The relationship between uterine, milk, and

environmental microbiota in postpartum

dairy cows

2020, March

NGUYEN THI THUONG

Graduate School of

Environmental and Life Science

(Doctor’s Course)

OKAYAMA UNIVERSITY

I

TABLE OF CONTENTS

Table of contents ......................................................................................................................... I

Declaration............................................................................................................................... III

Acknowledgments ................................................................................................................... IV

Publications arising from this thesis ......................................................................................... V

Conference proceedings ........................................................................................................... VI

List of figures ......................................................................................................................... VII

List of tables .......................................................................................................................... VIII

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

Chapter 2. Literature Reviews .................................................................................................. 2

The postpartum uterine microbiota............................................................................................. 2

Physiology of uterus after calving ........................................................................................ 2

The microbiota in uterus after calving .................................................................................. 5

The uterine diseases after parturition.................................................................................... 6

Therapy of the uterine diseases ............................................................................................ 8

The milk microbiota ................................................................................................................... 9

Physiology of lactation ......................................................................................................... 9

The microbiota in milk ....................................................................................................... 11

Mastitis ............................................................................................................................... 12

Treatment and prevention of mastitis ................................................................................. 13

The environment microbiota .................................................................................................... 13

The relationship between uterine and environmental microbiota ...................................... 13

The relationship between milk and environmental microbiota .......................................... 14

Chapter 3. The relationship between uterine, fecal, bedding, and airborne dust microbiota

from dairy cows and their environment .................................................................................... 15

Abstract ..................................................................................................................................... 15

Introduction .............................................................................................................................. 15

Materials and methods .............................................................................................................. 16

Results ...................................................................................................................................... 19

Discussion ................................................................................................................................. 21

Chapter 4. An investigation of seasonal variations in the microbiota of milk, feces, bedding,

and airborne dust ...................................................................................................................... 32

Abstract ..................................................................................................................................... 32

II

Introduction .............................................................................................................................. 32

Materials and methods .............................................................................................................. 34

Results ...................................................................................................................................... 36

Discussion ................................................................................................................................. 38

Chapter 5. General Conclusion .............................................................................................. 46

References ................................................................................................................................ 47

III

DECLARATION

I hereby declare that my thesis/dissertation entitled:

“The relationship between uterine, milk, and environmental microbiota in postpartum dairy

cows”

has been composed by myself, that the work contained here in is my own except where

explicitly stated otherwise in the text, and that this work has not been submitted for any other

degree or processional qualification except as specified.

Parts of this work have been published in Animals (2019).

Date

Signature

IV

ACKNOWLEGMENTS

Firstly, I would like to express my sincere gratitude to my supervisor Prof. Nishino

Naoki for the continuous support of my Ph.D study and research, for his patience, motivation,

and immense knowledge. His guidance helped me in all the time of research and writing of this

thesis. I could not have imagined having a better advisor and mentor for my Ph.D study.

I am very grateful to Prof. Saito Noboru, Prof. Funahashi Hiroaki and Prof. Morita

Hidetoshi for their support as co-supervisor. Their lectures and extensive knowledge helped me

a lot during my Ph.D study. And I would like to appreciate Assoc. Prof. Tsuruta Takeshi for his

kind support and encouragement in my Ph.D time.

I would like to thank Ms. Ayumi Miyake and Mr. Tanabe Yuji from Okayama Livestock

Research Institute for their enthusiastic help and instruction for collecting sample. Without their

support it would not be possible to conduct this research.

My sincere thanks also goes to Dr. Tang Thuy Minh, Dr. Tran Thi Minh Tu, and Dr Wu

Haoming for all of their kindness and thoughtfulness in laboratory work as well as student life.

And, I thank my labmates for the stimulating discussions, for the insightful comments in every

seminar, and for all the fun we have had together. Besides, I would like to send my honest

thanks to all of my Vietnamese friends for their support when I came to Japan. Their

companionship and friendship have been motivated me to overcome many obstacles.

Last but not the least, I would like to thank my family: my mother, my father, my sister,

my son and my partner with all my love and gratitude for supporting me spiritually throughout

my study in Japan and my life in general.

V

PUBLICATION ARISING FROM THIS THESIS

1. The Relationship between Uterine, Fecal, Bedding, and Airborne Dust Microbiota from

Dairy Cows and Their Environment: A Pilot Study. Thuong T. Nguyen, Ayumi Miyake,

Tu T.M. Tran, Takeshi Tsuruta and Naoki Nishino. Animals 2019, 9, 1007;

doi:10.3390/ani9121007.

2. An investigation of seasonal variations in the microbiota of milk, feces, bedding, and

airborne dust. Thuong T. Nguyen, Haoming Wu, Naoki Nishino. Asian-Australasian

Journal of Animal Sciences 2019, doi.org/10.5713/ajas.19.0506.

VI

CONFERENCES PROCEEDINGS

1. Tran Thi Minh Tu, Nguyen Thi Thuong, Tran Hoang Diep, Diep Tan Toan, Tsuruta Takeshi,

Nishino Naoki (2017). Relationship between fecal, uterus and milk microbiota in dairy farm in the

south of Vietnam. 122th Annual Meeting of Japanese Society of Animal Science, Kobe, Japan.

2. Nguyen Thi Thuong, Miyake Ayumi, Tanabe Yuji, Tsuruta Takeshi, Nishino Naoki (2018). An

investigation of fecal, uterus and milk microbiota of dairy cows after calving. 124th Annual

Meeting of Japanese Society of Animal Science, Tokyo, Japan.

3. Nguyen Thi Thuong, Miyake Ayumi, Tanabe Yuji, Tsuruta Takeshi, Nishino Naoki (2019). Uterine

microbiota of dairy cows as influenced by season and postpartum period. 125th Annual Meeting of

Japanese Society of Animal Science, Tokyo, Japan.

4. Nguyen Thi Thuong, Tran Thi Minh Tu, Tsuruta Takeshi, Nishino Naoki (2009). Stability and

variability of the uterine microbiota in postpartum dairy cows. National conference Animal and

Veterinary Sciences, Hochiminh city, Vietnam.

5. Nguyen Thi Thuong, Miyake Ayumi, Tran Thi Minh Tu, Tsuruta Takeshi, Nishino Naoki (2009).

Uterine microbiota of postpartum dairy cows as influenced by season and environment. The Second

International Conference on Animal Production and Environment, Can Tho City, Vietnam.

VII

LIST OF FIGURES

Chapter 2

Figure 1. Involution of the uterus in a cow following a normal calving .................................................. 3

Figure 2. Uterine horn diameter measured by ultrasonography in the cows ............................................ 3

Figure 3. The bovine estrus cycle ............................................................................................................. 4

Figure 4. Estrogen from growing follicle is transported through the blood stream to all parts of the

body .............................................................................................................................................. 4

Figure 5. Follicular growth occurs throughout the estrous cycle ............................................................. 5

Figure 6. Estrus cycle is divided into two phases..................................................................................... 5

Figure 7. Almost all cows have bacteria within the cavity of the uterus during the first 2 weeks of calving

and uterine disease ........................................................................................................................ 8

Figure 8. Schematic representation of bovine mammary gland ............................................................. 10

Figure 9. Schematic representation of relationships among lipid metabolism in adipose tissue, liver, and

mammary gland .......................................................................................................................... 10

Chapter 3

Figure 1. Uterine, fecal, bedding, and air-borne dust microbiota in the dairy farm examined during two

seasons ........................................................................................................................................ 30

Figure 2. Canonical analysis of principal coordinates plot characterizing the uterine, fecal, bedding, and

airborne dust microbiota in the dairy farm ................................................................................. 31

Chapter 4

Figure 1. Family-level proportions of the top 20 bacterial taxa of the fecal microbiota of the dairy cows

investigated during summer and winter ...................................................................................... 41

Figure 2. Family-level proportions of the top 20 bacterial taxa of the bedding microbiota of a dairy farm

investigated during summer and winter ...................................................................................... 42

Figure 3. Family-level proportions of the top 20 bacterial taxa of the airborne dust microbiota of a dairy

farm investigated during summer and winter ............................................................................. 43

Figure 4. Canonical analysis of principal coordinates plot characterizing the milk, fecal, bedding, and

airborne dust microbiota of the dairy farm ................................................................................. 44

VIII

LIST OF TABLES

Chapter 2

Table 1. List of bacteria according to their expected pathogenic potential in the uterus ......................... 6

Table 2. Nutrient composition of whole and whole, dry milk................................................................ 10

Chapter 3

Table 1. Milk yield and blood metabolites concentration of the dairy cow examined at one and two

months postpartum at two seasons. ............................................................................................. 25

Table 2. Relative abundance (%) of uterine microbiota of the dairy cows examined at one and two

months postpartum during the two seasons ................................................................................ 26

Table 3. Relative abundance (%) of fecal microbiota of the dairy cows examined at one and two months

postpartum during the two seasons ............................................................................................. 27

Table 4. Relative abundance (%) of bedding microbiota of the dairy farm cowshed examined at two

seasons ........................................................................................................................................ 28

Table 5. Relative abundance (%) of air-borne dust microbiota of the dairy farm cowshed examined at

two seasons ................................................................................................................................. 29

Chapter 4

Table 1. Milk yield, milk composition, and relative abundance of milk microbiota of the dairy cows

examined at one and two months postpartum during the two seasons ....................................... 43

1

CHAPTER 1

GENERAL INTRODUCTION

In postpartum dairy cows, the uterus is contaminated with diverse bacterial species,

including pathogens associated with uterine disease. Although one-third to two-thirds of cows

remain healthy, others may develop metritis and endometritis; this reduces their food intake

and milk production ability and renders them less likely to become pregnant. The bacteria

contaminating the uteri of postpartum dairy cows has been considered to originate from feces

and the environment. However, Trueperella spp. and Fusobacterium spp. can be found in the

uteri of virgin heifers and pregnant cows; hence, the belief that the pregnant uterus is sterile

until contamination with the environmental bacteria at calving, and metritis-causing bacteria

gain access to the uterus when cows calve should be reconsidered. Regardless, factors affecting

uterine microbiota need to be clarified to help prevent uterine disease, improve fertility, and

ensure high milk production from the dairy cows.

On the other hand, the assessment of milk microbiota is important for preventing mastitis

and maintaining herd health in dairy farms. If typical contagious bacteria, such as

Staphylococcus aureus, Streptococcus agalactiae, and Corynebacterium bovis, are found to

proliferate in the tank milk, the infected cows should be identified, and the procedure and

sequence of milking should be revised. If environmental bacteria, such as coliforms, coagulase-

negative Staphylococci, and Streptococci other than S. agalactiae, markedly increase, the

hygiene of the cowshed should be improved. The prevalence of mastitis varies according to the

season in dairy cows as does the somatic cell count (SCC), which is an indicator of the number

of leukocytes in the milk and therefore of udder health. Meanwhile, regardless of the symptoms

of mastitis, the milk microbiota may vary between seasons, because both the health of the cows

and the growth of milk bacteria are influenced by temperature and humidity. In fact, however,

it is unclear what causes seasonal variations in the milk microbiota.

Among the factors putatively involved in the uterine and milk microbiota, the bedding

microbiota may be the most important because the vulva and teats of a dairy cow can be in

direct contact with bedding material when the cow is resting. In this thesis, two experiments

were carried out to characterize the uterine, milk, fecal, bedding and airborne dust microbiota

during different seasons. The objectives were to investigate the relationship between uterine,

milk, and environmental microbiota in dairy farms.

2

CHAPTER 2

LITERATURE REVIEW

THE POSTPARTUM UTERINE MICROBIOTA

Physiology of uterus after calving

The period following parturition is known as the postpartum period (p.p). During this

time the uterus will be reduced in size and empty itself of bacteria, a process known as the

involution of the uterus. Contraction, tissue repair and loss of tissue are processes that will

decrease uterine weight from approximately 9 kg to 10 kg during a 30 days p.p (Sheldon, 2004).

The involution can be evaluated by rectal palpation at day 8-10 p.p. The reduction in weight

follows as 4 days p.p the uterine volume constitutes 50%, and by 8 days 33% of its pregnant

dimensions. The speed of the involution increases by day 10-14 p.p and is probably caused by

the uterine contractions (Figure 1, 2). The number of day’s p.p when the uterus is considered

being fully involuted varies among different studies. So the involution is completed by day 20-

25 p.p. However Sheldon et al. (2006) and Ghanem et al. (2015) indicate the involution of

cervical diameter and the size enlarging of uterus occur during from 1 to 2 months after

parturition. The uterine size reduced during the first one and a half month postpartum and

completed by 80% involution, while the uterus of dairy cows with uterine disease archived the

same involution until 2 months (Griffin et al., 1974; Heppelmann et al., 2013; 2015). In addition,

the epithelial regeneration of the uterine membrane was completed by 1 month after parturition,

whereas the endometrial layer was not fully restored until 2 months after calving (Gilbert at al.,

2005; Sheldon et al., 2008; Gautam et al., 2009). The criteria of a terminated involution are the

uterus in a normal position in the pelvic cavity, uterine horns being symmetrical or almost

symmetrical, no thickening of the uterine wall, and the cervix will also become shrinkage. The

number of lactations influences the cervical and uterine involution. Multiparae cows (≥6) have

a prolonged involution, which may be explained by the increased uterine size in these animals

(Morrow et al., 1969). The other factors also effect on the involution of the uterus such as age,

time of year, abnormalities associated with calving (dystocia, retained fetal, membranes,

hypocalcaemia, ketosis, twin births and metritis) and a delayed return to normal cyclic activity

in the ovaries (Noakes, 2009).

3

Figure 1. Involution of the uterus in a cow following a normal calving (Bekama et al,

1996)

Figure 2. Uterine horn diameter measured by ultrasonography in the cows (Emma, 2010)

In addition, the ovaries must resume normal cyclical activity for inseminating or mating

a cow shortly after calving, the aim being one calf born per cow per year, so it is important that

the postpartum proceeds normally. The estrous cycle normally repeat every 18 to 21 days

(Figure 3). The cycle works starting with a cow in heat on day zero. One ovary has a large

follicle approximately 15 to 20 mm in diameter. This follicle has a mature egg inside and ready

to be released. The cell lining the follicle are producing the hormone estrogen (Figure 4).

Estrogen is transported in the blood stream to all parts of the cow’s body. It makes the uterus

more sensitive to stimulation and aids in the transport of semen at the time of insemination. It

causes the cervix to secrete viscous mucus that flows and lubricates the vagina. Estrogen is also

4

responsible for all signs of heat including a red swollen vulva, allowing other cows to mount

her, going off feed, bellowing considerably and holding her ears erect are but a few of many

signs. One day one, the follicle ruptures or ovulates releasing the egg to the waiting

infundibulum. Several hours prior to ovulation estrogen production declines. As a result, the

cow no longer displays the familiar signs of heat. After ovulation, new types of cell called,

luteal cells, grow in the void on the ovary where the follicle was located. Quite rapidly over the

next five to six days these cells grow to form the corpus luteum (CL). The CL produces another

hormone, progesterone. Progesterone prepares the uterus for pregnancy. Under the influence of

progesterone, the uterus produces a nourishing substance of the embryo called uterine milk. At

the same time, progesterone causes a thick mucous plug to form in the cervix, preventing access

of bacteria or viruses into the uterus. Progesterone also prevents the animal from returning to

estrus by regulating the release of gonadotropins from the pituitary gland in the brain. There

are two important gonadotropins produced, stores and released from the pituitary gland. The

first is follicle stimulating hormone (FSH) which stimulates the growth of small follicles.

Luteinizing hormone (LH) is the other gonadotropin also produced. In addition to supporting

progesterone production by the CL, LH ca also stimulate estrogen production in large follicles.

High levels of estrogen would bring the animal back into heat and make life difficult for a new

embryo if she were pregnant. Thus, progesterone’s regulation of FSH and LH is very important

aspect of maintaining the pregnancy (Figure 5).

Days 16 through 18 of the estrus cycle are referred to as “the period of maternal

recognition”. During this time, the uterus sarches itself for the presence of a growing embryo.

If no embryo is detected, the uterus begins to produce prostaglandin which begins to detroy the

CL. When the CL is destroyed, no more progesterone is produced and the pituitary gland begins

to increase secretion of gonadotropins. Increased secrection of LH stimulate the dominant

folicle to produce estrogen and bring the animal back into estrus.

Figure 3. The bovine estrus cycle

(www.selectsires.com)

Figure 4. Estrogen from growing follicle is transported through

the blood stream to all parts of the body (www.selectsires.com)

5

The average total time is about 21 days. The estrous cycle is subdivided into two phases

based on the dominant hormone or ovarian struture during each phase (Figure 6). The luteal

phase begind when the corpus luteum is formed, about 5 to 6 days after cow was in heat, and

ands when the CL regresses, abnout day 17 to 19 of the cylce. Progesterone levels are high

during this phase of the cycle and estrogen levels are low. The other pahse of the cycle is the

follicular phase, begins when CL of one cycle is regressed and and when the new CL of the

following cylcle is formed. Thus, the follicular phase encompasses the period of time

surroungding estrus. During this phase of the cycle estrogen levels are typically high while

progesterone level are low. Although it is typical for much follicular growth to occur

throughtout the estrus cycle, low levels of LH during the luteal phase, prevents these follicles

from producing high levels of estrogen which would bring the animal back to heat. It is only

the domiant fillicle present at the time the CL regresses, when progesterone levels are low, that

is permitted to produce enough estrogen to bring the animal back into estrus and to continue on

to ovulation.

After calving, the muscles in the uterus begin to contract and eventually expel the calf

and membranes through a dilated cervix and vagina. Several hormones including progesterone,

estrogen, prolactin, relaxin and corticoids produces by the mother cow, the fetus and the

placenta, interact to bring about the parturition. Calving in a clean environment and proper

treatment of the cow after a difficult calving will help prevent reproductive problems.

The microbiota in uterus after calving

The vulva, vestibule, vagina and cervix function as anatomical barriers that protect the

uterus from being contaminated by bacteria. These structures enable a normal uterus to remain

sterile during pregnancy. During and after calving, however, the relaxed vulva and the dilated

cervix allow the entrance of bacteria in to the uterus (Sheldon, 2004). Bacterial contamination

of the uterus p.p is common and 33% of the animals show bacterial growth during the first week

Figure 5. Follicular growth occurs throughout the

estrous cycle (www.selectsires.com) Figure 6. Estrus cycle is divided into two phases

(www.selectsires.com)

6

after calving. By the second week the number of cows positive for bacterial culture has

increased to 44%.

In approximately 10-17% of the cows bacteria persist and cause uterine disease

(Borsberry and Dobson, 1989). Whether or not the bacteria are eliminated depends on the

involution, uterine contractions, endometrial regeneration and defence mechanisms such as

leukocyte migration, phagocytosis and inflammatory mediators. Due to this inflammatory

response an elevated rectal temperature is often seen in dairy cattle within the first ten days

after calving (Sheldon, 2004). Factors that delay the elimination of bacterial contamination are

the size of the bacterial load, the character of the bacterial flora, retained fetal membranes

(RFM), and depression in the cow’s immune status (Sheldon, 2006 and Noakes, 2009).

The most common species of bacteria isolated from the uterine lumen p.p are Escherichia

coli, Streptococci, Arcanobacterium pyogenes, Bacillus licheniformis, Prevotella spp and

Fusobacterium necrophorum. Among bacteria that are associated with uterine infection

(pathogenic species) the most common findings are E.coli, A. Pyogenes, F. necrophorum and

Prevotella species (Table 1)

Table 1. List of bacteria according to their expected pathogenic potential (1-3) in the uterus

1 2 3

A. pyogenes Bacillus licheniformis Clostridius perfringens P. melaninogenicus Enterococcus faecalis Klebsiella pneumoniae

E. coli M. haemolytica Micrococcus species F. necrophorum Pasteurella multocida Providencia stuartii

Peptostreptococcus species Proteus species

Staphylococcus aureus

Staphylococcus species, coagulase negative

Non-haemolytic Streptococci α-Haemolytic Streptococci

Streptococcus acidominimus Aspergillus species

Data: (1) uterine pathogens associated with uterine endometrial lesions; (2) potential pathogens frequently isolated

from the bovine uterine lumen and cases of endometritis but not commonly associated with endometrial lesions;

(3) opportunist contaminants transiently isolated from the uterine lumen and not associated with endometritis

(Sheldon, 2005).

The appearance and odour of vaginal mucus reflects the bacteria in the uterus, A.

pyogenes, Proteus species and F. necrophorum are related with purulent or mucupurulent

vaginal mucus. Furthermore, A.pyogenes, E.coli, non-haemolytic streptococci and M.

haemolytica are associated with a foul mucus odour (Williams et al., 2005).

Epithelial regeneration is complete by about 25 days after parturition, but the deeper

layers of tissues are not fully restored until 6–8 weeks after calving. The postpartum

environment of the uterine lumen supports the growth of a variety of aerobic and anaerobic

bacteria. Many of these bacteria are contaminants in the uterine lumen and are removed by the

mechanisms of defending from the uterus.

The uterine diseases after parturition

7

Bacterial contamination of the uterine lumen is common in cattle after parturition, often

leading to infection and uterine disease. Clinical disease can be diagnosed and scored by

examination of the vaginal mucus, which reflects the presence of pathogenic bacteria such as

Escherichia coli and Arcanobacterium pyogenes (Sheldon et al., 2008). The infertility

associated with uterine disease is caused by damage to the endometrium and disruption of

ovarian cyclic activity.

Uterine disease is commonly associated with Escherichia coli, Arcanobacterium

pyogenes, Fusobacterium necrophorum and Prevotella species. Indeed, A. pyogenes, F.

necrophorum and Prevotella species have been shown to enhance the likelihood of uterine

disease, and increase the risk of clinical endometritis and its severity (Olson et al., 1984).

Numerically the most prevalent pathogens are E. coli (37% of pathogenic bacteria isolated) and

A. pyogenes (49%) (Williams et al., 2005).

The placenta is normally expelled within 6 h of expulsion of the calf but if still present

by 24 h, it is defined as a retained placenta. The incidence of retained placenta (RP) is between

2% and 5% of animals in a herd, but can be increased in cows with abortion, twins, dystocia,

effects of diet and where infectious agents are endemic. Furthermore, retained placenta is

associated with a substantial reduction in milk yield that persists even after resolution of the

problem and in one study affected animals produced milk less than normal cows during the first

60 days of lactation (Sheldon et al., 2004).

The expression of clinical uterine infection depends on the balance between factors such

as the animal, immunity, the number and pathogenicity of the microbes, and the uterine

environment. Typically, 25–40% of animals have clinical metritis in the first 2 weeks after

calving, and disease persists in up to 20% of animals as clinical endometritis (Figure 7).

Clinical metritis (CM) was diagnosed in 18.6% (inter farm range; 15.2–23.5%) and 30%

(19.4–42.3%) of cows and heifers, respectively. RP was diagnosed in 13.1% (9.4–18.1%) and

9.2% (3.6–13.8%) of cows and heifers, respectively (Goshen et al., 2006).

Puerperal metritis is defined as an animal with an abnormally enlarged uterus and a fetid

watery red-brown uterine discharge, associated with signs of systemic illness (decreased milk

yield, dullness or other signs of toxaemia) and fever >39.50C, within 21 days after parturition.

Animals that are not systemically ill, but have an abnormally enlarged uterus and a purulent

uterine discharge detectable in the vagina, within 21 days after calving, may be classified as

having clinical metritis.

LeBlanc et al. (2002) defined endometritis was the presence of purulent uterine discharge

or cervical diameter >7.5 cm, or the presence of muco-purulent discharge after 26 DIM.

Clinical endometritis is characterized by the presence of purulent (>50% pus) uterine discharge

detecting in the vagina 21 days or more after parturition, or mucopurulent (approximately 50%

pus, 50% mucus) discharge detectable in the vagina after 26 days. In the absence of clinical

endometritis, a cow with subclinical endometritis is defined by >18% neutrophils in uterine

cytology samples collected 21–33 days after calving, or >10% neutrophils at 34–47 days.

8

Pyometra is defined as the accumulation of purulent material within the uterine lumen in the

presence of a persistent corpus luteum and a closed cervix.

Figure 7. Almost all cows have bacteria within the cavity of the uterus during the first 2 weeks of calving and

uterine disease is very common. Each marker (♦) indicates the percent of animals with bacteria isolated from the

uterine cavity; data are from four different studies, with animals sampled at various times between calving and 60

days after parturition (Elliot et al., 1968; Griffin et al., 1974; Sheldon et al., 2002b; Williams et al., 2005). The

shaded areas represent the proportion of animals with metritis ( ) within 2 weeks of calving and endometritis ( )

3–5 weeks after calving. The solid line ( ) indicates the percent of animal with histological evidence of

inflammation of the endometrium (Gilbert et al., 2005).

The risk factors for uterine infection include retention of the placenta, the calving

environment, twins, dystocia, and diet. There is bacterial contamination, clearance and re-

contamination of the uterine lumen during the first few weeks after calving, not just infection

around the time of parturition. Retained placenta, uterine bacterial infection and uterine disease

are common after parturition in cattle and cause considerable infertility. Uterine bacterial

infection stimulates a robust immune response but also modulates normal reproductive

physiology. The interaction between the environment, uterine infection, immunity and

reproduction are useful to improved control and treatment strategies for cattle infertility.

Therapy of the uterine diseases

Goshen et al. (2006) evaluated the intrauterine antibiotic treatment of clinical metritis

(CM) and retained placenta (RP) with 5 g Chlortetracycline by intrauterine installation, twice

weekly for 2 weeks. The results showed that Chlortetracycline treatment was proven to prevent

the detrimental effect of CM on reproductive performance in heifers and cows and on milk

production in cows only, while treatment of RP had no effect on reproductive performance or

milk production.

In dairy cows diagnosed with clinical endometritis between 20 and 33 days in milk (DIM),

LeBlanc et al. (2002) compared the effect of intrauterine with antibiotic 500 mg of cephapirin

benzathine or intramuscular with prostaglandin (PGF2α) 500 μg cloprostenol. The results

showed that there was no benefit of treatment of endometritis before 4 weeks postpartum.

9

Administration of between 20 and 26 DIM to cows with endometritis that did not have a

palpable corpus luteum was associated with a significant reduction in pregnancy rate. Between

27 and 33 DIM, cows with endometritis treated with cephapirin had a significantly shorter time

to pregnancy than untreated cows, and there was no difference in pregnancy rate between

PGF2α and untreated cows. The difference in pregnancy rate between cows treated with

cephapirin and with PGF2α was not statistically significant. In addition, Haimer et al. (2013)

indicate the meta-analysis did not reveal an improvement of reproductive performance of cows

with endometritis after treatment with PGF2α. Therefore, PGF2α was reconsidered as a routine

treatment for cows with chronic endometritis. This is particularly important because a blanket

treatment of PGF2α could be perceived by the public as unjustified hormone use.

Administration of PGF2α is the treatment of choice for cases of endometritis in which a

corpus luteum is present. Whereas, in the absence of a corpus luteum, a range of intrauterine

treatments have been administered including antiseptics and antibiotics. And Estradiol at doses

of 5-10 mg per animal has been used therapeutically for postpartum endometritis and is as

effective as PGF2α. However, the interval from treatment to conception was longer with

estradiol treatment than PGF2α or intrauterine antibiotic (Sheldon and Noakes, 1998).

THE MILK MICROBIOTA

Physiology of lactation

The bovine mammary gland (udder) grossly consists of four glands, commonly referred

to as quarters. A schematic representation of a longitudinal cross section of bovine udder is

shown in Figure 8. The glands are separated from one another by connective tissue and median

suspensory ligaments. Each gland has a single teat that opens to the exterior through the streak

canal through which the mammary secretions are drained. The teat canal is lined with skin-like

epidermis known as keratin that acts as the main physical barrier against pathogens that can

invade the mammary gland. The cavity within the teat seen above the teat canal is the teat

cistern and is continuous with the gland cistern of the mammary gland. The secretory tissue and

associated connective tissue constitute the interior of the mammary gland. Secretory tissue in

the udder is organized into lobes, each of which is subdivided into lobules. Each lobule contains

150-200 microscopic alveoli. Each alveolus is a sac-like structure lined with a single layer of

secretory epithelial cells, where milk is synthesized and then secreted. The epithelial lining of

the alveolus is surrounded by myoepithelial cells. In response to the release of oxytocin a

hormone from the pituitary gland, myoepithelial cells by their contractile nature assist in

ejection of milk from each alveolus into the mammary duct. Outside the myoepithelial cells,

the alveolus is surrounded with connective tissue, which consists of fibroblasts, adipocytes,

capillaries and extracellular matrix components. Milk synthesized in alveoli is drained to the

gland cistern through tubular structures known as ducts.

10

Figure 8. Schematic representation of bovine mammary gland

Nutrient composition of whole and whole, dry milk analyze in table 2.

Table 2. Nutrient composition of whole and whole, dry milk (Jenkins et al., 2006)

Whole Whole, dry

Water, % 88.32 2.47

Protein, % 3.22 26.32

Fat, % 3.25 26.71

Ash, % 0.69 6.08

Carbohydrate, % (by difference) 4.52 38.42

Energy, kcal/100 g 60 496

Cholesterol, mg/100 g 10 97

Fatty acids, % of total

Total saturated 64.9 66.1

Total monounsaturated 28.3 31.3

Total polyunsaturated 6.8 2.6

Figure 9. Schematic representation of relationships among lipid metabolism in adipose tissue, liver, and mammary

gland. Plus signs (+) indicate stimulatory effects, minus signs (−) indicate inhibitory effects. Dashed lines indicate

processes that occur at low rates or only during certain physiological states. Abbreviations: epi = epinephrine, TG

= triglyceride, VLDL = very-low-density lipoproteins, CPT-1 = carnitine palmitoyltransferase 1 (Drackley, 1999).

The seasonal pattern across the year has been known to effect the milk yield and milk

composition (Baul et al., 2014; Salfer et al., 2019). Protein and solid-none-fat (SNF) percentage

11

were observed higher in winter than these in summer (Gernand et al. 2019), whilst Salfer et al

(2019) showed the highest milk fat and protein concentration were during the winter and lowest

occurring in the summer. Extreme rates of lipid mobilization lead to increased uptake of NEFA

by liver and increased triglyceride (TG) accumulation (Figure 9). If this lipid infiltration

becomes severe, the syndrome of hepatic lipidosis or fatty liver may result, which can lead to

prolonged recovery for other disorders, increased incidence of health problems. Increased lipid

accumulation and decreased glycogen in the liver were associated with an increased

susceptibility to induction of ketosis (Drackley et al., 1992). Moreover, the primiparous cows

produced less milk than the multiparous group (Oikonomou et al., 1989; Meikle et al., 2004),

so milk composition of first-parity cows was higher than it in the second-, third-, and forth-

parity cows.

The microbiota in milk

The microbiota in milk can play many roles, such as facilitating dairy fermentations (e.g.

Lactococcus, Lactobacillus, Streptococcus, Propionibacterium and fungal populations),

causing spoilage (e.g. Pseudomonas, Clostridium, Bacillus and other spore-forming or

thermoduric microorganisms), promoting health (e.g. lactobacilli and bifidobacteria) or

causing disease (e.g. Listeria, Salmonella, Escherichia coli, Campylobacter and mycotoxin-

producing fungi) (Quigley et al., 2013)

Microorganisms can bring about the fermentation of milk through the production of

lactate and have a variety of different impacts on the sensory, texture, flavor and organoleptic

properties of resultant products (Wouters et al., 2002). Lactic acid bacteria (LAB) is a group of

bacteria that ferment lactose to lactate, which are a dominant population in dairy cow milk prior

to pasteurization. The most common LAB genera in milk include Lactococcus, Lactobacillus,

Leuconostoc, Streptococcus and Enterococcus. Other strains of non-LAB genera are also

encountered in milk, as well as various yeasts and molds (Quigley et al., 2011).

Microorganisms can also have the negative impacts of milk quality, such as

psychrotolerant bacteria can proliferate during refrigeration, which is a major component and

frequently include Pseudomonas and Acinetobacter spp, and through the production of

extracellular lipases and proteases, result in spoilage (Hantsis-Zacharov and Halpern, 2007).

The population of thermoduric bacteria (resisting pasteurisation) includes clostridia,

Listeria monocytogenes, Salmonella, coagulase-positive staphylococci, Escherichia coli,

Enterobacteriaceae, coliforms and Bacillus cereus. Masoud et al. (2012) compared the bacterial

population present in cows’ milk pre- and postpasteurisation. The milk was from a variety of

commercial producers, dominated by Lactococcus, Pseudomonas and Leuconostoc. The

number of anaerobic taxa was detected, including Bacteroides, Faecalibacterium, Prevotella

and Catenibacterium, which are more typically associated with the gut microbiota and may be

entering the milk through fecal contamination. The results indicate that the bacterial population

of pasteurised milk is more diverse than previously appreciated, but the nonthermoduric

12

bacteria that are present within these populations are likely to be in a damaged, nonculturable

form.

The microbial composition of milk can also influence on health of consumers when the

raw milk contaminated with pathogenic bacteria, in some cases, severe illness such as

Staphylococcus aureus, Campylobacter spp., Salmonella, Listeria monocytogenes, Shiga toxin-

producing E. coli (STEC). Staphylococcus aureus is one of major agents of mastitis in dairy

cows and relate to the ingestion of milk and dairy products as a clinical food poisoning problem

in humans characterized by vomiting and dehydration (Oliver et al., 2009; Gonzales-Barron et

al., 2017). Campylobacter spp. comprises several species, the most important is Campylobacter

jejuni which may be contaminated during milking from the intestinal tract of cattle and feces

(Del Collo et al., 2017). However, Salmonella and STEC are few outbreaks of the ingestion of

raw milk or milk products, in this case, the fecal contamination is their main source, thus

hygienic practices should be taken to prevent fecal contact with milk (Gonzales-Barron et al.,

2017). And the prevention of L. monocytogenes in raw milk is the application of hygienic

practices involving the use of detergents and sanitizers (Kumar et al., 2017).

Mastitis

Bovine mastitis is an important disease in the dairy industry, causing economic losses as

a result of milk reduces and treatment costs. Mastitis is inflammation of the mammary gland

detected by increased somatic cell count (SCC) or visible abnormalities in milk and is the most

common bacterial disease of dairy cattle. Although numerous bacteria can cause mastitis,

historically, most mastitis was caused by Streptococcus spp. or Staphylococcus spp. To ensure

accurate diagnosis, standardized culturing methods for milk samples have been developed

(Ruegg, 2017)

Several studies have suggested milk amyloid A (MAA) as a promising biomarker in the

diagnosis of mastitis, especially subclinical mastitis is difficult to detect because there are no

clinical signs to identify by visual inspection and palpation of the udder. Jaeger et al. (2016)

estimated the diagnostic test accuracy of a commercial MAA-ELISA, somatic cell count (SCC),

and bacteriological culture for subclinical mastitis. The results showed that MAA-ELISA result

of 3.9μg/mL to detect mastitis caused by major pathogens (e.g., Escherichia coli,

Staphylococcus aureus, streptococci, and lacto-/enterococci) and 1.6 μg/mL to detect mastitis

caused by all pathogens (major pathogens plus Corynebacterium bovis, coagulase-negative

staphylococci, Bacillus spp., Streptomyces spp.). The optimal SCC threshold for identification

of subclinical mastitis was 150,000 cells/mL. With the bacteriological culture, 63.5% milk

samples contained at least 1 colony of any bacterial species; among them, 20.4% contained

major pathogens and 79.6% contained minor pathogens. The MAA-ELISA is a valuable

addition to existing tools for the diagnosis of subclinical mastitis.

Haptoglobin (Hp) was also indicated as one of the promised biomarkers of mastitis and

the correlation between Hp and SCC has been related with the udder inflammation and mastitis

13

(Åkerstedt et al., 2011; Thomas et al., 2015). Beside that, Aspartate aminotransferase (AST)

and alanine aminotransferase (ALT) were the liver functional parameters to indicate the degree

of hepatocyte damage (Steen et al., 1997; Sun et al., 2015). The activities of ALT and AST

were seen higher in winter that suggested the risk factor occurred more relate to liver function

in winter. And ALT was found at 2 months higher than at 1 month after calving which followed

the days of lactation period (Stojevie et al., 2005). Other factors, the higher values of non-

esterified fatty acids (NEFA) was reported the relationship with mastitis by Melendez et al.

(2009), but some researches showed that urea nitrogen (BUN), serum albumin (Alb), total

cholesterol (T-Cho), and calcium (Ca) were not increased in mastitic cows (Sharma et al., 2016;

Sadek et al., 2017; Zhong et al., 2018).

Treatment and prevention of mastitis

The bovine mammary gland is a difficult target for antimicrobial treatment. Penetration

of substances into milk when administered parenteral or absorption and distribution throughout

the udder when infused intramammary (IMM) depends on the pharmacokinetic characteristics.

These are lipid solubility, degree of ionization, extent of binding to serum and udder proteins,

and the type of vehicle. Antimicrobial treatment of dairy cows creates residues into milk, and

residue avoidance is an important aspect of mastitis treatment (Wagner and Erskine 2006).

Several antimicrobial classes are approved for use in food animals for the treatment of mastitis

including β-lactams (e.g., penicillins, cephalosporins, ampicillin), tetracycline, macrolides (e.g.,

erythromycin), linocosamides (e.g., pirlimycin), and novobiocin (Mitchell et al., 1998).

The activity of macrolides, tetracyclines and trimethoprim-sulphonamides has been

shown to be reduced in milk (Louhi et al. 1992). Selecting a substance with a low minimum

inhibitory concentration (MIC) value for the target pathogen is preferable, particularly when

the antimicrobial is administered systemically. The antimicrobial should have bactericidal

rather than bacteriostatic action, because phagocytosis is impaired in the mammary gland.

Treatment of mastitis should be targeted towards the causative bacteria if possible, but in

some acute situations, treatment is initiated based on herd data and personal experience. Rapid

or on-farm bacteriological diagnosis would facilitate the selection of the most appropriate

antimicrobial. Treatment protocols and medicine selection for each farm should be made by

veterinarians familiar with the farm. The use of on-farm written protocols for mastitis treatment

also can promote judicious use of antimicrobials. Therapeutic response of the cows can be

monitored using individual somatic cell count data if available, or with bacteriological samples

in herds with contagious mastitis. However, the use of narrow-spectrum antimicrobials is

preferable.

THE ENVIRONMENT MICROBIOTA

The relationship between uterine and environmental microbiota

14

During parturition, the physical barriers of the cervix, vagina and vulva are compromised

providing the opportunity for bacteria to ascend the genital tract from the environment as well

as the animal’s skin and feces. The microbial sources could contaminate into the uterus from

the environment such as their feces (Sheldon and Dobson, 2004) or fecal contamination directed

to the angle of the vulva (Potter et al., 2010). However, according to Potter et al. (2010), the

risk factors of uterine inflammation were the trauma of reproductive tract rather than fecal

contamination.

The relationship between milk and environmental microbiota

The contaminated sources of milk microbiota were considered from environment

including feces, air-borne dust, and bedding (Vacheyrou et al. 2011). Ruminococcaceae were

present in milk and feces of healthy lactating cows (Young et al. 2015). Wu et al. (2018) found

Staphylococcaceae and Aerococcaceae were the most abundance taxa in the air-borne dust

associated with milk microbiota, while milk microbiota were associated with the microbiota

from the bedding more than from the air-born dust (Metzger et al. 2018). The milk microbiota

were not only affected by the environmental microbiota, but also varied by seasons. Indeed,

during summer, the milk microbiota were associated with the microbiota from the bedding more

than from the air-born dust (Metzger et al. 2018). However, both of the air-born dust and

bedding microbiota could be suggested to become the contaminated sources of the milk

microbiota in winter. Interestingly, the fecal microbiota were clearly separated from the milk,

air-born dust and bedding microbiota (Wu et al. 2018).

Moreover, Vacheyrou et al. (2011) also showed some microbiota presented in milk, but

not detected in the farm environment; and similarly, some microbiota taxa were detected in the

farm environment, but not in milk. Most of the fungal species and environmental bacteria found

in the milk were also found in the stable and milking parlour environments. While Lactobacilli

and propionic acid bacteria were frequently identified in the milk, but were rarely found in other

environments.

15

CHAPTER 3

THE RELATIONSHIP BETWEEN UTERINE, FECAL, BEDDING, AND

AIRBORNE DUST MICROBIOTA FROM DAIRY COWS AND

THEIR ENVIRONMENT

ABSTRACT

A total of 98 samples were analyzed to characterize uterine, fecal, bedding, and airborne

dust microbiota in a dairy farm managed by the free-stall housing system. Samples for the

microbiota analysis were collected during summer and winter at one and two months

postpartum. Blood samples were also collected to determine the levels of serum haptoglobin

(Hp) and biochemical components. Uterine microbiota varied between seasons but not between

one and two months postpartum; the five most prevalent taxa were Enterobacteriaceae,

Moraxellaceae, Ruminococcaceae, Staphylococcaceae, and Lactobacillaceae during summer,

and Ruminococcaceae, Lachnospiraceae, Bacteroidaceae, Moraxellaceae, and Clostridiaceae

during winter. Although one cow showed a great abundance (19.2%) of Actinomycetaceae, the

family Trueperella pyogenes belongs to, and four cows were considered to have uterine disease

based on high Hp levels, the relationship between uterine microbiota and serum metabolite

concentrations was unclear. The fecal microbiota was stable regardless of the season, whereas

bedding and airborne dust microbiota varied between summer and winter. With regards to

uterine, bedding, and airborne dust microbiota, Enterobacteriaceae, Moraxellaceae,

Staphylococcaceae, and Lactobacillaceae were more abundant during summer, and

Ruminococcaceae, Lachnospiraceae, Bacteroidaceae, and Clostridiaceae were more abundant

during winter. Canonical analysis of principal coordinates confirmed the relationship between

uterine and cowshed microbiota. Although our findings were derived mostly from healthy cows

in a dairy farm, this study clarified that uterine microbiota may vary with changes in the

microbiota from cowshed environments.

Keywords: airborne dust, bedding, dairy cow, microbiota, uterus

INTRODUCTION

The postpartum uterus of dairy cows has been shown to be contaminated with diverse

bacterial species, including pathogens associated with uterine disease (Sheldon et al., 2006;

Ghanem et al., 2015). Although one-third to two-thirds of cows remain healthy, others may

develop metritis and endometritis; this reduces their food intake and milk production ability

16

and renders them less likely to become pregnant (Lewis, 1997; LeBlanc et al., 2002).

Escherichia coli, Trueperella pyogenes, Fusobacterium necrophorum, and Bacteroides spp.

(e.g., Prevotella melaninogenica) are representative pathogens determined by isolation from

the uteri of cows with postpartum uterine disease using the plate-culture method (Sheldon et

al., 2006). The understanding of these pathogens has been expanded by employing culture-

independent microbiota analyses; several bacterial families, e.g., Porphyromonadaceae,

Mycoplasmataceae, Bacteroidaceae, and Leptotrichiaceae, have been recently considered as

pathogens associated with uterine disease (Santos et al., 2011; Machado et al., 2012; Knudsen

et al., 2016).

Restoration of the cervix diameter and regeneration of the uterine epithelium requires

three to four weeks (LeBlanc et al., 2002; Sheldon et al., 2008; Gautam et al., 2009); hence,

even in cows with no uterine disease, diverse bacterial species including anaerobes and

facultative anaerobes can be detected after parturition. The bacteria contaminating the uteri of

postpartum dairy cows are thus considered to originate from feces and the environment.

However, data for microbiota in cows from dairy farm environments are limited, and evidence

showing the relationship between uterine, fecal, and cowshed microbiota is lacking. Because

both Trueperella spp. and Fusobacterium spp. can be found in the uteri of virgin heifers and

pregnant cows (Moore et al., 2017; Karstrup et al., 2017), the belief that the pregnant uterus is

sterile until contamination with the environmental bacteria at calving, and metritis-causing

bacteria gain access to the uterus when cows calve should be reconsidered. Regardless, factors

affecting uterine microbiota need to be clarified to help prevent uterine disease, improve fertility,

and ensure high milk production from the dairy cows.

In this study, a total of 98 samples of uterine mucus, feces, bedding, and air-borne dust

collected in a dairy farm were analyzed by high-throughput amplicon sequencing. The cows

were managed by the free-stall housing system, and samples for microbiota assessment were

collected during summer and winter when the cows were at one and two months postpartum.

Blood samples were also collected to determine the serum levels of haptoglobin (Hp) and

biochemical components. The objective was to characterize the uterine microbiota of

postpartum dairy cows during different seasons, and examine if the uterine microbiota was

related with the fecal, bedding, and air-borne dust microbiota.

MATERIALS AND METHODS

Sampling

17

A total of 98 samples were collected from cows at the Okayama Prefecture Livestock

Research Institute (Okayama, Japan). The cows were housed in a free stall barn and fed total

mixed ration silage, which was formulated to contain 500–600 g/kg of dry matter (DM), 160–

180 g/kg DM of crude protein (N × 6.25), and 720–740 g/kg DM of total digestible nutrients.

The sampling was performed from 6 June to 22 August and from 17 November to 2 March in

2018; hereafter, the former series is referred to as the sampling during summer and the latter,

as the sampling during winter. Uterine mucus and feces samples were collected from nine cows

during summer and from eight cows during winter, with two sampling times each at one and

two months after the calving. Uterine and fecal sampling from cows at one and two months

postpartum was conducted occasionally on the same day. Accordingly, samples of bedding and

air-borne dust were collected six times during summer and nine times during winter.

Uterine mucus was collected by using a cytobrush (Fujihira Industry Co. Ltd., Tokyo).

The cow was restrained, the tail was held, and the perineum area, especially the vulva, was

cleaned by wiping with 70% ethanol. The cytobrush instrument was covered with a sanitary

plastic sheath and then inserted into the cervix. Inside the cervix, the plastic sheath was ruptured,

and the instrument was further passed through the cervix toward the base of the larger horn, at

which point the stainless-steel tube was retracted to expose the cytobrush. Uterine mucus was

collected by rotating the cytobrush while in contact with the uterine wall (Jeon et al., 2015);

then, the cytobrush was pulled back into the stainless-steel tube to avoid bacterial contamination

from the vagina, vulva, and feces. The cytobrush was cut and placed into an Eppendorf tube

and stored.

Fecal samples were collected from the rectum and blood samples were taken from the

caudal vein. Airborne dust samples were collected by placing three petri dishes approximately

1.0 m above the ground for five minutes; they were then gathered into a tube using sterile

physiological saline. Bedding samples were collected from three separate places in a cowshed.

In the free stall system, cows could move and rest freely, and determining their resting place

was difficult. Thus, a composite sample prepared from three separate samples was regarded as

a representative means of assessing the bedding and airborne dust microbiota at the time of

sampling. All the samples were kept on ice during their transportation to the laboratory and

stored at -20°C until further analyses. Procedures and protocols for the animal experiments

were approved by the Animal Care and Use Committee, Okayama University, Japan.

Blood analyses

18

The levels of serum albumin (Alb), urea nitrogen (BUN), total cholesterol (T-Cho), non-

esterified fatty acids (NEFA), aspartate aminotransferase (AST), alanine aminotransferase

(ALT), calcium (Ca), and phosphate were determined by using the respective commercial kits

(FUJIFILM Wako Pure Chemicals Co., Tokyo, Japan). The concentration of serum haptoglobin

(Hp) was determined by the quantitative sandwich enzyme-linked immunoassay technique

using the bovine Hp ELISA kit (Life Diagnostics, Inc., West Chester).

DNA extraction

For uterine samples, the thawed cytobrush was soaked in 1 mL of sterile physiological

saline for 30 minutes to release the uterine microbiota. Bacterial pellets were obtained by

centrifugation at 16,000 g for two minutes, and then washed with 500 µL of solution I

containing 0.05 M D-glucose, 0.025 M Tris-HCl (pH 8.0), and 0.01 M sodium EDTA (pH 8.0).

After further centrifugation at 16,000 g for two minutes, the bacterial cells were lysed with 180

µL of lysozyme solution (20 g/L lysozyme, 0.02 M Tris-HCl [pH 8.0], 0.002 M sodium EDTA

[pH 8.0], and 1.2 g/L Triton X-100) at 37°C for one hour. Air-borne dust samples dissolved in

sterile physiological saline were processed in the same way. Bacterial DNA was purified by

using the DNeasy blood & tissue kit (Qiagen, Germantown, MD, USA), according to the

manufacturer’s instructions. In case of the fecal and bedding samples, bacterial DNA was

extracted following the procedure for the repeated bead beating plus column method (Yu and

Morrison, 2004) and purified using the mini DNeasy stool kit (Qiagen, Germantown, MD,

USA).

Illumina MiSeq sequencing

Bacterial DNA was amplified by two-step polymerase chain reaction (PCR) to generate

amplicon libraries for next-generation sequencing. The primers targeting the V4 region of 16S

ribosomal RNA (rRNA) genes (forward: 5′-

ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAGCMGCCGCGGTAA-3′;

reverse: 5′-

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGACTACHVGGGTWTCTAAT-3′;

tail sequences are underlined) (Tang et al., 2017) were used for the first round of PCR, with the

following protocol: initial denaturation at 94°C for two minutes, followed by 35 cycles of

denaturation at 94°C for 30 seconds, annealing at 50°C for 30 sec, elongation at 72°C for 30

sec, and an elongation step at 72°C for five minutes. The PCR products were purified by

electrophoretic separation on a 2.0% agarose gel using a Fast Gene Gel/PCR Extraction Kit

19

(NIPPON Genetics Co., LTD., Tokyo, Japan). The second round of PCR, with adapter-attached

primers, followed the protocol of initial denaturation at 94°C for two minutes, 10 cycles of

denaturation at 94°C for 30 sec, annealing at 59°C for 30 sec, elongation at 72°C for 30 sec,

and an elongation step at 72°C for five minutes. The second-round PCR products were purified

in the same way as that in case of the first-round PCR products.

The purified amplicons were pair-end sequenced (2 × 250 bp) on an Illumina MiSeq

platform at FASMAC Co., Ltd. (Kanagawa, Japan). Raw sequence data were analyzed using

the Quantitative Insights Into Microbial Ecology (QIIME version 1.9.0). The 250-bp reads were

truncated at any site receiving an average quality score under 20. Truncated reads that were

shorter than 225 bp were discarded. In primer matching, sequences showing overlaps longer

than 200 bp were assembled. The final reads obtained after pair-end joining were grouped into

operational taxonomic units (OTUs) using a 97% similarity threshold. The sequence data were

analyzed and categorized from the phylum to the family level using the default settings of the

Ribosomal Database Project classifier.

Statistical analysis

Data for milk yield, blood metabolites concentration, and relative abundances of major

bacterial families (proportion of the family in at least one sample is >1.0%) of uterine and fecal

microbiota were analyzed by two-way analysis of variance (ANOVA). Models included the

fixed effects of season, sampling time (1 and 2 months postpartum), and interaction between

season and sampling time. Data for relative abundances of bedding and air-borne dust

microbiota were analyzed by one-way ANOVA to examine the effect of season. Microbiota data

were also subjected to canonical analysis of principal coordinates (CAP) to define the

assignment and clustering that explained variations in the microbiota. Discriminant vectors with

a Pearson correlation >0.7 were considered significant. Two-way ANOVA and t-test were

performed using JMP (version 11; SAS Institute, Tokyo, Japan) and CAP was carried out using

Primer version 7 with the Permanova+ add-on (Primer-E, Plymouth Marine Laboratory,

Plymouth, UK).

RESULTS

Time of calving for the cows examined during summer and winter were 2.0±0.7 and

2.3±1.3, respectively. Milk yield of the cows was 35–40 kg, and no differences were seen

between two seasons and between one and two months after the calving (Table 1). The content

of milk protein was greater, and those of fat and SNF were numerically higher during winter

20

than during summer. The average somatic cell count (SCC) of the milk at two months

postpartum during summer was as high as 429×103 cells/mL, because one cow showed a

substantially high SCC (2.8×106 cells/mL). The level of serum Alb was higher and that of serum

ALT was lower during summer than during winter (Table 1). Numerical differences were also

seen for the levels of serum NEFA (summer > winter) and AST (summer < winter). Likewise,

the levels of serum T-Cho and ALT were higher at two months than at one month postpartum.

The Illumina MiSeq sequencing revealed that the microbiota showed a huge taxonomic

diversity, including 34 phyla and 213 families, of which 10 phyla and 41 families were shared

among all samples. Firmicutes, Proteobacteria, and Bacteroidetes were the 3 major phyla, and

accounted for >90% of the total abundance regardless of the sampling time. The abundance of

Proteobacteria was higher and those of Firmicutes and Bacteroidetes were lower during summer

than during winter.

Differences owing to sample collection at one and two months postpartum were not found

in any families of the uterine microbiota (Table 2). The five most abundant taxa of the uterine

microbiota during summer were Enterobacteriaceae (12.4%), Moraxellaceae (12.1%),

Ruminococcaceae (11.0%), Staphylococcaceae (7.6%), and Lactobacillaceae (3.9%), and those

during winter were Ruminococcaceae (22.3%), Lachnospiraceae (7.3%), Bacteroidaceae

(5.5%), Moraxellaceae (4.5%), and Clostridiaceae (3.9%). Season-to-season differences were

seen for these families; relative abundances of Enterobacteriaceae, Moraxellaceae,

Staphylococcaceae, and Lactobacillaceae were greater and those of Ruminococcaceae,

Lachnospiraceae, Bacteroidaceae, and Clostridiaceae were lower during summer than during

winter. The abundances of Actinomycetaceae and Mycoplasmataceae were low, except that the

abundance of Actinomycetaceae in one cow was 19.2%, and that of Mycoplasmataceae in

another cow was 11.6% during summer. The relative abundance of Fusobacteriaceae was

substantially low (0.00–0.43%) in the uterine samples in this study.

The five most prevalent taxa of the fecal microbiota during summer were

Ruminococcaceae (33.9%), Bacteroidaceae (11.3%), Lachnospiraceae (9.2%), Rikenellaceae

(3.2%), and Clostridiaceae (3.2%), and those during winter were Ruminococcaceae (34.5%),

Lachnospiraceae (10.1%), Bacteroidaceae (9.2%), Rikenellaceae (3.4%), and Clostridiaceae

(3.0%). Except for Bacteroidaceae, the relative abundance of which was greater during summer

than during winter, differences owing to season were not seen. The relative abundance of

Mycoplasmataceae was 0.01% and those of Actinomycetaceae and Fusobacteriaceae were both

<0.005% in the fecal microbiota.

21

The five most abundant taxa of the bedding microbiota during summer were

Aerococcaceae (13.8%), Ruminococcaceae (10.8%), Moraxellaceae (8.3%),

Corynebacteriaceae (7.3%), and Staphylococcaceae (6.6%), and those during winter were

Ruminococcaceae (17.0%), Aerococcaceae (13.3%), Lachnospiraceae (6.6%),

Staphylococcaceae (6.3%), and Bacteroidaceae (6.0%). The relative abundance of

Moraxellaceae was higher and those of Ruminococcaceae, Lachnospiraceae, and

Bacteroidaceae were lower during summer than during winter. The abundances of

Actinomycetaceae (0.17%), Fusobacteriaceae (0.02%), and Mycoplasmataceae (0.02%) were

low in the bedding microbiota.

The five most abundant taxa of the air-borne dust microbiota during summer were

Staphylococcaceae (13.4%), Moraxellaceae (7.2%), Corynebacteriaceae (7.0%),

Pseudomonadaceae (6.7%), and Streptococcaceae (6.1%), and those during winter were

Ruminococcaceae (17.4%), Aerococcaceae (7.3%), Bacteroidaceae (7.1%), Lachnospiraceae

(6.8%), and Staphylococcaceae (4.7%). Season-to-season differences were found for the

relative abundances of these families, except for Aerococcaceae and Peptostreptococcaceae.

The abundances of Actinomycetaceae (0.20%), Fusobacteriaceae (0.12%), and

Mycoplasmataceae (0.02%) were also low in the air-borne dust microbiota.

Based on the CAP analysis, during summer, the uterine microbiota was grouped with the

air-borne dust microbiota, whereas bedding and fecal microbiota formed two other (separate)

groups. During winter, most of the uterine microbiota was grouped with the air-borne dust and

bedding microbiota, whereas some of the uterine microbiota formed the same group with the

fecal microbiota.

DISCUSSION

Milk yield and milk composition have been shown to follow a seasonal pattern across the

year, typically being the greatest during winter and reaching a nadir during summer; thus, the

higher protein and SNF contents of the milk obtained during summer were regarded to be

normal (Salfer et al., 2019). It has been demonstrated that the level of blood metabolites can

help describe postpartum uterine health and the resumption of postpartum cyclicity. Cows with

high milk-producing ability may face a severe negative energy balance after calving, and cows

with uterine disease may show lower levels of serum Alb, T-Cho, and Ca, and higher levels of

serum NEFA, BUN, and AST (LeBlanc et al., 2011; Shin et al., 2018; Kawashima et al., 2018).

In this study, although the results revealed that the cows showed lower serum Alb and higher

serum ALT levels, suggesting a greater risk of uterine disease during winter, they may have had

22

an appropriate protein-energy balance during both seasons, because the level of serum T-Cho

was sufficiently high and those of serum BUN and NEFA were acceptably low. No differences

between samples collected during different seasons were seen with regards to the serum

concentrations of Hp, an acute-phase protein that reflects the severity of inflammatory

responses in cattle with metritis (Chan et al., 2010; Nightingale et al., 2015; Pohl et al., 2015),

thereby supporting this finding.

In this study, 24 out of 34 cow samples showed a baseline level (<20 μg/L) of serum Hp.

Four out of 10 other samples showed a high Hp level, at >130 μg/L, which was indicated as an

upper cut-off value to differentiate between healthy and metritis-affected cows (Chan et al.,

2010). Regardless, the cows with elevated Hp concentration were indicated to have a normal

liver function, i.e., their serum AST and ALT levels were similar to those of others. Likewise,

the cows with high Hp levels apparently showed healthy uterine microbiota, i.e., low

abundances of typical pathogens such as members of Actinomycetaceae, Fusobacteriaceae, and

Mycoplasmataceae (detailed data are not shown).

In the uterine microbiota examined in this study, Firmicutes was the most abundant

phylum, followed by Proteobacteria and Bacteroidetes. Machado et al. (2012) and Wang et al.

(2018) also found that Firmicutes was the most abundant phylum, showing abundances of 52.3

and 76.7%, respectively, in the uterine microbiota of healthy cows at around one month

postpartum. The predominant taxa of Firmicutes in the healthy uterine microbiota, however, is

yet to be defined; Geobacillus spp. (Bacillaceae, Machado et al., 2012), Lactococcus spp.

(Streptococcaceae, Wang et al., 2018), and Ruminococcaceae (Knudsen et al., 2016) were

reported as the most abundant components of healthy uterine microbiota. Although standard

diagnosis methods, such as cytological assessment using polymorphonuclear neutrophils

percentage (PMN%) were not performed in this study, most of the cows examined can be

regarded to have healthy uterine microbiota, because the relative abundances of

Actinomycetaceae (the family T. pyogenes belongs to), Fusobacteriaceae (the family F.

necrophorum belongs to), and Mycoplasmataceae were substantially small.

Santos et al. (2011) and Wang et al. (2018) reported that Proteobacteria was the most

abundant phylum in the uterine microbiota of healthy cows; however, in this study,

Proteobacteria was the second and third most abundant phylum in the samples obtained during

summer and winter, respectively. The predominant taxa of Proteobacteria in the healthy uterine

microbiota are also unclear; Santos et al. (2011) have indicated Pasteurella spp., and Bicalho

et al. (2017) have demonstrated Escherichia spp. (Enterobacteriaceae), Shigella spp.

(Enterobacteriaceae), and Pseudomonas spp. (Pseudomonadaceae) as the major taxa of

23

Proteobacteria. Our results stated that Enterobacteriaceae and Moraxellaceae were the two

major taxa, which is, in part, similar to the results of the study by Bicalho et al. (2017).

The abundance of Bacteroidetes, particularly with regards to the families Bacteroidaceae

and Porphyromonadaceae, has been shown to increase in cows with metritis (Machado et al.,

2012; Bicalho et al., 2017). Meanwhile, the standard prevalence of Bacteroidetes among the

healthy uterine microbiota is yet to be defined. Machado et al. (2012) found an increase in the

abundance of Bacteroidetes from 12.7% in cows without metritis to 18.9% in those with metritis,

whereas Bicalho et al. (2017) reported an increase in the abundance of Bacteroidetes from 20%

in healthy cows to 29% in cows showing purulent vaginal discharge. The relative abundance of

Bacteroidetes in samples obtained during winter (about 20%) in this study could be regarded

as both a healthy- and metritis-level abundance. Greater abundance of Bacteroidetes during

winter was probably because of the greater abundance of this phylum in air-borne dust and

bedding microbiota during this season.

Even in healthy cows, Fusobacteria have been found as a prevalent phylum at one week,

and have shown >5% abundance at one month after calving (Santos et al., 2012; Machado et

al., 2012). In this study, in all uterine samples, Fusobacteria showed a relative abundance of

<0.5%, and thus, the data are not shown in Table 2. At two months postpartum during summer,

Moraxellaceae was found as the most abundant family in the uterine microbiota. Although

members of Acinetobacter, a genus belonging to the family Moraxellaceae, have been reported

to be associated with sub-clinical endometritis (Wang et al., 2018) and repeat breeder

(Pothmann et al., 2015), Acinetobacter spp. can be found in diverse environments and their

pathogenicity is regarded to be low. Similar to the finding for Bacteroidetes, differences in the

prevalence of Moraxellaceae at different sampling times could be due to the different

abundances of this family in the bedding and air-borne dust microbiota during the two seasons.

The average abundance of Actinomycetaceae was 2.3% in cows at two months

postpartum during summer, because one cow showed an extremely high abundance (19.4%) of

this family compared with the other cows (<0.4%). Likewise, the same cow showed a

substantially high abundance (3.1%) of Actinomycetaceae at one month postpartum. Even in

cows with metritis, the prevalence of T. pyogenes in the uterine microbiota was as high as 5%

at one month postpartum (Machado et al., 2012; Wagener et al., 2015; Bicalho et al., 2017).

The cow with a high abundance of Actinomycetaceae may have had uterine infection; however,

the levels of serum Hp and other metabolites were normal in this cow.

One cow showed a high abundance (11.6%) of Mycoplasmataceae at one month

postpartum during summer, and thus, the average abundance value of this family became 1.3%.

24

Although members of Mycoplasmataceae, particularly Ureaplasma spp., have been considered

as metritis-associated pathogens (Machado et al., 2012; Knudsen et al., 2016), a greater

abundance (13%) of this family than that in our study has been detected in healthy cows

(Knudsen et al., 2016). Thus, it is difficult to define whether the cow was healthy or had uterine

disease. The levels of serum metabolites were also normal in this cow.

The CAP analysis clarified that uterine microbiota was related with fecal, bedding, and

air-borne dust microbiota, and that their relationships may vary between seasons. According to

the conventional ANOVA results, the fecal microbiota was shown to be stable regardless of the

season, whereas uterine, bedding, and airborne dust microbiota were shown to be different in

the two seasons. Thus, the conventional ANOVA results did not suggest a significant association

between the uterine and fecal microbiota. Meanwhile, the CAP analysis clearly demonstrated

the relationship between uterine, bedding, and air-borne dust microbiota. During summer, the

dairy farm used fans with a mist of water to cool the bodies of the cows; hence, this enforcing

ventilation may have caused a difference in the association between the uterine, bedding, and

air-borne dust microbiota during the two seasons.

This study indicated the importance of microbiota in cowshed environments in

understanding the postpartum uterine microbiota of dairy cows, which might help account for

differences between the bacterial taxa among healthy uterine microbiota in previously

published studies. However, why some cows are affected by uterine disease while others remain

healthy and show sub-clinical symptoms despite being housed in the same cowshed

environment remains unanswered. Although air-borne dust and bedding microbiota can be

associated with uterine microbiota, the abundances of Actinomycetaceae (0.07–0.45% for air-

borne dust and 0.03–0.38% for bedding), Fusobacteriaceae (0.00–0.42% for air-borne dust and

0.00–0.08% for bedding), and Mycoplasmataceae (0.00–0.07% for air-borne dust and 0.00–

0.06% for bedding) in the cowshed were too low to exert large variations of the uterine

microbiota. Further research is necessary to understand the complex interactions within and

between uterine and cowshed environment microbiota, and differences between the

susceptibilities of cows housed in the same environment to uterine infection.

25

Table 1. Milk yield and blood metabolites concentration of the dairy cow examined at one and two months postpartum at two seasons.

Summer Winter SE Two-way ANOVA

1M

(n=9)

2M

(n=9)

1M

(n=8)

2M

(n=8)

S M S×M

Milk yield (kg/day) 39.0 39.2 34.6 38.2 2.89 NS NS NS

Blood metabolites

Albumin (g/dL) 3.57 3.56 3.29 3.11 0.10 ** NS NS BUN (mg/dL) 13.4 12.6 11.9 11.5 0.98 NS NS NS

Cholesterol (mg/dL) 151 197 148 175 9.37 NS ** NS NEFA (μEq/L) 0.37 0.20 0.14 0.13 0.08 NS NS NS

Calcium (mg/dL) 8.92 9.06 9.13 8.74 0.38 NS NS NS

Phosphorus (mg/dL) 6.72 5.69 5.84 5.84 0.33 NS NS NS AST (U/L) 45.1 52.5 55.2 52.2 2.58 NS NS NS

ALT (U/L) 11.1 12.5 12.7 13.9 0.72 * NS NS Haptoglobin (μg/L) 19.4 101 151 42.3 57.6 NS NS NS

Summer and winter stand for the sampling conducted between 6 June and 22 August and between 17 November

and 2 March respectively. 1M and 2M indicate one and two months postpartum respectively. BUN; blood urea

nitrogen, NEFA; non-esterified fatty acid, AST; aspartate aminotransferase, ALT; alanine aminotransferase. S;

effect of season, M; effect of the month postpartum, **; P<0.01, *; P<0.05, NS; not significant.

26

Table 2. Relative abundance (%) of uterine microbiota of the dairy cows examined at one and

two months postpartum during the two seasons. Summer Winter SE Two-way ANOVA

1M

(n=9)

2M

(n=9)

1M

(n=8)

2M

(n=8)

S M S×M

Actinobacteria 7.31 7.27 5.04 3.50 1.76 NS NS NS

Actinomycetaceae 0.53 2.34 0.37 0.23 1.13 NS NS NS

Corynebacteriaceae 2.75 2.03 1.76 1.15 0.53 NS NS NS

Micrococcaceae 1.67 1.17 0.38 0.25 0.39 ** NS NS

Bifidobacteriaceae 0.14 0.17 1.30 1.06 0.17 ** NS NS

Bacteroidetes 14.5 12.2 19.1 22.1 2.99 * NS NS

Bacteroidaceae 3.57 3.09 4.77 6.18 0.84 * NS NS

Porphyromonadaceae 2.38 1.16 2.06 2.23 0.64 NS NS NS

RF16 1.23 1.31 1.91 2.19 0.49 NS NS NS

Rikenellaceae 1.36 1.07 1.65 2.08 0.34 NS NS NS

S24-7 0.35 0.28 2.10 1.11 0.33 ** NS NS

Paraprevotellaceae 0.73 0.78 1.40 1.62 0.29 * NS NS

Firmicutes 44.2 42.1 55.8 59.7 1.99 ** NS NS

Bacillaceae 0.50 0.27 1.28 0.11 0.52 NS NS NS

Staphylococcaceae 8.14 7.12 2.45 1.75 1.20 ** NS NS

Exiguobacteraceae 0.09 1.01 0.14 0.04 0.35 NS NS NS

Aerococcaceae 2.30 1.01 1.22 1.42 0.63 NS NS NS

Lactobacillaceae 3.05 4.67 2.82 1.28 0.54 ** NS **

Streptococcaceae 2.03 2.44 1.96 1.69 0.43 NS NS NS

Turicibacteraceae 0.33 0.33 2.10 1.56 0.26 ** NS NS

Clostridiaceae 1.78 1.46 3.88 4.01 0.41 ** NS NS

Lachnospiraceae 3.57 3.12 6.63 7.97 0.70 ** NS NS

Peptostreptococcaceae 0.38 0.45 1.26 1.38 0.18 ** NS NS

Ruminococcaceae 11.8 10.1 19.6 24.9 2.64 ** NS NS

Tissierellaceae 2.80 3.02 0.83 0.60 0.41 ** NS NS

Erysipelotrichaceae 0.50 0.46 1.40 1.29 0.16 ** NS NS

Proteobacteria 29.0 35.0 13.5 8.40 3.65 ** NS NS

Succinivibrionaceae 1.28 0.97 0.71 1.10 0.47 NS NS NS

Enterobacteriaceae 11.9 12.8 1.41 1.12 1.85 ** NS NS

Moraxellaceae 9.94 14.2 5.87 2.99 1.79 ** NS NS

Pseudomonadaceae 1.51 2.32 1.63 0.83 0.31 * NS *

Tenericutes 2.54 1.13 2.33 2.62 0.72 NS NS NS

Mycoplasmataceae 1.32 0.02 0.41 0.10 0.71 NS NS NS

Phyla and families having relative abundance at > 1% in at least one sample are indicated. Summer and winter

stand for the sampling conducted between 6 June and 22 August and between 17 November and 2 March

respectively. 1M and 2M indicate one and two months postpartum respectively. S; effect of season, M; effect of

the month postpartum, **; P<0.01, *; P<0.05, NS; not significant.

27

Table 3. Relative abundance (%) of fecal microbiota of the dairy cows examined at one and

two months postpartum during the two seasons. Summer Winter SE Two-way ANOVA

1M

(n=9)

2M

(n=9)

1M

(n=8)

2M

(n=8)

S M S×M

Bacteroidetes 32.2 31.6 28.6 30.8 1.33 NS NS NS

Bacteroidaceae 10.3 12.3 9.68 8.65 0.85 * NS NS

Porphyromonadaceae 1.47 1.26 2.13 1.85 0.45 NS NS NS

RF16 1.01 1.33 1.51 2.48 0.33 * NS NS

Rikenellaceae 3.13 3.20 3.42 3.37 0.23 NS NS NS

S24-7 1.39 1.46 0.79 1.27 0.18 * NS NS

Paraprevotellaceae 2.55 2.27 1.61 2.70 0.33 NS NS *

Firmicutes 60.2 60.1 63.1 60.8 1.42 NS NS NS

Clostridiaceae 2.89 2.89 3.04 2.87 0.29 NS NS NS

Lachnospiraceae 9.32 9.06 9.38 10.7 1.22 NS NS NS

Ruminococcaceae 33.8 33.9 37.2 31.7 1.17 NS * *

Erysipelotrichaceae 1.22 1.44 0.94 1.26 0.15 NS NS NS

Proteobacteria 1.35 1.72 2.15 1.32 0.33 NS NS †

Succinivibrionaceae 1.11 1.15 1.16 0.72 0.31 NS NS NS

Spirochaetes 0.94 1.18 1.93 1.72 0.30 * NS NS

Spirochaetaceae 0.92 1.12 1.72 1.67 0.30 * NS NS

Phyla and families having relative abundance at > 1% in at least one sample are indicated. Summer and winter

stand for the sampling conducted between 6 June and 22 August and between 17 November and 2 March

respectively. 1M and 2M indicate one and two months postpartum respectively. S; effect of season, M; effect of

the month postpartum, **; P<0.01, *; P<0.05, NS; not significant.

28

Table 4. Relative abundance (%) of bedding microbiota of the dairy farm cowshed examined

at two seasons. Summer

(n=6)

Winter

(n=9)

SE One-way

ANOVA

Actinobacteria 11.4 6.97 1.48 *

Corynebacteriaceae 7.28 6.01 1.22 NS

Micrococcaceae 1.78 0.27 0.22 **

Bacteroidetes 11.4 16.8 1.64 *

Bacteroidaceae 3.75 5.95 0.67 *

Porphyromonadaceae 1.28 1.71 0.22 NS

RF16 0.93 1.65 0.24 NS

Rikenellaceae 1.04 1.74 0.17 *

Firmicutes 60.2 66.7 1.17 **

Planococcaceae 5.65 0.09 1.00 *

Staphylococcaceae 6.62 6.27 0.98 NS

Aerococcaceae 13.8 13.3 2.11 NS

Carnobacteriaceae 2.21 0.63 0.14 **

Clostridiaceae 2.11 2.45 0.11 *

Lachnospiraceae 3.40 6.59 0.59 **

Peptostreptococcaceae 1.18 1.75 0.16 *

Ruminococcaceae 10.8 17.0 1.41 **

Mogibacteriaceae 0.70 1.01 0.07 **

Tissierellaceae 2.32 1.11 0.34 *

Erysipelotrichaceae 1.32 1.56 0.12 NS

Proteobacteria 13.8 4.37 1.33 **

Succinivibrionaceae 0.55 1.01 0.22 NS

Idiomarinaceae 1.46 0.01 0.49 NS

Halomonadaceae 1.33 0.12 0.22 **

Moraxellaceae 8.32 1.80 0.92 **

Phyla and families having relative abundance at > 1% in at least one sample are indicated. Summer and winter

stand for the sampling conducted between 6 June and 22 August and between 17 November and 2 March

respectively. **; P<0.01, *; P<0.05, NS; not significant.

29

Table 5. Relative abundance (%) of air-borne dust microbiota of the dairy farm cowshed

examined at two seasons. Summer

(n=6)

Winter

(n=9)

SE One-way

ANOVA

Actinobacteria 12.4 4.31 1.10 **

Corynebacteriaceae 6.95 2.97 0.82 **

Micrococcaceae 1.52 0.35 0.23 **

Propionibacteriaceae 1.13 0.02 0.07 **

Bacteroidetes 5.33 23.1 0.55 **

Bacteroidaceae 1.13 7.05 0.27 **

Porphyromonadaceae 0.35 2.41 0.07 **

RF16 0.21 3.01 0.35 **

Rikenellaceae 0.44 2.28 0.09 **

Paraprevotellaceae 0.35 1.52 0.05 **

Firmicutes 56.9 58.1 0.65 NS

Planococcaceae 1.12 0.05 0.08 **

Staphylococcaceae 13.4 4.72 0.60 **

Aerococcaceae 4.75 7.28 1.04 NS

Lactobacillaceae 4.10 0.97 1.06 *

Streptococcaceae 6.10 0.31 0.81 **

Clostridiaceae 0.80 2.12 0.13 **

Lachnospiraceae 2.22 6.79 0.37 **

Peptostreptococcaceae 0.69 1.00 0.14 NS

Ruminococcaceae 3.73 17.4 0.77 **

Tissierellaceae 4.98 1.09 0.20 **

Erysipelotrichaceae 0.31 1.94 0.08 **

Proteobacteria 22.6 6.75 1.70 **

Enterobacteriaceae 1.32 0.35 0.18 **

Moraxellaceae 7.19 1.78 0.48 **

Pseudomonadaceae 6.72 1.34 0.63 **

Xanthomonadaceae 1.57 0.07 0.14 **

Spirochaetes 0.11 1.57 0.08 **

Spirochaetaceae 0.07 1.49 0.08 **

Phyla and families having relative abundance at > 1% in at least one sample are indicated. Summer and winter

stand for the sampling conducted between 6 June and 22 August and between 17 November and 2 March

respectively. **; P<0.01, *; P<0.05, NS; not significant.

30

Figure 1. Uterine, fecal, bedding, and air-borne dust microbiota in the dairy farm examined during two seasons.

AS, AW, BS, BW, FS, FW, US, and UW indicate air-borne dust during summer, air-borne dust during winter,

bedding during summer, bedding during winter, feces during summer, feces during winter, uterine mucus during

summer, and uterine mucus during winter, respectively.

31

Figure 2. Canonical analysis of principal coordinates plot characterizing the uterine, fecal, bedding, and airborne

dust microbiota in the dairy farm. Samples enclosed in a green circle are regarded to be in the same group at a

70% similarity level.

32

CHAPTER 4

AN INVESTIGATION OF SEASONAL VARIATIONS IN THE MICROBIOTA OF

MILK, FECES, BEDDING, AND AIRBORNE DUST

ABSTRACT

The microbiota of dairy cow milk varies with the season, and this accounts in part for the

seasonal variation in mastitis-causing bacteria and milk spoilage. The microbiota of the

cowshed may be the most important factor because the teats of a dairy cow come into contact

with bedding material when the cow is resting. The objectives of the present study were to

determine whether the microbiota of the milk and the cowshed vary between seasons, and to

elucidate the relationship between the microbiota. We used 16S rRNA gene amplicon

sequencing to investigate the microbiota of milk, feces, bedding, and airborne dust collected at

a dairy farm during summer and winter. The biochemical components of the cow’s blood were

also investigated to determine the nutrition status and health of the cow. The seasonal

differences in the milk yield, milk composition, and blood biochemical components were

marginal. The fecal microbiota was stable across the two seasons, except that Bacteroidaceae

was more abundant during summer than winter. Many bacterial taxa of the bedding and airborne

dust microbiota exhibited distinctive seasonal variation. In the milk microbiota, the abundances

of Corynebacteriaceae, Bacillaceae, Lactobacillaceae, Streptococcaceae, and

Microbacteriaceae were affected by the seasons, but the pattern of seasonal variation was not

the same in the cowshed microbiota. Nevertheless, canonical analysis of principle coordinates

revealed a distinctive group comprising the milk, bedding, and airborne dust microbiota.

Although the milk microbiota is related to the bedding and airborne dust microbiota, the

relationship may not account for the seasonal variation in the milk microbiota. Some major

bacterial families stably found in the bedding and airborne dust microbiota, e.g.,

Staphylococcaceae, Moraxellaceae, Ruminococcaceae, and Bacteroidaceae, may have greater

influences than those varied between seasons.

Keywords: Cowshed; Dairy Cow; Microbiota; Milk; Season

INTRODUCTION

The assessment of milk microbiota is important for preventing mastitis and maintaining

herd health (Jayarao et al., 2004; Olde et al., 2009; Kim et al., 2017) If typical contagious

bacteria, such as Staphylococcus aureus, Streptococcus agalactiae, and Corynebacterium bovis,

33

are found to proliferate in the tank milk, the infected cows should be identified, and the

procedure and sequence of milking should be revised. If environmental bacteria, such as

coliforms, coagulase-negative Staphylococci, and Streptococci other than S. agalactiae,

markedly increase, the hygiene of the cowshed should be improved. Likewise, pathogen

identification greatly facilitates the provision of appropriate treatment with antibiotics. With

regard to the quality control of milk, the abundance of Pseudomonas spp. may be of great

concern, because their heat-resistant enzymes can hydrolyze protein and fat, producing an

unpleasant flavor even after pasteurization (Vithanage et al., 2016; Ribeiro et al., 2018).

Moreover, several Pseudomonas spp. cause mastitis (Nam et al., 2009).

The prevalence of mastitis varies according to the season in dairy cows as does the

somatic cell count (SCC), which is an indicator of the number of leukocytes in the milk and

therefore of udder health. Makovec and Ruegg (2003) reported that mastitis caused by

Escherichia coli and Streptococcus spp. may occur more during summer, and Olde Riekerink

et al. (2007) described the greater risk of mastitis caused by S. aureus and Streptococcus

dysgalactiae during winter. Olde Riekerink et al. (2007) also reported the effect of housing on

seasonal variation; mastitis caused by E. coli can occur more during summer if cows are

managed on pasture, whereas the infection may be seen more during winter if managed as

confined herds. Mastitis caused by coagulase-negative S. aureus and C. bovis may occur

throughout the year. Milk spoilage caused by psychrophilic Pseudomonas spp. can become a

problem during winter.

Regardless of the symptoms of mastitis, the milk microbiota varies between seasons

(Metzger et al., 2018). Differences in temperature and humidity could account for the variation,

because both the health of the cows and the growth of milk bacteria are influenced by

temperature and humidity. However, Li et al. (2018) found that at the phylum level the milk

microbiota during summer was similar to that during winter. In fact, it is unclear what causes

seasonal variations in the milk microbiota.

Among the factors putatively involved in the milk microbiota and mastitis outbreak, the

bedding microbiota may be the most important because the teats of a dairy cow are in direct

contact with bedding material when the cow is resting. In a previous study, we examined the

microbiota of the gut, milk, and cowshed environment using 16S rRNA gene amplicon

sequencing, and demonstrated that the milk microbiota is associated with the bedding

microbiota but is clearly distinct from the feed, rumen fluid, fecal, and water microbiota (Wu

et al., 2019). We conducted the survey at two farms: one in April and one in September; hence,

the difference in the milk microbiota between the farms might have resulted from seasonal

34

variations. Although the importance of the bedding microbiota has long been recognized from

the perspective of mastitis prevention, few studies have examined the milk and cowshed

microbiota or the variation across seasons.

In the present study, we collected samples of milk, feces, bedding, and airborne dust from

a dairy farm during summer and winter at 1 and 2 months postpartum. The microbiota was

assessed by 16S rRNA gene amplicon sequencing. The composition of the milk and the

biochemical components of the blood were also investigated to determine the health and

nutrition status of the dairy cows. The objective was to determine if the milk and cowshed

microbiota vary between seasons, and if a variation in the milk microbiota is related to a

variation in the cowshed microbiota.

MATERIALS AND METHODS

Sampling

We collected samples from cows at the Okayama Prefecture Livestock Research Institute

(Okayama, Japan). The cows were housed in a free stall barn and fed a total mixed ration silage

throughout the year, except during the summer when their diet was supplemented with a fatty

acid salt (palmitic acid calcium) to fortify milk fat production. The contents of dry matter (DM),

crude protein (N × 6.25), and total digestible nutrients were 55–60%, 16–17% DM, and 72–

74% DM, respectively. The sampling was performed from 6 June to 22 August and from 17

November to 16 January. Hereafter, the former series is referred to as the summer sampling and

the latter as the winter sampling. The minimum and maximum temperatures were 16–31°C

during the summer and -2–15°C during the winter.

The milk and feces samples were collected from 9 cows during summer and from 8 cows

during winter, with 2 sampling times each at 1 and 2 months postpartum. Because milk and

fecal sampling from cows at 1 and 2 months postpartum was occasionally conducted on the

same day, bedding and airborne dust sampling was carried out 6 times during summer and 9

times during winter.

Each milk sample was collected after cleaning the surface of the udder, and the foremilk

was discarded before collecting the sample. Milk samples were taken manually from 4 udders,

then mixed to produce a composite sample. Fecal samples were collected from the rectum, and

blood samples were taken from the caudal vein. Airborne dust samples were collected by

placing 3 petri dishes approximately 1.0 m above the ground for 5 min. Bedding samples were

collected from 3 separate places in a cowshed. In the free stall system, cows can move and rest

freely, and determining their resting place was difficult. Thus, a composite sample prepared

35

from 3 separate samples was regarded as a representative means of assessing the bedding and

airborne dust microbiota at the time of sampling. All the samples were kept on ice during

transportation to the laboratory, and were stored at -20°C until required for further analyses.

The contents of protein, fat, and solids-not-fat (SNF), and the SCC of the milk were determined

using a CombiFoss FT+ analyzer (Foss Allé, Hillerød, Denmark). The procedures and protocols

for the animal experiments were approved by the Animal Care and Use Committee, Okayama

University, Japan.

DNA extraction

The 250 µL milk samples were centrifuged at 16,000 g for 2 min, and the pellet was

collected. For DNA extraction from airborne samples, a 1-mL aliquot of each sample was

transferred into an Eppendorf tube, and then centrifuged to collect the pellet. All the pellet

samples were washed with 500 µL of solution I containing 0.05 M D-glucose, 0.025 M Tris-

HCl (pH 8.0), and 0.01 M sodium EDTA (pH 8.0), and then lysed with 180 µL of lysozyme

solution (20 g/L lysozyme, 0.02 M Tris-HCl [pH 8.0], 0.002 M sodium EDTA [pH 8.0], 1.2

g/L Triton X-100) at 37°C for 1 h. Bacterial DNA of milk, airborne dust samples was purified

by using the DNeasy blood & tissue kit (Qiagen, Germantown, MD, USA), according to the

manufacturer’s instructions. In case of the fecal and bedding samples, bacterial DNA was

extracted following the procedure for the repeated bead beating plus column method (Yu et al.,

2014) and purified using the mini DNeasy stool kit (Qiagen, Germantown, MD, USA).

16S rRNA gene amplicon sequencing

Bacterial DNA was amplified by two-step polymerase chain reaction (PCR) to generate

amplicon libraries for next-generation sequencing (Tang et al., 2017). The primers targeting the

V4 region of 16S ribosomal RNA (rRNA) genes (forward: 5′-

ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAGCMGCCGCGGTAA-3′;

reverse: 5′-

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGACTACHVGGGTWTCTAAT-

3′; tail sequences are underlined) were used for the first round of PCR, with the following

protocol: initial denaturation at 94°C for 2 min, followed by 35 cycles of denaturation at 94°C

for 30 sec, annealing at 50°C for 30 sec, elongation at 72°C for 30 sec, and an elongation step

at 72°C for five min. The PCR products were purified by electrophoretic separation on a 2.0%

agarose gel using a Fast Gene Gel/PCR Extraction Kit (NIPPON Genetics Co., LTD., Tokyo,

Japan). The second round of PCR, with adapter-attached primers, followed the protocol of

36

initial denaturation at 94°C for two min, 10 cycles of denaturation at 94°C for 30 sec, annealing

at 59°C for 30 sec, elongation at 72°C for 30 sec, and an elongation step at 72°C for five min.

The second-round PCR products were purified in the same way as that in case of the first-round

PCR products.

The purified amplicons were pair-end sequenced (2 × 250 bp) on an Illumina MiSeq

platform at FASMAC Co., Ltd. (Kanagawa, Japan). Raw sequence data were analyzed using

the Quantitative Insights Into Microbial Ecology (QIIME version 1.9.0). The 250-bp reads were

truncated at any site receiving an average quality score under 20. Truncated reads that were

shorter than 225 bp were discarded. In primer matching, sequences showing overlaps longer

than 200 bp were assembled. The final reads obtained after pair-end joining were grouped into

operational taxonomic units (OTUs) using a 97% similarity threshold. The sequence data were

analyzed and categorized from the phylum to the family level using the default settings of the

Ribosomal Database Project classifier.

Statistical analysis

Data pertaining to the milk yields, milk compositions, blood metabolite concentrations,

and relative abundances of major bacterial families in the milk and fecal microbiota (where the

proportion of the family in at least one sample was > 1.0%) were analyzed by two-way analysis

of variance (ANOVA). Models included the fixed effects of season, the sampling time (1 and

2 months postpartum), and the interaction between season and sampling time. The data for the

relative abundances of the major bacterial families in the bedding and airborne dust microbiota

were analyzed by one-way ANOVA to determine the effect of season. The microbiota data

were also subjected to canonical analysis of principal coordinates (CAP) to define assignment

and clustering that explained the variations in the microbiota. Discriminant vectors with a

Pearson correlation > 0.7 were considered significant. Two-way ANOVA was performed using

JMP software (version 11; SAS Institute, Tokyo, Japan) and CAP was carried out using Primer

version 7 with the Permanova+ add-on (Primer-E, Plymouth Marine Laboratory, Plymouth,

UK).

RESULTS

The milk yield of the cows was 35–39 kg, and there were no differences between the two

seasons or between 1 and 2 months after calving (Table 1). The content of milk protein was

greater, and the contents of fat and SNF were numerically higher during winter than during

summer. The average SCC of the milk at 2 months postpartum during summer reached 429 ×

37

103 cells/mL, because one cow had a markedly high SCC (2.8 × 106 cells/mL). The serum level

of Alb was higher and that of ALT was lower during summer than during winter. Likewise, the

serum level of total cholesterol was greater at 2 months than at 1 month postpartum.

At the family level, the five most abundant taxa of the milk microbiota during summer

were Staphylococcaceae (10.3%), Ruminococcaceae (9.7%), Aerococcaceae (7.7%),

Lachnospiraceae (5.4%), and Corynebacteriaceae (4.3%), and those during winter were

Ruminococcaceae (10.2%), Staphylococcaceae (7.5%), Lactobacillaceae (6.5%),

Aerococcaceae (5.5%), and Lachnospiraceae (5.3%) (Table 1). There were seasonal variations

in the relative abundances of Corynebacteriaceae and Bacillaceae (summer > winter) and those

of Lactobacillaceae, Streptococcaceae, and Microbacteriaceae (summer < winter). There were

also variations in the relative abundances of Porphyromonadaceae (1 month > 2 months), and

Corynebacteriaceae and Tissierellaceae (1 month < 2 months) between samples taken 1 or 2

months postpartum.

The fecal microbiota was fairly stable at 1 and 2 months postpartum. Therefore, the data

are summed up to enable comparison between the two seasons (Figure 1). Ruminococcaceae

(34.1%), Bacteroidaceae (10.2%), Lachnospiraceae (9.6%), Rikenellaceae (3.3%), and

Clostridiaceae (2.9%) were the five most abundant taxa regardless of the season. There were

seasonal variations in the relative abundances of Bacteroidaceae, S24-7, Mogibacteriaceae, and

Methanobacteriaceae (summer > winter), and of RF16 and Spirochaetaceae (summer < winter).

In the bedding microbiota, Aerococcaceae (13.8%), Ruminococcaceae (10.8%), Moraxellaceae

(8.3%), Corynebacteriaceae (7.3%), and Staphylococcaceae (6.6%) were the five most

abundant taxa during summer, and Ruminococcaceae (17.0%), Aerococcaceae (13.3%),

Lachnospiraceae (6.6%), Staphylococcaceae (6.3%), and Corynebacteriaceae (6.0%) were the

five most abundance taxa during winter (Figure 2). There were seasonal variations in the

relative abundances of Moraxellaceae, Planococcaceae, Tissierellaceae, Carnobacteriaceae,

Micrococcaceae, Idiomarinaceae, and Halomonadaceae (summer > winter); and of

Ruminococcaceae, Bacteroidaceae, Lachnospiraceae, Clostridiaceae, Peptostreptococcaceae,

Rikenellaceae, RF16, and Mogibacteriaceae (summer < winter).

In the airborne dust microbiota, the five most abundant taxa during summer were

Staphylococcaceae (13.4%), Moraxellaceae (7.2%), Corynebacteriaceae (7.0%),

Pseudomonadaceae (6.7%), and Streptococcaceae (6.1%); and the five most abundant taxa

during winter were Ruminococcaceae (17.4%), Aerococcaceae (7.3%), Bacteroidaceae (7.1%),

Lachnospiraceae (6.8%), and Staphylococcaceae (4.7%) (Figure 3). There were seasonal

variations in the relative abundances of Staphylococcaceae, Moraxellaceae,

38

Corynebacteriaceae, Pseudomonadaceae, Streptococcaceae, Tissierellaceae, Lactobacillaceae,

Xanthomonadaceae, Micrococcacae, Enterobacteriaceae, Propionibacteriaceae, and

Planococcaceae (summer > winter); and of Ruminococcaceae, Lachnospiraceae,

Bacteroidaceae, Clostridiaceae, Rikenellaceae, and Paraprevotellaceae (summer < winter).

We used CAP to determine if the milk microbiota was related to the cowshed microbiota

(Figure 4). The results indicate that the milk microbiota was grouped together with the bedding

and airborne dust microbiota, and not with the fecal microbiota. Likewise, several airborne dust

samples taken during summer were low in Ruminococcaceae, Bacteroidaceae, and

Lachnospiraceae, and formed a separate group from the others.

DISCUSSION

Milk yield and protein content follow a seasonal pattern over the course of the year (Salfer

et al., 2019). They are typically greatest during winter and reach a nadir during summer; thus,

the higher protein content of the milk obtained during winter was regarded as normal. The dairy

cows investigated in the present study were given the same diet (total mixed ration silage)

throughout the year, except during the summer when their diet was supplemented with a small

amount of palmitic acid calcium. It is therefore difficult to explain why the relative abundances

of Bacteroidaceae, S24-7, Mogibacteriaceae, and Methanobacteriaceae were greater, and those

of RF16 and Spirochaetaceae were lower during summer.

Regardless of whether the cows had normal or high SCC, Metzger et al. (2018) found

variation of the milk microbiota between the sampling times after calving, i.e., 1 week, 2 weeks,

and 2, 3, 4, or 5 months of lactation. In the present study, only Corynebacteriaceae (increase),

Porphyromonadaceae (decrease), and Tissierellaceae (increase) exhibited changes in

abundance at 1–2 months postpartum. This differed from the finding of Metzger et al. (2018),

wherein the abundancies of numerous bacterial taxa varied between sampling times. Although

our data were mostly derived from healthy cows, the elevated abundance of Corynebacteriaceae,

the family that includes C. bovis, might indicate a greater risk of infection when lactation is at

its maximum.

Metzger et al. (2018) also demonstrated that variation of the milk microbiota was greater

between seasons than between the sampling times after calving; the abundancies of a large

number of bacterial taxa including Staphylococcus spp., Acinetobacter spp., and Aerococcus

spp. varied between the seasons. In the present study, only five families exhibited seasonal

variation; the abundances of Corynebacteriaceae and Bacillaceae were greater during summer,

and Microbacteriaceae, Lactobacillaceae, and Streptococcaceae were more prevalent during

39

winter. Li et al. (2018) reported that the abundances of Pseudomonas spp., Propionibacterium

spp., and Flavobacterium spp. were negatively correlated with temperature, and those of

Bacillus spp., Lactobacillus spp., and Bifidobacterium spp. were positively correlated with

temperature. In the present study, we observed a similar temperature effect to that described by

Li et al. (2018) for Bacillaceae (summer > winter), but the trend was reversed for

Lactobacillaceae.

With regard to the relationships between the milk and cowshed microbiota, the pattern of

seasonal variation was the same between the milk and bedding samples for Bacillaceae

(summer > winter), and between the milk and airborne dust samples for Corynebacteriaceae

(summer > winter). The pattern was reversed between the milk and bedding samples for

Microbacteriaceae, and between the milk and airborne dust samples for Microbacteriaceae,

Lactobacillaceae, and Streptococcaceae. With regard to the relationships between the milk and

fecal microbiota, the patterns of seasonal variation (summer > winter or summer < winter) was

not the same for any families.

The finding that the milk microbiota was related to the bedding and airborne dust

microbiota agreed with the results reported by Wu et al. (2019). Although the typical bacterial

taxa of feces, i.e., Ruminococcaceae, Bacteroidaceae, Lachnospiraceae, Clostridiaceae, and

Rikenellaceae, were stably detected in the milk microbiota, Aerococcaceae, Staphylococcaceae,

Moraxellaceae, Corynebacteriaceae, Streptococcaceae, Pseudomonadaceae, and

Tissierellaceae, which are regarded as typical bacterial taxa of bedding and airborne dust, were

not abundant in the fecal microbiota. Interestingly, Lachnospiraceae and Aerococcaceae were

not included in the discriminant vectors with a Pearson correlation > 0.7 in the CAP. Instead,

Carnobacteriaceae contributed to the differentiated groups. Although the relative abundance of

Carnobacteriaceae was < 1.0% in all the milk samples, the pattern of seasonal variation

(summer > winter) was the same in the milk and bedding samples. Carnobacteriaceae is known

as a spoilage-associated taxon in meat (Zhao et al., 2015), although its significance with regard

to milk quality has yet to be elucidated.

Despite the fact that the seasonal variation of the bedding and airborne dust microbiota

did not influence the milk microbiota, a distinctive group comprising the milk, bedding, and

airborne dust microbiota was formed by the CAP. Therefore, seasonal variation of the milk

microbiota mat result from factors that have greater influences than the seasonal variation of

the cowshed microbiota. Likewise, the serum concentration of biochemical components was

within acceptable levels; hence, seasonal changes in the milk microbiota may not be related to

40

the nutrition status or health of the cows. Thus, further research is required to clarify the factors

involved in the seasonal variation of the milk microbiota.

Although the importance of the cowshed microbiota has long been recognized, few

researchers have investigated the airborne dust microbiota of a dairy farm. Dutkiewicz et al.

(1994) examined cowshed microbiota by plate culture, and isolated numerous species including

those belonging to the following genera: Micrococcus, Arthrobacter, Staphylococcus, Bacillus,

Corynebacterium, Microbacterium, Streptomyces, Acinetobacter, Proteus, Pantoea,

Pseudomonas, Thermoactinomyces, and Saccharopolyspora. Although most of the

corresponding families, i.e., Micrococcaceae, Staphylococcaceae, Bacillaceae,

Corynebacteriaceae, Microbacteriaceae, Moraxellaceae, Enterobacteriaceae, and

Pseudomonadaceae, were detected by amplicon sequencing, the abundance of

Streptomycetaceae was quite low (< 0.01%), and we did not detect Thermoactinomycetaceae

in the present study.

The distinctive seasonal variation in the bedding and airborne dust microbiota is difficult

to explain, because few relevant surveys have been performed. The dairy farm used fans with

a mist of water to cool the bodies of the cows during summer; hence, this enforced ventilation

may have caused the differences in the bedding and airborne dust microbiota between the two

seasons. The lower abundances during summer than during winter of the five typical bacterial

taxa of feces, i.e., Ruminococcaceae, Bacteroidaceae, Lachnospiraceae, Clostridiaceae, and

Rikenellaceae, may have resulted from the water mist ventilation.

41

Figure 1. Family-level proportions of the top 20 bacterial taxa of the fecal microbiota of the dairy cows investigated

during summer and winter. Bars indicate mean values with standard deviations. Asterisks indicate significant

differences between summer and winter.

42

Figure 2. Family-level proportions of the top 20 bacterial taxa of the bedding microbiota of a dairy farm

investigated during summer and winter. Bars indicate mean values with standard deviations. Asterisks indicate

significant differences between summer and winter.

43

Figure 3. Family-level proportions of the top 20 bacterial taxa of the airborne dust microbiota of a dairy farm

investigated during summer and winter. Bars indicate mean values with standard deviations. Asterisks indicate

significant differences between summer and winter.

44

Figure 4. Canonical analysis of principal coordinates plot characterizing the milk, fecal, bedding, and airborne

dust microbiota of the dairy farm. Operational taxonomy units with Pearson’s correlations of > 0.7 are overlaid on

the plot as vectors. The samples enclosed by the green lines are considered to be in the same group (similarity

level 70%).

45

Table 1. Milk yield, milk composition, and relative abundance of milk microbiota of the dairy cows examined at one and two months postpartum during the two seasons.

Summer Winter SE Two-way ANOVA

1M

(n=9)

2M

(n=9)

1M

(n=8)

2M

(n=8)

S M S×M

Milk yield (kg/day) 39.0 39.2 34.6 38.2 2.89 NS NS NS

Milk composition

Protein (%) 2.69 2.74 2.94 3.03 0.07 * NS NS Fat (%) 3.48 3.27 3.61 3.56 0.17 NS NS NS

SNF (%) 8.29 8.37 8.45 8.63 0.11 NS NS NS SCC (×103 cells/mL) 74.1 429 65.1 65.6 173 NS NS NS

Milk microbiota

Actinobacteria 6.60 11.1 7.62 8.97 1.18 NS * NS

Corynebacteriaceae 3.17 5.39 2.84 3.22 0.60 * * NS

Microbacteriaceae 0.25 0.32 0.60 1.02 0.12 ** NS NS

Micrococcaceae 0.61 1.32 0.47 0.56 0.28 NS NS NS

Bifidobacteriaceae 1.28 2.07 1.95 2.44 0.43 NS NS NS

Bacteroidetes 12.1 9.57 12.8 13.0 1.16 NS NS NS

Bacteroidaceae 3.18 2.66 3.05 3.14 0.34 NS NS NS

Porphyromonadaceae 1.53 0.99 1.83 1.36 0.23 NS * NS

Rikenellaceae 0.90 0.86 1.13 0.99 0.15 NS NS NS

S24-7 1.96 0.59 2.31 1.55 0.66 NS NS NS

Firmicutes 64.9 61.2 61.3 60.2 2.67 NS NS NS

Bacillaceae 5.63 2.18 0.28 0.47 1.17 ** NS NS

Staphylococcaceae 9.47 11.2 7.50 7.51 1.93 NS NS NS

Aerococcaceae 5.34 9.96 4.92 6.07 1.94 NS NS NS

Lactobacillaceae 3.13 3.82 8.12 4.86 1.36 * NS NS

Streptococcaceae 1.58 1.59 2.86 2.51 0.37 ** NS NS

Turicibacteraceae 1.79 0.48 2.70 1.80 0.64 NS NS NS

Clostridiaceae 2.06 1.61 1.54 1.74 0.24 NS NS NS

Lachnospiraceae 5.91 4.84 4.90 5.66 0.59 NS NS NS

Peptostreptococcaceae 1.41 1.06 0.76 1.13 0.22 NS NS NS

Ruminococcaceae 11.1 8.30 9.72 10.8 1.15 NS NS NS

Mogibacteriaceae 1.08 0.86 0.98 0.87 0.12 NS NS NS

Tissierellaceae 0.86 1.58 1.16 1.59 0.22 NS * NS

Erysipelotrichaceae 1.66 0.98 2.07 1.39 0.50 NS NS NS

Proteobacteria 10.7 13.8 13.5 12.6 1.77 NS NS NS

Enterobacteriaceae 0.99 1.86 1.76 2.82 0.49 NS NS NS

Moraxellaceae 3.51 4.30 4.18 3.46 0.58 NS NS NS

Pseudomonadaceae 1.87 2.65 2.81 2.30 0.46 NS NS NS

Phyla and families having a relative abundance of > 1% in at least one sample are indicated. Summer and winter

stand for the sampling conducted between 6 June and 22 August and between 17 November and 2 March,

respectively. 1M and 2M indicate one and two months postpartum, respectively. S; effect of season, M; effect of

the month postpartum, **; P<0.01, *; P<0.05, NS; not significant.

46

CHAPTER 5

GENERAL CONCLUSION

In postpartum dairy cows, disorders such as mastitis, metritis and endometritis can occur

and reduce productivity and subsequent fertility. This study characterize the uterine, milk, fecal,

bedding, and airborne dust microbiota during different seasons, and clarify the relationship

between uterine, milk, and environmental microbiota in dairy farms.

The uterine microbiota varied between seasons but not between 1 and 2 months

postpartum; the five most prevalent taxa were Enterobacteriaceae, Moraxellaceae,

Ruminococcaceae, Staphylococcaceae, and Lactobacillaceae during summer, and

Ruminococcaceae, Lachnospiraceae, Bacteroidaceae, Moraxellaceae, and Clostridiaceae were

during winter. The fecal microbiota was stable regardless of the season, whereas bedding and

airborne dust microbiota varied between summer and winter. With regards to uterine, bedding,

and airborne dust microbiota, Enterobacteriaceae, Moraxellaceae, Staphylococcaceae, and

Lactobacillaceae were more abundant during summer, and Ruminococcaceae, Lachnospiraceae,

Bacteroidaceae, and Clostridiaceae were more abundant during winter. This finding clarified

that uterine microbiota may vary with changes in the microbiota from cowshed environments

across seasons.

In the milk microbiota, the abundances of Corynebacteriaceae, Bacillaceae,

Lactobacillaceae, Streptococcaceae, and Microbacteriaceae were affected by the seasons, but

the pattern of seasonal variation was not the same in the cowshed microbiota. Nevertheless,

canonical analysis of principle coordinates revealed a distinctive group comprising the milk,

bedding, and airborne dust microbiota. Although the milk microbiota is related to the bedding

and airborne dust microbiota, the relationship may not account for the seasonal variation in the

milk microbiota.

These results indicated the importance of microbiota in cowshed environments especially

in understanding the postpartum uterine microbiota of dairy cows, which may help account for

differences between the bacterial taxa among healthy uterine microbiota in previously

published studies. The distinctive seasonal variation in the bedding and airborne dust

microbiota is difficult to explain, because few relevant surveys have been performed. Thus,

research needs to be expanded to prevent uterine disease and ensure high milk production from

the dairy cows.

47

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