NUTRITIONAL AND ENVIRONMENTAL FACTORS REGULATING ...

The Pennsylvania State University

The Graduate School

Department of Animal Science

NUTRITIONAL AND ENVIRONMENTAL FACTORS REGULATING

BIOLOGICAL RHYTHMS OF MILK SYNTHESIS IN DAIRY CATTLE

A Dissertation in

Animal Science

by

Isaac J. Salfer

2019 Isaac J. Salfer

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2019

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The dissertation of Isaac J. Salfer was reviewed and approved* by the following

Kevin J. Harvatine

Associate Professor of Nutritional Physiology

Dissertation Advisor

Chair of Committee

Paul A. Bartell

Associate Professor of Avian Biology

W. Burt Staniar

Associate Professor of Equine Science

Connie J. Rogers

Associate Professor of Nutritional Immunology

Terry D. Etherton

Distinguished Professor of Animal Nutrition

Graduate Program Head – Animal Science Department

*Signatures are on file in the Graduate School

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ABSTRACT

Dairy cows exhibit well-characterized daily patterns of feed intake and milk synthesis.

These patterns may represent circadian rhythms, endogenous repeating cycles of approximately

24 h that govern most physiological processes. Circadian rhythms are controlled by a set of

‘clock’ transcription factors that oscillate over 24 h and govern gene expression across the day.

There is an increasing body of evidence demonstrating a role of nutrient intake in modifying

circadian rhythms in peripheral tissues. However, this effect has not been well-characterized in

dairy cattle. In addition to a daily rhythms of milk synthesis, dairy cows display seasonal changes

of milk production that are not well understood. The objective of this dissertation was to examine

the factors affecting daily and annual rhythms of milk synthesis. Specifically, we wanted to

examine the effects of nutrient intake on the daily rhythms of milk synthesis, and the relationships

between cow-level and environmental factors and the annual rhythms of milk production.

To accomplish these objectives, 6 experiments were conducted, with the first 4

examining the role of nutrient intake on the daily rhythms of milk synthesis, and the final 2

characterizing factors influencing annual rhythms of milk production. First, the effect of time-

restricted feeding on the daily patterns of milk synthesis and plasma hormones and metabolites

was examined in an experiment where feed was temporally restricted to cows for 16 h/d either

during the day (0700 to 2300 h) or at night (1900 to 1100 h). This experiment demonstrated that

night restricted feeding shifted the peak of milk yield from morning to evening, while shifting the

peak of milk fat and protein concentration from evening to morning. Moreover, the daily rhythms

of plasma glucose, nonesterified fatty acids, plasma urea nitrogen, and insulin were shifted about

8 h by night-restricted feeding. The objective of the second experiment was to investigate if the

changes in daily patterns of milk synthesis during time-restricted feeding were caused by changes

in the molecular circadian clock of the mammary gland. This experiment compared a control

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group with feed available continuously to cows under night-restricted feeding (feed available for

16 h/d from 2000 h to 1200 h). Results demonstrated that night-restricted feeding altered the daily

rhythms of expression of clock genes circadian locomoter output cycles kaput, cryptochrome 1

and REV-ERBα, suggesting that nutrients entrain the molecular circadian clock of the mammary

gland. The final two experiments examined the role of specific nutrients on the daily rhythms of

milk synthesis. In one experiment, a high C18:1 oil was abomasally infused either 24 h/d or for 8

h during the day (0900 to 1700 h) or the night (from 2100 to 0500). Day-infusion increased the

amplitudes of milk and milk fat and protein yield while reducing the amplitudes of milk fat and

protein concentration. However, treatment had little influence on the phase of the rhythms,

suggesting that the timing of fatty acid infusion did not entrain the mammary clock. In the other

experiment, sodium caseinate solution was abomasally infused either 24 h/d or for 8 h during the

day (0900 to 1700 h) or the night (from 2100 to 0500). Night-infusion induced a daily rhythm of

milk yield, but no rhythm was present during continuous or daytime infusion. Infusion of sodium

caseinate during the day reduced the amplitude of fat concentration and increased the amplitude

of protein concentration, while night-infusion decreased the amplitude of protein concentration.

Treatment had little effect on the time at peak of the rhythm, suggesting that the mammary

circadian clock was not entrained to the time of protein infusion. Furthermore, night-infusion

shifted the peak of the nonesterified fatty acid rhythm from morning to evening.

The final 2 experiments characterized factors influencing the annual rhythms of milk

synthesis. First, annual rhythms of milk fat and protein concentration were compared among

regions of the United States using monthly averages of Federal Milk Marketing Orders. The

annual rhythms had greater amplitudes in northern regions compared to southern regions

suggesting that they are related to photoperiod. In the same report, the effect of cow-level factors

on annual rhythms of milk, fat, and protein yield, and milk fat and protein concentration were

examined using data from 11 dairy herds in Pennsylvania. Rhythms were consistent regardless of

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herd, lactation number, and genotype, suggesting that they are highly conserved among individual

cows. Finally, annual rhythms of milk, fat and protein yield and milk fat and protein

concentration were studied using dataset obtained from Dairy Records Management Systems and,

relationships between annual rhythms of production and daylength, change in daylength, and

environmental temperature were determined. Annual rhythms differed between northern and

southern states, with northern states having greater amplitudes of milk fat and protein

concentration and southern states having greater amplitudes of milk yield. Moreover, the rhythms

of milk fat and protein concentration were highly correlated with absolute daylength, while the

rhythm of milk yield was highly correlated with the change in daylength, suggesting that they are

controlled through two separate oscillatory mechanisms.

In conclusion, the daily rhythms of milk synthesis are modified by the time of feeding

and this effect appears mediated by the molecular circadian clock. The timing of both fatty acids

and protein affect the robustness of milk synthesis rhythms, without large effects on their time of

peak. Finally, yearly patterns of milk production appear to constitute an annual rhythm, with the

rhythms of fat and protein concentration being entrained by daylength and the rhythm of milk

yield being entrained by the change in daylength.

Keywords: Biological rhythm, milk synthesis, dairy cattle, food entrainment

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TABLE OF CONTENTS

List of Figures .................................................................................................................... xi

List of Tables ................................................................................................................... xiii

List of Supplemental Figures ........................................................................................... xiv

List of Supplemental Tables ............................................................................................. xv

List of Abbreviations ....................................................................................................... xvi

Acknowledgements .......................................................................................................... xix

Chapter 1 Introduction ........................................................................................................ 1

Chapter 2 Literature Review ............................................................................................... 5

Biological Rhythms ......................................................................................................... 5

Molecular Structure of Biological Clocks ....................................................................... 7

Transcriptional-Translational Feedback Loops ........................................................... 7

Post-Transcriptional Modifications ............................................................................. 9

Post-Translational Modifications .............................................................................. 10

Epigenetics and the Clock ......................................................................................... 11

Entrainment of Biological Clocks ................................................................................. 11

Light Entrainment ...................................................................................................... 12

Food Entrainment ...................................................................................................... 13

Temperature Entrainment .......................................................................................... 15

Circadian Rhythms and Nutrient Metabolism ............................................................... 16

Cellular Energy and Redox Status and the Circadian Clock ..................................... 16

Glucose Metabolism and the Clock ........................................................................... 18

Lipid Metabolism and the Clock ............................................................................... 20

Circadian Rhythms in Microorganisms ..................................................................... 22

Annual Rhythms ............................................................................................................ 25

Annual Rhythms and Reproduction .......................................................................... 26

Seasonal Changes in Feeding Behavior and Metabolism .......................................... 28

Seasonal Changes in Immunity ................................................................................. 31

Epigenetic Control of Seasonality ............................................................................. 34

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Conclusions ................................................................................................................... 35

Figures ........................................................................................................................... 36

Chapter 3 The effects of day- versus night-restricted feeding of dairy cows on the daily

rhythms of feed intake, milk synthesis and plasma metabolites ....................................... 39

ABSTRACT .................................................................................................................. 40

INTRODUCTION ......................................................................................................... 41

MATERIALS & METHODS........................................................................................ 42

Animals and Treatments ............................................................................................ 42

Milk Sampling and Analysis ..................................................................................... 43

Feeding Behavior Observation and Analysis ............................................................ 44

Plasma Sampling and Analysis.................................................................................. 44

Body Temperature Recording ................................................................................... 45

Statistical Analysis .................................................................................................... 45

RESULTS...................................................................................................................... 46

Eating Behavior ......................................................................................................... 46

Rhythm of Milk and Milk Components .................................................................... 47

Milk FA Yield and Profile ......................................................................................... 48

Plasma Hormones and Metabolites ........................................................................... 49

Body Temperature ..................................................................................................... 50

DISCUSSION ............................................................................................................... 50

FIGURES ...................................................................................................................... 55

TABLES ........................................................................................................................ 60

SUPPLEMENTAL FIGURES ...................................................................................... 61

SUPPLEMENTAL TABLES ........................................................................................ 62

Chapter 4 The effects of night-restricted feeding on the molecular clock of the mammary

gland in lactating dairy cows ............................................................................................ 65

ABSTRACT .................................................................................................................. 66

INTRODUCTION ......................................................................................................... 68

MATERIALS AND METHODS .................................................................................. 70



Mammary Tissue Collection ..................................................................................... 71

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Gene Expression Analysis ......................................................................................... 73


RESULTS AND DISCUSSION ................................................................................... 74

Dry Matter Intake ...................................................................................................... 74

Milk Yield, Milk Composition, and Milk Fatty Acid Yields .................................... 75

Molecular Clock Gene Expression ............................................................................ 76

Plasma Hormones and Metabolites ........................................................................... 78

CONCLUSIONS ........................................................................................................... 80

FIGURES ...................................................................................................................... 82

TABLES ........................................................................................................................ 87

SUPPLEMENTAL TABLES ........................................................................................ 88

Chapter 5 The effects of time of fatty acid infusion on the daily rhythms of milk synthesis

and plasma hormones and metabolites in dairy cows ....................................................... 91

ABSTRACT .................................................................................................................. 92

INTRODUCTION ......................................................................................................... 94

MATERIALS & METHODS........................................................................................ 95




Body Temperature Analysis ...................................................................................... 98


RESULTS AND DISCUSSION ................................................................................... 99

Milk and Milk Components Synthesis ...................................................................... 99

Daily Yields and Daily Rhythms of Milk Fatty Acids ............................................ 101

Daily Rhythms of Plasma Metabolites .................................................................... 103

Daily Rhythm of Body Temperature ....................................................................... 105

CONCLUSIONS ......................................................................................................... 106

FIGURES .................................................................................................................... 108

SUPPLEMENTAL FIGURES .................................................................................... 116

SUPPLEMENTAL TABLES ...................................................................................... 117

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Chapter 6 The effect of timing of protein infusion on the daily rhythms of milk

production and plasma hormones and metabolites ......................................................... 119

ABSTRACT ................................................................................................................ 120

INTRODUCTION ....................................................................................................... 122

MATERIALS & METHODS...................................................................................... 124

Animals and Treatments .......................................................................................... 124

Milk Sampling and Analysis ................................................................................... 125

Plasma Sampling and Analysis................................................................................ 125

Statistical Analysis .................................................................................................. 126

RESULTS AND DISCUSSION ................................................................................. 127

Daily Milk Yield and Milk Components ................................................................. 127

Daily Rhythms of Milk Yield and Milk Components ............................................. 128

Daily Yields and Daily Rhythms of Milk Fatty Acids ............................................ 130

Daily Rhythms of Plasma Metabolites .................................................................... 131

CONCLUSIONS ......................................................................................................... 135

FIGURES .................................................................................................................... 136



Chapter 7 Annual rhythms of milk and milk fat and protein production in dairy cattle in

the United States ............................................................................................................. 145

INTRODUCTION ....................................................................................................... 148


USDA Milk Market Data ........................................................................................ 149

Cow-Level Data ....................................................................................................... 150

RESULTS.................................................................................................................... 151

USDA Milk Market Data ........................................................................................ 151

Cow-Level Data ....................................................................................................... 153

DISCUSSION ............................................................................................................. 156

CONCLUSIONS ......................................................................................................... 163

FIGURES .................................................................................................................... 164

TABLES ...................................................................................................................... 169


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Chapter 8 Annual rhythms of milk synthesis in dairy herds in four regions of the United

States and their relationships to environmental predictors. ............................................ 175

ABSTRACT ................................................................................................................ 176

INTRODUCTION ....................................................................................................... 178


Data Collection ........................................................................................................ 179

Cosinor rhythmometry ............................................................................................. 180

Effect of breed on annual rhythms of production .................................................... 180

Comparison of annual rhythm with temperature ..................................................... 181

Relationships among environmental variables and milk production responses ...... 181

RESULTS.................................................................................................................... 182

Annual Rhythms within Selected States .................................................................. 182

Annual Rhythms among Breeds .............................................................................. 183

Comparison of Annual Rhythm and Temperature Models ..................................... 185

Relationships among Environmental Variables and Milk Production .................... 186

Regression Equations to Adjust for Seasonal Changes in Production .................... 187

DISCUSSION ............................................................................................................. 187

CONCLUSIONS ......................................................................................................... 193

FIGURES .................................................................................................................... 195

TABLES ...................................................................................................................... 198


Chapter 9 Integrative Discussion .................................................................................... 205

References ....................................................................................................................... 212

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LIST OF FIGURES

Figure 2-1. Parameters used in the quantification of biological rhythms. ....................... 36

Figure 2-2. Structure of the mammalian transcriptional circadian clock. ........................ 37

Figure 2-3. Physiological mechanisms governing seasonal reproduction. ...................... 38

Figure 3-1. Effect of day- versus night-restricted feeding on the daily pattern of feed

intake. ................................................................................................................................ 55

Figure 3-2. Effect of day- versus night-restricted feeding on daily rhythms of milk yield

and milk components. ....................................................................................................... 56

Figure 3-3. Effect of day- versus night-restricted feeding on the daily production and

daily pattern of milk fatty acids. ....................................................................................... 57

Figure 3-4. Effect of day- versus night-restricted feeding on daily rhythms of plasma

hormones and metabolites................................................................................................. 58

Figure 3-5. Effect of day- versus night- restricted feeding on the circadian rhythm of

body temperature in dairy cows. ....................................................................................... 59

Figure 4-1. Schedule of feeding, lighting and sampling during the experiment. ............. 82

Figure 4-2. Effect of night-restricted feeding on average daily feed intake and milk

synthesis. ........................................................................................................................... 83

Figure 4-3. Effect of night-restricted feeding on milk fatty acid yields by origin. .......... 84

Figure 4-4. Effect of night-restricted feeding on the daily patterns of expression of

molecular clock genes in the mammary gland. ................................................................. 85

Figure 4-5. Effect of night-restricted feeding on the daily patterns of plasma metabolites.

........................................................................................................................................... 86

Figure 5-1. Schedule of feeding, lighting and sampling during the experiment. ........... 108

Figure 5-2. Effect of time of fatty acid infusion on dry matter intake. .......................... 109

Figure 5-3. Effect of time of fatty acid infusion on daily milk, fat and protein yield and

fat and protein concentration. ......................................................................................... 110

Figure 5-4. The effects of time of fatty acid infusion on the daily rhythms of milk

synthesis. ......................................................................................................................... 111

Figure 5-5. Effect of time of fatty acid infusion on the daily yields of fatty acids. ....... 112

Figure 5-6. Effect of time of fatty acid infusion on daily rhythms of fatty acids by source.

......................................................................................................................................... 113

Figure 5-7. The effects of time of fatty acid infusion on daily rhythms of plasma

metabolites. ..................................................................................................................... 114

Figure 5-8. The effect of the time of fatty acid infusion on the daily rhythms of core body

temperature. .................................................................................................................... 115

Figure 6-1. Schedule of feeding, lighting and sampling during the experiment. ........... 136

Figure 6-2. Effect of time of protein infusion on dry matter intake............................... 137

Figure 6-3. Effect of time of protein infusion on daily milk, fat and protein yield and fat

and protein concentration. ............................................................................................... 138

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Figure 6-4. The effects of time of protein infusion on daily rhythms of milk synthesis.

......................................................................................................................................... 139

Figure 6-5. Effect of time of protein infusion on daily yields and daily rhythms of milk

FA by source. .................................................................................................................. 140

Figure 6-6. The effects of time of protein infusion on daily rhythms of plasma

metabolites. ..................................................................................................................... 141

Figure 7-1. Map of United States Federal Milk Marketing Orders. .............................. 164

Figure 7-2. Annual rhythms of fat concentration in selected Federal Milk Market orders.

......................................................................................................................................... 165

Figure 7-3. Annual rhythms of protein concentration in selected Federal Milk Market

orders............................................................................................................................... 166

Figure 7-4. The effect of parity on annual rhythms of milk yield and composition. ..... 167

Figure 7-5. The effect of DGAT1 K232A polymorphism on annual rhythms of milk and

milk fat concentration and yield. .................................................................................... 168

Figure 8-1. Annual rhythms of milk, fat and protein yield and fat and protein

concentration in Holstein dairy herds in Florida (FL), Minnesota (MN), Pennsylvania

(PA), and Texas (TX). .................................................................................................... 195

Figure 8-2. Annual rhythms of milk and milk fat and protein yield and milk fat and

protein concentration by breed........................................................................................ 196

Figure 8-3. Relationship among environmental predictors and annual rhythms of milk

and milk fat and protein yield and milk fat and protein concentration by breed. ........... 197

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LIST OF TABLES

Table 3-1. Effect of day versus night-restricted feeding on total daily DMI, milk yield,

and milk composition. ....................................................................................................... 60

Table 4-1. Bovine primers used to quantify mammary gene expression with RT-qPCR. 87

Table 7-1. The acrophase and amplitude of a cosine function with a 12-month period fit

to the regional monthly averages of milk fat and protein concentration. ....................... 169

Table 8-1. Comparison between models testing the fit of a cosine function versus

maximum temperature. ................................................................................................... 198

Table 8-2. Equations for cosine regression equations for milk and milk fat and protein

yield and milk fat and protein concentration for Florida (FL), Minnesota (MN),

Pennsylvania (PA), and Texas (TX). .............................................................................. 199

Table 8-3. A summary of milk yield and milk fat and protein concentration responses in

experiments testing the effects of heat stress in environmental chambers. .................... 200

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LIST OF SUPPLEMENTAL FIGURES

Supplemental Figure S3-1. Effect of the day versus night-restricted feeding on feeding

behavior............................................................................................................................. 61

Supplemental Figure S5-1. Effect of time of fatty acid infusion on milk urea nitrogen

concentration. .................................................................................................................. 116

Supplemental Figure S 6-1. Effect of time of protein infusion on lactose yield. ......... 142

Supplemental Figure S7-1. Annual rhythms of fat concentration in Federal Milk Market

orders............................................................................................................................... 170

Supplemental Figure S7-2. Annual rhythms of protein concentration in Federal Milk

Market orders. ................................................................................................................. 171

Supplemental Figure S7-3. Variability in acrophase and amplitude of daily rhythms of

milk, fat and protein yield among 11 herds in Pennsylvania. ......................................... 172

Supplemental Figure S7-4. Variability in acrophase and amplitude of daily rhythms of

milk fat and protein concentration among 11 herds in Pennsylvania. ............................ 173

Supplemental Figure S7-5. Repeatability of annual rhythms across years. ................. 174

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LIST OF SUPPLEMENTAL TABLES

Supplemental Table S3-1. Diet and nutrient composition of the experimental diet. ...... 62

Supplemental Table S3-2. Effect of day- versus night-restricted feeding on total daily

and daily rhythms of individual milk fatty acids. ............................................................. 63

Supplemental Table S4-1. Diet and nutrient composition of experimental diet............. 88

Supplemental Table S4-2. Effect of night-restricted feeding on daily yields of individual

milk fatty acids. ................................................................................................................. 89

Supplemental Table S5-1. Diet and nutrient composition of the experimental diet. .... 117

Supplemental Table S5-2. Fatty acid profile of oil used for abomasal infusion of fatty

acids. ............................................................................................................................... 118

Supplemental Table S6-1. Diet and nutrient composition of the experimental diet. .... 143

Supplemental Table S6-2. Amino acid profile of sodium caseinate. ........................... 144

Supplemental Table S8-1. Adjustment factors to correct for annual rhythms of milk

production in Florida....................................................................................................... 201


production in Minnesota. ................................................................................................ 202


production in Pennsylvania. ............................................................................................ 203


production in Texas......................................................................................................... 204

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LIST OF ABBREVIATIONS

AA-NAT Arylalkylamine N-acetyl transferase

ACC Acetyl-CoA carboxylase

ACTB Beta-actin

ADF Acid detergent fiber

AICAR AMPK-inhibitors 5-aminoimidazole-4-carboxyamide ribonucleoside

AMPK (AMP)-activated protein kinase

AMS Agricultural Marketing Service

B2M Beta-2 microglobulin

BCFA Branched-chain fatty acid

BH Biohydrogenation

BHBA β-hydroxybutyrate

BMAL1 Brain and muscle ARNT-like 1

BVD Bovine viral diarrhea

CCG Clock controlled gene

CCK Cholecystokinin

CLOCK Circadian locomotor output cycles kaput

CIRP Cold-inducible RNA-binding proteins

CK1ε Casein kinase 1ε

CP Crude protein

CPT Carnitine palmitoyltranserase

CRY Cryptochrome

CYP7A1 7-alpha-hydroxylase

DBP D-box binding protein

DGAT1 Diacylglycerol O-acyltransferase 1

DHIA Dairy Herd Information Association

DIO Deiodinase

DM Dry matter

DMH Dorsomedial hypothalamus

DMI Dry matter intake

DRMS Dairy Records Management Systems

E4BP4 E4- promoter binding protein

EE Ether extract

FA Fatty acid

FAA Food anticipatory activity

FASN Fatty acid synthase

FBXL3 F-box/LRR-repeat protein 3

FEO Food-entrainable oscillator

FFA Free Fatty Acid

FMMO Federal milk marketing order

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FOXO1 Forkhead box protein O1

GIP Gastric inhibitory peptide

GLP1 Glucagon-like peptide 1

GRE Glucocorticoid-response element

HAT Histone acetyltransferase

HDAC Histone deacetylase

HLF Hepatic leukemia factor

HMG-CoA 3-hydroxy-3-methyl-glutaryl-Coenzyme A

HSF1 Heat-shock factor 1

IGF-1 Insulin-like growth factor

ipRGC Intrinsically photosensitive retinal ganglion cell

KLF15 Krüppel-like factor 15

LSM Least square means

MEL Melatonin

miRNA MicroRNA

mTOR Mechanistic target of rapamycin

MTP Microsomal triglyceride transfer protein

MUN Milk urea nitrogen

NAMPT Nicotinamide phosphoribosyltransferase

NDF Neutral detergent fiber

NE Norepinephrine

NEFA Nonesterified fatty acids

NF-κB Nuclear factor-κB

NK Natural killer

NPAS4 Neuronal PAS domain protein 4

OBCFA Odd- and branched-chain fatty acids

OCFA Odd-chain fatty acid

OTU Operational taxonomic unit

PBMC Polymorphonuclear blood monocyte

PER Period

PGC-1α Proliferator-activated receptor gamma cofactor 1α

PPAR Peroxisome proliferator-activated receptor

PPRE Peroxisome proliferator-activated receptors response element

PUN Plasma urea nitrogen

psbAI Photosystem II protein

PT Pars tuberalis

PTB Polypyrimidine tract-binding protein

PVN Paraventricular nucleus

qPCR Quantitative polymerase chain reaction

RAPTOR Regulatory-associated protein of mTOR

ROR Retinoic acid-related orphan receptor

RORE Retinoic acid-related orphan receptor binding element

RPS9 Ribosomal protein 9

xviii

S14 Spot 14

SCC Somatic cell count

SCD Stearoyl-CoA desaturase

SCN Suprachiasmatic nucleus

SDPP Short day photoperiod

SEM Standard error of the mean

SFA Saturated fatty acid

SHP Small heterodimer partner

siRNA Small interfering RNA

SIRT1 Sirtulin 1

SREBP Sterol regulatory element-binding protein

T3 Triiodothyronine

T4 Thyroxine

t10 trans-10 C18:1

t11 trans-11 C18:1

TEF Thyrotrophic embryonic factor

TMR Total mixed ration

TSH Thyroid stimulating hormone

TTFL Transcription translation feedback loop

USDA United States Department of Agriculture

UV Ultraviolet

VDR Vitamin D receptor

VFA Volatile fatty acid

VMH Ventromedial hypothalamus

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ACKNOWLEDGEMENTS

I have frequently joked that all the friends and family members that have provided love

and support throughout my Ph.D. program and should be listed as co-authors on all of my

manuscripts because the research truly could not have been performed without them. In the

sincerest sense, this is true. I have been extremely blessed to have an incredible support system

around me over the past 4 years, and there is no conceivable way I could have finished my Ph.D.

without them.

First, I would like to thank my advisor Dr. Kevin Harvatine. While his incredible

intellect, critical thinking skills and creativity are impressive, what I am most inspired by is his

quiet humility and the deep kindness he shows to everyone. I am truly fortunate to have had the

opportunity to work with him for my Ph.D.

I would also like to thank the other members of my graduate committee. Dr. Paul Bartell

is responsible for helping me develop a true understanding of circadian biology, and has been

instrumental in the design and analysis of my experiments. I learn something new and exciting

from him nearly every time we interact. Dr. Burt Staniar has been a great mentor and friend, and

always asks important questions I would never have considered. I have also learned many “non-

scientific” things from him about teaching, mentoring, and collaborating with other faculty

members, and always enjoy our discussions of opportunities to improve the academic system. Dr.

Connie Rogers has been a great resource about basic nutrient metabolism, and has sparked my

interest in nutritional immunology, which is a field I hope to pursue in the future.

There are several other members of the Penn State Animal Science Department that I

must thank. Dale Olver recruited me to help coach the dairy judging team on my first day at Penn

State, and since then he has continued to provide opportunities to be involved with the Dairy

Club, and the Pennsylvania dairy industry. I would like to thank Dr. “Doc” Dan Kniffen for

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always coming to visit our lab to tell jokes and stories, as well as for providing me with the

opportunity to help T.A. the beef management course. I would like to thank Dr. Judd Heinrichs

for our discussions about everything from cheese to dairy nutrition, to historical changes in the

dairy industry and for inviting me to celebrate Thanksgiving with his family and fellow graduate

students for the past two years. I would like to thank Dr. Chad Dechow for assistance with

statistical analysis, for providing data four our seasonal rhythm experiments, and for always being

kind and supportive. Thank you to Dr. Tara Felix for always bringing up excellent questions in

journal club and providing advice on how to be a young faculty member. Thank you to Dr. Larry

Specht for the conversations about the evolution the dairy industry, and for always making sure to

remind me when Penn State beat the University of Minnesota in sports. Thank you to Dr. Mike

O’Connor for the invitation to the Bracketology 101 class. Finally, I must thank Drs. Craig

Baumrucker, Dr. Joy Pate, Alan Johnson, Troy Ott, and Alex Hristov for generously allowing me

to use of their laboratory equipment.

The completion of my degree would truly not have happened if it were not for the

amazing technicians we have had in the lab. Jackie Ying was incredibly instrumental in teaching

me the laboratory techniques I needed to complete my PhD, and helped organize and execute my

early experiments. When Jackie left the lab, I thought she was irreplaceable, but we were

fortunate to catch lightning in a bottle twice when Dr. Harvatine hired Beckie Bomberger and

Elaine Barnoff. Both of them have been very helpful keeping me organized, helping with

circadian experiments so I could actually get some sleep, and performing lab analyses. They both

will be missed very much.

I must thank my other fellow labmates that I have had the pleasure to work with over the

past 4 years. Natalie Urrutia and Michel Baldin were very welcoming when I started in the lab

and will remain lifelong friends. I am also thankful to Richie Shepardson, Cesar Matamoros, Elle

Andreen, and Reilly Pierce, Yifan Fan, Jocely Souza, Li Junrong, Chengmin Li and Ahmed

xxi

Elzenary for their immense amount of help with sample collection. A special shout out goes to

“Harvateam” members Richie (the “Crystal Crusader”), Cesar (“Captain Ceviche”) and Elle (the

“RuminatoR”) for putting up with me through good times and bad, and for all the fun times we

had together. I am excited to see where all of us end up in the future.

I am incredibly grateful for the friendship I have shared with many other graduate

students in the department. I’d like to give special thanks to Kahina Ghanem for always providing

a spark of positivity and fun whenever I talk to her, for providing a ton of support when I was

finishing my dissertation, and for cooking unique and delicious food for myself and the rest of the

department. Special thanks also go out to Camilla Hughes for the discussions about physiology,

the academic system and politics, and to Isaac Haagen for inviting me to their family holidays on

several occasions, especially when I was new at Penn State. I also want to thank Pedro Carvalho,

Chen Lu, G. Ali Chisti, Susanna Ræsanen, Siga Laspinkas, Martyna Łupicka, Jasmine Dillon,

Lucas Mitchell, Alberto López, Mike Harper, Evelyn Weaver, Neha Oli, Sreelakshmi Vasudevan,

Manasi Kamat, Sonia Arnold, Lydia Hardie and Patricia Ochonski for the fun and friendship.

Lastly, I must thank my wonderful family. More than anyone else, they have made

completion of my PhD possible, If not for my dad’s love of dairy science and my mom’s love of

education; I would not have even thought to pursue a graduate degree in animal science. I cannot

express in words the gratitude I have for their unwavering support and love. I know me living

halfway across the country for 4 years was not easy for them, but they never discouraged me from

pursuing my degree and always made tremendous efforts to visit me here. I would be remiss if I

didn’t also thank my brother Lucas for always providing opportunities for intellectual

discussions, and my sister Hannah for constantly finding new ways to make me laugh.

1

Chapter 1

Introduction

Milk production from dairy cows provides an important source of nutrients for human

diets. As ruminants, dairy cows convert fibrous plant material into high-quality protein, making

them integral to sustainable food production. Moreover, the dairy industry in an important piece

of the United States agricultural economy and accounts for approximately 10% of total

agricultural sales according to the USDA Economic Research Service. Globally, the per capita

demand for dairy products has increased a total of 9% since 1970, and is expected to rise another

20% from now until 2030 as economic development continues throughout the world (FAO,

2012). The dairy industry, along with all of animal agriculture, has been receiving increased

scrutiny regarding potential negative environmental impacts. There has been public pressure to

reduce greenhouse gas emissions from livestock species in order to slow the effects of global

climate change. Additionally, over the past 4 years, the U.S. dairy industry has experienced a

period of unprecedentedly low milk prices, making margins for dairy producers low to

nonexistent. Together, these demands have created imminent desire for creative solutions to

improve the efficiency of milk production.

One potential approach to improve the efficiency of dairy cattle is by developing a better

understanding of the biological rhythms governing milk synthesis. Biological rhythms are

endogenous cycles that govern the behavior and physiology of nearly all organisms and allow

them to predict changes in the external environment before they occur. These rhythms occur

across multiple time scales ranging from a year to less than a day. Of these, the most commonly

described are circadian rhythms, which are approximately 24 h in length, and circannual rhythms,

2

which are approximately 1 year. Both circadian and circannual rhythms are controlled at a

cellular level through a host of proteins comprising a ‘molecular clock’. In recent years, there has

been an increasing body of literature demonstrating an intimate relationship between these

biological rhythms and metabolism in laboratory models and humans. Diseases such as obesity

and type II diabetes have been associated with disruption of circadian rhythms and nutritional

approaches such as time-restricted feeding appear to have potential benefits for metabolic health

and reduced aging (Kessler and Pivovarova-Ramich, 2019). However, there is still a dearth of

research examining the regulation of these biological rhythms in livestock species.

While still poorly understood, there is compelling evidence to suggest that biological

rhythms are important for milk synthesis in dairy cows. Dairy cows exhibit differences in milk

yield and composition across the day, with milk yield being greater in the morning than evening

and milk fat and protein concentration being greater in the evening (Gilbert et al., 1972). Initial

research has suggested that these patterns represent a daily rhythm and that this rhythm is

responsive to changes in feeding time (Rottman et al., 2014). Additionally, several hormones and

metabolites follow a diurnal pattern in dairy cows, suggesting that the metabolism of other tissues

is also controlled by circadian rhythms (Giannetto and Piccione, 2009). Milk yield also follows a

yearly pattern, with increases in milk and milk components during the winter and decreases in the

summer (Wood, 1976). While these rhythms appear to be important, little is known about their

regulation. Developing a better understanding of these biological rhythms may allow for

development of nutritional and management strategies to improve the efficiency of dairy

production.

The overall objective of this dissertation was to examine the relationship factors

regulating the biological rhythms of milk synthesis. Specifically, we investigated the effects of

nutrient intake on the daily rhythms of milk synthesis, as well as the relationships among cow-

and environmental factors and the annual rhythms of milk production. We expect that total food

3

intake and individual macronutrients will entrain the daily rhythms of milk synthesis by

modifying the circadian clock of the mammary gland. Moreover, we anticipate that an annual

rhythm governs yearly changes in milk production, and that this rhythm is consistent among

years, herds, and individual cows, but is responsive to changes in photoperiod.

First, a review of the literature providing a background of the basic molecular

mechanisms governing circadian and circannual rhythms, as well as their relationships to food

intake and metabolism is presented (Chapter 2). The aim of the first experiment (Chapter 3) was

to examine the effects of the time of feed intake on the daily rhythms of milk synthesis by

restricting the time of feed availability to either the day or night. We expected that the daily

rhythms of milk synthesis would be shifted by the time of feed restriction. To investigate if

changes in the rhythms of milk synthesis due to the time of feed intake were mediated by the

molecular circadian clock, a follow-up experiment examining the role of time-restricted feeding

on the daily patterns of clock gene expression in the mammary gland was conducted (Chapter 4).

We expected that expression of clock genes would be rhythmic and that the rhythms would be

shifted by night-restricted feeding. The next two experimented focused on the importance of

individual nutrients for entrainment of the daily rhythms of milk synthesis. Chapter 5 examined

the role of the timing of fatty acid infusion and Chapter 6 examined the role of the timing of

protein infusion over the day on the daily rhythm of milk synthesis. We expected that the time of

fat infusion would alter the daily rhythms of milk and milk fat synthesis, while the time of protein

infusion would alter the daily rhythms of milk and milk protein synthesis.

Chapters 7 and 8 focus on the factors contributing to annual rhythms of milk synthesis.

The analysis reported in Chapter 7, which has been published in the Journal of Dairy Science,

examines the effects of geographic region on annual rhythm of fat and protein concentration

using data from U.S. milk markets, as well as the factors such as lactation number and genotype.

We expected that lactation number and genotype will not affect annual rhythms, but that they will

4

be affected by geographic region. This experiment was followed by another analysis using a large

dataset from Dairy Records Management Systems to determine better elucidate the effects of

region of the U.S. on the annual rhythms of milk, fat and protein yield and fat and protein

concentration, as well as examine potential environmental signals entraining these rhythms

(Chapter 8). Based on the results of the previous experiment, we expected that geographic region

would affect the annual rhythms, and these rhythms would be highly correlated with the change

in photoperiod. Finally, an integrative discussion describing the potential implications, limitations

of these experiments, and future directions is provided in Chapter 9.

5

Chapter 2

Literature Review

Biological Rhythms

The ability of organisms to perceive time and coordinate behavior and metabolism

bestows a tremendous evolutionary advantage, allowing them to maximize energetic resources

and avoid harmful circumstances. Reliably predicting food availability and predator activity, even

by mere minutes, can be the difference between survival and death for many species.

Furthermore, the ability for an organism to monitor time of day allows them to temporally

segregate incompatible physiological processes and maximize energetic efficiency by preventing

futile cycles of metabolism. To facilitate the coordination between the temporal changes in the

external environment, and their own intrinsic metabolism, animals have developed inherent

biological rhythms. These rhythms persist in the absence of external cues and are responsive to

entrainment, or resetting environmental signals.

The most well-studied biological rhythms are circadian rhythms, which are repeating

biological cycles of approximately 24 h. The term circadian was coined in 1959 by Franz

Halberg and is derived from the Latin terms circa, meaning ‘about’; and diēm, meaning ‘day’

(Halberg et al., 1959). However, descriptions of daily rhythms had been made much earlier.

Androsthenes, a scribe for Alexander the Great discovered daily patterns of tamarind

(Tamarindus indica) leaf movements in the 4th century B.C. (Moore-Ede, 1982). These first

recorded observations indicating that daily rhythms may be endogenous were made Jean-Jacques

d’Ortous de Mairan in 1729. He noted that the flowering plant Mimosa pudica, which goes

6

through cycles of open leaves during the day and closed leaves overnight, continued to follow this

pattern even when plants were placed in constant darkness (de Mairan, 1729). This discovery was

revisited nearly 100 years later by Alphonse de Candolle who observed that the rhythm of plant

movement also persisted under constant light, and that the period of leaf movement was slightly

shorter than 24 h (De Candolle, 1832).

While these initial discoveries were important to developing the idea of endogenous daily

rhythms, the field of chronobiology – the study of biological rhythms - was not established until

the middle of the 20th century. Three scientists, Erwin Bünning, Jürgen Aschoff and Colin

Pittendrigh are credited for developing the foundational principles of biological rhythms.

Together they established that biological rhythms are endogenous, meaning that they are self-

sustained through physiological mechanisms in the animal, that they are free-running, meaning

that they can persist without external stimuli, and that they are entrainable, meaning that they are

adaptable to changes in the environment (Daan, 2000). At the same time, biological rhythms on

time scales greater or less than 24 h were proposed. Aschoff (1955) first suggested that seasonal

changes in behavior such as hibernation or migration may be due to an endogenous ‘circannual’

rhythm. This proposition was later confirmed in Willow Warblers (Phylloscopus trochilus),

which exhibited seasonal migratory behavior when placed in constant conditions (Gwinner,

1968). Moreover, great steps in the quantification of biological rhythms were made during this

time. Biological rhythms are characterized by their period length, which is the amount of time to

complete one cycle (e.g. 24 h, 12 m), acrophase, which is the time of the rhythm’s peak, and

amplitude, or the distance from mean to peak (Figure 2-1).

A dogmatic shift in the understanding of biological rhythms was initiated by the now

classic experiment performed by Konopka and Benzer (1971), who used mutagenesis to

demonstrate that the length of a free-running circadian rhythm was controlled at the level of the

gene. Later, an entire host of genes were discovered to make up what is now known as the

7

‘biological clock’, a network of transcription factors that differentially regulate gene expression

across the 24 h day (Huang, 2018). These same transcription factors were later discovered to be

responsible for the generation of circannual rhythms (Lincoln, 2019).

Molecular Structure of Biological Clocks

Circadian rhythms are generated in mammals through a hierarchical network of

independent cellular clocks that communicate through neural and humoral signals. Every

individual cell possesses their own molecular clock that is regulated by intracellular signaling

cascades. A region of ~20,000 neurons in the hypothalamus known as the suprachiasmatic

nucleus (SCN) is commonly acknowledged as the master pacemaker that is principally

responsible for entrainment of rhythms in peripheral tissues (Partch et al., 2014). In addition to

receiving inputs from the SCN, peripheral tissues relay rhythmic information to each other. For

example, melatonin produced by the pineal gland and glucocorticoids produced by the adrenal

cortex are important entraining signals for the clock of the pancreas, liver, and mammary gland

(Mohawk et al., 2012). Peripheral tissues can also be entrained by other cues, primarily feed

intake (Stokkan et al., 2001). Uncoupling of central SCN rhythms from rhythms of feed intake is

proposed as a mechanism for the development of the metabolic disturbances caused by circadian

maladies such as shift-work disorder chronic jet lag (Vetter and Scheer, 2017).

Transcriptional-Translational Feedback Loops

At its most basic level, the molecular circadian clock operates through interconnected

transcriptional-translational feedback loops employing an array of ‘clock’ transcription factors

(Figure 2-2). In mammals, the core negative feedback loop includes brain and muscle ARNT-like

1 (BMAL1), circadian locomotor output cycles kaput (CLOCK), period (PER) 1, 2 and 3, and

cryptochrome (CRY) 1 and 2. BMAL1 and CLOCK form heterodimers that bind to specific DNA

8

sites called E-boxes [canonically CANNTG] in the promoter region of clock-controlled genes.

Additionally, the BMAL1-CLOCK complex enhances the expression of PER and CRY proteins,

which themselves dimerize and feedback on the BMAL1 and CLOCK genes to repress their

expression (Takahashi, 2017). This cycle of activation and repression generates a rhythm of clock

gene expression that is completed in approximately 24 h, which leads to 24 h rhythms of clock-

controlled genes.

An ancillary feedback loop consisting of the orphan nuclear receptors REV-ERBα and β

and RAR-related orphan receptor (ROR) α, β, and γ adds addition level of regulation to stabilize

the core clock and provide secondary outputs for regulation of clock-controlled genes. Both

receptors bind to retinoic acid-related orphan receptor binding elements (ROREs)

[(G/A)GGTCA], with the ROR enhancing transcription and REV-ERB repressing transcription

(Takahashi, 2017). Both BMAL1 and CLOCK contain ROREs and competitive binding by REV-

ERB and ROR leads to either repression or activation of these transcription factors (Guillaumond

et al., 2005). A third level of clock regulation utilizes the transcription factors D-box binding

protein (DBP), thyrotrophic embryonic factor (TEF), hepatic leukemia factor (HLF) and E4-

promoter binding protein (E4BP4, also known as NFIL3). The expression of DBP, TEF, and HLF

is enhanced by the binding of the BMAL1-CLOCK complex to E-Box sequences, and themselves

act as transcription enhancers by binding to D-Box promoter elements. Alternatively, E4BP4

expression is activated by REV-ERBα, and represses expression of D-Box element-containing

genes through interference with DBP, TEF, and HLF binding (Papazyan et al., 2016). The

transcription of clock-controlled genes is therefore coordinated through regulation by a wide host

of transcription factors that act on either E-Box, RORE, or D-Box elements to influence their

expression.

9

Post-Transcriptional Modifications

Beyond the transcriptional-translational feedback loops, a number of post-transcriptional

modifications add an additional layer of regulation to affect both the rhythms of clock gene

expression and their outputs. The importance of these modifications is becoming increasingly

clear, with Reddy et al. (2006) demonstrating that nearly half of the rhythmic proteins present in

the liver lacked corresponding rhythms in mRNA expression. Transcriptional termination is an

important point of regulation for mRNA expression because it is required before transcriptional

initiation can proceed. In addition to its role in E-box mediated transcriptional repression, PER

represses transcriptional initiation through rhythmically binding to several RNA helicases, which

make up the transcriptional termination complex for certain genes (Lim and Allada, 2013).

Recent evidence suggests that alternative splicing of pre-mRNA is also regulated in a circadian

manner (McGlincy et al., 2012). Microarray data demonstrated that 62 of 227 measured splicing

factors displayed rhythmic gene expression (Grosso et al., 2008). Furthermore, Preußner et al.

(2017) illustrated that circadian rhythms of body temperature entrain cellular rhythms of alternate

splicing in the cerebellum and liver with the effect via heat-sensitive SR proteins.

The stability of mRNA is also influenced by circadian rhythms through silencing by

RNA-binding proteins and microRNA (miRNA). Period 2 and CRY1 stability is decreased by

RNA-binding proteins polypyrimidine tract-binding protein (PTB) and AU-rich element RNA-

binding protein 1. Deletions of these proteins result in increased amplitudes of the circadian

mRNA expression (Green, 2018). MicroRNA are small (~22 nt) non-coding RNA molecules that

interact with the 3’ untranslated region of mRNA to repress its translation. The light inducible

miRNA miR-132 augments the rhythms of PER1 expression in murine SCN neurons (Cheng et

al., 2007). In the same study, miR-219-1 was identified as clock-controlled miRNA whose

rhythm is ablated in CRY1/CRY2 double knockout mice. Later research implicated miR-219 as a

link between shift-work disorder and increased breast cancer. Night shift workers have greater

10

methylation of the miR-219 promoter, which downregulates its expression and attenuates its

anticarcinogenic activity (Shi et al., 2013). The miRNA 192/194 cluster was discovered to inhibit

translation of all three PER genes, and overexpression and knockdown of these miRNAs leads to

shortening and lengthening of the circadian period, respectively (Nagel et al., 2009). Furthermore,

miR-122 is regulated in a circadian manner in the liver and causes rhythmic degradation of

several genes involved in cholesterol and lipid metabolism (Mehta and Cheng, 2013).

Post-Translational Modifications

Post-translational modifications are also vital to the generation of 24-h rhythms within

cells. Modifications to clock proteins introduce a delay between the activating and repressing

arms of the transcriptional-translational feedback loop, ensuring that the cycle is maintained at a

period of ~ 24 h. Without this delay, the cycle of activation and repression would last just a few

hours (Gallego and Virshup, 2007). The most well-established example of post-translational

regulation of circadian period length in mammals occurs through casein kinase 1ε–mediated

phosphorylation. Casein kinase 1ε (CK1ε) decreases the stability of PER through

phosphorylation, leading to its degradation via the ubiquitin-mediated proteosomal pathway

(Vielhaber et al., 2000).

The importance of phosphorylation in the establishment of cell-autonomous rhythms is

perpetuated by presence of circadian rhythms in cells that are completely devoid of rhythmic gene

expression. Prokaryotic cyanobacteria can maintain 24-h cycles of growth, nitrogen fixation, and

photosynthesis with a molecular clock based only on rhythmic phosphorylation of KaiA, KaiB,

and KaiC proteins (Takai et al., 2006). Moreover, circadian oscillations of peroxiredoxin

oxidation occur in differentiated red blood cells which contain no nucleus or DNA, suggesting

that these oscillations are maintained through exclusively post-translational mechanisms (O’Neill

and Reddy, 2011). Phosphoproteomic analysis is further uncovering the indispensable role of

11

phosphorylation in generation of circadian rhythms. Robles et al. (2017) determined using mass

spectrometry that 25% (> 20,000 phosphosites) of the murine hepatic phosphoproteome are

follow a 24 h rhythmic pattern, with phosphorylation cycles having markedly greater amplitudes

than the rhythms of transcript or protein abundance.

Epigenetics and the Clock

Epigenetic modifications, such as DNA methylation and histone acetylation/deacetylation

have a bidirectional link with the circadian clock. In the liver of mice, acetylation of the H3 histone

follows a circadian rhythm, causing chromatin remodeling near the promoter regions of Per1, Per2,

and Cry1, permitting their expression (Etchegaray et al., 2003). The circadian transcription factor

CLOCK is itself a histone acetyltransferase (HAT), and its function is potentiated by its

heterodimer BMAL (Doi et al., 2006). REV-ERBα, a transcription factor of the ancillary molecular

clock, helps recruit the histone deacetylase 3 (HDAC3) to specific sites on histone 3, inhibiting the

transcription of target genes (Yin et al., 2007). Azzi et al. (2014) demonstrated that DNA

methylation patterns in the SCN of mice were associated with changes in the light-dark cycle.

Entrainment of Biological Clocks

Entrainment is the process of synchronizing endogenous biological clocks with external

environmental rhythms. Environmental cues, called Zeitgebers (German for “time giver”), confer

information about the environmental time to circadian oscillators to synchronize an organism’s

internal clock (Aschoff, 1963). All organisms have endogenous rhythms that differ slightly from

24 h which allow them to be responsive to entrainment signals from the environment. When

zeitgebers are absent, the rhythm is said to be freerunning, meaning that it expresses its

endogenous period length. The length of the freerunning period, also called tau (τ), differs

between species and even individuals. Humans, for example, have an average freerunning period

12

of 24.2 h (Czeisler et al., 1999). Entrainment of circadian clocks can occur through several cues,

including the light/dark cycle, nutrient intake, and body temperature.

Light Entrainment

Light is the most prominent zeitgeber known to entrain the central clock of the SCN.

Light is detected by the eye and transmitted to the SCN through two separate pathways. Rod and

cone cells are the primary visual photoreceptors in the retina. After sensing light, they signal the

SCN via retinal ganglion cells, setting its circadian clock (Drouyer et al., 2007). However, light

entrainment can occur completely independent of rod and cone cells. Foster et al. (1991)

demonstrated that mice possessing the rd/rd mutation, which causes degeneration of rod and cone

cells and complete blindness, still entrained to the light-dark cycle, suggesting that an additional

photoreceptor was involved in circadian entrainment. It was later discovered that the non-visual

photopigment melanopsin can entrain the master clock through excitement of intrinsically

photosensitive retinal ganglion cells (ipRGCs) in the eye (Panda et al., 2002). Exposure of

ipRGCs to light causes photoisomerization of the melanopsin protein, leading to a G-protein

signaling cascade which causes cell depolarization and increases the nerve firing rate (Hattar et

al., 2002). The ipRGCs directly innervate the SCN via the retinohypothalamic tract, conveying

light signals directly to the master clock. Panda et al. (2003) later confirmed the independence of

circadian oscillation and rod and cone cells using rd/rd mice, as well as mice lacking melanopsin

(Opn4-/-) and mice with both mutations. They found that photoentrainment occurred in mouse

strains with single deletions, but did not occur in the absence of both receptor systems. In addition

to establishing two separate pathways of circadian entrainment this study submitted that unlike

birds, reptiles, fish and invertebrates which can detect light extra-ocularly in the pineal gland or

deep-brain photoreceptors, mammals require ocular photoreceptors for photoentrainment.

13

Food Entrainment

Circadian rhythms of mammals can be entrained by nutrient intake. Early observations of

rat behavior revealed an increase in locomotor activity in the 2 to 4 h prior to feeding (Richter,

1922). These patterns of food anticipatory activity (FAA) were later shown to persist for up to 3

to 4 days after complete food removal (Stephan, 2002). While the light dark cycle is the

predominant zeitgeber for entraining behavioral rhythms, circadian rhythms of FAA can overtake

them during conditions of restricted food availability. Stokkan et al. (2001) detected a phase shift

of Per1 expression corresponding to feeding time in the liver and lung of mice, but not the SCN

when food was restricted to the inactive period of the day. The amount of entrainment is

proportional to the energy content of the feed, feeding a highly palatable, high-energy feed at a

specific time each day is sufficient to entrain FAA, even if food availability is not restricted

(Mistlberger, 1994). Additionally, food entrainment can maintain circadian behavioral rhythms

when signaling from the central clock is absent. Krieger et al. (1977) observed that rats with

lesions of their SCN still exhibited entrainment of daily locomotor activity, core body

temperature, and blood glucocorticoid concentration rhythms to once per day feeding. These

discoveries suggests that food entrainment is controlled by an independent oscillator separate

from the SCN.

While there has been evidence substantiating the presence of food-entrainable circadian

rhythms, the search for the so-called food-entrainable oscillator (FEO) has proven onerous.

Initially, the adrenal gland was targeted as the source of food-entrainable oscillations because of

the role of glucocorticoids in food anticipation, but adrenalectomy failed to affect the circadian

rhythms of FAA (Stephan, 2002). Several regions of the central nervous system have been

explored for their potential role as an FEO. Ablation of the ventromedial hypothalamus (VMH), a

region involved in satiety signaling and thermoregulation, eliminated the entrainment of body

temperature and glucocorticoids to meal time in some early short-term experiments, but this

14

effect did not occur when recovery of greater than 14 weeks was allowed following surgery

(Davidson, 2009). More recent experiments have failed to show correlations between VMH

lesions and FAA (Gooley et al., 2006). The paraventricular nucleus (PVN) is a region of the brain

involved in appetite regulation and stress. Lesioning of the PVN failed to reduce food-

anticipatory food-bin approach behavior, although general locomotion was reduced (Mistlberger

and Rusak, 1988).

The dorsomedial hypothalamus (DMH) has been highly implicated as the location of an

FEO, but results have been inconsistent. It was initially explored as a candidate because of its

previously discovered roles in feeding behavior regulation, stress response, and involvement in

the circadian rhythms of cortisol release (Chou et al., 2003). Gooley et al. (2006) observed that

food restriction synchronizes daily rhythms of DMH neuronal firing to the time of feed delivery.

Additionally, rhythmic expression of Per1 and Per2 is induced in the DMH during time-restricted

feeding (Mieda et al., 2006). However, Landry et al. (2006) failed to impair daily rhythms of

FAA upon partial or complete lesioning of the DMH. These results were later corroborated by

Landry et al. (2007) and Moriya et al. (2009). More recent evidence suggests that the effect of

DMH lesions on FAA is time-of-day dependent. Landry et al. (2011) demonstrated that DMH-

lesioned rats had less robust, but still present, rhythms of FAA when fed during the day, but that

FAA was unaffected when they were fed at night. The inability to isolate the FEO to a specific

region of the brain suggests that food-entrained rhythms may actually be generated by a network

of separate oscillators that communicate with each other to maintain food-anticipatory rhythms of

behavior, body temperature, and glucocorticoid signaling.

Communication from the viscera to the brain has been investigated as a mechanism for

entrainment by nutrient intake. However, ablation of both vagal and non-vagal afferents nerves

emanating from the viscera did not alter FAA (Davidson, 2009). Ghrelin is a peptide hormone

produced by the oxyntic cells of the stomach that is directly responsible for hunger signaling. The

15

expression of Per1 and Per2 in oxyntic cells is in antiphase with ghrelin secretion, and the

rhythms of their expression are altered by feeding time (LeSauter et al., 2009). Knockout of the

ghrelin receptor greatly reduces food-anticipatory locomotor activity in mice, suggesting that

ghrelin is important for this behavior (Blum et al., 2009). Research performed in goldfish

(Carassius auratus) demonstrated that ghrelin treatment elevated hepatic and hypothalamic Per1,

2 and 3 expression by over 2-fold and that treatment with a ghrelin antagonist completely

abolished daily rhythms of FAA (Nisembaum et al., 2014).

Temperature Entrainment

Environmental temperature is a common entraining cue for poikilotherms, who

experience dramatic changes in physiology when external temperature changes. Additionally,

Some plants and algae can entrain to just 1 to 2°C differences in temperature across the day

(Rensing and Ruoff, 2002). In mammals, the circadian rhythms of body temperature can entrain

biological clocks in peripheral tissues. Brown et al. (2002) ascertained that rhythms of Per2

expression could be entrained in rat fibroblast cells by temperature cycles that oscillated by 4°C.

Saini et al. (2012) later revealed similar results in human and mice fibroblast, but revealed that as

little as a 1°C change in temperature is sufficient for entrainment. These authors also revealed

that temperature entrainment was dependent on the protein heat-shock factor 1 (HSF1). In

addition to HSF1, cold-inducible RNA-binding proteins (CIRPs) link body temperature and the

circadian clock. Cycles of body temperature affect the regulation of polyadenylation sites by

CIRP, thus affecting the stability of target RNAs (Ki et al., 2015). While peripheral tissues appear

responsive to temperature, the SCN possesses mechanisms to resist feedback of body temperature

on the central clock. Buhr et al. (2010) discovered that communication between the dosoromedial

and ventrolateral regions of the SCN via L-type calcium channels prevents temperature from

resetting the SCN clock.

16

Circadian Rhythms and Nutrient Metabolism

As mentioned above, feeding behavior is a strong clue for circadian entrainment. In

addition to entrainment of whole-body rhythms by meal delivery or high-calorie feed

supplementation, specific nutrients can directly affect the molecular clock within individual cells.

Cellular energy status, redox status, and direct action by metabolically important hormones can

directly impact the circadian clock (Peek et al., 2012). Furthermore, the molecular clock exerts a

reciprocal relationship on cellular metabolism. By temporally segregating biochemically

incompatible processes, the circadian clock prevents futile cycling and maximized energetic

resources within the cell. Moreover, the efficiency of nutrient utilization is optimized by the

circadian clock because it coordinates the proper metabolic machinery with the time of nutrient

availability.

Cellular Energy and Redox Status and the Circadian Clock

Cellular energy status can entrain circadian rhythms through adenosine monophosphate

(AMP)-activated protein kinase (AMPK). During states of prolonged fasting and ATP depletion,

AMPK is activated. Activated AMPK can then affect the period length of the cellular circadian

clock by two separate pathways leading to CRY or PER degradation. First, AMPK

phosphorylates CK1ε at serine 389, which causes an increase in its activity leading to increased

degradation of PER2 and shortening of the circadian period (Jordan and Lamia, 2013).

Furthermore, AMPK can directly phosphorylate CRY1 and CRY2, which then interact with F-

box/LRR-repeat protein 3 (FBXL3), leading to their polyubiquitination and proteosomal

degradation (Lamia et al., 2009). Treatment with the AMPK antagonists, AMPK-inhibitors 5-

aminoimidazole-4-carboxyamide ribonucleoside (AICAR) or metformin, has been shown to

cause a phase shift in the circadian clock of the liver, suggesting that AMPK may play a role in

17

entrainment (Jordan and Lamia, 2013). In addition to its effects on circadian clock proteins,

AMPK appears to be responsible for circadian regulation of its target proteins. Both acetyl-coA

carboxylase (ACC), the rate-limiting enzyme for fatty acid biosynthesis, and regulatory-

associated protein of mTOR (RAPTOR), a protein involved in signaling cascade of insulin to

mechanistic target of rapamycin (mTOR), display 24 h rhythms of AMPK-mediated

phosphorylation (Davies et al., 1992; Lamia et al., 2009).

Cellular redox state is dependent on the ratio of the oxidized and reduced forms of the

cofactor nicotinamide adenine dinucleotide (NAD+/NADH). The molecular clock directly

impacts redox status through regulation of nicotinamide phosphoribosyltransferase (NAMPT), the

rate-limiting enzyme in the NAD+ salvage pathway (Peek et al., 2012). The CLOCK:BMAL1

complex binds to E-boxes in the NAMPT promoter, upregulating its expression and causing

NAMPT and NAD+ to oscillate in a diurnal manner (Ramsey et al., 2009). The diurnal pattern of

NAD+ also likely affects the circadian cycling of other metabolically important proteins though

its effect on sirtulin 1 (SIRT1). Sirtulin 1 is an NAD+-dependent deacetylase that regulates the

expression of a wide array of metabolically important transcription factors, including peroxisome

proliferator-activated receptor gamma cofactor 1α (PGC-1α, involved in mitochondrial biogenesis

and respiration), sterol regulatory element-binding protein 1c (SREBP-1c; involved in lipid

synthesis and glucose metabolism), and forkhead box protein O1 (FOXO1; involved in insulin

signaling) (Peek et al., 2012). Nakahata et al. (2009) confirmed that SIRT1 activity follows a

circadian pattern and that selective inhibition of NAMPT can abolish this rhythm. Furthermore,

SIRT1 can also feedback to inhibit the BMAL1-CLOCK complex through deacetylation of

BMAL1, preventing activation of clock-controlled genes (Tong et al., 2015).

The cellular redox state or peroxiredoxin proteins has also been shown to itself be a

circadian oscillator (Peek et al., 2012). Peroxiredoxins are proteins that both serve as antioxidants

which scavenge hydrogen peroxide (H2O2) and whose regulation is a component of H2O2-

18

mediated signal transduction (Rhee et al., 2005). Therefore, the oxidation status of peroxiredoxins

can affect the transcription and regulation of other target genes and proteins. O’Neill et al. (2011)

discovered that peroxiredoxins can maintain self-sustained circadian oscillations, independent of

transcription. They later discovered that this mechanism allows red blood cells, which lack a

nucleus, to express circadian rhythms (O’Neill and Reddy, 2011). The oxidation rhythms of

peroxiredoxins is out of phase with the rhythms of Bmal1 mRNA, and this phase relationship

differs among tissues, suggesting the peroxiredoxins may represent an entirely different oscillator

than the TTFL (Edgar et al., 2012).

Glucose Metabolism and the Clock

Glucose metabolism in mammals is highly influenced by the circadian system. Plasma

glucose and insulin concentrations exhibit 24 h rhythms in a variety of species including humans

(Van Cauter et al., 1991), mice (Sadacca et al., 2011), rats (Bellinger et al., 1975), sheep (McMillen

et al., 1987), and cattle (Rottman et al., 2014). Furthermore, liver glycogen concentration follows

an entrainable circadian rhythm (Halberg et al., 1960). Both Bmal1 and Clock are essential for

maintenance of these rhythms, and global knockout of either gene abolishes the daily oscillations

of plasma glucose concentration and hepatic gluconeogenesis (Rudic et al., 2004). In addition to

absolute glucose and insulin concentrations, insulin-stimulated glucose uptake is controlled by

circadian rhythms. In the medical field, knowledge that glucose tolerance in humans varies across

the day has existed since the late 1960’s (Bowen and Reeves, 1967). Van Cauter et al. (1997)

determined that 2 h post-infusion blood glucose concentrations are typically 30 to 50 mg/dL greater

when glucose infusion is performed the afternoon compared to in the morning. The insulin-

secreting islet β cells of the pancreas express 24 h rhythms of molecular clock genes in mice, and

when these rhythms are abolished by tissue-specific Bmal1 knockout, subjects exhibited severe

glucose tolerance and insufficient glucose-stimulated insulin production (Sadacca et al., 2011).

19

Besides its effects on glucose metabolism, the circadian clock appears to affect other aspects of

carbohydrate metabolism. Thaiss et al. (2016) determined using metabolomic analysis that the

conversion of sucrose to lactate was rhythmic.

Circadian rhythms of glucose metabolism may also be controlled through rhythms of

incretin release. Incretins, which include glucagon-like peptide-1 (GLP-1) and gastric inhibitory

peptide (GIP) are released from the enteroendocrine cells of the gut in response to feed intake and

enhance insulin release from the pancreas. Like insulin, the responsiveness of incretins to feed

intake shows robust circadian rhythms in humans and model organisms (Reimann and Reddy,

2014). L cells, responsible for the production of GLP-1, show 24 h oscillations of Bmal1 and

Per2 in vitro (Reimann and Reddy, 2014). Gil-Lozano et al. (2014) illustrated that GLP-1

secretion in response to various secretagogues followed a diurnal pattern. A less well-

characterized incretin, oxyntomodulin, appears to play an important role in food entrainment.

Landgraf et al. (2015) detected that oxyntomodulin entrains the rhythms of Per1 and Per2

expression via binding to the glucocorticoid receptor and that knockout of this protein blocks

food entrainment of the liver. Therefore, it is a promising candidate for the link between food

intake and the molecular clock.

Another link between circadian clocks and glucose metabolism is glucocorticoid

signaling. Glucocorticoids are a class of steroid hormones produced by the adrenal cortex that

have a somewhat antagonistic role to insulin, causing greater hepatic gluconeogenesis, reduced

glucose uptake, and increased lipid mobilization (Kuo et al., 2015). They are increased during

fasting, but are also greatly elevated in response to stress. In addition to their role in stress and

metabolism, glucocorticoids are a major output of the core circadian clock. Glucocorticoids

follows a consistent daily rhythm that peaks at the start of the active phase in both nocturnal and

diurnal animals (Dickmeis, 2009). This rhythm is modulated directly by the SCN, which

innervates the adrenal gland and affects its release in a circadian manner (Mohawk et al., 2012).

20

Glucocorticoids directly entrain circadian rhythms in peripheral tissues. This mechanism is

frequently exploited when studying circadian rhythms in vitro, when the synthetic glucocorticoid

dexamethasone is used to synchronize cultured cells (Balsalobre et al., 2000). The promoter

regions of Bmal1, Cry1, Per1, and Per2 contain glucocorticoid-response elements (GREs) that

regulate the circadian clock in peripheral tissues. (Mohawk et al., 2012). Furthermore, treatment

with glucocorticoids decrease the rate of entrainment of peripheral clocks to the time of food

intake (Minh et al., 2001). So et al. (2009) discovered that deletion of the GRE in Per2 protects

individuals from insulin resistance, demonstrating the intimate link between the glucocorticoid

rhythm and glucose metabolism.

Lipid Metabolism and the Clock

Several aspects of lipid metabolism are controlled by the molecular clock. Carnitine

palmitoyltranserase (CPT) 1 and 2 control fatty acid β-oxidation by limiting the rate of fatty acyl

entry through the inner and outer mitochondrial membranes, respectively. The protein abundance

of both CPT1 and CPT2 follows circadian rhythms with CPT1 peaking at 17 h post-light

exposure and CPT2 peaking at 6 h post-light exposure (Neufeld-Cohen et al., 2016). Acetyl-CoA

carboxylase, the enzyme that catalyzes the rate-limiting reaction of de novo lipogenesis, also

follows a diurnal pattern, likely due to regulation by AMPK (Gnocchi et al., 2015). The master

lipid regulator sterol regulatory element-binding transcription factor 1 (SREBP1) also displays

circadian rhythms of activity, which appear to be directly driven by REV-ERBα (Le Martelot et

al., 2009). Recent research has suggested that circadian rhythms of both SREBP1 and another

lipogenic factor, thyroid hormone-responsive Spot 14 (S14), are entrained by the time of feed

intake in the mammary gland of mice (Ma et al., 2013).

Lipid trafficking from the intestine also appears to be regulated in a circadian manner.

Intestinal mRNA and protein expression and activity of microsomal triglyceride transfer protein

21

(MTP), an enzyme involved in the assembly of chylomicrons, follows a daily pattern with greatest

expression at 2400 h. (Pan and Hussain, 2007). This daily pattern is regulated directly by CLOCK

through its effects on the transcription factor called “small heterodimer partner (SHP)”, which

enhances expression of MTP (Pan et al., 2010). Pan and Hussain (2007) revealed that absorption

of 3H -labeled triolein cholesterol followed a daily pattern with greater absorption at 2400 h than

1200 h. Another potential link between the circadian clock and lipid transport is the protein

nocturnin. Nocturnin controls gene expression by deadenlyation of the poly(A) tail of mRNA which

leads to its degradation, and its expression is activated by BMAL/CLOCK (Stubblefield, 2012).

Global knockout of the nocturnin gene results in reduced export of chylomicrons out of enterocytes

(Douris et al., 2011).

Bile acid metabolism and cholesterol synthesis are also intimately linked with the circadian

clock. 3-hydroxy-3-methyl-glutaryl-Coenzyme A (HMG-CoA) reductase, the rate limiting enzyme

for the synthesis of cholesterol, exhibits a diurnal pattern of expression leading to a circadian

rhythm of cholesterol synthesis (Shefer et al., 1972). Additionally the rate-limiting enzyme in bile

acid synthesis, 7-alpha-hydroxylase (CYP7A1) follows a circadian rhythm (Chiang, 2009). This

rhythm is directly regulated by REV-ERBα, which binds to D-box elements on the CYP7A1

promoter and enhances its transcription. Circadian rhythms of bile acid synthesis allow for greater

amounts of bile acids to enter the small intestine and emulsify dietary lipids during the active phase

when food intake is more likely to occur. Knockout of the circadian clock has detrimental effects

on bile acid homeostasis. Ma et al. (2009) discovered that Per1-/- Per2-/- double knockout mice lost

their circadian rhythms of bile acid synthesis, leading to a greater synthesis and over accumulation

of bile acids in the liver.

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors that act as lipid

sensors and master regulators of lipid and glucose metabolism. The three primary isoforms include

PPARα, PPARγ, and PPARδ, which have different binding affinities to specific fatty acids and are

22

distributed at different expression levels across tissues. All three PPAR isoforms, along with

peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1-α), display circadian

rhythms of expression (Chen and Yang, 2014). An E-Box element has been identified in the

promoter of PPARα in mice, suggesting direct regulation by BMAL/CLOCK (Oishi et al., 2005).

The rhythms of PPARα are likely responsible for diurnal regulation of SREBP1 and fatty acid

synthase (FAS) and HMG-CoA because it activates expression of these genes (Chen and Yang,

2014). Nuclear translocation of PPARγ in bone-marrow stromal cells is stimulated by the circadian

polyadenylase nocturnin (Kawai et al., 2010). Furthermore, liver PPARδ is expression is regulated

in a circadian manner by miR-122, a cyclic microRNA that is activated by REV-ERBα (Gatfield

et al., 2009). The relationship between PPARs and the clock is bidirectional. The promoter regions

of Bmal1 and Rev-erbα possess PPAR response elements (PPREs) and are activated upon PPAR

binding (Ribas-Latre and Eckel-Mahan, 2016). Through this mechanism, lipids feed back and affect

the molecular clock.

Circadian Rhythms in Microorganisms

A growing body of evidence has suggested an important role for circadian rhythms in

modulating growth and metabolism of microorganisms. The idea that microorganisms are capable

of maintaining biological rhythms is relatively new. Prior to the 1980’s prokaryotes were

considered too “simplistic” and too short-living to express circadian rhythms. Grobbelaar et al.

(1986) first observed endogenous circadian rhythms of nitrogen fixation in the cyanobacteria.

These findings were later validated by Kondo et al. (1993), who used a luciferase reporter to

demonstrate that the cyanobacterial species Synechococcus sp. PCC7942 exhibits daily rhythms of

photosystem II protein (psbAI) gene expression under constant conditions. Their results challenged

the previous dogma that bacteria do not possess rhythms by demonstrating that S. elongatus possess

the three criteria necessary for circadian rhythmicity: entrainment by environmental stimuli,

23

maintenance of approximately 24 h cycles after removal of external cues, and maintenance of

rhythms under a wide range of environmental temperatures. Since the initial confirmation of

rhythms in cyanobacteria, over 100 different clock mutant strains of bacteria have been identified

with circadian periods ranging from 16 to 60 h (Kondo et al., 1994).

The structure of the cyanobacterial clock differs considerably from the molecular clock of

eukaryotes. Unlike the core eukaryotic clock, the core cyanobacterial clock operates exclusively

through a post-translational oscillator centered around the protein KaiC (Swan et al., 2018). Along

with KaiC, two members of the same gene family, KaiA and KaiB, are responsible for generating

cycles of phosphorylation and dephosphorylation. Binding of KaiA to unphosphorylated KaiC

during the day promotes a conformational change that leads to autophosphorylation of KaiC using

cellular ATP as a phosphorus donor. The phosphorylation of KaiC leads to the recruitment of KaiB,

which creates a separate conformational state that inhibits KaiA binding (Kageyama et al., 2006).

The lack of KaiA binding decreases the ATPase activity of KaiC and causes

autodephosphorylation. As phosphorylation state decreases, KaiB disassociates with KaiC,

allowing KaiA to bind again and the cycle to repeat. The KaiABC oscillator activates a signaling

cascade that activates the response regulator RpaA which regulates the transcription of circadian-

associated genes (Swan et al., 2018). Entrainment of the cyanobacterial clock occurs both through

sensing the ratio of ATP to ADP within the organism, as well as through the presence of oxidized

quinone metabolites which act as electron shuttles during photosynthesis (Williams et al., 2002;

Ivleva et al., 2006). Interestingly, the growth cycle of cyanobacteria actually lasts only about 6 h,

but the phase of the circadian clocks is passed onto daughter cells to maintain their rhythms

(Schibler and Sassone‐Corsi, 2002).

Besides Synechococcus, several other bacterial species exhibit circadian rhythmicity.

Circadian rhythms of bioluminescence have been exhibited in the purple photosynthetic bacterial

species Rhodobacter sphaerodies (Min et al., 2005). Rhodopseudomonas palustris TIE-1 show

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self-sustained circadian rhythmicity of nitrogen-fixation that is entrained by oxygen (Ma, 2011).

Cycling patterns of anaerobic fermentation have been observed in continuous cultures of

Clostridium acetobutylicum, exhibiting 24-h cycles of acetate, propionate and butyrate, ethanol,

and biomass concentrations (Yerushalmi and Volesky, 1989). Additionally, transcriptomic analysis

uncovered 24-h temporal dynamics of metabolic gene expression in six oceanic bacterioplankton

(Ottesen et al., 2014).

Microorganisms may play an important role in modulating circadian rhythms in the host.

The intestinal microbiome and the host alimentary system have a bi-directional relationship through

which perturbations in either system can affect the circadian entrainment of the other. Estimates

suggest that 15 to 17% of the operational taxonomic units (OTU; proxy for species) and 23% of

the functional pathways of the colonic microbiome undergo daily rhythms (Thaiss et al., 2014;

Zarrinpar et al., 2014). These rhythms function to affect microbial metabolism, including growth,

energy metabolism, and amino acid metabolism, as well as regulate the spatial organization of

bacteria through affecting chemotaxis and mucosal adherence (Liang and FitzGerald, 2017).

Inhibition of the gut microbiome, either in germ-free models or through antibiotic treatment, leads

to damping of the circadian rhythms in the gut epithelium and liver (Leone et al., 2015; Thaiss et

al., 2016). Reciprocally, Per1/2 double knockout mice failed to demonstrate circadian rhythms of

microbial movement and mucosal attachment (Thaiss et al., 2016). The signaling mechanisms by

which the host and microbiome communicate temporal information are still being elucidated. Short

chain fatty acids, particularly butyrate, produced by hindgut fermentation follow daily rhythms,

and induce cyclicity in colonic epithelial cells, suggesting that they may be an important

entrainment signal derived from the microbiome (Leone et al., 2015). Murakami et al. (2016)

discovered using a fecal transplant model that PPARγ signaling may be responsible for

microbiome-entrainment of the circadian clock.

25

Humoral signals from the host also act directly to entrain the rhythms of the microbiome.

Paulose et al. (2016) discovered that the gastrointestinal bacterium Enterobacter aerogenes, which

displays daily patterns of motility, is responsive to treatment by exogenous melatonin. While it has

not yet been examined, cortisol may also be a mechanism whereby the host can entrain the

microbiome. As previously discussed, glucocorticoids follows a circadian rhythm that is directly

entrained by the master clock of the SCN. Feeding exogenous cortisol has been shown to modulate

the gut microbiome of pigs to promote the growth of coliform bacteria and suppress Salmonella

(Petrosus et al., 2018). Alverdy and Aoys (1991) implied that treatment with glucocorticoids can

cause increased bacterial adherence to the mucosal wall of the hindgut. Salivary cortisol

concentration follows a 24 h rhythm in humans (Katz and Shannon, 1969). Glucocorticoid signaling

through saliva may be particularly impactful for ruminants, because saliva enters the rumen

immediately after secretion and can directly impact the microbial community.

Annual Rhythms

In addition to circadian rhythms, many organisms have evolved a circannual timing system

to coordinate physiology across the year. This system uses environmental signals, primarily the

photoperiod, or length of daylight, to synchronize the organism’s physiology to the seasonal

changes in climate. Seasonal temperature changes occur due to the 23.5° angle of the earth’s axis

which causes the amount of solar radiation reaching different hemispheres of the earth’s surface to

change across the year (Foster and Kreitzman, 2009a). These changes in temperature are furthered

by albedo, the degree of sunlight reflectance from the earth’s surface back into space. Albedo is

greatest near the earth’s poles because ice reflects the greatest amount of sunlight, while water

reflects less solar energy than land (Sellers et al., 1997). Other annual changes such as jet streams,

26

ocean currents, tidal cycles, and cycles of rainfall create additional seasonal climates for organisms

to anticipate (Namias, 1976).

Many animals exhibit circannual rhythms regulating numerous physiological processes

including growth, reproduction, metabolism, and behavior. For example, migratory birds display

fattening and nocturnal activity (zugunrhue) coinciding with spring and fall migration. These

behaviors persist under constant light:dark (L:D) cycles, and can be modified by altering

photoperiod (Gwinner, 1996). Many mammals including horses, sheep, and hamsters exhibit

endogenous yearly rhythms of fertility (Gerlach and Aurich, 2000). Moreover, many mammals and

birds conserve energy in the winter through hibernation or through entering a state of winter torpor

(Geiser and Ruf, 1995).

Annual Rhythms and Reproduction

Many animals in seasonal environments have evolved to reproduce during a certain time

of the year. There is a clear advantage to birthing young during a time of high feed availability or

low predator abundance. In herbivorous mammals, births are typically timed to occur in the spring

when forage is readily available, providing the mother with ample calories to nurse her young

(Foster and Kreitzman, 2009b). Sheep (Ovis aries) are a classic example of seasonal breeders.

Domesticated ewes exhibit estrus and ovulation only during autumn and early winter resulting in

parturition in mid-spring (Robinson, 1951; Shelton and Morrow, 1965). Rams increase the

secretion of testosterone and production of sperm during the period of July through December,

corresponding to the time of estrus in the females (Lincoln, 1976; Dacheux et al., 1981).

Photoperiod is the major driver of the seasonality in sheep, but it is modulated by many

other factors such as nutritional status, previous lambing date and lactation (Lincoln, 1998).

Photoperiodic information is first sensed by the retina and transmitted to the suprachiasmatic nuclei

(SCN) of the hypothalamus by the photopigment melanopsin via photosensitive retinal ganglion

27

(Foster and Kreitzman, 2009b). Daylight increases the oscillation of the SCN (VanderLeest et al.,

2007). During periods of low SCN activity (i.e. dark photoperiod), norepinephrine signals the

release of melatonin from the pineal gland via efferent nerve signaling (Simonneaux and Ribelayga,

2003). The binding of norepinepherine causes an influx of calcium into pineal cells, activating

arylalkylamine N-acetyl transferase (AA-NAT), the rate limiting enzyme in melatonin production

(Klein et al., 1983).

The pituitary gland is responsible for integrating photoperiodic information with

reproductive signals. Thyrotroph cells in the pars tuberalis (PT) of the anterior pituitary gland are

rich in melatonin receptors (Morgan et al., 1989). These melatonin receptors are species-specific,

and are expressed at comparable levels throughout the year (Morgan et al., 1994). Briefly,

melatonin regulates the molecular clock of the PT by inducing Cry1 expression and repressing Per1

expression (Jilg et al., 2005; Johnston et al., 2006). This causes the relationship between abundance

of Per1 and Cry1 to expand and contract throughout the day, with Cry1 expression reaching a

maximum at dusk and Per1 peaking at dawn. The diurnal relationship of Per1 and Cry1 is an

example of an internal coincidence model that creates a yearly timekeeping mechanism implicated

in driving seasonal hormonal changes.

The seasonal effect of photoperiod modulates reproduction via thyroid hormone

production. Triiodothyronine (T3), the active form of thyroid hormone, affects the pulsatile release

of GnRH from the hypothalamus, subsequently affecting the release of LH and FSH from the

anterior pituitary gland (Misztal et al., 2002). The activity of thyroid hormone is dependent on the

synthesis of T3 from its inactive prohormone thyroxine (T4). The enzymatic conversion of T4 to T3

is dependent on the production of the enzyme thyroxine deiodinase 2 (DIO2), which cleaves an

iodine moiety from the outer ring of T4 (Watanabe et al., 2004). A second deiodinase, DIO3, can

inactivate T3 by cleaving a second iodine molecule, to synthesize diiodothyronine (T2).

Photoperiod-dependent melatonin release regulates the expression of both DIO2 and DIO3, as well

28

as the release of thyroid stimulating hormone (TSHB), which is responsible for the initial production

of T4 (Yoshimura, 2013). The production of DIO2 from specialized tanycyte cells in the ependyma

is increased, leading to greater T3 production (Dardente, 2015). Alternatively, DIO3 production is

increased during short-day photoperiods, leading to inactivation of T3 (Barrett et al., 2007). Both

mechanisms affect the season-dependent release of gonadotrophins, leading to the annual rhythm

of fertility (Figure 2-3).

Kisspeptin, a hypothalamic peptide that controls the release of GnRH via estrogen

feedback, may also play an intermediate role in modulating seasonal breeding pattern of sheep.

Short-day photoperiod downregulated kisspeptin gene expression, and subsequently reducing

negative feedback on GnRH secretion from the hypothalamus (Clarke et al., 2009; Castro, 2016).

In male hamsters, treatment with exogenous kisspeptin was able to restore reproductive function

during short-day lighting (Bailey et al., 2017). This effect appears to be melatonin-dependent

because the photoperiodic effect does not occur after pineal ablation (Revel et al., 2006). Bartzen‐

Sprauer et al. (2014) demonstrated that in addition to kisspeptin, its co-activators neurokinin B and

dynorphin are also inhibited during short-day lighting, potentially helping to direct the seasonality

of reproduction. While the relationship between photoperiodic melatonin signaling and kisspeptin

production is clear, the mechanism governing this relationship has yet to be discovered.

Seasonal Changes in Feeding Behavior and Metabolism

In the winter, reduced sunlight and cold temperatures lead to dormancy in most plants. As

a result, the availability of feed for herbivorous mammals is reduced. To avoid winter starvation,

these animals must alter their metabolism to prepare for food shortage. In modern livestock farming

systems, issues related to winter feed availability have been resolved through feed storage systems

such as ensiling forages, hay production, and the drying of cereal grains. However, the seasonal

rhythm of forage production is still relevant for rangeland animals, and many of the physiological

29

mechanisms that animals have adapted to cope with seasonal feed availability may still exist.

Furthermore, the seasonal changes in plant growth can have tremendous consequences on the

nutrient composition of feed harvested at different times of the year. In the northern United States,

digestibility of most legumes declines with the high-temperatures of mid-summer because of

increased lignification of stalks, causing second and third cuttings of alfalfa to be less digestible

than alfalfa harvested at first cutting (Van Soest, 1994). Understanding how yearly changes affect

feeding behavior and metabolism in domestic ruminants may provide management strategies to

improve animal efficiency.

In many wild animals, food intake and body weight exhibit dramatic fluctuations

throughout the year. The body weight of golden-mantled ground squirrels (Callospermophilus

lateralis) peaks from November to February and then declines to a nadir in June and July (Zucker

and Boshes, 1982). Pallid bats (Antrozous pallidus) have substantially greater body weight from

October to December than from April to September (Beasley et al., 1984). These seasonal changes

in feed intake appear to also be present in domesticated livestock. Recently, Ueda et al. (2016)

reported that total rumen dry matter and fiber pool size were greater in the autumn than in the spring

in dairy cows, with a reciprocal relationship of volatile fatty acid concentration within rumen fluid.

Alternatively, grazing sheep have been shown to increase feed intake in the spring (Milne et al.,

1978). While these changes may be related to changes in quality of pasture across the year, evidence

suggests that annual patterns of feed intake occur in non-pasture raised animals. Feedlot beef cattle

fed stored forage also have greater feed intake in the fall than in the spring (Mujibi et al., 2010).

Together, these results may indicate that cattle have an annual rhythm of feed intake.

Leptin may be one factor responsible for the seasonal changes in feed intake. Leptin is a

hormone produced by adipose tissue that acts as a satiety signal. Leptin secretion of male

woodchucks (Marmota monax) peaks in the spring, causing a concurrent minima of yearly feed

intake (Concannon et al., 2001). Sheep exhibit seasonal changes in leptin, with greater leptin

30

concentrations occurring in August compared to January (Henry et al., 2010). Rousseau et al.

(2003) discovered that the effect of leptin on adiposity in Siberian hamsters (Phodopus sungorus)

is affected by photoperiod, with long-day housed hamsters being more resistant to lipolysis-

inducing effects of leptin. However, administering exogenous melatonin appears to have no effect

on the annual rhythms of plasma leptin concentrations, suggesting that the photoperiodic effects on

leptin concentration may be melatonin-independent (Mustonen et al., 2005). Despite the intriguing

breadth of evidence correlating seasonal feed intake to leptin secretion, mechanisms governing this

relationship have not been explored.

Metabolic function is also under the influence of seasonality. An extreme example of the

seasonal role in metabolic rate occurs in animals living in polar climates. Many arctic animals adapt

to cold temperature by restricting blood flow to their extremities to maintain a higher core body

temperature. In species such as arctic wolves, foxes, muskrats, and reindeer this can mean that the

temperature of their feet can drop to near 0°C (Brix et al., 1990). Caribou maintain blood fluidity

in their cold extremities by increasing the concentration of fatty acids, especially unsaturated fatty

acids, in bone marrow (Meng et al., 1969). These changes are also often accompanied by a decrease

in metabolic rate and activity to conserve body fat reserves (Scholander et al., 1950). Many non-

arctic animal also exhibit seasonal changes in metabolic rate. The wild Przewalski horse (Equus

ferus przewalskii) displays an annual rhythm of basal metabolic rate with a peak occurring in

August (Arnold et al., 2006), and the North American moose (Alces alces) exhibits a 1.4-fold

increase in resting heat production during the summer compared to the winter (Regelin et al., 1985).

There has been a large amount of research examining the effect of photoperiod on milk

production dairy cattle. It has been known for several decades that a long light period increases dry

matter intake, milk production, and weight gain of dairy cattle (Peters et al., 1978, 1981). Dahl et

al. (1997) observed that exposing dairy cows to a long day photoperiod (18 h light: 6 h dark)

increased milk yield and concentrations of circulating IGF-1 compared to the natural winter

31

photoperiod of < 13 h of light. On the other hand, exposure to a short-day photoperiod in non-

lactating cows during the dry period before calving improved milk production in the subsequent

lactation (Miller et al., 2000). These production responses are likely due to the photoperiodic effects

on prolactin signaling. Crawford et al. (2015) demonstrated that the positive effects of long-day

lighting during lactation could be mimicked by the addition of exogenous prolactin, suggesting that

it is the primary driver of the effects of photoperiod on milk synthesis.

Despite many species demonstrating photoperiod-driven seasonal rhythms in feed intake

and metabolism in mammals, there have been few studies determining if these rhythms can be

maintained under constant conditions. Brinklow and Loudon, (1990) established that Red Deer

(Cervus elaphus) exhibit seasonal rhythms of feed intake even when placed in constant 18h light:6

h dark cycles and fed ad libitum. Conceivably, a circannual rhythm of feed intake and metabolism

could exist and be driven by endogenous yearly rhythms of prolactin. Prolactin plays a role in feed

intake as well as the initiation and maintenance of lactation in dairy cows (Bauman and Currie,

1980; Teyssot et al., 1981; Lawrence et al., 2000). Prolactin is released from the pars distalis (PD)

of the anterior pituitary in response to melatonin. An inherent yearly rhythm of prolactin

concentration is maintained in a constant photoperiod (Martinet et al., 1992). The signal that directs

seasonal prolactin release based on photoperiodic information has yet to be identified, but a

hypothetical ‘tuberalin’ protein is theorized to stimulate the release of prolactin (Foster and

Kreitzman, 2009b). Dupré et al. (2010) identified the tachykinins Substance P and Neurokinin A

as potential ‘tuberalin’ proteins. The Tac1 gene, which encodes both proteins was shown to increase

expression during long-day photoperiods, and both are secretagogues for prolactin.

Seasonal Changes in Immunity

Activation of the immune system is a highly energy-demanding process. It is estimated that

infection can lead to a 50% increase in basal metabolic rate (Lochmiller and Deerenberg, 2000).

32

Therefore, the animal must balance energy efficiency with the ability to mount an appropriate

immune response. Many infectious agents follow distinct yearly patterns. Seasonal variables such

as temperature, humidity, and sunlight, as well as host behavior, such as social interaction, all affect

the survivability of pathogens (Grassly et al., 2006). Influenza is one of the most well-known

examples of a seasonal disease in humans, with outbreaks occurring most frequently in the late fall

and winter of temperate climates (Tamerius et al., 2013). In pigs pneumonia infections are most

likely to occur during late winter and spring (Cowart et al., 1992). In the winter and spring, bovine

viral diarrhea (BVD) incidence is the greatest (Radostits and Littlejohns, 1988). The yearly pattern

of disease exposure creates predictable patterns that may be exploited by the immune system of

animals. By keeping the immune system ‘primed’ to respond to the pathogens that are most likely

to be encountered, it can most efficiently allocate resources without expending unnecessary energy.

Photoperiod can alter the growth and function of many immune cells. The number of

circulating leukocytes, B cells, T cells, and natural killer (NK) cells are increased by short day

photoperiod (SDPP) in Siberian hamsters (Bilbo et al., 2002). Furthermore, SDPP increases the

ability of NK cells to produce cytotoxicity via oxidative burst (Yellon et al., 1999). Non-lactating

dairy cattle increase neutrophil chemotaxis and lymphocyte proliferation in response to SDPP

(Auchtung et al., 2004). Additionally, season of the year can affect the functionality of specific

immune cells. Mann et al. (2000) observed a greater Th1: Th2 ratio in Rhesus macaques (Macaca

mulatta) in the summer versus winter. Alternatively, the response of polymorphonuclear blood

monocytes (PBMCs) to IL-2 stimulation was greater in the winter than summer.

One mechanism of photoperiodic changes in immune function may be melatonin signaling

in immune cells. Many immune cells including B cells, T cells, and PBMCs have membrane and

nuclear melatonin receptors (Pozo et al., 1997, 2004). The binding of melatonin to these receptors

can have multiple effects on the immune system. Treatment with exogenous melatonin increases

the systemic expression of IL-1α IL-1β, IL-5, IL-6, and IL-10 in endotoxemia-challenged mice

33

(Effenberger-Neidnicht et al., 2014). Removal of endogenous melatonin signaling via a

pinealectomy decreased the ability for splenocytes to react to antigen stimulation, and caused a near

depletion of lymphocytes (Maestroni et al., 1986). Melatonin itself can function as an antioxidant

by scavenging free radicals to ameliorate oxidative stress (Mayo et al., 2003).

The seasonal change in vitamin D may be another possible explanation for the effects of

photoperiod on the immune system. Vitamin D receptors (VDR) are present in nearly all immune

cells including macrophages, dendritic cells, neutrophils, T cells, and B cells (Baeke et al., 2010).

Photoperiod affects the function of vitamin D through sunlight-mediated vitamin D activation.

Briefly, ultraviolet radiation from the sun catalyzes the conversion of 7-dehydrocholesterol to

cholecalciferol (Vitamin D3) in the epidermal layer of the skin. After two successive hydroxylation

steps in the liver and kidney, respectively, the active form of vitamin D (1,25-dihydroxyvitamin

D3) is available for binding to the VDR (Horst et al., 2005). The role of sunlight in activation

implies a strong photoperiodic effect on vitamin D metabolism. Indeed, 1,25-dihydroxyvitamin D3

shows a strong yearly rhythm with plasma concentration peaking in the summer (Stumpf and

Privette, 1991).

While roles of melatonin and vitamin D may imply that yearly changes in immune function

are a result of strictly exogenous photoperiod, there is evidence to suggest an underlying

endogenous circannual rhythm of immune function. Brock detected a circannual rhythm in the

proliferation of B and T cells in C57BL/6 mice held in a constant photoperiod (12h light: 12h dark)

and temperature (22.5°C) (Brock, 1983). A similar experiment conducted in dogs under the same

photoperiod length also saw a circannual rhythm of total leukocytes and lymphocytes (Sothern et

al., 1993). These experiments provide some credence to the idea of an inherent circannual rhythm

of immune function, however, no mechanism has been described.

34

Epigenetic Control of Seasonality

Emerging evidence suggest that epigenetic modifications are important for the

maintenance and synchronization of circannual rhythms. Stevenson and Prendergast (2013)

demonstrated that SDPP increased the amount of DNA methylation at the promoter region of the

dio3 gene in the hypothalamus of Siberian hamsters. This led to decreased expression of diodinase

3, and greater inactivation of thyroid hormone. The authors concluded that hypermethylation at the

dio3 promoter helps precipitate seasonal reproduction by inhibition of reproductive activity during

summer months (Stevenson and Prendergast, 2013). Interestingly, DNA methyltransferase and

global methylation were actually lower in hamsters under SDPP, likely indicating a myriad of other

epigenetic changes are under photoperiodic control in the hypothalamus. The same authors

observed that the same mechanism of dio3 methylation can occur in a cell-autonomous manner

within immune cells. The increased methylation of deiodinase 3 caused an accumulation of T3

within lymphocytes under SDPP (Stevenson et al., 2014). Furthermore, supplementing with

exogenous T3 caused lymphocytes to exhibit greater sensitivity to inflammation.

Stoney et al. (2016) provided additional evidence that epigenetic programming may play a

role the seasonal changes of the immune system. They observed that expression of HDAC 4, 5, and

9 were found to be greater in mice under long-day (16h L: 8h D) versus short-day (8h L: 16h D)

photoperiod (Stoney et al., 2017). These histone deacetylases were shown to reduce the expression

of nuclear factor-κB (NF-κB), a master regulator of inflammation, during SDPP. Winter-related

behaviors also appear to be under the influence of epigenetic changes. Alvarado et al. (2015)

demonstrated that thirteen-lined ground squirrels (Ictidomys tridecemlineatus) exhibit a reduction

in global DNA methylation within muscle and liver at the onset of winter torpor.

Stevenson and Lincoln, (2017) have proposed a model of epigenetic control of circannual

rhythms, which postulates that circannual information is programmed into cells during early stem

cell development. They believe that this mechanism forms an ‘epigenetic memory’ of circannual

35

information, which can be reprogrammed in adult cells if seasonal inputs change. In insects, it

appears that seasonal information can be passed transgenerationally. Nasonia wasps (Nasonia

vitripennis) transmit seasonal signals of diapause from mother to an undifferentiated egg, which

was recently discovered to be a result of photoperiod-dependent DNA methylation (Pegoraro et al.,

2016). A similar mechanism appears to occur in Sarcophaga flies, which demonstrate the ability

to transmit photoperiodic signals from mother to embryo during early larval development.

Conclusions

Across multiple time-scales, biological rhythms serve to coordinate physiology with the

external environment. These biological rhythms are highly conserved across multiple organism,

illustrating their importance. Nutrient metabolism, in particular, is highly influenced by both

circadian and circannual rhythms. Moreover, nutrients themselves are important for entrainment

of biological clocks. Desynchronization of the day-light cycle from the time of feed intake can

lead to metabolic diseases such as obesity and type II diabetes in humans. Moreover, matching

the time of feed intake with the timing of the biological clock in livestock species may serve as an

opportunity to improve the efficiency of animal production.

36

Figures

Figure 2-1. Parameters used in the quantification of biological rhythms.

Period is the length of time to complete one cycle of the rhythm. Amplitude is the difference between peak

and mean. Double amplitude is the difference between peak and trough. Acrophase is the time at peak.

Bathyphase is the time at trough. Phase advance refers to shifting the rhythm curve so that the acrophase

occurs earlier than it did previously. Phase advance refers to shifting the rhythm curve so that acrophase

occurs later than it did previously.

37

Figure 2-2. Structure of the mammalian transcriptional circadian clock.

Key: BMAL1: Brain and muscles ARNT-like 1, CCG: Clock controlled gene, CK1ε: Casein kinase 1ε,

CLOCK: circadian locomotor output cycles kaput, CRY: Cryptochrome, E-Box: Enhancer-box

[canonically CANNTG], P: Phosphate group, PER: Period, RORα: Retinoic acid-related orphan receptor

α, RORE: Retinoic acid-related orphan receptor response element. Adapted from (Green et al., 2008).

38

Figure 2-3. Physiological mechanisms governing seasonal reproduction.

Key: DIO2: diodinase 2, GnRH: gonadotropin releasing hormone, MEL: Melatonin, NE: norepinephrine,

PT: Pars tuberalis, SCN: Suprachiasmatic nucleus, T3: Triiodothyronine, TSH:Thyroid stimulating

hormone, UV: Ultraviolet.

39

Chapter 3

The effects of day- versus night-restricted feeding of dairy cows on the daily

rhythms of feed intake, milk synthesis and plasma metabolites

Isaac J. Salfer and Kevin J. Harvatine

This chapter will be submitted as a manuscript to the Journal of Dairy Science.

40

ABSTRACT

The timing of feed intake can alter circadian rhythms of peripheral tissues. Dairy cows

have well-recognized daily rhythm of milk synthesis, but the effect of feeding time on these

rhythms is poorly characterized. The objective of this experiment was to determine if the time of

feed intake modifies the daily patterns of milk synthesis, key plasma metabolites, and body

temperature in dairy cows. Sixteen Holstein cows were randomly assigned to one of two

treatment sequences in a cross-over design with 17 d periods. Treatments included day-restricted

feeding (DRF) with feed available from 0700 to 2300 h and night-restricted feeding (NRF) with

feed available from 1900 to 1100 h. To determine a rhythm of milk synthesis, cows were milked

every 6 h on the last 7 d of each period and blood samples were collected to represent every 4 h

over the day and analyzed of glucose, insulin, non-esterified fatty acids (NEFA), and plasma urea

nitrogen (PUN). Body temperature was measured using indwelling vaginal temperature data

logger. The daily rhythms of milk yield and composition were modified by timing of feed

availability. Peak milk yield was shifted from morning in DRF to evening in NRF, while milk fat,

protein, and lactose concentration peaked in the evening in DRF and the morning in NRF. Plasma

glucose, insulin, NEFA, and PUN fit daily rhythms in all treatments. Night-feeding increased the

amplitude of glucose, insulin, and NEFA rhythms and shifted the daily rhythms by 8 to 12 h (P <

0.05). Night feeding also phase delayed the rhythm of core body temperature and DRF increased

its amplitude. Altering the time of feed availability shifts the daily rhythms of milk synthesis and

plasma hormone and metabolite concentrations and body temperature, suggesting that these

rhythms may be entrained by food intake.

Keywords: daily rhythm, milk synthesis, food entrainment, circadian

41

INTRODUCTION

Feeding behavior, milk synthesis, and plasma metabolites of dairy cows follow daily

patterns that may be regulated by endogenous circadian rhythms (Giannetto and Piccione, 2009).

Circadian rhythms are repeating cycles of about 24 h that govern many physiological functions

and allow organisms to anticipate changes in their environment. They are regulated at both the

systems level, through neural and humoral communication between tissues, and at the cellular

level, through transcriptional-translational feedback loops of ‘clock’ transcription factors and 24-

h protein phosphorylation cycles. In mammals, the master circadian pacemaker is located in the

suprachiasmatic nucleus (SCN) of the hypothalamus and is entrained by light through visual and

non-visual photoreceptors in the eye (Panda et al., 2002). However, food intake can entrain

circadian rhythms of peripheral tissues such as liver and adipose tissue, independent of the SCN

(Casey and Plaut, 2012).

Milk synthesis follows a daily rhythm in dairy cows with peak milk yield occurring in the

morning, and milk fat and protein concentration peaking in the evening (Rottman et al., 2014).

This adaptation may have evolved to provide neonates with energy-dense milk at night when

environmental temperature is lower and nursing and foraging activity is minimal. Evidence for a

role of the circadian system in lactation has been demonstrated in both rodents and humans, with

over 7% of the human mammary transcriptome oscillating in a circadian manner (Maningat et al.,

2009; Casey and Plaut, 2012).

Dairy cows also exhibit a crepuscular pattern of feed intake, consuming the majority of

their feed during the daylight hours with large bouts of intake in the morning and evening

(Albright, 1993). Furthermore, intake is stimulated by feed delivery and milking (DeVries et al.,

2005). Although most commercial dairy farms feed a total mixed ration (TMR) which is meant

provide cows with a consistent concentration of nutrients across the day, the daily pattern of

42

intake and sorting of feed cause nutrient intake and absorption to fluctuate diurnally (Rottman et

al., 2015). Moreover, the daily patterns of important peripheral hormones and metabolites such as

glucose, urea, nonesterfied fatty acids (NEFA), and insulin can be altered by the time of feed

intake (Nikkhah et al., 2008; Niu et al., 2014).

While there is evidence supporting a role of the mammary clock in lactation, little is

known about the relationship between feeding and daily rhythms of milk synthesis. Rottman et al.

(2014) observed that the amplitudes of milk fat and protein concentration are reduced by feeding

cows 4x/d compared to 1x/d. However, the response of the daily pattern of milk synthesis to

altering the timing of nutrient intake has not been examined. Time-restricted feeding is often

employed when studying food entrainment because it creates more robust changes in daily

rhythm compared to altering feeding time alone (Stokkan et al., 2001). The objectives of this

study were to determine the effects of the time of time-restricted feeding on daily rhythms of milk

synthesis and the daily rhythms of plasma hormones and metabolites and body temperature. Our

hypothesis was that restricting feed availability to 16 h over the dark period would invert the daily

rhythm of milk synthesis relative to feed available for 16 h during the light period and result in a

commensurate change in the daily pattern of plasma metabolites, with smaller changes in the

daily rhythm of body temperature.

MATERIALS & METHODS

Animals and Treatments

All experimental procedures were approved by the Pennsylvania State University

Institutional Care and Use Committee. Sixteen multiparous Holstein cows (183 ± 103 d

postpartum; mean ± SD) from the Pennsylvania State University Dairy Research and Teaching

Center were randomly assigned to one of two treatments sequences (n = 8 cows per sequence) in

43

a cross-over design. Treatment periods were 17 d and included 10 d of treatment adaptation with

2x/d milking and 7 d of 4x/d milking. Animals were housed in individual tie-stalls and had ad

libitum access to water. Cows were maintained in a 19 h light and 5 h dark photoperiod with

lights on from 0500 h to 0000 h. Treatments included (1) day-restricted feeding (DRF) with feed

available for 16 h/d from 0700 to 2300 and (2) night-restricted feeding (NRF) with feed available

for 16 h/d from 1900 to 1100 (Figure 3-1A). All cows were fed the same diet offered at 110% of

the previous day’s intake and intake was recorded daily. Feed was mixed once daily at 0700 and

immediately delivered to the DRF group. The remaining feed was compressed into plastic barrels,

covered with plastic, and stored at ambient temperature until feeding to NRF at 1900 h. Feed

samples were collected on d 7 and 14 of each period, composited by period and analyzed for dry

matter, crude protein, fiber, neutral detergent fiber, acid detergent fiber, and ash concentrations

according to Rico and Harvatine (2013) (Supplemental Table S3-1). The experiment was

conducted from February to March of 2016.

Milk Sampling and Analysis

Cows were milked 4x/d (0500, 1100, 1700 and 2300 h) for the last 7 d of each period to

observe the daily rhythm of milk synthesis. Milk collected at each time point represented milk

synthesis during previous 6 h, and data were expressed as the midpoint of the previous milking

interval (3 h prior to collection). Milk yield was measured at each milking using an integrated

milk meter (AfiMilk MPC Milk Meter; Afimilk Agricultural Cooperative Ltd., Kibbutz Afikim,

Israel) and corrected for the deviation of each individual stall according to Andreen et al. (2018).

Milk was sampled at all milkings on the last 2 d of each period. One subsample was stored at 4°C

with a preservative (Bronolab-WII; Advanced Instruments, Inc., Noorwood, MA) prior to

analysis of fat and protein concentration by Fourier transform infrared spectroscopy (Fossomatic

4000 Milko-Scan and 400 Fossomatic, Foss Electric; Dairy One DHIA). A second subsample was

44

stored without preservative at 4°C and centrifuged at 2300 x g within 12 h. The resulting fat

cakes were stored at -20°C and analyzed for concentrations of individual fatty acids (FA)

according to Baldin et al. (2018).

Feeding Behavior Observation and Analysis

The daily pattern of feed intake was monitored in nine of the 16 cows using an automated

feed observation system described by Rottman et al. (2015). Briefly, feed in hanging feed tubs

was weighed and recorded every 10 s from d 8 to 17 of each period by an electronic load cell

connected to a data acquisition system. To characterize feeding behavior, the number, length and

size of meals as well as the intermeal interval, eating time, and eating rate were determined as

described Niu et al. (2017). Hunger ratio was calculated as meal size divided by the previous

intermeal interval and satiety ratio was determined as meal size divided by the ensuing intermeal

interval. To characterize the rate of feed intake across the day, the data were smoothed by

calculating a running average over 180 s, the rate of feed intake was the determined over 10

minute intervals before averaging across 2 h intervals as described by Rottman et al. (2015).

Plasma Sampling and Analysis

Blood was collected by venipuncture of a coccygeal vessel using potassium EDTA

vacuum tubes (Greiner Bio-One North America, Inc., Monroe, NC). Samples were collected at

six time points on d 15 to 17 of each period to represent every 4 h across the day (0300, 0700,

1100, 1500, 1900, and 2300 h). Blood was immediately placed on ice and centrifuged within 1 h

at 2300 x g for 15 min at 4°C. Plasma was collected and stored at -20°C for analysis of glucose,

NEFA, plasma urea nitrogen (PUN), and insulin concentrations according to Rottman et al.

(2014). Briefly, plasma glucose concentration was analyzed using a glucose oxidase/peroxidase

enzymatic colorimetric assay (No. P 7119, Sigma-Aldrich, St. Louis, MO), NEFA concentration

45

was measured with an using acyl-CoA oxidase/peroxidase enzymatic colorimetric assay (NEFA-

HR (2), Wako Diagnostics, Richmond, VA), PUN was assayed using a modified Berthelot

methodology (Modified Enzymatic Urea Nitrogen Procedure No. 2050; Stanbio Laboratory,

Boerne, TX), and insulin was determined using a porcine 125I-insulin radioimmunoassay kit with

correction based on bovine insulin (PI-12K Porcine Insulin RIA, EMD Millipore, Billerica, MA).

Body Temperature Recording

Core body temperature was recorded every 10 min on d 12 to 17 of each period using an

intravaginal temperature data logger. A miniature plastic-coated thermometer (iBCod; Alpha

Mach Inc., Sainte-Julie, QC, Canada) was fastened to a progesterone-free CIDR vaginal implant

(Zoetis, Inc., Parsippany-Troy Hills, NJ) and placed centrally in the vagina of the cows using an

insertion tool. Raw data was averaged over 2 h intervals.

Statistical Analysis

All statistical analysis was performed using the MIXED procedure of SAS 9.4 (SAS

Institute, Cary, NC). Daily DMI, milk production, and FA yields were analyzed using the fixed

effects of treatment, period, and the interaction of treatment by period and the random effect of

cow. Repeated measures analysis was performed on data observed multiple times per day to test

the interaction of treatment and time of day on milk production, rate of feed intake, and plasma

metabolites and hormones. The model included the fixed effect of treatment, period, and the

interaction of treatment and period, the random effect of cow, and the repeated effect of time of

day. The AR(1) or ARH(1) covariance structure was selected based on convergence criteria, and

denominator degrees of freedom were adjusted using the Kenward-Roger method. Preplanned

contrasts tested the effect of treatment at each time point.

46

Time course data for milk production, plasma hormones and metabolites, and body

temperature were fit to the linear form of a cosine function using random regression in SAS 9.4.

The model included the fixed effect of treatment, cosine terms, and the interaction of treatment

with the cosine terms and the random effects of cow and period. A 12 h harmonic was also tested

for the daily patterns of plasma hormones and metabolites and body temperature but was removed

because it did not improve model fit according to Bayesian information criterion. The fit of the 24

h cosine was determined using a zero-amplitude test, an F-Test comparing a full model

containing the linear form of the cosine function to a reduced linear model. The acrophase (time

at peak) and the amplitude (difference between peak and mean) of the 24 h rhythm were

calculated and significance determined as described by Niu et al. (2014). In all analyses, points

with Studentized residuals outside of ± 3.5 were removed. Statistical significance was declared at

P < 0.05 and trends acknowledged at 0.05 < P < 0.10. High resolution figures were generated

using an add-in for Microsoft Excel (Kraus, 2014).

RESULTS

Eating Behavior

Total daily dry matter intake was not affected by the time of feed restriction (Table 1).

Both treatments consumed the greatest amount of feed in the first 2 h following feed delivery

(Figure 3-1B). However, the NRF treatment had a higher rate of intake after feed delivery,

consuming 21.6% (9.6 kg) of their feed per hour in the first two hours after feed delivery

compared to 15.6% (5.9 kg) in the DRF group (P < 0.0001).

The time of feed availability had no effect on meal size, number of meal bouts per day,

eating time/d, eating rate, or average intermeal interval (Supplemental Figure S3-1). However,

47

DRF increased average meal length 5.1 min/d (P = 0.02) and increased hunger ratio 40% (P =

0.01), while the satiety ratio was not modified.

Rhythm of Milk and Milk Components

The timing of feed restriction did not alter daily yield of milk and milk fat and protein or

daily average concentration of milk fat and protein (Table 1). However, milk yield fit a cosine

function with a 24 h period in both DRF (P = 0.04) and NRF (P < 0.0001; Figure 3-2A). The

acrophase of milk yield was delayed 8.1 h by NRF (P < 0.05), and the amplitude of the rhythm

was 32% greater in DRF than NRF.

Night-restricted feeding decreased total daily milk fat yield (P = 0.03; Table 1). Milk fat

yield fit a daily rhythm with an amplitude of 16.5 g and an acrophase at 0703 in NRF (P <

0.0001), but did not fit a 24 h rhythm in the DRF (Figure 3-2B). Milk fat concentration fit a 24 h

rhythm in NRF (P < 0.0001) and tended to fit a daily rhythm in DRF (P = 0.06; Figure 3-2C).

There was an effect of treatment on the rhythm, as milk fat concentration was phase shifted,

peaking at 1156 h in DRF compared to 0207 h in NRF (P < 0.05) and the amplitude was

increased by 64% in NRF compared to DRF (P < 0.05).

Milk protein yield fit a daily rhythm in both DRF and NRF (P < 0.001; Figure 3-2D)

with the peak of the rhythm occurring at 0211 h in DRF and at 0738 in NRF (P < 0.05). The

amplitude of the daily rhythm of protein yield was 30.1 g in DRF and was decreased to 10.4 g by

NRF (P < 0.05). Both DRF and NRF also exhibited daily rhythm of milk protein concentration (P

< 0.02; Figure 3-2E). Treatment modified the timing of the rhythm with DRF peaking at 1525 h

and NRF peaking at 0029 h (P < 0.05). The robustness of the rhythm was increased over two-fold

by NRF, which had an amplitude of 0.11% compared to 0.05% in DRF (P < 0.05).

48

Milk FA Yield and Profile

Compared to NRF, day-restricted feeding increased FA originating from de novo

synthesis in the mammary gland by 5% (Σ < 16C; P = 0.03) and mixed source FA that originate

both from de novo synthesis and preformed FA uptake from plasma by a 9% (Σ 16C; P = 0.001;

Figure 3-3A). However, no difference in yield of preformed FA (Σ >16C) was observed between

treatments.

Both de novo and mixed source FA fit a cosine function with a period of 24 h in both

treatments, but preformed FA only fit a cosine function in DRF (P < 0.05; Figure 3-3D-F). The

acrophase of the de novo FA rhythm was delayed 6.7 h by NRF (0108 vs 0750 h; P < 0.05) and

DRF increased the amplitude of the rhythm by 38% (P < 0.05). The acrophase and amplitude of

the mixed FA yield rhythms were also altered by treatments, with DRF peaking at 0032 h and

NRF peaking at 0721 (P < 0.05), and NRF having a 34% greater amplitude than DRF (P < 0.05).

Similar to the de novo FA rhythm, night-restricted feeding increased the robustness of the mixed

FA yield rhythm, increasing its amplitude 34% compared DRF (P < 0.05). Of the 44 individual

fatty acids quantified, 15 fit a daily rhythm in both DRF and NRF, 10 fit a rhythm in DRF only, 6

fit a rhythm in NRF only, and 13 did not fit a rhythm in either treatment (Supplemental Table S2).

Fatty acids generally followed a similar daily pattern as their source grouping (de novo, mixed

source, preformed).

To assess the potential cause of the reduced de novo fatty acid synthesis, milk fat

concentrations of trans-10 C18:1 (t10) and trans-11 C18:1 (t11) were determined. trans-10 C18:1

is produced by the alternate biohydrogenation pathways and is elevated during biohydrogenation-

induced milk fat depression and t11 is a product of the normal biohydrogention pathway and is

increased with slowing of the normal biohydrogenation pathway (Griinari et al., 1998). The

concentrations of t10 and t11 were analyzed as a percent of 18 carbon FA, rather than as a percent

of total FA to avoid bias of changes in de novo and mixed FA. Night-restricted feeding increased

49

t10 by 16.3% (P = 0.02) and t11 by 32.8% (P = 0.001) compared to NRF (Figure 3-3B). Both t10

and t11 fit daily rhythms in both treatments (P < 0.0001; Figure 3-3G&H). Milk fat t10 was

phase delayed 12.1 h by NRF, with NRF peaking at 1038 and DRF peaking at 2234 (P < 0.05).

The amplitude of t10 was increased by NRF compared to DRF (0.08% vs. 0.06%; P < 0.05). Like

t10, night-restricted feeding caused a complete inversion of the daily rhythms of t11

concentration, shifting the peak from 1816 in DRF to 0604 in NRF (P < 0.05). The amplitude of

t11 was not affected treatment (P > 0.10).

Plasma Hormones and Metabolites

Plasma glucose fit a 24 h cosine in both the DRF (P < 0.03) and NRF (P < 0.001), with

NRF increasing the amplitude of the rhythm by 149% (P < 0.05; Figure 3-4A). Night-restricted

feeding also phase shifted plasma glucose, with the acrophase occurring 10.2 h later in DRF than

NRF (P < 0.05).

Plasma insulin concentration fit a 24 h cosine function in both treatments (P < 0.001),

with no difference in mean insulin concentration between DRF and NRF (P = 0.69; Figure

3-4B). Night-restricted feeding shifted the acrophase of insulin production 8.0 h and increased the

amplitude 0.9 μIU/mL compared to DRF (P < 0.05).

Concomitant with the shift in plasma insulin rhythms, the rhythms of plasma NEFA

concentration were dramatically altered by treatment (Figure 3-4C). Both treatments exhibited 24

h rhythms in plasma NEFA (P < 0.01), but the acrophase of the rhythms were markedly different,

with DRF rhythms peaking at 0614 and NRF peaking at 1657 (P < 0.05). Additionally, NRF

increased the amplitude of the NEFA rhythm 3.3 fold (P < 0.05). This increase in amplitude in

the NRF treatment was due to a dramatic rise in NEFA concentration to 214 μEq/L at 1900 h, 6

hours into the fasting period.

50

Plasma urea nitrogen fit a 24 h rhythm in DRF (P = 0.02) and tended to fit a rhythm in

NRF (P = 0.08). The rhythm of NRF was completely inverted with respect to the DRF, with DRF

peaking at 0209 h and NRF peaking at 1420 (P < 0.05). Moreover, the amplitude of the PUN

rhythm was 20% greater in DRF than NRF (P < 0.05).

Body Temperature

Body temperature fit a 24 h cosine function in both treatments (P < 0.05) and was

modified by timing of feed restriction (Figure 3-5). NRF delayed the phase of the rhythm 15.5 h

(P < 0.05) and increased the amplitude 75% compared to DRF (P < 0.05).

DISCUSSION

The rate of feed intake was greater after food delivery in the NRF compared to DRF,

which is consistent with previous results reporting that feed delivery 1x/d at night increases intake

in the first the 2 to 3 h after feeding compared to feed delivery in the morning (Nikkhah et al.,

2008; Niu et al., 2014). Cows naturally exhibit a crepuscular pattern of intake, with highest intake

occurring at dusk and at dawn and a decline in intake overnight, and TMR-fed cows normally

have high intake at feed delivery and the late afternoon and early evening and low intake in the

overnight period (DeVries and von Keyserlingk, 2005). In NRF, feed was withheld during the

high-intake afternoon period of the day (1100 to 1900 h), whereas DRF cows were without feed

during the low-intake night. The greater rate of intake immediately after feeding in NRF suggests

greater hunger signaling presumably due to the circadian pattern of hunger and satiety. Other

species display 24 h rhythms of hunger. Humans, for example, exhibit the greatest appetite in the

evening, and lowest in the morning, independent of sleeping time or food intake (Scheer et al.,

2013). Moreover, rats display circadian rhythms of meal size and meal frequency under constant

51

illumination (Rosenwasser et al., 1981). Despite the difference in the daily pattern of feed intake,

total dry matter intake did not differ between treatments because DRF compensated by increasing

intake in the afternoon compared to the corresponding period in NRF (0100 to 0700 h).

Dairy cows generally have greatest milk yield in the morning and greater fat and protein

concentration in the evening when fed ad libitum with feed delivered in the morning (Quist et al.,

2008; Rottman et al., 2014). Our results confirmed these findings, but demonstrated that night-

restricted feeding shifts this daily pattern 8 hours later in the day. The change in the rhythms of

milk and milk component synthesis in response to altered feeding time is either due to a change in

available substrate for milk synthesis or entrainment of the mammary gland’s circadian clock. In

FVB mice, time-restricted feeding shifted the rhythms of clock genes in the mammary gland, and

affects the rhythmic gene expression of transcription factors (SREBP1c, Spot 14) and enzymes

(SCD1 and FASN) related to milk fat synthesis (Unpublished data). These results suggest that

alterations in the mammary molecular clock may mediate the response of feeding time on

rhythms of milk synthesis, but further research must be conducted to evaluate this effect.

The decrease in total milk fat yield and de novo and mixed fatty acids in NRF compared

to DRF indicates that de novo fatty acid synthesis was reduced by night-restricted feeding.

Concomitant with the decrease in apparent de novo fatty acid synthesis, the concentration of t10

C18:1 was elevated in NRF. Trans-10 C18:1 is highly correlated with reduced milk fat synthesis

in dairy cows as it is associated with production of bioactive intermediates of the trans-10

pathway (Lock et al., 2007). However, t11 C18:1, the intermediate of normal ruminal

biohydrogenation was also increased by NRF, which commonly occurs due to a slowing of the

normal pathway in conjunction with the shift to the alternate BH pathway (Rico and Harvatine,

2013). The decrease in ruminal biohydrogenation in NRF may be related to the daily pattern of

intake. The NRF group consumed a large percentage of their feed during the first 2 hours after

delivery, while DRF had more stable intake across the day. Previous research demonstrated that

52

stabilizing feed intake through 4x/d feeding increased milk fat yield and decreased t10 C18:0

(Rottman et al., 2014). The changes in daily patterns of de novo, mixed source, and preformed

FA closely reflected the daily pattern of milk yield. The daily pattern of t10 C18:1 appeared to be

highly impacted by time of feed restriction, peaking at the start of the fasting period in both

treatments. Trans-11 C18:1, meanwhile, peaked in the middle of the feeding period, 13 hours

after feed delivery in both treatments.

The effect of time of feed restriction on daily rhythms of fatty acids may also be due to

differences in entrainment of the mammary circadian clock. Previous research from other models

suggests circadian regulation of lipid metabolism. In lactating rats, mammary lipogenesis and

activity of acetyl-CoA carboxylase (ACC), the enzyme that catalyzes the rate-limiting step in FA

synthesis, follow clear daily pattern with a nadir at 1500 h (Munday and Williamson, 1983).

Furthermore, Hems et al. (1975) detected daily rhythms of FA synthesis in the livers of mice.

Similarly, the current study indicates that FA synthesis follows a daily pattern in the mammary

gland of cows. Several metabolic pathways linking the circadian clock to FA metabolism have

been characterized, including the peroxisome proliferator-activated receptor (PPAR) family of

nuclear receptors, which act as master regulators of lipid metabolism in multiple tissues and the

cellular energy sensor AMP-activated protein kinase (AMPK) influences the period of the

circadian clock while also increasing the expression of ACC (Jordan and Lamia, 2013; Chen and

Yang, 2014). The results of this experiment provide additional support that de novo FA synthesis

is controlled by the circadian rhythm of the mammary gland and suggest that this rhythm is

modified by timing of nutrient absorption.

Similar to previous reports in dairy cattle (Giannetto and Piccione, 2009) and other

species (Rudic et al., 2004), plasma glucose concentration fit a 24 h rhythm. Furthermore, both

glucose and insulin concentrations were phase shifted by NRF compared to DRF. Circadian

control over glucose metabolism in experimental models has been well-established. In mice, a

53

functioning circadian clock in pancreatic β-cells is required for maintenance of insulin sensitivity

(Sadacca et al., 2011). The amplitude of NEFA concentration was greatly increased in NRF

compared to DRF. This is likely a consequence of the daily pattern of hunger and satiety. Cattle

typically increase feed consumption during the afternoon, suggesting greater hunger signaling

during this time (Ray and Roubicek, 1971). The results of the current experiment suggest that

fasting during the high-intake afternoon period of the day causes greater lipid mobilization than

fasting during the low-intake overnight period. These results were similar to those reported in

rats, which displayed a shift in the daily pattern of insulin release when they were fasted during

the early part of their active period (Shimizu et al., 2018). Insulin in a potent inhibitor of hormone

sensitive lipase, the enzyme responsible for causing lipid mobilization from adipose tissue

(Locher et al., 2011). Peak NEFA concentration in both treatment groups coincided with the nadir

of insulin release. Shostak et al. (2013) reported that lipolysis from white adipose tissue follows a

daily pattern, and showed using ClockΔ19 and Bmal-/- mice that these patterns were controlled by

the molecular circadian clock. The current study provides evidence suggesting a similar

mechanism may occur in dairy cows.

The shift in body temperature by the time of time-restricted feeding likely demonstrates a

change in the central circadian rhythm. This result of the current study showed a more extreme

shift in the rhythm of body temperature than previously observed by Niu et al. (2014), who

reported a 3 hour phase delay after feeding at 2030 compared to 0830, without restricting the time

of feed availability. Our results also agreed with research performed in mice which showed shifts

in the body temperature rhythms when food was restricted to the inactive period (Damiola et al.,

2000). The change in body temperature may also be responsive to feeding pattern due to changes

in heat produced by rumen fermentation across the day. This may be a mechanism by which

ruminants can directly modify body temperature in response to feeding. Shifts in the rhythm of

body temperature may play a role in altering other daily rhythms within the animal because body

54

temperature has been shown to be capable of entraining peripheral circadian clocks in mammals

(Brown et al., 2002).

In conclusion, timing of feed intake dramatically altered daily rhythms of milk synthesis,

plasma hormones and metabolites, and body temperature. Modification of the phase of milk

synthesis by temporal changes in absorption of nutrients indicates possible changes in the

mammary molecular clock. Timing of nutrient intake also influences the central circadian clock,

evidenced by the shift in the body temperature rhythm. The fasting response was more dramatic

for cows on the NRF treatment, having a greater post-feeding feed consumption rate, and greater

pre-meal NEFA concentrations, which is likely due to the circadian rhythm of food intake. This

study supports the hypothesis that timing of nutrient absorption modified the daily rhythms of

milk synthesis, likely due to entrainment of the mammary circadian clock.

55

FIGURES

Figure 3-1. Effect of day- versus night-restricted feeding on the daily pattern of feed intake.

Panels show: (A) Daily schedule of feed availability and milking time for day-restricted feeding (DRF;

feed available for 16 h/d from 0700 to 2300) and night-restricted feeding (NRF; feed available for 16 h/d

from 1900 to 1100) treatments and milking times. Cows were adapted to feeding schedules for 10 d prior to

7 d of 4x milking. (B) Effects of day versus night feed availability on the rate of feed intake (kg DM/h).

Data are presented as the least square means with standard error bars for every 2 h period. Preplanned

contrasts of the effect of treatment at each time point are shown (*P < 0.05).

56

Figure 3-2. Effect of day- versus night-restricted feeding on daily rhythms of milk yield and milk

components.

Treatments were feed available for 16 h during the day [day-restricted feeding (DRF); feed from 0700 to

2300] or feed available for 16 during the night [night-restricted feeding (NRF); feed from 1900 to 1100].

Data are presented as LSM with SEM bars. Panels show the effect of day vs. night-restricted feeding on the

daily pattern of A. milk yield (kg), B. milk fat yield (g), C. milk fat concentration (%), D. milk protein yield

(g), E. milk fat concentration (%). 1Amplitude- difference between peak and mean. 2Acrophase-time at peak

of the rhythm. 3P-value of the zero-amplitude test. The black and white bars above the x-axis display the

light: dark cycle.

57

Figure 3-3. Effect of day- versus night-restricted feeding on the daily production and daily pattern

of milk fatty acids.



Panels show effect of day vs. night-restricted feeding on (A) total daily yield of de novo (Σ <16C FA), mixed

(Σ 16C FA), and preformed (Σ >16C FA) FA (g/d), (B) daily average milk fat concentration of trans-10

C18:1 (t10) trans-11 C18:1 (t11; % of total C18 FA), (C) daily patterns of milk denovo FA yield (g/d), (D)

daily patterns of milk t10 concentration (% of total C18 FA), (E) daily patterns of milk mixed source FA

yields (g/d), (F) daily patterns of milk t11 concentration (% of total C18 FA), (G) daily patterns of milk

preformed FA yields (g/d). 1Amplitude- difference between peak and mean. 2Acrophase-time at peak of the

rhythm. 3P-value of the zero-amplitude test. Data are presented as LSM with SEM bars. The black and white

bars above the x-axis display the light: dark cycle.

58

Figure 3-4. Effect of day- versus night-restricted feeding on daily rhythms of plasma hormones

and metabolites.



Panels show effect of day vs. night-restricted feeding on (A) daily patterns of plasma glucose concentration

(mg/dL), (B) daily patterns of plasma insulin concentration (μIU/mL), (C) daily patterns of plasma NEFA

concentration (μEq/L), (D) daily patterns of plasma urea nitrogen concentration (mg/dL), (E) daily patterns

of milk mixed source FA yields (g/d), (F) daily patterns of milk t11 concentration (% of total C18 FA), (G)

daily patterns of milk preformed FA yields (g/d). Data are presented as LSM with SEM bars. 1Amplitude-

difference between peak and mean. 2Acrophase-time at peak of the rhythm. 3P-value of the zero-amplitude

test. The black and white bars above the x-axis display the light: dark cycle.

59

Figure 3-5. Effect of day- versus night- restricted feeding on the circadian rhythm of body

temperature in dairy cows.



Data presented as 2 h means and SEM bars of body temperature collected every 10 min by a vaginal

temperature data logger. 1Amplitude- difference between peak and mean. 2Acrophase-time at peak of the

rhythm. 3P-value of the zero-amplitude test. The black and white bars above the x-axis display the light:

dark cycle.

60

TABLES

Table 3-1. Effect of day versus night-restricted feeding on total daily DMI, milk yield, and milk

composition.

1Treatments were feed available for 16 h during the day [day-restricted feeding (DRF); feed from 0700 to

2300] or feed available for 16 during the night [night-restricted feeding (NRF); feed from 1900 to 1100.

Treatment1

Item DRF NRF SEM2 P-value

DMI, kg/d 23.7 22.1 1.15 0.18

Milk yield, kg 33.7 33.2 1.34 0.68

Fat

Concentration, % 3.54 3.45 0.16 0.54

Yield, g/d 1125 1061 83.3 0.03

Protein

Concentration, % 3.28 3.23 0.10 0.64

Yield, g/d 1059 1010 59.5 0.43

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SUPPLEMENTAL FIGURES

Supplemental Figure S3-1. Effect of the day versus night-restricted feeding on feeding behavior.



Data are presented as LSM with SEM bars. Panels show effect of day vs. night-restricted feeding on A.

average meal size (kg) and average number of meal bouts/d, B. Meal length and intermeal interval (min),

C. eating time (min/d) and eating rate (kg/min), D. hunger and satiety ratios. Hunger ratio=meal

size/preceding intermeal interval. Satiety ratio=meal size/post-meal interval.

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SUPPLEMENTAL TABLES

Supplemental Table S3-1. Diet and nutrient composition of the experimental diet.

1Contained (% of DM): 6.8% CP, 36.4% NDF, 22.8% ADF.

2Contained (% of DM): 20.3% CP, 47.6% NDF, 41.6% ADF. 3Contained (% of DM): 9.2% CP, 70.9% NDF, 43.4% ADF. 4Contained (%, as-fed basis): 36.8% Calcium carbonate; 29.0% dried corn distillers grains; 24.9% salt;

4.2% magnesium oxide (54% Mg); 2.5% organic phosphorus (15% P); 1% zinc sulfate; 0.5% mineral oil.

Composition (DM-basis): 10.6% CP; 43.2% NDF; 7.0% ADF; 14.4% Ca; 0.75% P; 15.1% Cl; 0.28% K;

2.5% Mg; 0.5% S; 9.8% Na; 23.0 mg/kg Co; 651 mg/kg Cu; 796 mg/kg Fe; 54.0 mg/kg I; 1190 mg/kg Mn;

12.8 mg/kg Se; 3434 mg/kg Zn; 195,290 IU/kg vitamin A (retinyl acetate); 62,500 vitamin D (activated 7-

dehydrocholesterol); 1864 IU/kg vitamin E (dl-α tocopheryl acetate). 5Fed as coated urea (Optigen, Alltech Inc., Lexington, KY; 259% CP, DM basis).

Item Composition

Ingredients, % of DM

Corn silage1 40.3

Alfalfa haylage2 19.3

Canola meal 13.2

Ground corn 12.6

Roasted soybeans 4.6

Molasses 3.8

Grass hay/straw3 3.3

Vitamin/mineral mix4 2.5

Non-protein nitrogen5 0.40

Nutrients, % of DM

NDF 32.5%

ADF 22.2%

CP 16.8%

Ash 6.9%

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Supplemental Table S3-2. Effect of day- versus night-restricted feeding on total daily and daily rhythms of individual milk fatty acids.

g/d1 g/6 h1 Amplitude2 Acrophase3 P-Value4

Name DRF NRF DRF NRF DRF NRF DRF NRF DRF NRF

C4:0 47.1 44.7 11.9 11.7 - 0.94 - 0702 0.13 0.003

C6:0 26.1 24.5 6.63 6.44 0.37b 0.58a 0114a 0741b 0.08 0.002

C8:0 15.1 14.2 3.83 3.73 0.22b 0.39a 0044a 0735b 0.07 0.003

C10:0 36.4 34.2 9.19 8.97 0.64b 1.06a 0002a 0740b 0.01 < 0.001

C10:1 3.10 2.97 0.79 0.73 0.069a 0.046b 0307a 1007b 0.002 0.07

C11:0 1.03 0.92 0.26 0.23 0.041 0.044 2243b 0726a < 0.001 < 0.001

C12:0 42.5 39.7 10.7 10.4 0.78b 1.18a 0019a 0750b 0.007 < 0.001

C13:0 1.59 1.47 0.40 0.36 0.05 0.052 2336b 0747a < 0.001 < 0.001

C13:0 iso 0.17 0.16 0.042 0.038 0.012a 0.0069b 1027b 0628a < 0.001 < 0.001

C13:0 anteiso 0.88 0.83 0.22 0.20 0.021a 0.016b 0246a 1011b < 0.001 0.01

C14:0 129.5 120.7 32.7 31.2 2.22b 2.75a 0104a 0806b 0.01 0.001

C14:0 iso 0.87 0.75 0.23 0.19 0.013 - 0258 - 0.03 0.36

C14:1 10.3a 9.63b 2.61a 2.44b 0.23 0.15 0311a 0959b < 0.001 0.007

C15:0 14.0 13.0 3.52 3.25 0.32b 0.36a 0016a 0757b 0.001 0.001

C15:0 iso 2.27 2.08 0.57 0.54 - - - - 0.18 0.45

C15:0 anteiso 4.99 4.80 1.26 1.22 0.097 - 0322 - 0.006 0.24

C16:0 320.2a 290.4b 80.5 75.4 4.97b 7.08a 0109a 0721b 0.05 < 0.001

C16:0 iso 2.50a 1.97b 0.61a 0.52b - - - - 0.14 0.96

C16:1 13.5 12.7 3.37 3.21 0.29 - 0507 - 0.003 0.63

C17:0 6.47 6.39 1.60 1.61 - - - - 0.23 0.53

C17:0 iso 3.49 3.75 0.89 0.96 0.057 0.061 0324a 2345b 0.04 0.05

C17:0 anteiso 5.01 5.10 1.26 1.29 0.089 - 0423 - 0.03 0.75

C17:1 2.06 2.12 0.50 0.52 0.044a 0.030b 1823b 0731a 0.001 0.04

C18:0 117.3 114.1 29.8 29.9 - - - - 0.61 0.11

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trans-4 C18:1 0.21 0.25 0.048 0.056 0.018a 0.014b 1044a 1246b < 0.001 0.001

trans-5 C18:1 0.10 0.12 0.022 0.029 0.0073 0.0089 1004 0924 0.04 0.005

trans-6,8 C18:1 3.27 3.30 0.82 0.85 0.049 - 0449 - 0.07 0.28

trans-9 C18:1 2.61 2.63 0.65 0.68 - - - - 0.65 0.68

trans-10 C18:1 5.23 5.82 1.30 1.45 0.11a 0.095b 0417a 0937b 0.001 0.02

trans-11 C18:1 8.87 10.3 2.23b 2.64a - 0.15 - 0447 0.93 0.04

trans-12 C18:1 5.15 5.09 1.29 1.30 0.088 - 0403 - 0.04 0.66

trans-15 C18:1 2.70 2.64 0.68 0.68 0.043a 0.028b 0444a 0746b 0.06 0.02

cis-9 C18:1 196.4 191.7 48.5 47.8 4.58 - 0613 - 0.001 0.16

cis-11 C18:1 9.10 8.92 2.27 2.20 0.24 - 0504 - < 0.001 0.24

cis-12 C18:1 4.45 4.65 1.10 1.18 0.081a 0.055b 0402a 1008b 0.02 0.06

C18:2, n-6 29.1 28.4 7.33 7.16 0.70 - 0547 - 0.001 0.50

cis-9, trans-11 C18:2 4.59 4.56 1.28 1.15 0.077 - 0252 - 0.08 0.15

C18:3 n-3 6.09a 5.65b 1.53a 1.40b 0.045 - 0115 - 0.99 0.28

C20:0 1.60 1.49 0.41 0.39 - 0.029 - 0801 0.78 0.005

C20:1 0.43 0.41 0.12 0.11 - - - - 0.11 0.14

C20:2 0.62 0.61 0.15 0.15 - - - - 0.50 0.97

C20:3 1.38a 1.24b 0.35a 0.32b - - - - 0.87 0.50

C20:4 2.05a 1.88b 0.52a 0.48b - - - - 0.77 0.39

C22:0 0.33 0.33 0.074 0.08 - - - - 0.45 0.98 1Least squared means of day-restricted feeding (DRF) with feed available from 0700 h to 2300 h and night-restricted feeding (NRF) with feed available

from 1900 to 1100 h. Means with different superscripts are different at P < 0.05. 2Difference from mean to peak of 24-h rhythm, g. Amplitudes with different superscripts are different at P < 0.05. 3Time at peak of the 24-h rhythm hhmm. Acrophases with different superscripts are different at P < 0.05. 4P-value of the zero-amplitude test corresponding to the fit of a 24-h rhythm. The rhythm fit is significant if P < 0.05 with a tendency for a rhythm at 0.05 <

P < 0.10. Amplitude and acrophase are not presented if the P-value of the zero-amplitude test is greater than 0.10.

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Chapter 4

The effects of night-restricted feeding on the molecular clock of the

mammary gland in lactating dairy cows

Isaac J. Salfer, Paul A. Bartell, and Kevin J. Harvatine

66

ABSTRACT

Circadian rhythms are generated within tissues through a molecular clock made of a set

of transcription factors that oscillate in a 24 h manner. These rhythms can be entrained both by

photoperiod and feeding time. Dairy cows display daily rhythms of milk synthesis that are altered

by feeding time, but the role of molecular clocks in these rhythms is poorly understood. To

determine the impact of feeding on the mammary molecular clock, 11 lactating Holstein cows

were used in a crossover design that tested two feeding regimes, either (1) feed available for the

entire 24 h day (CON) or (2) night-restricted feeding where feed availability was limited to 16 h/d

from 2000 h to 1200 h (NR). All cows were housed in the same 19:5 light:dark cycle. Milk

samples were collected at 0700 and 1900 h on d 11 and 17 of each period and analyzed for fat and

protein concentration. Mammary tissue representing four times across the day (0400, 1000, 1600,

and 2200 h) was collected by needle biopsy. Expression of clock genes including brain and

muscle ARNT-Like 1 (BMAL1), circadian locomoter output cycles kaput (CLOCK),

cryptochrome 1 (CRY1), period (PER1), period 2 (PER2), and REV-ERBα was determined at each

time point using Real-Time RT-PCR. Blood was sampled on d 15 to 17 of each period to

represent every 4 h across the day (0200, 0600, 1000, 1400, 1800, and 2200 h), and analyzed for

concentrations of glucose, nonesterified fatty acids (NEFA), and plasma urea nitrogen (PUN).

Cosinor rhythmometry was performed using the MIXED procedure of SAS 9.4 to determine if

expression of clock genes and the concentrations of plasma metabolites fit a 24 h rhythm and if

the amplitude and acrophase (time at peak) were different between treatments. Night-restricted

feeding tended to reduce dry matter intake (P = 0.06) and decreased milk (P = 0.001), fat (P =

0.02) and protein yield (P < 0.001). Milk protein concentration was also reduced by NR (P =

0.01), but fat concentration was not changed. The expression of CLOCK fit a 24 h rhythm in

CON (P < 0.001) and tended to fit a rhythm in NR (P = 0.06), with NR increasing the amplitude

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75% and delaying the phase by 6.5 h compared to CON (P < 0.05). The expression of CRY1 fit a

24 h rhythm in both treatments and NR increased the amplitude by 53% and delayed the phase by

3 h. A 24 h rhythm of REV-ERBα expression was not present in CON, but a tendency for a

rhythm was induced by NR (P = 0.06). The expression of BMAL1, PER1, and PER2 did not fit a

24 h rhythm in either treatment. Plasma glucose, NEFA, and PUN concentrations fit a 24 h

rhythm (P < 0.005). The amplitudes of the daily rhythms of plasma glucose and PUN

concentration were increased 124% and 62% by NR, respectively (P < 0.05), but the amplitude of

NEFA was not altered. Night-restricted feeding phase delayed plasma glucose concentration by

12 h and PUN concentration by 9.9 h, but did not shift the phase of the daily NEFA rhythm.

Results indicate that key components of the mammary molecular clock are influenced by the

timing of feed intake.

Keywords: molecular clock, food entrainment, circadian rhythm.

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INTRODUCTION

All organisms have adapted a circadian timing system that allows them to anticipate daily

changes in their environment and correspondingly alter their behavior, metabolism, and cell

signaling. This system is arranged in a hierarchical manner with a master clock located in the

suprachiasmatic nucleus (SCN) of the hypothalamus, which is normally entrained by photoperiod

and sends neural and hormonal signals to synchronize biological clocks located within individual

cells of peripheral tissues (Plaut and Casey, 2012). Circadian oscillations in both the master clock

and peripheral cells are driven by a core set of clock transcription factors that maintain rhythms of

about 24 h by transcriptional-translational feedback loops (Panda, 2016). These transcription

factors include positive transcriptional regulators brain and muscle ARNT-like 1 (BMAL1) and

circadian locomotor output cycles kaput (CLOCK), which heterodimerize and bind to E-box

elements [canonically CANNTG] to activate transcription, and the negative transcriptional

regulators period (PER) and cryptochrome (CRY), which feedback to inhibit the expression of

BMAL1 and CLOCK. Additionally, an ancillary loop consisting of the orphan nuclear receptors

REV-ERBα and RAR-related orphan receptor-α (RORα) stabilize the transcriptional-translational

feedback loop by repressing or activating BMAL1 and CLOCK (Takahashi, 2017). Together,

these circadian transcription factors and nuclear receptors regulate the expression of a wide host

of clock-controlled genes, which are involved in diverse functions including growth, metabolism,

and immunity.

In addition to receiving signals from the master clock in the SCN, various other local and

systemic signals influence the oscillations of peripheral clocks. One of the most prominent of

these signals is the timing of feed intake. In other animals, the daily rhythms of locomotor activity

and blood glucocorticoid concentrations are entrained by restricting the time of feed availability

to the inactive phase of the day and these rhythms can persist several days after complete food

69

removal, even with ablation of the SCN (Stephan, 2002). Stokkan et al. (2001) established that

time-restricted feeding shifts the phase of the circadian clock of the liver, without a concomitant

effect in the SCN. This desynchronization of the rhythms of master and peripheral clocks has

been consistently observed during time-restricted feeding experiments in model organisms (Asher

and Sassone-Corsi, 2015).

Circadian rhythms appear to be important for metabolism within the mammary gland. In

several species, including humans, rodents and cattle, milk synthesis follows a daily pattern with

greater milk yield in the morning, and higher milk fat and protein concentration in the evening

(Plaut and Casey, 2012). Maningat et al. (2009) observed that 7% of the mammary epithelial

transcriptome follows circadian rhythms in humans. The circadian rhythm of the mammary gland

appears to be related to stage of lactation. Casey et al. (2014) observed an increase in BMAL1

and CLOCK protein abundance as well as a decrease in PER2 in differentiated cells compared to

undifferentiated cells. This is substantiated by evidence that short photoperiod during the dry

period increases mammary epithelial cell proliferation and increases milk synthesis in the

subsequent lactation (Wall et al., 2005). Furthermore, rate-limiting enzymes for fat, protein, and

lactose synthesis follow circadian rhythms in the mammary gland (Suárez-Trujillo and Casey,

2016).

Recent evidence indicates that the daily pattern of milk synthesis is responsive to the

timing of feed intake. Rottman et al. (2014) determined that feeding four times per day in equal

meals altered the daily pattern of milk synthesis relative to feeding once per day. Furthermore, we

previously established that time-restricted feeding shifts the phase of daily rhythms of milk

synthesis and in dairy cows (Chapter 3). However, it is unknown if these shifts are due to

entrainment of the molecular clock of the mammary gland by feeding or by the timing of nutrient

absorption. In mice, night-restricted feeding altered the expression of circadian transcription

factors. Ma et al. (2013) observed that time-restricted feeding altered clock gene expression and

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shifted the rhythms of expression of enzymes and transcription factors related to milk fat

synthesis in lactating mice. The objectives of this experiment were to investigate the effect of

time-restricted feeding on the molecular circadian clock of the mammary gland. The hypothesis

was that restricting the time of feeding to 16 h during the night, which is typically the lowest

period of feed intake, will cause a phase shift in the daily patterns of clock genes in mammary

tissue, demonstrating a change in the molecular clock of the mammary gland.

MATERIALS AND METHODS


Eleven multiparous mid-lactation Holstein cows (190 ± 31 d postpartum; mean ± SD)

from the Pennsylvania State University Dairy Research and Teaching Center were randomly

assigned to one of two treatments sequences in a cross-over design. Treatment periods were 22 d

long with 10 d of diet adaptation followed by 12 d of sampling. Treatments included (1) control

(CON) with feed delivered at 0800 h and available 24 h/d and (2) night-restricted feeding (NR)

with feed available for 16 h/d from 2000 h to 1200 h (Figure 4-1). All cows were fed the same

total mixed ration (TMR) that was mixed once daily at 0800 h and fed immediately to the CON

group (Supplemental Table S4-1). The remaining feed was compressed into plastic barrels,

covered with plastic, and stored at ambient temperature until NF feeding at 2000 h. Feed intake

was recorded daily and feed was offered at 110% of the previous day’s intake.

Feed samples were collected on d 7, 14, and 21 of each period and composited by period

prior to analysis. Dry matter, ash, starch, and CP were analyzed according to the AOAC (2000)

Official Methods of Analysis, and NDF and ADF were analyzed according to Van Soest et al.

(1991). Dry matter intake was determined on d 15 to 18 of each period. Animals were housed in

individual tie-stalls with sawdust bedded rubber mattresses and had ad libitum access to water.

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Cows were maintained in a 19 h light: 5 h dark photoperiod with lights on from 0500 to 0000 h.

The experiment was conducted from January to March of 2017. All experimental procedures

were approved by the Penn State University Institutional Care and Use Committee.


Cows were milked 2x/d at 0700 and 1900 h and milk yield was measured at each milking

using an integrated meter (AfiMilk MPC Milk Meter; Afimilk Agricultural Cooperative Ltd.,

Kibbutz Afikim, Israel). Milk yields were corrected for stall deviations according to Andreen et

al. (2018). Milk was sampled on d 10 and 17 of each period. One subsample was stored at 4°C in

preservative (Bronolab-WII; Advanced Instruments, Inc., Noorwood, MA) prior to analysis of fat,

protein, lactose, and MUN by Fourier transform infrared spectroscopy (Fossomatic 4000 Milko-

Scan and 400 Fossomatic, Foss Electric; Dairy One Laboratory, Ithaca, NY). A second subsample

was immediately centrifuged at 2300 x g at 4°C for 15 min and fat cakes were stored at -20°C for

analysis of fatty acid (FA) composition. Milk FA were extracted using hexane:isopropanol,

transmethylated using sodium methoxide, and FA methyl esters were quantified by GLC with a

flame-ionization detector according to Rico and Harvatine (2013) as modified by Baldin et al.

(2018).

Mammary Tissue Collection

Mammary biopsies were collected to represent every 6 h across the day (0400, 1000,

1600, and 2200 h). On d 11 to 14, one of the rear quarters was randomly chosen for a biopsy

collection at 2200 h, followed by a second biopsy on the opposite quarter at 1000 h the following

day. Following 7 d of recovery, a third and fourth biopsy was similarly collected from each rear

quarter on d 19 to 22 at 1600 and 0400 h. Cows were sedated using intravenous xylazine (0.02

mg/100 kg of BW) and local anesthesia was applied by subdermal block using 10 mL of 2%

72

lidocaine HCL in a line above the incision site. The biopsy site was disinfected with providone-

iodine scrub (Betadine, Avrio Health L.P., Stamford, CT) and ethanol rinse and a surgical blade

was used to make a 0.2 to 0.5 cm incision near the midpoint of the rear quarter. Tissue was

collected from a depth of 8 to 12 cm using a vacuum-assisted electronic biopsy gun system with a

10 gauge needle (Vacora; Bard Biopsy Systems, Tempe, AZ). Tissue samples were rinsed with

0.9% saline solution, snap frozen in liquid nitrogen, and stored at −80°C until RNA extraction

(~80 mg tissue/biopsy). After biopsy collection, manual pressure was placed on the biopsy site

for 5 to 10 min until bleeding ceased. Minimal bleeding occurred and milk appeared normal

within 3 to 5 milkings after the biopsy. No apparent intramammary infections or production

declines occurred as a result of the surgery.


Blood was collected using potassium EDTA vacuum tubes (Greiner Bio-One North

America, Inc., Monroe, NC) by venipuncture of a coccygeal vessel on d 15 to 17 of each period

to represent every 4 h across the day (0200, 0600, 1000, 1400, 1800, and 2200 h). Blood was

immediately placed on ice and centrifuged at 2300 x g for 15 min at 4° C within 30 min of

collection. Plasma was collected and stored at -20°C for analysis of glucose, plasma urea nitrogen

(PUN), and non-esterified FA (NEFA) according to Rottman et al. (2014). Briefly, Plasma

glucose concentration was analyzed using a glucose oxidase/peroxidase enzymatic colorimetric

assay (No. P 7119, Sigma-Aldrich, St. Louis, MO), PUN was assayed using a modified Berthelot


Boerne, TX), and NEFA concentration was measured using a acyl-CoA oxidase/peroxidase

enzymatic colorimetric assay [NEFA-HR (2), Wako Diagnostics, Richmond, VA].

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Gene Expression Analysis

Approximately 40 mg of mammary tissue was homogenized in 1.5 mL of RNA-Solv

(Omega Bio-Tek, Norcross, GA) and total RNA was isolated from approximately 40 mg of

mammary tissue using the E.Z.N.A. Total RNA Kit II with on-column DNase treatment (Omega

Bio-Tek). RNA concentration and quality were determined using a spectrophotometer (NanoDrop

1000, Thermo Scientific, Wilmington, DE). Total RNA was reverse transcribed with the High

Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Inc., Foster City, CA) using

random primers. Relative gene expression was performed using quantitative PCR (qPCR) using

an Applied Biosystems 7900HT Fast Real-Time PCR System (Applied Biosystems, Inc.) with

400 nM of gene-specific forward and reverse primers and SYBR Green reporter dye (PerfeCTa

SuperMix with ROX, Quanta Biosciences, Gaithersburg, MD). Primers were designed to span

exon boundaries and using Primer-BLAST (National Center for Biotechnology Information,

Bethesda, MD) to amplify a product length between 70 and 150 bp or were from previous

publications as indicated (Table 4-1). Reactions were performed in triplicate and transcript

abundance was determined relative to a dilution curve of pooled cDNA and normalized using the

geometric mean of reference genes [ribosomal protein 9 (RPS9), beta-2 microglobulin (B2M), and

beta-actin (ACTB)], which was included as a covariate in the statistical model. The presence of a

single product was verified by dissociation curve analysis.


All statistical analysis was performed using the MIXED procedure of SAS 9.4 (SAS

Institute, Cary, NC). Daily DMI, milk, fat, and protein yield, fat and protein concentration, and

FA yields were analyzed as a mixed effects model with the fixed effects of treatment and period

and the random effects of cow and day nested within period. The autoregressive covariance

74

structure was used and denominator degrees of freedom were adjusted using the Kenward-Roger

method.

Cosinor rhythmometry was performed on time course gene expression, plasma

metabolites, and body temperature data. Briefly, data were fit to the linear form a cosine function

by random regression in SAS 9.4 with a model that tested the fixed effects of treatment, linear

form of the cosine function, and the interaction of treatment with cosine function, and the random

effects of cow and period. The fit of the 24 h cosine was tested using a zero-amplitude test, and

the amplitude and acrophase (time at peak of rhythm) were calculated and significance

determined according to Niu et al. (2014). The presence of a 12 h harmonic was tested for plasma

hormone and metabolite data and was included for glucose and NEFA because it improved model

fit. Secondly, a model testing the fixed effects of treatment, time, and their interaction, and the

random effects of cow with preplanned contrasts testing the interaction of treatment and time was

performed on time course data. In all analyses, points with Studentized residuals ± 3.5 were

removed as outliers. Significance was declared at P < 0.05 with trends recognized at P < 0.10.

High resolution figures were generated using an add-in for Microsoft Excel (Kraus, 2014).

RESULTS AND DISCUSSION

Dry Matter Intake

Night-restricted feeding tended to reduced DMI with NR cows consuming 1.7 kg (7%)

less feed per day than CON (P = 0.06; Figure 4-2A). The decrease in intake is likely because NR

cows were fasted during the high intake period of the day. Dairy cow naturally prefer to eat the

greatest amount of feed during the morning and afternoon, with limited feed intake overnight

(Albright, 1993). Previous research revealed that night-restricted feeding numerically decreased

DMI by 1.6 kg relative to restricting feed availability to 16/d during the day (0900 to 1100), but

75

this change was not significant (Chapter 3). Niu et al. (2014) also observed a 1.2 kg (4%)

decrease in DMI by delivering feed at night (2030) versus the morning (0830) without restricting

the time of feed availability. Taken together, fasting during the afternoon when intake is typically

high reduces the total amount of feed consumed per day.

Milk Yield, Milk Composition, and Milk Fatty Acid Yields

Night-restricted feeding reduced milk yield by 8% (P = 0.001; Figure 4-2B).

Furthermore, NR decreased milk fat and protein yield by 11% (P = 0.02) and 10% (P < 0.001),

respectively (Figure 4-2D). Milk fat concentration was not affected by treatment (P = 0.91),

while milk protein concentration was decreased 2% by NR (P = 0.01; Figure 4-2C). We

previously failed to detect a difference in milk yield and milk fat and protein concentration

between cows undergoing time-restricted feeding for 16 h/d either during the day or the night,

although night-restricted feeding decreased milk fat yield 64 g (6%) compared to day-restriction

(Chapter 3). Rottman et al. (2014) also observed that feeding 4 x/d in equal meals increased the

yields and concentrations of both fat and protein compared to feeding 1x/d.

The reduction in milk synthesis in the current experiment may be related to modification

of circadian clock of the mammary gland. In rodent models, restricting the time of feeding to the

inactive phase without altering feed intake reduces the accretion of adipose tissue (Hatori et al.,

2012; Sherman et al., 2012). This change has been associated with increased robustness of daily

rhythms of adipocyte clock genes. Moreover, time-restricted feeding reduces adiposity in humans

(Manoogian and Panda, 2017). A similar mechanism may occur in cows, whereby alterations in

the circadian clock reduce the uptake and/or synthesis of nutrients in the mammary gland. Hu et

al. (2017) observed that small interfering RNA (siRNA)-mediated knockdown of PER2 mRNA

expression was associated with a reduction in the synthesis of αs1- and αs2-casein in mammary

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epithelial cell culture establishing a relationship between the mammary circadian clock and

protein synthesis.

The decrease in milk and milk fat and protein yield in the current experiment is possibly

confounded by the reduction in feed intake. While milk yield drives intake, unless another

limiting factor (rumen fill, energy excess) is present, fasting during the high-intake afternoon of

the day may have prevented cows from eating to meet their requirements for milk synthesis

(Allen, 2014). Future experiments examining the role of time of feed intake on the mammary

circadian clock may consider pair-feeding cows to eliminate this effect.

Night-restricted feeding did not alter the yield of FA originating from de novo synthesis

(Σ < 16C; P = 0.50), preformed FA originating from plasma (Σ >16C; P = 0.41), or mixed-source

FA (Σ 16C; P =0.37; Figure 4-3). These results differ from previous research, which observed a

5% reduction in de novo synthesized fatty acids and an 8% decrease in mixed-source fatty acids

in cows restricted to feeding for 16 h/d the night compared to the day (Chapter 3). However,

similar to the current paper, altering the timing of feed delivery without restricting the time of

feed availability did not affect FA profile (Niu et al., 2014). In the current experiment, individual

FA, iso-C14:1, iso-C15:1, iso-C16:1, C22:0 and C20:3-n6 were decreased by NR (P < 0.05),

C24:0 tended to be increased by NR feeding (P = 0.09), while NR tended to decrease both trans-

10 C18:1 and trans-11 C18:1 (0.05 < P < 0.10; Supplemental Table S4-2).

Molecular Clock Gene Expression

The expression of CRY1 fit a daily rhythm in both treatments (P < 0.05), CLOCK fit a

rhythm in CON (P < 0.001) and tended to fit a rhythm in NR (P = 0.06), and REV-ERBα failed

to fit a rhythm in CON (P = 0.62), but tended to fit a rhythm in NR (P = 0.06; Figure 4-4).

However, BMAL1, PER1, and PER2 did not fit daily rhythm in either treatment (P > 0.10). Night-

restricted feeding appeared to have a slight effect on the pattern of PER1 expression, with both

77

treatments having similar baseline expression with a noticeable rise of about 30% at 0400 in CON

that was shifted to 1600 in NR. However, this change was not significant. The daily rhythms of

CLOCK and CRY1 were affected by treatment. Night-restricted feeding increased the amplitude

of CLOCK by 75%, and phase-delayed the rhythm ~4.5 h. Night-restricted feeding did not alter

the amplitude of CRY1, but phase-delayed the rhythm by 3 h.

Time-restricted feeding can entrain circadian rhythms of other peripheral tissues in

rodent models. Damiola et al. (2000) detected a 12 h phase shift in the liver protein abundance of

PER1 and PER2 in mice that had feed availability restricted for 12 h/d during the light phase

(0600 to 1800) or the night phase (1800 to 0600), without a concomitant change in SCN clock

gene expression. These results were corroborated by Stokkan et al. (2001) in rats feed-restricted

to only 4 h during the inactive period. In addition to shifting the phase of the liver molecular

clock, they observed that the molecular clock in the lung was shifted by time-restricted feeding,

but the clock of the SCN was unaltered. The daily rhythms of mRNA abundance of Bmal1, Per1,

Per2, Cry2, and Rev-erbα in adipocytes of male C57BL/6 mice were phase shifted by restricting

the intake of a high-fat diet to 4 h/d during the inactive period of the day.

There has been limited research describing a role of the circadian clock in the mammary

gland. In human mammary epithelial cells, 7% of genes follow a 24 h rhythm (Maningat et al.,

2009). Moreover, Casey et al., (2009) suggested a potential role of the mammary clock in

modulating the homorhetic response to lactation by discovering an increases in expression and

phase shifts in rhythms of positive transcriptional regulators BMAL1, CLOCK, and RORα in

lactating compared to nonlactating mammary tissue. In mice, the amplitudes of BMAL1 and

CRY1 are increased after the start of lactation (Metz et al., 2006). Rhythms of mammary clock

gene expression can be entrained by altering the light-dark cycle (Plaut and Casey, 2012).

Furthermore, timing of nursing can entrain the mammary rhythms of PER1 in rabbits. The current

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experiment demonstrates that, in addition to these cues, time-restricted feeding can modestly

entrain the molecular clock of mammary gland of dairy cows.

Plasma Hormones and Metabolites

There was a treatment by time interaction for plasma glucose (P = 0.006) but no main

effect of treatment was present (P = 0.94; Figure 4-5A). The fit of the 24 h rhythm was improved

in both treatments by including a 12 h harmonic (P < 0.001). A 24 h rhythm with a 12 h harmonic

was present in both CON (P = 0.004) and NR (P < 0.0001). The acrophase of the 24 h rhythm

peaked at 0137 in CON, but the rhythm with phase delayed 12 h by NR, which peaked at 1337 (P

< 0.05). Furthermore, night-restricted feeding increased the amplitude of plasma glucose

concentration by 124% from 6.5 mg/dL in CON to 14.6 mg/dL in NR (P < 0.05).

Previous research has demonstrated that time of feeding alters the daily rhythms of plasma

glucose concentration. We previously detected a phase delay of 10.2 h in plasma glucose

concentrations in cows with feed available for 16 h/d from 1900 to 1100 compared to those with

feed available for 16 h/d from 0700 to 2300 (Chapter 3). Similar to the current experiment, they

observed a 149% increase the amplitude of the plasma glucose rhythms during night-restricted

feeding. Moreover, Niu et al. (2014) detected that the daily pattern of plasma glucose

concentration was altered by changing the time of feed delivery from 0830 to 2030 h without

restricting the time of feed availability.

Unlike previous experiments, we observed a 12 h harmonic in the daily pattern of plasma

glucose, suggesting that glucose in our experiment followed a 12 h ultradian rhythm in addition to

the daily rhythm. In both treatments, the harmonic approximately corresponded to the acrophase

of the opposite treatment. In humans subjected to continuous feeding, glucose concentration has

been reported to follow ultradian rhythms of various period lengths, including 12 h (Simon et al.,

1987; Shea et al., 2005). To our knowledge, however, there have been no reports of a 12 h

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harmonic in cattle. Lefcourt et al. (1993) observed that dairy cows express 3 h ultradian rhythms

of plasma cortisol concentration. Cortisol increases plasma glucose concentrations by increasing

gluconeogenesis and decreasing tissue glucose uptake, and may be partially responsible for the

ultradian rhythm of glucose concentration observed in the current experiment.

Night-restricted feeding did not affect average NEFA concentration (P = 0.74; Figure

4-5B). A single 24 h rhythm did not fit either treatment, but similar to glucose concentration, both

treatments fit a 24 h rhythm that included a 12 h harmonic (P < 0.01). The amplitude of the

primary 24 h rhythm was not altered by treatment (P > 0.05). Furthermore, the acrophase of the

main rhythm was not affected, with CON peaking at 1125 h and NR peaking at 1237 h (P >

0.05). These results suggest that night-restricted feeding did not alter the daily pattern of lipid

mobilization, despite fasting during the typically high-intake afternoon period of the day. Results

contrast with previous research from our lab which demonstrated that observed that night-

restricted feeding from 1900 to 1100 h shifted the rhythms of NEFA concentration by 10.7 h

relative to day-restricted feeding from 0700 to 2300 h (Chapter 3). Furthermore, we detected over

a 3-fold increase in the amplitude of the rhythm due to night-restricted feeding. The differences in

the current study and previous study may be related to the daily pattern of intake. The previous

experiment imposed day-restricted feeding which likely caused more extreme differences in the

pattern of feed intake. In the current experiment, maximum NEFA concentration in both

treatments corresponded to the period when the NR cows were fasted, with the harmonic

occurring at night, during which cows typically have reduced intake. Social interactions have

previously shown to have a strong influence on feeding behavior of dairy cattle (Grant and

Albright, 2001). Cows on CON may modified their daily pattern of feed intake to correspond to

the NR treatment. Moreover, cows on the current experiment may have been in more positive

energy balance, resulting in lower lipid mobilization in response to fasting. The results of our

experiment correspond more closely with experiments where the time of feed delivery was

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altered without restricting the time of feed availability. Nikkhah et al. (2008) observed little

difference in the daily pattern of NEFA concentration when cows were fed at 0900 h versus 2100.

Furthermore, Niu et al. (2014) detected no difference in the daily pattern of NEFA among cows

fed 1x/d at 0830, 1x/d at 2030, or 2x/d at both 0830 and 2030.

Plasma urea nitrogen concentration was not affected by treatment (P = 0.25; Figure

4-5C). A 24 h rhythm was present in both treatments (P < 0.01), with treatment affecting the

daily rhythm. Night-restricted feeding increased the amplitude of the daily rhythm 62% compared

to CON (P < 0.05) and delayed the phase 9.9 h from 1247 to 2241h. A 24 h rhythm of PUN has

been well-established in previous experiments. Our results agreed with a previous experiment in

our lab which demonstrated a 24 h rhythm in cows under night-restricted feeding that was phase

delayed 12.2 h relative to day-restricted feeding for 16 h/d (Chapter 3). Furthermore, Nikkhah et

al. (2008) observed an approximately 12 h delay in the peak of PUN in cows fed at 2100 versus

0900, but only in cows fed low fiber (38:62 forage: concentrate; 28.6 NDF), but not high fiber

diets (51:49 forage: concentrate; 33.8% NDF), suggesting that the effect may be diet dependent.

Results suggest that night-restricted feeding shifts the daily rhythms of amino acid utilization

efficiency.

CONCLUSIONS

Restricting feed availability to 16 h during the night increased the amplitude and phase-

shifted the daily rhythms of expression of several molecular clock genes in mammary tissue,

suggesting that nutrients can entrain the mammary molecular clock. These shifts may be partially

mediated by changes in plasma metabolites, because night-restricted feeding shifted the daily

patterns of plasma glucose and plasma urea nitrogen. Results may be partially influenced by a

reduction in total dry matter intake by night-restricted feeding, which was likely responsible for

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the decrease in milk, fat and protein yield observed in this treatment. Future studies examining

the role of the time of feeding on the molecular clock of the mammary gland may consider pair-

feeding cows to ensure intake is consistent among treatments. Additionally, this study was limited

to examining clock genes at the mRNA level. Additional post-transcriptional and post-

translational modifications of clock-related proteins also affect the manifestation of circadian

rhythms in tissues. Further research examining the role of food intake on the protein expression

and protein phosphorylation will provide a more complete depiction of the role of nutrients on the

mammary clock. However, this study provides initial compelling evidence to suggest that food

intake can entrain the molecular clock of the mammary gland.

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FIGURES

Figure 4-1. Schedule of feeding, lighting and sampling during the experiment.

Treatments were feed available continuously for 22 h/d starting at 0800 h (CON) or night restricted feeding

with feed available for 16 h/d from 2000 to 1200 h (NR). Panels show (A) the times of the day when feed

was available, lights were on/off, mammary biopsies were collected, and blood was collected and (B) the

days of each period when milk was sampled, biopsies were collected, and blood was collected.

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Figure 4-2. Effect of night-restricted feeding on average daily feed intake and milk synthesis.


with feed available for 16 h/d from 2000 to 1200 h (NR). Panels show the effect of night-restricted feeding

on (A) dry matter intake (kg/d), (B) milk yield (kg/d), (C) milk fat and protein concentration (%), and (D)

milk fat and protein yield (g/d).

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Figure 4-3. Effect of night-restricted feeding on milk fatty acid yields by origin.


with feed available for 16 h/d from 2000 to 1200 h (NR). Fatty acids grouped by biosynthetic origin

including FA derived from de novo synthesis in the mammary gland (De novo; Σ < 16C), mixed source

from de novo synthesis and preformed uptake (Mixed; Σ 16C), and preformed FA taken up by the

mammary gland from plasma (Preformed; Σ >16C).

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Figure 4-4. Effect of night-restricted feeding on the daily patterns of expression of molecular

clock genes in the mammary gland.


with feed available for 16 h/d from 2000 to 1200 h (NR). Panels show mammary mRNA expression of (A)

brain and muscle ARNT-like 1 (BMAL1) (B) circadian locomotor output cycles kaput (CLOCK), (C) period

1 (PER1), (D) period 2 (PER2), (E) cryptochrome 1 (CRY1), and (F) REV-ERBα across the day. LSM and

SEM bars and cosine fit of the control (solid line) and NR (dotted line) are shown. Cosinor analysis is

shown below each panel and include the amplitude (Amp; difference between peak and mean), acrophase

(Acro; time at peak of the rhythm), and the P-value of the zero-amplitude test. The black and white bars

above the x axis display the light: dark cycle.

86

Figure 4-5. Effect of night-restricted feeding on the daily patterns of plasma metabolites.


with feed available for 16 h/d from 2000 to 1200 h (NR). Panels show the effect of night-restricted feeding

on daily patterns of (A) glucose concentration (mg/dL), (B) nonesterified fatty acids (NEFA; μEq/L) and

(C) plasma urea nitrogen (mg/dL). Data are presented as relative expression LSM and SEM bars and cosine

fit of the control (solid line) and NR (dotted line) are shown. Cosinor analysis is shown below each panel

and include the amplitude (Amp; difference between peak and mean), acrophase (Acro; time at peak of the

rhythm), and the P-value of the zero-amplitude test. NS: Reduced model fits data significantly better than

daily rhythm model. The black and white bars above the x-axis display the light: dark cycle.

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TABLES

Table 4-1. Bovine primers used to quantify mammary gene expression with RT-qPCR.

1Gene names: ACTB = Beta-actin; B2M = Beta-2-microglobulin; BMAL1 = Brain and muscle ARNT-like protein-1; CLOCK = Circadian locomotor

output cycles kaput; CRY1 = Cryptochrome 1; PER1 = Period 1; PER2 = Period 2; RPS9 = Ribosomal protein S9. 2Encoded by the nuclear receptor subfamily 1 group d member 1 (NR1D1) gene.

Gene1

Accession

no. Forward Primer Reverse Primer

Amplicon

Size (nt) Reference

Genes of Interest

BMAL1 NC_037342.1 CAGCTCTCCTCCTCCGACAC TGGCCCAGGGGTTATATCTG 90

CLOCK NC_037333.1 TCAGTCTCAAGGAAGCGTTG GGAGTGCTAGTATCTGTTGGG 142

CRY1 NC_037332.1 TGCAAGTTGTACTCAAGGGAG CAATACTCTGCGTGTCCTCTTC 149

PER1 NC_037346.1 CCAGGAGTTCTACCAGCAATG GAGACAGCCACGGAGAAG 141

PER2 NC_037330.1 ACGTTTTCCAGTCTCGCTG CGTTTGGACTTCAGTTTTCGG 148

REV-ERBα2 NC_037346.1 TGGTGTTGCTGTGTAAGGTG CCTTTTGTACTGGATGTTCTGC 122

Reference Genes

ACTB NC_037352.1 GCGTGGCTACAGCTTCACC CTTGATGTCACGGACGATTTC 55 (Kadegowda et al., 2009)

B2M NC_037337.1 TTTGGAACAACCCCGGATAG AAACCTCGATGGTGCTGCTT 61 (Urrutia and Harvatine, 2017)

RPS9 NC_037345.1 CCTCGACCAAGAGCTGAAG CCTCCAGACCTCACGTTTGTTC 64 (Urrutia and Harvatine, 2017)

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SUPPLEMENTAL TABLES

Supplemental Table S4-1. Diet and nutrient composition of experimental diet.

1Contained (% of DM): 33.0% NDF, 16.2% ADF, 42.3% Starch. 2Contained (% of DM): 42.5% NDF, 31.3% ADF, 0.7% Starch. 3Contained (% of DM): 73.2% NDF, 40.8% ADF, 1.2% Starch. 4Contained (%, as-fed basis): 37.0% Calcium carbonate; 29.9% dried corn distillers grains; 24.5% salt;

4.2% magnesium oxide (54% Mg); 2.4% organic phosphorus (15% P); 0.5% zinc sulfate; 0.2% mineral oil.

Composition (DM-basis): 7.2% CP; 7.1% NDF; 3.7% ADF; 15.0% Ca; 0.75% P; 0.33% K; 2.6% Mg; 0.5%

S; 9.8% Na; 23.0 mg/kg Co; 652 mg/kg Cu; 783 mg/kg Fe; 54.0 mg/kg I; 1190 mg/kg Mn; 12.8 mg/kg Se;

1718 mg/kg Zn; 88,700 IU/kg vitamin A (retinyl acetate); 28,400 vitamin D (activated 7-


Item Composition


Corn silage1 43.6


Canola meal 12.4

Ground corn 10.9




Molasses 2.0

Controlled-release N5 0.26

Nutrients, % of DM

NDF 30.6

ADF 17.9

Starch 27.8

Ash 8.6

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Supplemental Table S4-2. Effect of night-restricted feeding on daily yields of individual milk

fatty acids.

Treatment

FA, g/d1 CON NR SEM P-value

C4:0 56.53 56.75 5.12 0.97

C6:0 36.45 33.95 2.93 0.52

C8:0 18.10 17.67 1.70 0.80

C10:0 44.14 42.65 3.76 0.70

cis-9 C10:1 3.89 3.58 0.30 0.32

C11:0 0.90 0.84 0.10 0.54

C12:0 52.99 50.40 3.57 0.48

C13:0 0.81 0.60 0.22 0.37

C13:0 iso 0.31 0.26 0.04 0.22

C13:0 anteiso 0.59 0.38 0.16 0.36

C14:0 161.87 153.98 6.10 0.21

C14:0 iso 1.55 1.03 0.16 0.004

cis-9 C14:1 12.73 12.07 0.83 0.43

C15:0 14.95 14.04 0.65 0.18

C15:0 iso 2.73 2.51 0.10 0.04

C15:0 anteiso 4.96 4.76 0.78 0.81

C16:0 375.38 362.77 13.68 0.37

C16:0 iso 3.51 2.68 0.21 0.0006

cis-9 C16:1 16.83 16.92 0.90 0.92

C17:0 6.73 6.59 0.25 0.58

C17:0 iso 2.62 2.59 0.58 0.96

C17:0 anteiso 5.64 6.01 0.23 0.12

cis-9 C17:1 2.19 2.40 0.16 0.22

OCFA2 42.22 41.15 1.93 0.59

BCFA3 21.82 19.98 1.86 0.33

OBCFA4 47.45 45.04 2.06 0.26

C18:0 139.31 127.95 7.04 0.12

trans-4 C18:1 0.35 0.37 0.05 0.60

trans-5 C18:1 0.23 0.23 0.04 0.92

trans-6,8 C18:1 4.36 4.46 0.18 0.57

trans-9 C18:1 3.54 3.55 0.14 0.92

trans-10 C18:1 5.81 6.61 0.46 0.09

trans-11 C18:1 12.33 13.56 0.71 0.10

t10:t11 C18:15 0.52 0.52 0.02 0.76

trans-12 C18:1 7.11 7.16 0.31 0.87

trans-15 C18:1 4.78 5.19 0.39 0.30

cis-9 C18:1 258.94 251.40 9.77 0.45

cis-11 C18:1 9.90 10.01 0.74 0.88

90

cis-12 C18:1 6.15 6.59 0.38 0.26

Total C18:1 315.12 310.14 12.05 0.68

C18:2 n-6 35.29 35.70 2.34 0.86

cis-9, trans-11 C18:2 6.53 7.01 0.48 0.33

C18:3 n-6 0.27 0.19 0.06 0.21

C18:3 n-3 4.02 3.52 1.21 0.78

Total C18 FA 505.08 489.19 17.84 0.38

C20:0 1.65 1.50 0.09 0.12

cis-11 C20:1 5.30 4.89 1.83 0.99

C20:3 n-6 1.80 1.63 0.07 0.02

C20:4 n-6 1.96 1.91 0.13 0.74

C20:5 n-6 0.43 0.36 0.04 0.14

C22:0 0.69 0.57 0.05 0.02

C22:4 n-3 0.33 0.25 0.07 0.27

C24:0 0.43 0.32 0.06 0.09 1Least squared means of control (CON) with feed available continuously for 22 h/d fed at 0800, and night-

restricted feeding (NR) with feed available 16 h/d from 1900 to 1100 h (n = 11 per treatment. 2Total odd-chain fatty acids (Σ 11:0, 13:0, 13:0 iso, 13:0 anteiso, 15:0, 15:0 iso, 15:0 anteiso, 17:0, 17:0

iso, 17:0 anteiso, cis-9 17:1). 3Total branched-chain fatty acids (Σ 13:0 iso, 13:0 anteiso, 14:0 iso, 15:0 iso, 15:0 anteiso, 16:0 iso, 17:0

iso, 17:0 anteiso). 4Total odd- and branched-chain fatty acids (Σ 11:0, 13:0, 13:0 iso, 13:0 anteiso, 14:0 iso, 15:0, 15:0 iso,

15:0 anteiso, 16:0 iso, 17:0, 17:0 iso, 17:0 anteiso, cis-9 17:1). 5Ratio of trans-10 18:1 to trans-11 18:1.

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Chapter 5

The effects of time of fatty acid infusion on the daily rhythms of milk

synthesis and plasma hormones and metabolites in dairy cows

Isaac J. Salfer, P. A Bartell, and Kevin J. Harvatine

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ABSTRACT

Dairy cows display daily rhythms of milk synthesis that appear to be driven by the

circadian clock of the mammary gland and are altered by the time of feed availability. Fatty acids

have been shown to entrain circadian rhythms in other peripheral tissues such as the liver and

adipose tissue in experimental models, but their role in the mammary gland has not been well-

investigated. The objective was to determine the effects of the timing of fatty acid absorption on

the daily rhythms of milk synthesis. Nine lactating Holstein cows were arranged in a 3 x 3 Latin

square design. Treatments were abomasal infusions of 350 g/d of an oil enriched in C18:1 either

continuously throughout the day (CON) or over 8 h/d from 0900 to 1700 h (DAY) or from 2100

to 0500 (NGT). Treatment periods were 12 d with a 5 d washout. Cows were milked every 6 h

during the final 7 d of each period to determine the daily patterns of milk synthesis. A 24 h

rhythm was fit to time-course data using cosine analysis and the amplitude and acrophase (time at

peak) were determined. Daily milk, fat, and protein yield and fat and protein concentration were

not affected by treatment. Milk yield fit a 24 h rhythm in CON and DAY, and tended to fit a

rhythm in NGT. The amplitude of the rhythm of milk yield was increased 29% in DAY compared

to CON (P < 0.05), but NGT was not affected. Furthermore, DAY delayed the acrophase of the

daily rhythm of milk yield by 2 h compared to CON and NGT (P < 0.05). Fat and protein

concentration exhibited a daily rhythms in CON and NGT (P < 0.05), but not DAY. The

amplitude of the rhythm of fat concentration was increased 29% by NGT compared to CON. Fat

yield tended to fit a 24 h rhythm in DAY (P = 0.07), but not in CON or NGT. Moreover, protein

yield fit a 24 h rhythm in all treatments and the amplitude of the rhythm was increased 30% by

DAY and decreased 47% by NGT. Both de novo and mixed source FA yield was reduced by

DAY and NGT, suggesting that restricting the time of FA infusion may reduce de novo fatty acid

synthesis. Glucose failed to fit a daily rhythm in any treatment, while nonesterified fatty acids fit

93

a rhythm in CON and NGT, but the rhythm was eliminated by DAY. Plasma urea nitrogen fit a

daily rhythm in all treatments, and both the mean and amplitude were increased by DAY. Fatty

acid infusion during the daytime modified the daily rhythms of milk synthesis by increasing the

amplitude of milk yield and decreasing the amplitude of milk fat and protein concentration

whereas infusion at night had little effect. Day-infusion also modified the daily rhythms of

plasma metabolites by reducing the amplitude of nonesterified fatty acids and increasing the

amplitude of plasma urea nitrogen.

Keywords: Daily rhythm, milk synthesis, nutrient entrainment, circadian rhythm

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INTRODUCTION

Dairy cows exhibit a daily pattern of feed intake that is naturally crepuscular, with high

rates of intake during the morning and afternoon and a decline in intake overnight (Albright,

1993). Furthermore, intake is stimulated by delivery of fresh feed and milking (Menzi and Chase,

1994; DeVries et al., 2005). While most cows in the United States are fed a total mixed ration

(TMR), which provides a consistent composition of nutrients over the day, the daily pattern of

feed intake causes the consumption of nutrients to vary across the day (Ying et al., 2015). The

daily pattern of feed intake can be altered by modifying the time of feed delivery, as well as

increasing the frequency of feeding (e.g. Niu et al., 2014; Rottman et al., 2014).

In addition to a daily pattern of feed intake, cows display daily rhythms of milk and milk

component synthesis. Typically, milk yield is greatest in the morning, while milk fat and protein

concentration are greatest in the evening (Rottman et al., 2014; Ying et al., 2015). However,

night-restricted feeding shifts these rhythms so that milk yield peaks in the evening and milk fat

and protein concentration peak in the morning (Chapter 3). These daily rhythms appear to be

driven by the molecular circadian clock of the mammary gland (Chapter 4). The molecular clock

is composed of a transcriptional-translational feedback loop that includes a variety of

transcription factors that oscillate over 24 h and regulate the expression of gene in a circadian

manner (Takahashi, 2017). Moreover, a 24 h pattern post-translational regulation via

phosphorylation and dephosphorylation adds another level of regulation to the molecular clock

(Robles et al., 2017).

Light is the primary signal that entrains circadian rhythms, but an increasing body of

evidence has demonstrating a role for timing of food intake in circadian entrainment of peripheral

tissues. Fatty acids, in particular, play an important role in entrainment of circadian rhythms. The

lipid-sensing family of nuclear receptors known as peroxisome proliferator-activated receptors

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(PPARs) display 24 h rhythms of expression, and possess E-box elements, suggesting direct

regulation by BMAL1/CLOCK (Oishi et al., 2005). Moreover, the expression of BMAL1 is

activated by PPARα though binding to a PPAR-response element present in the BMAL1 promoter

(Ribas-Latre and Eckel-Mahan, 2016). Feeding a high-fat diet has been shown to alter the

molecular clock and the daily pattern of adiponectin expression in the liver of male C57BL/6

mice (Barnea et al., 2009; Branecky et al., 2015). Moreover, CLOCK-/- and BMAL-/- double-

mutant mice have increased triglyceride accumulation in white adipose tissue, as well as

disrupted rhythms of free fatty acids in blood (Shostak et al., 2013).

Evidence suggests that time-restricted feeding alters the molecular circadian clock of the

mammary gland. We previously discovered that the daily rhythms of CLOCK and CRY1 mRNA

expression were shifted 4 h by restricting the time of feed availability from 7 PM to 11 AM

(Chapter 4). Furthermore, the daily rhythms of both clock genes and transcription factors

involved in milk fat synthesis were altered by time restricted feeding in mice (Ma et al., 2013).

While feed intake entrains the daily rhythms of milk synthesis, the role of individual nutrients,

such as FA, is unknown. The objective of this experiment was to determine the effect of time of

FA absorption on the daily rhythms of milk synthesis, plasma metabolites, and body temperature.

We hypothesize that altering the time of FA absorption through abomasally infusing FA either

continuously throughout the day or over 8 h during the day or the night will shift the phase of the

daily rhythms of milk fat synthesis. Furthermore, we expect the daily rhythm of plasma

nonesterified fatty acid concentration and core body to be shifted by time of fatty acid infusion.

MATERIALS & METHODS

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Nine multiparous mid-lactation (132 ± 90 d postpartum; mean ± SD) Holstein cows from

the Penn State University Dairy Research and Teaching Center fitted with rumen cannulas and

randomly assigned to treatment sequences in a 3x3 Latin Square design. Treatment periods were

12 d long with 10 d of treatment adaptation and 2 d of sampling. There was a 5 d washout

between periods. During the final 7 d of each period, cows were milked every 6 h (4 x/d) to allow

a daily rhythm to be fit to milk yield and components data.

Treatments were abomasal infusion of 350 g/d of an oil mixture either continuously

throughout the day (CON) or over 8 h/d from 0900 h to 1700 h (DAY) or from 2100 h to 0500 h

(NGT; Figure 5-1). The treatment consisted of a high oleate mixture of free fatty acids (KIC

Chemicals, Inc, New Paltz, NY; Supplemental Table S5-2) with 10% w/w Tween 80 added to

aid in emulsification. Cows in the CON treatment were infused a total of 22 h/d with no infusion

occurring for 30 min during each milking. All cows were provided the same basal TMR fed once

daily at 0600 h at 110% of the previous day’s intake (Supplemental Table S5-1). Feed samples

were collected on day 7 of each period and analyzed for dry matter, ash, neutral detergent fiber

(NDF), acid detergent fiber (ADF), starch, and crude protein according to Rico and Harvatine

(2013). The experiment was conducted from May 7 to June 22, 2018. All experimental

procedures were approved by the Penn State University Institutional Care and Use Committee.


Cows were milked every 6 h on the final 7 d of each period (0100, 0700, 1300, and 1900

h) to observe the daily rhythm of milk synthesis. Milk collected at each time point represented the

sum of milk synthesis over the previous 6 h interval and is plotted as the midpoint of the previous

milking interval (3 h prior to collection). Milk yield was measured at each milking using an

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integrated milk meter (AfiMilk MPC Milk Meter; Afimilk Agricultural Cooperative Ltd., Kibbutz

Afikim, Israel). Yields were corrected for the deviation of each individual stall according to

Andreen et al. (2018). Milk was sampled at each milking on the final 2 d of each period. One

subsample was stored at 4°C with preservative (Bronolab-WII; Advanced Instruments, Inc.,

Noorwood, MA) prior to analysis of fat and protein concentration by Fourier transform infrared

spectroscopy (Fossomatic 4000 Milko-Scan and 400 Fossomatic, Foss Electric; Dairy One DHIA,

Ithaca, NY). A second subsample was stored at 4°C and centrifuged at 2300 x g within 24 h. The

resulting fat cakes were stored at -20°C and analyzed for concentrations of individual FA

according to Baldin et al. (2018).


Blood was collected by veinipuncture of a coccygeal vessel using potassium EDTA

vacuum tubes (BD Vacutainer, Becton Dickinson, Franklin Lakes, NJ). Blood sampling occurred

6 times across d 11 to 12 representing every 4 h across the day (0300, 0700, 1100, 1500, 1900,

and 2300 h). Samples were immediately placed on ice and centrifuged within 30 min at 2300 x g

for 15 min at 4°C. Plasma was collected and stored at -20°C for analysis of glucose, nonesterified

fatty acids (NEFA), and plasma urea nitrogen (PUN) as described by Rottman et al. (2014).

Briefly, plasma glucose concentration was analyzed using a glucose oxidase/peroxidase

enzymatic colorimetric assay (No. P 7119, Sigma-Aldrich, St. Louis, MO), NEFA concentration

was measured with an using acyl-CoA oxidase/peroxidase enzymatic colorimetric assay (NEFA-

HR (2), Wako Diagnostics, Richmond, VA), and PUN was assayed using a modified Berthelot


Boerne, TX).

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Body Temperature Analysis

An intravaginal temperature logger was used to record core body temperature every 10

min on d 10 to 12 of each period as described by Niu et al. (2017). Briefly, a miniature plastic

coated thermometer fastened to a vaginal implant was placed centrally in the vagina. Body

temperature was averaged over 2 h intervals.


All data were analyzed using the MIXED procedure of SAS 9.4 (SAS Institute, Cary,

NC). Models testing the effect of treatment on daily dry matter intake, milk production, and FA

yields included the fixed effect of treatment and the random effects of cow and period. The AR(1)

or ARH(1) covariance structure was selected based on convergence criteria, and denominator

degrees of freedom were adjusted using the Kenward-Roger method. Individual treatment least

squared means were compared using preplanned contrasts of CON versus DAY, CON versus

NGT and DAY versus NGT.

Time course data for milk production, plasma metabolites, and body temperature were fit

to the linear form of a cosine function with a period of 24 h using random regression in SAS 9.4.

The model included the fixed effects of treatment, cosine terms, and the interaction between

treatment and cosine terms, as well as the random effects of cow and period. The presence of a 12

h harmonic was tested for body temperature and plasma hormone and metabolite data, and was

included if it improved model fit. The fit of the 24 h cosine was determined using a zero-

amplitude test, and the amplitude and acrophase (time at peak of rhythm) were determined

according to Niu et al. (2014). In all analyses, points with Studentized residuals outside ± 3.5

were removed. A separate model testing the fixed effects of treatment, time, and their interaction,

with random effects of cow and period, and preplanned contrasts testing the interaction of

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treatment and time was also conducted on all time course data. Statistical significance was

declared at P < 0.05 and trends acknowledged at 0.05 < P < 0.10. High resolution figures were

generated using an add-in for Microsoft Excel (Daniel's XL Toolbox; Kraus, 2014).


Milk and Milk Components Synthesis

Dry matter intake was not affected by treatment (P = 0.25; Figure 5-2). Furthermore,

treatment did not affect daily milk or milk fat and protein yield or milk fat and protein

concentration (P > 0.10; Figure 5-3). High oleic acid oils, like the one used in our experiment,

have previously been shown to increase milk yield and milk fat concentration (LaCount et al.,

1994). The results of our study suggest that the timing of FA absorption does not affect daily milk

or milk component production.

Treatment altered the daily rhythms of milk synthesis (Figure 5-4). Milk yield fit a 24 h

rhythm in CON and DAY (P < 0.002), and tended to fit a rhythm in NGT (P = 0.08). Relative to

CON, DAY increased the amplitude of milk yield by 29% (P < 0.05), while NGT was unaffected

(P > 0.10). Milk yield peaked in the morning in all treatments, similar to previous

characterizations of the daily rhythm of milk synthesis in dairy cows (Rottman et al., 2014). Day-

infusion phase-delayed the rhythm of milk yield by 2 h compared to CON and NGT (P < 0.05).

Fat yield did not fit a rhythm in CON or NGT, but a rhythm was induced by DAY (P = 0.02),

with the amplitude being 115% and 200% greater than CON and NGT, respectively (P < 0.05;

Figure 5-4B). The time of fat infusion did not alter the phase of fat yield. A daily rhythm of

protein yield was present in all treatments (Figure 5-4D). The amplitude was increased 30% by

DAY, and decreased 32% by NGT. The phase of the rhythm was delayed 2 h by DAY (P < 0.05),

but was not affected by NGT (P > 0.10).

100

Fat concentration fit a daily rhythm in CON and NGT (P < 0.05), but not DAY (P > 0.10;

Figure 5-4C). Corresponding with the loss of rhythm, the amplitude of fat concentration was

dramatically decreased by DAY (67%, P < 0.05), but was increased 29% by NGT, relative to

CON (P < 0.05). Similar to previous reports, fat concentration peaked in the middle of the day in

both CON and NGT (Rottman et al., 2014; Salfer et al., 2016). However, the phase of the rhythm

was altered by the timing of infusion, with DAY phase-advancing the rhythm 2.75 h and NGT

phase-delaying the rhythm 1 h relative to CON (P < 0.05). Similarly, protein concentration fit a

daily rhythm in CON and NGT (P < 0.05), but not DAY (P > 0.10; Figure 5-4E). The amplitude

was reduced by 50% in DAY (P < 0.05), but did not differ between CON and NGT (P > 0.10).

Furthermore, the acrophase of CON occurred at 1555 h and did not differ from DAY or NGT (P

> 0.10), but the phase occurred 1 h and 40 min earlier in DAY (1446 h) compared to NGT (1626

h; (P < 0.05).

Fat metabolism has been previously linked to circadian rhythms of milk synthesis. Ma et

al., (2013) observed differences in both the daily rhythms of expression of circadian clock and

lipid synthesis genes in wild type mice compared to mice lacking the gene encoding lipid-

metabolism regulator Thyroid-Hormone Responsive Spot 14. In the current experiment, phase

shifts in the daily rhythms of fat and protein yield and concentration occurred, these effects were

small (less than 3 hours) and likely not of biological significance. In contrast, altering the timing

of restricted feeding by 8 h caused an approximately 8 h shift in the phases of milk and protein

yield and fat and protein concentration (Chapter 3). The larger effects of FA infusion on the daily

rhythms of milk synthesis occurred through modification of the amplitude. Fatty acid infusion

during the day increased the robustness of the oscillations of milk, fat, and protein yield, while

reducing the amplitudes of fat and protein concentration. These changes in amplitude may occur

through altering amplitudes of molecular clock genes in the mammary gland. Dietary fat has been

shown to reduce the amplitude of daily rhythms of molecular clock expression and expression of

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genes related to FA synthesis in the adipose tissue and liver of mice (Kohsaka et al., 2007).

However, to our knowledge, the direct effects of FA on mammary circadian clock gene

expression have not been investigated. Future research examining the role of the timing of FA

infusion on the molecular clock should be conducted.

Daily Yields and Daily Rhythms of Milk Fatty Acids

In addition to examining total fat yield, the effect of time of FA infusion on the origin of

milk FA was examined. The yield of FA originating from de novo synthesis in the mammary

gland (Σ < 16C) was affected by treatment (P = 0.001), with DAY and NGT reducing yields by

12% and 11% compared to CON (Figure 5-5A). Similarly, yields of mixed-source FA

originating both from de novo synthesis and preformed FA uptake from plasma (Σ 16C) were

decreased 9% by DAY and 6% by NGT compared to CON (P = 0.02). Yield of preformed FA (Σ

>16C) was not affected by treatment, however (P = 0.24). These results suggest that limiting the

time of FA absorption from the intestine reduced de novo FA synthesis in the mammary gland,

but that the time during which it is limited is irrelevant. Altering the timing or frequency of

feeding has previously been demonstrated to have no effect on apparent de novo FA synthesis

(Niu et al., 2014; Rottman et al., 2014). However, restricting feed intake to 16 h during the night

decreased de novo and mixed FA yields relative to 16 h feed restriction during the day,

suggesting a role for meal timing in mammary FA synthesis (Salfer et al., 2016).

Of the individual FA, three odd-chain FA (OCFA) emerged as having the greatest

decrease from CON in both DAY and NGT treatments (Figure 5-5C). These included C11:0

(28% decrease in DAY; 27% decrease in NGT; P = 0.03), C13:0 (28% decrease in DAY; 23%

decrease in NGT; P = 0.002), and C15:0 (23% decrease in DAY; 16% decrease in NGT; P =

0.0004). Along with this, OCFA and total odd- and branched-chain fatty acids (OBCFA) were

reduced in DAY and NGT relative to CON (P < 0.02; Figure 5-5B). Odd and branched chain FA

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are derived primarily from microbial synthesis in the rumen, and found almost exclusively in

adipose tissue and milk of ruminants (Vlaeminck et al., 2006). Moreover, they may have a may

have a role in human heath because they have been shown to have anti-carcinogenic properties

(Yang et al., 2000). In the current experiment, oil was infused post-ruminally and major changes

in rumen fermentation would be unexpected. The effect may be indirectly modulated through gut

peptides. The gut peptides glucagon-like peptide 1 (GLP-1) and cholecystokinin (CCK) are

increased by intestinally-available unsaturated fatty acids and may modify rumen motility

(Bradford et al., 2008). Additionally, de novo synthesis of OBCFA can occur in the mammary

gland through elongation of the 3-carbon precursor propionyl -CoA, derived from ruminally-

produced propionate (Fievez et al., 2012). Similar to reducing total de novo FA synthesis,

restricting the time of FA availability appears to inhibit elongation of propionyl-CoA or other

OCFA. The effect appears to be fatty-acid specific because previous research demonstrates that

altering meal pattern through time-restricted feeding does not affect total or individual OBCFA

yields (Salfer et al., 2016; Salfer and Harvatine, 2018).

The daily rhythms of de novo, mixed, and preformed FA were affected by treatment.

Similar to previous reports (Rottman et al., 2014), de novo FA yield fit a daily rhythm in CON (P

= 0.01) and DAY (P = 0.03), but this rhythm was eliminated by NGT (P = 0.63; Figure 5-6A).

The amplitude of the rhythm was increased 19% by DAY and decreased 46% by NGT. The

acrophase of de novo FA yield occurred at 0603 h in CON and was phase-delayed 1.1 h by DAY

and phase-advanced 1.3 h by NGT. These small phase shifts are unlikely to be of biological

relevance. Similar changes due to time of FA infusion were observed for mixed FA yield, which

exhibited a daily rhythm in CON (P = 0.03) and DAY (P = 0.01), but not NGT (P = 0.68, Figure

5-6B). The amplitude of the daily rhythm of mixed source FA yield was increased 17% by DAY

and decreased 42% by NGT. Phase was also modified slightly by treatment with CON peaking at

0632, and being shifted 1 h later by DAY and 30 min earlier by NGT. These results suggest that

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in addition to total de novo FA synthesis being affected by time of FA infusion, the daily rhythms

of de novo FA synthesis are also affected. Preformed FA failed to fit a rhythm in CON (P = 0.43)

or NGT (P = 0.68), but a rhythm was induced by DAY (P = 0.02; Figure 5-6C). Corresponding

with the induction of a rhythm according to the zero-amplitude test, DAY increased the amplitude

of the daily rhythm by 38% compared to CON and 33% compared to NGT. Moreover, the fitted

24 h rhythm was advanced 0.9 h by DAY and delayed 2.8 h by NGT, compared to CON which

peaked at 0812 h.

Daily Rhythms of Plasma Metabolites

Plasma glucose concentration has been demonstrated to follow a daily rhythm in cows

(Giannetto and Piccione, 2009; Niu et al., 2014). However, in our experiment, glucose failed to fit

a 24 h rhythm in any treatment according to the zero-amplitude test, nor did it fit a 24 h rhythm

with a 12 h harmonic (P > 0.20; Figure 5-7A). In model organisms, these rhythms are shown to

be influenced by peripheral circadian clocks in the liver and pancreas (Qian and Scheer, 2016).

However, nutrient intake has been shown to affect the daily rhythm of cows. Feeding 4x/d has

been shown to dampen the daily rhythm (Rottman et al., 2014), and the phase of the rhythm is

shifted by altering the time of feed availability (Chapter 3). Our results may suggest that FA

infusion reduces the amplitude of the daily pattern of glucose concentration, such that it fails to fit

a 24 h rhythm according to the zero-amplitude test, but a negative control was not included in this

experiment so this cannot be confirmed.

A daily rhythm of NEFA concentration was present in both CON (P = 0.003) and NGT

(P < 0.0001), but this rhythm was eliminated by DAY (P = 0.35; Figure 5-7B). In conjunction

with an elimination of the rhythm according to the zero-amplitude test, the amplitude of the 24 h

cosine function was reduced by DAY (P < 0.05). The phase of the 24 h rhythm was also affected

by treatment, with DAY phase advancing the rhythm 4.5 h and NGT phase delay the rhythm ~9 h

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relative to CON. These results suggest that the time of fat absorption alters the daily patterns of

lipid mobilization from adipose tissue. Specifically, infusion during the morning dampens the

rhythm, while infusion overnight does not. Moreover, NEFA concentration decreased during

infusion in both DAY an NGT treatments, both reaching a nadir near the end of the infusion

period. The change in the daily pattern of NEFA concentration may be through entrainment of the

adipocyte circadian clock. In laboratory models, plasma free FA concentrations follow daily

rhythms that are under the control of the adipocyte molecular clock (Shostak et al., 2013).

Furthermore, lipids can entrain the molecular circadian clock of peripheral tissues through the

peroxisome proliferator-activated receptor (PPAR) family of nuclear receptors, which can bind to

response elements on the promoter regions of circadian clock genes BMAL1 and REV-ERBα and

enhance their expression. Interestingly, our study suggests that altering the time of FA absorption

may uncouple FA metabolism from glucose metabolism. While the phase of the NEFA rhythm is

shifted by the time of infusion during the day, glucose metabolism was not. Direct absorption of

FA may desynchronize the circadian clock of adipose tissue from the clocks of the liver and

pancreas that drive the daily rhythm of glucose concentration.

Treatment tended to affect concentration of plasma urea nitrogen (P = 0.06), with DAY

increasing PUN 20% compared to CON (P = 0.03) and 16% compared to NGT (P = 0.07; Figure

5-7C). This change in PUN was associated with a 16% increase in milk urea nitrogen in DAY

compared to control (Supplemental Figure S5-1), but no difference in milk protein

concentration or yield. Plasma and milk urea nitrogen concentrations are measures of nitrogen

utilization efficiency (Jonker et al., 1998; Kohn et al., 2005). Results suggest that FA infusion

during the day may decrease the efficiency of amino acid utilization by muscle and other non-

mammary tissues. A 24 h rhythm fit all treatments (P < 0.0005), and treatment affected the daily

rhythm. These results agree with previous research suggesting that cows exhibit daily rhythms of

plasma urea nitrogen concentration (Giannetto and Piccione, 2009; Niu et al., 2017). Day-

105

infusion increased the amplitude of the daily rhythm 26% compared to CON and 18% compared

to NGT (P < 0.05). This increase in amplitude may be related to the increase in average PUN by

NGT. The phase of the rhythm was slightly modified by treatment with the acrophase of NGT

occurring 29 min later than CON and 41 min later than DAY (P < 0.05), but this effect is likely

not biologically relevant. Salfer et al. (2016) previously observed that altering the time of time-

restricted feeding inverts the daily rhythms of PUN, but our results suggest that this effect was

not mediated by FA intake.

Daily Rhythm of Body Temperature

The time of FA infusion altered average body temperature (P = 0.02), with NGT

decreasing body temperature compared to CON and DAY (Figure 5-8). Body temperature fit a

24 h rhythm in all treatments (P < 0.0001) and the rhythm was altered by treatment. The

amplitude of the daily rhythm was increased 92% by DAY and 125% by NGT (P < 0.05).

Furthermore, the phase of the daily rhythm of body temperature was delayed 50 min by DAY, but

was not affected by NGT.

Previous research has suggested that altering the time of feed restriction dramatically

shifts the phase of the daily rhythm of body temperature. We previously observed a ~9 h phase

shift in the body temperature rhythm when feed was restricted to 16 h either during the day or

night (Chapter 4). Similar results were observed by Niu et al. (2014) who detected a 3 h shift in

the phase of body temperature when cows were fed at 0830 versus 2030 h, without restricting the

time of feed availability. The results of the current study contrast this previous work and suggest

that the time of fat absorption has very little effect on the phase of the rhythm of body

temperature. The discrepancy between the experiments may be related to rumen temperature.

Rumen temperature also oscillates in a circadian manner (Piccione et al., 2014). Moreover, rumen

temperature is directly correlated to core body temperature, and is related to dry matter intake

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(Beatty et al., 2008). This relationship may explain why only slight differences in the daily

rhythm of body temperature occurred after abomasal infusion of fat, whereas changing the time

of feed intake has previously been shown to alter the rhythm, perhaps by changing the daily

rhythm of body temperature through altered rumen fermentation.

Interestingly, the amplitude of the daily rhythm of body temperature was dramatically

altered by time of feed restriction, with night-infusion increasing the rhythm to a greater extent

than day infusion. Similarly, we previously reported a 4-fold increase in the amplitude of the

daily rhythm when time of feed restricted was limited to the overnight period compared to the day

(Chapter 4). Therefore, the timing of fat infusion may influence the daily rhythm of body

temperature thorough altering the robustness, but not the phase of the rhythm. The increase in

amplitude of the rhythm may be important in modifying the circadian rhythms of other tissues

because core body temperature has been shown to be capable of circadian entrainment in

mammals (Brown et al., 2002).

CONCLUSIONS

The daily rhythms of milk synthesis were altered by modifying the time of FA infusion.

Most notably, infusion during the day increased the amplitude of milk yield, but decreased

concentration of fat and protein. However, FA do not appear to robustly entrain the mammary

clock because the phase of the rhythms of milk, fat, and protein yield and milk fat and protein

concentration were not markedly shifted by treatment. De novo FA synthesis was reduced by

restricting the time of FA infusion, regardless if time restriction occurred during the day or night.

Daily rhythms of glucose and nitrogen metabolism were not greatly affected by time of FA

infusion, but total nitrogen efficiency appeared to be decreased by day-infusion. The daily pattern

of lipid mobilization was altered by time of fat absorption, however, with day-infusion flattening

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the daily rhythm. The amplitude of the daily body temperature was increased both by restricting

fat infusion to either the day or night, indicating that time-restricted fat absorption may increase

the robustness of the central circadian clock. The effects of time of FA infusion on the daily

rhythms of milk synthesis may be related to increasing the robustness of oscillations of the

mammary molecular clock. Moreover, the changes in the daily patterns of NEFA concentrations

may be related to the adipocyte clock. The role of FA on the molecular circadian clocks of

mammary and adipose tissue should be investigated in the future.

108

FIGURES


Treatments were 350g/d of fatty acids abomasally infused continuously for 22 h/d (CON); for 8 h/d during

the day [(DAY); infused from 0900 to 1700] or for 8 h/d during the night [(NGT); feed from 2100 to 0500].

109

Figure 5-2. Effect of time of fatty acid infusion on dry matter intake.


the day [(DAY); infused from 0900 to 1700] or for 8 h/d during the night [(NGT); feed from 2100 to 0500].

Data are presented as LSM with SEM bars.

110

Figure 5-3. Effect of time of fatty acid infusion on daily milk, fat and protein yield and fat and

protein concentration.


the day [(DAY); infused from 0900 to 1700] or for 8 h/d during the night [(NGT); feed from 2100 to

0500]. Panels the effect of time of fatty acid infusion on (A) milk yield (kg), (B) fat and protein yield (g)

and (C) fat and protein concentration. Data are presented as LSM with SEM bars.

111

Figure 5-4. The effects of time of fatty acid infusion on the daily rhythms of milk synthesis.



0500]. Panels show the effect of time of fatty acid infusion on the daily rhythms of milk (A) yield (kg), (B)

fat yield (g), (C) protein yield (g), (D) fat concentration (%), and (E) protein concentration (%). Data are

presented as relative expression LSM and SEM bars and cosine fit are shown. Cosinor analysis is shown

below each panel and include the amplitude (Amp; difference between peak and mean), acrophase (Acro;

time at peak of the rhythm), and the P-value of the zero-amplitude test. NS: Reduced model fits data

significantly better than daily rhythm model. The black and white bars above the x-axis display the light:

dark cycle.

112

Figure 5-5. Effect of time of fatty acid infusion on the daily yields of fatty acids.



0500]. Panels show the effect of time of fatty acid infusion on the average daily yields of (A) de novo (Σ <

16C), mixed (Σ 16C) and preformed (Σ >16C) sources of fatty acids (g/d), (B) odd and branched chain fatty

acids (OBCFA), odd chain fatty acids (OCFA), and branched chain fatty acid (BCFA) (g/d), and (C)

C11:0, C13:0 and C15:0 fatty acids (g/d). Data are presented as LSM with SEM bars. Means with differing

superscripts are different at P < 0.05.

113

Figure 5-6. Effect of time of fatty acid infusion on daily rhythms of fatty acids by source.



0500]. Panels show the effect of time of fatty acid infusion on the daily rhythms of (A) de novo synthesized

(Σ < 16C) FA yield (g/d) (B) mixed source (Σ 16C) FA yield (g/d), and (C) preformed (Σ >16C) FA yield

(g/d). Data are presented as relative expression LSM and SEM bars and cosine fit are shown. Cosinor

analysis is shown below each panel and include the amplitude (Amp; difference between peak and mean),

acrophase (Acro; time at peak of the rhythm), and the P-value of the zero-amplitude test. NS: Reduced

model fits data significantly better than daily rhythm model. The black and white bars above the x-axis

display the light: dark cycle.

114

Figure 5-7. The effects of time of fatty acid infusion on daily rhythms of plasma metabolites.



0500]. Panels include the effects of time of fatty acid infusion on daily rhythms of plasma (A) glucose

concentration (mg/dL), (B) nonesterified fatty acid concentration (NEFA; μEq/L), and (C) urea nitrogen

concentration (mg/dL). Data are presented as relative expression LSM and SEM bars and cosine fit are

shown. Cosinor analysis is shown below each panel and include the amplitude (Amp; difference between

peak and mean), acrophase (Acro; time at peak of the rhythm), and the P-value of the zero-amplitude test.

NS: Reduced model fits data significantly better than daily rhythm model. The black and white bars above

the x-axis display the light: dark cycle.

115

Figure 5-8. The effect of the time of fatty acid infusion on the daily rhythms of core body

temperature.



0500]. Data presented as 2 h means and SEM bars of body temperature collected every 10 min by a vaginal

temperature data logger, with cosine fit are shown. Cosinor analysis is shown below each panel and include

the amplitude (Amp; difference between peak and mean), acrophase (Acro; time at peak of the rhythm),

and the P-value of the zero-amplitude test. NS: Reduced model fits data significantly better than daily

rhythm model. The black and white bars above the x-axis display the light: dark cycle

116


Supplemental Figure S5-1. Effect of time of fatty acid infusion on milk urea nitrogen

concentration.



0500]. Data are presented as LSM with SEM bars. Means with differing superscripts are different at P <

0.05.

117

SUPPLEMENTAL TABLES


1Contained (% of DM): 33.0% NDF, 16.9% ADF 2Contained (% of DM): 45.1% NDF, 34.7% ADF 3Contained (% of DM): 63.2% NDF, 33.5% ADF 4AminoPlus, Ag Processing Inc., Omaha, NE 5Contained (%, as-fed basis): 37.0% Calcium carbonate; 29.9% dried corn distillers grains; 24.5% salt;





dehydrocholesterol); 850 IU/kg vitamin E (dl-α tocopheryl acetate). 5Fed as coated urea (Optigen, Alltech Inc., Lexington, KY; 259% CP, DM basis.

Item Composition


Corn silage1 49.6


Canola meal 12.5

Ground corn 11.6



Mechanically-extracted soybean meal4 2.9



Nutrients, % of DM

NDF 33.1

ADF 18.3

Ash 5.6

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Supplemental Table S5-2. Fatty acid profile of oil used for abomasal infusion of fatty acids.

Name g/100 g

14:0 0.22

15:0 0.01

16:0 5.11

cis-9 16:1 0.04

17:0 0.03

C18:0 1.39

trans-9 18:1 0.26

trans-10 18:1 0.02

trans-11 18:1 0.38

cis-9 18:1 79.6

cis-11 18:1 0.56

cis-9, cis-12 18:2 10.0

cis-6, cis-9, cis-12 18:2 0.15

cis-9, cis-12, cis-15 18:2 0.36

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Chapter 6

The effect of timing of protein infusion on the daily rhythms of milk

production and plasma hormones and metabolites

Isaac J. Salfer, Rebecca A. Bomberger, Cesar I. Matamoros, P.A. Bartell, Kevin J. Harvatine

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ABSTRACT

Dairy cows have a daily rhythm of milk synthesis that appears to be driven by the

molecular clock of the mammary gland and is modified the time of feed availability. Protein

metabolism is intimately linked to circadian rhythms in other species, but the effect of amino

acids on the daily rhythm of milk synthesis is not known. The objective of this experiment was to

determine the effects of intestinally-absorbed protein on the daily rhythms of milk and milk

component synthesis in dairy cows. Nine lactating Holstein cows were randomly assigned to one

of three treatment sequences in a 3 x 3 Latin square. Treatments were absomasal infusions of 500

g/d of sodium caseinate either continuous throughout the day (CON), for 8 h/d from 0900 to 1700

h (DAY), or for 8 h/d from 2100 to 0500 (NGT). Treatment periods were 14 d with 11 d of

treatment adaptation and 3 d of sampling. Cows were milked every 6 h during the final 8 d of

each period. A 24-h rhythm was fit to time-course production data using cosine analysis and the

amplitude and acrophase (time at peak) were determined. Daily milk and protein yield were

decreased 7.5% and 8% by NGT compared to CON, respectively, while milk fat yield was

increased by DAY (P < 0.05). Daily milk fat and protein concentration were not affected by

treatment. Milk yield failed to fit a 24 h rhythm in CON or DAY, but a rhythm was induced by

NGT (P = 0.03). Neither fat yield nor protein yield fit a rhythm in either treatment. However, fat

concentration fit a daily rhythm in all treatments (P < 0.05) and the amplitude of the rhythm was

decreased 57% by DAY and 26% by NGT (P < 0.05). The rhythm of milk fat concentration was

phase advanced ~2 h by DAY and phase delayed ~1 h by NGT (P < 0.05). Protein concentration

fit a daily rhythm in CON and DAY but not NGT. The phase of the rhythm of protein

concentration was delayed ~1.25 h by DAY and the amplitude was increased 2-fold relative to

CON (P < 0.05). The daily yields of de novo (Σ < C16), mixed (Σ 16C), and preformed (Σ >16C)

fatty acids in milk were not altered by treatment (P > 0.10), and none of these sources fit a daily

121

rhythm in any treatment (P > 0.10). Plasma glucose concentration fit a daily rhythm in CON and

NGT, but the rhythm was eliminated by DAY. Furthermore, the phase of glucose concentration

was not altered by treatment. Nonesterified fatty acid concentration did not fit a daily rhythm in

CON or NGT (P > 0.10), but a rhythm was induced by DAY (P < 0.05). A rhythm of plasma urea

nitrogen was present in CON and NGT (P < 0.0001) but not DAY, (P < 0.05), with no difference

in phase between CON and NGT. The time of protein infusion influenced the daily rhythms of

milk and milk protein synthesis, with night infusion abolishing rhythms of protein concentration

and inducing rhythms of milk yield, and day infusion increasing the amplitude of the rhythm of

protein concentration. Moreover, infusion of protein during the day damped the rhythms of

plasma glucose and PUN concentration, but increased the amplitudes of nonesterified FA,

suggesting that the daily rhythms of nutrient metabolism are also altered by the time of protein

infusion.

Keywords: Daily rhythm, milk synthesis, nutrient entrainment, circadian rhythm

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INTRODUCTION

Dairy cows exhibit rhythms of milk synthesis, with milk yield typically peaking in the

morning and milk fat and protein concentration peaking in the evening (Rottman et al., 2014).

These daily rhythms appear to be related to changes in the molecular clock of the mammary gland

(Plaut and Casey, 2012). The molecular clock is responsible for generating circadian rhythms,

which are repeating ~24 h cycles that allow animals to anticipate daily changes in their

environment. This molecular clock is comprised of a set of ‘clock’ transcription factors and daily

rhythms of phosphorylation and dephosphorylation events that generate 24 h rhythms of gene

expression and intracellular signaling (Robles et al., 2017).

A master circadian oscillator located in the suprachiasmatic nucleus (SCN) of the

hypothalamus serves as the central control point for setting the circadian rhythms of peripheral

tissues. This master oscillator is entrained by the light-dark cycle and initiates neural and

hormonal signaling cascades to entrain the molecular clock of cells throughout the body. In

addition to receiving signals from the SCN, the molecular clock in peripheral tissues can be

entrained by other signals, including nutrient intake (Stokkan et al., 2001). Entrainment by

nutrients can occur independently of the SCN and can cause desynchronization of master and

peripheral circadian rhythms (Asher and Sassone-Corsi, 2015). Desynchronization of these

rhythms has been implicated in development of metabolic disorders such as obesity and type II

diabetes (Vetter and Scheer, 2017).

Nutrient intake appears to influence the circadian rhythms of milk synthesis in dairy

cows. Feeding cows four times per day in equal meals dampened the daily rhythms of milk fat

and protein concentration, as well as shifted peaks of milk, fat, and protein yield and fat and

protein concentration (Rottman et al., 2014). Salfer et al. (2016) discovered that restricting the

time of feed availability to 16 h/d at night (1900 to 1100) shifted the daily rhythms of milk yield

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and fat and protein concentration approximately 8 h relative to restricting feed availability to the

same amount of time during the day (0700 to 2300). The change in these daily patterns of milk

synthesis appear to be at least partially modulated through modifying the molecular clock because

night-restricted feeding shifts the daily rhythms of some components of the molecular clock

(Chapter 4). Similar results were observed in mice under night-restricted feeding, which exhibited

shifts in the rhythms of milk fat synthesis, along with changes in the mammary molecular clock

(Ma et al., 2013). In addition to modifying the time of feed intake through time-restricted feeding,

individual nutrients have been shown to be capable of entraining molecular clocks (Oike, 2017).

In dairy cows, altering the time of fatty acid (FA) infusion shifts the amplitude and phase of milk,

fat, and protein synthesis in the mammary gland (Chapter 5).

While fatty acids appear to entrain daily rhythms of milk synthesis of dairy cows, to our

knowledge, other nutrients have not been examined. Amino acids may have a potential role in the

circadian rhythm of the mammary gland. Amino acid metabolism is intimately linked with the

molecular clock of mammals. In humans, plasma concentrations of total amino acids, and all

individual proteogenic amino acids follow circadian rhythms that are entrained by alterations to

the sleep-wake cycle (Feigin et al., 1968). Furthermore, Krüppel-like factor 15 (KLF15), a

transcription factor responsible for upregulation of pathways related to amino acid mobilization

from muscle and their use for gluconeogenesis during fasting, is regulated in a circadian manner

by glucocorticoid signaling (Fan et al., 2018). This pathway links amino acid metabolism with

circadian rhythms of plasma glucose concentrations.

Amino acids are important for milk protein synthesis in the mammary gland and play a

role in the synthesis of lactose because α-lactalbumin is a milk protein and is a regulatory

component of the lactose synthase enzyme complex. Altering the time of amino acid absorption

may affect the efficiency of protein synthesis in the mammary gland and/or alter the daily

rhythms of milk synthesis. The objective of this experiment was to examine the effect of time of

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protein absorption on the daily rhythms of milk synthesis and plasma metabolites. We expect that

altering the timing of amino acid availability in the small intestine through abomasally infusing

sodium caseinate either continuously throughout the day, for 8 h during the day, or for 8 h during

the night, will shift the phase of the daily rhythms of milk yield and milk protein concentration

and yield and will shift the daily rhythms of plasma glucose concentrations.

MATERIALS & METHODS


Nine cannulated multiparous mid-lactation (128 ± 46 d postpartum; mean ± SD) Holstein

cows from the Penn State University Dairy Research and Teaching Center were randomly

assigned to treatment sequences in a 3x3 Latin Square design. Treatment periods were 14 d with

11 d of treatment adaptation followed by 3 d of sampling. A 7 d washout was used between

treatment periods. During the final 7 d of each period, cows were milked 4 times per day to allow

observation of daily rhythm. Treatments included abomasal infusion of 500 g/d day of sodium

caseinate dissolved in 6 L of distilled water for either 24 h/d (CON), for 8 h/d from 0900 h to

1700 h (DAY), or for 8 h/d from 2100 h to 0500 h (NGT; Figure 6-1A). Cows on the CON

treatment received infusion for approximately of 22 h/d as infusion occurred for 30 min during

each milking. Sodium caseinate (Erie Foods International, Erie, IL) was used as a protein source

because of its high biological value for milk protein synthesis. Amino acid profile of sodium

caseinate was analyzed by The Experiment Station Chemical Laboratories at the University of

Missouri (Columbia, MO) according to (AOAC, 2006) (Supplemental Table S6-2). All cows

were provided the same basal TMR fed once daily at 0600 h at 110% of the previous day’s intake

(Supplemental Table S6-1). Feed samples were collected weekly throughout the course of the

experiment, composited by period, and analyzed for dry matter, crude protein, starch, neutral

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detergent fiber (NDF), acid detergent fiber (ADF), and ash according to Rico and Harvatine

(2013). The experiment was conducted from July 30 to September 23, 2018 and all experimental

procedures were approved by the Penn State University Institutional Care and Use Committee.


Cows were milked 4 x/d every 6 h (0500, 1100, 1700, and 2300) on the final 8 d of each

period to observe the daily pattern of milk synthesis. Milk collected at each time point

represented total milk synthesis over the previous 6 h and is plotted as the midpoint of the

previous milking interval (3 h prior to collection). Milk yield was measured at each milking using

an integrated milk meter (AfiMilk MPC Milk Meter; Afimilk Agricultural Cooperative Ltd.,

Kibbutz Afikim, Israel), and yields were corrected for the deviation of each individual stall

according to Andreen et al. (2018). Milk was sampled at all milkings on d 13 and 14 of each

period, with one subsample used for analysis of fat and protein concentration by Fourier

transform infrared spectroscopy (Fossomatic 4000 Milko-Scan and 400 Fossomatic, Foss

Electric; Dairy One DHIA, Ithaca, NY), and one subsample used for analysis of FA

concentrations according to Baldin et al. (2018).


Blood was collected via venipuncture of a coccygeal blood vessel into potassium-EDTA

vacuum tubes (BD Vacutainer, Becton Dickinson, Franklin Lakes, NJ) on d 13 to 14 of each

period. Sampling occurred at 6 time points representing every 4 h across the day (0300, 0700,

1100, 1500, 1900, and 2300 h). Samples were placed on ice immediately after collection and

centrifuged at 2300 x g for 15 min at 4°C within 30 min of collection. Plasma was collected and

stored at -20°C for analysis of glucose, nonesterified fatty acids (NEFA), and plasma urea

nitrogen concentrations (PUN) as described by Rottman et al. (2014). Briefly, glucose was

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measured using a glucose oxidase/peroxidase enzymatic colorimetric assay (No. P 7119, Sigma-

Aldrich, St. Louis, MO), NEFA were measured using an acyl-CoA oxidase peroxidase enzymatic

colorimetric assay (NEFA-HR (2), Wako Diagnostics, Richmond, VA) and urea nitrogen was

measured using a modified Berthelot methodology (Modified Enzymatic Urea Nitrogen

Procedure No. 2050; Stanbio Laboratory, Boerne, TX).


All data were analyzed using the MIXED procedure of SAS 9.4 (SAS Institute, Cary,

NC). Effects of time of protein absorption on daily dry matter intake, milk production, and milk

FA yields were tested using models that included the fixed effect of treatment and the random

effects of cow and period. Denominator degrees of freedom were adjusted using the Kenward-

Roger method, and the AR(1) or ARH(1) covariance structure was used based on convergence

criteria. Preplanned contrasts were used to compare least squared means among individual

treatments.

Time course data for milk production and plasma metabolites were fit to the linear form

of a cosine function with a 24 h period using random regression in SAS 9.4. Model parameters

included the fixed effects of treatment, cosine terms, and the interaction between treatment and

cosine terms as well as the random effects of cow and period. The daily patterns of plasma

metabolites were tested for the presence of a 12 h harmonic, and the harmonic was included in the

24 h rhythm if it improved model fit. The fit of the 24 h cosine function was determined using a

zero-amplitude test and the amplitude and acrophase (time at peak) and their significance were

determined according to Niu et al. (2014). Data points with Studentized residuals outside ± 3.5

were removed. Another linear model including the fixed effects of treatment, time, and their

interaction, with random effects of cow and period was also tested. Statistical significance was

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declared at P < 0.05 and trends acknowledged at 0.05 < P < 0.10. High resolution figures were

generated using an add-in for Microsoft Excel (Kraus, 2014).


Daily Milk Yield and Milk Components

Dry matter intake was not altered by treatment (P = 0.46; Figure 6-2). Daily milk yield

was decreased 7.4% by NGT relative to CON but NGT and DAY were not different (P = 0.02;

Figure 6-3A). Treatment tended to affect fat yield (P = 0.07), with DAY increasing fat yield

4.5% relative to CON and 4.6% relative to NGT (Figure 6-3B). The yield of protein was also

affected by treatment (P = 0.005; Figure 6-3B), with NGT decreasing protein yield 8.3% relative

to CON (P = 0.01) and 6.9% relative to DAY (P = 0.002). However, daily average concentration

of fat (P = 0.34) and protein (P = 0.61) were not affected by treatment (Figure 6-3C).

The results of the current experiment differ from previous research in our lab examining

the timing of FA infusion on milk synthesis, which exhibited no differences in milk or milk

components when FA were infused either continuously or over 8 h/d from 0900 to 1700 or 8 h/d

from 2100 to 0500 (Chapter 4). Postruminal sodium caseinate infusion consistently increases

milk protein yield in dairy cows (Clark, 1975; Mackle et al., 1999). This increase in milk protein

is attributed to the high biological value of sodium caseinate, with the amino acid profile of

sodium caseinate closely matching that required for milk protein synthesis. Furthermore, specific

amino acids, such as leucine and isoleucine can directly increase milk protein synthesis via

mechanistic target of rapamycin (mTOR) signaling (Appuhamy et al., 2012). Furthermore,

protein supply may increase milk lactose by increasing α-lactalbumin, a regulator of the lactose

synthesis pathway, or by providing glucogenic precursors that may increase lactose by increasing

plasma glucose availability (Kuhn et al., 1980). Lactose is an osmotic regulator of milk and is

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drives milk yield (Schingoethe, 1996). These mechanisms by which protein can increase milk and

milk protein synthesis are variable, and depend on the availability of protein and glucogenic

precursors. In the current experiment, lactose yield tended to be decreased by NGT, but not DAY

(Supplemental Figure S 6-1). This reduction in lactose yield may be responsible for the

reduction in milk yield. The effect of time of protein infusion on milk, fat, and protein yield may

be related to the mammary clock. Hu et al. (2017) discovered that siRNA-mediated knockdown

of the PER2 gene in bovine mammary epithelial cells increased mRNA and protein abundance of

αs1- and αs2- casein. Restricting time of protein infusion to the night may similarly affect the

mammary clock, which may reduce milk yield.

Daily Rhythms of Milk Yield and Milk Components

Previous research has demonstrated that milk yield follows a 24 h rhythm (Gilbert et al.,

1972; Rottman et al., 2014). In the current experiment, milk yield fit a daily rhythm in NGT (P =

0.03), with an amplitude of 0.72 kg and an acrophase of 0330 (Figure 6-4A). However, milk

yield failed to fit a daily rhythm in CON (P = 0.12) or DAY (P = 0.25). These results imply that

infusion of protein throughout the day or during the morning dampened the rhythm of milk

synthesis, whereas infusion at night does not. A negative control treatment without protein

infusion was not included due to limitations in conducting the intensive experiment and the

hypothesis was focused on the timing of absorption.

Fat concentration fit daily rhythms with similar amplitudes in CON (P = 0.003) and NGT

(P = 0.01), but the rhythm was abolished by DAY (P = 0.26; Figure 6-4C). The acrophase of the

24 h rhythm was phase-advanced 1.5 h by DAY and phase advanced 1.75 h by NGT, relative to

CON (P < 0.05). Protein concentration fit a daily rhythm in CON (P = 0.0004) and DAY (P <

0.0001), but not NGT (P = 0.49; Figure 6-4E). Day-infusion increased the amplitude 2-fold

compared to CON, while NGT decreased the amplitude by 50% (P < 0.05). Similar to previous

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reports (Rottman et al., 2014), the acrophase occurred in the afternoon in all treatments, with

DAY and NGT phase delaying the rhythm 1.25 and 4 h, respectively (P < 0.05).

Fat yield failed to fit a daily rhythm in any treatment (P > 0.38 Figure 6-4B). Despite the

failure to fit a rhythm via the zero-amplitude test, a post-hoc analysis comparing the amplitude

and acrophase of the fitted cosine function were performed. The acrophase of the fitted 24 h

cosine appeared to be phase-advanced 5.25 h from 0917 h in CON to 0402 h in DAY (P < 0.05).

Protein yield similarly failed to fit a daily rhythm in any treatment according to the zero-

amplitude test (P > 0.13; Figure 6-4D). The amplitude of the 24 h cosine function was increased

106% by NGT, compared to CON (P < 0.05). Moreover, the phase of the fitted cosine function

was shifted 4.25 earlier by DAY but was not affected by NGT.

The timing of nutrient intake has previously been shown to impact the daily rhythms of

milk synthesis in dairy cows. Rottman et al. (2014) observed a reduction in the amplitude of milk

fat and protein concentration when cows were fed 4x/d in equal meals compared to 1x/d.

Moreover, restricting the time of feed availability to 16 h/d during the night shifted the peak of

milk and milk protein yield to the afternoon, and the peak of fat and protein concentration to the

morning, relative to restricted feeding for 16 h during the day (Chapter 3). Furthermore, timing of

FA infusion has been shown to affect the daily rhythms of milk synthesis, with infusion during

the day causing a reduction in the amplitudes of milk and milk fat and protein yield, but increased

amplitudes of fat and protein concentration (Chapter 5). Results of the current study demonstrate

that the time of protein infusion also modifies the daily rhythms of milk synthesis. Specifically,

protein infusion during the day reduces the amplitude of milk fat concentration and increases the

amplitude of milk protein concentration, while protein infusion at night reduces the amplitude of

milk protein concentration.

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Daily Yields and Daily Rhythms of Milk Fatty Acids

Because the timing of protein infusion tended to affect total daily fat yield, we examined

the effect of treatment on yields of FA by source. Time of protein infusion did not affect the daily

yields of FA originating from de novo synthesis in the mammary gland (Σ < 16C; P = 0.12),

preformed FA taken up from plasma (Σ >16C; P = 0.14), or mixed source FA (Σ 16C; P = 0.66;

Figure 6-5A). Day-infusion numerically increased the yields of de novo, mixed, and preformed

FA, suggesting that the increased total FA yield was due to the cumulative effects of slight

increases in both de novo FA synthesis and preformed FA uptake from plasma. We have

previously observed that the time of feed intake affects de novo FA synthesis, with night-

restricted feeding for 16 h/d (1900 to 1100) reducing de novo and mixed FA yields, but not

preformed FA, relative to day-restricted feeding for 16 h/d (0700 to 2300; Chapter 3).

Furthermore, we observed that restricting the time of FA infusion to the day (0900 to 1700) or

night (2100 to 0500) reduced de novo synthesis relative to continuous infusion across the day

(Chapter 5). Results of the current study suggest that unlike altering the time of feed restriction or

the time of FA infusion, the time of protein infusion does not alter de novo FA synthesis.

In addition to the daily yields of FA, we examined the effect of time of protein infusion

on the 24 h rhythms of de novo, mixed, and preformed FA yield. Previous research has

demonstrated that these sources of FA oscillate over 24 h (Rottman et al., 2014; Ma et al., 2015).

According to the zero-amplitude test, none of the treatments fit daily rhythms of de novo, mixed,

or preformed FA (P > 0.20; Figure 6-5B-D). The present study may indicate that infusion of

sodium caseinate eliminates these rhythms, but this cannot be definitively determined without a

negative control. Abomasal infusions of FA during the NGT have been shown to eliminate the

daily rhythms of de novo, mixed and preformed FA yields, whereas infusion during the day did

not (Chapter 4). Despite not fitting a rhythm in any treatment, there were detectable changes in

the fitted 24 h cosine functions of de novo, mixed, and preformed FA yield due to treatment. The

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amplitude of the rhythm of de novo FA yield was increased over 6-fold by DAY and over 3-fold

by NGT, relative to CON (P < 0.05), with DAY having a 92% greater amplitude than NGT

(Figure 6-5B). Moreover, the phase of the daily rhythm was advanced 11.6 h by DAY and

delayed 10.2 h by NGT (P < 0.05). However, these results should be interpreted with caution

because their low amplitudes hinder the ability to fit a cosine function with an appropriate phase.

Relative to CON, the amplitude of mixed FA yield was increased nearly 4-fold by DAY, but was

reduced 52% by NGT (P < 0.05). Similar to de novo FA, the phase was advanced 10 h by DAY

and delayed 10.4 h by NGT, relative to CON (P < 0.05). Day-infusion decreased the amplitude of

preformed FA yield 48%, but NGT increased the amplitude 7% compared to CON (P < 0.05).

The phase of the rhythm was shifted 6.8 h earlier by DAY and 3.9 h earlier by NGT (P < 0.05).

Taken together, these results imply that although sodium caseinate seemingly causes dramatic

reductions in the amplitude of the daily rhythms of the three major sources of FA such that they

do not fit a 24 h rhythm, time of protein infusion still causes apparent changes in these low-

amplitude rhythms.

Daily Rhythms of Plasma Metabolites

Treatment affected the daily rhythms of plasma glucose concentration (Figure 6-6A). A

24 h rhythm was present in CON (P = 0.03) and NGT (P = 0.02), but was abolished in DAY (P =

0.85). Moreover, the amplitude of the rhythm was decreased 66% by DAY, but was increased

47% by NGT, relative to CON. Alternatively, the acrophase of the daily rhythm was not altered

by treatment. Furthermore, average plasma glucose concentration was not affected by treatment

(P = 0.14). Results were similar to previous research demonstrating that altering the time of FA

infusion had no effect on average glucose concentrations or the phase of the daily rhythm

(Chapter 4). However, altering the time of feed availability has previously been shown to entrain

daily rhythms of glucose concentration. Feeding 4x/d reduced the amplitude and phase-delayed

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the rhythm 8 h relative to 1 x/d feeding (Rottman et al., 2014). Moreover, we previously

demonstrated that restricting the time of feed availability to 16 h/d during the night (1900 to

1100) increased the amplitude 2.5-fold and phase delayed the rhythm by 9.8 h relative to day-

restricted feeding (16 h /d from 0700 to 2300 h) (Chapter 3).

In dairy cows, plasma glucose is almost exclusively derived from gluconeogenesis in the

liver using propionate as a substrate (Aschenbach et al., 2010). Therefore, the circadian clock of

the liver may be linked to daily rhythms of glucose metabolism. In model organisms, the

circadian clock of the liver is highly responsive to food entrainment (Stokkan et al., 2001).

Maugeri et al. (2018) proposed that the molecular clock of the liver can be entrained by a protein-

only diet or treatment with cysteine, and that this effect was mediated through glucagon and IGF-

1 signaling. The changes in circadian amplitude due to the time of protein infusion may be

through increasing or decreasing the robustness of oscillations of the liver clock. The daily

rhythms of glucose metabolism may also be controlled by rhythms of insulin secretion. We have

previously demonstrated that insulin concentration follows a 24 h rhythm that is responsive to

feeding time (Chapter 3). In model organisms, the daily rhythms of insulin secretion are secreted

by a circadian clock in the pancreas, and this clock may be entrained by timing of nutrient

absorption (Allaman-Pillet et al., 2004). Further research examining the role of protein and amino

acids on the molecular clocks of the liver and pancreas should be conducted. The increase in

amplitude of the plasma glucose concentrations due to protein infusion at night may be

responsible for the increased amplitude of milk yield. Glucose is required for synthesis of lactose,

which is the osmotic driver of milk yield. The increased amplitude of glucose concentration may

increase the amplitude of conversion to lactose, and therefore the amplitude of milk yield.

Plasma NEFA concentration has previously been demonstrated to exhibit a 24 h rhythm

in dairy cows (Giannetto and Piccione, 2009; Niu et al., 2014). In the current experiment, NEFA

concentrated exhibited a daily rhythm in DAY (P = 0.003), but this rhythm was abolished by

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CON (P = 0.93) and NGT (P = 0.69; Figure 6-6B). The elimination of the rhythms of CON and

NGT corresponded with a 73% and 61% decrease compared to DAY, respectively (P < 0.05).

The fitted daily rhythm peaked at 0313 in the CON group and was phase-advanced 10.5 h by

NGT (P < 0.05), but was not affected by DAY (P > 0.10). Average plasma NEFA concentration

was not affected by treatment (P = 0.19).

The time of feed intake has previously been demonstrated to alter the daily rhythm of

plasma NEFA concentration in dairy cows. Rottman et al. (2014) observed that the amplitude of

NEFA concentration was reduced by feeding 4x/d compared to 1x/d. Moreover, research from

our lab demonstrated that night-restricted feeding for 16 h from 1900 to 1100 delayed the phase

by 10 h and increased the amplitude over 3-fold compared to day-restricted feeding (Chapter 3).

Furthermore, plasma NEFA concentrations are affected by the time of fatty acid absorption. In a

previous experiment from our lab, the amplitude of NEFA was reduced 4–fold, and the phase was

delayed 9 h when the time of FA infusion was limited to 8 h during the day (0900 to 1700 h)

versus continuous infusion (Chapter 5). In contrast, the current experiment demonstrated that

protein infusion at during the day increased the amplitude of plasma NEFA nearly 4 fold.

Moreover, the previous experiment observed a 5 h phase delay due to day-restricted fat in fusion,

whereas the current experiment detected a phase-advance of 9.5 due to protein infusion during the

same time period. These disparate results suggest differential regulation of the daily rhythm of

lipid mobilization by fat and protein.

The daily rhythms of NEFA mobilization may be regulated by the molecular clock of

adipocytes. In model organisms, lipolysis follows a daily rhythm, and this rhythm is modulated

by the adipocyte clock (Shostak et al., 2013). However, to our knowledge, there has been no

research examining the role of amino acids on the circadian clock of adipose tissue. Future

research using explant or cell culture to examine the direct effects of protein on the adipocyte

clock should be conducted. Similar to previous research from our lab testing the time of fatty acid

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infusion (Chapter 3), the time of protein infusion uncouples the daily rhythm of glucose

concentration from the daily rhythm of NEFA concentration. This suggests that protein infusion

during the night may desynchronize the peripheral circadian clocks governing glucose and fatty

acid metabolism.

Similar to previous reports (Giannetto and Piccione, 2009; Niu et al., 2014), a daily

rhythm of plasma urea nitrogen concentration was present in CON (P < 0.0001) and NGT (P <

0.0001), but the rhythm was ablated by DAY (P = 0.22; Figure 6-6C). The amplitude of the daily

rhythm was decreased 30% by DAY and increased 66% by NGT. Furthermore, relative to CON

the daily rhythm was phase-delayed 6.1 h and phase-advanced 1.4 h by DAY and NGT,

respectively. Treatment did not affect the concentration of PUN (P = 0.81). Unlike protein

infusion, previous unpublished research from our lab demonstrated that the time of FA infusion

alters PUN concentration, with 8 h of night-infusion causing a 20% increase compared to

continuous infusion (Chapter 5). In the same experiment, however, the phase of the rhythm of

PUN was only slightly (less than 1 h) shifted by treatment. Plasma urea nitrogen concentration is

an indicator of nitrogen utilization efficiency, with higher PUN values indicating poorer

efficiency (Kohn et al., 2005). Results of the current experiment suggest that the daily rhythm of

efficiency of nitrogen use is dependent on the time of protein absorption. Nitrogen homeostasis

has previously been shown to display endogenous circadian rhythmicity in humans (Minami et

al., 2009). Research conducted in model organisms revealed an integral role for Krüppel-like

factor 15 (KLF15), a zinc-finger DNA-binding protein, in circadian regulation of nitrogen

metabolism (Jeyaraj et al., 2012). Branched-chain amino acids suppress KLF15 expression, and

may be a potential avenue for entrainment of circadian rhythms of nitrogen utilization (Liu et al.,

2017).

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CONCLUSIONS

The time of sodium caseinate absorption affected the daily rhythms of milk synthesis.

This effect appears to be primarily through affecting the robustness of the rhythm without

entraining the phase. In particular, restricting infusion to the night increased the amplitude of the

milk yield and decreased the amplitude of protein concentration, while day-infusion damped the

rhythms of milk yield and fat concentration. The changes in amplitude of milk yield may be

driven by changes in the daily rhythms of glucose concentration, because, similar to milk yield,

the amplitude of plasma glucose was decreased by day-infusion and increased by night-infusion.

The time of protein infusion also appears to affect the daily pattern of lipogenesis, with the peak

of the NEFA rhythm corresponding to the time of protein infusion, and day-infusion dramatically

increasing the amplitude. The nadir of PUN was shifted to the time when protein was infused,

suggesting that protein may entrain the daily rhythms of efficiency of nitrogen utilization. The

changes in the daily rhythms of milk synthesis and plasma metabolites may be mediated through

alterations in the circadian clock of peripheral tissues. Future research examining the role of

amino acids on the molecular clocks of the mammary gland, liver, and adipose should be

conducted.

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FIGURES


Treatments were 500g/d of sodium caseinate abomasally infused continuously for 22 h/d (CON); for 8 h/d

during the day [(DAY); infused from 0900 to 1700] or for 8 h/d during the night [(NGT); feed from 2100 to

0500].

137

Figure 6-2. Effect of time of protein infusion on dry matter intake.



0500]. Data are presented as LSM with SEM bars.

138

Figure 6-3. Effect of time of protein infusion on daily milk, fat and protein yield and fat and




0500]. Data are presented as LSM with SEM bars. Means with differing superscripts are different at P <

0.05.

139

Figure 6-4. The effects of time of protein infusion on daily rhythms of milk synthesis.



0500]. Panels show the effect of time of fatty acid infusion on milk (A) yield (kg), (B) fat yield (g), (C)

protein yield (g), (D) fat concentration (%), and (E) protein concentration (%). Data are presented as

relative expression LSM and SEM bars and cosine fit are shown. Cosinor analysis is shown below each

panel and include the amplitude (Amp; difference between peak and mean), acrophase (Acro; time at peak

of the rhythm), and the P-value of the zero-amplitude test. The black and white bars above the x-axis

display the light: dark cycle.

140

Figure 6-5. Effect of time of protein infusion on daily yields and daily rhythms of milk FA by

source.



0500]. Data are presented as LSM with SEM bars Panels show the effect of protein infusion on (A) daily

yields of de novo (Σ < 16C), mixed (Σ 16C) and preformed (Σ >16C) sources of fatty acids (g/d). (B) daily

rhythms of de novo synthesized (Σ < 16C) FA yield (g/d) (c) daily rhythms of mixed source (Σ 16C) FA

yield (g/d), and (C) daily rhythms of preformed (Σ >16C) FA yield (g/d). Data are presented as relative

expression LSM and SEM bars and cosine fit are shown. Cosinor analysis is shown below each panel and

include the amplitude (Amp; difference between peak and mean), acrophase (Acro; time at peak of the

rhythm), and the P-value of the zero-amplitude test. The black and white bars above the x-axis display the

light: dark cycle.

141

Figure 6-6. The effects of time of protein infusion on daily rhythms of plasma metabolites.



0500]. Panels show the effects of time of fatty acid infusion on daily rhythms of plasma (A) glucose

concentration (mg/dL), (B) nonesterified fatty acid concentration (NEFA; μEq/L), and (C) urea nitrogen

concentration (mg/dL). Data are presented as relative expression LSM and SEM bars and cosine fit are

shown. Cosinor analysis is shown below each panel and include the amplitude (Amp; difference between

peak and mean), acrophase (Acro; time at peak of the rhythm), and the P-value of the zero-amplitude test.

The black and white bars above the x-axis display the light: dark cycle.

142


Supplemental Figure S 6-1. Effect of time of protein infusion on lactose yield.



0500]. Data are presented as LSM with SEM bars. Treatments with different superscripts tend to be

different at P < 0.10.

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SUPPLEMENTAL TABLES


1Contained (% of DM): 42.3% NDF, 21.7% ADF 2Contained (% of DM): 47.2% NDF, 33.5% ADF 3Contained (% of DM): 69.4% NDF, 35.8% ADF 4AminoPlus, Ag Processing Inc., Omaha, NE. 5Contained (%, as-fed basis): 37.0% Calcium carbonate; 29.9% dried corn distillers grains; 24.5% salt;






Item Composition


Corn silage1 45.5


Canola meal 14.3

Ground corn 11.2





Nutrients, % of DM

NDF 38.2

ADF 21.0

CP

Ash 4.7

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Supplemental Table S6-2. Amino acid profile of sodium caseinate.

Amino Acid g/100 g AA

Glutamic Acid 21.1

Proline 10.1

Leucine 9.04

Lysine 7.59

Aspartic Acid 6.80

Valine 6.52

Tyrosine 5.32

Isoleucine 5.22

Phenylalanine 4.99

Serine 4.37

Threonine 3.85

Arginine 3.58

Alanine 2.91

Histidine 2.90

Methionine 2.71

Glycine 1.85

Tryptophan 1.38

Cysteine 0.36

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Chapter 7

Annual rhythms of milk and milk fat and protein production in dairy cattle

in the United States

Isaac J. Salfer, Chad D. Dechow, and Kevin J. Harvatine

This chapter was published as a manuscript in the Journal of Dairy Science:

Salfer, I.J., C.D. Dechow, and K.J. Harvatine. 2019. Annual rhythms of milk and milk fat and

protein production in dairy cattle in the United States. J. Dairy Sci. 102:742–753.

doi:10.3168/jds.2018-15040.

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ABSTRACT

An annual pattern of milk composition has been well appreciated in dairy cattle, with

highest milk fat and protein concentration observed during the winter and lowest occurring in the

summer. However, rhythms of milk yield and composition have not been well quantified.

Cosinor rhythmometry is commonly used to model repeating daily and annual rhythms and

allows determination of the amplitude (peak to mean), acrophase (time at peak), and period (time

between peaks) of the rhythm. The objective of this study was to use cosinor rhythmometry to

characterize the annual rhythms of milk yield and milk fat and protein concentration and yield

using both national milk market and cow-level data. First, ten years of monthly average milk

butterfat and protein concentration for each Federal Milk Marketing Order (FMMO) were

obtained from the USDA Agricultural Marketing Service database. Fat and protein concentration

fit a cosine function with a 12-month period in all milk markets. There was an interaction

between milk marketing order and milk fat and protein concentration (P < 0.01). The acrophase

(time at peak) of the fat concentration rhythm ranged from December 4th to January 19th in all

regions, while the rhythm of protein concentration peaked between December 27th and January

6th. The amplitude (peak to mean) of the annual rhythm ranged from 0.07 to 0.14 percentage

points for milk fat and from 0.08 to 0.12 percentage points for milk protein. The amplitude of the

milk fat rhythm generally was lower in southern markets and higher in northern markets.

Secondly, the annual rhythm of milk yield and milk fat and protein yield and concentration were

analyzed in monthly test day data from 1684 cows from eleven tie-stall herds in Pennsylvania. Fat

and protein concentration fit an annual rhythm in all herds, while milk and milk fat and protein

yield only fit rhythms in 8 of the eleven herds. On average, milk yield peaked in April, fat and

protein yield peaked in February, fat concentration peaked in January, and protein concentration

peaked in December. Amplitudes of milk, fat, and protein yield averaged 0.82 kg, 55.3 g, and

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30.4 g, respectively. Milk fat and protein concentration had average amplitudes of 0.12 and 0.07,

respectively, similar to the results of the milk market data. Generally, milk yield and milk

components fit annual rhythm regardless of parity or DGAT1 K232A polymorphism, with only

cows of the low frequency AA genotype (5.2% of total cows) failing to fit rhythm of milk yield.

In conclusion, the yearly rhythms of milk yield and fat and protein concentration and yield

consistently occur regardless of region, herd, parity, or DGAT1 genotype, and supports

generation by a conserved endogenous annual rhythm.

Keywords: Annual rhythm, seasonal rhythm, milk synthesis, milk fat

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INTRODUCTION

Milk production and milk component concentrations follow a seasonal pattern across the

year that is well-recognized by dairy producers and nutritionists. Milk yield and milk fat and

protein concentrations are typically greatest in the winter and reach a nadir in the summer.

Seasonal variation has long been quantified and accounted for by animal breeders, but has not

been well integrated into dairy management (Wood, 1970, 1976). The causes of these yearly

changes are not fully understood and are often attributed to environmental factors such as heat

stress or changes in forage quality. However, these yearly patterns may be the result of an

endogenous annual rhythm controlling milk synthesis.

In nature, annual rhythms occur as a mechanism for organisms to predict seasonal

environmental changes before they occur to allow proactive adaptations. Many animal species

exhibit yearly cycles of reproductive activity, hibernation, migration, hair growth, and feeding

behavior, allowing them to better prepare for changes in weather conditions and food supply

(Lincoln et al., 2006; Schwartz and Andrews, 2013). A classic example of annual rhythmicity in

production animals is seasonal breeding observed in ewes, which exhibit estrus only from early

autumn to late January, restricting lambing to the spring (Robinson, 1951; Shelton and Morrow,

1965). These annual rhythms of estrus are the result of interactions between photoperiod and an

endogenous physiological timekeeping mechanism (Malpaux et al., 1989). Feed intake of sheep

may also be regulated via annual rhythms, as the secretion of leptin, ghrelin, and orexin is

impacted by photoperiod length (Kirsz et al., 2012). In dairy cattle, the secretion of prolactin

(Chew et al., 1979) and serotonin (Philo and Reiter, 1980) follow melatonin-controlled annual

rhythms, with prolactin levels peaking in summer and serotonin peaking in winter. Furthermore,

Piccione et al. (2012) determined that circulating concentrations of β-hydroxybutyrate, bilirubin,

creatinine, and triglycerides followed annual rhythms in Italian Brown dairy cattle.

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Biological rhythms, including annual rhythms, can be analyzed using cosinor

rhythmometry. This technique fits a linear form of the cosine function with a 12-month period to

multiple years of data to determine if it fits an annual rhythm. The degree to which the data

follow an annual rhythm is assessed using a zero-amplitude test, which determines if the linear

form of the cosine function models that data significantly better than the linear effect of time

(Went, 2006). Cosinor rhythmometry also determines the amplitude, difference from mean to

peak, and acrophase, or time at peak, of a biological rhythm (Bourdon et al., 1995). Quantifying

the annual rhythms of milk and milk component production can improve management decisions

and may provide insight into physiological mechanism governing annual rhythms. The objective

of this study was to quantify the annual rhythms of milk and milk component production in dairy

cattle in the USA and examine cow specific factors affecting these rhythms.

MATERIALS & METHODS

USDA Milk Market Data

Milk composition data from January 2000 through October 2015 were obtained from the

Agricultural Marketing Service (AMS) agency of the United States Department of Agriculture

(USDA). Monthly milk butterfat and protein concentration from 2000 through 2015 were

downloaded for each US federal milk marketing order (FMMO). Orders included: Northeast

(Order 1), Appalachian (5), Florida (6), Southeast (7), Upper Midwest (30), Central (32), Mideast

(33), Pacific Northwest (124), Southwest (126), Arizona-Las Vegas (131), and Western (135;

Figure 7-1). Butterfat data were available for all 11 orders; however, milk protein percentages

were not available for the Appalachian, Florida, Southeast, or Arizona-Las Vegas regions. Only

four years of data were available from the Western region because the order was terminated in

2004 (Tosi, 2004).

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All statistical analysis was performed using the PROC MIXED procedure of SAS 9.4

(SAS Institute, Cary, NC). Monthly butterfat and protein concentration were fit to the linear form

of a cosine function with a 12-month period according to Bourdon et al. (1995) using random

regression (Seltman, 1997) as described by Niu et al. (2014). The model tested the random effect

of year and the fixed effects of marketing order, the linear form of a cosine function with a period

of 12-months, and the interaction of order and the linear form of cosine functions. Denominator

degrees of freedom were estimated using the Kenward-Roger method and the AR(1) covariance

structure was used. The fit of the 12-month rhythms was determined using a zero amplitude test,

which employs an F-Test to compare the full model containing the linear form of the cosine

function to a reduced linear model testing the linear effect of month (Went, 2006). The acrophase

and amplitude of each marketing order were calculated, with subsequent significance tests, using

a spreadsheet program developed in Microsoft Excel® (Bourdon et al., 1995).

Cow-Level Data

Data from a previously published experiment by Vallimont et al. (2010) were used to

examine the effect of seasonal rhythms on milk production at the individual cow level. Briefly,

test day milk yield and butterfat and protein concentration were collected from 1684 individual

cows in eleven Pennsylvania tie-stall dairy herds during the years 2002 to 2011. Of these cows,

731 had been tested for the diacylglycerol O-acyltransferase 1 (DGAT1) K232A polymorphism

using a panel of 121 candidate gene markers (Dekleva et al., 2012; Igenity, Neogen Corporation,

Lincoln, NE). Butterfat and protein yield were calculated as the product of milk yield and

butterfat and protein concentration. Test days when cows were less than 40 days in milk (DIM)

were removed from the analysis as high milk fat is expected during early lactation.

Milk, fat, and protein yield and milk fat and protein concentration were fit to the linear

form of a cosine function with a 12-month period using the MIXED procedure of SAS 9.4. Fixed

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effects included herd, DGAT1 genotype, lactation group (1, 2, 3+ lactations), and days in milk,

along with the random effects of cow and test year. The levels of each main effect (herd, DGAT1

genotype, and lactation group), along with the average of all herds, were fit to a 12-month cosine

function and rhythm fit, amplitude and acrophase were determined using the methodology

described above for the milk market data.

RESULTS

USDA Milk Market Data

A cosine function with a 12-month rhythm was used to test for an annual rhythm in milk

composition using milk fat and protein concentration data from USDA federal milk marketing

orders. There was an interaction of milk marketing order and the cosine function on fat

concentration so the fit, amplitude, and acrophase of the 12-month cosine function were

determined for each milk market order (Table 7-1). Fat concentration in all marketing orders fit

the 12-month rhythm (P < 0.0001). Mean fat percent was lowest in the Arizona-Las Vegas region

(3.56%) and greatest in the Pacific Northwest (3.73%). The amplitude of the rhythm of fat

concentration ranged from 0.07 to 0.14 percentage units between orders. The Southwest, Central

(Figure 7-2C), Southeast, Mideast, Appalachian, and Western regions had 12-month rhythms

with the greatest amplitudes (0.13 to 0.14 percentage units) and did not differ between each other

(P > 0.10). The Northeast, Upper Midwest (Figure 7-2B), and Pacific Northwest marketing

orders all exhibited an amplitude of 0.11 percentage units, which was lower than the Southwest,

Central, Southeast, Mideast, Appalachian, and Western regions (P < 0.05). The amplitudes of fat

concentration rhythms were lowest in Arizona-Las Vegas (0.09 percentage units) and Florida

(0.07 percentage units), with Florida having a lower amplitude than Arizona-Las Vegas (P <

0.05; Figure 7-2A).

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The acrophase, or time at peak, of butterfat concentration ranged from December 4th to

January 18th across milk marketing orders. In six of eleven marketing orders, including Arizona-

Las Vegas, Northeast, Upper Midwest, Mideast, Southwest, and Southeast, the 12-month rhythm

of fat concentration peaked within 4 days of January 1st (P > 0.10). No difference was observed

between the acrophases of the Western, Central, and Appalachian regions, and all peaked

between January 17th and January 19th (P > 0.10). Florida’s annual rhythm peaked on December

4th, markedly earlier than the other regions (P < 0.05).

An interaction between milk marketing order and the cosine function was observed for

protein concentration, so the fit, amplitude, and acrophase of the 12-month rhythm were

compared between marketing orders. Protein concentration fit the 12-month function in all seven

regions with protein data available (P < 0.001; Table 7-1). Mean protein concentration was

greatest in the Pacific Northwest, Southwest, and Western Regions, followed by Central and

Mideast (P < 0.05). The Northeast and Upper Midwest had the lowest mean protein

concentrations. The Northeast and Upper Midwest averaged 3.04% protein, the lowest of all

regions (P < 0.05). The amplitudes of annual rhythms in all regions were between 0.08 and 0.10

percentage units. The greatest amplitudes (0.10 percentage units) were observed in the Central

and Southwest (Figure 7-3B) regions. The Upper Midwest and Mideast had intermediate

amplitudes (0.09 percentage units), while the Northeast (Figure 7-3A), Pacific Northwest and

Western regions displayed amplitudes of 0.08 percentage units (P < 0.05). The acrophases of

annual protein concentration rhythms occurred between December 27th and January 6th in all

regions (P > 0.10). These results are consistent with those observed by Bailey et al. (2005), who

reported that three-year average milk production peaks were greatest in December and January in

the Mideast milk market.

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Cow-Level Data

Data from 11 tie-stall herds in Pennsylvania were analyzed to characterize seasonal

patterns of yield of milk and milk fat and protein, determine how cow-level factors including

parity or DGAT1 genotype influence annual production rhythms, and examine variation in annual

rhythms between individual herds. Milk yield of the 11 herds fit an annual rhythm with an

amplitude of 1.26 kg and an acrophase of March 13st. A yearly rhythm of milk yield was

observed in ten of the eleven herds (P < 0.05; Supplemental Figure S7-3A&B). Eight of the ten

herds that exhibited a yearly rhythm of milk yield peaked between March 13th and May 5th, while

the remaining two herds peaked in mid-summer. These results agree with previous data

suggesting that the average amount of milk shipped from farms is greatest in March, April, and

May (Bailey et al., 2005). The amplitudes of the milk yield rhythms ranged from 0.84 to 1.64

kg/d, suggesting that annual rhythms may be responsible for as much as 3.3 kg/d difference in

milk production across the year.

Overall, the average fat yield of all herds fit a 12-month rhythm with a mean of 1313 g/d,

an amplitude of 55.3 g/d, and an acrophase occurring on February 23rd (P < 0.0001;

Supplemental Figure S7-3C&D). All but one of the eleven herds exhibited a yearly rhythm,

while one exhibited a rhythm with a drastically lower amplitude than the other nine herds (16.5

g/d; P < 0.0001). The nine remaining herds had amplitudes ranging from 97.2 to 47.3 g/d. The

non-rhythmic and low amplitude herds peaked on July 5th and 6th, respectively, in contrast to the

other 9 herds, which peaked between January 22nd and April 3rd. Average protein yield also fit a

yearly rhythm, with a mean of 1081 g, an amplitude of 30.4 g, and an acrophase occurring on

February 19th (Supplemental Figure S7-3E&F). A yearly rhythm was observed in 10 of the 11

herds. The amplitudes of annual rhythms of protein yield were highly variable between herds and

ranged from 14.3 to 47.0 g g/d. There was, however, low variability in the acrophases, with 8

herds peaking between January 31st and March 1st and one herd peaking on April 24th.

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Fat and protein concentration fit 12-month rhythms in all herds as expected and exhibited

much more consistent yearly patterns than milk, fat, and protein yield (P < 0.0001; Supplemental

Figure S7-4A&B). The average rhythm of fat concentration for the 11 herds peaked on January

25th with an amplitude of 0.12 percentage units. Of the 11 herds, 9 peaked within 30 days of

January 25th, with the remaining two peaking on March 27th and April 7th. The amplitude of the

yearly rhythm averaged 0.12 percentage units, equivalent to a 0.24 unit change in fat percent

throughout the year and was highly variable between herds (0.07 to 0.19 units). Milk protein

concentration similarly fit an annual rhythm in all herds (P < 0.001), with an average amplitude

of 0.07 units and the average acrophase occurring on December 30th (Supplemental Figure

S7-4C&D). All herds peaked between December 8th and January 23rd with amplitudes ranging

from 0.04 to 0.11 percentage units, suggesting that annual rhythms of protein percent are highly

consistent between herds.

The effect of parity on fit of the cosine function and the mean, amplitude and acrophase

of annual production rhythms was evaluated (Figure 7-4). Groups included cows in first, second,

or third and greater lactations. Milk yield increased with increasing parity (P < 0.05) and fit an

annual rhythm in all three parity groups (P < 0.0001). The amplitude of the 12-month milk yield

rhythm was greater for cows in their third and greater lactations than 1st and 2nd lactation (P <

0.05), but was not different between 1st and 2nd lactation cows (P > 0.10). Furthermore, acrophase

did not differ between 1st and 2nd lactation, but occurred over a month earlier in third and greater

lactation cows (P < 0.05).

A yearly rhythm of fat yield was also observed in all lactation groups (P < 0.0001). Mean

fat yield increased with increasing parity, while cows in third and greater lactation had 80% and

55% greater amplitudes than 1st and 2nd lactation cows, respectively (P < 0.05). The acrophase of

fat yield did not differ between cows in second or third and greater lactations (P > 0.10), but

occurred 23 and 35 days later in 1st lactation cows compared with 2nd and 3rd lactation,

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respectively (P < 0.05). Protein yield fit a 12-month rhythm in all lactation groups (P < 0.0001)

and mean protein yield increased from 936 g in 1st lactation to 1130 g in 2nd lactation and 1260 g

in 3rd and greater lactations (P < 0.05). Amplitude was greatest in third and greater lactation (73.1

g; P < 0.05), but did not differ between cows in first and second lactation (P > 0.10). The

acrophase of the yearly rhythm was similar between second and third and greater lactations, and

occurred on March 11th and March 1st, respectively (P > 0.10). Alternatively, the acrophase of the

annual rhythm of protein yield occurred later in first lactation cows (April 10th; P < 0.05).

Milk fat and protein concentration fit a 12-month rhythm in all three parity groups (P <

0.0001). Mean milk fat concentration decreased with increasing parity (P < 0.05), but no

difference in amplitude was observed between lactation groups (P > 0.10). Similarly, parity

exerted no effect on the acrophase of fat concentration rhythms, with all peaking between January

14th and January 25th (P > 0.10). Milk protein concentration fit a yearly rhythm in all three

lactation groups (P < 0.05). Mean protein concentration did not differ between 1st and 2nd

lactation (P > 0.10), but was 8% and 6% lower in 3rd and greater lactation cows than 1st and 2nd

lactation, respectively (P < 0.05). No difference occurred in the acrophase or amplitude of the

rhythms of protein concentration between lactation groups, with amplitudes ranging from 0.08 to

0.09 percentage units and all acrophases occurring between December 12th and December 26th (P

> 0.10).

DGAT1 genotype influenced average milk fat concentration, with the cows of the AA

genotype averaging 4.19% milk fat, KA genotype averaging 3.94% fat, and KK genotype

averaging 3.58% fat (P < 0.05; Figure 7-5). Fat yield did not differ between the AA (1296 g) and

KA genotype (1256 g; P > 0.10), but was lower the KK genotype (1178 g; P < 0.05). These

results are consistent with previous reports showing that the K232A polymorphism is associated

with milk fat percent and yield (Schennink et al., 2007). An annual rhythm of fat concentration

was observed regardless of DGAT1 genotype (P < 0.0001) and DGAT1 genotype did not affect

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the amplitude or acrophase of the rhythm (P > 0.10). Similarly, fat yield fit a rhythm in all three

genotypes (P < 0.0001) with no difference in mean fat yield. Amplitude was similar between all

genotypes and ranged from 54.6 to 70.0 g (P > 0.10). Likewise, acrophase was unaffected by

DGAT1 genotype and all rhythms peaked between February 15th and March 1st. Average milk

yield did not differ between the AA and KA genotypes (P > 0.10), but was 2.4 and 1.2 kg greater

in cows with the KK genotype than AA and KA (P < 0.05). Milk yield fit a 12-month rhythm in

cows with the KA and KK genotype, but not AA. The failure of AA to fit a rhythm may have

been because of its low frequency within the population limited observations within the dataset

(only 5% of cows are AA). Between the KA and KK groups, no difference was observed for

either amplitude or acrophase.

DISCUSSION

Regional milk market and cow-level data both confirmed the presence of 12-month

rhythms of fat and protein concentration and cow level data demonstrated a rhythm in milk and

milk fat and protein yield, which is biologically and economically important. The consistency of

these rhythms between years, regions and herds suggests that the annual patterns of production

seen on dairy farms may be the result of a biological rhythm, rather than the outcome of

environmental factors. Heat stress is the most notable environmental factor correlated to seasonal

production declines, and begins to occur when the temperature-humidity index rises above 70

(Bohmanova et al., 2007). Cows in the United States are most likely to experience during July

and August. If the summer decrease in production were strictly due to the acute effects of heat

stress, yearly production would be expected to remain relatively stable, with a sharp decline in the

summer, followed by recovery in the fall when the stress is alleviated. However, the results of

this study suggest a consistent and predictable cosine decrease from the peak of production to the

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nadir. For example, fat and protein concentration, which peak in January in the majority of

regions, begins to decrease in February and March, much earlier than would be expected if the

effect is simply a consequence of heat stress. Furthermore, milk yield was shown to reach a

minimum between September and November, rather than mid-summer when heat stress is

expected to be the greatest. Lastly, the rhythm is very consistent between years and the degree

heat stress is expected to differ between years, especially in more northern regions

(Supplemental Figure S7-5).

The annual rhythms of fat and protein concentration consistently peaked in mid-winter in

both the milk market and cow level datasets. These results are consistent with previous reports

showing that maximal fat and protein concentration occur during December and January (Bailey

et al., 2005). Research in grazing cattle has alternately suggested that the occurrence of milk fat

depression is greatest in May, however this may be influenced by changes in the fermentability of

grass throughout the year (Carty et al., 2017). Dunshea et al. (2008) observed that milk

concentrations of trans-10 C18:1 isomers, associated with biohydrogenation-induced milk fat

depression, were greatest in August and September which is consistent with the trough of fat

yield production observed in the current experiment.

The yearly change in photoperiod with lengthening and shortening days is a strong cue

for entraining annual rhythms and greater amplitude rhythms are often associated with greater

variation in the light-dark cycle across the year (Hut et al., 2013). In the Federal Milk Market

Order data the lowest amplitude annual rhythm occurred in Florida, followed by Arizona –Las

Vegas. Florida has the lowest latitude (Tallahassee, FL: 30.4° N) of all the regions investigated,

and consequently has the smallest change in photoperiod across the year. Similarly, Arizona-Las

Vegas has a lower latitude (Las Vegas, NV: 36.2° N) than the majority of the other marketing

orders. However, the amplitude of the other nine orders did not follow a pattern based on

photoperiod length. For example, the Southeast and Southwest regions had the greatest amplitude

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rhythms, along with the Appalachian, Mideast and Western regions. The northernmost regions

including the Northeast, Upper Midwest, and Pacific Northeast had intermediate amplitudes. One

potential cause for the inconsistency between regions is the differences in housing systems among

various U.S. regions. In Florida and Arizona a large proportion of cows are housed in shaded dry

lots with access to natural lighting, while in northern states cows are more likely to be housed in

enclosed tie-stall or free-stall barns under the influence of artificial lighting. However, this does

not account for the high amplitudes observed in the Southeast and Southwest, where cows are

also commonly housed in open dry lots. Data from dairy herds in the southern hemisphere would

provide additional insight into the role of change in yearly photoperiod on annual rhythms of

production. If the effects is a direct effect of photoperiod, rhythms in the southern hemisphere

would be expected to be inverted compared to those in the northern hemisphere. Auldist et al.

(1998) demonstrated that in New Zealand, milk fat and protein concentration are greatest during

their winter (June-August), while milk yield peaks in their spring and early summer (September-

December). Results from data in the southern hemisphere are difficult to interpret, however,

because grazing systems are common in those countries, and effects of the annual rhythm may be

confounded by differences in the nutrient composition of pasture.

Herd level data suggested that, unlike fat and protein concentration, which peak between

December and February, fat and protein yield reach an acrophase between February and April

and milk yield reaches an acrophase between March and May in most herds. Between herds,

variability was present in the annual rhythms of milk, fat, and protein yield. Two herds in

particular stood out for having no annual rhythms, a low amplitude of fat and protein yield, and

acrophases of fat, protein, and milk yield that occurred during the summer, about four to six

months later than the other herds. One herd also notably failed to demonstrate a yearly rhythm of

milk yield. These herds possessed no identifiable feeding or management characteristics to

differentiate them from the other eight (Vallimont et al., 2010). Results suggest that management

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factors such as changes in diet or feed intake across the year may mask an endogenous annual

rhythm driving yearly changes in milk production.

We examined the genotype at the diacylglycerol O-acyltransferase 1 (DGAT1) locus to

determine its influence on the yearly rhythms of fat concentration, fat yield, and milk yield. The

K2332A polymorphism in the DGAT1 gene is responsible for up to 50% of the genetic variation

in milk fat production (Schennink et al., 2007). The AA genotype is associated with high milk fat

concentration, the KK genotype is associated with low milk fat, and the KA heterozygote results

in an intermediate phenotype (Winter et al., 2002). The current experiment confirmed these

previous results, with AA cows producing milk with a 0.25% greater milk fat concentration than

KA cows, which was 0.36% greater than KK. Annual rhythms of nearly all production variables

persisted regardless of DGAT1 genotype, with a lone exception being milk yield in cows with the

AA genotype. The reason for the lack of an annual rhythm in this group is unclear, but may be a

consequence of a lower prevalence of this genotype. Only 1196 test day observations from 40

unique cows carried the AA genotype, which was equivalent to 5% of the total cows in the study.

This low number of cows, combined with management variability between herds may have

contributed to the inability to detect an annual rhythm. However, despite the low number of

observations, annual rhythms were detected for fat concentration and yield within the AA group.

The DGAT1 genotype of the animals did not affect the shape of the annual rhythms of fat percent

or fat yield, with similar amplitudes and phases in all groups. These results are similar to those

observed by Duchemin et al. (2013), who observed that milk fat concentration was not affected

by the interaction of DGAT1 genotype and season, although they did observe an interaction

between DGAT1 and summer vs winter milk for unsaturated fatty acid concentration.

Lactation number had little effect on the shape of annual production rhythms. Increased

amplitudes of milk, fat, and protein yield occurred in cows in their third or greater lactation.

These results suggest that as cows age, greater oscillation in the yearly rhythms of production

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occurs. Furthermore, greater lactation number was associated with slightly earlier peaks in yearly

rhythms of milk protein and fat yield. The cause of changes is unclear, but may be related to

overall increase in production observed as lactation number increases.

Previous experiments have supported that dairy cattle are influenced by annual rhythms.

Although cows are not generally considered to be seasonal breeders, Hansen (1985) observed

modest effects of season and photoperiod length on reproduction in cattle. Furthermore, Kendall

and Webster (2009) observed greater daily fluctuations in body temperature in the summer

compared to winter. Several circulating hormones and metabolites also follow annual rhythms

across the year. Petitclerc et al. (1983) observed that circulating prolactin concentrations are over

six times greater in summer than winter, and that this effect occurs even when melatonin

signaling is blocked through blinding or pinealectomy. Plasma concentrations of bilirubin,

creatinine, triglycerides, and β-hydroxybutyrate (BHBA) follow 12-month rhythms, with BHBA

peaking on April 1st, bilirubin peaking on July 14th, creatinine peaking on June 12th, and

triglycerides peaking June 16th (Piccione et al., 2012). The annual rhythms observed in these

metabolites may be regulated by the same mechanism as the annual rhythms of production. The

annual patterns of production may also be a consequence of seasonal changes in feed intake.

Cattle typically increase feed intake in the winter and Ueda et al. (2016) reported dry matter and

fiber in the rumen of dairy cows is greater in autumn than spring, with a reciprocal relationship

for ruminal VFA concentration. Endocrine factors regulating intake have been reported to follow

an annual rhythm in other animals. Circulating leptin concentrations vary across the year in sheep

and Siberian hamsters (Rousseau et al., 2003; Henry et al., 2010) and leptin, orexin and ghrelin

appear to be modulated by photoperiod in sheep (Kirsz et al., 2012).

A seasonal rhythm in milk yield and composition may be adaptive to improve nutrition of

the calf. From a biological perspective, there is a clear advantage to providing nursing offspring

with high-energy milk during the winter months. In other organisms, seasonal rhythms function

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to maximize reproductive success by scheduling parturition to allow neonates to be born during

periods of high food availability and favorable climatic conditions (Lincoln et al., 2003). As an

important component of mammalian reproduction, it is reasonable to expect lactation to similarly

follow an annual rhythm and provide more milk and higher fat and protein to neonates in the

winter when energetic demands are greater. Seasonal reproduction in sheep and hamsters appears

to be coordinated by interactions photoperiod and circadian clock genes located within the

hypothalamus. Melatonin is released from the pineal gland during the dark phase of the

photoperiod and binds with high affinity to the pars tuberalis (PT) of the hypothalamus (Johnston

et al., 2006). The duration of melatonin release affects the phase of the core clock genes Per and

Cry within the PT, and which acts downstream to influence the pulsatile frequency of GnRH, thus

affecting reproductive cyclicity (Misztal et al., 2002). Although this mechanism is well-

characterized for controlling seasonal reproduction, there is a dearth of research examining the

presence of mechanisms governing annual rhythms of lactation.

The amplitude, acrophase, and period length of annual rhythms can be manipulated

through photoperiod alterations. Gwinner (1981) demonstrated that yearly cycles of testes growth

and molting in migrating common starlings (Sturnus vulgaris) can be shorted by accelerating

changes in photoperiod. Furthermore, accelerated photoperiods can speed up the reproductive

cycles of rainbow trout (Oncorhynchus mykiss; Bon et al., 1997). Photoperiod manipulation has

been extensively studied for its effects on milk production (Peters et al., 1978; Dahl et al., 1997;

Miller et al., 1999). Cows consistently increase milk yield when placed in a photoperiod with

over 12 h of light, with maximal response occurring in a 16L:8D photoperiod (Reksen et al.,

1999). This effect of may be related to hormonal changes caused by long-day lighting. Dahl et al.

(1997) established that increased milk synthesis due to an artificial 18L: 6D photoperiod is

associated with increased concentrations of circulating IGF-1. While the direct effect of IGF-1 on

milk synthesis is unclear, its concentration is increased after exogenous treatment with

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recombinant bovine somatotropin, suggesting it may increase milk yield (Bauman and Vernon,

1993). Plasma prolactin concentrations are also consistently elevated under long-day lighting

(Tucker et al., 1984; Stanisiewski et al., 1988; Lacasse et al., 2014). Prolactin is an important

driver of seasonal physiological changes in other mammalian species, drives changes in

reproduction and molting (Martinet et al., 1984; Gómez-Brunet et al., 2008). While an association

between prolactin and milk synthesis has been suggested, the effect does not appear to be direct

because no response to exogenous prolactin on milk yield has been observed in cattle (Plaut et al.,

1987; Lacasse et al., 2012).

There appears to be a paradox between the effects of long-day lighting and annual

rhythms of production. In natural conditions milk increases across the winter when the duration

of the light cycle is less than 12 h, whereas photoperiod research suggests that milk yield should

be greatest when more than 12 h of light is present. The effect may be explained by induction of

photorefractoriness to the annual rhythm by constant-long day photoperiods. In other mammalian

species, long-term exposure to a constant photoperiod causes spontaneous reversion, or

photorefractoriness, of a seasonal physiological response to the state expected in the opposite

photoperiod (Lincoln et al., 2005). Suffolk sheep exposed to a fixed short 8L: 16D photoperiod

exhibit summer-type physiology, characterized by a decrease in serum luteinizing hormone

concentrations and anestrous (Robinson and Karsch, 1984). In long-day breeding Syrian hamsters

(Mesocricetus auratus) gonadal degeneration spontaneously occurs after long-term administration

of a fixed 14L: 10D photoperiod. The mechanism for this phenomenon is thought to be through

dissociation of the endogenous annual timer in the PT with melatonin-based signals from the

light-dark cycle (Lincoln et al., 2005). The long-term treatment needed to elicit the effects of long

day photoperiod on milk synthesis provides additional support for this mechanism. In other

species, a fixed photoperiod must be applied for 4 to 12 weeks prior to induction of

photorefractoriness, which is consistent with data suggesting that the effect of long day lighting

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on milk yield does not manifest until 4 weeks after administration (Dahl et al., 2000). While this

mechanism seems like a promising explanation for the incongruence between the annual rhythm

and photoperiod research, it has not been well-examined in cows and further research should be

performed to investigate this effect.

CONCLUSIONS

Milk yield and milk fat and protein concentration and yield fit annual rhythm at both the

regional and cow level. These rhythms appear to exist independent of environmental effects such

as heat stress and may be the result of endogenous circannual rhythms. Rhythms of fat and

protein concentration peak in the middle of winter, and reach a nadir in the summer, whereas

yields of milk, fat and protein peak in the spring. Region of the U.S. appears to have mild effects

on annual rhythms, with lower latitude regions having lower-amplitude rhythms. Moderate

variability in annual rhythms exists between herds, but DGAT1 genotype and lactation group

exert little effects. Dairy producers and consultants should consider the annual rhythms of

production when making management decisions. An understanding of these rhythms will allow

better benchmarking across the year.

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FIGURES

Figure 7-1. Map of United States Federal Milk Marketing Orders.

Image provided courtesy of the USDA Agricultural Marketing Service. The Western (135) marketing order

was terminated in 2004.

165

Figure 7-2. Annual rhythms of fat concentration in selected Federal Milk Market orders.

Orders include (A) Florida (Order 6), (B) Upper Midwest (Order 30) and (C) Central (Order 32). Data

presented as least-squares means with error bars representing SEM of each month and the line representing

a fitted cosine function with a 12-month period. Characteristics of fitted rhythm are displayed including:

Mean, 1Amplitude (peak minus mean), 2Acrophase (time at peak), and 3P-value for the zero-amplitude test

corresponding to the fit of the 12-month cosine rhythm. Annual rhythms of fat concentration for the other

milk market orders are displayed in Supplemental Figure 1.

166

Figure 7-3. Annual rhythms of protein concentration in selected Federal Milk Market orders.

Orders include (A) Northeast (Order 1) and (B) Southwest (Order 126). Data presented as least-squares

means with error bars representing SEM of each month and the solid line representing a fitted cosine

function with a 12-month period. Characteristics of fitted rhythm are displayed including: Mean, 1Amplitude (peak minus mean), 2Acrophase (time at peak), and 3P-value for the zero-amplitude test

corresponding to the fit of the 12-month cosine rhythm. Annual rhythms of fat concentration for the other

milk market orders are displayed in Supplemental Figure 2.

167

Figure 7-4. The effect of parity on annual rhythms of milk yield and composition.

Annual rhythm by parity shown for (A) milk yield, (B) fat yield, (C) fat concentration, (D) protein yield,

and (E) protein concentration. Data presented as least-squares means with error bars representing SEM of

each month and the line representing a fitted cosine function with a period of 12-months. Mean, 1Amplitude (peak minus mean), 2Acrophase (time at peak), and 3P-value for the zero-amplitude test

corresponding to the fit of a 12-month cosine rhythm are displayed in tables below each corresponding

graph.

168

Figure 7-5. The effect of DGAT1 K232A polymorphism on annual rhythms of milk and milk fat

concentration and yield.

Effect of DGAT1 polymorphism on annual rhythms of (A) milk yield, (B) fat concentration, and (C) fat

yield are shown. Data presented as least-squares means with error bars representing SEM of each month

and the line representing a fitted cosine function with a 12-month period. Mean, 1Amplitude (peak minus

mean), 2Acrophase (time at peak), and 3P-value for the zero-amplitude test corresponding to the fit of a 12-

month cosine rhythm are displayed in tables below each corresponding graph.

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TABLES

Table 7-1. The acrophase and amplitude of a cosine function with a 12-month period fit to the regional monthly averages of milk fat and


1Federal Milk Marketing Order. 2Means that do not share a superscript differ P < 0.05 3Distance from mean to peak of the 12-month rhythm. Values that do not share a superscript differ P < 0.05. 4Date at peak of the 12-month fitted rhythm as month and day of month. Values that do not share a superscript differ P < 0.05. 5P-value for the zero-amplitude test corresponding to the fit of a 12-month cosine rhythm.

Item FMMO1 Region N Mean2 Amplitude3 (%) Acrophase4 (date) P-value5

Fat, % 1 Northeast 189 3.71ab 0.11b Dec 31a < 0.001

5 Appalachian 190 3.66cd 0.13a Jan 17bc < 0.001

6 Florida 189 3.61ef 0.07d Dec 4d < 0.001

7 Southeast 189 3.66d 0.14a Jan 3a < 0.001

30 Upper MW 189 3.72a 0.11b Dec 31a < 0.001

32 Central 189 3.67cd 0.14a Jan 19c < 0.001

33 Mideast 189 3.69bc 0.13a Dec 31a < 0.001

124 Pacific NW 189 3.73ab 0.11b Jan 12b < 0.001

126 Southwest 188 3.64de 0.14a Dec 31a < 0.001

131 AZ-LV 190 3.56f 0.09c Dec 29a < 0.001

135 Western 51 3.62de 0.13a Jan 18bc < 0.001

Protein, % 1 Northeast 190 3.04c 0.08c Dec 31 < 0.001

30 Upper MW 190 3.04c 0.09bc Dec 30 < 0.001

32 Central 189 3.07b 0.10ab Jan 6 < 0.001

33 Mideast 190 3.06bc 0.09ab Dec 30 < 0.001

124 Pacific NW 189 3.10a 0.08c Dec 27 < 0.001

126 Southwest 189 3.09ab 0.10a Dec 30 < 0.001

135 Western 51 3.09ab 0.08abc Jan 2 < 0.001

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Supplemental Figure S7-1. Annual rhythms of fat concentration in Federal Milk Market orders.

Orders include (A) Northeast (Order 1) (B) Appalachian (Order 5), (C) Southeast (Order 7), (D) Mideast

(Order 33), (E) Pacific Northwest (Order 124), (F) Southwest (Order 126), (G) Arizona-Las Vegas (Order

131), and (H) Western (Order 135). Data presented as least-squares means with error bars representing

SEM of each month and the solid line representing a fitted cosine function with a period of 12-months.

Characteristics of fitted rhythm are displayed including: Mean, 1Amplitude, 2Acrophase, and 3P-value for

the zero-amplitude test corresponding to the fit of a 12-month cosine rhythm.

171

Supplemental Figure S7-2. Annual rhythms of protein concentration in Federal Milk Market

orders.

Orders include (A) Upper Midwest (Order 30), (B) Central (Order 32), (C) Mideast (Order 33), (D) Pacific

Northwest (Order 124), and (E) Western (Order 135). Data presented as least-squares means with error bars

representing SEM of each month and the solid line representing a fitted cosine function with a period of 12-

months. Characteristics of fitted rhythm are displayed including: Mean, 1Amplitude, 2Acrophase, and 3P-

value corresponding to the zero-amplitude test of the 12-month rhythm.

172

Supplemental Figure S7-3. Variability in acrophase and amplitude of daily rhythms of milk, fat

and protein yield among 11 herds in Pennsylvania.

Panels show: (A&C) Acrophases (date at peak) of the 12-month (A) fat concentration and (C) protein

concentration rhythms among herds. The average acrophase of the 11 herds are shown at the top of the

figures in grey. Fit of a 12-month rhythm as determined by zero-amplitude test is shown on the right side of

the figures. (B&D). Amplitudes of the 12-month (B) fat concentration and (D) protein concentration

rhythms among herds. The average amplitude of the 11 herds is shown at the right side of the figures in

grey. Data are presented as means with error bars representing the 95% confidence interval.

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Supplemental Figure S7-4. Variability in acrophase and amplitude of daily rhythms of milk fat

and protein concentration among 11 herds in Pennsylvania.

Panels show: (A & C) Acrophases (date at peak) of the 12-month (A) fat and (C) protein concentration

rhythms among herds. The average acrophase of the 11 herds are shown at the top of the figures in grey.

Fit of a 12-month rhythm as determined by zero-amplitude test is shown on the right side of the figures. (B

& D). Amplitudes of the 12-month (B) fat concentration and (D) protein concentration rhythms among

herds. The average amplitude of the 11 herds is shown at the right side of the figures in grey. Data are

presented as means with error bars representing the 95% confidence interval.

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Supplemental Figure S7-5. Repeatability of annual rhythms across years.

Panels show monthly average fat and protein concentration in (A) the Northeast region (Order 1) and the

(B) Central region (Order 32) from 2000 to 2015.

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Chapter 8

Annual rhythms of milk synthesis in dairy herds in four regions of the United

States and their relationships to environmental predictors.

Isaac J. Salfer, Paul A. Bartell, C.D. Dechow, Kevin J. Harvatine

176

ABSTRACT

Annual rhythms of milk fat and protein concentration are well described, but the rhythm

of milk and milk component yield is not well described and is important to dairy management.

Recent analysis of Federal Milk Marketing orders in the USA suggested that the amplitude and

time at peak (acrophase) of the rhythm differ between regions. Our objective was to determine the

annual rhythm of milk production and the effect of U.S. region at the herd level, and examine

potential environmental factors entraining this rhythm. Monthly DHIA records of all available

herds in Pennsylvania, Minnesota, Texas and Florida from the years 2003 to 2016 were obtained

from Dairy Records Managements Systems. Milk yield, fat and protein yield, and fat and protein

concentration were fit to the linear form of the cosine function with a 12-month period using a

linear mixed effects model. Model parameters included the fixed effects of state, cosine

parameters, the interaction of state and cosine parameters, and breed and the random effects of

herd and year. A zero-amplitude test was performed to determine cosine function. The fit of

models containing either the cosine function or environmental temperature were compared using

an F-test. Additionally, raw milk production responses and the predicted response from the cosine

function were correlated with daylength, change in daylength, and maximum temperature. Milk

yield and fat and protein yield and concentration fit a cosine function in all four states, indicating

an annual rhythm (P < 0.001). The amplitude of the rhythm of milk yield varied by state and was

lower in PA (2.8 kg) and MN (2.4 kg) compared to TX (6.9 kg) and FL (8.1 kg; P < 0.05). Fat

and protein yield similarly showed a greater amplitude in the southern versus northern states (P <

0.05). The fat and protein concentrations was opposite, with greater amplitudes occurring in MN

and PA than in TX and FL (P < 0.05). The acrophase of milk yield, fat and protein yield, and

concentration also varied by state, but all peaked between October and March (P < 0.05). The

annual rhythm fit the data better than environmental temperature for all responses in all states (P

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< 0.001), except for fat and protein concentration in Florida. Both raw milk yield (r =0.19) and

the annual rhythm (r = 0.85) were highly correlated with the change in daylength (P < 0.0001),

while raw milk fat and protein concentration and their annual rhythms were highly correlated

with absolute daylength. Results suggest that region of the U.S. impacts annual production

rhythms, with a greater yearly variation in milk, fat and protein yield occurring in the south.

Moreover, the annual rhythms of milk yield and milk components appear to be controlled by two

separate oscillatory mechanisms with change in daylength controlling milk yield and absolute

daylength controlling milk fat and protein concentration.

Keywords: Annual rhythms, milk synthesis, yearly pattern

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INTRODUCTION

Annual rhythms commonly occur in nature as a mechanism for animals to anticipate

seasonal changes in their environment before they occur and improve adaptation to environmental

changes. These rhythms drive remarkable biological changes including migration, hibernation,

and seasonal reproduction. The change in photoperiod across the year is a primary driver of

annual rhythms for organisms living in temperate climates. For example, in short day breeders

like sheep, estrus is triggered as photoperiod shortens resulting in mid-spring lambing (Woodfill

et al., 1991). True annual rhythms are endogenously generated and continue even if animals are

placed under constant photoperiods. The most extreme documented example of this occurred the

African stonechat (Saxicola torquatus axillaris), a species of thrush, which maintained annual

rhythms for 15 years when held in a constant 12.25 light: 11.75 dark photoperiod (Gwinner,

1996). Recent evidence suggests that specialized cells within the hypothalamus integrate changes

in photoperiod with a molecular circadian clock, causing time-of-year specific signals that can be

maintained even after the photoperiod becomes fixed (Hazlerigg et al., 2018).

Dairy cattle display seasonal changes in milk production that are well-appreciated by

dairy producers and nutritionists. We have previously characterized annual rhythms of milk and

milk components using USDA milk market data and test day data from 1684 cows in 11

Pennsylvania dairy herds (Salfer et al., 2019). Fat and protein concentration peaked near early

January in all regions within the USDA milk market dataset, although the amplitudes varied with

southern regions having a lower amplitude rhythm than northern regions. Moreover, cow-level

data revealed that milk yield peaked in late March and fat and protein yield peaked in late

February and that neither parity nor diacylglycerol O-acyltransferase 1 (DGAT1) K232A

polymorphism, associated a majority of the genetic variation in milk fat concentration, exert

substantial influence over annual rhythms of production (Salfer et al., 2019)

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While U.S. milk market and cow-level data have been insightful for characterization of

annual rhythms, a more comprehensive dataset was desired to better quantify these rhythms. The

objective of this study was to model the annual rhythms of milk and milk fat and protein yield

and milk fat and protein concentration using a robust dataset containing a large number of herds

in northern and southern states. The hypothesis was that the annual rhythms of milk production

would vary by latitude, with Florida and Texas having lower amplitude rhythms than

Pennsylvania and Minnesota. Furthermore, the fit of a cosine function was compared to the

effects of environmental temperature, period, and change in photoperiod to determine which

factors correspond to the annual rhythms of milk production.

MATERIALS & METHODS

Data Collection

Monthly DHIA test day records from January 2004 to October 2016 were obtained from

Dairy Records Management Systems (DRMS) for all available herds in Florida (FL), Minnesota

(MN), Pennsylvania (PA), and Texas (TX). These states were selected because they represented

four distinct geographic regions of the U.S. and had a large amount of data available within the

DRMS system. Herd codes were blinded and records included test date, herd size, average DIM,

percent of cows in 1st, 2nd, and 3rd or greater lactation, milk yield, fat concentration, protein

concentration and somatic cell count. For comparisons between states, data were filtered to

include only herds designated as having Holsteins to eliminate the potentially confounding effect

of breed. A total of 764,196 records from 9757 Holstein herds were included in the dataset (5,021

records from 98 herds in FL, 184,521 records from 2708 herds in MN, 552,542 records from

6647 herds in PA, and 15,825 records from 303 herds in TX).

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Cosinor rhythmometry

The fit, amplitude, and time at peak of a 12 month rhythm was determined among

selected states and breeds using cosinor rhythmometry as described by (Salfer et al., 2019).

Briefly, response variables were fit to the linear form of cosine functions with a 12 month period

in ASREML version 14 with the following model:

𝑦𝑖𝑗𝑘𝑙𝑚 = 𝜇 + 𝑆𝑖 + 𝐴 + 𝐵𝑘 + 𝑆𝑖 × 𝐴𝑗 + 𝑆𝑖 × 𝐵𝑘 + 𝑀𝑙 + 𝑌𝑚 + 𝜀𝑖𝑗𝑘𝑙𝑚

Where yijklm is the response variable of interest, μ is the overall mean, Si is the fixed effect of state,

Aj is the linear form of the sin function, Bk is the linear form of the cosine function, Ml is the fixed

effect of average days in milk, Ym is the random effect of year (I = 2004 to 2016) and εijklm is the

residual error. The fit of the linear form of cosine functions was compared to a reduced model

testing the linear effect of day of year:

𝑦𝑖𝑗𝑘𝑙 = 𝜇 + 𝑆𝑖 + 𝐷𝑗 + 𝑆𝑖 × 𝐷𝑗 + 𝑀𝑘 + 𝑌𝑙 + 𝜀𝑖𝑗𝑘𝑙

Where yijkl is the response variable of interest, μ is the overall mean, Si is the fixed effect of state,

Dj is the fixed effect of day of year (i= 1 to 365), Mk is the fixed effect of average days in milk, Yl

is the random effect of year (i= 2004 to 2016), and εijkl is the residual error. The fit of the annual

rhythms was determined by a zero amplitude test comparing the residual sums of squares between

the full model containing linearized cosine functions and the reduced model (Cornelissen, 2014).

The amplitude (peak minus mean) and acrophase (time at peak) and their significance tests were

determined using a script written in R ver. 3.4.3 according to equations of Bourdon et al. (1995).

Effect of breed on annual rhythms of production

The effect of breed was tested based on breed designation codes in the database. Breeds

within fewer than 50 herds were removed, leaving Holsteins (n = 8,790 herds, 675,068 records),

Jerseys (n = 398 herds, 21,510 records), Brown Swiss (n = 67 herds, 2,913 records), and

crossbreds (n = 1463 herds; 53,444 records). A full model containing the fixed effects of breed,

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cosine terms, the interaction of breed with cosine terms, and average days in milk and the random

effect of year was compared to a reduced model containing the fixed effects of breed, day of year,

and average days in milk, and the random effect of year.

Comparison of annual rhythm with temperature

To compare the fit of the annual rhythm with the fit of a model testing the effect of

environmental temperature, daily climate data were collected from the U.S. National Weather

Service from representative weather stations centrally located near heavy populations of dairy

herds in each state (Tallahassee, FL; Litchfield, MN; Harrisburg, PA, Abilene, TX). The effect of

daily maximum temperature on milk and milk fat and protein yield and milk fat and protein

concentration was tested using the following model:

𝑦𝑖𝑗𝑘𝑙 = 𝜇 + 𝑆𝑖 + 𝑇𝑗 + 𝑆𝑖 × 𝑇𝑗 + 𝑀𝑘 + 𝑌𝑙 + 𝜀𝑖𝑗𝑘𝑙

Where yijkl is the response variable of interest, μ is the overall mean, Si is the fixed effect of state Tj

is the maximum daily temperature, Mk is the fixed effect of average days in milk, Yl is the random

effect of year (i= 2004 to 2016), and εijkl is the residual error. The coefficient of determination

(R2) was calculated for both the model containing cosine functions with a 12-month period

(Equation 1) and the model with maximum temperature (Equation 3). An F-test was performed to

compare the fit of the two models (Glatting et al., 2007).

Relationships among environmental variables and milk production responses

The relationship between daylength, change in daylength, and maximum temperature and

the annual rhythms of milk synthesis were tested to better understand which environmental

predictors best explain these annual rhythms. Daylength (min of daylight) for each day of the year

was determined using the latitude of the weather stations used for collection of temperature data

(Tallahassee, FL: 30.4°N; Litchfield, MN: 45.1°N; Harrisburg, PA: 40.3°N, Abilene, TX: 32.4°N)

182

of the from the United States Naval Observatory website

(http://aa.usno.navy.mil/data/docs/Dur_OneYear.php). The change in day length across the year

was computed for each region as described by Forsythe et al. (1995). These environmental

predictors were correlated with both the raw milk and milk component responses and with the

fitted 12 month cosine function as described by Roenneberg and Aschoff (1990). Pearson

correlations were performed using the cor.test() function of R ver. 3.4.3 (R Development Core

Team, 2013).

RESULTS

Annual Rhythms within Selected States

An interaction between state (PA, MN, TX, and FL) and the cosine function with a 12

month period occurred for milk and milk fat and protein yield and milk fat and protein

concentration so the fit, amplitude, and acrophase of the rhythm were determined for each state

(P < 0.001). Average milk yield was greatest in Pennsylvania (26.8 kg), followed by Minnesota

(26.7), Texas (25.8), and Florida (23.9; P < 0.001). In all four states, milk yield fit the 12 month

cosine function, indicating the presence of an annual rhythm (P < 0.001; Figure 8-1A). The

amplitude of the 12-month rhythm of milk yield differed by state, with Florida (3.31 kg) and

Texas (3.06) having amplitudes over twice that of Pennsylvania (1.35) and Minnesota (1.29; P <

0.001). The acrophase, or day at peak, occurred on April 11 for both Minnesota and

Pennsylvania, while Florida peaked 4 days earlier on April 7, and Texas peaked 8 days earlier on

April 3.

Milk fat and protein concentration also fit an annual rhythm in all states (P < 0.002;

Figure 8-1C&E). The amplitude of the 12-month rhythm of milk fat concentration was greatest

in PA (0.16%), followed by MN (0.14), TX (0.09), and FL (0.06). The acrophase of the annual

http://aa.usno.navy.mil/data/docs/Dur_OneYear.php

183

rhythm occurred near the first of the year and was similar between PA (Jan 7) and TX (Jan 8), but

occurred over one week earlier in MN (Dec 29), and nearly a month earlier in FL (Dec 3). The

annual rhythm of milk protein concentration differed between regions similar to milk fat

concentration, with FL having a lower amplitude (0.05%) than MN (0.10), TX (0.10), and PA

(0.09; P < 0.001). The acrophase of the protein rhythm was similar between MN, PA, and TX,

with all occurring within one day of Dec 21, while FL had an acrophase occurring 19 d earlier on

Dec 2.

The annual rhythms in milk yield and milk fat and protein concentration caused a rhythm

in milk fat and protein yield, which fit an annual rhythm regardless of state, with differences in

amplitude and acrophase among states (P < 0.001; Figure 8-1B&D). The amplitude of the fat

yield rhythm was greatest in TX (117 g), followed by FL (110), with much lower amplitudes

observed in PA (61.6) and MN (52.6). The acrophase of the 12-month rhythm of fat yield

occurred on April 2 in FL and was 10 days earlier in TX (March 23). The annual rhythm of fat

yield peaked about a month earlier in PA and MN, occurring on March 1 and March 2,

respectively (P < 0.001). The annual rhythm of protein yield did not differ in amplitude between

FL (94.3 g) and TX (93.1), which both oscillated more than MN (39.4) and PA (39.3; P < 0.001).

The acrophase of protein yield was different between all states, peaking on March 31 in FL,

March 17 in TX, March 7 in FL, and March 5 in MN (P < 0.001).

Annual Rhythms among Breeds

Annual rhythms were detected for milk and milk fat and protein yield and milk fat and

protein concentration regardless of breed (P < 0.001). As expected, average milk yield was

greatest for Holsteins (26.6 kg), followed by crossbreds (21.6), Brown Swiss (21.1), and Jerseys

(17.7; Figure 8-2A). The amplitude of milk yield varied by breed with Holsteins having a 106,

54, and 40%, greater amplitude than crossbreds, Jerseys, and Brown Swiss, respectively (P <

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0.05). However, no differences were detected among the other three breeds (P > 0.05). The

greater amplitude in Holsteins is likely related to greater milk yield, and when amplitude was

examined as a percent of average milk yield, there was little difference among breeds (3% for

Brown Swiss, 5% for Holsteins, 5% for Jerseys, and 4% for crossbreds). Breeds also varied in

acrophase of the annual rhythm of milk yield with Holsteins (Apr. 1) and crossbreds (Apr. 22)

peaking earlier than Jerseys (May 11) and Brown Swiss (May 16). The annual rhythms of fat and

protein concentration also varied by breed (Figure 8-2D&E). Fat concentration was greatest in

Jerseys (4.71%), followed by Brown Swiss (4.07), crossbreds (3.95), and Holsteins (3.72). The

amplitude of the annual rhythm of fat concentration was greatest in Jerseys (0.27%) and lowest in

Holsteins (0.14%; P < 0.05), with Brown Swiss (0.23%) and Crossbreds (0.20%) having a similar

rhythm amplitude (P > 0.05). All breeds had an acrophase of milk fat concentration that occurred

between Jan 5th and 7th (P > 0.05). Similar to fat concentration, the amplitude of protein

concentration was greatest for Jerseys (0.15%), followed by Brown Swiss (0.14), crossbreds

(0.12), and Holsteins (0.08). The acrophase of the protein concentration rhythm occurred on Dec.

21 for both Holsteins and Jerseys, two days earlier than crossbreds and 8 d earlier than Brown

Swiss.

Milk fat yield was greatest in Holsteins (990 g; P < 0.05), followed by Brown Swiss (899

g) and crossbreds (850g), and was lowest in Jerseys (836 g; P < 0.05). Similar to fat yield, protein

yield was greatest in Holsteins (811 g), did not differ between Brown Swiss (745 g) and

crossbreds (689 g), and was lowest in Jerseys (635 g). The annual rhythm of milk fat yield had an

amplitude of 64.2 g in Holsteins 69, 55, and 42% greater than Brown Swiss, Jerseys and

crossbreds, respectively (P < 0.05; Figure 8-2B). The acrophase of fat yield did not differ among

Brown Swiss (Feb. 6), crossbreds (Feb. 23), or Holsteins (Feb. 24 P > 0.05), but the acrophase of

Jerseys (Mar 3) acrophase was delayed 25 d compared to Brown Swiss (P < 0.05). Holsteins also

had a larger amplitude of milk protein (42.8 g) than the other three breeds. Protein yield peaked

185

on Feb 12 in Brown Swiss, which was 16 d earlier than Holsteins, 18 d earlier than crossbreds

and 31 d earlier than Jerseys (P < 0.05; Figure 8-2C).

Comparison of Annual Rhythm and Temperature Models

To determine the influence of environmental temperature versus the annual rhythm, a

model containing the daily maximum temperature of a representative weather station in each state

was compared to the cosine function with a period of twelve months. Milk yield fit a 12 month

cosine function better than the model including the effect of maximum temperature (P < 0.0001;

Table 8-1). Furthermore, among the four states (Florida, Minnesota, Pennsylvania and Texas), all

fit a cosine function better than maximum temperature (P < 0.0001). Model R2 was increased

from 0.16 in the temperature model to 0.19 in the 12 month cosine model for the combined

dataset, and was also greater for the cosine function in all individual states. Florida (0.14 vs 0.32)

and TX (0.23 vs. 0.35) had the greatest improvements in model R2 from the temperature model to

the cosine function model.

Fat concentration fit a cosine function better than the temperature model in the combined

data set (P < 0.0001) and in MN, PA, and TX (P < 0.0001; Table 8-1). However, for FL, the

temperature model fit fat concentration better than the cosine function (P < 0.0001). Model R2 of

the combined dataset was improved from 0.30 in the temperature model to 0.31 in the cosine

model. Furthermore, the R2 of the cosine model was 0.01 units greater in MN, PA, and TX, but

were the same for both models in FL. Similarly, milk protein concentration fit a 12 month cosine

function better than maximum temperature in the combined dataset and in MN, PA, and TX (P <

0.0001). Like fat concentration, milk protein concentration fit temperature better than the cosine

function in FL (P < 0.0001). Model R2 of protein concentration was improved from 0.36 in the

temperature model to 0.39 in the cosine model in the combined dataset. Among the individual

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states the R2 of the cosine model was greater than the temperature model for protein

concentration in MN, PA, and TX, but was not different between models in FL.

Fat and protein yield also both fit a cosine function better than temperature model for the

combined model (P < 0.0001) and each individual state (P < 0.0001). Model R2 of the combined

dataset was improved from 0.12 in the maximum temperature model to 0.15 in the cosine

function model for fat yield and improved from 0.12 to 0.14 for protein yield.

Relationships among Environmental Variables and Milk Production

Daylight and change in daylight change across the year, and appear to follow similar

daily patterns to milk component concentrations and milk yield, respectively (Figure 8-3B&C).

There was a significant relationship among daylength, change in daylength, and environmental

temperature with all production responses (milk yield, milk fat concentration, milk protein

concentration, milk fat yield, milk protein yield; P < 0.0001), with the exception of daylength and

milk protein concentration (P = 0.14; Figure 8-3A). Change in daylength was the environmental

predictor with the strongest relationship to both milk yield (r = 0.19) and the fitted cosine

function (r = 0.85). Maximum temperature had a negative correlation with both milk yield (r = -

0.06) and cosine-adjusted milk yield (r = -0.14), while daylength had a weak positive relationship

(raw: r = 0.04; fitted: r = 0.33).

For both raw data and the fitted cosine function, absolute day length had a strong

negative relationship with milk fat (raw; r = -0.31; fitted: -0.88) and protein (raw; r = -0.40; fitted:

-0.92) concentrations (Figure 8-3A). Maximum temperature also exhibited a strong negative

correlation with both milk fat (raw; r = -0.30; fitted: -0.85) and protein concentration (raw; r = -

0.36; fitted: -0.77). Alternatively, the correlation between change in daylength and raw and fitted

milk fat concentration was relatively low (raw; r = 0.07; fitted: 0.23). Milk protein concentration

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had a weak negative relationship with change in daylength (r = -0.003) and the fitted cosine

function was not correlated (r = 0.002; P = 0.14).

Fat yield, like milk yield, was the most strongly correlated with change in daylength (r =

0.22). Moreover, milk fat yield was also negatively correlated with maximum temperature (r = -

0.19) and daylength (r = 0.10). The fitted cosine function of fat yield also had a strong positive

relationship with change in daylength (r = 0.83) and had a reasonably strong negative

relationships with maximum temperature (r = -0.62) and daylength (r = -0.28). Similarly, both

raw protein yield and the fitted cosine function had the strongest relationship with change in

daylength (raw: r = 0.19; fitted: 0.77), followed by a strong negative relationship with maximum

temperature (raw: r = -0.14; fitted: -0.58), and a weaker negative relationship with absolute

daylength (r = -0.07; -0.27).

Regression Equations to Adjust for Seasonal Changes in Production

The consistency of annual rhythms among years allowed for the derivation of regression

equations to adjust expected production for each month. Equations derived from the linear form

of the cosine function significantly predicted actual milk and milk fat and protein yield and milk

fat and protein concentration for each of the 4 regions (P < 0.001; Table 8-2). Furthermore, the

expected deviations from mean responses were computed from these equations for rapid

adjustment of milk and milk fat and protein yield and milk fat and protein concentration to

account for the seasonal rhythm (Supplemental Table S8-1 - Supplemental Table S8-4).

DISCUSSION

Milk synthesis followed an annual rhythm in all four states that were examined that

represent different regions of the U.S. In all states, milk yield peaked in early April, whereas fat

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and protein concentration peaked at the start of the year. These results were similar to previous

accounts of seasonal variation in milk yield. Wood (1970) detected a ‘spring hump’ in milk yield

across multiple years. Moreover, Bailey et al. (2005) determined that the maximal quantity of

milk shipped per farm in the Mideast Federal Milk Market Order occurred in March, April, and

May. Salfer et al. (2019) observed that in 11 Pennsylvania dairy herds, the yearly rhythm of milk

yield peaked on March 13, while fat concentration peaked on January 25, and protein

concentration peaked on December 20.

The region of the U.S. had a clear effect on the robustness of the annual rhythms of milk

synthesis. States in the southern U.S. (FL and TX) had greater amplitudes of milk and milk fat

and protein yield, whereas states in the northern U.S. (MN and PA) had greater amplitudes of

milk fat and protein concentration. These results concurred with those observed by Salfer et al.

(2019), which demonstrated that the Milk Marketing Order located in Florida had a lower

amplitude of fat and protein concentration than other orders. Together, these results suggest that

the amplitude of annual rhythms is directly influenced by the latitude, perhaps related to

differences in photoperiod. While data from the southern hemisphere would be insightful for

better understanding the relationship between photoperiod and annual rhythms of milk synthesis,

these data are limited and confounded by management system. In New Zealand, milk yield peaks

in September through January, and fat and protein concentration peak from June through August

(Auldist et al., 1998). This would suggest that annual rhythms of milk synthesis are opposite in

the southern compared to the northern hemisphere. However, the cows in this experiment were

housed on pasture and results were likely affected by changes in pasture composition across the

year and perhaps by seasonal breeding.

Of the four breeds examined, all displayed annual rhythms of production with little

differences in the rhythms among breeds. Milk yield peaked approximately one month later in

Brown Swiss and Jerseys than Holsteins and crossbreds, but this difference is unlikely to be

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biologically significant. All of major breeds studied are of the Bos taurus species and were

developed in northern Europe and are relatively genetically similar. An interesting comparison

would be between Bos taurus breeds and animals of the Bos indicus species, which were

developed in tropical climates where the change in photoperiod throughout the year is smaller,

but data is not available to our knowledge. Chital (Axis axis), a species of deer native to the

Indian subcontinent near the equator, show asynchronous annual rhythms of gonadal activity both

in the wild and captivity, whereas these rhythms are well-synchronized in related species of deer

native to Europe (Loudon and Curlewis, 1988).

The annual changes in milk production are often attributed to heat stress. Heat stress

occurs during mid-to late summer because of high ambient temperature and humidity (West,

2003). While the effects of heat stress on milk production are well-established, several factors

suggest that the annual rhythm of milk synthesis is influenced by factors beyond heat stress alone.

First, if the summer decrease in production was caused only by heat stress, yearly production

would be expected to remain stable during the fall through spring, with an acute decline in

production during the summer. Furthermore, the nadir of milk yield occurs during early October,

well past peak temperature, but could be a carry-over effect. Interestingly, average milk, fat and

protein yield fall below the fitted cosine function in June, July, and August, particularly in Texas

and Florida. This suggests that there is an additional depression in milk yield below the annual

rhythm during the summer, likely due to the additive effect of heat stress.

In the current study, the effect of heat stress was directly compared to the annual rhythm

using an F-test that tested the fit of a model testing the main effect of maximum daily

temperature, to a model testing the linear form of the cosine function. Milk, fat and protein yield

were better predicted by the annual rhythm than by maximum temperature in all regions

examined. Furthermore, the annual rhythm was a better predictor of fat and protein concentration

in Minnesota, Pennsylvania and Texas. In Florida, however, fat and protein concentration were

190

better predicted by maximum temperature. This result may be because Florida has a lower

amplitude annual rhythm than other states, so variability in environmental temperature may have

a greater influence on production. Maximum temperature was highly correlated with fat and

protein concentration and their respective fitted cosine functions. However, this effect appears to

be confounded with daylength. Daylength follows a similar yearly pattern to maximum

temperature (Figure 8-3B&D). Furthermore, daylength has a stronger negative relationship with

fat and protein concentration and their fitted cosine functions than maximum temperature.

Importantly, a summary of controlled experiments testing the high ambient temperature using

environmental chambers demonstrate that, while milk yield is consistently decreased by heat

stress, fat concentration is consistently increased, and the effects on protein yield are small and

inconsistent (Table 8-3). While milk yield was decreased by heat stress in all 6 experiments, with

an average decrease of 6.7 kg, fat concentration was increased by heat stress in 5 out of the 6

experiments and was increased 0.22 percentage units on average. Protein concentration was

decreased in 4 and increased in 2 experiments and was decreased 0.12 percentage units on

average. The decrease in milk yield is consistent with the annual rhythm of milk yield, but the

increase in milk fat during heat stress is opposite of the rhythm. These results suggest that the

yearly pattern of milk fat is regulated by a different mechanism.

The pattern of milk synthesis appears to represent an endogenous annual rhythm rather

than a consequence of heat stress. Annual rhythms occur in a wide variety of organisms as a

mechanism to predict seasonal changes in the environment before they occur (Lincoln, 2019).

Commonly, these rhythms are involved in optimizing reproductive processes to improve the

likelihood of offspring survival. For example, ewes express estrous only during late fall and early

winter, restricting the time of lambing to the spring (Shelton and Morrow, 1965). While dairy

cows are not generally considered to be seasonal breeders, Hansen (1985) detected modest effects

of season and photoperiod length on postpartum calving interval. As a component of

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reproduction, annual rhythms of milk fat and protein concentration may exist to provide neonates

with nutrient-dense milk during the winter when energetic demands are higher. Evidence suggests

that other physiological responses follow annual rhythms in dairy cows. Piccione et al. (2012)

discovered that the concentrations of creatinine, triacylglycerides, and β-hydroxybutyrate follow

12-month rhythms. Moreover, Petitclerc et al. (1983) observed that circulating prolactin follows

an annual rhythm that persists even after blinding or pinealectomy. The core body temperature is

also influenced by season, with greater daily fluctuation in temperature during the summer

compared to winter (Kendall and Webster, 2009).

Annual rhythms are governed by endogenous circannual clocks that convert

environmental signals to time-of-year cues that influence physiology in a seasonal manner. In

mammals and birds, a central circannual pacemaker located in the pars tuberalis (PT) of the

pituitary gland has been described (Wood and Loudon, 2018). Notably, annual rhythms can

persist for long periods of time after organisms are placed in constant conditions (Pengelley et al.,

1976; Gwinner, 2003). When annual rhythms are allowed to freerun in the absence of

environmental stimuli, they typically have a period of only 10 to 11 months and require

entrainment of circannual clocks to maintain a 12 month period (Lincoln, 2019). Several

entrainment cues such as photoperiod, social stimuli, and nutritional cycles can synchronize

endogenous annual rhythms to the external environment (Lincoln et al., 2006).

To study the potential role of photoperiod on entrainment of annual rhythms of milk

synthesis, the relationships among milk production responses and both absolute photoperiod

(daylength) and change in daylength were tested. In addition to correlating these predictors with

raw production responses, they were correlated with the fitted response from the linear form of

the cosine function. Fat and protein concentration were most highly correlated with daylength,

and in particular the 12 month fitted cosine function was very highly correlated. These results

suggest that fat and protein concentration may be controlled by a circannual oscillator that is

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entrained by photoperiod. A wealth of research has described the relationship between

photoperiod length and milk synthesis in dairy cattle. Placing cows in an long days (> 12 h

daylight) consistently stimulates milk yield, with the optimal response occurring with a 16 h light:

8 h dark (16L: 8D) photoperiod (Dahl et al., 2000). However, there are a few interesting

contrasts between the results of studies using artificial photoperiod manipulation and the results

of the current study examining the naturally-occurring annual rhythm. First, while long-day

lighting stimulates milk yield, it does not alter fat and protein concentration. In the current study,

not only are fat and protein concentration strongly correlated to daylength, the long days are

associated with decreases in fat and protein concentration. Moreover, milk yield is poorly

correlated with daylength, but maximal milk yield occurs during the spring, when daylength is

shorter than 12 h.

The paradox between controlled photoperiod manipulation trials and the annual rhythm

of milk yield may be caused by animals becoming photorefractory to artificial long-day lighting.

In seasonally-breeding mammals such as Suffolk sheep (Ovis aries) and Syrian hamsters

(Mesocricetus auratus), exposure to a fixed photoperiod for long periods of time causes a

spontaneous reversion to a physiological state expected during the opposite photoperiod

(Robinson and Karsch, 1984; Lincoln et al., 2005). In short-day breeding sheep, estrus is

inhibited by fixed 8L:16D photoperiods, while the estrus of long-day breeding hamsters is

conversely inhibited by fixed 14 light:10 dark photoperiods. Induction of photorefractoriness

requires 4 to 12 weeks of a fixed photoperiod prior to a physiological change, which is consistent

with results of long-day lighting experiments, which require 4 weeks of adaptation before

increased milk yield is observed (Dahl et al., 2000).

As mentioned above, milk, fat and protein yield were poorly correlated with absolute

daylength, but had a strong positive relationship with change in daylength. This suggests that,

rather than being entrained by the absolute daylength, a separate circadian oscillator controlled

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change in daylength may be responsible for entraining the annual rhythms of milk yield.

Furthermore, the annual rhythm of milk yield may be related to the phase-relationship between an

oscillator entrained by photoperiod, and a daily circadian oscillator. Bartell and Gwinner (2005)

proposed that the degree of synchronization between a circannual and a circadian oscillator can

entrain seasonal responses.

The consistency of annual rhythms among years and herds allowed for the derivation of

regression equations to normalize milk and milk fat and protein yield and milk fat and protein

concentration. Furthermore, these regression equations were used to calculate adjustment factors

to standardize these responses across the year. Using these adjustment factors may allow dairy

producers to remove the annual variation in milk production to make better-controlled nutritional

and management decisions. A spreadsheet developed in Microsoft Excel that uses these

regression equations for rapid calculation of yearly rhythm-adjusted milk and milk fat and protein

yield and milk fat and protein concentration are included in the supplement.

CONCLUSIONS

The milk synthesis of dairy cows follows an annual rhythm that is affected by region of

the United States. The annual rhythm of milk yield appears to be related to, and is perhaps

entrained by the change in daylength. Fat and protein concentration have a strong relationship to

absolute daylength, suggesting that they may be entrained by photoperiod. While maximum

environmental temperature is also highly correlated to fat and protein concentration, the cosine

function fits the data better, suggesting it better explains their yearly pattern. Fat and protein

concentration closely align with the lengthening and shortening of days, while milk yield aligns

with the change in daylength. These annual rhythms may to be driven by endogenous circannual

oscillators, and may not be able to be influenced through management interventions. Further

194

research must be conducted to understand the physiological mechanisms governing the annual

rhythms of milk synthesis. Dairy producers can account for the annual rhythms of production by

standardizing milk production for this rhythm across the year.

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FIGURES

Figure 8-1. Annual rhythms of milk, fat and protein yield and fat and protein concentration in

Holstein dairy herds in Florida (FL), Minnesota (MN), Pennsylvania (PA), and Texas (TX).

Data includes Dairy Herd Information Association (DHIA) test day results from individual herds in FL (98

herds, 5,021 records), MN (2708 herds, 184,521 records), PA (6647 herds, 552,542 records), and TX (303

herds, 15,825 records) acquired from Dairy Records Management Systems (DRMS). Panels show: (A)

Effect of state on annual rhythms of milk yield, (B) Effect of state on annual rhythms of fat yield, (C)

Effect of state on annual rhythms of fat concentration, (D) Effect of state on annual rhythms of protein

yield, (E) Effect of state on annual rhythms of protein concentration. Data are presented as the least

squares mean of each month with error bars representing the SEM and the line is the fitted cosine function

with a period of 12 months. Cosinor analysis is shown below each panel and include the amplitude (Amp;

difference between peak and mean), acrophase (Acro; time at peak of the rhythm), and the P-value of the

zero-amplitude test.

196

Figure 8-2. Annual rhythms of milk and milk fat and protein yield and milk fat and protein

concentration by breed.

Data includes Dairy Herd Information Association (DHIA) test day results from individual herds

designated to contain Brown Swiss (BS; 67 herds, 2,913 records), Holsteins (HO, 8790 herds, 675,071

records), Jerseys (JE; 398 herds, 21,510 records), and crossbreds (1463 herds, 53,444 records) acquired

from Dairy Records Management Systems (DRMS). Panels show: (A) Effect of breed on annual rhythms

of milk yield, (B) Effect of breed on annual rhythms of fat yield, (C) Effect of breed on annual rhythms of

fat concentration, (D) Effect of breed on annual rhythms of protein yield, (E) Effect of breed on annual

rhythms of protein concentration. Data are presented as the least squares mean of each month with error

bars representing the SEM and the line is the fitted cosine function with a period of 12 months. Cosinor

analysis is shown below each panel and include the amplitude (Amp; difference between peak and mean),

acrophase (Acro; time at peak of the rhythm), and the P-value of the zero-amplitude test.

197

Figure 8-3. Relationship among environmental predictors and annual rhythms of milk and milk

fat and protein yield and milk fat and protein concentration by breed.

Panels show: (A) Correlogram showing relationship among environmental variables [daylength, change in

daylength (ΔDaylength), and maximum temperature (Max. Temp)] and milk production responses (Milk

yield, fat concentration, protein concentration, fat yield and protein yield). For each relationship, the

correlation is listed first, followed by the P-value, (B) daylength, (C) change in daylength, and (D) average

maximum temperature across the year for Florida (FL), Minnesota (MN), Pennsylvania (PA), and Texas

(TX). Fitted correlations show relationship of milk and milk fat and protein yield and fat and protein

concentration with the response predicted by a the model: 𝑦 = 𝑦𝑖𝑗𝑘𝑙𝑚 = 𝜇 + 𝑆𝑖 + 𝐴 + 𝐵𝑘+. 𝑆𝑖 × 𝐴𝑗 +

𝑆𝑖 × 𝐵𝑘 + 𝑀𝑙 + 𝑌𝑚 + 𝜀𝑖𝑗𝑘𝑙𝑚 where yijklm is the response variable of interest, μ is the overall mean, Si is the

fixed effect of state, Aj is the linear form of the sin function, Bk is the linear form of the cosine function, Ml

is the fixed effect of average days in milk, Ym is the random effect of year (i= 2004 to 2016) and εijklm is the

residual error.

198

TABLES

Table 8-1. Comparison between models testing the fit of a cosine function versus maximum

temperature.

1Cosine model: 𝑦𝑖𝑗𝑘𝑙𝑚 = 𝜇 + 𝑆𝑖 + 𝐴 + 𝐵𝑘+. 𝑆𝑖 × 𝐴𝑗 + 𝑆𝑖 × 𝐵𝑘 + 𝑀𝑙 + 𝑌𝑚 + 𝜀𝑖𝑗𝑘𝑙𝑚where yijklm is the

response variable of interest, μ is the overall mean, Si is the fixed effect of state, Aj is the linear form of the

sin function, Bk is the linear form of the cosine function, Ml is the fixed effect of average days in milk, Ym

is the random effect of year (i= 2004 to 2016) and εijklm is the residual error. 2Temperature model: 𝑦𝑖𝑗𝑘𝑙 = 𝜇 + 𝑆𝑖 + 𝑇𝑗 + 𝑆𝑖 × 𝑇𝑗 + 𝑀𝑘 + 𝑌𝑙 + 𝜀𝑖𝑗𝑘𝑙where yijkl is the response variable

of interest, μ is the overall mean, Si is the fixed effect of state Tj is the maximum daily temperature, Mk is

the fixed effect of average days in milk, Yl is the random effect of year (i= 2004 to 2016) and εijkl is the

residual error. 3F-test of temperature model fits data better than the cosine model (P < 0.0001).

R2 Cosine

Model1

R2 Temperature

Model2 F-Value P-value

Milk yield, kg/d

Combined 0.19 0.16 9695.7 <0.0001

FL 0.32 0.14 558.5 <0.0001

MN 0.13 0.11 1468.8 <0.0001

PA 0.20 0.17 6972.2 <0.0001

TX 0.35 0.23 875.4 <0.0001

Fat yield, g/d

Combined 0.15 0.12 6885.0 <0.0001

FL 0.19 0.07 161.4 <0.0001

MN 0.10 0.09 860.9 <0.0001

PA 0.15 0.12 5274.4 <0.0001

TX 0.23 0.11 618.0 <0.0001

Protein yield, g/d

Combined 0.14 0.12 8152.6 <0.0001

FL 0.24 0.10 183.9 <0.0001

MN 0.10 0.09 1324.5 <0.0001

PA 0.14 0.12 6147.3 <0.0001

TX 0.27 0.17 515.7 <0.0001

Fat concentration, %

Combined 0.31 0.30 3030.7 <0.0001

FL 0.07 0.07 -55.6 NS3

MN 0.22 0.21 584.2 <0.0001

PA 0.33 0.32 2803.3 <0.0001

TX 0.30 0.29 7.38 0.0001

Protein concentration, %

Combined 0.39 0.36 10504.9 <0.0001

FL 0.06 0.06 -7.22 NS3

MN 0.34 0.31 2276.1 <0.0001

PA 0.41 0.38 8573.5 <0.0001

TX 0.21 0.19 138.9 <0.0001

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Table 8-2. Equations for cosine regression equations for milk and milk fat and protein yield and

milk fat and protein concentration for Florida (FL), Minnesota (MN), Pennsylvania (PA), and

Texas (TX).

1Parameters derived from fitting Dairy Herd Information Association (DHIA) test day data acquired from

Dairy Records Management Systems (DRMS) to a 12 month cosine function with the following equation:

𝑦 = 𝑥 − 𝐴 ∗ cos(2𝜋𝑚

12) + 𝐵 ∗ sin(

2𝜋𝑚

12). A cosine by state interaction was included so parameters could be

derived within each state.

Cosine Term1

Response A B R2 P-value

Milk, kg

FL -0.381 3.30 0.04 0.001

MN -0.249 1.26 0.15 <0.0001

PA -0.261 1.33 0.20 <0.0001

TX -0.175 3.06 0.41 <0.0001

Fat, g

FL -3.90 110.4 0.38 <0.0001

MN 25.0 46.3 0.19 <0.0001

PA 30.2 53.6 0.26 <0.0001

TX 15.5 116.4 0.43 <0.0001

Protein, g

FL -0.151 94.3 0.47 <0.0001

MN 16.8 35.7 0.19 <0.0001

PA 15.3 36.2 0.23 <0.0001

TX 21.9 90.5 0.44 <0.0001

Fat, %

FL 0.049 -0.025 0.10 <0.0001

MN 0.135 -0.005 0.28 <0.0001

PA 0.155 0.017 0.20 <0.0001

TX 0.093 0.013 0.17 <0.0001

Protein, %

FL 0.046 -0.025 0.23 <0.0001

MN 0.095 -0.014 0.40 <0.0001

PA 0.091 -0.015 0.41 <0.0001

TX 0.107 -0.022 0.35 <0.0001

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Table 8-3. A summary of milk yield and milk fat and protein concentration responses in

experiments testing the effects of heat stress in environmental chambers.

Reference Milk, kg Fat, % Protein, %

Rhoads et al. (2009) -10.6 0.34 -0.13

Schwartz et al. (2009) -10.1 0.06 -0.22

Wheelock et al. (2010) -9.6 0.60 -0.27

Zimbelman et al. (2010) -0.1 -0.17 0.13

Baumgard et al. (2011) -6.2 0.28 -0.12

Rungruang et al. (2014) -3.4 0.20 -0.10

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SUPPLEMENTAL TABLES

Supplemental Table S8-1. Adjustment factors to correct for annual rhythms of milk production in Florida.

Milk,

kg/d

Milk lb/d Fat, % Protein, % Fat, g/d Fat, lb/d Protein, g/d Protein, lb/d

Jan 0.00 0.00 -0.05 -0.05 0.0 0.00 0.00 0.00

Feb -1.86 -3.79 -0.03 -0.03 -59.8 -0.12 -47.22 -0.10

Mar -3.32 -6.18 0.00 0.00 -104.6 -0.20 -76.00 -0.17

Apr -3.99 -6.55 0.03 0.03 -122.4 -0.20 -78.61 -0.17

May -3.70 -4.77 0.05 0.05 -108.5 -0.14 -54.36 -0.12

Jun -2.52 -1.35 0.05 0.05 -66.5 -0.02 -9.75 -0.02

Jul -0.76 2.82 0.04 0.04 -7.8 0.12 43.27 0.10

Aug 1.10 6.61 0.02 0.02 52.0 0.24 90.50 0.20

Sep 2.56 9.00 -0.01 -0.01 96.8 0.32 119.27 0.26

Oct 3.23 9.37 -0.04 -0.04 114.6 0.32 121.88 0.27

Nov 2.94 7.59 -0.06 -0.06 100.7 0.26 97.63 0.22

Dec 1.75 4.17 -0.06 -0.06 58.7 0.14 53.02 0.12

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Supplemental Table S8-2. Adjustment factors to correct for annual rhythms of milk production in Minnesota.

Milk,

kg/d


Jan 0.00 0.00 -0.14 -0.11 0.0 0.00 0.0 0.00

Feb -0.62 -3.28 -0.16 -0.11 -59.8 -0.12 -47.2 -0.10

Mar -1.14 -5.68 -0.14 -0.09 -104.6 -0.20 -76.0 -0.17

Apr -1.42 -6.55 -0.08 -0.05 -122.4 -0.20 -78.6 -0.17

May -1.38 -5.65 0.00 0.00 -108.5 -0.14 -54.4 -0.12

Jun -1.05 -3.24 0.08 0.05 -66.5 -0.02 -9.7 -0.02

Jul -0.50 0.05 0.13 0.08 -7.8 0.12 43.3 0.10

Aug 0.12 3.33 0.15 0.09 52.0 0.24 90.5 0.20

Sep 0.64 5.73 0.13 0.07 96.8 0.32 119.3 0.26

Oct 0.92 6.60 0.07 0.03 114.6 0.32 121.9 0.27

Nov 0.89 5.70 -0.01 -0.02 100.7 0.26 97.6 0.22

Dec 0.55 3.29 -0.09 -0.07 58.7 0.14 53.0 0.12

203

Supplemental Table S8-3. Adjustment factors to correct for annual rhythms of milk production in Pennsylvania. Milk,

kg/d


Jan 0.00 0.00 -0.17 -0.11 -31.0 -0.07 -19.7 0.05

Feb -0.70 -1.49 -0.16 -0.09 -54.0 -0.12 -10.7 0.01

Mar -1.29 -2.61 -0.11 -0.05 -62.7 -0.13 0.0 0.00

Apr -1.60 -3.07 -0.03 0.00 -54.8 -0.11 9.5 0.01

May -1.55 -2.73 0.05 0.04 -32.5 -0.05 15.3 0.04

Jun -1.16 -1.69 0.11 0.07 -1.6 0.02 15.8 0.09

Jul -0.52 -0.22 0.14 0.08 29.4 0.09 10.9 0.14

Aug 0.18 1.27 0.13 0.06 52.3 0.14 1.9 0.17

Sep 0.77 2.39 0.08 0.02 61.0 0.15 -8.7 0.18

Oct 1.08 2.84 0.00 -0.03 53.2 0.13 -18.2 0.17

Nov 1.03 2.50 -0.08 -0.07 30.8 0.07 -24.0 0.14

Dec 0.63 1.46 -0.14 -0.10 0.0 0.00 -24.6 0.09

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Supplemental Table S8-4. Adjustment factors to correct for annual rhythms of milk production in Texas. Milk,

kg/d


Jan 0.00 0.00 -0.10 -0.13 -31.1 -0.20 -43.7 -0.19

Feb -1.65 -3.92 -0.09 -0.10 -90.2 -0.34 -86.0 -0.31

Mar -2.91 -6.32 -0.06 -0.06 -129.3 -0.41 -111.1 -0.37

Apr -3.43 -6.55 -0.02 0.00 -137.9 -0.40 -112.3 -0.36

May -3.08 -4.55 0.03 0.05 -113.7 -0.31 -89.3 -0.28

Jun -1.96 -0.86 0.07 0.08 -63.3 -0.16 -48.2 -0.15

Jul -0.35 3.54 0.08 0.08 0.0 0.00 0.0 0.00

Aug 1.30 7.46 0.08 0.06 59.1 0.14 42.3 0.12

Sep 2.56 9.85 0.05 0.01 98.2 0.21 67.4 0.18

Oct 3.08 10.08 0.00 -0.04 106.8 0.20 68.6 0.17

Nov 2.73 8.08 -0.05 -0.09 82.7 0.11 45.6 0.09

Dec 1.60 4.39 -0.09 -0.13 32.2 -0.04 4.5 -0.04

205

Chapter 9

Integrative Discussion

Dairy producers are well aware of daily and seasonal changes in milk production.

Moreover, differences in milk and milk components between morning and evening milking

(Gilbert et al., 1972), and among different seasons of the year (Wood, 1976) have been

characterized in the literature. However, the regulation of these changes have been largely

unexplored and are not considered in modern dairy management. Developing a better

understanding of the biological rhythms governing milk synthesis may uncover opportunities to

improve the efficiency of dairy production. Therefore, the research conducted for this dissertation

sought to examine the nutritional and environmental factors affecting daily and annual rhythms of

milk synthesis.

The first four experiments focused on the relationship between feed intake and daily

rhythms of milk synthesis using an in vivo approach with lactating cows. Previous research has

provided groundwork to suggest that a relationship between feed intake and daily rhythms of milk

production exists. Rottman et al. (2014) demonstrated that increasing the frequency of feeding

from 1x to 4x/d damped the rhythms of milk yield, while Niu et al. (2014) observed differences in

the ratio of morning to evening milk production and daily patterns of plasma metabolites when

cows were fed at 8:30 AM versus 8:30 PM. Based on these initial reports, we wanted to

investigate the impact of the time of feed intake of the rhythms of milk synthesis more

completely. To do this, we employed time-restricted feeding to impose a more dramatic shift in

the feeding pattern of cows, while milking cows 4x/d to determine the daily rhythm of milk

synthesis (Chapter 3). Time-restricted feeding caused a substantial shift in the daily rhythms of

206

milk yield and milk components corresponding with the 12 h difference in feed delivery. These

results confirmed in a very clear fashion that feeding time does indeed affect the daily rhythms of

milk synthesis. Plasma hormone and metabolite concentrations were similarly shifted by the time

of feed intake, corresponding with the fasting cycle. Most interestingly, the response to fasting

was much greater in the night-restricted feeding group, who had much greater fasting non-

esterified fatty acid concentrations, indicating greater lipid mobilization. Cows typically increase

feed intake during the afternoon (Albright, 1993). Our results demonstrate that this daily pattern

of intake appears to be an inherent component of the cow’s physiology, because the cow appears

to continue to want to eat in the afternoon, even after being adapted to night-feeding. Although

not quantified, the cows on the night-restricted treatment also exhibited more food seeking

behavior and greater irritability than day-restricted cows.

In laboratory models, time-restricted feeding can shift physiological rhythms by

modifying the daily pattern of molecular clock gene expression (Stokkan et al., 2001). We wanted

to determine if this mechanism was responsible for the changes in the daily rhythms of milk

synthesis in Chapter 3. We used mammary biopsies to collect tissue from the mammary gland at

four times across the day in cows that were either fed 24 h/d or 16 h/d at night. Of the 6 clock

genes measured, 3 followed a daily rhythm. Time-restricted feeding shifted the daily rhythms of

expression of two clock genes CLOCK and CRY1, and induced a rhythm in REV-ERBα.

To our knowledge, this is the first demonstration that feeding can entrain the mammary

molecular circadian clock in cattle, and one of the first to demonstrate this in any species. On

many large-scale modern dairy farms, milking parlors are operating 24 h/d, leading to cows being

milked at all hours of the day and night. However, feeding of all cows is usually performed

within the same timeframe. Differences in the relationship between feeding and milking time

among individual cows may cause differences in the entrainment of their mammary gland. Future

research should be done to better elucidate this relationship. The results of this experiment not

207

only has exciting implications for dairy production, but human lactation as well. In humans 7% of

the transcriptome follows a daily rhythm (Maningat et al., 2009). Because humans have a less

consistent daily eating pattern than cows, they may be even more responsive to food entrainment.

Therefore, nutritional composition of breastmilk may be affected by meal pattern of the mother.

Research into this area may develop potential avenues to improve post-natal nutrition.

Unfortunately, while we observed compelling evidence to suggest the circadian clock of

the mammary gland is entrained by food intake, the results were not as clear cut as we hoped.

Unexpectedly, 3 of the measured clock genes failed to follow a daily rhythm according to the

zero-amplitude test. One potential cause of this is that we had an unexpectedly high variability in

the samples. Visually, all of the genes analyzed appear to be somewhat rhythmic. Moreover,

PER1 seems to be influenced by treatment with a large spike in expression at 0400 in control that

occurs 12 h later under night-restricted feeding, but again this change is not statistically

significant. The high sample variability may have been due to several potential factors. First, the

mammary tissue collected may have been more heterogeneous than anticipated. The mammary

gland is a complex organ containing multiple cell types including epithelial cells, smooth muscle

cells, and adipocytes (Inman et al., 2015). While biopsy collection was performed as consistently

as possible, variation in the proportions of individual cell types and their potential entrainment

may have contributed to the variation in the gene expression results. More frequent sampling

could have been employed to develop a better characterization of the daily pattern of gene

expression and perhaps helped us detect a rhythm, but mammary biopsy is an invasive species

and we wanted to avoid excess stress on the cows for both animal welfare and research purposes.

An increased number of experimental subjects could have also been used to increase the power of

the experiment, and this will likely be done in the future.

Recent research has illustrated the importance of post-transcriptional and post-

translational regulation of circadian rhythms (Robles et al., 2014, 2017). In Chapter 4, the

208

molecular circadian clock was only examined at the mRNA level, limiting its scope. In the future,

research should consider using proteomic and phosphoproteomic analyses, or antibody-based

techniques to understand these additional levels of regulation.

In Chapters 5 and 6, the effects of the time of fat and protein availability on the daily

rhythms of milk synthesis were studied by infusing cannulated cows with these nutrients post-

ruminally. The time of protein infusion had considerable effects on daily milk yield. If maintained

in commercial herds, the 7 lb reduction in milk yield by restricting protein infusion to the night

would have momentous implications for dairy farmers. This experiment provides compelling

evidence that if protein availability is limited during the day, milk synthesis will also be limited.

Alternatively, the time of fat infusion did not affect milk or milk components. Day infusion of fat

increased the robustness of milk yield and decreased the robustness of fat and protein

concentration (Chapter 5). Alternately, night infusion of protein increased the robustness of milk

yield and decreased the robustness of protein concentration (Chapter 6). These experiments were

the first to study individual nutrients on daily rhythms in the mammary gland. Interestingly, the

response of the daily rhythms of milk synthesis differed between infusions of fat and protein,

suggesting that these nutrients differentially affect the clock. Future research looking at the direct

effect of these nutrients on the mammary circadian clock should be performed.

In Chapters 5 and 6, mixtures of fatty acids or amino acids were used. Presumably,

individual fatty acids and/or amino acids may regulate the mammary clock differently. The

choice to use nutrient mixtures was made partially because no previous characterizations of the

role of nutrients on the mammary clock had been made. Providing a mixture of nutrients allowed

for detection of a general effect of each nutrient on the daily rhythms. Furthermore, performing in

vivo experiments with cows is expensive and time-consuming, and obtaining quantities of pure

fatty acids or amino acids large enough to perform these experiments would be very difficult and

costly. Therefore, experiments using readily available sources of fat and protein were used.

209

Because we have demonstrated that these nutrients do affect daily rhythms, future research

examining specific fatty acids and amino acids would be compelling. The most cost-effective

approach would be to test individual nutrients in a cell culture system first, before developing an

in vivo experiment.

Altering the time of fatty acid and protein infusion also influenced the daily rhythms of

plasma glucose, nonesterified fatty acid and urea nitrogen concentrations. These changes may

have been due to entrainment of peripheral tissues besides the mammary gland. Future research

should consider rhythms in other tissues. The liver of dairy cows is an incredible metabolic organ

that is responsible for producing nearly all of the glucose used by the animal via gluconeogenesis.

Additionally, adipose tissue and muscle are important metabolic organs for dairy cows. Currently

the molecular clocks in these tissues are poorly characterized but developing a better

understanding of them and their relationship to metabolism could reveal opportunities to improve

dairy cow efficiency. Another unexplored aspect of circadian biology in dairy cows is circadian

rhythms of the rumen microbiome. As ruminants, dairy cows are dependent on microbial

fermentation to meet their nutritional requirements. Recent research has shown that microbes in

the hindgut of laboratory models display daily rhythms that are related to the circadian rhythms of

the host (Heath-Heckman, 2016). However, this has been unexplored in ruminants.

Understanding the daily rhythms of microbial metabolism and their relationship to the host could

uncover promising nutritional strategies to improve dairy cow efficiency.

In Chapter 6, milk synthesis failed to exhibit a daily rhythm in the control treatment with

continuous infusion of sodium caseinate. This may suggest that sodium caseinate for 24 h/d

abolishes the rhythm of milk synthesis, but this cannot be confirmed without a negative control

where no sodium caseinate was infusion. Unfortunately in both Chapters 5 and 6, the availability

of cannulated cows was limited so no negative controls were included.

210

Importantly, the daily rhythms of milk synthesis observed in these experiments do not

represent true circadian rhythms. Detection of a true circadian rhythm requires that subjects be

placed in constant lighting conditions. Maintaining lactating cows in a constant lighting

environment is technically challenging because they would need to be housed in an environment

with no access to outside light with the necessary equipment to milk cows in place. At the

Pennsylvania State University, all cows must be moved through an alleyway exposed to natural

light on their way to the milking parlor. Development of a facility where cows could be placed

under constant lighting conditions would open up interesting avenues of research. Not only could

the presence or absences of a true circadian rhythm of milk synthesis be determined, research into

the relationships between light entrainment and food entrainment could be performed.

The characterizations of the rhythms of milk synthesis in Chapters 3, 5, and 6 were

limited by the frequency of milking. Practically, at the Penn State facility it was only possible to

milk cows 4x/d without drastically altering behavior. A facility where cows are milked in-place

would allow more frequent milking and better characterization of the rhythm.

Chapters 7 and 8 of this dissertation characterize annual rhythms of milk and milk

component production. In the Chapter 7, results demonstrated that annual rhythms are highly

conserved among individual cows, but vary by geographic region. To our knowledge, this

experiment was the first to describe yearly changes of milk production as an annual rhythm.

Using a much more robust dataset, we discovered that the annual rhythms differed between the

northern and southern U.S, with the amplitude of milk yield being greater in the south and the

amplitude of milk fat and protein concentration being greater in the north. Moreover, milk yield

was found to be highly related to change in daylength, while milk component concentrations were

highly correlated with absolute daylength, suggesting that two separate circannual oscillators may

regulate these responses. The observations made in these experiments represent a dogmatic shift

in the way that the dairy industry views seasonal changes in milk production. Previously, the

211

changes were thought to be strictly a consequence of environmental conditions, such as heat

stress. Our results clearly suggest that there is an additional effect of an annual rhythm. So far in

the dairy industry, this work has received significant attention. We hope that it will allow dairy

farmers and consultants to better manage cows across the year.

While characterizing the annual rhythms of milk synthesis is a strong initial step,

mechanistic work really must be performed in order to both better understand these rhythms and

develop approaches to modify them. Across all species, there is limited research examining the

mechanisms governing annual rhythms due to the long time-course required to conduct

experiments. In dairy cows, this is complicated even further because of the 9 month lactation

period, which would conflict with the annual rhythm. In other model organisms, ‘accelerated

year’ experiments with controlled lighting have been conducted that allow an entire annual cycle

to be compressed into about 3 months. This approach could be used to better understand the

annual rhythms governing milk production in dairy cows. Additionally, if the mechanisms

causing these rhythms become more well-understood, lighting strategies could be developed to

modify them and potentially improve milk production.

212

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VITA

Isaac James Salfer was born on February 6, 1991 in Maplewood, Minnesota. He began

showing dairy heifers at the age of 11 and working on commercial dairy farms during high

school, which ignited his passion for the dairy industry. Isaac enrolled in the University of

Minnesota in the fall of 2009. As an undergraduate he was highly active within the campus

community, including being a member of the university dairy judging team, the dairy challenge

team, serving as the treasurer of Gopher Dairy Club, and being the president of FarmHouse

Fraternity. He completed his bachelor’s degree in Animal Science in spring of 2013 and stayed at

the University of Minnesota to start master’s degree program in the laboratory of Dr. Marshall

Stern. His master’s research used dual-flow continuous culture fermenters to study potential

modifiers of rumen fermentation and the rumen microbiome. Isaac finished his master’s degree in

Animal Science in the summer of 2015, and immediately began a Ph.D. at The Pennsylvania

State University. At Penn State, his Ph.D. dissertation focused on the nutritional and

environmental factors influencing daily and annual rhythms of milk synthesis in dairy cows. After

graduation, Isaac will remain in academia as an assistant professor of dairy science at South

Dakota State University.

NUTRITIONAL AND ENVIRONMENTAL FACTORS REGULATING ...

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