponsah et al., 2019; Thornton al., 2022; Cheng et al., 2022; Khan et
al., 2023; Kwon et al., 2024). Selective breeding has traditionally been used to improve production
efficiency but could also become an attractive solution for mitigating heat stress experienced by
cattle (Renaudeau et al., 2012; Osei-Amponsah et al., 2019; Cheng et al., 2022).

1.3 Identifying Heat Stress
1.3.1 Heat Stress in Cattle
Heat stress is the result of an imbalance between the metabolic heat produced inside an
animal’s body and the dissipation to its surroundings (Das et al., 2016). Homeothermy is a
thermoregulation process performed by endotherms to maintain a stable internal body temperature;
it requires minimal expenditure of energy if the animal’s external environment is within the
respective thermoneutral zone. Once the environmental temperature is outside of the thermoneutral
zone, extra energy is required to thermoregulate, and the animal will experience stress to maintain
homeothermy. (Nardone et al., 2006; Collier et al., 2015; Khan et al., 2023). During periods of
excess heat in the animal’s surrounding environment, homeostatic mechanisms like panting and
sweating are activated to reestablish or regulate the animal’s internal environment. When an
animal is unable to dissipate excess heat, heat stress will occur (Collier et al., 2015; Herbut et al.,
2019).

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The average core body temperature (CBT) for cattle is found to range from 38°C to 39°C with
a mean value of 38.6°C ± 0.5°C (Collier et al., 2015; Ammer et al., 2016; Herbut et al., 2019;
Summer et al., 2019; Islam et al., 2023). Cattle are homeothermic and will maintain their body
temperature over a range of conditions (Collier et al., 2015; Khan et al., 2023). In conditions
outside of the thermoneutral zone, cattle may fail to dissipate self-generated and absorbed heat
energy, causing an elevated CBT which leads to heat stress (Islam et al., 2023). In general., a
temperature range of -0.5°C to 26°C is accepted as a thermoneutral zone for dairy cattle (Kadzere
et al., 2002; Liu et al., 2019; Herbut et al., 2019), and as temperature passes 26°C, they begin to
exhibit heat stress behaviors (Kadzere et al., 2002; Cheng et al., 2022). In addition to air
temperature, relative humidity is an important factor that determines the exchange of heat between
an animal’s body and surroundings, therefore it must be accounted for when predicting heat stress
(Herbut et al., 2019; Islam et al., 2023, VanderZaag et al., 2023).

1.3.2 The Temperature Humidity Index
To readily identify key heat stress points for cattle, a temperature-humidity index (THI) is
commonly used (Dikmen et al., 2008; Kim et al., 2020; VanderZaag et al., 2023). This index
represents the combined effects of air temperature and relative humidity to assess the risks of heat
stress. (Wang et al., 2018; VanderZaag et al., 2023). A chart for how these two variables impacts
the THI is displayed in Figure 1. Different thresholds for heat stress in cattle have been reported,
with THI values as low as 68 found to impact the milk yield of high production dairy cattle
(Zimbelman et al., 2009). Beef cattle tend to have a higher THI threshold than dairy cattle given
that they have been found to tolerate temperatures up to 30°C while humidity is less than 80%
(Summer et al., 2019). Overall, the heat stress threshold for cattle is THI 70 and the point where

4

beef cattle begin to experience mild heat stress, and cattle will elicit a response when THI is ≥72
(Dikmen et al., 2008; Liu et al., 2019; Herbut et al., 2019; Kim et al., 2020). As the THI increases,
heat stress becomes more severe and will begin to cause adverse effects in cattle at indices of 80
and higher. However, even prolonged exposure to conditions of THI ≥ 72 can lead to chronic heat
stress and physiological problems (Herbut et al., 2019; Kim et al., 2020; Cheng et al., 2022).

Figure 1. Temperature Humidity Index chart used by farmers to assess heat stress thresholds for
cattle (University of Georgia, 2023).

5

1.4 Characteristics of Heat Stress in Cattle
Heat stress in cattle is demonstrated by various behavioral, metabolic and physiological
changes that can significantly impact the animal’s health and productivity (Collier et al., 2019).
The primary autonomic responses include sweating and panting which are exhibited by cattle to
increase the evaporative heat loss from the skin surface (Yadav et al. 2013; Collier et al., 2014).
Cattle will also reduce feed intake to reduce heat gain, which is recognized as one of the first
indicators of heat stress and is linked to many additional consequences (Godde et al., 2021).
Reducing feed intake is also caused by decreased appetite and rumination time due to heat stress
which directly results in weight loss, reduced fat thickness, and poor growth rates (Nardone et al.,
2006; Cheng et al., 2022).
In addition to underweight cattle, reduced feed intake also results in lower amounts of
nutrients and protein needed for proper physiological functions. Heat stressed cattle have
decreased milk quantity and quality due to decreased fat and protein content. (Summer et al., 2019;
Godde et al., 2021). Meat quality is also affected as it is reported to have less fat and therefore less
water holding capacity, poor colour, tenderness, and marbling (Gregory 2010; Gonzalez-Rivas et
al., 2019; Godde et al., 2021).
Chronic heat stress can also suppress immune function and impact the fertility of cattle as
it begins to interfere with the endocrine system and hormonal pathways (Bagath et al., 2019;
Lovarelli et al., 2024). Heat stress increases peripheral levels of glucocorticoids and blood cortisol
which inhibit the synthesis and release of cytokines and ultimately suppress the immune system,
resulting in cattle becoming more susceptible to infection and disease (Bagath et al., 2019; Godde
et al., 2021).

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Heat stress also disrupts the hormonal balance that is necessary for successful reproduction.
In females, it reduces the luteinizing hormone (LH) and progesterone levels which leads to a
decline in estrus behavior and problems with follicle development, compromised oocyte growth,
poor embryo development, longer gestation periods and lower birthing rates. Furthermore, poor
milk quality can compromise calf development and health. For males, semen concentration,
spermatozoa motility and quality of fertile sperm are all reduced (Nardone et al., 2006; Khan et
al., 2023).

Figure 2. Representation of the adverse effects that can be caused by heat stress in cattle
(BioRender, 2024).

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1.5 The Significance of the SLICK Phenotype
1.5.1 Hair Coat Morphology and Evaporative Heat Loss
Cattle predominantly dissipate heat through latent heat exchange, known as evaporative
heat loss (EVHL) in the form of sweating (Collier et al., 2008). The hair coat of an endotherm is
one of the most significant factors that affect heat dispersion and moisture transfer from the animal
to the environment. Physical properties of the hair coat such as hair length, diameter, coat density
and thickness will all affect the efficacy of EVHL cooling from the skin (Collier et al., 2014). The
variation in EVHL among different breeds of cattle suggest there is an opportunity to improve
thermal tolerance (Dikmen et al., 2008; Collier et al., 2008).

1.5.2 The SLICK Phenotype
The SLICK phenotype is characterized by a short, sleek haircoat and larger sweat glands
that provide cattle an increased resistance to heat stress (Olson et al., 2003; Dikmen et al., 2014).
The SLICK1 mutation (c.1382del; rs517047387) follows a dominant inheritance pattern and has
originated from the tropically adapted cattle breed Senepol, that arose in the Caribbean (Sosa et
al., 2022). It is identified as a deletion mutation in the PRLR (prolactin receptor) gene on bovine
chromosome 20 that causes a premature stop codon and results in a truncated C-terminal protein
(Mariasegaram et al., 2007; Sosa et al., 2022). Mammalian hair growth is modulated by the
prolactin hormone and its signalling pathways; a mutation to the PRLR gene affects the activity of
prolactin and is linked to shorter hair morphology and the SLICK phenotype (Littlejohn et al.,
2014). The SLICK phenotype has also been successfully inherited by other breeds of cattle as
previous studies have shown that Senepol cattle and their crosses with Holstein and Angus animals
are equally as heat tolerant (Mariasegaram et al., 2007). In fact, the gene-editing tool CRISPR-

8

Cas9 has been used to introduce an intentional genomic alteration (IGA) to the PRLR gene to
create SLICK cattle, with the goal of enhancing heat tolerance for animal welfare purposes
(Pozzebon et al., 2024; Cuellar., 2024). Cuellar et al. (2024) reported that SLICK animals achieved
from gene editing not only had improved thermotolerance but were heavier on average compared
to non-SLICK animals making them favourable for beef production. Furthermore, the IGA is also
heritable and will be passed on through breeding with a PRLR-SLICK cattle lineage (Pozzebon et
al., 2024). The gene editing approach is preferred over crossbreeding because it allows for the
modification of only the target gene without potentially compromising other favoured traits
(Cuellar et al., 2024).
1.6 Heat Shock Proteins
Heat shock proteins (HSPs) are a family of chaperone proteins that are produced by cells
in response to stressful conditions such as heat stress (Archana et al., 2017). The expression of
HSPs is a strong indicator of heat stress at the molecular level. Within the HSP family, heat shock
protein 70 (HSP70) has the most sensitive response to environmental change and has been
identified as the ideal biological marker for quantifying heat stress in animals (Archana et al.,
2017; Herbut et al., 2019; Guzman et al., 2023).
When an animal experiences heat stress, a highly conserved process of protein activation
and gene expression occurs to mitigate the impacts and sustain normal cellular function (Collier et
al., 2008). The upregulation of chaperone genes like the HSP70 gene is mediated by heat shock
transcription factors (HSFs) and an activation-attenuation cycle controlled by HSPs that is
illustrated in Figure 3. (Pessa et al., 2023). In unstressed conditions the HSF1 monomer is bound
to a HSP in the cytoplasm. The stress induced accumulation of misfolded proteins causes the HSF1

9

monomer to dissociate and bind with others for HSF1 trimerization, which is translocated into the
nucleus. Within the nucleus, the homotrimetric HSF binds to the heat shock element – a sequence
in the promoter region of heat shock genes – which results in the expression of HSP mRNA
(Collier et al., 2008; Archana et al., 2017; Pessa et al., 2023). In mammalian cells, HSF1 will also
recruit RNA polymerases and chromatin remodellers to ensure heat inducible genes are in an open
state (Pessa et al., 2023).
The expression levels of HSP genes, particularly HSP70, have been used to establish the
severity of heat stress experienced by cattle (Collier et al., 2008; Kim et al., 2020; Guzman et al.,
2023). Tolerance to heat stress and THI thresholds can be identified through the quantification of
HSPs (Guzman et al., 2023).

Figure 3. Schematic representation of the HSF1 and HSP activation-attenuation cycle that
mediates the expression of HSP genes (Pessa et al., 2023).

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1.7 Objective
The analysis of HSP70 expression provides a reliable alternative to monitoring behavioral
traits in cattle for assessing heat stress. Given that HSP70 expression levels serve as a clear
indicator of the onset and severity of heat stress, it can be utilized as an optimal biomarker to
identify environmental conditions based on the Temperature-Humidity Index (THI) where heat
stress poses a threat to cattle welfare and productivity. Recognizing the potential impact of heatstressed cattle on Canadian beef production, this study aims to determine whether the presence of
the SLICK phenotype incorporated into an Angus herd offers an advantage in mitigating heat
stress. In order to confirm that the two groups used – traditional Angus type and Senepol-Angus
hybrids – are phenotypically different, haircoat characteristics will be investigated by measuring
hair length and diameter. To determine the difference in heat stress experienced, HSP70 gene
expression levels will be quantified and compared between two cattle groups.

2. MATERIALS AND METHODS
2.1 Ethical Approval and Biosafety
An application for the use of animals in research was submitted to TRU’s Animal Care
Committee (ACC). Ethical approval for using the cattle was granted by ACC at Thompson Rivers
University (File Number: 103700).
A Biohazardous Materials Application was submitted and approved by the Biosafety
Committee at Thompson Rivers University (File Number: 103740). Biosafety modules were also

11

completed to work in a CL2 lab and approved by the Office of Safety & Emergency Management
at Thompson Rivers University.
2.2 Herd Access and Animal Populations
All cattle used for this project were sampled at a farm in Heffley Creek, British Columbia
(50.84632° N, 119.96735° W) with permission from Joanne Niklas, the farm owner. During two
of the earlier sampling dates, 9 of the cattle used for testing spent time at Tod Mountain, before
moving to the sampling location in August. Specifically, in June there were 5 SLICK cattle and 5
Angus type, while in August, there were 12 SLICK and 7 Angus type cattle. All cattle were
approximately 4 months old at the beginning of sampling in June, and reported to be in good health
during and between sampling times.
The two groups of cattle that were used include a traditional Angus type (Wild Type), and
a Senepol-Angus hybrid (SLICK) that is composed of ¼ Angus and ¾ Senepol. The SenepolAngus hybrid carries the SLICK1 mutation and exhibits the SLICK phenotype being assessed. The
initial hybrid embryos were synthesized approximately 20 years ago and supplied to TRU by Davis
Rairdan Embryo Transplants in Calgary, Alberta (Davis Rairdan, 2024).
2.3 Genotyping
To determine if cattle carried the SLICK1 mutation, all animals were genotyped for the
rs517047397 deletion. Blood samples were taken from each calf and stored at 5°C until processed.
DNA was extracted from each blood sample following the protocol supplied with the Qiagen
DNeasy Blood & Tissue Kit (Qiagen). PCR amplicons were produced using a reaction containing:
0.1uL Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific), 2uL of 5X buffer, 1

12

uL 0.4mM dNTPs, 1 uL of each primer at 5pmol, 2 uL Betaine, 2 uL of 1.5 ng/uL to 20 ng/uL of
template DNA, and 0.9 uL dH2O. The forward primer and reverse primer were 5’ –
CCTGGATCTTGACAGTGACTCTG – 3’ and 5’ – TTTGGGAACAGAGCCAGCAC – 3’,
respectively. All reactions were carried out with an annealing temperature of 62°C in a
SimpliAmp™ Thermal Cycler (Thermo Fisher Scientific). The ExoSAP-ITä Express Kit (Thermo
Fisher Scientific) was used to cleanup all PCR amplicons in preparation for Sanger sequencing.
The sequencing reactions were carried out using the BigDyeä Terminator V3.1 Cycle Sequencing
Kit (Thermo Fisher Scientific) and purified with the BigDye Xterminatorä Purification Kit
(Thermo Fisher Scientific). Sanger sequencing was preformed using a SeqStudioä machine
(Thermo Fisher Scientific) and genotypes were determined by using the electropherograms that
were produced. Only electropherograms with Phred scores above 30 were confidently used to
determine the rs517047387 genotype.
All genotyping was completed by Robert J. Wester, MSc, at the Applied Genomics Centre,
Kwantlen Polytechnic University.
2.4 Hair Measurement with ImageJ
Hair clippings were taken from animals using electric clippers by shaving a small portion
(~5 x 5 cm) at the shoulder, and saving in separate paper envelopes for transportation. To
accurately measure the length and diameter of hair strands, the imaging software ImageJ-2
(National Institutes of Health, USA) was used. When taking pictures of the hair pieces, a small
ruler was placed in the frame to use as a reference for measurements and to convert from pixels to
mm. Photos were uploaded to the computer and imported into ImageJ in JPEG format. To measure
the diameter, images of hair samples were taken while using a dissecting microscope for better
13

resolution (Figure 4). To set a reference scale, the straight-line tool was selected and drawn across
a known distance in mm, the number of pixels that related to that measurement was recorded. To
measure the subject of interest, the straight-line tool was used, and the number of pixels related to
that measurement was recorded. To measure the length of hair strands, each piece was taped with
clear tape to a blank surface to ensure steady measurements. If hair pieces were wavy, the
segmented-line tool was used to trace over each piece of hair, and the total pixel measurement was
recorded. 5 hairs were selected from each individual for measurement of diameter and length.

Figure 4. Image taken of a hair follicle underneath a dissecting microscope and used for
measurement with ImageJ-2.
In order convert the pixel measurement to metric measurements, the following formula was used:

Measurement in mm =

!"#$%&"'"() +( ,+-".$
/+-".$ ,"& ''

14

2.5 HSP70 Gene Expression Analysis
2.5.1 Collection of Hair Samples
For gene expression analysis, hair from the tail of each calf was used. Calves were
contained in cattle-squeeze prior to pulling hair, and hairs were grasped as close to the base as
possible to ensure the follicles were attached. After pulling hair samples from the tail, they were
immediately placed in sterile 10mL Falcon tubes and the follicles were submerged in RNAlaterä
Stabilization Solution (Themo Fisher Scientific) to protect the RNA. The Falcon tubes were placed
in a cooler at the collection site and then moved to Thompson Rivers University for long term
storage at -20°C.
2.5.2 THI Measurements
Environmental conditions for the sample site at Heffley Creek, was obtained by entering
the location into Visual Crossing Weather Data (Visual Crossing Corporation, 2024), and
requesting the historical hourly weather data for the dates of interest. Once the query was approved,
hourly weather data was downloaded and exported to Excel. The downloaded data was also
compared with historical data supplied by other weather databases such as Weather Underground,
AccuWeather, and The Weather Network. The herd was sampled at three separate points
throughout the summer months and the environmental parameters from each collection are
displayed on Table 1. Environmental conditions that the herd would have experienced in the 72
hours prior to sampling have also been collected. The formula developed by Ravagnolo et al.
(2000) that is widely used to calculate THI measure heat stress in cattle (Dikmen et al., 2008;

15

Davila et al., 2019; Kim et al., 2020; Ekine-Dzvinu et al., 2020) is as follows:
THI = (1.8x T + 32) – [(0.55-0.0055 x RH) x (1.8 x T – 26)],

where T = dry bulb temperature (°C) and RH = relative humidity (%).

Table 1. Environmental conditions during hair sample collections.
Collection Date Time
Humidity (%)
Temperature (C°)

THI

June 13th, 2023

9:00 AM

19.4

51.1

64.5

August 2nd, 2023

9:30 AM

20.5

36.0

56.9

August21st, 2023

10:00 AM

15.5

45.2

57.46

2.5.3 RNA Extraction
Hair samples frozen in RNAlaterä (Thermo Fisher Scientific) were thawed on ice prior to
homogenization. Approximately 15-20 hairs were placed follicle first into a 2.0 mL BeadBugä
(Millipore Solutions) tube and were cut approximately 1cm in length to fit. 1mL of TRIzolä was
added to each tube and samples were placed on ice. To homogenize the samples, they were placed
on a desktop vortex machine at the maximum speed for a runtime of 30 seconds and rest time of
30 seconds on ice, which was repeated 3 times.
For phase separation, 200 µL of chloroform was added to each sample and hand shaken
vigorously for 15 seconds, incubated at room temperature for 3 minutes and then centrifuged at
12,000 x g for 15 minutes at 4°C using a High Performance Refrigerated Microcentrifuge (Labnet
International, Z233 Series). After centrifugation, the solution separated into a clear layer
containing the RNA and a pink layer which contains DNA. 400 µL of the clear phase was

16

transferred into a RNase-free microcentrifuge tube with an equal volume of 70% ethanol and
vortexed to disperse any visible precipitate that had formed.
A PureLinkä RNA Mini Kit (Thermo Fischer Scientific) was used for column purification
of the sample. 400 µL of the sample was transferred to a Spin Cartridge and centrifuged at 12,000
x g for 15 seconds at room temperature; the flow-through was discarded. This step was repeated
until the entire sample was processed through the Spin Cartridge. Next, 350 µl of Wash Buffer I
was added to the Spin Cartridge, which was then centrifuged at 12,000 x g for 15 seconds at room
temperature, and the flow-through was discarded. 80 µl of a DNase Treatment containing 80 µL
of 10X DNase 1 Buffer, 100 µL of Resuspended DNase, and 620 µL of RNase Free Water was
added directly onto the membrane and incubated at room temperature for 20 minutes. After
incubation, 350 µl of Wash Buffer I was added to the Spin Cartridge, which was then centrifuged
at 12,000 x g for 15 seconds at room temperature; the flow-through and collection tube were
discarded. 500 µl of Wash Buffer II was then added to the Spin Cartridge, which was centrifuged
at 12,000 x g for 15 seconds at room temperature, and the flow-through was discarded. This wash
step was repeated once.
To dry the membrane with bound RNA, the Spin Cartridge was centrifuged at 12,000 x g
for 1 minute. The collection tube was discarded, and the Cartridge was placed in a Recovery Tube.
Then, 39 µl of RNase-free water was added to the center of the Spin Cartridge, incubated at room
temperature for 1 minute, and centrifuged at 12,000 x g for 1 minute at room temperature. 1 µl of
RiboLock RNase Inhibitor (40 U/µl) was added to the eluted sample, and the samples were placed
in the -80°C freezer.

17

The purity and concentration of RNA samples was determined by using a Nanodrop One
(Thermo Fischer Scientific), and gel electrophoresis was preformed to visualize the integrity of
RNA samples.

2.5.4 cDNA Generation
A High-Capacity cDNA Reverse Transcription Kit (Thermo Fischer Scientific) was used
to generate cDNA samples from RNA extractions. Prior to reactions, all regents from the kit and
RNA samples from the -80°C freezer were thawed on ice. The High-Capacity cDNA Reverse
Transcription Kit user manual was followed to prepare all reactions (Thermo Fischer Scientific,
2018). A 2X Master Mix was prepared for each reaction using the following components: 2.0 µl
of 10X RT Buffer, 0.8 µl of 25X dNTP Mix (100 mM), 2.0 µl of 10X RT Random Primers, 1.0 µl
of Multiscribe Reverse Transcriptase, and 4.2 µl of nuclease-free water, resulting in a total volume
of 10 µl. The 2X Master Mix was gently mixed and kept on ice until use.
The volumes for this Master Mix are for one reaction and were upscaled based on how
many samples were being processed.
For the reverse transcription reactions, 10 µL of the 2X Master Mix was pipetted into each
0.2 mL RNase-free PCR tube along with 10 µL of RNA sample. The capacity of the Reverse
Transcriptase Kit was 2 µg of RNA, therefore some samples required dilution prior to carrying out
the reactions. The reactions were briefly vortexed to ensure thorough mixing and then centrifuged
to remove any bubbles. The tubes were kept on ice until they were loaded into the thermocycler.

18

The reverse transcription reaction was performed using the following thermal cycling
conditions: Step 1 at 25°C for 10 minutes, Step 2 at 37°C for 120 minutes, Step 3 at 85°C for 5
minutes, and Step 4 at 4°C (or 10°C) for holding. The purity and concentration of cDNA samples
was determined using a NanoDrop One. cDNA samples were diluted in autoclaved water and used
directly for qPCR.
2.5.5 Quantitative PCR
Relative gene expression of HSP70 was measured through Quantitative PCR (qPCR).
Custom primers for HSPA1A (HSP70 gene) and the endogenous controls, Glyceraldehyde 3phosphate dehydrogenase (GAPDH) and b-tubulin isoform 5, were designed with the Primer
Express 3.0.1 Software (Life Technologies) and ordered through Custom Oligo Design Tools
(ThermoFisher Scientific) in a dry format. Primer sequences for each gene are displayed in Table
3. Primer design was completed by Robert J. Wester, MSc, at the Applied Genomics Centre,
Kwantlen Polytechnic University.
All qPCR reactions were performed in triplicate in a total reaction volume of 20µL per
well, in a 96-well plate and consisted of 10µL of 2X PowerTrackä SYBR Green Master Mix
(Thermo Fisher Scientific), 1µL of each primer at 10µM, 2µL of autoclaved water, and 6µL of
cDNA at approximately 20ng/µL. Thermal cycling conditions for all reactions are as follows:
initial incubation at 95°C for 3 minutes, followed by 40 cycles of denaturation at 95°C for 10
seconds, annealing at 60°C for 30 seconds and extension at 72°C for 30 seconds. After, samples
were heated to 95°C for 10 seconds and cooled to 65°C for 5 seconds, then reheated to 95°C at a
rate of 0.5°C/second to produce a melt curve. All reactions were carried out and analyzed using a
QuantStudioä 3 (Thermo Fisher Scientific).
19

Table 2. Primer sequences used for gene expression analysis.
Gene
Primer Component Sequence (5’-3’)

Fluorescence

HSP70

Forward
Reverse

CCGGTGCCCTGCCTTT
GGCCGTTTTCAGGTTTGAAG

GAPDH

Forward
Reverse

CCCTCCACGATGCCAAAGT
SYBR
GGCGTGAACCACGAGAAGTATAA

b-Tubulin

Forward
Reverse

GCTGTTCTTATTCTGCACGTTGA

SYBR

SYBR

CGTGGGCGGATGTCCAT

The QuantStudio 3ä instrument and corresponding Design and Analysis Software 2.8.0
(Thermo Fisher Scientific), were used to collect and analyze cycle threshold (Ct) values. Analysis
of gene expression data was completed through normalization of the target gene to control genes
by utilizing part of the 2-DDCt method (Livak and Schmittgen, 2001). To examine the relative
expression of HSP 70 between wild-type and SLICK cattle relative to the control genes the
following equation is used:
DCt = (Ct target) - (Ct control)
where Ct target is the mean Ct value for HSP70 gene expression and Ct control is the mean Ct value
for endogenous control genes, GAPDH and b-tubulin. The DCt values for HSP 70 Angus type and
SLICK cattle were compared using a t-test to determine the presence of a significant difference in
expression levels. To display the relative expression between the two groups, values were
displayed using: 2-DCt.

20

2.6 Statistical Analysis
To assess the statistical significance between groups for both hair coat characteristics and
gene expression levels, independent t-tests were performed. These analyses were conducted in
Microsoft Excel, employing a two-tailed distribution with a two-sample unequal variance
(heteroscedastic) assumption. A p-value threshold of 0.05 was used to determine statistical
significance between selected data sets.
3. RESULTS
3.1 ImageJ Hair Measurements
Hair samples were taken for measurement from animals exhibiting both traditional Angus,
and SLICK cattle (Senepol x Angus cross) phenotypes. All hair pieces selected for measurement
included the follicle and natural end of strand. Table 3 reports values for hair length and diameter
from each group as mean ± standard deviation. Overall, there was a large variation in hair length
observed in both groups and an expected difference in the average hair length between groups (P
= 0.0287). Less variation in diameter was measured and there is no significant difference between
the two groups (p = 0.841).

Table 3. Measurements for hair characteristics pulled from Angus and SLICK cattle.
n
Hair Length
Hair Diameter
SLICK

65

37.059 ± 8.836

0.084 ± 0.0083

Angus Type

30

51.549 ± 10.735

0.082 ± 0.0243

0.0287

0.841

p value

21

3.2 Genotypes
Genotypes for all animals were successfully obtained from blood samples to determine the
presence of the SLICK1 mutation.
Table 4. Animals used in the assessment of the SLICK Phenotype with corresponding genotypes.
Cattle ID

PRLR_387 Genotype

Phenotype

H1

CC

Wild type

H2

CC

Wild type

H3

CC

Wild type

L1

NN

SLICK

L2*

NC

SLICK

L3*

NN

SLICK

L4

NC

SLICK

L5

NN

SLICK

L6*

NN

SLICK

L7*

NN

SLICK

L8*

NC

SLICK

L9

CC

Wild type

L10

NN

SLICK

L11

NC

SLICK

L12*

NC

SLICK

L13*

CC

Wild type

L14*

NC

SLICK

L15*
L16

NC
CC

SLICK
Wild type

Note: N represents the deletion associated with the SLICK phenotype. NN and CC indicate homozygous, NC indicates
heterozygous, Cattle ID* indicate cattle that were not available for all sampling date.

22

3.3 RNA Extractions
All RNA extractions that were preformed using samples that had been properly stored were
successful and yielded intact RNA for further application. RNA concentrations ranged from 254.2
ng/µL to 658.1 ng/µL with an average of 435.9 ng/µL. 260/280 ratios ranged from 1.85 to 2.10,
with an average of 2.03. Better readings for the 260/280 ratios were obtained when the PureLink
RNA columns were treated with DNase for 20 minutes, rather than 15 minutes. All RNA
extractions were run on a 1% agarose gel in 1X TBE buffer for visualization; 10 extractions are
displayed in Figure 5. The most noticeable and brightest bands are consistent with the highly
conserved 28S and 18S rRNA bands that should be predominant in eukaryotic organisms. Other
bands present are as expected from other mRNAs, small RNAs and long-noncoding RNAs.

Figure 5. Gel electrophoresis of 10 RNA extractions used to visualize the integrity and quantity
of samples.
3.4 cDNA Generation

23

All reverse-transcription reactions were performed by following the manufacturers
instruction manual to generate single stranded, first strand cDNA as the final product. The reversetranscription PCR conditions did not include amplification cycles and the MultiScribeä Reverse
Transcriptase has RNase H activity, therefore a 1:1 conversion of RNA to cDNA was assumed.
260/280 ratios of cDNA samples ranged from 1.84 to 1.99, with an average of 1.89. Figure 6
includes gel electrophoresis that confirmed cDNA generation and functioning MultiScribeä
Reverse Transcriptase.

NA
cD

rR
te
af

R
PC
T-

A
RN

H1

no

M

ul

ti

e
rib
Sc

NA
cD

rR
te
af

R
PC
T-

A
RN

no

M

H2

ul

ti

e
rib
Sc

NA
cD

rR
te
af

R
PC
T-

A
RN

H3

M
no

ul

ti

e
rib
Sc
A
RN

M
no

ul

ti

e
rib
Sc

NA
cD

T
rR
te
af

CR
-P

L1

1

Figure 6. Reverse transcription products run alongside respective RNA sample run without
MultiScribe Reverse Transcriptase.
3.5 HSP70 Gene Expression Analysis
HSP70 gene expression in cattle was determined by using qPCR and calculating the
difference in expression to reference genes. The relative expression values for HSP70 between
SLICK and Angus type cattle, were determined using 2(-DCt) and are displayed in Figure 7. The
difference in cycle threshold values (DCt) from the housekeeping genes is presented in Table 5 as
mean ± standard deviation. All DCt values report that the HSP70 gene was downregulated in

24

respect to the housekeeping genes. Data collected on June 13th does not have a statistically
significant difference of HSP70 gene expression between SLICK and Wild type cattle (p=0.210).
Data from August 21st does report a statistically significant difference in HSP70 gene expression
between the two cattle groups (p=0.048). The hourly THI for the 72 hours prior to sample
collection is displayed in Figures 8a. and 8b. Samples collected August 2nd, 2023, were ineligible
due to incorrect storage of RNA extractions.

Relative Expression of The HSP70 Gene
0.300

Relative Expression (2-DCt)

0.250
0.200
0.150
0.100
0.050
0.000
SLICK

Angus Type
13-Jun

SLICK

Angus Type
21-Aug

Figure 7. Bar graph of the median HSP70 gene expression (2-DCt) for each cattle group during
different sample collection dates.

25

Table 5. Difference in cycle threshold values between HSP70 and endogenous controls (DCt).
Sampling Date
June 13th

August 21st

Cattle Group Mean Relative
Expression (DCt)
SLICK
6.557

Standard
Deviation
0.880

Wild type

5.7096

1.072

SLICK

7.351

2.721

Wild type

3.883

4.153

p value
0.210

0.048

80.00

Temperature Humidity Index

75.00

70.00

65.00

60.00

55.00
0

10

THI Threshold for Heat Stress

20

30

40

50

60

70

80

Hours

Sample Collection

Figure 8.a. Hourly THI for the 72 hours prior to sample collection on June 13th, 2023.

26

75.00

Temperature Humidity Index

70.00

65.00

60.00

55.00

50.00
0

10

THI Threshold for Heat Stress

20

30

Sample Collection

40

50

60

70

80

Hours

Figure 8.b. Hourly THI for the 72 hours prior to sample collection on August 21st, 2023.

4 DISCUSSION
This study aimed to assess the effectiveness of the SLICK phenotype, caused by the
SLICK1 mutation, on the thermotolerant capabilities of cattle. Two groups of cattle, Red Angus
(Wild type) and Angus-Senepol hybrids (¼ Angus, ¾ Senepol; SLICK), were used to analyze the
haircoat properties of the phenotype, while HSP70 gene expression levels were studied as an
indicator of heat stress. Hair coat analysis determined a statistically significant difference between
hair lengths. Furthermore, the HSP70 gene showed varying levels of expression between the two
groups of cattle during different THI conditions.

27

4.1 Hair Coat Measurements
Hair strands were collected form cattle in order to confirm that the Angus-Senepol hybrids
that have inherited the SLICK1 mutation do exhibit a noticeably different hair coat physiology
than the purebred Red Angus group. As expected, the SLICK cattle presented with a significantly
shorter haircoats, averaging 3.7 cm, compared to Angus’ 5.15 cm (p = 0.0287). Hair length is an
important factor that can impact the efficacy of evaporative cooling from the skin. Because cattle
dissipate excess heat through cutaneous EVHL, a longer hair coat becomes an obstruction to free
evaporation of sweat from the skin surface and reducing the animals ‘ability to regulate body
temperature. Confirmation that the two cattle groups present with different hair coat lengths
suggests that any observed differences in heat stress could be attributed to variation in their EVHL
capacity.
Hair diameter has also been found to affect EVHL as it can impact the airflow at the skins
surface (Collier et al, 2014). The diameter of hair pieces was also measured but did not report a
significant difference between the two cattle groups (P = 0.841). Although this does not allow us
to confirm that hair diameter is a major factor in differentiating thermoregulatory abilities between
the two cattle groups, it is still possible that hair diameter and hair density combined can affect
airflow regulation at the skins surface.
Another reason that SLICK cattle are better able to regulate their body temperature is due
to their increased sweating rate, as demonstrated by Dikmen at al. (2008). This trait is affected by
properties such as sweat gland density and size. As explained by Collier et al. (2008), cattle have
apocrine sweat glands – there is one sweat gland associated with each hair fiber – therefore hair
density directly affects the number of sweat glands. Unfortunately, the exact area from which cattle
were shaved for hair samples was not recorded and some hair breakage occurred. As a result, it

28

was not possible to accurately calculate the number of hair strands per unit area or determine hair
coat density for each group.
Furthermore, although hair diameter measurements were taken, they are not indicative of
sweat gland size. According to Hernandez et al. (2024) and Mateescu et al. (2023), skin biopsies
are required to measure sweat gland size in cattle. Both of these studies explored the effect of sweat
glad characteristics for heat tolerance on beef cattle. Although they did not investigate the same
breed of SLICK cattle, both reported a difference in sweat gland area between Angus and Brahman
cattle, another heat tolerant breed (Mateescu et al, 2023; Hernandez et al, 2024).

4.2 Genotypes
The genotyping results allowed us to confirm which cattle carried the SLICK1 mutation in
order to confidently split the herd into two groups based on genotype and not phenotype alone.
The genotypes align with the observed phenotypic differences and corresponding hair lengths.
Although the genotypes themselves do not warrant an extensive discussion, they successfully
provided a genetic basis needed to organize the other results from this study.

4.3 RNA Extractions and cDNA Generations
Individual RNA extractions were required to analyze HSP70 gene expression levels in each
animal studied. Although the RNA extractions themselves are a routine part to quantify gene
expression, the successful extracts validate the modified RNA extraction method that was used.
RNA extractions that were not successful and yielded a poor-quality sample, came from hair
follicles that were not correctly stored and ended up degrading. The successful cDNA generations
also indicated that the RNA was intact and suitable for further applications. Results from the qPCR

29

reactions confirmed that cDNA generations were successful and intact to yield gene expression
results.

4.4 HSP70 Gene Expression Analysis
To evaluate the efficacy of the SLICK phenotype on thermotolerance, HSP70 gene
expression levels were measured as an indicator of a heat stress response. We predicted that
because the Angus-Senepol hybrids (SLICK) presented with a shorter haircoat, they would have
an advantage in heat dissipation and consequently experience less heat stress, resulting in lower
HSP70 expression compared to the Angus (Wild type) group under the same conditions. The
relative expression of HSP70 (2-DCt) between SLICK and Angus cattle were compared for each
individual sampling condition.
Overall, we found inconsistent HSP70 gene expression results in relation to the respective
THI conditions. Both groups of cattle demonstrated an under expression of HSP70 in relation to
the housekeeping genes (positive DCt values) for June and August sampling times. Although the
expression levels of HSP70 genes were low—falling below those of the housekeeping genes—
expression was still detectable, indicating that some activation of HSP70 occurred in both cattle
groups.
Data collected from the June sampling period indicates that there was no significant
difference in HSP70 gene expression between the two cattle groups (p=0.210). When comparing
the THI conditions that the cattle would have experiences for 3 days prior to collection, it is notable
that the THI does pass the threshold for heat stress (72), reaching 76.4. As no significant
differences were found between the two groups, we can speculate that similar HSP70 responses
were induced in both groups. Although there was no significant difference between DCt values,

30

the 2-DCt values presented in Figure 7 display a higher expression of HSP70 in Red Angus cattle
than in SLICK cattle, which aligns with our hypothesis that SLICK cattle experience less heat
stress.
As for data collected in August, the difference in HSP70 expression between the cattle
groups is significant (P = 0.048). This is unexpected given the THI for the 3 days prior to collection
of these samples did not cross the threshold for heat stress. It would be expected that in
environmental conditions where the THI does not reach a point high enough to induce heat stress,
there should be little to no HSP70 gene expression. When examining the 2(-DCt) values for
the SLICK group between June and August, the decrease in HSP70 expression aligns with
expectations. However, the Angus group displayed an unexpected increase in HSP70 expression
despite the lower THI conditions, which may be reflective of the cumulative heat stress
experienced by the Angus (Wild type) throughout the summer.
Because the sampling date was later in the summer, it is important to consider not only the
intensity of the THI, but also the duration and the heat stress response it may elicit. A study
performed by Ouellet et al. (2019), showed that prolonged exposure to THI above 65 negatively
impacted the production of dairy cattle in Quebec, Canada. Given that the cattle in the present
study consistently experienced conditions where the THI exceeded 65 throughout the summer –
reaching a high of 69 in the days before sampling – this prolonged exposure may have contributed
to the unexpected increase in HSP70 expression observed in the Angus group. Although the THI
recorded in the days prior to sample collection in August did not cross the heat stress threshold,
the cumulative effects of sustained, lower-level heat stress over the course of hotter months, may
have resulted in a delayed or prolonged stress response. This persistent environmental pressure

31

could explain why the Angus cattle group exhibited higher HSP70 expression, compared to the
SLICK cattle, even in the absence of high THI conditions.
It is also possible a major heat stress event occurred earlier than the THI conditions
displayed in Figure 8b. Previous research conducted by Kim et al. (2020) indicates that HSP70
expression levels increase, and peak at 3 days following a heat stress event, before returning to
baseline within 6 days, when the cattle remain in the same environmental conditions. This
expression pattern may align with the data collected, suggesting that the increase in HSP70
expression observed in the Angus (Wild type) group could be linked to a previous heat stress event.
However, it should be noted that Kim et al. (2020) observed this pattern in cattle that experienced
THI conditions of 88.
There is also the speculation that the age of animals during sampling could play a role in
HSP70 gene expression, as many genes are affected by age (Kim et al, 2020). A study presented
by Kaushik et al. (2022) investigated the differential expression of HSP70 in ruminants during a
growth phase in response to heat stress. Although they recorded a noticeable difference in HSP70
gene expression between heat-stress tolerant versus heat-stress susceptible goats, they also noticed
significantly higher mRNA expression in animals at 9 months of age compared to all other age
groups. In addition to a protective role, HSP70 can also be involved in cell growth and proliferation
(Kaushik et al, 2022). Given that the animals used in this study were less than a year old, it is
possible we had sampled during a growth phase which may provide an explanation for the jump
in relative HSP70 gene expression seen in the Angus type group in August. Because the two groups
of cattle are composed of different lineages, growth phases may not be identical. However, these
hypotheses would need to be further investigated in respect to calf development stages.

32

Another explanation for this discrepancy may be due to the increase in cattle sampled in
August versus in June. It should also be considered that the additional cattle did not experience the
same conditions as the original cattle for the previous months. As mentioned before, 9 of the cattle
available in August were previously kept at Tod Mountain during earlier sampling dates. The
addition of new cattle, majority of which were SLICK, altered the sample sizes and increased the
standard deviation values for both groups. The sample sizes changed from n = 6 to n = 12 for the
SLICK group and from n = 6 to n = 7 for the Angus (Wild type) group. The standard deviations
increased from 1.07 and 0.88, to 2.72 and 4.15 for the SLICK and Angus (Wild type) groups,
respectively. These cattle may have had an increased HSP70 gene expression for reasons aside
from heat stress, as mentioned above. This is reflected in Figure 7, as the error bars for the Angus
(Wild type) group are noticeably larger than the rest, indicating the presence of an outlier.
Methodological explanations for the unexpected HSP70 expression levels observed from
the Angus type group may be due to non-specific amplification or primer dimers detected during
qPCR analysis. The gene expression assay was changed from TaqMan to SYBR Green chemistries
due to problems experienced with the TaqMan assay probes. However, forward and reverse
primers were not complementary to each other and melt curves obtained from qPCR analysis
suggests that this did not happen. It is also unlikely that these errors would have occurred among
one set on samples and not the others.
Overall, HSP70 gene expression levels obtained from the two cattle groups did not
correlate with the THI conditions. Given that other studies with similar objectives have
consistently reported significant findings, suggests that the environment where these cattle were
raised may be beneficial for mitigating heat stress. For instance, Kim et al. (2020) observed a
significant difference in HSP70 expression (P = 0.0120) in beef calves under different sampling

33

conditions in Korea, where the THI reached 83.04. Similarly, Taborda-Charris et al. (2023)
reported a significant change in HSP70 expression (P = 0.0011) in cattle (from Northern Colombia)
as the THI increased from 76 to 83.
Due to our sampling site being located just 20 minutes from Sun Peaks Mountain, the local
geography may have limited the occurrence and duration of high THI values experienced by cattle.
While this may have prevented us from observing significant and consistent changes in HSP70
expression, it also suggests that moderate environments like Heffley Creek offer optimal
conditions to raise beef cattle and limit heat stress over temperate or tropical conditions.

5 CONCLUSION AND FUTURE WORK
The increasing risk that climate change and rising temperatures poses for cattle production,
met with the increasing global demand for high quality meat and dairy products – particularly
Canadian beef – highlights the need for effective strategies to mitigate heat stress in livestock. A
promising solution to alleviate the heat stress experienced by cattle is the introduction of the
SLICK1 mutation, and corresponding SLICK phenotype. To determine if the SLICK phenotype
was effective at increasing the thermotolerance of Canada’s top beef producing breed, Red Angus
cattle, we investigated the expression levels of the HSP70 gene.
While we did confirm that the Angus (Wild type) cattle and Angus-Senepol hybrid
(SLICK) cattle differed in haircoat morphology, HSP70 gene expression results were inconsistent.
The identification of the SLICK1 mutation, along with its associated shorter hair length and the
variability in other haircoat characteristics observed in supporting research, suggests that there is
an opportunity to improve cutaneous EVHL in production cattle. Moreover, selecting for these

34

traits in beef cattle could improve resilience to heat stress without compromising production
performance.
In order to really determine if the SLICK1 mutation is beneficial to Red Angus beef cattle,
future research would have to include more consistent sampling times before, during, and after
known heat stress events, where the THI exceeds the stress threshold for longer amounts of time.
It would be ideal to extract samples from the same herd over multiple hours and days in order to
more thoroughly investigate the fold changes in HSP70 expression within each group of cattle. In
order to better characterize the hair coat morphology of each cattle group, future work may include
skin biopsies where hair follicle and sweat gland density could be measured. The difference in
pore size for sweat excretion could be determined by subtracting the diameter of hair from the area
to provide an idea of how much room there is for sweat dissipation at the skins surface. If future
studies identify differences in haircoat traits between Wild type and SLICK cattle, and establish a
relationship with EVHL rates, these may be traits that could be selected for in the future to improve
heat tolerance.

5.1 LIMITATIONS
The major limitation in this study was herd access. Sampling times had to work within the
availability of the farm owner which limited when samples from calves could be taken.
Furthermore, although the hair tail extractions are not considered invasive, the actual process was
timely to separate each calf from the herd and could be stressful, which we did not want to subject
young calves to repeatedly. Another limitation that could have supported our results was that we
did not consider other physiological parameters that could indicate heat stress such as core body
temperature and respiration rates.

35

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temperature in lactating cows under different climatic conditions. J Dairy Res. 83(2):165–172.
doi:10.1017/S0022029916000182.
Archana PR, Aleena J, Pragna P, et al. 2017. Role of heat shock proteins in livestock adaptation
to heat stress. Journal of Dairy, Vetrinary & Animal Research.5(1):13-19.
doi: 10.15406/jdvar.2017.05.00127
Asseng S, Spänkuch D, Hernandez-Ochoa Ixchel M, Laporta J. 2021. The upper temperature
thresholds of life. The Lancet Planetary Health. 5(6):e378–e385. doi:10.1016/S25425196(21)00079-6.
Bagath M, Krishnan G, Devaraj C, Rashamol VP, Pragna P, Lees AM, Sejian V. 2019. The impact
of heat stress on the immune system in dairy cattle: A review. Research in Veterinary Science.
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