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Investigation of the microbiota of dairy
cow milk in relation to mastitis
prevention and milk quality control
2019, March
Wu Haoming
Graduate School of
Environmental and Life Science
(Doctor’s Course)
OKAYAMA UNIVERSITY
CONTENTS
FIGURE CONTENTS ................................................................................ I
TABLE CONTENTS ................................................................................ IV
AKNOLAGEMENT ................................................................................. 1
ABSTRACT ............................................................................................ 2
CHAPTER 1 GENERAL INTRODUCTION .................................................. 7
1.1 EFFECTS OF MILK CONTENTS ON THE HEALTH OF DAIRY COWS ................ 7
1.2 PROGRESS AND SHORTCOMINGS OF AMS MILKING MACHINE .................. 8
1.3 BACTERIA IN MILK ........................................................................ 9
1.3.1 Mastitis ............................................................................. 9
1.3.2 Spoilage .......................................................................... 11
REFERENCE ................................................................................. 12
CHAPTER 2 MONITORING THE MICROBIOTA AND NUTRIENT
COMPOSITION IN DAIRY COW MILK COLLECTED FROM A HERD
MANAGED BY AUTOMATIC MILKING SYSTEMS .................................. 19
2.1 INTRODUCTION ..................................................................... 19
2.2 MATERIALS AND METHODS ................................................... 20
2.2.1 Milk samples ................................................................... 20
2.2.2 Preparation of bacterial DNA .......................................... 21
2.2.3 qPCR ............................................................................... 21
2.2.4 Illumina MiSeq sequencing ............................................. 22
2.2.5 Statistical analysis .......................................................... 22
2.3 RESULTS ................................................................................ 23
2.4 DISCUSSION .......................................................................... 28
2.5 CONCLUSION ......................................................................... 33
2.6 REFERENCE ............................................................................ 33
CHAPTER 3 ASSESSMENT OF MILK MICROBIOTA OF DAIRY COWS
MANAGED BY AUTOMATIC MILKING SYSTEMS .................................. 39
3.1 INTRODUCTION ..................................................................... 39
3.2 MATERIALS AND METHODS ................................................... 40
3.2.1 Sample collection ............................................................ 40
3.2.2 Preparation of bacterial DNA .......................................... 41
3.2.3 Quantitative real-time PCR ............................................. 41
3.2.4 Illumina MiSeq sequencing ............................................. 42
3.2.5 Statistical analysis .......................................................... 42
3.3 RESULTS ................................................................................ 43
3.4 DISCUSSION .......................................................................... 48
3.5 CONCLUSION ......................................................................... 54
3.6 REFERENCES .......................................................................... 54
CHAPTER 4 RUMEN FLUID, FECES, MILK, WATER, FEED, AIRBORNE
DUST, AND BEDDING MICROBIOTA IN DAIRY FARMS MANAGED BY
AUTOMATIC MILKING SYSTEMS ......................................................... 59
4.1 INTRODUCTION ..................................................................... 59
4.2 MATERIALS AND METHODS ................................................... 60
4.2.1 Sample collection ............................................................ 60
4.2.2 Preparation of bacterial DNA .......................................... 61
4.2.3 Quantitative real-time PCR for total bacteria ................. 61
4.2.4 Illumina MiSeq sequencing ............................................. 62
4.2.5 Bioinformatics and microbiota characterization ............. 62
4.2.6 Statistical analyses ......................................................... 63
4.3 RESULTS ................................................................................ 63
4.4 DISCUSSION .......................................................................... 67
4.5 CONCLUSION ......................................................................... 72
4.6 REFERENCES .......................................................................... 73
CHAPTER 5 MILK COMPOSITION AND MICROBIOTA IN DAIRY FARMS IN
CHINA ................................................................................................ 77
5.1 INTRODUCTION .......................................................................... 77
5.2MATERIALS AND METHODS ............................................................ 78
5.2.1 Sample collection ............................................................ 78
5.2.2 Preparation of bacterial DNA .......................................... 78
5.2.3 Quantitative real-time PCR ............................................. 79
5.2.4 Illumina MiSeq sequencing ............................................. 79
5.2.5 Statistical analysis .......................................................... 80
5.3 RESULTS ................................................................................... 80
5.4 DISCUSSION .............................................................................. 86
5.5 CONCLUSION ............................................................................. 87
5.6 REFERENCE ............................................................................... 87
CHAPTER 6 MICROBIOTA OF MANUALLY COLLECTED MILK BETWEEN
CHINESE, VIETNAMESE, AND JAPANESE DAIRY FARMS ....................... 90
6.1 INTRODUCTION .......................................................................... 90
6.2MATERIALS AND METHODS ............................................................ 91
6.2.1 Sample collection ............................................................ 91
6.2.1.1 Collection of Mastitis and Healthy milk ....................................... 91
6.2.1.2 Collection of milk samples from different countries ................... 92
6.2.2 Preparation of bacterial DNA .......................................... 92
6.2.4 Illumina MiSeq sequencing ............................................. 92
6.2.5 Statistical analysis .......................................................... 93
6.3 RESULTS ................................................................................... 94
6.3.1 Experiment 1 ................................................................... 94
6.3.2 Experiment 2 ................................................................... 97
6.4 DISCUSSION ............................................................................ 101
6.5 CONCLUSION ........................................................................... 102
6.6 REFERENCE ............................................................................. 103
I
Figure Contents
Figure 2.1 Seasonal variation of milk composition by one-way ANOVA ________ 23
Figure 2.2 (A) Relation between Milk Indicators (n=201). (B) The somatic cell
count (SCC) in milk has a significant linear positive relationship with the total
bacterial count (TBC) (n=60). __________________________________________ 24
Figure 2.3 alpha diversity index of milk microbiota by one-way ANOVA. _______ 25
Figure 2.4 LEfSe analysis at multiple taxonomic levels of each month data. ____ 27
Figure 2.5 (A) stacked bar chart of phyla proportion in each month (B) LEfSe
analysis at family levels of each month data. ______________________________ 28
Figure 2.6 The correlation network of different milk components and predominant
family(proportion>1%). _______________________________________________ 31
Figure 3.1 Association heatmap between the relative abundance of bacterial
families, sampling procedure, and sampling month for the milk of cows managed
by automatic milking systems. __________________________________________ 46
Figure 3.2 Principal coordinates analyses of (a) bacterial families and (b) KEGG
pathways in the microbiota of manually and automatically collected milk sampled
in May and July from dairy cows managed by automatic milking systems. ______ 47
Figure 3.3 The KEGG pathways encoded in the microbiota of (a) manually
collected and (b) automatically collected milk. Only the KEGG pathways
significantly affected by sampling month are presented. _____________________ 48
Figure 3.4 The KEGG pathways encoded in the microbiota of manually collected
and automatically collected milk. _______________________________________ 49
II
Figure 3.5 The predicted functions based on FAPROTAX database in the
microbiota of manually collected and automatically collected milk. ____________ 53
Figure 4.1 Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) in feed, rumen fluid, feces, milk, bedding,
water, and airborne dust microbiota in two dairy farms managed by an automatic
milking system. ______________________________________________________ 65
Figure 4.2 Family level proportions of feed, rumen fluid, feces, milk, bedding,
water, and airborne dust microbiota in two dairy farms managed by an automatic
milking system. ______________________________________________________ 66
Figure 4.3 Canonical analysis of principal coordinates plot characterizing the
microbiota of feed, rumen fluid, feces, milk, bedding, water, and airborne dust in
the two dairy farms managed by an automatic milking system.________________ 68
Figure 4.4 Pie charts of the percentages of inferred sources of milk microbiota in
two dairy farms managed by an automatic milking system. ___________________ 69
Figure 4.5 The five most abundant taxa in rumen fluid, milk, feces, bedding,
airborne dust, water, and feed microbiota in two dairy farms managed by an
automatic milking system. _____________________________________________ 71
Figure 5.1 Farm variation of milk composition(A) and alpha diversity(B) by one-
way ANOVA. _______________________________________________________ 81
Figure 5.2 Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) in milk microbiota in five dairy farms
collected from Heilongjiang and Shanxi. _________________________________ 82
Figure 5.3 Principal coordinates analyses of bacterial families in the microbiota of
manually collected milk sampled from different farm in China. _______________ 83
III
Figure 2.5 LEfSe analysis at family levels of Heilongjiang and Shanxi in China.
___________________________________________________________________ 84
Figure 5.5 The correlation network of different milk components and predominant
family(proportion>1%). _______________________________________________ 85
Figure 6.1 Alpha diversity of Bacteria in Milk of Healthy and Mastitis Cows by one-
way ANOVA. _______________________________________________________ 94
Figure 6.2 Phyla level (A) and Family level (B) proportions of Mastitis and healthy
cow’s milk which manually collected in different month managed by an automatic
milking system. ______________________________________________________ 96
Figure 6.3 (A) Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) (B) Significant changed family in milk
between healthy and mastitis dairy cows by one-way ANOVA. (C) Principal
coordinates analyses of bacterial families in Mastitis and healthy cow’s milk which
manually collected in different month managed by an automatic milking system. 97
Figure 6.4 Country variation of milk bacterial alpha diversity by one-way ANOVA.
___________________________________________________________________ 98
Figure 6.5 LEfSe analysis at multiple taxonomic levels of each country milk data.
___________________________________________________________________ 99
Figure 6.6 Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) in milk microbiota from china, Japan and
Vietnam. __________________________________________________________ 100
Figure 6.7 Principal coordinates analyses of bacterial families in the microbiota of
manually collected milk sampled from china, Japan and Vietnam. ___________ 101
IV
Table Contents
Table 3.1. Milk yield and composition of dairy cows and total bacterial
population, total sequence reads, and alpha diversity indices of
microbiota of manually and automatically collected milk sampled in May
and July. ................................................................................................... 43
Table 3.2. Relative abundances of bacterial families in the microbiota of
manually and automatically collected milk sampled in May and July
from dairy cows managed by automatic milking systems. ..................... 44
Table 3.3. The KEGG pathways encoded in the microbiota of manually
and automatically collected milk sampled in May and July from dairy
cows managed by automatic milking systems. ........................................ 51
1
AKNOLAGEMENT
I would like to thank my supervisor, Professor Naoki NISHINO, in particular.
From my arrival in Japan to the present six years, Professor Nishino has shown great
concern for my life and research. With his help and guidance, I can smoothly complete
the experiment design, operation and result analysis. And then compile papers for
publication. He guided and witnessed every step of my scientific research.
I exceedingly appreciate my co-supervisors, Assoc. Prof. Takeshi TSURUTA and
Prof. Hiroaki FUNAHASHI, for their academic comments and persistent help in my
research.
I want to thank to Ph.D. Hongyan Han, Ph.D. Xiaomin Ao, Ph.D. Baiyila Wu,
Ph.D. Gaowa Bai, Ph.D. Tang Thuy Minh, Ph.D. Tran Thi Minh Tu, Ph.D. Kuikui Ni
and students who are studying hard in the Animal Nutrition Laboratory for their help,
accompany, and encouragement.
The scholarship from China Scholarship Council financially supported me during
entire doctoral period.
Finally, I express my deep love to my family, thanks for their continued support
and encouragement.
2
Abstract
In the production of milk and dairy products, bacteria in raw milk may closely be
related to health of the cows, quality of the milk, shelf life and flavor of the dairy
products. Contamination of cow's mammary glands by pathogenic bacteria, such as
Esherichia coli, Staphylococcus aureus, and Streptococcus agalactiae, will cause a
large increase in somatic cell count (SCC) in milk and even the occurrence of mastitis.
Mastitis lowers productivity and increases farm costs, including veterinary diagnostics,
medicines, unpaid special feeding, and even risks leading to elimination of the disease’s
dairy cows. In addition, as a source of various dairy products, bacteria in raw milk, such
as Pseudomonas spp. and Acinetobacter spp. can affect flavor and shelf life of the dairy
products by secreting heat-resistant proteases and lipases. Although the milk
disinfection system can effectively eliminate bacteria in milk, heat-resistant proteases
and lipases in milk may continue to be activated and deteriorate quality of the dairy
products. Overall, the more bacteria are present in milk, the greater risk of cow disease
and milk deterioration the dairy farm may have; hence, in the current milk production,
SCC and total bacterial count (TBC) are important indicators to understand cow health
and milk quality. Effective and reasonable control of bacteria in milk can be a key to
prevent mastitis and improve milk quality, and thus it is of great significance to clarify
factors affecting bacteria in raw milk. In this thesis, five experiments were carried out
to examine the effects of season, regions, milking methods, and cowshed environment
on the microbiota of raw milk. Microbiota was assessed by Illumina MiSeq sequencing
and the gene functions were predicted by Phylogenetic Investigation of Communities
by Reconstruction of Unobserved States (PICRUSt).
Seasonal difference in milk composition and milk microbiota
The microbiota, SCC, and composition of milk collected from a herd managed
by automatic milking systems were examined through a 1-year survey. From February
to December 2014 every two months, a total of 201 milk-test day samples were
collected. Milk composition and SCC were determined using a CombiFoss FT+, and
milk microbiota total bacterial population were assessed by MiSeq amplicon
sequencing and real time-PCR respectively. Among 201 samples, 37 samples (18.4%)
3
had SCC of >300×103/mL and 9 samples had >1,000×103/mL. The SCC was positively
correlated with yield, fat content, protein content, and total bacterial count of the milk.
The proportion of Firmicutes appeared to be greater and that of Proteobacteria were
smaller in hot seasons compared with other seasons. Relative abundances of
Pseudomonadaceae, Enterobacteriaceae, Staphylococcaceae, Micrococcaceae, and
Ruminococcaceae were greater in winter (Dec. and Feb.), those of Streptococcaceae,
Microbacteriaceae, and Enterococcaceae were greater in spring (Apr. and Jun.), and
that of Moraxellaceae was greater in autumn (Oct.) than other seasons. In addition, the
abundances of Pseudomonadaceae and Enterobacteriaceae were positively and those
of Staphylococcaceae, Micrococcaceae, Ruminococcaceae, and Moraxellaceae were
negatively related with milk urea nitrogen, suggesting that milk microbiota could be
manipulated by diet and nutritional management.
Microbiota between manually collected and mechanically collected milk
In the previous study, we assessed the microbiota of individual cow’s milk
automatically stored in cooling cups, which became unable because of technical
development with AMS. Although the sample can be regarded as raw milk, the data for
milk microbiota may not be identical to those for udder milk, because of the effect of
cooling. To clarify the differences between manually collected and automatically
collected milk, samples were collected in May and July from three lactating Holstein
cows managed by AMS. Large differences were seen between May- and July-collected
samples in the microbiota of manually collected milk; the total abundances of
Methylobacteriaceae, Sphingomonadaceae, and Staphylococcaceae were
approximately half (45%) in May-collected samples, whereas there was an
overwhelming abundance (>70%) of Enterobacteriaceae in July-collected samples.
The microbiota of automatically collected milk was substantially different;
Moraxellaceae, Pseudomonadaceae, and Enterococcaceae were found as major
families in both May- and July-collected samples, although their proportions were
marginal in manually collected milk. Assessment by PICRUSt demonstrated that gene
functions related with fermentation, diarrhea, gastroenteritis, septicemia, aromatic
compound degradation, and nosocomial and pneumonia were higher in manually
collected than automatically collected milk. Effect of the collection procedure on the
diversity and predicted gene functions of milk microbiota was clear and samples of
4
manually collected milk were shown appropriate.
Characterization of gut, milk, and cowshed environment microbiota
To examine possible contamination source of milk microbiota, rumen fluid,
feces, milk, water, feed (total mixed ration silage), bedding, and airborne dust were
sampled at two dairy farms managed by AMS. The microbiota on each was assessed
by Illumina MiSeq sequencing. The three most prevalent taxa (Aerococcaceae,
Staphylococcaceae, and Ruminococcaceae at farm 1 and Staphylococcaceae,
Lactobacillaceae, and Ruminococcaceae at farm 2) were shared between milk and
airborne dust microbiota. Indeed, Source Tracker indicated that milk microbiota was
related with airborne dust microbiota. Meanwhile, hierarchical clustering and canonical
analysis of principal coordinates demonstrated that the milk microbiota was associated
with the bedding microbiota but clearly separated from feed, rumen fluid, feces, and
water microbiota. Importance of cowshed management for milk quality control and
mastitis prevention was confirmed and emphasized from this study.
A practical survey of composition and microbiota of the milk in dairy farms
in China
From five dairy farms implementing milking parlor and free barn housing in
Heilongjiang and Shanxi provinces, China, milk samples were manually collected from
five cows and their microbiotas were assessed by Illumina MiSeq sequencing. Milk
composition and SCC were determined using CombiFoss FT+, and N-acetyl-β-D-
glycosaminidase (NAGase) activity was determined by a fluorometric method. Milk
microbiota varied across farms and individual cows, and thus differences between
regions were obscure. Enterobacteriaceae was a most predominant family in about one-
third of the milk samples, and Pseudomonadaceae, Staphylococcaceae, Moraxellaceae,
and Lactobacillaceae were detected at high relative abundances in other samples. The
SCC was positively correlated with fat content, protein content, and total bacterial count
in the milk. The abundance of Enterobacteriaceae was positively and those of
Pseudomonadaceae and Lactobacillaceae were negatively related with lactose content,
and that of Moraxellaceae was negatively related with milk urea nitrogen; hence,
appropriate management of protein-energy balance in cows could be regarded as a
5
measure to suppress contamination of Moraxellaceae (Acinetobacter spp.) in the milk.
Unlike the results in experiment 4, cow-to-cow differences were evident even in the
same farm in this survey.
Microbiota of manually collected milk between Chinese, Vietnamese, and
Japanese dairy farms
Using the data for milk microbiota obtained by ourselves and our co-workers,
the similarities and differences between Chinese, Vietnamese, and Japanese practices
were examined. All milk samples were obtained manually, and diets consisting of corn
silage and concentrates, elephant grass and concentrates, and total mixed ration silage
were fed at Chinese, Vietnamese, and Japanese d, respectively. Milking parlor and free
barn housing was implemented at Chinese and Vietnamese farms, and AMS was
operated at Japanese farms. The milk microbiota was assessed by Illumina MiSeq
sequencing. Diversity of the milk microbiota was higher in Japanese than Chinese and
Vietnamese cows; the number of operational taxonomy unit, Chao 1 index, and
Shannon index were all greater for the milk of Japanese than those of Chinese and
Vietnamese cows. Moreover, relative abundances of Enterobacteriaceae and
Propionibacteriaceae were greater for Chinese and those of Streptococcaceae and
Methylobacteriaceae were greater for Vietnamese milk samples. Japanese milk
samples were characterized by low abundances of potential pathogens. It is yet unsure
if these differences can be due to the differences between AMS and parlor milking;
however, compared with regional difference examined in experiment 4, meteorological
difference could influence on the milk microbiota of dairy cows.
These experiments have revealed a number of factors affecting the milk
microbiota of dairy cows. The fact that substantial differences were seen in the milk
microbiota between manually collected and automatically collected samples should be
considered for both mastitis prevention and milk quality control. Although
Staphylococcaceae and Enterobacteriaceae were present at high proportions in udder
milk, the milk microbiota could be represented by Moraxellaceae and
Pseudomonadaceae in cooling tank, which may lead to an underestimate of mastitis
risk and irrelevant cow health management. Likewise, even though high abundances of
typical pathogens like Staphylococcaceae, Enterobacteriaceae, and Streptococcaceae
6
were shared in cows, the SCC was substantially different; hence, in addition to
monitoring of the milk microbiota, cow factors associated with infection and
inflammation need to be considered for better prevention and treatment. Our finding
that milk composition, especially milk urea nitrogen, was related with the abundance
of Moraxellaceae in the milk can suggest that appropriate protein-energy balance may
work as a measure to suppress pathogen contaminations of the milk.
7
CHAPTER 1 General Introduction
Bovine milk contains the nutrients needed for growth and development of the calf,
and is a resource of lipids, proteins, amino acids, vitamins and minerals. The lipids in
milk are emulsified in globules coated with membranes and proteins are in colloidal
dispersions as casein micelles. The casein micelles occur as colloidal complexes of
protein and salts, primarily calcium. Lactose and most minerals are in solution. Milk
composition has a dynamic nature, and the composition varies with stage of lactation,
age, breed, nutrition, and energy balance and health status of the udder. Milk contains
many different types of fatty acids. All these components make milk a nutrient rich
food item. Similarly, it has become a good environment for the growth of many kinds
of bacteria. Bacterial infection of the mammary gland results in an increase in the
concentration of somatic cells in cow’s milk.
1.1 Effects of Milk Contents on the Health of Dairy Cows
Many data show that milk content is related to mammary gland health and bacteria
in milk. Fat-to-protein ratio (F:P) may serve as a measure of cow’s energy balance
(Buttchereit et al., 2010). Patterns of changes in milk yield, F:P and SCC during
lactation was used to detect cases of mastitis in dairy cows (Jamrozik & Schaeffer,
2012). Heuer indicated that F:P was a risk factor for many diseases, including mastitis
(Heuer et al., 1999). Windig showed that cows with short and intensive peaks of SCS
related to infections with environmental pathogens exhibited an increase in F:P,
whereas infections caused by contagious pathogens resulted in decrease in F:P (Windig
et al., 2010). Kayano reported that Some mastitis pathogens, such as Streptococcus and
Escherichia coli, seem to be associated with long-term changes in fat, protein and MUN
(Kayano et al., 2018).
Milk triglyceride is synthesized from more than 400 different fatty acids, making
it the most complex of all-natural fats (Haug et al., 2007; Jensen, 2002). Almost all of
these acids exist in trace amounts, with only 1% or higher levels of about 15 acids.
Many factors are related to the changes of lipid content and fatty acid composition in
milk (Jensen, 2002; Palmquist et al., 1993). They may originate from animals, i.e.
genetics, lactation, mastitis and rumen fermentation, or they may be feed-related factors,
8
i.e. fiber and energy intake, dietary fat, seasonal and regional effects (Lindmark
Månsson, 2008). The fat content would be significant changed when the mastitis was
occurs (Kitchen, 1981).
Urea is an end product of protein metabolism. Rajala reported negative effects of
blood urea nitrogen or Mun on reproductive performance of dairy cows, and pointed
out that overfeeding CP resulted in reproductive stress (Rajala-Schultz et al., 2001).
However, others did not find that high concentrations of mirabilite had such a negative
effect on the reproductive capacity of dairy cows (Godden et al., 2001). Milk urea
nitrogen (MUN) is also used to monitor the utilization of protein in the gastrointestinal
tract of dairy cows in modern dairy farming. Rumen microorganisms break down feed
protein into ammonia. Rumen microorganisms can capture ammonia and use its
nitrogen to synthesize amino acids and proteins when their production is carried out in
the optimum amount of fermentable carbohydrates. However, in practice, especially in
high-yielding dairy cows, excessive ammonia occurs in the rumen, where ammonia is
absorbed through the rumen wall, penetrates into the blood, enters the liver, and the
liver cells convert it into urea in the urea cycle. Therefore, the urea content in milk
conveys detailed information about volume. Nitrogen, which is not used for the growth
or synthesis of milk proteins, is found in animal feed that is eaten every day. Feed
rationing contains too much protein, and excessive nitrogen is excreted into body fluids,
namely plasma (pun - plasma urea nitrogen), milk and urine (non-urine nitrogen).
Therefore, the level of urea in milk is a very useful indicator, which tells modern dairy
farmers that cows do not make full use of feed protein intake and excretion of excessive
nitrogen, or in the case of low protein content in feed. Actual use of information on
milk urea levels helps to assess the energy and protein balance of rations, reduce feed
costs and potential loss of nitrogen in dairy farms (Piotr Guliński et al., 2016; Wattiaux
1994). MUN concentration could be affected by season. Guli showed high levels of
protein associated with milk production season and feed. In their study, milk produced
in summer had the highest urea content (225 mg/l), while milk produced in winter had
the lowest urea content (172 mg/l) (P Guliński et al., 2008).
1.2 Progress and shortcomings of AMS milking machine
Since 1992, Automatic milking systems (AMS) were started to use in the
Netherlands and become more and more popular from Europe to all over the world
9
(Jacobs & Siegford, 2012; Shortall et al., 2016). The milk collection separates from
human participation, it has brought a vast reduce of stress and fear response (Innocente
& Biasutti, 2013). Moreover, the use of AMS enables cows to be milked whenever they
want; hence, compared with regular twice per day milking, milk production may
increase as the milking frequency of individual cows increases (Kruip et al., 2002). On
the same time, mastitis monitoring system is well going for quality assurance of milk
(Castro et al., 2015; Jacobs & Siegford, 2012; Kamphuis et al., 2010; Mollenhorst et
al., 2012). The AMS is equipped with instruments to detect individual affected breast
seasons. Milk conductance and color analysis were used to detect affected milk
(Hovinen et al., 2006). These systems make it possible to detect affected breast seasons
and separate milk from those affected quarters. However, udder health is also a
worrying problem on farms (Dohmen et al., 2010; Hovinen & Pyörälä, 2011). Honvinen
reported somatic cell count (SCC) was increased after AMS introduction first year and
continued to be higher (Hovinen et al., 2009). And, under AMS management, Mastitis
morbidity did not change in Primiparous cows, but Multiparous cows were increased.
it cannot be ruled out that the hypothetical healthy composite milk may contain high
SCC milk from various breast regions, which may affect bulk canned milk during
storage due to enzyme activity (Berglund et al., 2004). And the alert may increase the
antibiotic using for mastitis treatment (Meijering et al., 2004).
1.3 Bacteria in Milk
Microbiome of milk has always been attention. From prevent and treatment of
mastitis to prevent spoilage and quality identification and then to process of dairy
products (e.g. cheese, yogurt), every step was correlated with microflora in milk. A
wide variety study of bacterial of milk was contributed with culture-dependent and -
independent to investigate the source of milk bacteria (Dogan et al., 2006; Jonghe et al.,
2011; Thomas et al., 2003). In the vast majority of studies, there are two main categories:
cow health (mastitis) and milk quality (spoilage).
1.3.1 Mastitis
Mastitis is an inflammation of the breast, which is a common and expensive
disease in many dairy cattle around the world (Halasa et al., 2007). Milk cows
experienced milk loss and poor milk quality (Ogola et al., 2007). Milk loss is a major
10
component of the cost of mastitis. Poor milk quality, such as high somatic cell count
(SCC) and changes in milk ingredients, may damage profits because it may lower milk
prices. It is very important for dairy industry, especially for cheese production and
quality (Blum et al., 2014). Clinical mastitis may be associated with changes in fat,
protein and MUM in milk (Kayano et al., 2018). The changes in fat and protein may be
caused by clinical mastitis. Although some of them are not pathogen specific, they show
high fat and low protein content (Coulon et al., 2002; Jamrozik & Schaeffer, 2012); on
the contrary, low fat content is caused by streptococcal infection (Bezman et al., 2015).
Other studies have shown no significant changes in fat and protein content due to
mastitis (Botaro et al., 2014; Tomazi et al., 2015).
Mastitis affects total milk production and changes milk composition and technical
availability. In dairy cows, somatic cell count (SCC) is a useful predictor of subclinical
mastitis. It is an important component of milk in terms of quality, hygiene and mastitis
control (Harmon 1994). The increase of SCC in milk was also related to the change of
protein quality, fatty acid composition, lactose, ion and mineral concentration, the
increase of enzyme activity and the higher pH value of raw milk (Auldist et al., 1996;
Coulon et al., 2002). SCC is lowest during the winter, highest during the summer, and
greater for multiparous than primiparous cows (Olde Riekerink et al., 2007). Both
contagious and environmental pathogens contribute to the high SCC observed in
summer.
A variety of bacterial pathogens are known to be related to mastitis,
Staphylococcus aureus and Streptococcus agalactiae are regarded as the most common
contagious pathogens, and coagulase-negative Staphylococci, Escherichia coli,
Streptococcus uberis, and Streptococcus dysagalactiae are regarded as the most
common environmental pathogens (Bradley, 2002). Kayano reported that Some
mastitis pathogens, such as Streptococcus and Escherichia coli, seem to be associated
with long-term changes in fat, protein and MUN (Kayano et al., 2018). And long-term
milk losses were caused by the contamination of Escherichia coli and Streptococcus
spp. (Bezman et al., 2015; Kayano et al., 2018). However, approximately 10–40% of
clinical mastitis cases are culture negative in routine clinical assays (Makovec & Ruegg,
2003). Chryseobacterium spp. are opportunistic bacteria in raw milk;(Hagi et al., 2013)
found these bacteria in the milk of healthy cows, and (Kuang et al., 2009) detected these
bacteria in the milk of cows with mastitis. Based on NGS, (Zhang et al., 2014) detected
Chryseobacterium spp. as the most abundant species, whereas (Oikonomou et al., 2014)
11
detected these bacteria as rather minor species in raw milk. Enterococcus spp. are also
often found in raw milk samples, and the source of contamination is thought to be
milking equipment rather than the animal body and faeces (Kagkli et al., 2007).
Propionibacterium acnes is a regular inhabitant of animal skin, and (Oikonomou et al.,
2014) detected this species by NGS as the predominant species in healthy milk samples.
Despite the finding that Burkholderia spp. were more frequent in clinically diseased
milk than in healthy milk (Kuehn et al., 2013).
1.3.2 Spoilage
Certainly, pasteurized milk is as safe as a house, however, the residual secretion
of bacterial in raw milk still induce to spoilage and quality defects (Kable et al., 2016).
As a well-known measure, refrigerated storage of raw milk is widely used for prolong
storage time and mesophilic bacteria spoilage prevention (Marchand, et al., 2017).
Indeed, the low temp storage due to an outgrowth of psychrophilic bacteria in raw milk.
The predominant psychrophilic bacterial secrete heat-stable enzymes while milk was
heat-treated (e.g. UHT). Lafarge report that psychrophilic bacterial were significant
increase in low temp storage (Decimo et al., 2014; Machado et al., 2015; Marchand et
al., 2009; Ogier et al., 2004). Pseudomonas spp. are well-known psychrotrophic
bacteria and are found more often during the winter in raw milk samples. (Hagi et al.,
2013; Uraz & Citak, 1998) Pseudomonadaceae family and Moraxellaceae family
would produce heat-stable extracellular enzymes aprX-lipA, principally pseudomonas
secreted proteases and Acinetobacter, microbacterium, pseudomonas and Enterobacter
secreted lipases (Vithanage et al., 2016). The proteases casein micelles to casein gather
and precipitate to disequilibria milk gelatinize and lipase can hydrolyze acylglycerol to
release fatty acids like unpleasant flavors short-chain fatty acids (e.g. butyric, caprylic
and caproic acids) and lead to soapy taste medium-chain fatty acids (Chen et al., 2003;
Machado et al., 2017). Therefore, the increase of somatic cell count may reduce the
storage time of milk. Therefore, (Jonghe et al., 2011) suggested that proteolysis and
lipolysis was an important spoilage potential as determined. N2 gas flushing was used
to inhibit psychrotrophic pseudomonas increasing at low temp. However, the
psychrotrophic bacterial source is not clear yet (Gschwendtner et al., 2016). On the
other hand, A study using next-generation sequencing (NGS) indicated that the
proportion of Pseudomonas spp. in raw milk was greater in healthy cows than in cows
12
exhibiting clinical disease (Kuehn et al., 2013). Similarly, Barbano ma’s paper was
found that the speed of spoilage of milk with high somatic cell count was much higher
than that of milk with low somatic cell counts (Barbano et al., 2006). Ahmed Gargouri’s
paper found that the polymorphonuclear leukocytes (PMN) were significantly
correlated (P<0.001) with total SCC, and lipolysis level in milk. The PMN was
suggested that it can response to sufficient lipolysis of milk fat (Gargouri et al., 2008).
In any case, the high SCC would accelerate the flavor lost or spoilage in milk.
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CHAPTER 2 Monitoring the microbiota and Nutrient
composition in dairy cow milk collected from a herd
managed by automatic milking systems
2.1 INTRODUCTION
In recent years, the dairy industry has experienced many technological
advancements. Among these advancements, implementation of Automatic milking
systems (AMS) is one of the most important events occurring within the last 20 years.
AMS have greatly affected milk production, milk quality, cow behaviour, cow welfare,
herd management, and herd health (Jacobs & Siegford, 2012). In the milk production
process, all operations follow the system settings completely automated by the machine,
greatly reducing the risk of bacterial contamination caused by manual operation. At the
same time, the AMS will automatically collect individual dairy cow samples, not only
for the convenience of individual management of cattle health, but also for the quality
of milk and dairy products to provide effective sample support. However, the majority
of published research still has examined the microbiota of milk in tanks (Frétin et al.,
2017; Kable et al., 2016; Kim et al., 2017; Li et al., 2018; Meng et al., 2017; Quigley
et al., 2013; Rodrigues et al., 2017; Zhang et al., 2014). Data on individual milk quality
and milk flora are still few.
The bacteria in milk play a direct role in uber health as well as milk and dairy
products. Some bacterial pathogens are known to be related to mastitis. Staphylococcus
aureus and Streptococcus agalactiae are regarded as the most common contagious
pathogens, and coagulase-negative Staphylococci, Escherichia coli, Streptococcus
uberis, and Streptococcus dysagalactiae are regarded as the most common
environmental pathogens (Bradley, 2002). Some bacteria in milk, including
Lactococcus, Lactobacillus, Streptococcus, Propionibacterium as well as fungi can
directly affect the flavor, and organoleptic properties of dairy products (Wouters et al.,
2002). Psychrophilic bacterial, including Pseudomonas spp. and Acinetobacter spp.,
can grow in milk at low temperature. These bacteria can produce proteases and lipases
even after pasteurization, leading to nutrition and organoleptic changes associated with
spoilage (Ribeiro Júnior et al., 2017; Vithanage et al., 2016). The quality of milk,
20
including somatic cell count (SCC) and total bacterial count (TBC), will obviously
affect the milk shelf life and the yield of cheese. (Auldist et al., 1996; Barbano et al.,
2006). Moreover, Gabriel Leitner suggests that the quality of milk that affects cheese
production may be due to subclinical uber infections when milk is mixed in milk tank
(Leitner et al., 2008). Therefore, it is necessary to investigate the flora of individual
milk with different milk quality levels.
Milk microbiota has been shown to be influenced by many factors including region,
season, cowshed environment, and the hygienic management of milking (Doyle et al.,
2017b; Kable et al., 2016; Kim et al., 2017; Latorre et al., 2010) It is worth noting that
the content in milk can also affect the quality of dairy products. Milk fat and protein
content will directly affect the yield of cheese (Verdier-Metz et al., 2001). And, the
amount of urea in milk affects the taste of cheese (Martin et al., 1997). Interestingly,
even in the same feeding conditions, the components in different milk is not the same.
For example, milk protein and fat in different seasons, parity and Lactation period
content will be significantly different (Yoon et al., 2004). In addition, MUN as an index
of dietary crude protein intake (Nousiainen et al., 2004), also different levels in different
seasons (Hwang et al., 2000; Yoon et al., 2004). However, there was less paper show
the effects of content on the bacterial flora in milk.
In this study, we examined the microbiota and milk contents of dairy cow milk
collected from a herd managed by AMS. Because both microbiota and milk contents
can be influenced by season, we performed a 1-year survey using test-day milk samples.
Select and carry out microbial analysis of milk with different levels of quality (SCC)
per month. Microbiota was analysed by MiSeq, and the total bacterial population was
measured by qPCR. The objectives of this study were to provide information about the
milk microbiota of the cows managed by AMS and to clarify changes in the milk
microbiota in relation to milk contents and varying levels of milk quality.
2.2 MATERIALS AND METHODS
2.2.1 Milk samples
Milk samples were collected from a Holstein herd managed by AMS (Lely
Astronout A4, Cornes AG. Ltd., Eniwa, Japan) at Okayama Prefecture Livestock
Research Institute. The cows were fed total mixed ration silage throughout the year and
their milk samples were collected once a month for the herd test. Concentrations of dry
21
matter (DM), crude protein (N × 6.25), and total digestible nutrients in the total mixed
ration silage were 500–600 g kg–1, 160–180 g kg–1DM, and 720–740 g kg–1DM,
respectively, and dairy cows showed a high level of milk production without other
supplements. (Han et al., 2014) The test-day samples mechanically taken by the AMS
were collected every 2 months from February 2014 to December 2014. Milk samples
were transferred to a 1.5-mL sterile centrifuge tube and stored on ice until transported
to the university. The tubes were stored at –30°C until analysis. SCC and milk
component were determined using a CombiFoss FT+ (Foss Alle, Hillerød, Denmark)
after milk treated with Bronopol (Chemical Land○R). A total of 201 samples were
collected in this 1-year survey.
2.2.2 Preparation of bacterial DNA
Totally 36 samples of somatic cells with varying degrees in each sampling time,
including less than 100,000 cells/ml, 101,000 to 300,000 cells/ml, and greater than
301,000 cells/ml were selected for analysis. Bacterial pellets were obtained by
centrifugation at 8000 × g for 15 min, and then washed once with 1 mL of solution
containing 0.05 M D-glucose, 0.025 M Tris-HCl (pH 8.0), and 0.01 M sodium EDTA
(pH 8.0). After centrifugation at 15,000 × g for 2 min, 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], 1.2 g/L Triton X-100) at 37°C for 1 h. Subsequent bacterial
DNA purification was performed using a commercial kit (DNeasy Blood & Tissue Kit;
Qiagen, Germantown, MD, USA) according to the manufacturer’s
recommendations(Ni et al., 2017).
2.2.3 qPCR
qPCR was carried out on a MiniOpticon System (Bio-Rad Laboratories, Inc.,
Tokyo, Japan). For quantification of both total bacteria, 2 μL of DNA solution was
added to 23 μL of a PCR mixture containing 12.5 μL of KAPA SYBR FAST Master
Mix (Kapa Biosystems, Inc., Wilmington, MA, USA) and 1.0 μL of each primer (8 μM,
357f and 517r, as described above). A dilution series of plasmids carrying the nearly
full-length 16S rDNA gene of E. coli was used as known concentrations of the standard
plasmid. The cycle parameters for the total bacterial assay were as follows: 30 s at 95°C
and 35 cycles of 15 s at 95°C, 20 s at 60°C, and 30 s at 72°C. The copy number of the
22
standard plasmid was calculated using the molecular weight of the nucleic acid and the
length (base pairs) of the cloned plasmid.
2.2.4 Illumina MiSeq sequencing
The PCR amplification using primers targeting the V4 region of the 16S rRNA
genes (forward: 5ʹ-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAG
CMGCCGCGGTAA-3′; reverse: 5′-GTGACTGGAGTTCAGACGTGTGCTCTT
CCGATCTGGACTACHVGGG TWTCTAAT-3′) was employed (Tang et al. 2017).
The PCR protocol was as follows: initiation at 94°C for 2 min and followed by 25 cycles
of 94°C for 30 s, 50°C for 30 s, 72°C for 30 s, and a final elongation of 72°C for 5 min.
The products were purified using Fast Gene Gel/PCR Extraction Kit (NIPPON
Genetics Co., Ltd., Tokyo, Japan) and moved to a second round of PCR with adapter-
attached primers. The second PCR protocol was as follows: initiation at 94°C for 2 min
followed by 10 cycles of 94°C for 30 s, 59°C for 30 s, 72°C for 30 s and a final
elongation of 72°C for 5 min. The PCR products were again purified as described above.
The purified DNA was then ligated to the 16S rRNA amplicons prior to 2x250-bp
paired-end sequencing performed on an Illumina MiSeq platform at FASMAC Co., Ltd.
(Kanagawa, Japan).
Raw sequences were processed using QIIME (version 1.9.2) running the virtual
box microbial ecology pipeline. Each pair raw sequences data was joined longer than
200bp after cut off the low-quality sequences (Q<30). Chimeric sequences were
identified with USEARCH and removed. The remaining DNA sequences were grouped
into OTU with 97% matched with the closed-reference OTU picking method in QIIME,
with default settings. Both chimera checking and OTU picking used Greengenes 13.8
as the reference database. Sequences were aligned using PyNAST.
2.2.5 Statistical analysis
Quantitative NGS data were subjected to one-way analysis of variance. Tukey’s
multiple range test was performed using the JMP software (version 11; SAS Institute,
Tokyo, Japan) to examine the differences between sampling month and SCC degrees.
Data for microbiota were used to perform principle coordinate analysis and heatmap
construction using Primer version 7 with Permanova+ add-on software (Primer-E,
Plymouth Marine Laboratory, Plymouth, UK). Alpha diversity indices, i.e. Evenness,
23
observed otus, and Shannon indices, were rarefied to an equal depth of 30,000
sequences by QIIME. LEfSe Analysis of Seasonal Change by Galaxy
(http://huttenhower.sph.harvard.edu/galaxy). The correlation between the milk
components and relative genera were calculated by Spearman’s rank correlation
coefficient by SPSS (Statistics 24).
Figure 2.1 Seasonal variation of milk composition by one-way ANOVA. Feb
n=36; Apr n=52; Jun n=22; Aug n=31; Oct n=37; Dec n=23
2.3 RESULTS
A total of 201 samples were collected and analyzed in different months in cattle
farms that supplied the same TMR feed throughout the year. The protein content was
the highest in December while the MUN value was the lowest in the whole year. In
spring and autumn (April and October), the content of SNF in milk was the highest
(fig1). Among 201 milk samples, SCC of thirty-seven samples (18.4%) more than
300,000 cells mL-1, moreover, SCC of nine samples more than 1,000,000 cells mL-1.
24
However, no matter milk yield, milk fat content, or SCC in different months did not
change significantly (P > 0.05). In addition, there was a significant correlation between
milk components in person analysis. SCC was positively correlated with milk yield,
milk fat and milk protein (P < 0.05). At the same time, SCC in milk increased with the
increase of month age and parity of dairy cows. There was a significant positive
correlation between month age and Parity of dairy cows and milk production (fig2 a, P
< 0.01). There was a linear positive correlation between total bacterial count (TBC) and
SCC in milk collected by Automatic milking machine.
Figure 2.2 (A) Relation between Milk Indicators (n=201). (B) The somatic cell
count (SCC) in milk has a significant linear positive relationship with the total
bacterial count (TBC) (n=60). Data was calculated by Spearman’s rank correlation
coefficient and plotted in RStudio (Corrplot packages). SNF: Solid Non-Fat; MUN:
Milk Urea Nitrogen; MA: Month Age; DIM: Day in Milk’ *: p<0.05; **:P<0.01;
***:P<0.001
In MiSeq analysis, the average non-chimeric sequence reads in all samples was
38746. The highest bacterial species richness index (Chao1) was found in December
25
milk samples. Whether it is Shannon (Microbial diversity index) or Simpson (Flora
evenness), the milk collected in February is the highest and the lowest in August among
the whole year milk samples (fig 3). Firmicutes, Proteobacteria, Bacteroidetes,
Actinobacteria and Fusobacteria are the major Phylum in milk. From February to
August, the temperature of the environment gradually increased, and the content of
Firmicutes in milk samples increased significantly, and reached the highest level in
August. Meanwhile, the content of Proteobacteria decreased. Furthermore, the
Bacteroidetes proportion in milk samples were less than 1% in June, August and
October, more than 10 times lower than other months (fig 4a). The proportion of
bacteria in milk changed significantly in different months. In LEfSe analysis, the
contents of 11 species of family,such as Pseudomonadaceae and Enterobacteriaceae,
were significantly higher than those in other months. Streptococcaceae and
Microbacteriaceae are abundant in April milk samples. Enterococcaceae,
Planococcaceae and Bacillaceae were most in June milk than others month. The
contents of Carnobacteriaceae in August and Moraxellaceae in October were
significantly higher than those in other months, respectively. In December , The
contents of nine fungi including Staphylococcaceae, Micrococcaceae , and
Ruminococcaceae were significantly higher than those in other months (fig 4b). There
were significant differences between warm seasonal milks (Jun, Aug and Oct) and cool
seasonal milks (Feb, Apr and Dec) in bacterial flora (fig 4c).
Figure 2.3 alpha diversity index of milk microbiota by one-way ANOVA.
26
Nutritional components, quality indicators and milk production factors in milk are
related to the proportion of bacteria in milk. There was a significant positive correlation
between TBC and protein content in milk (correlation coefficient 0.45; P < 0.01). When
TBC and protein content in milk increased, the proportion of Proteobacteria also
increased (table1, P < 0.05). On the contrary, milk protein content (P < 0.05) and SNF
content (P < 0.01) were negatively correlated with Actinobacteria. Milk yield was
positively correlated with Acetobacteraceae and negatively correlated with
Alcaligenaceae. The changes of MUN were significantly correlated with nineteen
family. Among them, eight family such as Staphylococcaceae, Ruminococcaceae and
Moraxellaceae were negatively correlated, while include Enterobacteriaceae,
Pseudomonadaceae and Microbacteriaceae total eleven family were positively
correlated. With the increase of DIM, the contents of Actinobacteria (P < 0.01) and
Prevotellaceae in milk also decreased. With the increase of month age, the proportion
of Acetobacteraceae in milk also increased (table1, P < 0.05), while the proportion of
Caulobacteraceae in milk decreased.
27
Figure 2.4 LEfSe analysis at multiple taxonomic levels of each month data. Cladogram illustrating the taxonomic groups explaining the most variation among
months. Each ring represents a taxonomic level, with phylum (p), class (c), order (o)
and family (f) emanating from the center to the periphery. Each circle is a taxonomic
unit found in the dataset, with circles or nodes shown in colors (other than yellow)
indicating where a taxon was significantly more abundant. Factorial Kruskal-Wallis
test(p<0.05), pairwise Wilcoxon test(p<0.05) and the LDA log-score(value>2.0) data
was marked on this figure. *: p<0.05; **:P<0.01; ***:P<0.001
28
2.4 DISCUSSION
Even with the same feed, the nutrients in milk still showed significant seasonal
changes. Milk in August had the lowest levels of fat and protein throughout the year,
whereas protein and fat content was highest in December (P < 0.05). Although the
month-to-month variations were significant, however, all data did not change over
within the normal range of nutritional criteria (fig1). In the Ralucapavel study, there
was no significant change in nutritional components (Ralucapavel & Gavan, 2011). On
the other hand, there was no monthly change in the number of somatic cells, because
the standard deviation in the number of SCC in the same month was very large,
especially in October milk (n = 37), and the standard deviation in the SCC exceeded
even 50% of the average. This shows that individual differences among cows are
obvious. It is very necessary to monitor individual cows.
Figure 2.5 (A) stacked bar chart of phyla proportion in each month (B) LEfSe
analysis at family levels of each month data. Cladogram illustrating the taxonomic
groups explaining the most variation among months. Each circle is a taxonomic unit
found in the dataset, with circles or nodes shown in colors (other than yellow) indicating
where a taxon was significantly more abundant. Factorial Kruskal-Wallis test(p<0.05),
pairwise Wilcoxon test(p<0.05) and the LDA log-score(value>2.0) data was marked on
this figure. *: p<0.05; **:P<0.01; ***:P<0.001
29
Compared with the stability of milk nutrient composition, the microflora of milk
showed obvious monthly changes and individual differences. Microbiota in milk
samples collected during the relatively warm months of June, August and October were
more than sixty percent similar to each other, while those collected in February and
April were more than sixty percent similar based on S17 Bray-Curtis similarity analysis
by Primer 7 (Data not show). Samples taken in December were different from those
collected in other months. In February, a large proportion of Pseudomonadaceae and
Enterobacteriaceae were found in the milk flora (Fig 4.) It was well-known
psychrotrophic bacteria and are found more often during the winter in raw milk samples
(Hagi et al., 2013; Uraz & Citak, 1998). And some cold-loving Enterobacteriaceae and
Pseudomonadaceae have been found to secrete proteases and lipolytic enzymes that
cause a decline in the sensory properties of cheese (Franciosi et al., 2011). The
proportion of Streptococcaceae and Microbacteriaceae were highest in April than that
in others month. Franciosi found that 45% strains of Streptococcaceae isolates had
obvious thermophilic ability and lipolytic activity. And all Streptococcaceae have
acidifying activity (Franciosi et al., 2011). In June, Enterococcaceae increased
significantly in milk bacteria. Enterococcaceae is in the ascendant of most cheese,
attribute the success to its numerous enzymatic systems, which enable Enterococcaceae
to effectively utilize the nutrients in milk (Montel et al., 2014). In October, the
proportion of Moraxellaceae was the only significant high family compare with others
season. This family are also often found to be associated with milk spoilage (Franciosi
et al., 2011). Staphylococcaceae, also known as a psychrophile, was found in large
quantities in milk collected in December (Franciosi et al., 2011). It can enhance the
flavor, aroma and color of cheese during fermentation (Frétin et al., 2018). These results
suggest that, even in the same farm, the milk microflora produced by different month
may have different effects on the quality of dairy products. These bacterial mainly come
from teat canals and environment (Frétin et al., 2018; Gill et al., 2006)
The total bacterial count (TBC) was positively correlated with somatic cell count
(SCC) (fig 2). it's widely acknowledged that when the number of somatic cells is at a
high level, the shelf life of milk is greatly shortened (Barbano et al., 2006). In this
experiment, TBC should include the TBC1 of milk in the milk breast and TBC2 of
contamination from machines during milking. From the results, we speculate that the
linear correlation between TBC and SCC in Automatic machine collected milk may be
due to the increase of TBC from machines by adhesion to cells. Therefore, in the future,
30
we will further investigate the relationship between the adhesion mechanism of
psychrotrophic bacteria in machines and SCC.
In the selected samples, the quality standards of milk (including TBC and SCC)
were closely related to many bacterial families. When SCC increased, Prevotellaceae
and Bradyrhizobiaceae decreased significantly (P < 0.05). Similarly, TBC was also
negatively correlated with Prevotellaceae. Prevotellaceae, as the dominant bacteria in
intestinal bacteria, was found to be related to grain feeding (Khafipour et al., 2016;
Tang, Han, Yu, Tsuruta, & Nishino, 2017). Meanwhile, TBC was positively correlated
with Enterobacteriaceae (0.341, P < 0.05), which family can lead to disease and
deterioration of milk quality (Bradley, 2002; Franciosi et al., 2011). This indicates that
the proportion of feed may affect the health of dairy cows and the quality of milk.
31
Figure 2.6 The correlation network of different milk components and
predominant family(proportion>1%). Red lines indicate a positive correlation and
Green Line representation has negative correlation. Data was calculated by Spearman’s
rank correlation coefficient with primer 7 and result with significant correlation were
plotted in Cytoscape (Version 3.7.0).
32
On the other hand, the nutrients in milk also can affect the bacteria in milk. When
milk protein and SNF increase, Moraxellaceae in milk will also increase, meanwhile,
Methylobacterium and Bradyrhizobium were deceased. In this experiment,
Moraxellaceae family is mainly composed of Acinetobacter, which would produce
heat-stable extracellular enzymes proteases and lipases to cause the spoilage of milk
(Vithanage et al., 2016). Simultaneously, Propionibacteriaceae in milk are decreasing
while the SNF increasing. Furthermore, Propionibacteriaceae was mainly found in all
milk samples by Propionibacterium acnes, which was considered to be a non-specific
immunodefences (Pyörälä, 2002). In the study of J.S. Hogan, they found the incidence
of clinical mastitis was decreased for cow infused intravenously with 4 mg of killed
Propionibacterium acnes compared with no treatment group (Hogan et al., 1994). It is
also reported that Propionibacteriaceae was a very good probiotic, which regulates
intestinal flora and metabolic activity, promotes intestinal peristalsis, prevents enteritis,
and contains potential inhibitory activity of cancer factors (Cousin et al., 2010; Rabah
et al., 2017). Fat content in milk was positively correlated with Xanthomonadaceae in
milk (0.338, p < 0.05). The Xanthomonadaceae were frequently found in cow feed,
grass and silo samples (Doyle et al., 2017a; Tang et al., 2017; Zeineldin et al., 2017)
Urea content (MUN) in milk has always been used as a monitoring index for nitrogen
use efficiency in dairy cattle (Baker et al., 1995). The content of MUN in milk had
obvious seasonal variation, and the content of MUN in December samples was
significantly lower than that in other months (fig1). In B Martin's study, cheeses made
from milk with high urea content, whether firm, pasty or chalky, were lower than those
made from the control group. However, comparing the two kinds of cheese, the content
of PH or Ca will not change due to the increase of urea (Martin et al., 1997). In our data,
we found that there was a significant positive correlation between urea content in milk
and bacteria that reduced cheese sensory properties and accelerated spoilage of milk,
such as Enterobacteriaceae and Pseudomonadaceae (Table 1; p < 0.01) (Franciosi et
al., 2011). Pseudomonadaceae family and would produce heat-stable extracellular
enzymes aprX-lipA, principally pseudomonas secreted proteases and
Enterobacteriaceae secreted lipases (Vithanage et al., 2016). The proteases casein
micelles to casein gather and precipitate to disequilibria milk gelatinize and lipase can
hydrolyze acylglycerol to release fatty acids like unpleasant flavors short-chain fatty
acids (e.g. butyric, caprylic and caproic acids) and lead to soapy taste medium-chain
fatty acids (Chen et al., 2003; Machado et al., 2017). At the same time, with the increase
33
of MUN, the proportion of Staphylococceae, that enhance flavor in cheese production
will decrease (Frétin et al., 2018; Seitz, 1974).
In addition, we carried out the verification test of the cultivation method. The
results showed that the growth rate of bacteria in E. coli culture medium with 20 mg/dl
urea was significantly faster than that in the control group (PBS group). On the contrary,
the proliferation rate of Staphylococcus. aureus in the culture group with 20 mg/dl urea
was lower than that in the control group (PBS). Similarly, we used milk sold in
supermarkets for validation tests. The rate of PH reduction in milk with artificial urea
(20mg/dl) was significantly higher than that in the control group (P < 0.05) (Data not
displayed).
2.5 CONCLUSION
Results from the present study clearly showed the monthly variation of microbiota
in individual milk. Under the same feeding conditions, the composition of milk
produced in different seasons has obvious seasonal variation. And, the bacteria in milk
also have obvious seasonal changes. These changes may affect the sensory perception
of cheese. In addition, changes in flora in milk may be associated with changes in milk
composition (including fat, protein and MUN). These results suggested that the
composition and microflora of individual milk will affect the quality of tank-milk,
whether in the monitoring of dairy products or milk quality. Monitoring individual flora
and components in milk is very important. According to the season, reasonable
improvement of Feed proportioning may play a positive role in improving the quality
of milk.
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Datta, N. (2016). Biodiversity of culturable psychrotrophic microbiota in raw
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milk attributable to refrigeration conditions, seasonality and their spoilage
potential. International Dairy Journal, 57(July), 80–90.
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milk production, season, parity and lactation period on variations of milk urea
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CHAPTER 3 Assessment of milk microbiota of dairy cows
managed by automatic milking systems
3.1 INTRODUCTION
The automatic milking system (AMS) was introduced in 1992 and has become
increasingly popular in countries that operate high productivity dairy farming. Without
human participation, an AMS can collect milk at any time when the cows become ready
for milking. The frequency of milking in a day can exceed three times; hence, in
addition to the increase in milk yield, the risk of udder contamination associated with
the farmer’s contact is believed to be minimized (Jacobs & Siegford, 2012). However,
the recovery of the mammary gland could be retarded by the curtailed milking interval,
and infectious mastitis, if present, could potentially spread because the sequence of
milking is not controlled. Therefore, the (Berglund & Pettersson, 2002; Dufour et al.,
2011; Kruip et al., 2002)
Understanding of the milk microbiota is helpful for mastitis prevention (Olde
Riekerink et al., 2007) and food quality management (Jayarao et al., 2004; Quigley et
al., 2013; Vithanage et al., 2016) If mastitis occurs with infectious pathogens, e.g.
Staphylococcus aureus, Streptococcus agalactiae, Corynebacterium bovis, or
Mycoplasma spp., the farmer should reconsider the procedure and sequence of milking.
If mastitis is caused by environmental pathogens, e.g. coliforms, coagulase negative
Staphylococci, or Streptococci other than S. agalactiae, the farmer may take greater
care to improve the hygiene of the cowshed. An understanding of causative
microorganisms helps to decide the appropriate antibiotics for clinical mastitis.
The analysis of milk microbiota is also important for improving the shelf life of
milk. A number of psychrophilic bacteria, e.g. Pseudomonas spp. and Acinetobacter
spp., can produce proteases and lipases even after pasteurization, leading to
organoleptic changes associated with spoilage (Hantsis-Zacharov & Halpern, 2007;
Ribeiro Júnior et al., 2017; Vithanage et al., 2016). The select growth of psychrophilic
bacteria during refrigeration in a bulk cooler is difficult to avoid; however,
contamination through water and the environment needs to be minimized.
A lot of research has been conducted to analyze milk microbiota by both plate-
40
culture and culture-independent methods. Milk microbiota has been shown to be
influenced by many factors including region, season, cowshed environment, and the
hygienic management of milking (Doyle et al., 2017; Elmoslemany et al., 2010; Kable
et al., 2016; Kim et al., 2017). However, the results of next-generation sequencing
(NGS) analyses were highly variable between experiments and the understanding of
high-throughput data is still largely complicated regarding mastitis prevention and milk
quality management (Bhatt et al., 2012; Bonsaglia et al., 2017; Doyle et al., 2017;
Falentin et al., 2016a; Kuehn et al., 2013a; Oikonomou et al., 2014; Young et al., 2015).
Likewise, although the term “raw milk” is often used, the majority of published research
has examined the microbiota of milk stored in cooling tanks (Frétin et al., 2018; Kable
et al., 2016; Kim et al., 2017; Li et al., 2018; Quigley et al., 2013; Rodrigues et al., 2017;
Zhang et al., 2015). Monitoring the microbiota in the tank milk is indeed helpful, but
the data may not be identical to those for udder milk, because of the effect of cooling
(Ogier et al., 2004). Regardless, little information is available on the milk microbiota
of cows managed by AMS.
The advantages of 16S rRNA amplicon sequencing by NGS can be further
improved by incorporating predicted metagenomes using Phylogenetic Investigation of
Communities by Reconstruction (PICRUSt) (Langille et al., 2013). In this study,
microbiota analysis by MiSeq followed with PICRUSt was performed for the milk
collected manually and automatically from the cows managed by an AMS. The aim
was to obtain information on the milk microbiota of the AMS-managed cows and the
effect of the collection procedure on the diversity and predicted gene functions of milk
microbiota.
3.2 MATERIALS AND METHODS
3.2.1 Sample collection
From approximately 40 lactating Holstein cows raised in the Okayama Prefectural
Livestock Research Institute, three cows were randomly selected. All cows were
managed by AMS (Lely Astronout A4, Cornes AG. Ltd., Eniwa, Japan) with exclusive
feeding of total mixed ration silage throughout the year. The contents of dry matter
(DM), crude protein (N × 6.25), and total digestible nutrients in the total mixed ration
silage were 500–600 g/kg, 160–180 g/kg DM, and 720–740 g/kg DM, respectively.
Milk samples were collected from the same cows in May and July. Manual collection
41
was made between 10:00-11:00, and samples from four quarters were mixed to obtain
a composite sample. Following surface cleaning, several streams of foremilk were
discarded prior to sample collection. Automatic collection was made through an AMS
device into sterile sample cups with preservative (bronopol; 2-bromo-2-nitropropane-
1,3-diol). Samples from the latest visit to the AMS were taken as counterparts of the
manually collected samples. The difference in the time of collection between the
manual and automatic samplings was <6 h. All samples were immediately refrigerated
on ice and transported to Okayama University. Milk yield was recorded from the AMS
samples, and protein, fat, solid-not-fat, and the somatic cell count in milk was
determined using a CombiFoss FT+ (Foss Alle, Hillerød, Denmark).
3.2.2 Preparation of bacterial DNA
Bacterial pellets were obtained by centrifugation at 8000 × g for 15 min, and then
washed once with 1 mL of solution containing 0.05 M D-glucose, 0.025 M Tris-HCl
(pH 8.0), and 0.01 M sodium EDTA (pH 8.0). After centrifugation at 15,000 × g for 2
min, 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], 1.2 g/L Triton X-100) at
37°C for 1 h. Subsequent bacterial DNA purification was performed using a
commercial kit (DNeasy Blood & Tissue Kit; Qiagen, Germantown, MD, USA)
according to the manufacturer’s recommendations (Ni et al., 2017).
3.2.3 Quantitative real-time PCR
The total bacterial count was determined by quantitative PCR (qPCR). 2 μL of
each sample DNA solution was added to 23 μL of a PCR mixture containing 12.5 μL
of KAPA SYBR FAST Master Mix (Kapa Biosystems, Inc., Wilmington, MA, USA)
and 8 μM primers targeting the V3 region of the 16S rRNA genes (forward: 5ʹ-ACG
GGGGGCCTACGGGAGGCAGCAG-3′; reverse: 5ʹ-ATTACCGCGGCTGCTGG-
3ʹ). qPCR was performed with a Mini Opticom real time PCR system (Bio-Rad
Laboratories Inc., Tokyo, Japan), with initiation at 95°C for 30 s followed by 35 cycles
of 15 s at 95°C, 20 s at 60°C, and 30 s at 72°C. A standard curve was prepared from
plasmid DNA employing 16S rRNA genes of Escherichia coli (JCM 1649). The copy
number of the standard plasmid was calculated using the molecular weight of the
nucleic acid and the length (base pairs) of the cloned plasmid.
42
3.2.4 Illumina MiSeq sequencing
The PCR amplification using primers targeting the V4 region of the 16S rRNA
genes (forward: 5ʹ-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAG
CMGCCGCGGTAA-3′; reverse: 5′-GTGACTGGAGTTCAGACGTGTGCTCTT
CCGATCTGGACTACHVGGGTWTCTAAT-3′ ) was employed(Tang, Han, Yu,
Tsuruta, & Nishino, 2017). The PCR protocol was as follows: initiation at 94°C for 2
min and followed by 25 cycles of 94°C for 30 s, 50°C for 30 s, 72°C for 30 s, and a
final elongation of 72°C for 5 min. The products were purified using Fast Gene
Gel/PCR Extraction Kit (NIPPON Genetics Co., Ltd., Tokyo, Japan) and moved to a
second round of PCR with adapter-attached primers. The second PCR protocol was as
follows: initiation at 94°C for 2 min followed by 10 cycles of 94°C for 30 s, 59°C for
30 s, 72°C for 30 s and a final elongation of 72°C for 5 min. The PCR products were
again purified as described above. The purified DNA was then ligated to the 16S rRNA
amplicons prior to 250-bp paired-end sequencing performed on an Illumina MiSeq
platform at FASMAC Co., Ltd. (Kanagawa, Japan).
Raw sequences were processed using QIIME (version 1.9.0) running the virtual
box microbial ecology pipeline. Before pair-end joining, raw sequence data were
sheared using “sickle pe” to obtain a phred quality score above 30 and ensure that
sequences were longer than 135 bp. Paired-end sequences were joined using fastq-join
with more than 20 bp overlap required between all paired sequences. Chimeric
sequences were identified with USEARCH and removed. The remaining DNA
sequences were grouped into OTU with 97% matched with the closed-reference OTU
picking method in QIIME, with default settings. Both chimera checking and OTU
picking used Greengenes 13.8 as the reference database. Sequences were aligned using
PyNAST. The OTU table was used in the bioinformatics tool PICRUSt (version 1.1.3)
for functional metagenomics. Functional predictions were made using the KEGG
Orthology Pathways. Within PICRUSt, the 16S rRNA copy number was normalized,
molecular functions were predicted, and all results were summarized into KEGG
pathways.
3.2.5 Statistical analysis
Quantitative NGS data were subjected to one-way analysis of variance. Tukey’s
multiple range test was performed using the JMP software (version 11; SAS Institute,
43
Tokyo, Japan) to examine the differences between months and sampling procedures.
Data for microbiota and predicted gene functions were used to perform principle
coordinate analysis and heat map construction using Primer version 7 with Permanova+
add-on software (Primer-E, Plymouth Marine Laboratory, Plymouth, UK). Alpha
diversity indices, i.e. Chao1, Simpson, and Shannon indices, were analyzed by QIIME.
Table 3.1. Milk yield and composition of dairy cows and total bacterial population,
total sequence reads, and alpha diversity indices of microbiota of manually and
automatically collected milk sampled in May and July.
May July
Manual Automatic Manual Automatic
Milk yield (kg/day) 38.7 ± 9.50 40.8 ± 11.2
Milk composition
Protein (%) 3.13 ± 0.41 3.06 ± 0.14
Fat (%) 4.01 ± 0.26 3.27 ± 0.60
Solid-not-fat (%) 8.84 ± 0.52 8.69 ± 0.18
Somatic cell count
(X 103 cells/mL) 189 ± 151 220 ± 148
Milk microbiota
Total bacterial population
(log10 copies/mL) 6.57 ± 0.26 b 7.48 ± 0.71 a 6.93 ± 0.58 b 7.93 ± 1.71 a
Sequence reads 72231 ± 3512 b 96602 ± 7766 a 85483 ± 325 ab 98225 ± 7959 a
Chao 1 index 1649 ± 18 b 1391 ± 133 b 2330 ± 207 a 1361 ± 135 b
Simpson index 0.927 ± 0.01 a 0.883 ± 0.03 a 0.575 ± 0.09 b 0.855 ± 0.01 a
Shannon index 5.96 ± 0.29 a 4.21 ± 0.21 b 3.78 ± 0.56 b 3.95 ± 0.14 b
Values for milk microbiota in the same row with different following superscripted letters are
significantly different (P<0.05).
3.3 RESULTS
Milk yields on the day of sampling were 32.4–49.6 and 32.8–53.6 kg/day in May
and July, respectively. No differences were seen in protein, fat, solid-not-fat contents
in milk between months, although the values appeared lower in July compared with
May. The somatic cell counts (SCC) were 82–362×103 and 50–315×103 cells/mL for
May and July samples, respectively. The cow showing the highest milk yield had an
SCC of >300×103 cells/mL regardless of the month; hence, although there were no
outward signs, the cow could be regarded as having sub-clinical mastitis.
44
Table 3.2. Relative abundances of bacterial families in the microbiota of manually
and automatically collected milk sampled in May and July from dairy cows
managed by automatic milking systems.
May July
Manual Automatic Manual Automatic
Actinomycetaceae 0.53 ± 0.48 0.00 ± 0.00 0.09 ± 0.03 0.01 ± 0.00
Aerococcaceae 11.2 ± 10.4 0.20 ± 0.19 5.29 ± 2.51 0.16 ± 0.16
Bacillaceae 0.08 ± 0.05 0.01 ± 0.01 0.51 ± 0.40 0.07 ± 0.02
Bacteroidaceae 6.44 ± 2.34 a 0.11 ± 0.14 b 1.23 ± 0.48 b 0.29 ± 0.15 b
Clostridiaceae 4.61 ± 2.57 a 0.08 ± 0.09 b 0.59 ± 0.20 ab 0.01 ± 0.00 b
Corynebacteriaceae 4.33 ± 1.41 a 0.09 ± 0.08 b 2.79 ± 1.33 ab 0.16 ± 0.08 b
Enterobacteriaceae 0.74 ± 0.77 c 4.29 ± 0.98 c 73.6 ± 5.72 a 50.4 ± 3.46 b
Enterococcaceae 0.10 ± 0.04 9.95 ± 6.17 0.06 ± 0.02 4.78 ± 1.13
Lachnospiraceae 3.01 ± 0.93 a 0.04 ± 0.04 b 0.73 ± 0.03 b 0.02 ± 0.01 b
Lactobacillaceae 0.54 ± 0.36 0.01 ± 0.01 0.10 ± 0.03 0.03 ± 0.01
Methylobacteriaceae 17.8 ± 2.1 a 0.32 ± 0.36 b 0.01 ± 0.01 b 0.00 ± 0.00 b
Microbacteriaceae 0.04 ± 0.03 0.50 ± 0.45 0.04 ± 0.00 0.04 ± 0.03
Micrococcaceae 0.54 ± 0.09 b 4.25 ± 1.59 a 0.24 ± 0.09 b 2.22 ± 0.78 ab
[Mogibacteriaceae] 1.00 ± 0.36 a 0.01 ± 0.02 b 0.23 ± 0.05 b 0.00 ± 0.00 b
Moraxellaceae 0.86 ± 0.19 b 45.1 ± 10.4 a 0.60 ± 0.26 b 34.1 ± 3.74 a
Peptostreptococcaceae 2.11 ± 1.49 0.04 ± 0.05 0.12 ± 0.03 0.00 ± 0.00
Pseudomonadaceae 0.20 ± 0.07 b 22.5 ± 8.98 a 0.13 ± 0.05 b 6.01 ± 0.84 b
Ruminococcaceae 5.17 ± 1.29 a 0.09 ± 0.10 b 3.82 ± 0.98 a 0.07 ± 0.04 b
Sphingomonadaceae 14.9 ± 5.57 a 0.34 ± 0.43 b 0.01 ± 0.00 b 0.01 ± 0.00 b
Staphylococcaceae 12.4 ± 4.05 a 0.26 ± 0.13 b 2.29 ± 0.93 b 0.29 ± 0.12 b
Streptococcaceae 1.57 ± 1.38 b 11.2 ± 4.43 a 0.87 ± 0.65 b 0.40 ± 0.23 b
[Tissierellaceae] 1.37 ± 0.35 a 0.02 ± 0.03 c 0.68 ± 0.12 b 0.24 ± 0.16 bc
Turicibacteraceae 1.25 ± 0.96 0.01 ± 0.01 0.08 ± 0.01 0.00 ± 0.00
Others 9.25 ± 2.22 a 0.46 ± 0.38 b 5.83 ± 0.88 a 0.66 ± 0.31 b
The families detected at >1% of the total in at least one sample are presented. Values in the same
row with different following superscripted letters are significantly different (P<0.05).
Although milk samples were obtained from the same cows in May and July, large
differences were seen in the microbiota between the two sampling months.
Methylobacteriaceae (17.8%), Sphingomonadaceae (14.9%), Staphylococcaceae
(12.4%), Aerococcaceae (11.2%), and Bacteroidaceae (6.4%) were the five most
abundant families in samples manually collected in May. In contrast, an overwhelming
abundance of Enterobacteriaceae (73.6%) and a low abundance of Aerococcaceae
(5.3%), Ruminococcaceae (3.8%), Staphylococcaceae (2.3%), and Bacteroidaceae
(1.2%) were seen in samples manually collected in July.
Substantial differences of milk microbiota were demonstrated between manually
collected and automatically collected samples. Moraxellaceae (45.1%),
45
Pseudomonadaceae (22.5%), Streptococcaceae (11.2%), Enterobacteriaceae (4.3%),
and Micrococcaceae (4.2%) were predominant in samples automatically collected in
May, and Enterobacteriaceae (50.4%), Moraxellaceae (34.1%), Pseudomonadaceae
(6.0%), Enterococcaceae (4.8%), and Micrococcaceae (2.2%) were major families in
samples automatically collected in July. Regarding the five most abundant families in
the May-collected samples, no taxa were found in common between manually collected
and automatically collected samples. Likewise, only Enterobacteriaceae was found as
a common predominant taxon between samples manually collected and automatically
collected in July. Accordingly, Principal Coordinates Analysis (PCoA) at the 60%
similarity level indicated a clear separation of milk microbiota due to months and
sampling procedures (Fig 2a). At the 80% similarity level, samples collected in May
were separated almost individually both for manually collected and automatically
collected milk.
Total bacterial populations were greater for automatically collected than manually
collected samples, whereas no differences were seen between the May and July samples.
Data for manually collected samples indicated that the Simpson and Shannon indexes
were greater and Chao 1 was lower in May than in the July-collected samples.
There were 6908 KEGG orthology groups aligned with normalized OTU data, and
55 second level pathways from eight first level pathways contributed to the description
of gene functions and metabolic activities. The comparison of manually collected
samples with criteria of differences between months at >0.5% indicated that amino acid
metabolism, energy metabolism, nucleotide metabolism, replication and repair,
translation, xenobiotics biodegradation and metabolism were greater in the May-
collected samples, whereas glycan biosynthesis and metabolism and membrane
transport were higher in the July-collected samples. For automatically collected
samples, amino acid metabolism, replication and repair, translation, and xenobiotics
biodegradation and metabolism were greater in the May-collected samples, and
membrane transport was higher in the July-collected samples. Unlike manually
collected samples, energy metabolism and nucleotide metabolism were similar between
May and July, but lipid metabolism and metabolism of terpenoids and polyketides were
greater in May for automatically collected samples. The PCoA of the predicted gene
functions demonstrated that, regardless of 60 and 80% similarity levels, all milk
samples were regarded as belonging to the same group.
46
Figure 3.1 Association heatmap between the relative abundance of bacterial
families, sampling procedure, and sampling month for the milk of cows managed
by automatic milking systems. The families detected at >1% of the total in at least
one milk sample are presented. M and A stand for manually collected and automatically
collected sample, respectively.
47
Figure 3.2 Principal coordinates analyses of (a) bacterial families and (b) KEGG
pathways in the microbiota of manually and automatically collected milk sampled
in May and July from dairy cows managed by automatic milking systems. The
families and pathways detected at >1% of the total in at least one milk sample are
presented. M and A stand for manually collected and automatically collected sample,
respectively.
48
Figure 3.3 The KEGG pathways encoded in the microbiota of (a) manually
collected and (b) automatically collected milk. Only the KEGG pathways
significantly affected by sampling month are presented. Positive values indicate an
increased representation in May compared with July.
3.4 DISCUSSION
In this study, healthy cows with no outward signs of clinical mastitis were used to
examine milk microbiota for two months and for two sampling procedures. Differences
between May and July were quite large especially for the manually collected samples,
and Enterobacteriaceae exceeded 70% in the samples that were manually collected
July. Li et al. (Li et al., 2018) reported a large variation in the microbiota of tank milk
according to the months and seasons, and the relative abundance of Proteobacteria was
about 60% in July. Enterobacteriaceae is regarded as a gut-associated bacterial group,
and several genera like Escherichia coli, Klebsiella spp., and Proteus spp. are known
to be involved in environmental mastitis (Makovec & Ruegg, 2003). High temperature
49
and humidity in summer might have enhanced their growth in faeces and bedding.
However, Kagkli et al. indicated that the ribotypes of coliform bacteria isolated from
milk were not the same as those isolated from bovine faeces; hence, a large proportion
of Enterobacteriaceae detected in July samples may not be originated from faeces and
bedding (Kagkli et al., 2007). Relative abundance of Enterobacteriaceae could increase
to 60% in the milk of cows with mastitis (Bhatt et al., 2012); however, the abundance
itself would not help detect a cow with mastitis, because even the cow showing SCC of
<50×103 cells/mL had Enterobacteriaceae at 60% abundance in this study.
Figure 3.4 The KEGG pathways encoded in the microbiota of manually collected
and automatically collected milk. Only the KEGG pathways significantly affected by
sampling method are presented. Positive values indicate an increased representation in
Manual compared with AMS milk.
Methylobacteriaceae, Sphingomonadaceae, and Staphylococcaceae were
abundant in samples manually collected in May. Methylobacteriaceae are oligotrophic
bacteria and, although several reports indicated their presence in bovine milk (Bracke
50
et al., 2014), they are regarded as a minor group and unrelated to mastitis and milk
quality. Sphingomonas spp. have the ability to grow in a wide range of environments
that are not tolerated by most other bacteria. Although Kuehn et al. reported that, based
on NGS analysis, the relative abundance of Sphingomonas spp. was 20.4% in the
mastitis-affected quarter and 4.0% in the healthy quarter, Sphingomonas spp. are not
regarded as mastitis-causing bacteria (Kuehn et al., 2013b). Staphylococcus aureus is a
representative agent of infectious mastitis (Makovec & Ruegg, 2003), and Bhatt et al.
detected as much as 50% of Staphylococcus spp. in milk from cows with mastitis (Bhatt
et al., 2012).
Aerococcaceae was most abundant (25.8%) in one sample manually collected in
May. Aerococcus spp. can be detected in milk as the predominant bacteria (Quigley et
al., 2013), and faeces is regarded as the source of Aerococcus spp. Although A. viridians
is known as a mastitis-associated pathogen (Liu et al., 2015), the cow that showed the
highest abundance of Aerococcaceae in this study had an SCC of 82×103 cells/mL.
Thus, a high abundance of Aerococcus spp. does not necessarily indicate sub-clinical
and clinical mastitis.
There were no distinctive differences in diet composition between May- and July-
collected samples and, although protein, fat, and solid-not-fat contents appeared lower
in July, no significant differences were seen in milk composition and SCC between
May and July in this study. However, a large variation was demonstrated in milk
microbiota, especially when manually collected milk was used for assessment. Cow-
to-cow variation was small compared with month-to-month variation in milk
microbiota; hence, a group of cows managed in the same environment displayed similar
monthly variations of milk microbiota.
51
Table 3.3. The KEGG pathways encoded in the microbiota of manually and automatically collected milk sampled in May and July from dairy cows managed
by automatic milking systems.
May July
Manual Automatic Manual Automatic
Amino acid metabolism 9.76 ± 0.20 b 10.61 ± 0.28 a 8.80 ± 0.02 c 9.27 ± 0.05 bc
Carbohydrate metabolism 10.5 ± 0.08 ab 9.71 ± 0.58 b 10.2 ± 0.04 ab 10.9 ± 0.09 a
Cell motility 2.99 ± 0.31 a 2.05 ± 0.36 bc 2.43 ± 0.07 ab 1.61 ± 0.04 c
Energy metabolism 6.42 ± 0.09 a 5.97 ± 0.13 b 5.95 ± 0.03 b 5.76 ± 0.02 b
Enzyme families 1.95 ± 0.02 b 1.75 ± 0.02 c 2.02 ± 0.00 a 1.95 ± 0.01 b
Folding, sorting and degradation 2.31 ± 0.02 ab 2.40 ± 0.08 a 2.31 ± 0.01 ab 2.19 ± 0.02 b
Function unknown 1.52 ± 0.08 c 1.97 ± 0.09 b 2.14 ± 0.04 ab 2.20 ± 0.01 a
General function prediction only 3.65 ± 0.02 a 3.59 ± 0.01 b 3.49 ± 0.01 c 3.33 ± 0.01 d
Glycan biosynthesis and metabolism 1.83 ± 0.10 c 1.98 ± 0.05 c 2.55 ± 0.05 a 2.30 ± 0.01 b
Lipid metabolism 3.25 ± 0.10 b 3.94 ± 0.18 a 3.04 ± 0.00 b 3.31 ± 0.04 b
Membrane transport 11.5 ± 1.07 b 12.8 ± 0.70 b 15.8 ± 0.11 a 17.0 ± 0.19 a
Metabolism of cofactors and vitamins 4.64 ± 0.18 a 4.19 ± 0.06 b 4.23 ± 0.01 b 4.13 ± 0.01 b
Metabolism of other amino acids 1.79 ± 0.08 bc 1.96 ± 0.05 a 1.63 ± 0.00 c 1.79 ± 0.01 b
Metabolism of terpenoids and polyketides 1.86 ± 0.04 b 2.18 ± 0.08 a 1.51 ± 0.02 c 1.63 ± 0.03 c
Nucleotide metabolism 3.87 ± 0.15 a 3.38 ± 0.17 bc 3.45 ± 0.05 b 3.05 ± 0.01 c
Other ion-coupled transporters 1.34 ± 0.08 c 1.60 ± 0.04 b 1.70 ± 0.01 b 1.87 ± 0.01 a
Replication and repair 8.21 ± 0.30 a 6.99 ± 0.18 b 7.01 ± 0.12 b 5.97 ± 0.02 c
Signal transduction 2.19 ± 0.17 b 2.26 ± 0.15 ab 2.58 ± 0.05 a 2.55 ± 0.00 ab
Transcription 2.40 ± 0.12 b 2.40 ± 0.09 b 2.88 ± 0.01 a 2.98 ± 0.03 a
Translation 5.09 ± 0.29 a 4.44 ± 0.13 b 4.13 ± 0.10 b 3.56 ± 0.02 c
Xenobiotics biodegradation and metabolism 2.67 ± 0.12 b 3.82 ± 0.24 a 2.10 ± 0.02 c 2.76 ± 0.07 b
Others 10.3 ± 0.47 10.0 ± 0.15 10.1 ± 0.10 9.91 ± 0.03
The pathways detected at >1% of the total in at least one milk sample are presented. Values in the same row with different following superscripted letters are significantly
different (P<0.05).
52
In addition to the major families detected in this study (Aerococcaceae,
Enterobacteriaceae, Methylobacteriaceae, Moraxellaceae, Pseudomonadaceae,
Sphingomonadaceae, Staphylococcaceae, and Streptococcaceae), NGS analysis has
demonstrated that Bacillaceae (Li et al., 2018), Bifidobacteriaceae (Frétin et al., 2018),
Corynebacteriaceae (Bonsaglia et al., 2017), Flavobacteriaceae (Zhang et al., 2015),
Lachnospiraceae (Falentin et al., 2016b), Ruminococcaceae (Rodrigues et al., 2017),
and Propionibacteriaceae (Oikonomou, Bicalho, Meira, Rossi, Foditsch, Machado,
Gustavo, et al., 2014) could be detected at >10% abundance in raw milk. Bacillaceae,
Bifidobacteriaceae, Flavobacteriaceae, and Propionibacteriaceae were always <1.0%,
but Ruminococcaceae, Corynebacteriaceae, and Lachnospiraceae exceeded 3.0% in
several samples regardless of the sampling month in this study. Alpha diversity data
indicated that a great number of bacterial taxa were dispersed in samples manually
collected in May, whereas an extreme deviation of Enterobacteriaceae was evident in
samples manually collected in July. This distinctive difference in diversity between
May- and July-collected samples was not clear when microbiota was assessed with
automatically collected milk. Likewise, the five most abundant families were
completely different between samples manually collected (Methylobacteriaceae,
Sphingomonadaceae, Staphylococcaceae, Aerococcaceae, and Bacteroidaceae) and
automatically collected (Moraxellaceae, Pseudomonadaceae, Streptococcaceae,
Enterobacteriaceae, and Micrococcaceae) in May. Further, Moraxellaceae,
Pseudomonadaceae, Enterococcaceae, and Enterobacteriaceae were the four most
abundant families regardless of sampling months in automatically collected samples.
Therefore, although milk microbiota varied greatly because of changing environment
and other undefined factors, the variation may not be clearly seen when assessed with
automatically collected milk. In this study, the time differences between manual and
automatic samplings were <6 h. For psychrophilic bacterial proliferation in milk, 6 h
may be too short. Therefore, major families (Moraxellaceae and Pseudomonadaceae)
stably found in automatically collected samples were probably derived from teat cups,
pipe lines, and hoses in the AMS device.
Although microbiota was substantially different between May and July and
between manually and automatically collected samples, gene functions predicted by
PICRUSt were mostly unchanged and the extent of the differences was rather small.
Regardless of sampling procedures, the activities of amino acid metabolism, replication
53
and repair, translation, and xenobiotics biodegradation and metabolism were higher in
May, and the activity of membrane transport was greater in July. An overwhelming
proportion of Enterobacteriaceae in July-collected samples was probably involved in
enhanced membrane transport, but the relationship between Enterobacteriaceae
proportion and membrane transport function is difficult to explain.
Despite the fact that the microbiota was substantially different between manually
collected samples from May and July, differences in gene functions were <2%, except
for membrane transport, which was about 5%. This finding suggested that dysbiosis of
microbiota would not imply disorganization of gene functions to the same extent. The
fact that all four milk samples grouped together by gene function, but clearly separated
by microbiota supports this.
Figure 3.5 The predicted functions based on FAPROTAX database in the
microbiota of manually collected and automatically collected milk. Only the KEGG
pathways significantly affected by sampling method are presented. Positive values
indicate an increased representation in Manual compared with AMS milk.
54
The distinctive differences in gene functions between samples manually collected
May and July were in the categories of energy metabolism, glycan biosynthesis and
metabolism, and nucleotide metabolism, and those between samples automatically
collected May and July were lipid metabolism, metabolism of terpenoids and
polyketides, and transcription. The meaning of these differences is not clear, but the
emphasized lipid metabolism in automatically collected milk could be related to the
abundance of psychrophilic Moraxellaceae and Pseudomonadaceae bacterial species.
3.5 CONCLUSION
The milk microbiota of healthy dairy cows managed by AMS was examined.
Large differences were seen between samples manually collected May and July; the
total abundance of Methylobacteriaceae, Sphingomonadaceae, and Staphylococcaceae
bacteria was about half as large in May-collected samples compared to July-collected
samples, but an overwhelming abundance (>70%) of Enterobacteriaceae was seen in
July-collected samples. The microbiota of automatically collected milk was
substantially different from manually collected milk; Moraxellaceae,
Pseudomonadaceae, and Enterococcaceae were major families present in both May-
and July-collected samples. Although the milk microbiota greatly differed between the
May and July samples, the differences in predicted gene functions were <2%, except
for membrane transport. Although milk microbiota can vary greatly between months in
cows managed by AMS, the variation may be less pronounced when milk samples are
collected automatically compared with manually.
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CHAPTER 4 Rumen fluid, feces, milk, water, feed, airborne
dust, and bedding microbiota in dairy farms managed by
automatic milking systems
4.1 INTRODUCTION
Mastitis, an inflammation of the mammary gland regarded as the most important
disease affecting dairy herds, is triggered by pathogens derived from infectious and
environmental bacteria. Assessment of milk microbiota is thus important for preventing
mastitis and maintaining herd health (Jayarao et al., 2004; Olde Riekerink et al., 2007).
Typical infectious bacteria include Staphylococcus aureus, Streptococcus agalactiae,
Corynebacterium bovis, and Mycoplasma spp. Therefore, if mastitis is caused by these
pathogens the farmer should revise their milking procedure and sequence. If
environmental bacteria, such as coliforms, coagulase negative Staphylococci, and
Streptococci other than S. agalactiae, are pathogenic agents, the farmer should improve
the hygiene of their cowshed.
Implementation of automatic milking systems (AMS) is one of the most important
technological advancements in the dairy industry in the past 20 years. The use of AMS
enables cows to be milked whenever they want. Therefore, milk production may
increase as the milking frequency of individual cow increases as compared with regular
twice per day milking (Kruip et al., 2002). Moreover, milk quality, cow behavior, cow
welfare, and herd management have been shown to be affected by AMS (Jacobs &
Siegford, 2012) and the risk of udder contamination associated with farmer contact is
expected to be minimized. However, the teat orifice could be damaged more when using
an AMS than when employing conventional milking techniques, because the curtailed
milking interval may retard the recovery of the mammary gland. Further, infectious
mastitis, if present, could spread throughout the herd via the AMS, because the
sequence of milking is not controlled. Though, studies have shown inconsistent results
regarding the risk of mastitis during the transition from conventional milking to AMS
(Berglund & Pettersson, 2002; Dufour et al., 2011; Jacobs & Siegford, 2012).
A lot of research has been conducted, by both plate-culture and culture-
independent methods, to analyze milk microbiota in association with milking practices
and the farm management. Milk microbiota is shown to be influenced by the microbiota
60
present on teat skin, bedding, feed (hay), and in the surrounding air (Doyle et al., 2017;
Quigley et al., 2013; Vacheyrou et al., 2011). Likewise, region, season, cowshed
environment, and hygiene of the milking practices are known environmental factors
that influence the microbiota (Elmoslemany et al., 2010; Kable et al., 2016; Kim et al.,
2017).
Although increasing data for milk microbiota, determined by next-generation
sequencing (NGS) analyses, have become available, the results were highly variable
between experiments and the understanding of high-throughput data is largely
complicated by factors such as milk quality control and mastitis prevention (Bhatt et
al., 2012; Bonsaglia et al., 2017; Doyle et al., 2017; Falentin et al., 2016; Kuehn et al.,
2013; Oikonomou et al., 2014; Young et al., 2015). Further, information on the milk
microbiota of cows managed by AMS is lacking.
In this study, microbiota analysis by NGS (MiSeq) was performed for rumen
fluid, feces, milk, water, feed (total mixed ration silage), bedding, and airborne dust
collected at two dairy farms managed by AMS. The aim of this study was to
characterize the microbiota of the gut, milk, and cowshed environment. Diet and
nutrition were shown to affect the composition of milk microbiota and the risk of
mastitis (Zhang et al., 2015). Therefore, feed, rumen fluid, and feces were examined to
see how diet and gut microbiota associate with each other and raw milk.
4.2 MATERIALS AND METHODS
4.2.1 Sample collection
Samples were collected at two farms at Hiroshima and Okayama Japan, located
at >100 km away from each other, which operated AMS (Lely Astronout A4, Cornes
AG. Ltd., Eniwa, Japan) for management of lactating dairy cows. At both farms the
cows were housed in a free stall barn and fed total mixed ration silage, which was
formulated to have 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. Samples were
collected between 10:00–12:00 in July at farm 1 and between 13:00–15:00 in October
at farm 2. Rumen fluid was obtained using a flexible stainless spring tube (Lumenar
stomach evacuator outfit, Fujihira Industry Co. Ltd., Tokyo, Japan), and fecal samples
were collected from the rectum. Milk samples were collected manually from four
udders and then mixed as a composite sample. Following surface cleaning, several
61
streams of foremilk were discarded prior to sample collection. Airborne dust samples
were collected by placing three petri dishes for five minutes approximately 1.0 m above
the ground. Bedding samples were collected from three separate places in a cowshed
and water was collected from three different water cups. Feed was sampled by taking
three piles of total mixed ration silage. 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 thus regarded as a representative means of
assessing airborne dust, water, bedding, and feed microbiota at a farm. All samples
were immediately refrigerated on ice and transported to Okayama University.
Procedures and protocols for the animal experiments were approved by the Animal
Care and Use Committee, Okayama University, Japan.
4.2.2 Preparation of bacterial DNA
Bacterial DNA from feed, milk(250µL), water(250µL), and airborne dust(250µL)
samples was extracted and purified using the DNeasy Blood & Tissue Kit (Qiagen,
Germantown, MD, USA). Bacterial pellets were obtained by centrifugation at 16,000
× g for 2 min. For feed samples, 10 g of silage was vigorously mixed with 90 mL of
sterilized saline and the gauze-filtered extract was centrifuged to obtain pellets (Ni et
al., 2016). All pellet samples 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], 1.2 g/L Triton
X-100) at 37°C for 1 hr. Subsequent bacterial DNA purification was performed
following the manufacturer’s recommendations. For rumen fluid, feces, and bedding
samples, 0.1 g of the sample was used to prepare bacterial pellets and bacterial DNA
was purified using the DNeasy Stool Mini Kit (Qiagen, Germantown, MD, USA).
4.2.3 Quantitative real-time PCR for total bacteria
The total bacterial count was determined by quantitative PCR (qPCR). Each
sample DNA solution (2 μL) was added to 23 μL of a PCR mixture containing 12.5 μL
of KAPA SYBR FAST Master Mix (Kapa Biosystems, Inc., Wilmington, MA, USA)
and 8 μM primers targeting the V3 region of the 16S rRNA genes (forward: 5ʹ-
ACGGGGGGCCTACGGGAGGCAGCAG-3′; reverse: 5ʹ-ATTACCGCGGCTGCTG
G-3ʹ). The qPCR was performed with a Mini Opticon real time PCR system (Bio-Rad
Laboratories Inc., Tokyo, Japan) with initiation at 95°C for 30 sec followed by 35 cycles
62
of 15 sec at 95°C, 20 sec at 60°C, and 30 sec at 72°C. A standard curve was prepared
from plasmid DNA employing 16S rRNA genes from Escherichia coli (JCM 1649).
The copy number of the standard plasmid was calculated using the molecular weight of
the nucleic acid and the length (base pairs) of the cloned plasmid.
4.2.4 Illumina MiSeq sequencing
The PCR amplification using primers targeting the V4 region of the 16S rRNA
genes (forward: 5ʹ-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAG
CMGCCGCGGTAA-3′; reverse: 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCC
GATCTGGACTACHVGGGTWTCTAAT-3′) was employed (Tang, Han, Yu, Tsuruta,
& Nishino, 2017). The PCR protocol was as follows: initiation at 94°C for 2 min and
followed by 25 cycles of 94°C for 30 sec, 50°C for 30 sec, 72°C for 30 sec, and a final
elongation of 72°C for 5 min. The products were purified using the Fast Gene Gel/PCR
Extraction Kit (NIPPON Genetics Co., Ltd., Tokyo, Japan) and moved to a second
round of PCR with adapter-attached primers. The second PCR protocol was as follows:
initiation at 94°C for 2 min followed by 10 cycles of 94°C for 30 sec, 59°C for 30 sec,
72°C for 30 sec and a final elongation of 72°C for 5 min. The PCR products were again
purified as described above. The purified DNA was then ligated to the 16S rRNA
amplicons prior to 250 bp paired-end sequencing performed on an Illumina MiSeq
platform at FASMAC Co., Ltd. (Kanagawa, Japan).
4.2.5 Bioinformatics and microbiota characterization
Raw sequences were processed using QIIME (version 1.9.0) running the virtual
box microbial ecology pipeline. Before pair-end joining, raw sequence data were
sheared using “sickle pe” to obtain a phred quality score above 30 and ensure that
sequences were longer than 135 bp. Paired-end sequences were joined using fastq-join
with more than 20 bp overlap required between all paired sequences. Chimeric
sequences were identified with USEARCH and removed. The remaining DNA
sequences were grouped into an OTU with 97% matched with the closed-reference
OTU picking method in QIIME, when assessed with default settings. Both chimera
checking and OTU picking used Greengenes 13.8 as the reference database and
sequences were aligned using PyNAST. The results of the sequence analysis are
available in the DDBJ Sequence Read Archive under project identification number
63
PRJDB7427.
4.2.6 Statistical analyses
Comparison of total population and bacterial composition between the two farms
was examined by analysis of variance. Microbiota data were also subjected to canonical
analysis of principal coordinates to define assignment and clustering that explained
variations in the microbiota. Discriminant vectors with a Pearson correlation >0.7 were
considered significant. Likewise, hierarchical clustering and heat map construction
were done. These analyses were performed using Primer version 7 with Permanova+
add-on software (Primer-E, Plymouth Marine Laboratory, Plymouth, UK).
Sources of environmental contamination of milk were assessed using the
SourceTracker algorithm (Knights et al., 2011). During this source tracking, airborne
dust, bedding, water, and feed microbiota were regarded to be a common source of
contamination on a farm, i.e. the source of milk contamination could vary between cows
even if they were kept in a same housing.
4.3 RESULTS
Regardless of the farms, Prevotellaceae (31.9 and 25.5% respectively at farm 1
and 2) was the most abundant taxa in rumen fluid microbiota (Fig. 1, Fig. S1, and Table
S1). Other bacteria identified included Ruminococcaceae (11.2%), Lachnospiraceae
(9.3%), Paraprevotellaceae (2.9%), and Veillonellaceae (1.7%) at farm 1, and
Succinivibrionaceae (13.3%), Ruminococcaceae (10.8%), Lachnospiraceae (5.2%),
and Veillonellaceae (4.4%) at farm 2.
The most abundant taxa in the feces microbiota were different from those in rumen
fluid microbiota. Ruminococcaceae was found at 38.5 and 39.2% at farm 1 and 2
respectively. Additionally, Lachnospiraceae (7.8%), Clostridiaceae (6.6%),
Bacteroidaceae (6.1%), and Peptostreptcoccaceae (3.0%) were found at farm 1, and
Bacteroidaceae (11.5%), Lachnospiraceae (5.1%), Clostridiaceae (4.5%), and
Rikenellaceae (3.5%) were detected at farm 2.
The five most abundant taxa in the milk microbiota were Aerococcaceae (24.3%),
Staphylococcaceae (12.3%), Ruminococcaceae (11.4%), Corynebacteriaceae (5.9%),
and Lachnospiraceae (5.1%) at farm 1, and Staphylococcaceae (21.0%),
Lactobacillaceae (10.8%), Ruminococcaceae (6.3%), Corynebacteriaceae (6.1%), and
64
Enterobacteriaceae (5.6%) at farm 2. Although Aerococcaceae and Staphylococcaceae
were present in the two highest proportions in the milk microbiota at farm 1 and 2, one
out of three cows had Ruminococcaceae as the most abundant taxa at both farms.
Although only one composite sample of bedding was examined for each farm,
Ruminococcaceae (19.5 and 10.8% at farm 1 and 2 respectively) was found as the
predominant taxa at the two farms. Some taxa varied greatly between farms with
Aerococcaceae (15.0%), Staphylococcaceae (9.7%), Corynebacteriaceae (8.8%), and
Lachnospiraceae (6.4%) observed at farm 1, and Moraxellaceae (10.4%),
Idiomarinaceae (8.5%), Halomonadaceae (8.2%), and Corynebacteriaceae (7.0%)
observed at farm 2.
In airborne dust microbiota, Aerococcaceae (25.2%) was the most abundant at
farm 1 followed by Ruminococcaceae (12.0%), Staphylococcaceae (10.3%),
Lachnospiraceae (5.8%), and Corynebacteriaceae (5.7%). At farm 2, Lactobacillaceae
(64.5%) were found at far greater proportions than Staphylococcaceae (5.6%),
Ruminococcaceae (3.1%), Pseudomonadaceae (2.2%), and Aerococcaceae (1.8%).
Regardless of the farms, Lactobacillaceae (38.8 and 55.7% at farm 1 and 2
respectively) was the most abundant taxa in the water microbiota. Other taxa were seen
at proportions <10% including Comamonadaceae (6.8%), Moraxellaceae (5.7%),
Pseudomonadaceae (5.1%), and Staphylococcaceae (3.6%) at farm 1, and
Moraxellaceae (9.5%), Aeromonadaceae (4.3%), Neisseriaceae (4.2%), and
Weeksellaceae (3.8%) at farm 2.
At both farms, total mixed ration silage was exclusively fed to dairy cows and the
proportions of Lactobacillaceae exceeded 95% in the feed microbiota. The second most
prominent taxon was Leuconostocaceae, but the proportions were as low as 1.0–2.6%.
According to heatmap, the rumen fluid and feces microbiota were clearly separated
with the bedding microbiota (Fig. 2). Few differences were seen in the rumen fluid and
feces microbiota between individual cows across the two farms. Farm-to-farm and cow-
to-cow differences in milk microbiota appeared to be greater compared with the feed,
rumen fluid, and feces microbiota. The milk microbiota at farm 1 were grouped with
the airborne dust and bedding microbiota. Although the milk microbiota for two
samples at farm 2 was grouped with the bedding microbiota, that for one sample was
related with the airborne dust and water microbiota.
65
Figure 4.1 Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) in feed, rumen fluid, feces, milk, bedding, water, and
airborne dust microbiota in two dairy farms managed by an automatic milking system. Clustering
was performed using the Euclidean distance as a similarity metric. F1, F2, rumen, and air indicate
farm 1, farm 2, rumen fluid, and airborne dust, respectively
66
Canonical analysis of principal coordinates further clarified the taxa associated
with rumen fluid, feces, milk, water, feed, airborne dust, and bedding samples (Fig. 3).
Rumen fluid and feces, which were characterized by Prevotellaecae and
Ruminococcaceae respectively, were regarded as separate groups at a 60% similarity
level. As depicted in the heatmap, the milk, airborne dust, and bedding at farm 1 were
considered to be in a same group, which was characterized with a high abundance of
Aerococcaceae. Feed, airborne dust, and water at farm 2 formed one group and was
characterized by the high abundance of Lactobacillaceae. Differences with respect to
airborne dust and bedding between farm 1 and 2 were apparent.
Figure 4.2 Family level proportions of feed, rumen fluid, feces, milk, bedding,
water, and airborne dust microbiota in two dairy farms managed by an automatic
milking system. F1, F2, rumen, and air indicate farm 1, farm 2, rumen fluid, and
airborne dust, respectively
67
The SourceTracker algorithm was used to identify the likely source of milk
microbiota in the dairy farm environment (Fig. 3). Regardless of the farms, airborne
dust was identified as the greatest contributor (53.0 and 37.9% at farm 1 and 2,
respectively) to the milk microbiota, followed by feces (13.8%), bedding (13.7%), and
water (4.30%) at farm 1, and bedding (9,70%), feces (8.00%), and rumen fluid (6.40%)
at farm 2. Farm-to-farm differences were not evident (P>0.05) for any source of
contamination. When SourceTracker analysis was performed by combining the data
from the two farms; the contributions of airborne dust, feces, and bedding were
calculated to be 45.5, 10.9, and 10.1%, respectively.
4.4 DISCUSSION
Although the proportion of Prevotellaceae is the most abundant taxa (>25%) in
the rumen fluid, the proportion was <0.3% in feces. Ruminococcaceae was the second
(farm 1) and the third (farm 2) most abundant taxa in the rumen fluid, but the relative
abundance was the highest in feces for any cows regardless of the farm. Prevotellaceae
is regarded as a major soluble carbohydrate degrader and the proportion correlated with
grain feeding (Khafipour et al., 2016). Therefore, differences in the taxon proportions
between rumen fluid and feces indicated that availability of the soluble carbohydrates
was greatly lowered over digestion from the rumen to the large intestine.
Lachnospiraceae was found at similar proportions in rumen fluid and feces, whereas
Veillonellaceae was higher in rumen fluid and Bacteroidaceae and Clostridiaceae were
higher in feces. Ruminococcaceae, Bacteroidaceae, Lachnospiraceae, and
Clostridiaceae were identified as the four most abundant taxa in feces, which was
similar to that seen in our previous study (Tang et al., 2017). The observation that
Prevotellaceae is predominant in rumen fluid whereas the abundance was greatly
different between the rumen fluid and feces was also similar to that reported by Mao
(Mao et al., 2015).
Well-preserved TMR silage with Lactobacillaceae at >95% was exclusively fed
to the cows at both farms. Therefore, the observation that the mean proportion of
Succinivibrionaceae in rumen microbiota was numerically higher at farm 2 (13.3%)
than farm 1 (0.92%) was difficult to explain. However, one cow among three cows at
farm 2 had Succinivibrionaceae at <1.0%, which was similar to the mean proportion of
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Succinivibrionaceae in the rumen fluid at farm 1. Thus, although Succinivibrionaceae
has been known to correlate with grain feeding (Khafipour et al., 2016), the taxa may
show a large cow-to-cow variation in rumen fluid.
Figure 4.3 Canonical analysis of principal coordinates plot characterizing the
microbiota of feed, rumen fluid, feces, milk, bedding, water, and airborne dust in
the two dairy farms managed by an automatic milking system. The operational
taxonomy unit with Pearson's correlation >0.7 is overlaid on the plot as vectors.
Samples for farm 1 and 2 are presented as red and blue plots, respectively. Samples
enclosed in a green circle are regarded to be in the same group at a 60% similarity level.
F1, F2, rumen, and air indicate farm 1, farm 2, rumen fluid, and airborne dust,
respectively
69
Figure 4.4 Pie charts of the percentages of inferred sources of milk
microbiota in two dairy farms managed by an automatic milking system. Cows
moved freely and determining their resting place was difficult in the free stall system.
A composite sample prepared from three separate samples was thus regarded as a
representative means of assessing airborne dust, water, bedding, and feed microbiota at
a farm. The values are the means and standard deviations for three cows
Staphylococcaceae, Ruminococcaceae, and Corynebacteriaceae were
identified as the most abundant taxa in the milk microbiota at both farms.
Staphylococcus aureus is the representative agent of infectious mastitis and the
proportion of Staphylococcus spp. was shown to increase to >50% in clinical mastitis
(Bhatt et al., 2012). Relative abundances of Staphylococcaceae detected in this study
(12.3% and 21.0% at farm 1 and 2 respectively) should be regarded as high levels, but
the cows from which milk was collected did not show any symptoms of clinical or
subclinical mastitis in this study.
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Although a lot of studies have examined the milk microbiota of cows with and
without mastitis, no typical microbiota has been defined as the microbiota for healthy
cow’s milk. Bonsaglia reported that Corynebacterium spp. (Corynebacteriaceae) and
Psychrobacter spp. (Moraxellaceae) were the two most abundant taxa in milk samples
and Acinetobacter spp. (Moraxellaceae), Staphylococcus spp (Bonsaglia et al., 2017).
(Staphylococcaceae), and Micrococcus spp. (Micrococcaceae) were found at >5.0%
abundance. Falentin found that Oscillospira spp. (Ruminococcaceae) and
Staphylococcus spp. (Staphylococcaceae) were prevalent taxa and Bifidobacterium spp.
(Bifidobacteriaceae) was found as the next most abundant taxon in the milk microbiota
(Falentin et al., 2016). Because milk microbiota can vary between seasons (Li et al.,
2018), to define the microbiota of healthy cow’s milk may be difficult.
In the water microbiota, Lactobacillaceae and Moraxellaceae were found in both
farm 1 and 2. A high proportion of Lactobacillaceae may indicate a transfer from mouth
to cows fed Lactobacillaceae-rich TMR silage, and that of Moraxellaceae could
indicate the presence of the psychrophilic Acinetobacter spp. The relative abundance
of Lactobacillaceae was high in airborne dust at farm 2. Therefore, a high proportion
of Lactobacillaceae in water may also indicate a transfer from the surrounding air.
At both farm 1 and 2, Ruminococcaceae and Corynebacteriaceae were found as
the major taxa in the bedding microbiota, which agrees with the findings of Doyle
(Doyle et al., 2017). Ruminococcaceae is regarded as a gut inhabitant, whereas
Corynebacteriaceae is known to inhabit diverse environments. Our results indicating
that milk and feces microbiota are separately grouped from bedding microbiota were
similar to those of Doyle (Doyle et al., 2017). Ruminococcaceae and Lachnospiraceae
were identified as the major taxa in feces, bedding, and milk microbiota at farm 1,
indicating that milk microbiota could be contaminated by gut-associated groups
(Oikonomou et al., 2014). However, neither Ruminococcaceae nor Lachnospiraceae
are considered to be mastitis pathogens. Rather, Aerococcaceae, Staphylococcaceae,
and Corynebacteriaceae were found at high relative abundances. Therefore, non-gut
groups like A. viridans, S. aureus, and C. bovis might provoke mastitis at farm 1.
Regardless, at two farms examined in this study, and apparently well managed by AMS,
gut-associated microbiota was not a primary risk factor for mastitis.
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Figure 4.5 The five most abundant taxa in rumen fluid, milk, feces, bedding,
airborne dust, water, and feed microbiota in two dairy farms managed by an
automatic milking system.
Aerococcaceae, Ruminococcaceae, and Staphylococcaceae were major taxa in the
airborne dust microbiota at farm 1 and 2. Evgrafov reported that the major taxa of
microbiota of airborne dust collected at a milking parlor were Lachnospiraceae (26%),
Aerococcaceae (12.9%), Peptostreptococcaceae (11.0%), and Moraxellaceae (6.2%)
and Staphylococcaceae (2.0%), Corynebacteriaceae (2.5%), Lactobacillaceae (1.1%),
and Pseudomonadaceae (1.7%) were low in relative abundance (De Evgrafov et al.,
2013). Although they reported that these bacteria were detectable in non-farm outdoor
samples collected 8 km away from the dairy farm, they did not detect Ruminococcaceae
in either the cowshed or non-farm outdoor environment. In this study, three airborne
72
dust samples collected at the free-barn were composited. Therefore, Ruminococcaceae
have a greater likelihood of detection than those samples collected near milking parlor.
The observation that Lactobacillaceae is a major taxon in airborne dust at farm 2 was
difficult to explain, but Lactobacillaceae was found at a high relative abundance
(10.8%) in water at this farm. Thus, although the route and source were unclear,
Lactobacillaceae could become a major taxon of the airborne dust microbiota in the
cow shed environment. Luongo et al. (2017) reported that Lactobacillus spp.
(Lactobacillaceae), Streptococcus spp. (Streptococcaceae), Micrococcus spp.
(Micrococcaceae), Corynebacterium spp. (Corynebacteriaceae), Haemophilus spp.
(Pasteurellaceae), and Finegoldia spp. (Peptoniphilaceae) were found as the major
taxa in the airborne dust microbiota of non-farm indoor samples (university dormitory
rooms).
Although relative abundances were different between milk and airborne dust,
Ruminococcaceae, Aerococcaceae, and Staphylococcaceae were the three most
abundant taxa found in common between milk and airborne dust at farm 1. Likewise,
Staphylococcaceae, Lactobacillaceae, and Ruminococcaceae were the three most
abundant taxa found in common between milk and airborne dust at farm 2. Indeed,
SourceTracker indicated that the milk microbiota may be related to the airborne dust
microbiota in non-mastitis healthy cows. Further, Corynebacteriaceae was found at
both farm 1 and 2 at stable relative abundances (5.8―8.8%) in milk and bedding
microbiota. Corynebacteriaceae in milk may have been derived from that in the
bedding. Therefore, management of the bedding should be a primary consideration as
a measure to prevent mastitis.
Based on hierarchical clustering and canonical analysis of principal coordinates,
milk microbiota, particularly at farm 1, was associated with the bedding microbiota.
Although Doyle et al. (2016) clarified that the teat microbiota showed the greatest
similarity with the milk microbiota, we did not examine teat or udder skin microbiota
in this study. The relationship between airborne dust, bedding, and the teat microbiota
should be determined in the forthcoming studies.
4.5 CONCLUSION
This study examining the microbiota of the gut, milk, and cowshed environment
in dairy farms managed by AMS displayed greater farm-to-farm and cow-to-cow
73
differences in milk microbiota compared with the feed, rumen fluid, and feces
microbiota. Milk microbiota appeared to be influenced by airborne dust based on the
source tracking. Hierarchical clustering and canonical analysis of principal coordinates
demonstrated that the milk microbiota was associated with the bedding microbiota but
clearly separated from feed, rumen fluid, feces, and water microbiota. Our findings
were derived from only two case studies at Chugoku Region in Japan. Therefore, it is
unclear if these findings should be limited to the farms managed by AMS. Regardless,
the importance of cowshed management should be emphasized to maintain cow’s
health and prevent mastitis.
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77
CHAPTER 5 Milk composition and microbiota in dairy
farms in China
5.1 Introduction
Mastitis is one of the most expensive livestock diseases. Infected cows will not be
able to produce milk for nearly a month. At the same time, the feeding costs of dairy
cows, including labor and feed costs, have been continuously consumed. Furthermore,
in order to treat disease costs, including veterinary costs, drug costs, and so on, and
further increase the burden of breeding. As an infectious disease, infected dairy cows
also have the possibility of infecting other dairy cows. Some cattle farms choose to
eliminate sick cows under multiple considerations (Jayarao et al, 2004; Ma et al., 2016).
Therefore, finding the law of mastitis infection will bring great benefits to milk
production.
Generally, the main cause of mastitis is the invasion of standard pathogens,
including Escherichia coli, Staphylococcus, Streptococcus and so on, into the
mammary glands of dairy cows (Patel et al., 2017). As the main source of bacteria in
milk, the bacteria in the environment will directly affect the bacterial flora in milk.
When the feeding mode changes, the microflora in milk produced by indoor feeding
and grazing cows will change significantly (Doyle et al., 2017). As the main feeding
method of modern dairy cows, the bacteria in milk produced by indoor feeding cows
mainly come from dust bacteria in the air and bacteria on the ground (Wu et al., 2019).
Therefore, a reasonable method of dairy cattle feeding and management will obviously
affect the bacteria in milk produced by dairy cattle in the dairy farm. It is also important
to understand whether the flora in milk of cows’ mammary glands in different areas are
identical or similar under the same or similar feeding and management methods, which
will play a decisive role in the formulation of cow farm management.
As a nutritious food, milk is an excellent hotbed for bacteria. The contents of milk,
including protein, fat, lactose and so on, will provide the nutrition needed for the large-
scale reproduction of bacteria in milk. As we know, when the nutrients in the culture
medium change, the bacterial flora will also change significantly. With the development
of the times, the current sequencing of 16srRNA gene for bacteria MiSeq can clearly
analyze the proportion of bacteria in the sample (Kable et al., 2016; Li et al., 2018).
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The total number of bacteria can be collected by QPCR. By calculating, the absolute
number of bacteria in each sample will be presented to us. Combining the nutritional
components in milk and the total bacterial count in milk, it may provide beneficial data
support for the prevention of pathogenic bacteria of cow mastitis. Therefore, we plan
to investigate the bacterial flora in milk of individual dairy cows from different cattle
farms in different regions under similar management conditions to see if the same
management is significantly different due to geographical differences. In addition, no
matter which cattle farm, the relationship between nutritional components in milk and
bacterial flora in milk will be analyzed.
5.2Materials and methods
5.2.1 Sample collection
From five dairy farms implementing milking parlor and free barn housing in
Heilongjiang and Shanxi provinces, China. All dairy cows were fed with commercial
silage. Milk is collected manually and in accordance with the collection procedure.
Before the dairy cows were required to produce milk, the on-duty veterinarians in the
dairy hall used alcohol cotton to clean the nipples and manually collected and discarded
the milk that had just been squeezed under 3-4 times. About 15 ml of milk was collected
separately from the breasts of randomly selected five dairy cows in each farm. The
collected milk is kept in ice until it is analyzed. Milk protein, fat, solid-not-fat, lactose, MUN
and the somatic cell count in milk was determined using a CombiFoss FT+ (Foss Alle, Hillerød,
Denmark).
5.2.2 Preparation of bacterial DNA
A total of 25 samples from three farms in Heilongjiang Province and two farms in
Shanxi Province were used for DNA extraction. Bacterial pellets were obtained by
centrifugation at 8000 × g for 15 min, and then washed once with 1 mL of solution
containing 0.05 M D-glucose, 0.025 M Tris-HCl (pH 8.0), and 0.01 M sodium EDTA
(pH 8.0). After centrifugation at 15,000 × g for 2 min, 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], 1.2 g/L Triton X-100) at 37°C for 1 h. Subsequent bacterial
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DNA purification was performed using a commercial kit (DNeasy Blood & Tissue Kit;
Qiagen, Germantown, MD, USA) according to the manufacturer’s recommendations
(Ni et al., 2017).
5.2.3 Quantitative real-time PCR
The total bacterial count was determined by quantitative PCR (qPCR). 2 μL of
each sample DNA solution was added to 23 μL of a PCR mixture containing 12.5 μL
of KAPA SYBR FAST Master Mix (Kapa Biosystems, Inc., Wilmington, MA, USA)
and 8 μM primers targeting the V3 region of the 16S rRNA genes (forward: 5ʹ-
ACGGGGGGCCTACGGGAGGCAGCAG-3′; reverse: 5ʹ-
ATTACCGCGGCTGCTGG-3ʹ). qPCR was performed with a Mini Opticon real time
PCR system (Bio-Rad Laboratories Inc., Tokyo, Japan), with initiation at 95°C for 30
s followed by 35 cycles of 15 s at 95°C, 20 s at 60°C, and 30 s at 72°C. A standard
curve was prepared from plasmid DNA employing 16S rRNA genes of Escherichia coli
(JCM 1649). The copy number of the standard plasmid was calculated using the
molecular weight of the nucleic acid and the length (base pairs) of the cloned plasmid.
5.2.4 Illumina MiSeq sequencing
The PCR amplification using primers targeting the V4 region of the 16S rRNA
genes (forward: 5ʹ-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAG
CMGCCGCGGTAA-3′; reverse:5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCG
ATCTGGACTACHVGGGTWTCTAAT-3′) was employed (Tang, Han, Yu, Tsuruta,
& Nishino, 2017). The PCR protocol was as follows: initiation at 94°C for 2 min and
followed by 25 cycles of 94°C for 30 s, 50°C for 30 s, 72°C for 30 s, and a final
elongation of 72°C for 5 min. The products were purified using Fast Gene Gel/PCR
Extraction Kit (NIPPON Genetics Co., Ltd., Tokyo, Japan) and moved to a second
round of PCR with adapter-attached primers. The second PCR protocol was as follows:
initiation at 94°C for 2 min followed by 10 cycles of 94°C for 30 s, 59°C for 30 s, 72°C
for 30 s and a final elongation of 72°C for 5 min. The PCR products were again purified
as described above. The purified DNA was then ligated to the 16S rRNA amplicons
prior to 250-bp paired-end sequencing performed on an Illumina MiSeq platform at
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FASMAC Co., Ltd. (Kanagawa, Japan).
Raw sequences were processed using QIIME (version 1.9.2) running the virtual
box microbial ecology pipeline. Before pair-end joining, raw sequence data were
sheared using “sickle pe” to obtain a phred quality score above 30 and ensure that
sequences were longer than 135 bp. Paired-end sequences were joined using fastq-join
with more than 20 bp overlap required between all paired sequences. Chimeric
sequences were identified with USEARCH and removed. The remaining DNA
sequences were grouped into OTU with 97% matched with the closed-reference OTU
picking method in QIIME, with default settings. Both chimera checking and OTU
picking used Greengenes 13.8 as the reference database. Sequences were aligned using
PyNAST.
5.2.5 Statistical analysis
Quantitative NGS data were subjected to one-way analysis of variance. Tukey’s
multiple range test was performed using the JMP software (version 11; SAS Institute,
Tokyo, Japan) to examine the differences between farms and regions. Data for
microbiota and predicted gene functions were used to perform principle coordinate
analysis and heat map construction using Primer version 7 with Permanova+ add-on
software (Primer-E, Plymouth Marine Laboratory, Plymouth, UK). Alpha diversity
indices, i.e. Evenness, observed otus, and Shannon indices, were rarefied to an equal
depth of 30,000 sequences by QIIME. LEfSe Analysis of Seasonal Change by
Galaxy(http://huttenhower.sph.harvard.edu/galaxy). The correlation between the milk
components and relative genera were calculated by Spearman’s rank correlation
coefficient by SPSS (Statistics 24).
5.3 Results
The fat content, lactose content and urea nitrogen content in milk have obvious
differences between farm and farm. However, there are no obvious regional differences.
In addition, the total bacterial count and somatic cell count in milk did not show
significant differences between cattle farms and between regions. The milk of
Heilongjiang cattle farm B had the highest fat content, the largest variation of somatic
81
cell number and the most bacterial species.
A
B
Figure 5.1 Farm variation of milk composition(A) and alpha diversity(B) by one-
way ANOVA. Heilongjiang farm A(HA), farm B (HB) and farm C(HC), and Shanxi
farm A(SA) and farm B(SB) was abbreviated
Pathogenic bacteria of mastitis, including Enterobacteriaceae, Staphylococcaceae
and streptococcaceae, can be found in milk of individual cows. However, the
emergence of pathogenic bacteria does not have obvious changes between farm and
farm, between regions and regions. In addition, pseudomonadaceae and moraxellaceae
often appear in the milk of individual cows, and no obvious changes between regions
or between cattle farms have been found.
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Figure 5.2 Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) in milk microbiota in five dairy farms
collected from Heilongjiang and Shanxi. Clustering was performed using the
Euclidean distance as a similarity metric. Heilongjiang farm A(HA), farm B (HB) and
farm C(HC), and Shanxi farm A(SA) and farm B(SB) was abbreviated
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Figure 5.3 Principal coordinates analyses of bacterial families in the microbiota of
manually collected milk sampled from different farm in China. The families and
pathways detected at >1% of the total in at least one milk sample are presented.
Heilongjiang farm A(HA), farm B (HB) and farm C(HC), and Shanxi farm A(SA) and
farm B(SB) was abbreviated
There are obvious individual differences between dairy cows in the same cattle
farm. When the Enterobacteriaceae in milk became the dominant bacterial sample, the
bacterial flora phase velocity in milk was higher than 60% regardless of region to region
or farm to farm. Similarly, when Staphylococcaceae is becoming the dominant bacteria
in milk, the similarity of bacteria in milk is higher than 40%. No obvious regional
differences were found.
In lefse analysis, milk produced by Heilongjiang cattle farm and Shanxi cattle farm
had higher proportion of bacteria, such as Bacteroidetes phylum, Cyanobacteria
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phylum Chloroplast class, Cytophagia class, Anaerolineae class, Streptophyta ordor,
GMD14H09 ordor, Cyrophagaordor family and parapretellaceae family in Shanxi
milk than Heilongjiang milk. Only Yaniellaceae family in Heilongjiang milk samples
was significantly higher than that in Shanxi milk samples.
Figure 2.4 LEfSe analysis at family levels of Heilongjiang and Shanxi in China.
Each circle is a taxonomic unit found in the dataset, with circles or nodes shown in
colors indicating where a taxon was significantly more abundant. Factorial Kruskal-
Wallis test(p<0.05), pairwise Wilcoxon test(p<0.05) and the LDA log-score(value>2.0)
SCC in milk was positively correlated with fat content, protein content and total
bacterial count (TBC). Lachnospiraceae, Planococcaceae, Ruminococcaceae,
Hyphomicrobiaceae, Alteromonadaceae, Intrasporangiaceae and Micrococcaceae in
milk were positively correlated. Actinobacteria, Proteobacteria, Firmicutes and
Bacteroidetes in milk increase with the increase of total bacteria in milk, regardless of
region or cattle farm. The protein content in milk was positively correlated with
Enterobacteriaceae, Ruminococcaceae, Aerococcaceae, Planococcaceae and
Lachnospiraceae. The fat content in milk was correlated with Planococcaceae,
Hyphomicrobiaceae and Pseudomonadaceae. The TBC in milk increased with the
increase of pathogenic-related family such as Streptococcaceae, Enterococcaceae and
Staphylococcaceae.
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Figure 5.5 The correlation network of different milk components and
predominant family(proportion>1%). Red lines indicate a positive correlation and
Green Line representation has negative correlation. Data was calculated by Spearman’s
rank correlation coefficient with primer 7 and result with significant correlation were
plotted in Cytoscape (Version 3.7.0).
86
5.4 Discussion
In this experiment, the two areas were nearly 1,000 kilometers away and all the
samples were collected manually. The ambient temperature of Heilongjiang cattle farm
was 22-33 degrees, and that of Shanxi cattle farm was 16-25 degrees. Meanwhile, the
sampling days in Shanxi Province were rainy and cloudy while those in Heilongjiang
Province were sunny. Under such a huge environmental difference, only a few non-
dominant bacteria have obvious regional changes, and the main bacterial changes are
mainly individual differences. Although some individual dairy cows have higher SCC
content in their milk, because they do not show any symptoms associated with mastitis,
all dairy cows are certified as healthy by veterinarians on the dairy farm. The fat content
of milk produced in Heilongjiang B cattle farm is obviously higher than that in other
cattle farms, and the individual difference of somatic cell number is the greatest. Further
analysis also showed that there was a significant positive correlation between fat
content and somatic cell number in milk. This may indicate that fat content in milk may
stimulate mammary epithelial cells by some means, leading to a significant increase in
SCC in milk. Gargouri suggested that the increase of somatic cell count in milk might
be related to lipase in milk (Gargouri et al., 2008). In this study, fat content and SCC
content in milk were positively correlated with Planococcaceae and
Hyphomicrobiaceae. Although little research has been done on the ability of these two
bacteria to cause diseases, data analysis shows that these bacteria may be directly or
indirectly related to the increase of SCC.
When the standard pathogenic bacteria, Enterobacteriaceae and
Staphylococcaceae in milk showed a large proportion (more than 5%), the bacterial
flora in milk tended to be similar in regardless of farm to farm or region to region. It
can be found that the protein content in milk is positively correlated with the percentage
of Enterobacteriaceae in the relationship between nutrients and bacteria (Zhang et al.,
2015). Similarly, in the second chapter, we also found that urea nitrogen in milk was
positively correlated with Enterobacteriaceae. This may indicate that the proportion of
Enterobacteriaceae in milk will increase significantly when the protein digestion
balance of cows’ changes (Bi et al., 2016). Increased proportion of Enterobacteriaceae
may lead to mastitis (Blum et al., 2017). On the other hand, from the introduction of
the first chapter, we can know that the incidence of Enterobacteriaceae mastitis in
summer milk is significantly higher than that in other seasons. In previous studies, we
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found that this may be related to the fact that Enterobacteriaceae in the air caused by
the continuous use of fans in cattle farms in summer is more likely to contaminate the
breast of dairy cows than in other seasons (Kable et al., 2016; Laura et al., 2009). In
this season, cows with abnormal protein digestion are prone to Enterobacteriaceae
mastitis (Wu et al., 2019).
Unlike environmental pollution, Ruminococcaceae in milk has a significant
positive correlation with SCC, but it is difficult to enter contaminated milk in vitro.
This is because Ruminococcaceae is more frequently found in rumen samples and cow
feces samples. And as an anaerobic bacterium, Ruminococcaceae family is very
difficult to survive in the aerobic environment in vitro. Now, some studies are further
exploring how intestinal bacteria can transfer bacteria through in vivo mechanisms.
This correlation was also found in Cornell's laboratory studies (Rodrigues et al., 2017).
5.5 Conclusion
In this study, bacterial flora in milk from five dairy farms, which were produced
in different areas but managed similarly, were investigated. Under similar management
conditions, the regional differences of bacterial flora in cow milk were not obvious. The
change of dominant bacteria is mainly the individual difference between cattle and
cattle. When the content of pathogenic bacteria family increased significantly, the
characteristic flora changes appeared in milk. The nutritional content of milk has
obvious correlation with bacteria in milk. The level of nutrient secretion in milk of dairy
cows may significantly affect the microflora in milk, thus leading to the occurrence of
diseases in dairy cows.
5.6 Reference
Bi, Y., Wang, Y. J., Qin, Y., Vallverdú, R. G., & García, J. M. (2016). Prevalence of
bovine mastitis pathogens in bulk tank milk in China. PLoS One,
11(5):e0155621. https://doi.org/10.1371/journal.pone.0155621
Blum, S. E., Heller, E. D., Jacoby, S., Krifucks, O., & Leitner, G. (2017). Comparison
of the immune responses associated with experimental bovine mastitis caused by
different strains of Escherichia coli. Journal of Dairy Research, 84(2), 190–197.
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https://doi.org/10.1017/S0022029917000206
Doyle, C. J., Gleeson, D., O’Toole, P. W., & Cotter, P. D. (2017). Impacts of seasonal
housing and teat preparation on raw milk microbiota: A high-throughput
sequencing study. Applied and Environmental Microbiology, 83(2).
https://doi.org/10.1128/AEM.02694-16
Gargouri, A., Hamed, H., & ElFeki, A. (2008). Total and differential bulk cow milk
somatic cell counts and their relation with lipolysis. Livestock Science, 113(2–
3), 274–279. https://doi.org/10.1016/j.livsci.2007.05.007
Jayarao, B. M., Pillai, S. R., Sawant, A. A., Wolfgang, D. R., & Hegde, N. V. (2004).
Guidelines for monitoring bulk tank milk somatic cell and bacterial counts.
Journal of Dairy Science, 87(10), 3561–3573. https://doi.org/10.3168/jds.S0022-
0302(04)73493-1
Kable, M. E., Srisengfa, Y., Laird, M., Zaragoza, J., Mcleod, J., Heidenreich, J., &
Marco, L. (2016). The core and seasonal microbiota of raw bovine milk in tanker
trucks and the impact of transfer to a milk processing facility. MBio, 7(4).
https://doi.org/10.1128/mBio.00836-16.Editor
Laura, M., Marzotto, M., Dellaglio, F., & Feligini, M. (2009). International journal of
food microbiology study of microbial diversity in raw milk and fresh curd used
for Fontina cheese production by culture-independent methods. International
Journal of Food Microbiology, 130(3), 188–195.
https://doi.org/10.1016/j.ijfoodmicro.2009.01.022
Li, N., Wang, Y., You, C., Ren, J., Chen, W., & Zheng, H. (2018). Variation in raw
milk microbiota throughout 12 months and the impact of weather conditions.
Scientific Reports, 1–10. https://doi.org/10.1038/s41598-018-20862-8
Ma, C., Zhao, J., Xi, X., ng, J. D., Wang, H., Zhang, H., & Kwok, L. Y. (2016).
Bovine mastitis may be associated with the deprivation of gut Lactobacillus.
Beneficial Microbes, 7(1), 95–102. https://doi.org/10.3920/BM2015.0048
Ni, K., Minh, T. T., Tu, T. T. M., Tsuruta, T., Pang, H., & Nishino, N. (2017).
Comparative microbiota assessment of wilted Italian ryegrass, whole crop corn,
and wilted alfalfa silage using denaturing gradient gel electrophoresis and next-
generation sequencing. Applied Microbiology and Biotechnology, 101(4), 1385–
1394. https://doi.org/10.1007/s00253-016-7900-2
Patel, R. J., Pandit, R. J., Bhatt, V. D., Kunjadia, P. D., Nauriyal, D. S., Koringa, P.
G., … Kunjadia, A. P. (2017). Metagenomic approach to study the bacterial
89
community in clinical and subclinical mastitis in buffalo. Meta Gene, 12, 4–12.
https://doi.org/10.1016/j.mgene.2016.12.014
Rodrigues, M. X., Lima, S. F., Canniatti-Brazaca, S. G., & Bicalho, R. C. (2017). The
microbiome of bulk tank milk: Characterization and associations with somatic
cell count and bacterial count. Journal of Dairy Science, 100(4), 2536–2552.
https://doi.org/10.3168/jds.2016-11540
Tang, M. T., Han, H., Yu, Z., Tsuruta, T., & Nishino, N. (2017). Variability, stability,
and resilience of fecal microbiota in dairy cows fed whole crop corn silage.
Applied Microbiology and Biotechnology, 101(16), 6355–6364.
https://doi.org/10.1007/s00253-017-8348-8
Wu, H., Nguyen, Q. D., Tran, T. T. M., Tang, M. T., Tsuruta, T., & Nishino, N.
(2019). Rumen fluid , feces , milk , water , feed , airborne dust , and bedding
microbiota in dairy farms managed by automatic milking systems. Anim Sci J.
90(3):445-452. https://doi.org/10.1111/asj.13175
Zhang, R., Huo, W., Zhu, W., & Mao, S. (2015). Characterization of bacterial
community of raw milk from dairy cows during subacute ruminal acidosis
challenge by high-throughput sequencing. Journal of the Science of Food and
Agriculture, 95(5), 1072–1079. https://doi.org/10.1002/jsfa.6800
90
CHAPTER 6 Microbiota of manually collected milk between
Chinese, Vietnamese, and Japanese dairy farms
6.1 Introduction
Mastitis is one of the most expensive animal diseases. Generally, the main cause
of mastitis is the invasion of standard pathogens, including Escherichia coli,
Staphylococcus, Streptococcus and so on, into the mammary glands of dairy cows (Bi
et al., 2016). As the main source of bacteria in milk, the bacteria in the environment
will directly affect the bacterial flora in milk. When the feeding mode changes, the
microflora in milk produced by indoor feeding and grazing cows will change
significantly (Doyle et al., 2017). As the main feeding method of modern dairy cows,
the bacteria in milk produced by indoor feeding cows mainly come from dust bacteria
in the air and bacteria on the bedding (Wu et al., 2019). In addition, it is pointed out in
the previous chapters that bacterial flora in milk can change significantly under
different collection methods. Therefore, in order to understand the true flora in the
breast of dairy cows, it is necessary to collect samples manually. However, as the
environment in which mastitis pathogens exist, the theory that only part of dairy cows
living in the same environment will be infected by mastitis pathogens is
incomprehensible.
With the development of science and technology, the methods of microbial flora
analysis in samples are constantly improving. From the past culture-dependent method
to the present culture-independent method, which relies on DNA sequencing analysis,
we have greatly increased the breadth and speed of bacterial flora in samples. High-
throughput sequencing of 16srRNA for bacteria can well describe the composition of
bacterial flora in samples, and further improve our understanding of bacterial flora in
samples. Nowadays, many studies have found that there are also a lot of bacteria in
healthy milk (Bhatt et al., 2012; Bi et al., 2016; Jayarao et al., 2004; Laura et al., 2009;
Li et al., 2018; Zhang et al., 2015). These bacteria in milk will change obviously with
the changes of health level, storage temperature and other conditions of dairy cows
(Kable et al., 2016; Quigley et al., 2013; Rodrigues et al., 2017). However, there is still
a lack of papers directly aimed at the comparison of bacterial flora in healthy milk and
mastitis milk.
On the other hand, the stability of the flora in the mammary glands of dairy cows
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has not been determined. Milk research is mostly concentrated in a dairy farm, region
or country. Whether the flora in the mammary glands of dairy cows does not have the
difference between countries is still unclear. That is to say, cows of the same breed may
not produce the same quality of milk in different countries. Therefore, in this chapter,
we plan two experiments. The first is to investigate whether the bacterial flora in
mastitis milk is different from that in healthy milk. Experiments 2 investigated whether
milk from cows in different countries had similar bacterial flora.
6.2Materials and methods
6.2.1 Sample collection
6.2.1.1 Collection of Mastitis and Healthy milk
From approximately 40 lactating Holstein cows raised in the Okayama Prefectural
Livestock Research Institute, three cows were randomly selected. All cows were
managed by AMS (Lely Astronout A4, Cornes AG. Ltd., Eniwa, Japan) with exclusive
feeding of total mixed ration silage throughout the year. The contents of dry matter
(DM), crude protein (N × 6.25), and total digestible nutrients in the total mixed ration
silage were 500–600 g/kg, 160–180 g/kg DM, and 720–740 g/kg DM, respectively.
Samples are collected when cows develop mastitis symptoms and healthy milk samples
are collected in the same or similar months. Manual collection was made between
10:00-11:00, and samples from four quarters were mixed to obtain a composite sample.
Following surface cleaning, several streams of foremilk were discarded prior to sample
collection. Automatic collection was made through an AMS device into sterile sample
cups with preservative (bronopol; 2-bromo-2-nitropropane-1,3-diol). Samples from the
latest visit to the AMS were taken as counterparts of the manually collected samples.
The difference in the time of collection between the manual and automatic samplings
was <6 h. All samples were immediately refrigerated on ice and transported to
Okayama University. Milk yield was recorded from the AMS samples, and protein, fat,
solid-not-fat, and the somatic cell count in milk was determined using a CombiFoss
FT+ (Foss Alle, Hillerød, Denmark).
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6.2.1.2 Collection of milk samples from different countries
From Hei Longjiang in China, Okayama and Hiroshima in Japan and Ho Chi Minh
in Vietnam, a total of six cattle farms were selected for two cattle farms in each country.
Three dairy cows were randomly selected from each dairy farm for analysis. All dairy
cows in China and Japan farms were fed with commercial silage, and cows in Vietnam
farms were fed with formula and fresh grass. In China and Vietnam, milk collection is
managed through the parlor system. In Japan, Automatic milking system (AMS) was
used for milking management. Milk is collected manually and in accordance with the
collection procedure. Before the dairy cows were required to produce milk, the on-duty
veterinarians in the dairy hall used alcohol cotton to clean the nipples and manually
collected and discarded the milk that had just been squeezed under 3-4 times. About 15
ml of milk was collected separately from the breasts of randomly selected five dairy
cows in each farm. The collected milk is kept in ice until it is analyzed.
6.2.2 Preparation of bacterial DNA
In experiment 1, nine samples of mastitis milk and nine samples of healthy milk
were used for DNA extraction. And total of 18 samples from each two farms from Hei
Longjiang in China, Okayama and Hiroshima in Japan and Ho Chi Minh in Vietnam
were used for DNA extraction in experiment 2. Bacterial pellets were obtained by
centrifugation at 8000 × g for 15 min, and then washed once with 1 mL of solution
containing 0.05 M D-glucose, 0.025 M Tris-HCl (pH 8.0), and 0.01 M sodium EDTA
(pH 8.0). After centrifugation at 15,000 × g for 2 min, 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], 1.2 g/L Triton X-100) at 37°C for 1 h. Subsequent bacterial
DNA purification was performed using a commercial kit (DNeasy Blood & Tissue Kit;
Qiagen, Germantown, MD, USA) according to the manufacturer’s
recommendations(Ni et al., 2017).
6.2.4 Illumina MiSeq sequencing
The PCR amplification using primers targeting the V4 region of the 16S rRNA
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genes (forward: 5ʹ-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAG
CMGCCGCGGTAA-3′; reverse:5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCG
ATCTGGACTACHVGGGTWTCTAAT-3′) was employed (Tang, Han, Yu, Tsuruta,
& Nishino, 2017). The PCR protocol was as follows: initiation at 94°C for 2 min and
followed by 25 cycles of 94°C for 30 s, 50°C for 30 s, 72°C for 30 s, and a final
elongation of 72°C for 5 min. The products were purified using Fast Gene Gel/PCR
Extraction Kit (NIPPON Genetics Co., Ltd., Tokyo, Japan) and moved to a second
round of PCR with adapter-attached primers. The second PCR protocol was as follows:
initiation at 94°C for 2 min followed by 10 cycles of 94°C for 30 s, 59°C for 30 s, 72°C
for 30 s and a final elongation of 72°C for 5 min. The PCR products were again purified
as described above. The purified DNA was then ligated to the 16S rRNA amplicons
prior to 250-bp paired-end sequencing performed on an Illumina MiSeq platform at
FASMAC Co., Ltd. (Kanagawa, Japan).
Raw sequences were processed using QIIME (version 1.9.2) running the virtual
box microbial ecology pipeline. Before pair-end joining, raw sequence data were
sheared using “sickle pe” to obtain a phred quality score above 30 and ensure that
sequences were longer than 135 bp. Paired-end sequences were joined using fastq-join
with more than 20 bp overlap required between all paired sequences. Chimeric
sequences were identified with USEARCH and removed. The remaining DNA
sequences were grouped into OTU with 97% matched with the closed-reference OTU
picking method in QIIME, with default settings. Both chimera checking and OTU
picking used Greengenes 13.8 as the reference database. Sequences were aligned using
PyNAST.
6.2.5 Statistical analysis
Quantitative NGS data were subjected to one-way analysis of variance. Tukey’s
multiple range test was performed using the JMP software (version 11; SAS Institute,
Tokyo, Japan) to examine the differences between farms and regions. Data for
microbiota and predicted gene functions were used to perform principle coordinate
analysis and heat map construction using Primer version 7 with Permanova+ add-on
software (Primer-E, Plymouth Marine Laboratory, Plymouth, UK). Alpha diversity
indices, i.e. Evenness, observed otus, and Shannon indices, were rarefied to an equal
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depth of 30,000 sequences by QIIME. LEfSe Analysis of Seasonal Change by
Galaxy(http://huttenhower.sph.harvard.edu/galaxy). The correlation between the milk
components and relative genera were calculated by Spearman’s rank correlation
coefficient by SPSS (Statistics 24).
6.3 Results
6.3.1 Experiment 1
The reading of bacterial flora in mastitis milk (mean 25898) was significantly
lower than that in healthy milk (mean 39881). At the same time, although the significant
value of alpha diversity analysis is higher than 0.05, it can be clearly seen that the
diversity of bacteria and evenness in healthy milk are higher than those in mastitis milk.
Figure 6.1 Alpha diversity of Bacteria in Milk of Healthy and Mastitis Cows
by one-way ANOVA.
There was no significant difference in phylum level between healthy and diseased
dairy cows. However, in warmer seasons, the percentage of Proteobacteria phylum in
individual milk suddenly increases. In the level of family, the incidence of pathogen-
related family in mastitis milk increased significantly to a very large proportion, such
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as Enterobacteriaceae, Streptococcaceae, Enterococcaceae were found to be a
significant proportion in mastitis milk. In the warm season, especially in the samples
from July to October, the individual differences of bacterial flora in milk were the most
obvious in both healthy cows and mastitis cows. Enterobacteriaceae also appeared in a
large proportion of one healthy milk in July. Meanwhile, pathogenic bacteria, including
Enterobacteriaceae, Streptococcaceae, Enterococcaceae and Staphylococcaceae, were
found in healthy milk.
Similar changes in other bacterial flora occur when a large number of pathogenic
bacteria are present in milk with mastitis. However, in some samples of mastitis, there
is no obvious pathogenic bacteria associated with the bacteriology. On the contrary, the
flora of these mastitis milk was very similar to that of healthy milk samples (>60%). In
the results of one-way ANOVA, the bacterial family in mastitis milk did not have a
higher prevalence than that in a certain bacterial family. However, Staphylococcaceae
content in these samples was generally higher than 1% in these samples. The contents
of Aerococcaceae, Bacteroidaceae and Tissierellaceae in milk of healthy cows were
significantly higher than those of milk with mastitis. Aerococcaceae is nearly five times
more common in healthy milk than in mastitis milk.
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A
B
Figure 6.2 Phyla level (A) and Family level (B) proportions of Mastitis and healthy
cow’s milk which manually collected in different month managed by an automatic
milking system.
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A
B C
Figure 6.3 (A) Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) (B) Significant changed family in milk
between healthy and mastitis dairy cows by one-way ANOVA. (C) Principal
coordinates analyses of bacterial families in Mastitis and healthy cow’s milk which
manually collected in different month managed by an automatic milking system.
6.3.2 Experiment 2
Two cattle farms in each country were randomly selected and a total of 18 samples
were analyzed. Diversity of the milk microbiota was higher in Japan than in China and
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Vietnamese cows; the number of operational taxonomy units, Chao 1 index, and
Shannon index were all greater for the milk of Japan than those of China and
Vietnamese cows.
Figure 6.4 Country variation of milk bacterial alpha diversity by one-way ANOVA.
Among the Japanese samples, Bacteroidetes phylum, Cyanobacteria phylum,
Firmicutes phylum, Fusobacteria phylum, Euryarchaeota phylum and Verrucomicrobia
phylum were significantly higher than those of China and Vietnam. Among them,
Sphingobacteriia class in Bacteroidetes phylum and Lactobacillaceae family in
Firmicutes phylum were significantly higher in Chinese samples than in Japanese and
Vietnamese milk samples. Proteobacteria phylum and Actinobacteria phylum were
significantly higher in China and Vietnam than in other countries. At the family level,
the contents of Bacillaceae, Dermacoccaceae and Methylobacteriaceae in Vietnamese
samples were much higher than those in Chinese and Japanese samples. Aerococcaceae
content in Japanese samples was significantly higher than that in other two countries.
Among the Chinese samples, the most distinguishing family is Lactobacillaceae and
Pseudomonadaceae.
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Figure 6.5 LEfSe analysis at multiple taxonomic levels of each country milk data. Cladogram illustrating the taxonomic groups explaining the most variation among
country. Each ring represents a taxonomic level, with phylum (p), class (c), order (o)
and family (f) emanating from the center to the periphery. Each circle is a taxonomic
unit found in the dataset, with circles or nodes shown in colors (other than yellow)
indicating where a taxon was significantly more abundant. Factorial Kruskal-Wallis
test(p<0.05), pairwise Wilcoxon test(p<0.05) and the LDA log-score(value>2.0) data
was marked on this figure. *: p<0.05; **:P<0.01; ***:P<0.001
100
Relative abundances of Enterobacteriaceae and Propionibacteriaceae were
greater for Chinese and those of Streptococcaceae and Methylobacteriaceae were
greater for Vietnamese milk samples. Japanese milk samples were characterized by low
abundances of potential pathogens. The similarity of bacterial flora in milk samples
from China, Japan and Vietnam is very low (less than 40%). Within the same country,
differences between cattle farms and between cattle and cattle are obvious, but smaller
than differences between countries.
Figure 6.6 Heatmap showing the relative abundance of major taxa (detected
at >1.0% at least two different samples) in milk microbiota from china, Japan and Vietnam.
Clustering was performed using the Euclidean distance as a similarity metric.
101
Figure 6.7 Principal coordinates analyses of bacterial families in the microbiota of
manually collected milk sampled from china, Japan and Vietnam. The families and
pathways detected at >1% of the total in at least one milk sample are presented.
6.4 Discussion
In the first experiment, all the bacterial family that appeared in large quantities in
mastitis samples were also found in healthy milk. This indicates that under the same
management conditions, all dairy cows have the same probability of invading
pathogenic bacteria. Every cow's milk is attacked by pathogens, but only part of the
cow suffers from disease (Bhatt et al., 2012). This may be due to some external
conditions, such as changes in temperature (season), Day in milk, Metritis, parity, etc.,
may lead to changes in the feeding capacity of dairy cows (Kable et al., 2016; Kayano
et al., 2018; Li et al., 2018; Wittrock et al., 2011). This in turn leads to the production
of milk content changes from the usual ingredients (Kayano et al., 2018). These changes
in milk content can help milk pathogens to proliferate in large quantities while reducing
probiotics that may also inhibit the growth of pathogenic bacteria. As a result, a large
number of pathogenic bacteria related bacteria suddenly appeared in milk. At this time,
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the number of white blood cells in milk began to increase, some cows will begin to
appear mastitis symptoms. In addition, the tolerance level of dairy cows to pathogenic
bacteria also has individual differences. Some dairy cows, even with a high proportion
of pathogenic bacteria infection, can maintain milk production without mastitis
symptoms (Kayano et al., 2018; Li et al., 2018; Zhang et al., 2015).
In addition, we found that Aerococcaceae, one of the lactic acid bacteria, is
abundant in healthy milk. In previous studies, this bacterium mainly appeared in
Airborne Dust and was the main source of Aerococcaceae in milk (Wu et al., 2019). In
the analysis of the yearly milk flora survey (Chapter 2), the change of the bacteria has
a significant positive correlation with the protein in milk. In Chinese samples (Chapter
5), it was found that there was also a positive correlation between urea nitrogen and
milk. This indicates that the protein content in milk may have a positive correlation
with the breast health of dairy cows. In experiment 2, Aerococcaceae appeared in a
large number of Japanese samples and was significantly higher than that of Chinese and
Vietnamese samples. The study found that the content of Aerococcaceae in milk from
Okayama and Hiroshima was different, and the content of Aerococcaceae in air had
obvious changes. Therefore, we believe that this may be due to the different
management methods which lead to the different content of Aerococcaceae in the
replacement.
Silage is the main feed for dairy cows in China and Japan, while fresh grass is the
main feed for dairy cows in Vietnam. This may be the reason why Vietnamese milk
samples are rich in Actinobacteria phylum. In terms of management, the Chinese and
Vietnamese cattle farms we selected are mainly managed by parlor system. In Japan,
AMS is used for milk management. Parlor system is a milking system requiring manual
participation. Therefore, this may be the reason why milk from China and Vietnam
contains high levels of pathogenic bacteria in milk.
6.5 Conclusion
It is yet unsure if these differences can be due to the differences between AMS and
parlor milking; however, compared with regional difference examined in experiment 4,
meteorological difference could influence on the milk microbiota of dairy cows. Dairy
cow mastitis may not only be caused by pathogenic bacteria infection. The content of
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milk and the tolerance of cows may have a greater impact on the occurrence of mastitis.
The study in this chapter shows that the diversity of flora in cow breasts under AMS
management will increase significantly. However, the content of pathogenic bacteria in
the breasts of cows under AMS management will be significantly reduced, which is
very powerful in the prevention of mastitis.
6.6 Reference
Bhatt, V. D., Ahir, V. B., Koringa, P. G., Jakhesara, S. J., Rank, D. N., Nauriyal, D.
S., … Joshi, C. G. (2012). Milk microbiome signatures of subclinical mastitis-
affected cattle analysed by shotgun sequencing. Journal of Applied
Microbiology, 112(4), 639–650. https://doi.org/10.1111/j.1365-
2672.2012.05244.x
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