Robustness and resilience of gut microbiota in ruminants
in relation to feeding and nutritional management

September 2017
TANG THUY MINH

Graduate School of Environmental and Life Science(Doctor’s Course)
OKAYAMA UNIVERSITYITABLE OF CONTENTS
Table of contents ..................................................................................................................... II
Declaration .............................................................................................................................. V
Acknowledgments................................................................................................................... VI
Publications arising from this thesis ...................................................................................... VII
Conference proceedings .........................................................................................................VIII
List of figures ....................................................................................................................... IX
List of tables............................................................................................................................ X
Chapter 1: General Introduction ................................................................................................ 1
Chapter 2: Literature Reviews ................................................................................................... 3
2.1. The role of gut bacteria .................................................................................................... 3
2.1.1. Metabolic function ..................................................................................................... 3
2.1.1.1. Carbohydrate metabolism ................................................................................. 3
2.1.1.2. Protein metabolism ........................................................................................... 4
2.1.1.3. Vitamin synthesis .............................................................................................. 4
2.1.2. Trophic function......................................................................................................... 4
2.1.2.1. Epithelial cell growth and differentiation ......................................................... 4
2.1.2.2. Interactions between gut bacteria and host immunity....................................... 5
2.1.3. Protective function ..................................................................................................... 5
2.2. Temporal colonization of gut bacteria communities ....................................................... 5
2.3. Characterizing microbiota across gastrointestinal tract of ruminants ........................... 10
2.3.1. Microbial composition of digesta-associated population across gastrointestinal tract..................................................................................................................................... 122.3.2. Microbial composition of mucosa-associated population across gastrointestinal tract.......................................................................................................................................142.3.3. Microbial composition of food-associated population in related to bacterial
communities across gastrointestinal tract .................................................................16
2.3.4. Prediction of molecular function of microbiota across gastrointestinal tract ...........16
2.4. Factors influencing gut bacterial communities ...............................................................212.4.1. Diet............................................................................................................................212.4.1.1. Dietary carbohydrates ......................................................................................212.4.1.2. Dietary proteins ................................................................................................222.4.1.3. Dietary fat ........................................................................................................22II

2.4.2. Antibiotics usage.......................................................................................................23
2.4.3. Prebiotics and probiotics ...........................................................................................24
2.4.4. Host-related factors ...................................................................................................25
2.4.5. Other factors..............................................................................................................27
Chapter 3: Changes in Gut Microbiota of Holstein Heifers during Growing Stage .................29
3.1. Abstract ...........................................................................................................................29
3.2. Introduction .....................................................................................................................29
3.3. Materials and methods ....................................................................................................30
3.4. Results .............................................................................................................................32
3.4.1. Changes in bacterial population and structure during growing stage .......................32
3.4.2. Comparison between Holstein heifers and Japanese Black calves ...........................33
3.5. Discussion .......................................................................................................................36
Chapter 4: Variability, Stability, Resilience of Fecal Microbiota of Dairy Cows Fed Whole
Crop Corn Silage.....................................................................................................41
4.1. Abstract ...........................................................................................................................41
4.2. Introduction .....................................................................................................................41
4.3. Materials and methods ....................................................................................................424.4. Results .............................................................................................................................44
4.4.1. Silage fermentation and microbiota ..........................................................................44
4.4.2. Fecal microbiota........................................................................................................45
4.4.3. Variations between cows, farms, and countries ........................................................45
4.5. Discussion .......................................................................................................................49
Chapter 5: Microbial Community in the Gastrointestinal Tract of Goats Fed Whole Crop Corn
Silage ........................................................................................................................56
5.1. Abstract ...........................................................................................................................56
5.2. Introduction .....................................................................................................................56
5.3. Materials and methods ....................................................................................................57
5.4. Results .............................................................................................................................60
5.4.1. Habitation of Lactobacillus community from silage throughout the gastrointestinal
tract of goat ...............................................................................................................60
5.4.2. Microbial population changing across the gastrointestinal tract of goat ..................62
5.4.3. Comparative evaluation of digesta- and mucosa-associated microbial population
across the gastrointestinal tract of goat ....................................................................63
5.5. Discussion .......................................................................................................................65

III

Chapter 6: General Conclusion ................................................................................................69
References .................................................................................................................................71

IV

DECLARATIONI hereby declare that my thesis/dissertation entitled:“Robustness and resilience of gut microbiota in ruminants in relation to feeding and nutritional
management”
has been composed by myself, that the work contained here in is my own except where
explicitly stated otherwise in the text, and that this work has not been submitted for any other
degree or processional qualification except as specified.
Parts of this work have been published in Journal of Applied Microbiology and Biotechnology(2017).

Date

SignatureV

ACKNOWLEGMENTSFirstly, I would like to express my sincere gratitude to my supervisor Prof. Nishino
Naoki for the continuous support of my Ph.D study and related research, for his patience,motivation, and immense knowledge. His guidance helped me in all the time of research andwriting of this thesis. I could not have imagined having a better advisor and mentor for my Ph.D
study.
I am very grateful to Prof. Funahashi Hiroaki, Prof. Saito Noboru for their keen support
as co-supervisor. Their lectures and extensive knowledge helped me a lot during my Ph.D study.
I would like to appreciate Assoc. Prof. Tsuruta Takeshi for his kind support andencouragement in my Ph.D time.
I would like to thank Mr. Sugimoto Yusuke and Mr. Tanabe Yuji from Okayama
Livestock Research Institute for their enthusiastic help and instruction for collecting sample.
Without their support it would not be possible to conduct this research.
My sincere thanks also goes to Dr. Han Hongyan, Dr. Wu Baiyila, Dr. Ni Kuikui for allof their kindness and thoughtfulness in laboratory work as well as student life. Their enthusiasm
has inspired me a lot during my Ph.D study. And, I thank my fellow labmates for the stimulating
discussions, for the insightful comments in every seminar, and for all the fun we have had
together. With their encouragement and support, I have kept staying strong and enduring for allof the time.
Besides, I would like to send my honest thanks to all of my Vietnamese friends for theirprecious support since the very first time I came to Japan. Their companionship and friendshiphave been motivated me to overcome many obstacles.
Last but not the least, I would like to thank my family: my parents and to my brotherand sister for supporting me spiritually throughout my study in Japan and my life in general.

VI

PUBLICATION ARISING FROM THIS THESIS

1. Tang TM, Han H, Yu Z, Tsuruta T, Nishino N (2017) Variability, stability, and resilienceof fecal microbiota in dairy cows fed whole crop corn silage. Appl Microbiol Biotechnol.
doi: 10.1007/s00253-017-8348-8

VIICONFERENCES PROCEEDINGS

1. Tang TM, Sugimoto Y, Tanabe Y, Tsuruta T, Nishino N (2016) Gut bacterial communities
in Holstein heifers during growing stage. 121th Annual Meeting of Japanese Society ofAnimal Science, Tokyo, Japan.
2. Tang TM, Sugimoto Y, Tanabe Y, Tsuruta T, Nishino N (2016) Changes in Gut Microbiota
in Japanese Black and Holstein Calves during Growing Stage. 17th AAAP Animal Science
Congress, Fukuoka, Japan.
3. Tang TM, Han H, Tsuruta T, Nishino N (2017) A comparative survey of fecal microbiota
of silage-fed dairy cows in Japan and China. 122th Annual Meeting of Japanese Society ofAnimal Science, Kobe, Japan.

VIII

LIST OF FIGURES
Figure 2.1. Age classification of calf ruminal samples according to phyla relative abundance determinedwith TREE package of SYSTAT from birth to weaning ........................................................... 8
Figure 2.2. Age classification of calf ruminal samples according to genera relative abundance
determined with TREE package of SYSTAT from birth to weaning ........................................ 9
Figure 2.3. Median relative abundances of the three most abundant phyla during the six timepoints . 10
Figure 2.4. Aggregate microbiota composition at the phylum level by week of life............................ 10
Figure 2.5. Predicted function of the gut micorbiota in the sheep of GIT ............................................ 16
Figure 2.6. Distribution of 16S rRNA sequences from the feed and GIT of a Nelore steer ................. 18
Figure 3.1. Cluster analysis results for Lactobacillus, Bifidobacterium, Clostridium cluster I&II,
Bacteroides-Prevotella-Porphyromonas, and total bacterial community structure based on
DGGE banding patterns of seven Holstein heifers (A, B, C, D, E, F, G, and H) during growing
stage ............................................................................................................................................ 34
Figure 3.2. Cluster analysis results for Lactobacillus, Bifidobacterium, Clostridium cluster I&II,
Bacteroides-Prevotella-Porphyromonas, and total bacterial community structure based on
DGGE banding patterns of seven Japanese Black calves (A, B, C, D, E, F, G, and H) during
growing stage ............................................................................................................................. 35
Figure 3.3. DGGE profile and species identification in Lactobacillus group of seven Holstein calves(A, B, C, D, E, F, and G) during growing stage ......................................................................... 35
Figure 3.4. DGGE profile and species identification in Bifidobacterium group of seven Holstein calves(A, B, C, D, E, F, and G) during growing stage ......................................................................... 36
Figure 3.5. Comparison of total bacteria population (A), Bacteroides-Prevotella-Porphyromonas (B),
Clostridium cluster XIVa (C), Clostridium I&II (D), Lactobacillus (E), and Bifidobacterium (F)population between Holstein heifers and Japanese Black calves during growing stage ............ 38
Figure 4.1. Heatmap representing the relative abundance of the major families (50 most abundant taxa)
in silage and fecal microbiota ..................................................................................................... 46
Figure 5.1. DGGE banding patterns and species identification of Lactobacillus group of silage sample(S) and digesta (d) and mucosa-associated samples (m) across gastrointestinal tract of goat ... 61
Figure 5.2. Principal Coordinate Analysis (PCoA) profile of Lactobacillus group based on DGGE
banding pattern of digesta and mucosa-associated microbiota across the gastrointestinal tract of
4 goats and silage samples .......................................................................................................... 62
Figure 5.3. Relative abundance of total bacteria (A), R. albus (B), Lactobacillus group (C), F.
prausnitzii (D), Enterobacteriaceae (E), F. succinogenes (F), Bacteroides–Prevotella–Porphyromonas group (G) population of digesta- (log 16S rRNA gene copies/g) and mucosaassociated (log 16S rRNA gene copies/cm2) samples across the gastrointestinal tract of goat .

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.................................................................................................................................................... 64

LIST OF TABLES
Table 2.1. Relative abundance of the 28 most abundant genera (relative abundance > 0.1%) of
Simmental calves from birth to weaning .................................................................................... 11
Table 2.2. Total bacterial density (numbers per gram wet weight) throughout the gastrointestinal tractsof dairy cattle .............................................................................................................................. 13
Table 2.3. Comparison of the phyla in digesta samples across the gastrointestinal tract of dairy cattle.................................................................................................................................................... 19
Table 2.4. Comparison of the phyla in mucosal samples across the gastrointestinal tract of dairy cattle.................................................................................................................................................... 20
Table 3.1. Quantification PCR results (log 16S rRNA gene copy number/g fresh matter) of total
bacteria, Bacteroides-Prevotella-Porphyromonas, Clostridium cluster XIVa, Clostridium I&II,
Bifidobacterium, and Lactobacillus population ......................................................................... 33
Table 4.1. Feeding management, herd milk yield, diet formulation, and fermentation products of wholecrop corn silage used in the six farms ........................................................................................ 48
Table 4.2. Number of sequence reads, taxa, and Shannon diversity index values determined by next
generation sequencing at the family level for the microbiota of whole crop corn silage and silagefed dairy cows ............................................................................................................................ 49
Table 4.3. Relative abundance of taxa detected at >0.1% of the total population in silage samples .... 50
Table 4.4. Relative abundance of the taxa detected at >0.1% of the total population in feces samples
from silage-fed dairy cows at 3 Japanese and 3 Chinese farms ................................................. 53
Table 4.5. Coefficient of variation calculated for the taxa detect at >0.1% of total population in feces
samples collected from silage-fed dairy cows at 3 Japanese and 3 Chinese farms .................... 54
Table 5.1. Proportion of Lactobacillus group, R. albus, F. prausnitzii, Enterobacteriaceae,
Bacteroides–Prevotella–Porphyromonas group, F. succinogenes in digesta (D) and mucosaadhered microbiota (M) of different sampling sites across the gastrointestinal tract of goat .... 60

XCHAPTER 1
GENERAL INTRODUCTION
Gastrointestinal tract of animal is the natural habitat for a dense and dynamic microbialcommunity. Ruminants and their diverse community of symbiotic microbiota is a typical example.
This microbiota is in charge of digesting and fermenting plant materials into nutrient sources usableby the host. The ruminants acquire the fermentation products for body maintenance, growth, andmilk production.
During early life, major changes take place in the composition of gut microbiota. The early
and rapid colonization helps protect from pathogen invasion but the climax population establish forseveral years. In adults, the composition of this microbiota across the digestive tract is determinedby a gradient of oxygen from food and water consumption and a gradient from tissue into the lumen.
In summary, establishment of gut microbiota is a complex process which is influenced by microbeshost interactions and by several external and internal factors. The important extrinsic factors arebacteria of the environment, composition of maternal microbiota, diet, mode of delivery andmedication, and the host-related factors like anatomical development of intestinal tract, peristalsis,
bile acids, intestinal pH and immune systemic responses, microbial interactions and mucosal
receptors.
Although several studies have been conducted for understanding the gut microbiota ofruminants, several questions remain. A substantial part of this resident microbiota is still to bediscovered. Theoretically, this population has been contributed by digesta-associated and mucosaassociated microbiota. While mucosa-associated microbiota would be crucial for immunologicalpriming, digesta-associated would be important for nutrient digestion. Rather than mucosaassociated microbiota, digesta-associated microbiota has intensively been studied targeting torumen and feces microbiota, due to the easy and convenient sampling. Despite reports indicatingthat the core bacterial community is shared, other studies have observed high variation in therelative microbial abundance across samples. The divergence of abundance was suggested to be
due to differences in genotype, aging, diet formulation, farm management, and geographicallocations, food-borne microbiota. Similarly, differences were also observed in gut microbiota
structure even when animals were kept under the same condition and showed the similar
physiological profiles. In additon, recent studies have paid attention to individual intra-variation ofgut microbiota in different segments across the gastrointestinal tract. Reports indicated that gut
microbiota varied across the tract in abundance as well as in microbial composition. However,understanding about this fluctuation of gut microbiota is still limited.

1

Because microbial activity and metabolism in the gut is a key to secure health andproductivity of ruminants, understanding robustness and resilience of the gut microbiota is of great
importance. In this thesis, three experiments were carried out to examine variability, stability, andadaptability of gut microbiota in relation to feeding and nutritional management. Feces were used
to determine the gut microbiota in heifer and dairy cow experiments, and digesta and mucosa
samples collected from various gut segments were used in goat experiment. Silage, a rich source of
lactic acid bacteria, was given to animals in the entire or a part of test period; hence, the effect offood-borne bacteria on the gut microbiota was also examined.
In the first experiment, fecal microbiota of seven Holstein calves were examined during
growing stage, wherein weaning and commencement of silage feeding were practiced. The
objective was to understand how gut microbial community changed over the time and if weaningand silage feeding had influence on gut microbiota during growing stage.
In the second experiment, microbiota of whole crop corn silage and feces of 18 silage-fed
lactating Holstein cows were examined in Japan and China. The objective was to understand thevariation and similarity of the fecal microbiota of dairy cows with regards to individuals, farms,
and countries. And, we also examined if silage-associated microbiota affected the gut microbiotaof lactating cows.
In the third experiment, gut microbiota was characterized across the gastrointestinal tract
for both digesta and mucosa-associated microbiota using goat model. The Lactobacillus community
was also examined to understand how this community inhabited from feed (silage) throughout
different segments along the gastrointestinal tract.

2

CHAPTER 2
LITERATURE REVIEW
2.1. The role of gut bacteria
Gut bacteria have main functions including metabolic activities for rescue of energy andnutrients, trophic effects on intestinal epithelia, development of immune system and protection thehost from invasion of foreign microbes (Guarner et al. 2003). Gut bacteria harbor different
dominant bacterial phyla regarding host species. For human, the most popular phyla are Firmicutes,
Bacteroides, Actinobacteria, Proteobacteria and Verrucomicrobia; for cattle, those are Firmicutes,
Bacteroides, Proteobacteria, Euryarchaeota (Eckburg et al. 2005; Hogenova et al. 2011).
2.1.1. Metabolic function
The critical metabolic function of gut bacteria is related to the fermented process of nondigestible substances (exogenous substrates that are provided by food and endogenous substrates
synthesized by host) and converting into chemical compounds, which subsequently are absorbed
and digested by host animals (Guarner et al. 2003; Rambaud et al. 2006).
2.1.1.1. Carbohydrate metabolism
Non-digestible carbohydrates like resistant starch, cellulose, hemicelluloses, pectin,
unabsorbed sugars and alcohols are converted into short chain fatty acids (mainly acetate,
propionate and butyrate) and gases by the interaction of different kinds of bacteria. These fatty acids
have an important contribution to host physiology. Butyrate which is the main source of energy forcolonocytes is consumed by colonic epithelium while acetate and propionate play a role inmodulation of glucose metabolism. These fermenting activities of bacteria are supposed to recoverenergy and nutrients for host as well as energy and nutrients supplying for their own growth andmultiplication (Rambaud et al. 2006). Taking part in these processes, it’s necessarily mentioned toamylolytic species (Bacteroides), cellulolytic species (Ruminococcus, Clostridium, Eubacterium,
Enterococcus and Propionibacterium), xylanolytic species (Clostridium, Butyrivibrio andBacteroides), pectinolytic species (Bacteroides, Bifidiobacterium, Eubacterium and Clostridium).
In addition, many endogenous substrates that secreted by the host animal (mucopolysaccharides,
mucins) were degraded by Bacteroides, Bifidobacterium, Ruminococcus based such enzymes assialidase, specific α-glucosidase, β-D-galactosidase (Rambaud et al. 2006).

3

2.1.1.2. Protein metabolism
Food proteins are hydrolysed to peptides and amino acids by gut bacteria proteases
consisting of serine-, cystine- and metallo-proteases. Abundance of bacterial species which hasproteolytic

activity

belongs

togeneraBacteroides,

Clostridium,

Propionibacterium,

Fusobacterium, Streptococcus, and Lactobacillus. Especially in ruminant, hydrolysed amino acids
are degraded further to create organic acids, ammonia and carbon dioxide. The rumen bacteria used
the resulted ammonia and some small peptides, free amino acids for synthesis of microbial proteins.
Once these organisms travel through to abomasum and intestine, then, microbial proteins aredigested and absorbed. The formation of microbial protein implicated an important point thatbacteria can synthesize essential and non – essential amino acids which adapt to the host’s protein
requirements (McDonald et al. 1995; Rambaud et al. 2006).
2.1.1.3. Vitamin synthesis
Gut bacteria also contribute partly to the vitamin synthesis. In ruminant, all the members ofvitamin B complex and vitamin K are synthesized by rumen microorganisms. Vitamin B synthesis
depends on the amounts of B vitamins supplied by feeds. This process enhances when feed-derived
vitamin B is small, and conversely, decreases when vitamin B supply is relatively adequate.
Therefore, in adult ruminant, these vitamins are independent of dietary source (McDonald et al.
1995).
2.1.2. Trophic function
2.1.2.1. Epithelial cell growth and differentiation
The role of gut bacteria on colonic physiology is based on their production of short chain
fatty acids. A study of Alam et al. (1994) showed that the rate of crypt cells production decreased
in colon of rats that were bred in germ-free environment than in conventional environment. It’s
implicated that commensal bacteria influence cell multiplication in the colon (Alam et al. 1994).
The differentiation of epithelial cells is affected by resident bacterial interactions (Hopper et al.
2001; Guarner et al. 2003). Among fatty acids that stimulate cell proliferation and differentiation,butyrate inhibits cell proliferation but stimulates cell differentiation in neoplastic organ cell, and it
also supports reversion from neoplastic to non-neoplastic phenotypes (Siavoshian et al. 2000;
Guarner et al. 2003). Another study of Hooper et al. (1999) indicated that the Bacteroides
thetaiaotamicron can secret a signal molecule stimulating the expression of fucose used as anutrient resource by bacteria (Hooper et al. 1999; Callaway et al. 2012). The presence of B.
thetaiotamicron is related to fucose expression among epithelial cells (Callaway et al. 2012).

4

2.1.2.2. Interactions between gut bacteria and host immunity
The communication between gut bacteria and host at mucosal interface plays a role indevelopment of immune system. Microbial colonization is considered to affect the composition ofgut-associated lymphoid tissue. Components of gut bacteria have an important role in the postnatal
immune system development. In the early post natal period, a transient physiological inflammatory
response with expansion of the mucosal-associated lymphatic tissue was induced by componentsof gut bacteria (Hrncir et al. 2008; Hogenova et al. 2011). Early exposure to microbes induces
expansion and development of immune cells and tissues; and diversity of microorganisms partiallyinfluence subsequent ability of immune system to respond to allergens and infection (Bjorksten etal. 2001; Callaway et al. 2012). Reports showed that bacterial colonization of animal bred in agerm-free environment indicated an enhancement in the production of specific antibodies, a highdensity of lymphoid cells in gut mucosa and an increase in circulating concentration of
immunoglobulin in the blood (Butler et al. 2000; Guarner et al. 2003; Tannock et al. 2001;
Stepankova et al. 1998; Hogenova et al. 2011). Hrncir et al. (2008) reported that the developmentof regulatory T lymphocytes depends on the presence of the dietary bacterial components. Germfree mice fed with small amount of lipopolysaccharide had fewer T lymphocytes (Hrncir et al.
2008). Interestingly, germ-free mice’s bacterial colonization also stimulates the biochemical
maturation of enterocytes, creating an alternation of brush-border enzymes close to those inconventional mice (Stepankova et al. 1998; Hogenova et al. 2011).
2.1.3. Protective function
Resident bacteria can be considered as an essential line of resistance to exogenous microbes’
colonization, then, greatly relevant in prevention of pathogens. When the bacteria have an optimalcomposition, adherent non-pathogenic bacteria can compete for attachment sites in brush-border ofintestinal cells in order to prevent attachment and invasion of the pathogenic microorganisms intoepithelial cells and the circulation. As well, bacteria compete for nutrient availability and maintaintheir habitat by administering and consuming all resources. This symbiotic relationship between
host-bacteria prevents undesirable overproduction of nutrient, which attract potentially pathogenic
competitors. Finally, bacteria can inhibit the growth of their competitors by secreting antimicrobial
substances – bacteriocins (Bernet et al. 1994; Hooper et al. 1999; Lievin et al. 2000; Guarner et al.
2003; Hogenova et al. 2011).
2.2. Temporal colonization of gut bacteria communities
During early life, major changes take place in the composition of gut bacteria. The fetal
intestine is sterile and soaked in amniotic fluid. At birth, the intestine is still sterile. Following

5

delivery, multiple antigens challenge intestine of newborn. Within a few hours, bacteria appear in
the feces. Facultative aerobes rapidly colonize the intestinal tract, gradually, consumption ofoxygen leads the intestinal environment into a reduced one, allowing strict anaerobes to sequentiallycolonize it (Bezirtzoglou et al. 1997; Farano et al. 2003; Penders et al. 2006; Dominguez-Bello et
al. 2011; Koenig et al. 2011; Callaway et al. 2012).
The climax intestinal bacterial composition is attained in successive stages. During passage
through vagina new-born is exposed to maternal vaginal flora. According to studies, vaginal
microbiota of mother and of the external ear canal of newborn showed the close qualitative andquantitative association; similar microbes were determined in 85% of mother–newborn pairs(Mandar et al. 1996; Farano et al. 2003). However, vaginal microbes usually do not settle inintestinal tract. The maternal intestinal flora is actually the source of bacteria for newborn. But infact, some neonates acquire bacteria from environment rather than from mother (Farano et al. 2003).
The early and rapid colonization helps protect from pathogen invasion but the climax
population establish for several years (Marques et al. 2010; Callaway et al. 2012). In adults, thecomposition of bacteria along digestive tract is determined by a gradient of oxygen from food andwater consumption and a gradient from tissue into the lumen (Wilkinson et al. 2012; Callaway et
al. 2012). In summary, establishment of gut bacteria is a complex process which is influenced by
microbes-host interactions and by external and internal factors. The important extrinsic factors arebacteria of the environment, composition of maternal microbiota, diet, mode of delivery andmedication, and the host-related factors like anatomical development of intestinal tract, peristalsis,
bile acids, intestinal pH and immune systemic responses, microbial interactions and mucosal
receptors (Mackie et al. 1999; Farano et al. 2003). The stability of intestinal microbiota changes
throughout life (Spor et al. 2011; Claesson et al. 2011; Callaway et al. 2012).
Recently, several studies have been carried out to investigate the composition of bacterial
communities in newborn calves and their changes with growth stages throughout the animal’s life.
Rumen and feces samples have been employed in this approaching (Jami et al. 2013; Li et al. 2012;
Rey et al. 2013; Klein-Jobstl et al. 2014; Oikonomou et al. 2013).
In ruminal bacteria of calves, the dominant phyla found in all age groups were Firmicutes,
Bacteroidetes, and Proteobacteria (Jami et al. 2013). And, Rey et al. (2013) reported that threephyla Proteobacteria, Fusobacteria and Bacteroidetes changed significantly over time. At 2 daysof age, Proteobacteria, Bacteroidetes and Firmicutes were the dominant phyla and accounted for
70.4, 13.9 and 10.8% of total sequences, respectively (Fig. 2.1.). Jami et al. (2013) characterizedthat phylum Firmicutes in the samples taken from the 1–3-day-old-calves was abundant comparedwith the other groups, with the vast majority of the reads belonging to the genus Streptococcus.

6

This phylum decreased in the 2-month-old-calves and significantly increased again in the other
older age groups. Phylum Bacteroidetes was significantly lower in samples taken from the 1–3day-old-calves compared with all other groups, but became the most abundant phylum in samples
from older animals. The genus composition of this phylum also changed considerably between theage groups: in the newborn animals (1- and 3-day-old) it was mainly composed of the genus
Bacteroides, whereas in the older age groups, it was almost exclusively composed of Prevotella. It
indicated that the changes occurred in rumen ecosystem after birth, reflected by a decline in aerobic
and facultative anaerobic taxa and an increase in anaerobic ones (Jami et al. 2013). In a report ofRey et al. (2013), from 3 days of age, Proteobacteria relative abundance fell rapidly, exhibiting thelowest values between 3 and 15 days, while the Bacteroidetes phylum became dominant (56.3%).
Bacteroides became prominent genus, others were Prevotella, Actinobacillus, Fusobacterium, andStreptococcus (Rey et al. 2013). Several specific functional bacterial species like Ruminococcus
flavefaciens, Ruminococcus albus could already be found in the first days of life in the prefunctioning rumen. This proved that establishment in the rumen of bacterial species that areimportant for its proper function in adult animals begins on the first day of life, when animals arestill being fed exclusively colostrum, before the intake of plant material (Jami et al. 2013). Ruminal
function can be covered by different microbiota with quite different phylogenetic composition (Liet al. 2012). Hence, it is difficult to find relationships between bacterial community and ruminal
functions or fermentation parameters (Rey et al. 2013).
When calves began to eat concentrate, a fall in Bacteroides abundance (from 16.9% to 7.1%
after one day) and an increase in Succinivibrio (from 0.2% to 2.1%). Succinivibrio was foundpositively correlated with concentrate intake. As the concentrate intake progressively increased,
several dominant genera declined or no longer detected, including Granulicatella, Actinomyces,
Bacteroides, Streptococcus, Gallibacterium, Fusobacterium and Pelistega. The Proteobacteria
phylum increased to 27.6%. Prevotella genus and Coriobacterineae family increased to achieve
41.5 and 2.9% relative abundances, respectively (Fig. 2.2). At the age of 1 month, calves ate more
and more starter concentrate and started eating some hay leading to a significant increase in ruminalpH, cause disruption in the bacterial community: a selection phase of bacterial community would
be set up with bacterial taxa that were more specific and adapted to these new substrates. Several
genera belonging mainly to Proteobacteria and Firmicutes phyla, were no longer detected. At thesame time, Dialister and Syntrophococcus increased to 1.2 and 0.6% relative abundances,respectively (Rey et al. 2013). However, in study of Jami et al. (2013), even though animals fromthe 6-month and 2-year-old groups received the same diet, their microbiota were still significantly
different, which might indicate that at 6 months of age, the rumen microbiota undergoes

7developmental changes that are independent of diet. Also, the study highlighted the mature rumen
microbiota are more diverse, more specific, and more homogenous than that of primary microbiota
which allows form more heterogeneity between different animals (Jami et al. 2013).

Figure 2.1. Age classification of calf ruminal samples according to phyla relative abundance determinedwith TREE package of SYSTAT from birth to weaning, values expressed as mean percentages ± standard
deviation of calf means. PRE indicates proportion of variability explained by division into age-classes.
Significance of age in the repeated measurements analysis is referred as ***P ≤ 0.001, **P ≤ 0.01, NS for P> 0.05 (Rey et al. 2013).

In fecal microbiota of calves from birth until weaning, the predominant phyla wereFirmicutes, Bacteroidetes, and Proteobacteria. However, relative abundance was different acrossstudies. Oikonomou et al. (2013) reported that Firmicutes was the major phylum in dairy calves,
showing a prevalence that ranged from 63.84% to 81.90%, followed by Bacteroidetes (8.36% to23.93%), Proteobacteria (3.72% to 9.75%), Fusobacteria (0.76% to 5.67%), and Actinobacteria(1.02% to 2.35%). Klein-Jobstl et al. (2014) characterized Bacteroidetes (69.3%), Proteobacteria(15.7%), and Firmicutes (14.8%), accounting for 99.8% of all reads in case of Simmental calves(Fig. 2.3). The diversity of fecal microbiota was found increased with advanced age and solid feed
consumption (Oikonomou et al. 2013; Klein-Jobstl et al. 2014). In dairy calves, the abundance ofFirmicutes increased from the first to the fourth week of life and then progressively decreased. A
reverse pattern was observed for Bacteroidetes prevalence. It is possible that these changes reflect
the gradual adaptation of the calf gastrointestinal tract first to milk consumption and later toconsumption of solid feed. An observation made at the genus level supports this assumption. The
prevalence of Lactobacillus spp. (known to be related to digestion of milk) reached a 14.74%
maximum during the fourth week of calf life and then progressively decreased to reach 2.15%
during the seventh week. The prevalence of Bifidobacterium spp. (also known to be related todigestion of milk) showed a similar pattern. It is known that the consumption of solid feed by dairy
calves significantly increases after the fourth week of calf life (Oikonomou et al. 2013). But in caseof Simmental calves, while Bacteroidetes decreased, Firmicutes increased over time. The overall8Firmicutes to Bacteroidetes ratio was 0.2 varying between 0.1 and 0.4 during the different stagesof growing, with a tendency to increase with age. Genus Bacteroides is dominant from birth until
calves were provided concentrate to diet.; and generally, this genus was decreasing overtime. The
same trend of variation was found in Lactobacillus with a significant decrease over time (KleinJobstl et al. 2014).
Faecalibacterium prausnitzii is a butyrate-producing microorganism. Butyrate has thehighest energy value per mole of the major rumen volatile fatty acids, is extensively metabolized
by the rumen epithelium, and has mitogenic effects on the epithelium during development.
Additionally, butyrate concentrations were found to be higher in the rumen fluid of steers thatshowed higher feed efficiency. Klein-Jobstl et al. (2014) reported that in Simmental calves,
Faecalibacterium spp. decreased significantly over time. Oikonomou et al. (2013) indicated that
Faecalibacterium spp. prevalence during the first week of calf life was found to be significantly
associated with body weight gain during the pre-weaning period. Calves with higher abundance ofFaecalibacterium spp. in the first week of life had showed the lower incidence of diarrhea(Oikonomou et al. 2013).

Figure 2.2 Age classification of calf ruminal samples according to genera relative abundance determinedwith TREE package of SYSTAT from birth to weaning, values expressed as mean percentages ± standard
deviation of calf means. PRE indicates proportion of variability explained by division into age-classes. ND
indicates not detected genera. Significance of age in the repeated measurements analysis is referred as ***P
≤ 0.001, **P ≤ 0.01, *P ≤ 0.05, NS for P> 0.05 (Rey et al. 2013).

9

Figure 2.3. Median relative abundances of the three most abundant phyla during the six timepoints (12hours, 2 weeks, 3 weeks, 6 weeks after calving, one week before and after weaning) (Klein-Jobstl et al.
2014).

Figure 2.4. Aggregate microbiota composition at the phylum level by week of life (Oikonomou et al. 2013).

2.3. Characterizing microbiota across gastrointestinal tract of ruminants
The symbiosis between gastrointestinal bacterial communities and their mammalian hosthave been proved to have essential implications for overall animal health. The typical example ofthis relationship is exposed in the rumen of ruminants, where plant materials are digested in order
to converse plant fibers into chemical compounds absorbed by the animal. Hence, the microbiota
in bovine rumen have been examined extensively. In addition, fecal microbiota has been alsostudied due to the convenient sampling procedure. However, the role of microbiota in othersegments such as small intestine and large intestine have received little consideration, and, limited
understanding of characteristic microbiota in different parts along the gastrointestinal tract wasachieved. Recently, there have been studies exploring the structure of microbiota across the10gastrointestinal tract of ruminant. These works have revealed there is a high intra-individual
variation in the microbiota inhabiting within the different components of the gastrointestinal tract(de Oliveira et al. 2013; Mao et al. 2015; Zeng et al. 2017).

Table 2.1. Relative abundance of the 28 most abundant genera (relative abundance > 0.1%) of Simmental
calves from birth to weaning (Klein-Jobstl et al. 2014).

The symbiotic bacteria are traditionally including digesta-associated and mucosa-associatedbacteria. These bacterial populations were supposed to interact with each other and contribute tohost animal function.
Total bacterial populations in various regions were estimated with a real-time PCR analysis.
Regional sites affected bacterial density of dairy cattle significantly. Higher digesta-associatedbacterial numbers were observed in the colon and omasum, and higher mucosa-associated bacterial
numbers were present in the rumen and omasum. In addition, mucosa-associated bacterial densities
in the jejunum, ileum, cecum, colon and rectum were significantly lower than in their corresponding
digesta. However, digesta-associated bacterial numbers in the duodenum were lower than mucosaassociated bacterial densities (Table 2.2) (Mao et al. 2015).

11

2.3.1. Microbial composition of digesta-associated population across gastrointestinal tract
The gastrointestinal tract of ruminant was differentiated into three regions, namely theforestomach (rumen, reticulum, omasum, and abomasum), small (duodenum, jejunum, and ileum)and large intestine (cecum, colon, and feces).
The diversity of microbiota was found higher in the stomach than in the small and large
intestine. For instance, among different site along the gastrointestinal tract of dairy cow, rumen andabomasum harbored most of the phyla (19 phyla), while the lowest number was seen in cecum (12phyla) (Mao et al. 2015). In dairy cow, within the gastrointestinal tract, the most abundant phyla
were Firmicutes (64.81%), Bacteroidetes (15.06%), and Proteobacteria (13.29%). At genus level,
Prevotella (5.27%), Ruminococcus (3.29%), Acetitomaculum (2.89%), Butyrivibrio (5.06%), as
well as those unclassified derived from Peptostreptococcaceae (16.9%), Ruminococcaceae(8.69%), Enterobacteriaceae (11.1%), Clostridiales (2.46%), Rikenellaceae (3.64%) and
Bacteroidales (4.03%) were predominant (Table 2.3) (Mao et al. 2015).
One report of Brazilian Nelore steer showed that the majority belonged to the phyla
Firmicutes (41.22%), Bacteroidetes (33.51%), and Proteobacteria (12.15%). And, the most highly
represented families included the Ruminococcaceae (11.14%), Lachnospiraceae (9.98%) andPrevotellaceae (8.67%) (de Oliveira et al. 2013). Zeng et al. (2017) indicated that in thegastrointestinal tract of Chinese Mongolian sheep harbored phyla Firmicutes (44.62%),
Bacteroidetes (38.49%), Proteobacteria (4.11%), Spirochaetes (3.44%), and Euryarchaeota(1.78%); classes Clostridia (42.02%), Bacteroidia (37.30%), Spirochaetes (3.36%), Unclassified(2.80%), and Fibrobacteria (0.58%); 20 orders Clostridiales (42.01%), Bacteroidales (37.30%),
Spirochaetales (3.26%), Unclassified (2.90%) and Methanobacteriales (1.83%); 38 families
Ruminococcaceae (20.76%), Prevotellaceae (16.60%), Lachnospiraceae (8.37%), Unclassified(6.88%), and Bacteroidaceae (5.23%); 40 genera Unknown (20.76%), Unclassified (19.92%),
Prevotella (15.56%), Ruminococcus (6.35%), and Treponema (3.26%) and 18 species Unknown(63.42%) Unclassified (21.70%), Prevotella ruminicola (5.45%), Ruminococcus flavefaciens(3.63%), and Ruminobacter albus (1.72%) (Zeng et al. 2017).

12

Table 2.2. Total bacterial density (numbers per gram wet weight) throughout the gastrointestinal tracts ofdairy cattle*. Note. Means within the same column with different subscripts are significantly different fromone another. *Copy number of 16S rRNA gene (copy.g-1). ‡Sampling site effect on bacterial density
throughout the GIT of dairy cattle. §Regional effect on bacterial density throughout the GIT of dairy calves(Mao et al. 2015).

Although Firmicutes, Bacteroidetes, and Proteobacteria were found represented withingastrointestinal tract of ruminant, they varied considerably among regions in abundance and in thenumber of genera composing them. In steer, Bacteroidetes was found predominantly from thereticulum to abomasum, whereas Firmicutes dominate within the small intestine (duodenum,
jejunum and ileum) and within the large intestine (cecum, colon and feces) (de Oliveira et al. 2013).
The gastrointestinal tract of sheep indicated Firmicutes was higher in the small intestine than in thestomach and large intestine, while Bacteroidetes showed the opposite trend of variation. In phylum
Firmicutes, the two genera Ruminococcus and Oscillospira were the most abundant across the tract.
While genus Ruminococcus showed a decrease trend in abundance from the stomach (15.45% of
total bacterial population), small intestine (4.03%), to larger intestine (1.99%), genus Oscillospiraindicated a gradual increase in abundance from 1.94% of total population in the stomach, 3.03% insmall intestine to 4.42% in large intestine. Genus Prevotella, the most predominant genus within
phylum Bacteroidetes, showed great variation between segments. This genus accounted for 34.88%(of total bacterial population) in the stomach, decreased into 7.78% in the small intestine, and lightly
increased again in large intestine (13.42%). Genus Bacteroides was only detected in jejunum(9.27%), colon (10.63%), and feces (15.32%). The relative abundance of other genera [Prevotella],Parabacteroides, and CF231 were found greater from the segment of ileum to feces, while they
were not detected at remarkable level in other segments (Zeng et al. 2017).
In dairy cow, phylum Firmicutes dominated all bacterial communities along the tract except
for in the duodenum, where Proteobacteria (53.9%) was predominant. Bacteroidetes was thesecond most prevalent in the forestomach, while Proteobacteria was the second most prevalent in13

digesta samples of the jejunum, ileum, cecum and colon. Firmicutes was mainly composed of
unclassified Ruminococcaceae (15.2%), unclassified Christensenellaceae (7.51%), andunclassified Lachnospiraceae (6.29%) in forestomach; unclassified Lachnospiraceae (6.06%),
Buryrivibrio (8.19%), and Ruminococcus (4.41%) in small intestine; and, unclassified
Peptostreptococcaceae (43.1%), Turicibacter (13.2%), and Clostridium (10.1%) in large intestine.
Phylum Bacteroidetes was dominated by Prevotella (12.9%), unclassified Rikenellaceae (8.42%),and unclassified Bacteroidales (9.46%) in forestomach. High abundance of genus Prevotella inforestomach is thought to relate to its high genetic variability, which enables them to occupy various
ecological niches within the rumen. However, the exact mechanism explaining the result that
Prevotella was less abundant in small and large intestines is not clear yet. Phylum Proteobacteria
was overwhelmed by unclassified Enterobacteriaceae in small intestine (27.3%), and large
intestine (8.92%), but could not be detected at significant level in forestomach (Mao et al. 2015).
2.3.2. Microbial composition of mucosa-associated population across gastrointestinal tract
Gastrointestinal mucosa-associated microbiota could play important biological roles due totheir close proximity to the animal host, but knowledge of their composition in cattle still is limited.
Mao et al. (2015) indicated that significant differences in the diversity, composition, and structureof mucosa-associated bacteria communities among sections. Firmicutes (42.2%), Bacteroidetes(21%) and Proteobacteria (17.6%) were the most abundant phyla across the tract, and Firmicutes,
Bacteroidetes, Proteobacteria, Actinobacteria, Tenericutes, Spirochaetae, Cyanobacteria andLentisphaerae were found in all samples. Firmicutes dominated all mucosa-associated bacterial
communities along the tract except for in the duodenum, where Proteobacteria (37.3%) was
predominant. Bacteroidetes was the second most prevalent phyla in mucosal samples of thereticulum, omasum, abomasum, colon and rectum, whereas Proteobacteria was the second mostprevalent in mucosal tissues of the rumen, jejunum and ileum. Firmicutes and Spirochaetae were
the second most dominant phyla in mucosal samples of the duodenum and cecum, respectively. In
Firmicutes, unclassified Ruminoccocaceae were found more abundant in forestomach (9.12%) andlarge intestine (16.1%) (Table 2.4). Unclassified Peptostreptococcaceae (6.32%), Anaerovibrio(4.05%) appeared predominant in large intestine. Butyrivibirio showed a higher abundance inrumen (12.1%) and reticulum (12.1%). As the mucosal butyrate producers release butyrate close to
the epithelium, species of Butyrivibrio may enhance butyrate bioavailability for the host, which
may be particularly useful in proliferating rumen and reticulum epithelium. The study highlighted
the dominant presence of the aerobic bacterial genus Acinetobacter (12.1%), belonged to phylum
Proteobacteria, in small intestine mucosa. This could be explained by the presence of oxygen at14

the apical surface of the intestinal epithelial cells, representing a possible mechanism of exclusionof strictly anaerobic, extremely oxygen-sensitive microorganisms. In addition, the enrichment ofgenus Treponema (12.5%) (belonged to phylum Spirochaetae) in large intestine mucosa may have
health implications. Treponema spp. has been reported that they were well adapted to oxidative
stress. Additionally, Treponema spp. are believed to be associated with ulcerative mammarydermatitis and bovine digital dermatitis in cattle and contagious ovine digital dermatitis in. Thus,
enhanced Treponema in large intestine mucosa could have deleterious effects on hindgut health(Mao et al. 2015).
When comparing between digesta and mucosa samples of each region along gastrointestinal
tract, significant differences in composition and structure of bacterial communities were observed.
Mao et al. (2015) showed that higher digesta-associated bacterial numbers were observed in thecolon and omasum, and higher mucosa-associated bacterial numbers were present in the rumen andomasum. In addition, mucosa-associated bacterial densities in the jejunum, ileum, cecum, colonand rectum were significantly lower than in their corresponding digesta. However, digestaassociated bacterial numbers in the duodenum were lower than mucosa-associated bacterial
densities.
At the phylum level, the proportion of Firmicutes in the digesta of the forestomach, jejunumand large intestine was significantly higher than in their corresponding mucosal tissues. The
abundance of Bacteroidetes was in the digesta of the rumen and reticulum, while it was lower inthe digesta of the duodenum, jejunum and large intestine when compared with their corresponding
mucosal tissues. Mucosal tissues of the forestomach and rectum presented higher proportions ofProteobacteria than their corresponding digesta samples, while digesta of the duodenum showed acomparatively higher proportion of Proteobacteria. At genus level, higher proportions of the
predominant Prevotella, unclassified Ruminococcaceae, unclassified Enterobacteriaceae andunclassified Peptostreptococcaceae in the digesta-associated microbiota and a larger percentage of
Butyrivibrio, Acinetobacter and Treponema in the mucosa. Previous studies revealed that the genus
Prevotella, unclassified Ruminococcaceae and unclassified Peptostreptococcaceae might play
important roles in feed digestion and that members of the genera Butyrivibrio, Acinetobacter andTreponema were more involved in epithelium proliferation and diseases (Mao et al. 2015).

15

Figure 2.5. Predicted function of the gut micorbiota in the sheep of GIT. KEGG pathways were shown in
two heatmaps. The bootstrap Mann–Whitney u-test was used to detect the gene distribution with cutoffs of
P < 0.05, FDR <0.2, Mean counts >10,000 (a) and P < 0.01, FDR <0.1, Mean counts >10 (b) (Zeng et al.
2017).

2.3.3. Microbial composition of food-associated population in related to bacterial
communities across gastrointestinal tract
There have been limited studies which involved in the relationship between food-associatedand gut microbiota of cattle. de Oliveira et al. (2013) reported that corn and grass feed which
provided to cattle was dominated by members of phylum Proteobacteria, and indicated no relation
to gut microbiota of cattle across the gastrointestinal tract. According to the authors, this is not
surprising given that the rumen, which is the first major organ that feed enters, is adept at selecting
a very specific microbiota (Fig. 2.6). However, in human case, several studies have been carriedout to shed light on the consequence of consumption of naturally fermented food. Lee et al. (1996)
demonstrated that consumption of naturally fermented kimchi, which has been shown to have
Lactobacillaceae and Leuconostocaceae as the dominant taxa (Jung et al. 2014), increased the
population of Lactobacillus spp. and Leuconostoc spp. in human feces (Lee et al. 1996). Similarly,
Han et al. (2015) found that intake of fermented kimchi raised up the populations of two phyla,
Proteobacteria and Actinobacteria, and two genera Bacteroides spp. and Prevotella spp. in humanfeces (Han et al. 2015).
2.3.4. Prediction of molecular function of microbiota across gastrointestinal tract
The gastrointestinal microbiota presents many physiological functions that are lacking in
the host, and therefore, they can be considered essential to cattle life. To gain insight into the16molecular functions of bacterial microbiota across cattle GITs, in many studies, PICRUSt was used
to predict the metagenomic contribution of the communities observed. PICRUSt predicts
metagenomic potential by imputing the available annotated genes within a known sequenced
database, such as the Kyoto Encyclopaedia of Genes and Genomes (KEGG) and the Clusters of
Orthologs Groups (COGs) catalogue, based on the presence/absence of OTUs in a 16S rRNA
survey. With PICRUSt, one can calculate nearest sequenced taxon index (NSTI), which measures
how closely related the average 16S rRNA sequence in an environmental sample is to an available
sequenced genome. When this number is low, PICRUSt is likely to perform well in predicting the
genomes of the organisms in an environmental sample.
Using PICRUSt as a predictive exploratory tool, Mao et al. (2015) inferred that 39 genefamilies were identified in the digesta and mucosa-associated microbiota samples. The results
indicated that molecular function of digesta- and mucosa-associated microbiota were differentiatedbetween forestomach and intestine (except for rectum samples). The majority of the genes belonged
to membrane transport (17.8% indigesta-associated microbiota and 17.6% in mucosa-associatedmicrobiota, respectively), carbohydrate metabolism (10.68% in digesta, 10.8% in mucosa), aminoacid metabolism (8.36% in digesta, 9.13% in mucosa), replication and repair (7.70% in digesta,
7.62% in mucosa) and energy metabolism (4.69% in digesta, 4.84% in mucosa). The abundance of
these genes is consistent with the general metabolic functions being essential for microbial survival.
This study also revealed that significant differences in bacterial function among region across thegastrointestinal tract. For instance, in the digesta samples, the relative abundances of the genes
involved in carbohydrate metabolism and replication and repair in the microbiota of forestomach
samples were significantly higher than in the microbiota of the cecum and colon. In mucosal
samples, there was a notable enrichment of genes related to amino acid metabolism in the
duodenum when compared with those in the forestomach and large intestine. When comparing
between digesta and mucosa, genes associated with amino acid metabolism were more enriched in
the mucosa-associated microbiota than in their corresponding digesta. One possibility is that the
mucosal tissue provides a decreased supply of carbohydrates and that bacteria may derive energy
from amino acid fermentation, and the mucosal microbiota may be more necessary to amino aciddegradation (Mao et al. 2015).
Zeng et al. (2017) also used PICRUSt to predict bacterial functional across gastrointestinal
tract of sheep. Genes related to carbohydrate metabolism and bacterial flagellar assembly(“Carbohydrate metabolism”, “Peptidoglycan biosynthesis”, “Ethylbenzene degradation”,“Geraniol degradation”, “Primary immunodeficiency”, “Arachidonic acid metabolism”,“Biosynthesis of siderophore group nonribosomal peptides”, and “Flagellar assembly”) were more

17

abundant in the stomach and small intestine than in the large intestine. Genes primarily related tothe metabolism of cofactors and vitamins (“Membrane and intracellular structural molecules”,“Ubiquinone and other terpenoid-quinonebiosynthesis”, and “Adipocytokine signaling pathway”)
were significantly abundant only in the stomach. And, two pathways (“Ether lipid metabolism” and“RIG-I-like recep- tor signaling pathway”) were significantly abundant only in the small intestine(Fig. 2.5) (Zeng et al. 2017).

Figure 2.6. Distribution of 16S rRNA sequences from the feed and GIT of a Nelore steer. Depicted aresampling locations and the relative sequence abundances in phyla (de Oliveira et al. 2013).

18

Table 2.3. Comparison of the phyla in digesta samples across the gastrointestinal tract of dairy cattle (the multiple comparisons results were only presented forthe phyla which average relative abundance ≥1% in at least one region). Mean in the same row with different superscripts represents a significant difference (P< 0.05) (Mao et al. 2015).

Phylum

Rumen

ReticulumOmasumAbomasum

Duodenum

Jejunum

Ileum

Cecum

Colon

RectumSEM

P value

FDR*

Firmicutes

50.49ef

53.86de50.96ef

64.37cd

38.28f68.52bc

67.49bc

79.15ab83.71a91.26a2.211<0.001<0.001Bacteroidetes

42.39

a

38.98

a

40.59

a

19.66

b

c

c

c

c

c

c

2.353

<0.001<0.001

Proteobacteria

1.15

e

1.12

e

1.21

e

de

53.87

a

17.49

bc

26.86

e

2.223

<0.001<0.001

Actinobacteria

1.27

b

1.23

b

b

5.18

b

4.29

b

9.62

a

b

3.97

b

0.402

<0.001<0.001

1.43

b

1.43

b

1.03

b

c

0.23

c

0.24

c

c

c

0.096

<0.001<0.001

1.02

b

0.91

d

d

d

cd

bc

bc

Tenericutes
Spirochaetae
Lentisphaerae

0.35

cde

0.3

abcd

2.11
0.59

a

bcd

0.5

b

2.57

2.03

a

1.12

a

a

2.23

ab

0.2

0.06
0.05
1.23

1.66

0.07

bc

cdef

0.14

abc

3.12
0.04
0.05

bc

0.41
15.65
0.1

bcd

0.08
0.01

b

d

bc

def

0.59

f

0.99
10.62

cde

3.32

b

0.18

c

2.87
1.4

2.96

0.35

0.31

cd

0.089

<0.001<0.001

0.01

bc

0.01

c

0.053

<0.001<0.001

def

ef

0.105

<0.001<0.001

0.13

0.1

0.06

0.05

0.03

0.02

0.02

0.005

0.004

0.003

0.009

0.004

0.005

Acidobacteria<0.001

ND

ND

ND

ND

ND

ND

ND

ND

ND

<0.001<0.001<0.001

Armatimonadetes

0.01

0.01

0.02

0.02

0.002

0.001

ND

ND

0.003

ND

0.001

<0.001<0.001Chlamydiae<0.001

ND

ND

ND

ND

ND

<0.001

ND

ND

ND

0.000

0.537

0.537

Chlorobi

ND

ND

ND

0.01

0.01

ND

ND

ND

ND

ND

0.001

0.524

0.537

Cyanobacteria

0.07

0.26

0.17

0.25

0.09

0.19

0.04

0.03

0.04

0.06

0.021

0.038

0.04

Deinococcus-Thermus

0.002

ND

<0.001

0.001

ND

ND

<0.001

ND

<0.00

ND

0.000

0.003

0.004

Elusimicrobia

0.02

0.01

0.04

0.09

0.003

0.003

0.001

ND

ND

<0.001

0.005<0.001<0.001

Fibrobacteres

0.02

0.08

0.02

0.35

0.01

0.01

0.002

ND

<0.001

0.003

0.019

<0.001<0.001

Fusobacteria

0.02

0.02

0.01

0.06

0.06

0.06

0.02

0.005

0.005

0.01

0.006

0.017

0.021

Planctomycetes

ND

ND<0.001

0.03

0.02

ND

ND

ND

ND

ND

0.003

0.526

0.537

Synergistetes

0.02

0.01

0.01

0.04

0.001

0.003

0.003

ND

ND

<0.001

0.002<0.001<0.0010.02

0.01<0.001

0.01

0.002

0.003

0.01

0.02

0.05

0.13

0.007

<0.001<0.001

19

0.76

0.27

Chloroflexi

ND, not detected FDR: False discovery rate.

0.78

b

1.6b

*

1.97

bc

1.31

Unclassified Bacteria

Verrucomicrobia

1.67

bc

1.13

3.48

1.78

0.68

Table 2.4. Comparison of the phyla in mucosal samples across the gastrointestinal tract of dairy cattle (the multiple comparisons results were only presented
for the phyla which average relative abundance ≥1% in at least one region). Mean in the same row with different superscripts represents a significant difference(P < 0.05) (Mao et al. 2015).
Phylum

Rumen

ReticulumOmasumAbomasum

Duodenum

Jejunum

Ileum

Cecum

Colon

RectumSEM

P value

FDR*

Firmicutes

43.79ab43.01ab39.43ab27.4b34.21ab42.24ab52.15a48.29ab45.17ab46.57ab

1.614

0.026

0.047

bc

39.08a

bc

cd

d

12.04

cd

bcd

bc

30.94

ab

1.438

<0.001<0.001

bc

18.27

bc

8.48

c

5.69c

1.663

<0.001<0.001

1.85

c

1.79c

Bacteroidetes

21.98

26.95

ab

Proteobacteria

abc

17.44

bc

10.88

c

c

22

Actinobacteria

0.81

Spirochaetae

2.49

bc

1.88

c

bc

1.13

c

1.12
2.52

20.95
7.85

7.87

10.94

a

12.62

a

10.59

c

0.31

c

0.63

c

0.39

c

18.75

a

13.85

a

b

0.56

b

0.54

b

0.55

b

0.7

b

0.84

4.85

1.27

Tenericutes

1.03

1.16

b

1.49

b

17.95

Unclassified Bacteria

6.59

5.93

4.23

2.2

b

b

0.65

b

7.92
3.09

bc

ab

10.64

0.686

<0.001<0.001abc

1.105<0.001<0.001b

0.774<0.001<0.001

6.03

4.75

2.27

6.53

1.6

0.533

0.262

0.317

a

0.25

b

0.48b

0.47b

0.28b

0.22b

0.31b

0.047

<0.001<0.001

Lentisphaerae

0.16

Acidobacteria

ND<0.001ND<0.001

0.05

0.17

0.01

0.001

0.02

0.005

0.015

0.250

0.317

Aquificae

ND

ND

ND

ND

ND

0.001

ND

ND

ND

ND

0.000

0.452

0.506

Armatimonadetes

0.01

0.002

0.01

0.01

0.03

ND

ND

<0.001

0.002

0.01

0.002

0.085

0.130

Chlamydiae

ND

ND

ND

ND

ND

<0.001

0.03

0.002

ND

ND

0.002

0.225

0.305

Chlorobi

ND<0.001

ND

ND

0.01

0

0.01

0.01

ND

ND

0.001

0.635

0.635

Chloroflexi

0.02

0.03

0.03

0.03

0.06

0.22

0.07

0.01

0.02

0.004

0.018

0.225

0.305

Cyanobacteria

0.06

0.1

0.41

0.76

0.48

0.62

0.36

0.14

0.33

0.29

0.044

0.001

0.003

Deinococcus-Thermus

0.01

0.07

0.005

0.01

0.28

0.21

0.03

0.01

0.01

0.003

0.023

0.045

0.074

Elusimicrobia

0.08

0.13

0.13

0.18

0.03

0.07

0.1

0.02

0.02

0.02

0.010

<0.001<0.001

Fibrobacteres

0.1

0.13

0.36

0.28

0.01

0.04

0.06

0.07

0.08

0.14

0.018

<0.001<0.001

Fusobacteria

0.1

0.13

0.04

0.04

0.19

0.33

0.09

0.02

0.03

0.01

0.017

<0.001<0.001

Gemmatimonadetes

ND

ND

ND

ND

ND

0.03

ND

ND

0.004

0.004

0.003

0.462

0.506

Planctomycetes

0.01

0.002

<0.001

ND

0.003

ND

ND

0.001

0.03

0.01

0.003

0.570

0.595

Synergistetes

0.77

0.97

0.21

0.18

0.05

0.01

0.01

0.01

0.01

0.02

0.046

<0.001<0.001

0.01

0.01

0.03

0.01

0.03

0.03

0.04

0.39

0.9

1.13

0.092

0.016

0.031

Verrucomicrobia

1.06

0.64

a

c

21.75

37.26

27.84

ab

18.16

ab

b

0.29

10.33

a

19.82

abc

*

ND, not detected; FDR: False discovery rate20

2.4. Factors influencing gut bacterial communities
2.4.1. Diet
Diet has been the most concerned parameter which drives the composition and metabolism of
the colonic microbiota. Especially, the great impact of amount, type and balance of the main
dietary macronutrients (carbohydrates, proteins and fats) is noticed (Scott et al. 2013).
2.4.1.1. Dietary carbohydrates
Cumming et al. (1991) reported that approximately 40 g of dietary carbohydrates per
day have been escaped from digestion by host enzymes (Cummings et al. 1991; Scott et al.
2013). Many studies revealed that changing the amount and type of carbohydrate over periods(about four weeks) could create an extreme and rapid influence on the composition of the gut
bacteria and its metabolites (Duncan et al. 2007; Russell et al. 2011; Walker et al. 2011; Scottet al. 2013), and dietary habit also makes an profound effect on bacterial composition. The
studies of Qin et al. (2010) and of Yatsunenko et al. (2005) demonstrated that there has been ahigher similarity between fecal bacteria of European and USA adults than that of SouthAmerica and Malawi (Qui et al. 2010; Yatsunenko et al. 2012). The microbial communities ofMalawians clusters were grouped together with those of Amerindian, but both were separated
from USA adults’ communities (Yatsunenko et al. 2012). It could be explained that Malawians
and Amerindian groups owned a diet consisting of a large amount of plant-derived
polysaccharides in comparison with a protein-rich diet of USA adults. Another study of De
Filippo et al. (2010) showed that microbiota of the African children was dominated by
Bacteroidetes (73%) compared to 27% Bacteroidetes and 51% Firmicutes in the EU children(De Filippo et al. 2010). The dominant Bacteroidetes species of African children, which are
adept at fermenting xylans and other components of plant fiber, belonged to Prevotella and
Xylanibacter genera which were absent in EU children (Hogenova et al. 2011).
Starch is a complex polysaccharide consisting of a mixture of amylose and amylopectin.
The relative proportion of amylose and amylopectin also influences the bacterial composition
to use different kinds of starch for growth (Scott et al. 2013). Mao et al. (2013) showed thatwhen the proportion of dietary corn grain increased, the abundance of phylum Actinobacteria
and Planctomycetes linearly decreased; phylum Firmicutes linearly increased; but there wereno significant changes in the abundance of Bacteroidetes, Chloroflexi, Cyanobacteria,
Proteobacteria and Tenericutes (Mao et al. 2013). The abundance and digestibility of starch ingrains created a shift in microbial community structure in grain-fed animals. An observation in
cattle announced that across an increasing starch gradient, the relative abundance of phylum

21Firmicutes decreased and phylum Bacteroidetes increased, and, the bacterial composition
composing to these phyla also changed (Shanks et al. 2011).
2.4.1.2. Dietary proteins
The colon is an active site of protein which provides nitrogen for growth ofsaccharolytic bacteria and amino acids for fermentation by saccharolytic species. In humanfeces, Bacteroides species and Clostridium perfringens, propionibacteria, streptpcocci, bacilli,
staphylococci are determined as the predominant proteolytic bacteria. Amongst fecal bacteria
Bacteroides species has strong pepsidase activity. And, several bacterial groups can ferment
amino acids and possess weak saccharolytic activity like peptococci, acidaminococci,
veillonella, and some fusobacteria, eubacteria, clostridia (Macfarlane et al. 1997; Walker et al.
2005; Hamer et al. 2011; Scott et al. 2013).
Fermentation of protein results in the production of various potentially toxic products,
such as amines and ammonia, phenols, indoles, and often accompanies with growth of potential
pathogens. Excessive protein intake has been related to stimulate the growth of pathogenic
species like Clostridium perfringens, and to decrease amount of beneficial bifidobacteria (Ristet al. 2013). Principally, dietary protein becomes favorable for bacterial fermentation when it
escapes the digestion by host enzymes (Libao-Mercado et al. 2009; Rist et al. 2013). The sourceof protein may influence the microbial fermentation. For instance, highly digestible proteins
like casein are digested by host enzymes, so that they are not available for microbial
fermentation. In case of plant proteins, they are usually not completely digested and certainly
become available for microbial fermentation especially at higher dietary protein levels (Pluske
et al. 2002). One study showed that protein source (soybean meal, casein) has no influence onbifidiobacteria, lactobacilli and clostridia in ileal digesta of weaned piglets, but these bacterialgroups were increased in piglets fed with soybean meal-based diet (Rist et al. 2013).
It has been suggested that increasing of carbohydrate supply can restrict proteolytic
fermentation by enhancing bacterial proliferation, and then, utilization of peptides for bacterial
proteosynthesis. Evidence from many studies indicated that a shift of carbohydrate:proteinbalance changes the profile of fermentation products, but changes in micobitota are still unclear(Bernalier-Donadille et al 2010; Scott et al. 2013).
2.4.1.3. Dietary fat
Zhang et al. (2012) have reported that the microbiota of mice fed with high-fat diet to
induce obesity for 12 weeks and followed by a normal diet for 10 weeks has shown a different

22microbial composition in obese group in high-fat diet but become indiscernible from control
group after 10 weeks of normal diet treatment. Hence, the response of microbiota to the highfat diet indicated that microbiota responds to the diet not to the obese phenotype (Zhang et al.
2012).
Data obtained from animal showed that the high-fat diet can force an increase in
Firmicutes and decrease in Bacteroidetes. Gut flora can be considered as an essential target tomanipulate obesity and related diseases (D’Aversa et al. 2013). A study of Mujico et al. (2013)indicated that high-fat diet administration induced an increase in body weight, all groups ofFirmicutes (phylum Firmicutes, Lactobacillus group and Clostridial cluster XIVa); an decrease
in Bifidobacterium spp. and the phylum Bacteroidetes. But for the high-fat diet administration
supplemented with oleic acid-derived compound, the microbial changes were counteracted: adecrease in all groups of Firmicutes and an increase in Bifidobacterium spp. and the phylum
Bacteroidetes, up to the level even higher than the control diet group. These data support a rolefor fatty acids related to obesity prevention (Mujico et al. 2013).
2.4.2. Antibiotics usage
Many antibiotics that have been used followed the idea of “detoxifying” the body by
the rectal administration (Rambaud et al. 2006). They can reach the colon directly or after
entering an enterohepatic cycle, and create an impact on the microbiota depending on class,spectrum or pharmacological properties. Hence, the adverse effects may happen: decreasing
capacity of fermentation (increasing risk of diarrhea), decreasing in the barrier effect(increasing risk of the emergence of pathogens) and the occurrence of antibiotic resistance(Rambaud et al. 2006; Perez-Cobas et al. 2013). Perez-Cobas et al. (2013) reported that specificproperties of antibiotics such as mode of action forced the selection of intestinal microbiota,
and were responsible for the changes of bacterial composition during therapy. This study was
carried out with 4 patients (A, B, C and D) who were treated different kinds of antibiotics
owning bactericidal, antimicrobial and bacteriostatic effect. In patient A (treated with cell
replication inhibitor antibiotic), both total (16S rRNA gene) and active (16S rRNA transcripts)
microbiota indicated high dominance of the families Lachnospiraceae and Ruminococcaceae
during treatment. Treatment of patient B (inhibitor of protein synthesis) resulted in highpresence of Enterobacteriaceae and in active microbiota, there was an increase of Bacteroides
genus after 5-day treatment. For patient C (cell envelop synthesis inhibitor antibiotic),
Oscillibacteriaceae, Ruminococcaceae, Rikenellaceae and Bacteroidaceae were seen as themost abundant taxa. The first significant change in this case took place on day 6 with an increase23

in Parabacteroides. And, on day 10 after treatment, Enterobacteriaceae and Enterococcaceae
families increased. In patient D (cell envelop synthesis inhibitor antibiotic), Enteriobacteraceae
and Ruminococcaceae were the most popular at the initial composition but, after that, were
extremely affected by antibiotics due to an increase in resistant bacterial taxa of Bacteroides
genus. After all, the bacterial composition and abundance of patients who suffered bactericidal
antimicrobial agent (A, C and D) clustered together, and apart from those of patient who
received bacteriostatic antibiotic (B) (Perez-Cobas et al. 2013). For the infant in the study ofPenders et al. (2006), during the first 1 month of life, oral use of antibiotics resulted in adecrease of bifidobacteria and Bacteroides fragilis group species (Penders et al. 2006). A
different study indicated that antibiotics created a suppression of all anaerobic bacteria, except
for clostridia, and increased levels of Klebsiella, Enterobacter, Citrobacter and Pseudomonas
spp. The action of antibiotics on bacterial communities is selective and its impacts can maintaineven after the treatment has been terminated (Fanaro et al. 2003).
2.4.3. Prebiotics and probiotics
Prebiotics are defined as non-digestible food ingredients that are resistant to digestion
and absorption, are fermented by cecal/colonic microbiota, and selectively stimulate growthand/or activity of bacteria that contribute to colonic and host health (Callaway et al. 2012). It’s
focused on oligosaccharides as health-promoting substrates, which can be food additives
because of their low caloric value and ability of enhancing mineral absorption. Many
oligosaccharides can modulate microbiota of large bowel by increasing bifidobacteria andlactobacilli populations and decreasing clostridia populations. There are three oligosaccharides
classified as prebiotics: fructans, galactooligosaccharides and lactulose. Yusrizal et al. (2003)
showed that supplementation of fructans created an improvement in weight gain, carcass weight
and an increase in lactobacilli counts, concomitantly, a decrease in Campylobacter andSalmonella (Yusriszal et al. 2003; Gaggia et al. 2010). Another study reported the decrease of
Clostridium perfringens number and bacterial endotoxin levels due to adding 0.5% of fructanrich jerusalem artichokes syrup to drinking water in broilers (Kleessen et al. 2003; Gaggia etal. 2010).
Probiotics are defined as a preparation or a product containing viable, defined
microorganisms in sufficient numbers, which alter the microbiota (by implantation orcolonization) in a compartment of the host and by that exert beneficial health effects in this host(Schrezenmeir et al. 2001; Callaway et al. 2012).

24

The practice of supplementing probiotics and prebiotics to domestic animals during
their growth becomes more widespread. It is possible that effectiveness of probiotics andprebiotics in some animal species may be an indirect consequence of speeding up theestablishment of the dominant microbiota characteristic of the adult gastrointestinal tract. A
study of Siew et al. (2013) showed that treatment of probiotics mix VSL#3 (contained 900
billion viable lyophilized bacteria consisting of four strains of Lactobacillus (L. casei, L.
plantarum, L. acidophilus and L. delbrueckii subsp. bulgaricus), three strains ofBifidobacterium (B. longum, B. breve and B. infantis), and one strain of Streptococcus(Streptococcus salivarius subsp. thermophilus)) demonstrated a significant reduction ofBacteroides and improved evenness of microbial profile (Ng et al. 2013). Younts-Dahl et al.
(2004) reported a large-scale trial of steers fed with a standard steam-flaked corn-based
finishing diet containing L. acidophilus NP51 demontrated a reduction of E. coli O157 fecal
shedding by 57% (Younts-Dahl et al. 2004). Another field trial clearly showed that E. coli
O157:H7 fecal shedding decreased (35%) in beef cattle, following daily administration of L.
acidophilus NP51 (Peterson et al. 2007). Higgins et al. (2007) found that Lactobacillus-based
probiotic cultures significantly reduced Salmonella enteritidis recovery in challenged neonatal
broiler chicks but there were no relevant results towards S. typhimurium (Higgins et al. 2007;
Higgins et al. 2008).
2.4.4. Host-related factors
In intestinal tract, the abiotic ecological factors controlling the microbiota are acidity,
bile, pancreatic and intestinal secretions, mucus and defensins. Gastric acid secretion is theessential defense factor against colonization of the gastrointestinal tract by pathogens. Intestinal
infection extremely increased in case of achlorhydria or administration of anti-secretory therapy
because the number of bacteria needed for a trigger of infection is decreased (Marteau et al.
1997; Rambaud et al. 2006). Resistant ability to acid are greatly different among
microorganisms, and some can survive through the stomach, which are selected as probiotics.
Biliary acids and pancreatic juice also have affected bacterial membranes because of theirantimicrobial properties (Drouault et al. 1999; Rambaud et al. 2006). The influence of mucus
and defensins is still poorly understood. Mucus performs a physical barrier between lumen andepithelial cells of stomach, intestine and the colon. The secret of mucus, which is viscous,
composed of mucoglycoprotein polymer, is decreased by fasting and total parenteral feeding.
Bacterial flora can change its composition and properties by not only partial degradation but
also influencing its synthesis. Mucus creates a physical barrier and also concentrates many25

antimicrobial substances such as secretory immunoglobulin A, lactoferrin, lactoperoxidase andlysozyme. Besides, some of its sugars which can mimic bacterial receptors have the ability tobind various microorganisms. Defensins are antimicrobial peptides, acting by destroying thebacterial cell membrane, especially against E. coli, Listeria monocytogenes, Salmonella andCandida albicans (Ganz et al. 2003; Rambaud et al. 2006). However, their possible action on
the saprophytic microbiota is not known.
Intestinal immune system has the function of controlling the pathogenic intestinal
microbiota, indicated by the observation of disturbances in pathogenic flora in immunedeficient hosts. Hence, in case of IgA selective deficiency or common variable
immunodeficiency, recurrent intestinal infection is increased. Depending on the severity of
immune deficiency, the risk of bacterial colitis (Campylobacter jejuni and coli, Salmonella,
Clostridium difficile), and then, cytomegalovirus infections, infections with different protozoa,
mycobacteria or fungi is observed (Rambaud et al. 2006).
Differences in small intestine and colon’s motility are responsible for the primary
localization of microbiota. In adult animals, there is a gradient of oxygen from food and water
consumption and from tissues into lumen that has effects on bacterial composition along
digestive tract (Wilkinson et al. 2002; Callaway et al. 2012). There is an increase in total
numbers of bacteria along the small intestinal tract, in the cecum, and in the proximal and distallarge intestine resulting in falcutative microorganism being <0.01 – 1.0% of total population(Callaway et al. 2012).
A study of Khachatryan et al. (2008) showed the link between host genotype and thecorresponding shifts in the gut microbiota. Mutations in a single host gene named MEFV(Mediterranean fever), which is involved in regulation of innate immunity, lead to specificrestructuring of commensal gut microbiota. Analysis of gut bacterial diversity revealed highly
specific, well-separated and distinct grouping, which depended on the allele carrier status of
the host (Khachatryan et al. 2008). Guan et al. (2008) reported the important role of hostgenetics in rumen microbial structure. Analysis of detectable bacterial PCR-DGGE profiles
showed that efficient steers (who have low residual feed intake) clustered together and wereclearly separated from those obtained from inefficient steers (who have high residual feedintake), implicating that specific group may only inhabit in efficient steers. Likewise, thesimilarity of bacterial component in the rumen of efficient steers (91%) was more than those of
inefficient steers (71%) (Guan et al. 2008). In another study, Li et al. (2016) examined therumen microbiota of the parents and their offspring using the hybridization of sika deer and elk.
The results indicated microbiota of the hybrids significantly differed from that of their parents,

26

suggesting the rumen microbiota were largely affected by host genetics, which may be related
to the transmission of microbiota to the offspring during birth (Li et al. 2016).
2.4.5. Other factors
Penders et al. (2006) reported that delivery and birth characteristics have influence on
infant’s microbiota. Infant who was born through cesarean section had lower colonization rates
and counts of bifidobacteria and Bacteroides fragilis group species, whereas prevalence andcounts of Clostridium difficile and counts of E. coli were higher in comparison with vaginal
delivery (Penders et al. 2006). However, the influence of delivery methods is less effective indeveloping countries because of some reports showed the same early colonization withenterobacteria in newborns delivered either vaginally or by caesarean section, in the hospital orat home (Adlerberth et al. 1991; Mackie et al. 1999; Fanaro et at. 2003). Preterm birth infants
were colonized more often by C. difficile with higher counts compared with term infants.
Formula – fed infants showed the colonization of E. coli, C. difficile, B. fragilis group andlactobacilli more than breastfed infants (Penders et al. 2006). Staphylococci have been foundmore frequently in breastfed infants, compared to bottled-fed infants (Fanaro et al. 2003).
Another study of Harmsen et al. (2000) reported that in all breastfed infants the flora wasdominated by bifidobacteria, whereas similar numbers of Bacteroides spp. and bifidiobacteria
were found in most formula-fed infants (Harmsen et al. 2000; Fanaro et al. 2003). Interestingly,
infants who have older siblings owned lower total bacterial counts per gram of feces than thatof infants who had no sibling. Moreover, infants with older siblings indicated the greater
proportion of bifidobacteria compared with infants without siblings (Penders et al. 2006).
Shanks et al. (2011) emphasized the role of management practices to community
structures of fecal bacteria in cattle. Data suggested that animals subjected to similar
management practices are more closely associated with one another than with animals fromdifferent groups. Respective key metabolic processes are most likely linked to particularmanagement practices rather than to site-specific attributes, such as water source, elevation,
humidity, or other factors closely associated with a particular geographic location (Shanks et
al. 2011). The role of geography in shaping diversity of the gut microbiota was confirmed incase of house mice and human. Lewis et al. (2017) indicated that 8.4% of total variation of the
fecal microbiota of breast-fed infants from Armenia and Georgia was contributed by thedifferences in location. Armenian infants had more Enterobacteriaceae, Planococcaceae,
Streptococcus, Enterococcus, Bacteriodes, Clostridium, and Staphylococcus than those ofGeorgian infants (Lewis et al. 2017). In house mice, Linnenbrink et al. (2013) determined the27most significant contributing factor to microbial diversity between individuals to be geography,
as measured by the distance between sampling sites. The authors showed that the variation of
not only digesta-associated microbiota (11%) but also mucosa-associated microbiota (16%)
was derived from geographic distance. The geography might be viewed as an approximation
for the sum of environment effects such as local weather patterns, food intake, and behavior,culture in case of human. Therefore, the mechanism which drive the differences in the gut
microbiota has not been identifiable yet (Linnenbrink et al. 2013).

28

CHAPTER 3
CHANGES IN GUT MICROBIOTA OF HOLSTEIN HEIFERS
DURING GROWING STAGE

3.1. Abstract
Fecal microbiota of seven Holstein heifers was examined during growing stage, wherein
weaning and commencement of silage feeding were practiced. Denaturing gradient gelelectrophoresis (DGGE) and quantitative PCR (qPCR) were employed for microbiota analyses.
Populations of total bacteria and Bacteroides-Prevotella-Porphyromonas, Clostridium XIVa,
and Clostridium I&II groups were relatively stable in abundance during the growing stage, andthose of Lactobacillus and Bifidobacterium groups decreased after weaning. Regarding bacterial
composition, distinctive changes were shown after weaning in all bacterial taxa examined,
whereas no changes were observed thereafter. The composition of Lactobacillus group changed
over the growing stage, while maintaining high similarity between individual heifers. Thesefindings indicated that weaning had a marked influence on the gut microbiota especially in thecomposition. Management during this transition period may be critical in shaping gut
microbiota, whereas silage microbiota would have no effects.
3.2. Introduction
During early life, the primary colonization of bacteria is rapid but the climax microbiota
population is gradually established over the time. Efficient growth of pre-weaned dairy calf isa precondition for the optimal performance in post-weaning period and further productivity(Soberon et al. 2012). The perception that calf microbiota is related to growth and health wereindicated (Donovan et al. 2002), that inferred to the development of the immune system, theinfluence on host’s physiology, including energy balance (Mazmanian et al. 2005; Backhed etal. 2004). A newborn calf is considered as a non-ruminant from a functional viewpoint. Calfrelied almost on milk or milk replacer and a small amount of solid feed as a negligible level
during the early weeks of life. Later, since weaning, the calf started increasing amount of solid
feed, that supporting to the development of the forestomach, and leading to full function of aruminant. These changes in anatomy and physiology of the gastrointestinal tract areaccompanied with the succession of gastrointestinal microbiota. The transition of diet frommilk to solid feed, for instance, roughage and concentrates, has also been demonstrated a
substantial effect on the gastrointestinal microbiota (Callaway et al. 2010). Although several

29

studies had been conducted to clarify the establishment of gut microbiota in early life after
calving, most of them focused on the very early time of colonization of gut microbiota in preweaned calves (Li et al. 2012; Oikonomou et al. 2013; Rey et al. 2013; Klein-Jobstl et al. 2014).
Others focused on the development of gut microbiota in bovine from birth to adulthood byexamining different groups of age (Jami et al. 2012). The sequential succession of gut
microbiota during growing stage in dairy calves has not been elucidated. Hence, in this study,
feces samples were repeatedly collected from seven Holstein heifers prior and posterior to
weaning, and at the termination of growing stage to examine sequential changes taken place ingut microbiota during growing stage.
3.3. Materials and Methods
Feces sample collection and DNA extraction – Seven Holstein heifers were involved in thisstudy. Feces samples were taken repeatedly three times during growing: prior and posterior to
weaning, and at the termination of growing stage (3 weeks, 4 months, and 8 months after
calving). At the third sampling time, calves had already started to take silages. Feces samples
were kept on ice and transferred to laboratory, and further stored at -30oC for downstream
processing.
Bacterial DNA was extracted from 200 mg frozen feces sample using repeated bead beatingplus column method (Yu and Morrison, 2004).
PCR amplification – Extracted DNA was subjected to PCR with universal primers including
GC357f (5’-CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGGCCTACGGAGGCAGCAG-3’)

and

517r

(5’-ATTACCGCGGCTGCTGG-3’)

for

V3

regionamplification. PCRs were also performed using the primer pairs of Bac303f (5’-GAAGGTCCCCCACATTG-3’)

andBac708r(5’-CAATCGGAGTTCTTCGTG-3’)

fortargeted

Bacteroides-Prevotella-Porphyromonas community (Bartosch et al. 2004), Clost-f (5’AAAGGRAGATTAATACCGCA TAA-3’) and Clost-r (5’-TTCTTCCTAATCTCTACGCA3’) for targeted Clostridium cluster I&II community (Hung et al. 2008), Lab159f (5’-GGAAACAGRTGCTAATACCG-3’) and Lab617r (5’-CACCGCTACACATGGAG-3’) for targeted
Lactobacillus community (Heilig et al. 2002), and Bif 164f (5’-GGGTGGTAATGCCGGATG3’) and Bif 601r (5’-TAAGCGATGGACTTTCACACC-3’) for targeted Bifidobacterium
community (Bernhard et al. 2000). Nested PCRs were employed with universal primers on
previous generated products from amplification of Bacteroides, Clostridium and Lactobacillus
groups. PCR products were separated by electrophoresis on 2% agarose gel including ethidium
bromide.

30

DGGE analysis – PCR products were separated by DGGE by using the DCode Universal
Mutation Detection System (Bio-Rad Laboratory Inc.). Polyacrylamide gels consisted of 8%(v/v) polyacrylamide. Denaturing gradient (Urea and Formamide) of 25 - 50% was used for the
separation of the generated. Electrophoresis was performed for 8 h at 150 V in TAE buffer at aconstant temperature of 60°C. Gels were stained with SYBR Green, observed by UVtransilluminator and photographed.
Species identification – PCR amplicons (with 357f (without GC clamp) and 517r primers)
resulted from excised bands on DGGE gel were purified using FastGene® Gel/PCR Extraction
Kits (Nippon Genetics Co., Ltd., Japan) according to the manufacturer’s instructions, inserted
in to pTAC–1 vector and cloned into Escherichia coli strain DH5α competent cells(DynaExpress TA Cloning Kit; BioDynamics Laboratory Inc., Tokyo, Japan). Positive whiteclones are randomly selected and confirmed for the presence of expected size inserts by PCR.
Subsequently, positive colonies were sub-cultured into LB broth media. Extracted plasmids
should be employed for a new PCR amplification followed by purification. To enhance thequality of sequencing, the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied
Biosystems) was used according to the manufacturer’s instructions. DNA sequences wereanalyzed using an ABI PRISM 3130 sequencer (Applied Biosystems Inc., Foster City, Calif.,
U.S.A.). The Basic Local Alignment Search Tool (BLAST) program andGenBank databases
were used to determine the closest relatives of partial 16S rRNA gene sequences. Unknown
sequences that shared greater than 98% identity with a sequence in the BLAST database wereconsidered as identified.
Quantitative PCR – A master mix for total quantification (357f (5’-ACGGGGGGCCTACGGAGGCAGCAG-3’) and 517r (5’-ATTACCGCGGCTGCTGG-3’)), Bacteroides-PrevotellaPorphyromonas population (BPP fwd (5’-GGTGTCGGCTTAAGTGCC AT-3’) and BPP rev(5’-CGGA(C/T)GTAAGGGCCGTGC-3’)), Clostridium cluster I&II population (Cl-perf fwd(5’-ATGCAAGTCGAGCGA(G/T)G-3’) and Cl-perf rev (5’-TATGCGGTATTAATCT(C/T)CC TTT-3’)), Clostridum cluster XIVa (Clost-Eub fwd (5′-CGGTACCTGACTAAGAA
GC-3′) and Clost-Eub rev (5′-AGTTTYATTCTTGCGAACG-3′)), Lactobacillus population(Lact fwd (5’-AGCAGTAGGGAATCTTCCA-3’) and Lact rev (5’-CACCGCTACACATGG
AG-3’)), Bifidobacterium population (Bifdo fwd (5′-TCGCGTCYGGTGTGAAAG-3′) andBifdo rev (5′-CCACATCCAGCRTCCAC-3′)) (Rinttilä et al. 2004) was prepared for each wellcontained following reagents: 2 x KAPA SYBR FAST qPCR Master Mix Universal (Kapa
Biosystem Inc.) that contained integrated antibody-mediated hot start, SYBR Green I
fluorescent dye, MgCl2, dNTPs, forward and reverse primers, and distilled water to a total of3123 µl per well. To these, 2 µl of each sample or standard was added, and the plates were briefly
spun down and placed in MiniOpticon Real-Time PCR Detection System (Bio-Rad Laboratory,Inc.) for analysis. Amplification programme involved an initial denaturation step at 95oC for 5min, followed by 35 cycles of denaturation at 95oC for 15s, primer annealing at optimal
temperatures for each target bacterial group for 20 s, extension at 72oC for 30 s and an additional
incubation step at 80-85oC for 30s to collect the fluorescent data (Rinttilä et al. 2004).
Statistical analysis – Binary matrix resulted from DGGE banding patterns of Lactobacillus
group was created to describe the presence or absence of individual band in all lanes. Resulted
binary matrix was engaged to create Cluster Analysis to demonstrate similarity and differences
between sampling times and individual calves, using Primer version 7 with Permanova + addon software (Primer-E, Plymouth Marine Laboratory, and Plymouth, UK). Data for bacterial
population were subjected to repeated measures of ANOVA and mean values were separated
by Tukey’s multiple range test. All data are presented as means with their standard deviation.
3.4. Results
3.4.1. Changes in bacterial population and structure during growing stage
Total bacterial population, the major bacterial groups (Bacteroides-PrevotellaPorphyromonas; Clostridium XIVa), pathogenic group (Clostridium I&II), and so-called
beneficial groups (Bifidobacterium, Lactobacillus) were examined during growing stage ofseven Holstein heifers (Table 3.1). In general, bacterial number was relatively stable during
growing stage for total bacteria, Bacteroides-Prevotella-Porphyromonas, Clostridium XIVa,
and Clostridium I&II population. These populations showed a light decrease after weaning andincrease again in bacterial number after taking silage (Changes were not significant). Only
Lactobacillus and Bifidobacterium population indicated a significant decrease in number after
weaning (Table 3.1).
Cluster analysis results showed that similarity of total bacterial structure of Holsteinheifers increased after weaning and no change was observed thereafter. Taking an insight into
individual groups, the changing pattern was different between groups. Bacteroides-PrevotellaPorphyromonas, Bifidobacterium and Clostridium I&II groups formed two clear separated
groups before and after weaning point. This indicated these bacterial structures were influenced
by weaning than silage taking. Lactobacillus group showed significant changes in structure
over the growing stage. This was demonstrated by three different groups at each sampling time,
while maintaining synchronized composition between calves (Fig. 3.3).

32

Table 3.1. Quantification PCR results (log 16S rRNA gene copy number/g fresh matter) of total bacteria,
Bacteroides-Prevotella-Porphyromonas, Clostridium cluster XIVa, Clostridium I&II, Bifidobacterium,
and Lactobacillus population (Period 1, 2, and 3 - 3 weeks, 4 months and 8 months after calving,
respectively).
Totalbacteria

BPP

Clostridium
XIVa

Clostridium
I&II

Bifidobacterium

Lactobacillus

Period 1

10.7 ± 0.66

10.1 ± 1.59

9.42 ± 1.18

6.19 ± 0.56

8.83 ± 0.62x

6.28 ± 0.48x

Period 2

10.6 ± 0.28

9.91 ± 0.34

8.96 ± 0.39

6.17 ± 0.30

7.85 ± 0.16y

5.39 ± 0.25y

Period 3

11.1 ± 0.17

10.5 ± 0.31

9.30 ± 0.24

6.02 ± 0.84

8.51 ± 1.11xy

5.67 ± 0.20y

Results are means ± standard deviation of seven samples in every sampling time. Samples that arelabeled by different characters are considered statistically different.

DGGE profile and species identification of Lactobacillus community were represented
in Figure 3.3. Banding pattern was different between periods. Composition of this community
also changed during growing. Before weaning, Lactobacillus community was dominated by L.
delbrueckii (band 1), L crispatus (band 3), Faecalicoccus pleomorphus (band 5), L. gasseri(band 6), Streptococcus constellatus (band 9), L. amylolyticus (band 10), F. acidiformans (band11), S. constellatus (band 12), L. johnsonii (band 13, 14). After weaning, only few of thesebacterial species were further maintained their presences, for instance, L. gasseri (band 6), L.
amylolyticus (band 10), L. johnsonii (band 13). And, several species newly appeared instead,such as uncultured bacterium (band 2), L. jonhsonii (band 4), L. pontis (band 8). When animals
took silages, L. gasseri (band 6) was gone, L. pontis (band 7) exclusively came to sight at thisperiod in several heifeirs (Fig. 3.3).
Bifidobacterium community were also examined as Figure 3.4. The diversity ofBifidobacterium

communitywasincreased

after

weaning.

Beforeweaning,

this

Bifidobacterium community was varied between calves, several species (band 2, band 6, B.
boum-band 7, B. pseudocatenulatum-band 8, and band 9) was found in this period. After
weaning, Bifidobacterium structure became similar between calves and there was no significant
difference after calves consumed silage. B. longum (band 1), B. pseudolongum (band 4), anduncultured bacterium (band 5) were steadily present at period 2 and 3 in several calves (Fig.
3.4.).
3.4.2. Comparison between Holstein heifers and Japanese Black calves
The same examination was conducted for Japanese Black calves at the same periods
during growing stage. The simple comparison was carried to understand the dynamic changes
in gut microbiota regarding to breed. In general, similarity between cattle appeared to increase

33

after weaning period for both Japanese Black calves and Holstein heifers. While Holsteinheifers revealed significant changes in composition before and after weaning, Japanese Black
calves showed changes in bacterial structure at every single sampling time (Fig. 3.2).
The changing pattern of individual groups was different between breeds. In JapaneseBlack calves, Bacteroides-Prevotella-Porphyromonas and Clostridium I&II communities
showed different banding pattern at every sampling time. Cluster analysis indicated that calfto-calf variation of Bacteroides-Prevotella-Porphyromo composition became the lowest at thetermination of growing period (period 3). However, in Clostridium I&II, the community
showed the highest synchronization between individuals at 4 months (Fig. 3.2). The otherperiods inferred the differences in composition among individual calves. Interestingly, in both
breeds, Lactobacillus community showed significant changes in diversity over the growing
stage while maintaining synchronized composition between calves.

Figure 3.1. Cluster analysis results for Lactobacillus, Bifidobacterium, Clostridium cluster I&II,
Bacteroides-Prevotella-Porphyromonas, and total bacterial community structure based on DGGE
banding patterns of seven Holstein heifers (A, B, C, D, E, F, G, and H) during growing stage (1, 2, and3 - 3 weeks, 4 months and 8 months after calving, respectively).

Japanese Black calves also disclosed changes taken place in bacterial population (Fig.
3.5). Total bacterial number tended to increase after weaning, whereas Lactobacillus population34

decreased after weaning and reduced their size with advancement of age. Nevertheless,population size decreased in all cases at the last period. On the contrary, in Holstein heifers,
bacterial population was relatively stable. Only Lactobacillus population showed the same
trend of changing across periods (Fig. 3.5).

Figure 3.2. Cluster analysis results for Lactobacillus, Bifidobacterium, Clostridium cluster I&II,
Bacteroides-Prevotella-Porphyromonas, and total bacterial community structure based on DGGE
banding patterns of seven Japanese Black calves (A, B, C, D, E, F, G, and H) during growing stage (1,2, and 3 - 3 weeks, 4 months and 8 months after calving, respectively).

Figure 3.3. DGGE profile and species identification in Lactobacillus group of seven Holstein calves (A, B, C, D,
E, F, and G) during growing stage (1, 2, and 3 - 3 weeks, 4 months and 8 months after calving, respectively).

35

Figure 3.4. DGGE profile and species identification in Bifidobacterium group of seven Holstein calves(A, B, C, D, E, F, and G) during growing stage (1, 2, and 3 - 3 weeks, 4 months and 8 months after
calving, respectively).

3.5. Discussion
In this study, fecal microbiota of seven young Holstein heifers was examined during
growing stage, wherein weaning and commencement of silage feeding were employed. PCRDGGE and qPCR were used for microbiota analyses. The changing pattern in gut microbiotaof Holstein heifers during growing was different regarding to composition and abundance, andalso with regard to bacterial groups. Weaning is the event that involving to the diet transition
from liquid (milk) to solid feed, had a marked influence on the gut microbiota composition in
our study.
Young ruminants at birth have an undeveloped rumen, until the system is fully matured
they function as non-ruminant fed on milk-based diet that are not digested in the rumen but inthe abomasum. The development of the rumen is an important physiological challenge foryoung ruminants, involves in the anatomical development, functional achievement andmicrobialcolonization.

In

this

study,

totalbacteria

andBacteroides-Prevotella-Porphyromonas, Clostridium XIVa, Clostridium I&II groups were stable in abundance over
the time. Regarding to Bacteroides-Prevotella-Porphyromonas group, this group is the
combination of the two most abundant genera (Bacteroides and Preovotella) of phylum
Bacteroidetes in calves (Li et al. 2012; Rey et al. 2013). Even though the abundance ofcombination of these two genera was showed constant in Holstein heifers of our study, changing
pattern of these individual genera was reported to be different by aging. In a report of KleinJobstl et al. (2014), during early development of Simmental calves, genus Bacteroides in feces36generally decreased in relative abundance over the time despite of its particularly highabundance before weaning (Klein-Jobstl et al. 2014). This was consistent to the findings in therumen of developing calves (Li et al. 2012; Jami et al. 2013). The reason was supposed to thepresence of Bacteroides in the vagina of healthy cows, and calves received Bacteroides byingestion during passage through the birth canal (Klein-Jobstl et al. 2014). In adult cattle,
relative abundance of Bacteroides was negatively associated with high fiber diet (Kim et al.
2014). Hence, the increase of fiber and decrease of milk consumption could explain for thedecrease of Bacteroides with the advancement of age (Klein-Jobstl et al. 2014). In contrast toBacteroides, genus Prevotella indicated an increase in abundance over the time (Jami et al.
2013). However, the concrete timing for Prevotella to take over the exclusive position ofBacteroides is yet to be known. Jami et al. (2013) reported the prevalence of Prevotella wasseen from 2 months of age in rumen of Holstein calves, while Rey et al. (2013) detected thehigh abundance of this genus as early as 15 days of age (Jami et al. 2013; Rey et al. 2013). On
the opposite side of other reports, Li et al. (2011) discovered the increase of Bacteroides anddecrease of Prevotella from 14 days of age to 42 days of age of pre-ruminant calves (Li et al.
2011). After all, it is obvious to see the mutual compensation between these two genera inabundance in gut microbiota of cattle. This partly explained for the relatively stable in the
combined abundance of these two genera in our study.
In the gastrointestinal tract, genus Clostridium make up a substantial part of the total
bacteria in the gut microbiota. It is likely that genus Clostridium play a crucial role in gut
homeostasis by interacting with the other resident microbe populations, but also by providing
specific and essential functions, such as maintenance of overall gut function (Lopetuso et al.
2013). However, it has not been paid attention in several reports. In this study, we examined
the two important groups of Clostridia in the gut, Clostridium XIVa and Clostridium I&II.
Principally, the cluster Clostridium XIVa includes species belonging to the Clostridium,
Eubacterium, Ruminococcus, Coprococcus, Dorea, Lachnospira, Roseburia and Butyrivibrio
genera. And, cluster Clostridium I&II (or Clostridium perfringens group) includes Clostridium
perfringens and Clostridium tetani. While members of cluster Clostridium XIVa related tobutyrate production that supporting to overall gut health, members of cluster Clostridium I&II
possessed pathogenic species (Lopetuso et al. 2013). The stable abundance of genus
Clostridium may implicate a certain balance status in gut microbiota promoting to a goodcondition of gut health of Holstein calves in our study.
Lactobacillus and Bifidobacterium was considered as beneficial bacterial groups incattle (Callaway et al. 2012). The abundance of these two genera were found positively

37

correlated with the expression level of miRNAs, that relating to the gut development
mechanism, for instance, the differentiation and proliferation of the cells of gastrointestinal tract(Liang et al. 2014). The gradual decrease in abundance of genera Lactobacillus andBifidobacterium after weaning was consistent with previous reports (Oikonomou et al. 2013;
Klein-Jobstl et al. 2014). Oikonomou et al. (2013) reported that the prevalence of Lactobacillus
and Bifidobacterium reached the maximum during the first fourth week of calf life, and thenprogressively decreased over the time. The two genera were supposed to be related to digestionof milk, that explained for the low abundance after weaning (Oikonomou et al. 2013). KleinJobstl et al. (2014) showed that Lactobacillus had relatively low abundances after weaning. The
early appearance of Lactobacillus might be due to milk feeding or the incorporation of thesebacteria during passage of the vagina during natural birth (Dominguez-Bello et al. 2010; Klein-

Total bacteria

9

a

12

a

b

9

Period 1

Japanese
Black calves

b

6

b

4

Period 3

Period 2

a
b

10
9

b

8
7

Period 1

C
12

Period 3

Period 2
Clostridium cluster XIVa

11
10

a

a

9
8
7

b

6
5

Period 1

Period 2

Period 3

Period 1E
10Bacteroides-Prevotella-Porphyromonas

11

4

Holsteinheifers

a

5

8

6

Clostridium cluster I&II

7

10

B
12

D

8

11

7

Log 16S rRNA gene copy numberA

Log 16S rRNA gene copy number13

Log 16S rRNA gene copy number

Log 16S rRNA gene copy number

Log 16S rRNA gene copy number

Log 16S rRNA gene copy number

Jobstl et al. 2014).

Period 3

Period 2
Lactobacillus
a

9
8
x

7

b
y

6

y

b

5
4

12

Period 1

Period 3

Period 2

F

Bifidobacterium
xy

11
10

x

9

y

8

a

b

7

c

6
5
4

Period 1

Period 2

Period 3

Figure 3.5. Comparison of total bacteria population (A), Bacteroides-Prevotella-Porphyromonas (B),
Clostridium cluster XIVa (C), Clostridium I&II (D), Lactobacillus (E), and Bifidobacterium (F)population between Holstein heifers and Japanese Black calves during growing stage (Period 1, 2, and3 - 3 weeks, 4 months and 8 months after calving, respectively). Boxes that are labeled by different
characters are considered statistically different, for Japanese Black calves (a, b, and c) and for Holsteinheifers (x, y).

38

In current study, the composition of total bacteria and several bacterial groups showed
great differences before and after weaning but no change was observed thereafter. This finding
indicated that weaning had a marked influence on gut microbiota (Fig. 3.1.). The similarity incomposition between seven heifers increased with the advancement of age. Among bacterialgroups examined, Lactobacillus showed higher similar between cows than other bacterialgroups. The finding of our study were in agreement with the report of Jami et al. (2013). Report
indicated that the diversity and within-group similarity increased with age, suggesting a morediverse but homogenous and specific mature community, compared with the moreheterogenerous and less diverse primary community (Jami et al. 2013).
Holstein heifers and Japan Black calves examined in this study were raised withdifferent dietary regime and in different farm environments. Shanks et al. (2011) showed thatbovine management played an integral role in fecal microbial community structure than thesite-specific attributes, such as water source, elevation, humidity and particular geographic
location. This could account for the similarity of bacterial structure between calves kept under
the same management, which was proven by cluster analysis (Fig 3.1&3.2). Likewise, Holsteinheifers and Japanese Black calves evidently possessed their own pattern of changing in gut
microbiota during growing. Combined analyses of DGGE and qPCR revealed that weaning
period implicated greater on bacterial population than on bacterial composition in beef cattle,
whereas contrary state was found in dairy cattle. Lactobacillus spp. community showedsignificant changes in composition over the growing stage regardless of breed, while
maintaining the highest similarity between individual calves. The difference between two
systems may also derived from the managing of young ruminants. In dairy farming, heifers are
typically separated from their mother, on the contrary, in beef farming, the calves remain with
their mothers until weaning. Abecia et al. (2014) implied that kid goats reared with mothers hadgreater rumen development than their twins that were fed on milk replacer and separated fromadult animals (Abecia et al. 2014). This is consistent with report of De Paula Vieira et al. (2012)that calves grew up with the presence of older adult animals exhibited more frequent and longer
visits to the feeder. That was hypothesized as a consequence of social learning (De Paula Vieira
et al. 2012).
In conclusion, in Holstein heifers, populations of total bacteria and BacteroidesPrevotella-Porphyromonas, Clostridium XIVa, and Clostridium I&II groups were relatively
stable in abundance during the growing stage. Lactobacillus and Bifidobacterium groups
decreased their abundance after weaning. Regarding bacterial composition, distinctive changes
were shown after weaning in all bacterial taxa examined, whereas no changes were observed

39thereafter. The composition of Lactobacillus group changed over the growing stage, while
maintaining high similarity between individual heifers. These findings indicated that weaning
had a marked influence on the gut microbiota especially in the composition. Management
during this transition period may be critical in shaping gut microbiota, whereas silage
microbiota would have no effects.

40

CHAPTER 4
VARIABILITY, STABILITY, AND RESILIENCE OF FECAL MICROBIOTA IN
DAIRY COWS FED WHOLE CROP CORN SILAGE
4.1. Abstract
The microbiota of whole crop corn silage and feces of silage-fed dairy cows were
examined. A total of 18 dairy cow feces were collected from six farms in Japan and China, andhigh-throughput Illumina sequencing of the V4 hypervariable region of 16S rRNA genes was
performed. Lactobacillaceae were dominant in all silages, followed by Acetobacteraceae,
Bacillaceae,

andEnterobacteriaceae.

In

feces,

thepredominantfamilies

wereRuminococcaceae, Bacteroidaceae, Clostridiaceae, Lachnospiraceae, Rikenellaceae, andParaprevotellaceae. Therefore, Lactobacillaceae of corn silage appeared to be eliminated in
the gastrointestinal tract. Although fecal microbiota composition was similar in most samples,
relative abundances of several families, such as Ruminococcaceae, Christensenellaceae,
Turicibacteraceae, and Succinivibrionaceae varied between farms and countries. In addition to
the geographical location, differences in feeding management between total mixed ration
feeding and separate feeding appeared to be involved in the variations. Moreover, a cow-tocow variation for concentrate-associated families was demonstrated at the same farm; two cows
showed high abundance of Succinivibrionaceae and Prevotellaceae, whereas another had ahigh abundance of Porphyromonadaceae. There was a negative correlation between forageassociatedRuminococcaceae

andconcentrate-associatedSuccinivibrionaceae

andPrevotellaceae in 18 feces samples. Succinivibrionaceae, Prevotellaceae, p-2534-18B5, andSpirochaetaceae were regarded as highly variable taxa in this study. These findings help toimprove our understanding of variation and similarity of the fecal microbiota of dairy cows
with regard to individuals, farms, and countries. Microbiota of naturally fermented corn silage
had no influence on the fecal microbiota of dairy cows.
4.2. Introduction
The relationship between gut microbiota and their host has been suggested to provide
critical benefits to animals. Cattle and their diverse community of symbiotic microbiota is atypical example. These symbionts are in charge of digesting and fermenting plant materials into
nutrient sources usable by the host. The cattle acquire the fermentation products for bodymaintenance, growth, and milk production (Jami and Mizrahi 2012a, b; Jewell et al. 2015).
Although several studies have been conducted using next generation sequencing (NGS) for41

understanding the gut microbiota of cattle, several questions remain (Menezes et al. 2011; Maoet al. 2015; Liu et al. 2016). Despite reports indicating that the core bacterial community isshared by all cattle; other studies have observed high variation in the relative microbialabundance across samples (Jami and Mizrahi 2012a, b; Jewell et al. 2015; Lima et al. 2015).
The divergence of abundance was suggested to be due to differences in diet formulation, farmmanagement, and geographical locations (Menezes et al. 2011; Shanks et al. 2011; Anderson
et al. 2016; Khafipour et al. 2016). Similarly, differences were also observed in gut microbiota
structure even when cows were supplied with the same diet and had similar milk yields andrumen fluid profiles (Welkie et al. 2009). There is therefore a need to fully understand the extent
of variation between microbiota from individual cows under similar diet and management.
In dairy practice, silage feeding is a popular management method (Castillo et al. 2001;
Broderick and Radloff 2004; Beauchemin and Wang 2005; Benchaar et al. 2007). Silage is a
product of the ensiling process, in which lactic acid fermentation usually occurs. Therefore, it
can be considered as a lactic acid bacteria (LAB)-rich nutrient source for dairy cows. Because
dairy cows receive the silage microbiota steadily through daily meals, silage could confer
probiosis especially when silage LAB have acceptable survivability and compatibility
throughout the gastrointestinal tract. Although several studies have emphasized the effects ofdiet on shaping gut microbiota, whether food microbiota influence the gut microbiota has notbeen elucidated.
In the present study, we conducted a survey of the fecal microbiota of dairy cows fed
corn silages in Japan and China. Corn silage has been reported to harbor a predominant
proportion of Lactobacillus spp. (Ni et al. 2017), and NGS was utilized to get insight into themicrobiota of silage and feces of silage-fed dairy cows. The objective was to improve ourknowledge of variations in the fecal microbiota of dairy cows with regard to country, farm, andindividual cows. We also examined whether silage microbiota influence gut microbiota.
4.3. Materials and methods
Sampling – A total of 18 feces samples were collected from six dairy farms located in
Kumamoto Province (farms A, B, and C), Japan, and in Shan Xi Province (farm D), He Bei
Province (farm E), and Beijing city (farm E), China. Holstein dairy cows were fed a dietcontaining whole-crop corn silage that was self-produced at each farm using a bunker silo
without bacterial inoculants. In farms A, C, D, and E, feed was provided as a total mixed ration(TMR), and in farms B and F, forage and concentrate were provided separately (Table 4.1).
The proportion of corn silage in the total diet varied from 0.20 to 0.35 on a DM basis. Silage

42samples were collected by digging into the silo face to approximately a 0.2 m depth andmanually removing approximately 500 g of silage. Five samples (2 outer samples from the top
layer, 2 outer samples from the bottom layer, and 1 sample from the central part) were obtained
from each bunker silo and were thoroughly mixed to prepare a representative sample.
Approximately 500 g of silage and 1 g of feces were placed in a plastic bag and an Eppendorf
tube, respectively, and kept on ice during transportation to the laboratory.
The DM content of silage was determined after oven drying at 60°C for 48 h. The pH
value and fermentation products in water extracts were determined. Lactic acid, acetic acid, andethanol contents were measured by ion-exclusion polymeric high-performance liquid
chromatography with refractive index detection as previously described (Ni et al. 2017).
Illumina MiSeq sequencing – Silage samples were added to a 20× volume of sterilized
phosphate-buffered saline (pH 7.4), and extraction was achieved through vigorous shaking for
10 min at ambient temperature. Bacterial pellets were obtained by centrifugation at 8000 × g
for 15 min, 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. Bacterial DNA of the feces was purified using the mini DNeasy Stool Kit(Qiagen, Germantown, MD, USA).
The resulting DNA was subjected to 2-step polymerase chain reaction (PCR)
procedures to generate amplicon libraries for NGS. In the first round PCR, primers targeting
the V4 region of 16S rRNA genes (forward: 5’-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCCAGCMGCCGCGGTAA-3′; reverse: 5’-GTGACTGGAGTTCAGACGTGTG
CTCTTCCGATCTGGACTACHVG GGTWTCTAAT-3′; tail sequences are underlined) wereused. The PCR protocol included initial denaturation at 94°C for 2 min, followed by 25 cycles
of denaturation at 94°C for 30 s, annealing at 50°C for 30 s, elongation at 72°C for 30 s, and a
final elongation at 72°C for 5 min. The PCR products were purified by electrophoretic
separation on a 2.0% agarose gel using a Fast Gene Gel/PCR Extraction Kit (NIPPON Genetics
Co., Ltd., Tokyo, Japan), and moved to a second round of PCR with adapter-attached primers.
The PCR protocol included initial denaturation at 94°C for 2 min, followed by 10 cycles ofdenaturation at 94°C for 30 s, annealing at 59°C for 30 s, elongation at 72°C for 30 s, and a

43

final elongation at 72°C for 5 min. PCR products were purified as described above for the firstround products.
The purified amplicons were pair-end sequenced (2 × 250) on an Illumina MiSeq
platform at the FASMAC Co., Ltd. (Kanagawa, Japan). Raw sequences were processed using
the QIIME (version 1.9.0) microbial ecology pipeline. The 250 bp reads were truncated at any
site receiving an average quality score < 20 over a 20 bp sliding window. Truncated reads thatwere shorter than 50 bp were discarded. Similarly, two nucleotide mismatches in primer
matching were removed, sequences that overlapped more than 50 bp were assembled, andchimeric sequences were identified and removed. Only sequences of at least 130 bp long after
quality filtering were grouped into operational taxonomic units (OTUs) using a 97% similarity
threshold. Sequences were classified from the phylum to the genus level using the default
settings of the Ribosomal Database Project classifier. The results of the sequence analysis areavailable in the DDBJ Sequence Read Archive under project identification number
PRJDB5800.
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 countries. Major families in bothsilage and feces samples were engaged to create a heat map using Primer version 7 with
Permanova+ add-on software (Primer-E, Plymouth Marine Laboratory, Plymouth, UK). In
addition, the data were subjected to the Shannon diversity index and coefficient of variation
calculations.
4.4. Results
4.4.1. Silage fermentation and microbiota
Fermentation products of whole crop corn silage produced in bunker silos of Japanand China are presented in Table 4.1. The DM content of silage was observed different between
Chinese (221 g/kg) and Japanese (299 g/kg) samples. Mean lactic acid, acetic acid, ethanol, and1-propanol contents in Chinese silages were greater than those in Japanese silages. Althoughlactic acid was predominant in all silages, propionic acid was present in silages Cs and Ds anda small amount of butyric acid (<1 g/kg DM) was detected in silages Ds and Es. 1,2-propanediol
was found in all silages except silage Cs.
The NGS results of the silage microbiota indicated huge taxonomic diversity including
29 phyla, 200 families, and 302 genera. Among them, 12 phyla, 50 families, and 30 genera
were shared in all samples. At the family level, the number of taxa was 118, 96, 113, 141, 119,44

and 91 for silage As, Bs, Cs, Ds, Es, and Fs, respectively (Table 4.2). Lactobacillaceae wasfound to be exclusively dominant, accounting for 75.2, 87.5, 45.7, 91.6, 73.6, and 82.2% of thetotal population (Table 4.3). Clusters based on the family level indicated three separate groups
regardless of the country (Fig. 4.1). Silage Cs showed a distinctive bacterial structure with thelowest proportion of Lactobacillaceae (45.7%) among the silages. This was followed by the
Acetobacteraceae(32.4%),

Bacillaceae(4.63%),

Enterobacteriaceae(3.44%),

Comamonadaceae (3.32%), Enterococcaceae (2.65%), Moraxellaceae (2.29%), andPlanococcaceae (1.53%) families, which were shown to be greater than those in other silages.
The Bs, Es, and Fs silage groups were different from the As and Ds groups, because of thehigher proportion of Acetobacteraceae representing 9.14, 20.6, and 16.8%, respectively, in theformer. The Shannon index showed numerically greater value for silage Cs (1.581) than forother Lactobacillaceae-dominated silages.

4.4.2. Fecal microbiota
The heat map in Figure 1 shows 50 of the most abundant taxa at the family level. The
fecal microbiota was shown to be unrelated to the silage microbiota by a distinct grouping
pattern. The NGS demonstrated a total number of 35 phyla, 184 families, and 263 genera
allocating for all feces samples. Among them, 9 phyla, 28 families, and 34 genera wereconsistently present in all feces samples and covered 98.9, 75.6, and 32.7% of the total bacterial
populations.

Ruminococcaceae,

Bacteroidaceae,

Clostridiaceae,

Lachnospiraceae,

Rikenellaceae, and Paraprevotellaceae were the most abundant families, accounting for 22.8,
10.6, 7.17, 7.13, 5.94, and 5.59% of the total population on average across 18 dairy cows (Table
4.4). Lactobacillaceae contributed to only 0.04% of the total population in feces samples. The
numbers of taxa (ranging from 71.0 to 83.3) were numerically lower, and the Shannon index
values (ranging from 2.205 to 2.549) were numerically greater for feces than silages (Table
4.2).
4.4.3. Variations between cows, farms, and countries
The coefficient of variation (CV) was employed for taxa detected at >1% of the total
population in order to understand the variation of fecal microbiota across 18 cows (Table 4.5).
If the threshold was defined as >1.0, 4 families (Succinivibrionaceae, Prevotellaceae, p-253418B5, and Spirochaetaceae) and 3 genera (Succinivibrio spp., Prevotella spp., and Treponema
spp.) were evaluated as highly variable taxa. If the threshold was lowered to >0.5, 4 families(Veillonellaceae, RF16, S24-7, and Porphyromonadaceae) and one genus (Prevotella spp.
belonging to family Paraprevotellaceae) were added as moderately variable taxa.

45

Cs
Fs
Bs
Es
Ds
As
B2
B3
B1
A2
A3
C3
C1
C2
A1
F1
F2
F3
D2
D1
E1
E2
E3
D3

10

5

0

Figure 4.1. Heatmap representing the relative abundance of the major families (50 most abundant taxa) in silage and fecal microbiota. Clustering was performed
using the Euclidean distance as a similarity metric. As, Bs, Cs, Ds, Es, and Fs indicate silage samples from farms A, B, C, D, E, and F, respectively. A-(1, 2, 3),
B (1, 2, 3), C (1, 2, 3), D (1, 2, 3), E (1, 2, 3), and F (1, 2, 3) indicate feces samples from farms A, B, C, D, E, and F, respectively.

46

Enterobacteriaceae
Acetobacteraceae
Lactobacillaceae
Moraxellaceae
Comamonadaceae
Xanthomonadaceae
Paenibacillaceae
Bacillaceae[Weeksellaceae]
Enterococcaceae
Oxalobacteraceae
Planococcaceae
Corynebacteriaceae
Leuconostocaceae
Microbacteriaceae
Methylobacteriaceae
Bifidobacteriaceae
Streptococcaceae
Alcaligenaceae
Succinivibrionaceae
Prevotellaceae
Porphyromonadaceae
Anaeroplasmataceae
RF16
Veillonellaceae
S24-7
Peptostreptococcaceae
Turicibacteraceae
Peptococcaceae
Ruminococcaceae[Mogibacteriaceae]
Erysipelotrichaceae
Clostridiaceae
Lachnospiraceae[Barnesiellaceae]
Rikenellaceae
Undefined taxa
Bacteroidaceae[Paraprevotellaceae]
Spirochaetaceae
Verrucomicrobiaceae
Christensenellaceae
Methanobacteriaceae
BS11
Desulfovibrionaceae
p-2534-18B5
Elusimicrobiaceae
RFP12
Victivallaceae
Fibrobacteraceae

Three dairy farms (A, B, and C) in Japan were similar with regard to the number of taxa
and Shannon index value for fecal microbiota at the family level (Table 4.2). However, therelative abundance of several families, such as Ruminococcaceae, Peptococcaceae,
Christensenellaceae, Peptostreptococcaceae, Turicibacteraceae, Succinivibrionaceae, andFibrobacteraceae was statistically different between samples from the three farms (Table 4.4).
In China, although the number of taxa was similar for samples from farms D, E, and F, theabundance of the bacteria varied, with respect to the families Ruminococcaceae,
Veillonellaceae,

Mogibacteriaceae,

Christensenellaceae,

Turicibacteraceae,

Succinivibrionaceae, and Spirochaetaceae.
Firmicutes and Bacteroidetes were the first- and second-most predominant phyla insamples from both countries (Table 4.4). These two phyla contributed to 88.6 and 93.7% of
total bacterial population in samples from Japan and China, respectively. The populations of
Ruminococcaceae, Clostridiaceae, Mogibacteriaceae, and Christensenellaceae were higher,
and those of Paraprevotellaceae, RF16, and Prevotellaceae were lower in samples from China
than in those from Japan. The Firmicutes to Bacteroidetes ratio was, therefore, greater in
Chinese samples (>1) than in Japanese samples (<1). Additionally, the unidentified family p2534-18B5,

Succinivibrionaceae,

Anaeroplasmataceae,

Methanobacteriaceae,

andFibrobacteraceae indicated statistical differences between the bacterial populations in samples
from the two countries.
Cluster analysis revealed that, although differences between individual cows wereobserved, the composition of fecal microbiota was clearly different in the samples from the twocountries. Moreover, two samples from farm B (B2 and B3) were grouped in a cluster separate
from the cluster that included the other 16 feces samples. This grouping was determined basedon the extremely high proportions of Succinivibrionaceae (9.83%), Prevotellaceae (4.41%),
Peptostreptococcaceae (0.37%), and Turicibacteraceae (0.24%), and lower proportion of
Ruminococcaceae (10.9%) in the B2 and B3 samples compared to those of other samples (Table
4.4). Therefore, cow-to-cow variation in farm B was greater than farm-to-farm and country-tocountry variation.

47

Table 4.1. Feeding management, herd milk yield, diet formulation, and fermentation products of whole crop corn silage used in the six farms.

Feeding management
Milk yield (g/day)Diet formulation
Corn silage
Other forages
Concentrates
Silage fermentation
Dry matter (g/kg)
pH
Lactic acid (g/kg DM)
Acetic acid (g/kg DM)
Propionic acid (g/kg DM)
Butyric acid (g/kg DM)
Ethanol (g/kg DM)
1,2-Propanediol (g/kg DM)
1-Propanol (g/kg DM)

A
Total mixed
ration27

Japan
B
Separate
feeding
32

C
Total mixed
ration
30

0.35
0.16
0.49

0.29
0.21
0.50

313
3.98
47.1
33.8
0.00
0.00
3.94
25.2
2.39

295
3.94
57.2
46.7
0.00
0.00
10.2
42.9
0.78

48

D
Total mixed
ration21

China
E
Total mixed
ration28

F
Separate
feeding
30

0.20
0.39
0.41

0.29
0.11
0.38

0.32
0.18
0.50

0.32
0.16
0.52

290
3.88
73.7
29.2
11.7
0.00
9.78
0.00
2.49

219
3.92
59.5
51.1
14.7
0.62
15.1
5.50
16.2

219
3.51
129
45.4
0.00
0.74
34.2
25.1
10.5

225
3.52
97.3
26.6
0.00
0.00
19.7
13.3
3.60

Table 4.2. Number of sequence reads, taxa, and Shannon diversity index values determined by next
generation sequencing at the family level for the microbiota of whole crop corn silage and silage-fed
dairy cows.
As
Bs
Cs
Ds
Es
Fs
A
B
C
D
E
F
Japan
China

Read
63574
71458
10557376133
76427
10314969612 ± 14319
69232 ± 13384
64671 ± 6569
78106 ± 11620
65035 ± 11527
64308 ± 4394
67838 ± 10607
69150 ± 10817

Taxa
118
96
113
141
119
91
78.7 ± 29.8a
71.0 ± 21.6a
75.7 ± 22.9a
83.3 ± 24.0a
79.3 ± 21.4a
71.0 ± 19.9a
75.1 ± 21.9a
77.9 ± 19.7a

Shannon
0.781
0.507
1.581
0.556
0.866
0.537
2.319 ± 0.147a
2.549 ± 0.022a
2.296 ± 0.100a2.226 ± 0.038b
2.205 ± 0.021b
2.319 ± 0.043a
2.388 ± 0.150a
2.250 ± 0.061b

4.5. Discussion
Several factors including diet formulation, management practice, and geographicallocation have been shown to influence the gut microbiota (Menezes et al. 2011; Shanks et al.
2011; Anderson et al. 2016; Khafipour et al. 2016). Regarding diet formulation, the forage-toconcentrate ratio is considered to have substantial effects on gut microbiota (Shanks et al. 2011;
Khafipour et al. 2016). In the present study, dairy cows received diets with the forage-toconcentrate ratio varying from 62:38 to 52:48. The difference may be large enough to result indifferences in fecal microbiota; however, farm-to-farm and country-to-country differences wereobserved even with similar forage-to-concentrate ratio. Because all cows were of the Holstein
breed, farm-to-farm and country-to-country differences could, at least in part, be attributed tothe differences in the geographical location.
Lactobacillaceae species accounted for the highest proportion of the total population ofsilage microbiota in all the six corn silages. At the family level, three separate groups wereformed, and none of them were related to the country in which silage was produced. A groupof silage Bs, Es, and Fs, another group of silage As and Ds, and silage Cs possessed their own
patterns, and the divergence of Acetobacteraceae was the main cause for the groupdifferentiation. Acetobacter spp. were the main contributor to the Acetobacteraceae family inthis study. Acetobacter spp. are known to have strong crop specificity with respect to corn andcereal silages (Oude Elferink et al. 2001), and to have the ability to initiate aerobic deterioration49

by oxidizing ethanol to acetic acid. Other typical spoilage-associated bacteria, e.g., Bacillaceae(Bacillus spp.) and Enterobacteriaceae (Enterobacter spp.), were also detected, and were
especially high in silage Cs. However, all the silages examined were not obviously spoiled andwere considered acceptable based on the pH and fermentation product content (Table 4.1). An
exception of low abundance of Lactobacillaceae in silage Cs was similar to the finding of
Kraut-Cohen et al. (2016), who observed a farm silage harboring <35% Lactobacillaceae,
despite having pH and fermentation product content at acceptable levels. Among the six silage
samples examined in the present study, only silage Cs had no detectable 1,2-propnediol, but therelevance to a large proportion of Acetobacteraceae was not known.
Table 4.3. Relative abundance of taxa detected at >0.1% of the total population in silage samples.
PhylumFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyPhylumFamilyFamilyFamilyFamilyFamilyFamilyFamilyPhylumFamilyFamilyPhylumFirmicutes
Lactobacillaceae
Bacillaceae
Clostridiaceae
Enterococcaceae
Planococcaceae
Ruminococcaceae
Lachnospiraceae
Leuconostocaceae
Paenibacillaceae
Proteobacteria
Acetobacteraceae
Enterobacteriaceae
Comamonadaceae
Moraxellaceae
Methylobacteriaceae
Xanthomonadaceae
Oxalobacteraceae
Actinobacteria
Microbacteriaceae
Bifidobacteriaceae
Bacteroidetes

As
76.6
75.2
0.15
0.35
0.03
0.04
0.34
0.16
0.01
0.02
22.1
0.42
21.1
0.10
0.05
0.08
0.05
0.01
0.10
0.01
0.003
0.68

Bs
87.8
87.5
0.08
0.04
0.02
0.02
0.06
0.05
0.004
0.01
11.6
9.14
2.28
0.03
0.01
0.01
0.02
0.01
0.07
0.003
0.004
0.13

Cs
55.5
45.7
4.63
0.05
2.65
1.53
0.04
0.05
0.34
0.35
43.1
32.4
3.44
3.32
2.29
0.01
0.86
0.45
0.52
0.02
0.24
0.48

Ds
93.8
91.6
0.16
0.73
0.05
0.31
0.24
0.28
0.01
0.03
3.20
0.51
0.12
0.20
0.20
1.36
0.07
0.07
1.92
0.97
0.68
0.45

Es
76.9
73.6
0.06
2.54
0.09
0.09
0.15
0.05
0.13
0.03
22.0
20.6
0.26
0.15
0.13
0.35
0.04
0.09
0.56
0.44
0.01
0.16

Fs
82.3
82.2
0.02
0.01
0.01
0.01
0.03
0.01
0.003
0.001
17.1
16.8
0.01
0.04
0.02
0.16
0.01
0.01
0.15
0.11
0.001
0.05

As-Fs indicate silages produced in bunker silos at farms A, B, C, D, E, and F, respectively.

The fact that Lactobacillus spp. dominate the microbiota of corn silage (Ni et al. 2017)
was confirmed in this study. Lactobacillaceae ranged from 45.7% to 91.6% of the total bacterial
population. However, in feces samples, Lactobacillaceae accounted for a very small proportion50

(<0.1%), and appeared to be replaced by the predominance of Ruminococcaceae,
Bacteroidaceae, Clostridiaceae, Lachnospiraceae, Rikenellaceae, and Paraprevotellaceae.
Although the decrease in Lactobacillaceae proportion could occur if silage microbiota wasdiluted in the rumen, the survival of Lactobacillaceae throughout the gastrointestinal tract ofdairy cows was apparently not possible owing to the low compatibility. Indeed, cluster analysis
indicated that silage microbiota was distinctively separated from fecal microbiota, and thegrouping pattern of silage samples showed no relation to that of feces samples (Fig. 4.1).
Similarly, although great variation in microbial diversity was observed in the six silage samples,
especially for silage Cs with a low Lactobacillaceae and a high Acetobacteraceae proportion,no effects on the fecal microbiota of silage-fed dairy cows were observed. The number ofdetected taxa was higher in silage than in feces. Because of the unique dominance and moreeven distribution of Lactobacillaceae, however, the Shannon index value was lower for silage
than feces.
The repeated microbiota consumption of silage in dairy cows is comparable to that ofnaturally fermented food in humans. Lee et al. (1996) demonstrated that consumption ofnaturally fermented kimchi, which has been shown to have Lactobacillaceae andLeuconostocaceae as the dominant taxa (Jung et al. 2014), increased the population of
Lactobacillus spp. and Leuconostoc spp. in human feces. Similarly, Han et al. (2015) found that
intake of fermented kimchi raised up the populations of two phyla, Proteobacteria andActinobacteria, and two genera Bacteroides spp. and Prevotella spp. in human feces. Because
only silage-fed dairy cows were examined in this study, the comparison between fermented andnon-fermented crop feeding was not possible. Regardless, there were no distinctive differencesin any taxa of fecal microbiota in response to ingestion of various silage microbiota, whichwere seprated into three separate clusters (Fig. 4.1). The low impact of diet microbiota on fecal
microbiota of cattle was likely due to the polygastric digestive system compared to themonogastric system in humans.
Although several NGS studies on gut microbiota of dairy cows have been conducted, it
is not easy to perform cross comparisons because of the differences in diet formulation,
production level (lactating and non-lactating), examined gut segments (e.g., rumen and rectum),and targeted regions of 16S rRNA genes. Mao et al. (2015) studied the gut microbiota oflactating Holstein cows in China, but their reported proportion of Firmicutes and Bacteroidetes(91.3 and 2.87%, respectively) for rectum digesta (feces) was greatly different from that foundin our study (46.1 and 45.1%, respectively). This discrepancy could be due to differences intargeted regions. Mao et al. (2015) analyzed V3/V4 regions whereas we examined the V451

region of 16S rRNA genes. Claesson et al. (2010) demonstrated that, regardless ofpyrosequencing and Illumina platforms, the Firmicutes-to-Bacteroidetes ratio was muchgreater when the V3/V4 region was targeted than other regions such as V1/V2, V2/V3, V4/V5,
V5/V6, and V7/V8. Similar to Mao et al. (2015), Derakhshani et al. (2016) reported the
proportion of Firmicutes and Bacteroidetes at 80.3 and 11.1%, respectively, although they
analyzed the same V4 region as targeted in this study. Since Derakhshani et al. (2016) examined
the feces of Holstein bull calves, the difference in diet formulation may have affected theoutcome of the microbiota analysis. Regarding the proportion of Firmicutes and Bacteroidetes
for feces samples, our data was similar to that obtained for steers fed a late growing diet(moderate grain) containing 26% corn silage (Kim et al. 2014).
Among the families evaluated as highly variable taxa, Succinivibrionaceae andPrevotellaceae have been shown to correlate with grain feeding (Khafipour et al. 2016).
Furthermore, Spirochaetaceae (mainly the genus Treponema spp.) has been shown to relate to
the fiber degrading ability (De Filippo et al. 2010; Liu et al. 2016). At the phylum level,
Proteobacteria (CV = 1.312) was the highest variance taxa in our study, which was inagreement with a study by Jami and Mizrahi (2012a) for rumen microbiota. Our finding that
Bacteroidetes (CV = 0.121) was consistent was different from previously reported findings for
cattle (Jami and Mizrahi 2012a) and human (Kurokawa et al. 2007; Arumugam et al. 2011).
Variations in Succinivibrionaceae and Prevotellaceae were also seen between farms andcountries. This difference could be seen from a high abundance of these two families (9.83%
and 4.41% for Succinivibrionaceae and Prevotellaceae, respectively) for farm B (Table 4.4),
especially with B2 and B3 cows. In B1 cow raised under the same management as B2 and B3,
Porphyromonadaceae, which also showed a higher proportion under a high grain diet (Kim etal. 2014), was detected at a high abundance compared to Succinivibrionaceae andPrevotellaceae, indicating that gut microbiota may have adapted strategies under the same
feeding regimen. Because the forage-to-concentrate ratio was similar between farms A and B,
the high abundance of concentrate-associated taxa in farm B cows could not be attributed todiet formulation. Rather, the difference in feeding management, i.e., TMR and separate feeding,
may have affected the abundance of forage- and concentrate-associated families. It has been
demonstrated that, although cows may feed on diet of an intended formulation under TMR
feeding, cows may consume a greater amount of concentrate than designed under separate
feeding (Nocek et al. 1986). Maekawa et al. (2002) found that, even if the diet was provided at
a 50:50 forage-to-concentrate ratio, the actual ratio consumed by cows was approximately
60:40 under separate feeding. In farm F cows that were also managed by separate feeding,52

although the differences were small, the lowest proportion of forage-associatedRuminococcaceae and the highest proportion of concentrate-associated Succinivibrionaceae
were observed, compared to other Chinese cows. Therefore, rather than the geographicallocation, feeding management could have led to the farm-to-farm differences in fecal
microbiota in this study.
Table 4.4. Relative abundance of the taxa detected at >0.1% of the total population in feces samples
from silage-fed dairy cows at 3 Japanese and 3 Chinese farms.
Farm-to-Farm variation

PhylumFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyPhylumFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyFamilyPhylumFamilyFamilyFamilyPhylumFamilyPhylumFamilyPhylumFamilyFamilyPhylumPhylumFamilyPhylumFamilyPhylumFirmicutes
Ruminococcaceae
Clostridiaceae
Lachnospiraceae
Veillonellaceae
Erysipelotrichaceae
Peptococcaceae[Mogibacteriaceae]
Christensenellaceae
Peptostreptococcaceae
Turicibacteraceae
Bacteroidetes
Bacteroidaceae
Rikenellaceae[Paraprevotellaceae]
RF16
S24-7
Prevotellaceae
Porphyromonadaceae
p-2534-18B5
BS11[Barnesiellaceae]
Proteobacteria
Succinivibrionaceae
Desulfovibrionaceae
Alcaligenaceae
Tenericutes
Anaeroplasmataceae
Spirochaetes
Spirochaetaceae
Verrucomicrobia
Verrucomicrobiaceae
RFP12
Cyanobacteria
Euryarchaeota
Methanobacteriaceae
Fibrobacteres
Fibrobacteraceae
Lentisphaerae

Japan
A
B
41.2
37.2
23.2a 10.9b
4.53
7.29
4.83
9.76
0.73
2.37
0.47
0.71
0.47ab 0.77a
0.20
0.12
0.14a 0.03b
0.07b 0.37a
0.04b 0.24a
48.6
46.7
9.06
11.7
5.84
4.79
5.45
6.96
5.98
1.88
1.28
4.33
1.33
4.41
1.86
1.22
0.67
0.27
0.56
0.20
0.17
0.16
1.78b 10.7a
0.95b 9.83a
0.26
0.06
0.13
0.48
2.44
1.42
0.22
0.28
2.85
2.98
2.74
2.97
0.81
0.12
0.32
0.04
0.47
0.08
0.43
0.12
0.09
0.11
0.08
0.10
0.28ab 0.001b
0.28ab 0.001b0.21 0.004

C
40.6
18.7ab
5.04
7.73
0.63
0.47
0.29b
0.22
0.08ab0.11b0.10ab
51.4
10.4
6.25
7.15
5.49
1.84
0.10
1.37
0.004
0.22
0.21
2.06b
1.45b
0.09
0.11
2.50
0.30
1.45
1.45
0.20
0.19
0.01
0.17
0.11
0.10
0.45a
0.45a
0.01

SE
2.63
1.85
1.42
1.73
0.66
0.15
0.08
0.03
0.02
0.06
0.04
2.27
1.05
0.90
0.82
1.47
1.18
0.96
0.63
0.24
0.16
0.03
1.12
0.95
0.08
0.23
0.48
0.05
1.31
1.33
0.28
0.10
0.20
0.20
0.04
0.04
0.10
0.10
0.09

D
51.9
28.2ab
8.97
5.74
0.63b
0.92
0.39
0.30b
0.20ab
0.08
0.05b
42.8
10.8
5.82
4.87
1.54
2.07
0.21
1.01
2.31
0.65
0.10
0.90
0.35ab
0.19
0.07
1.67
0.16
0.39b
0.36b
0.62ab
0.32
0.28
0.29
0.22
0.19
0.06
0.06
0.17

Country-toCountry
variation
ChinaE
F
55.1 50.3
31.7a 23.9b
8.24 8.99
5.71 9.03
0.69ab 1.08a
0.45 0.64
0.78 0.37
0.49a 0.41ab
0.46a 0.15b
0.12 0.09
0.10a 0.11a
37.4 43.8
11.8 10.1
5.83 7.13
4.06 5.04
0.81 1.49
1.38 2.42
0.14 0.52
0.46 0.90
1.46 1.77
0.67 0.27
0.14 0.16
1.06 0.92
0.04b 0.51a
0.46 0.11
0.08 0.10
2.47 1.42
0.04 0.16
0.40b 2.05a
0.39b 1.94a
1.48a 0.31b
0.85 0.20
0.58 0.11
0.54 0.05
0.39 0.27
0.38 0.24
0.10 0.02
0.10 0.02
0.16 0.02

SE
1.53
1.21
0.96
1.07
0.10
0.15
0.15
0.04
0.06
0.03
0.01
1.62
1.94
0.68
0.46
0.22
0.72
0.09
0.13
0.97
0.13
0.04
0.31
0.09
0.16
0.03
0.53
0.04
0.30
0.25
0.26
0.22
0.14
0.20
0.08
0.08
0.04
0.04
0.07

Japan
39.7b
17.6b
5.62b
7.44
1.24
0.55
0.51
0.18b
0.09b
0.19
0.13
48.9a
10.4
5.63
6.52a
4.45a
2.48
2.25a
1.48
0.31b
0.33
0.18
4.84a
4.08a
0.14
0.24
2.12
0.27a
2.42
2.39
0.37
0.18
0.18
0.24
0.10b
0.09b
0.24a
0.24a
0.07

China
52.4a
27.9a
8.74a
6.83
0.79
0.67
0.51
0.40a
0.27a
0.10
0.08
41.3b
10.9
6.26
4.65b
1.28b
1.96
0.29b
0.79
1.85a
0.53
0.13
0.96b
0.30b
0.25
0.08
1.85
0.12b
0.95
0.89
0.80
0.46
0.32
0.29
0.29a
0.27a
0.06b
0.06b
0.12

A-F indicate feces samples collected from farms A, B, C, D, E, and F, respectively. Mean values with different
superscript letters are significantly different (p < 0.05).

53

Table 4.5. Coefficient of variation calculated for the taxa detect at >0.1% of total population in feces
samples collected from silage-fed dairy cows at 3 Japanese and 3 Chinese farms.
PhylumFamilyGenus
FamilyGenus
FamilyFamilyPhylumFamilyGenus
FamilyFamilyGenus
Genus
FamilyFamilyFamilyGenus
FamilyFamilyPhylumFamilyGenus
PhylumPhylumFamily
Genus

SD
7.519
7.241
0.380
2.549
2.453
2.821
0.940
5.440
1.672
1.990
1.358
1.507
1.345
0.693
2.613
1.769
1.807
1.807
0.793
1.337
3.804
3.682
2.735
0.897
1.753
1.748
1.749

Firmicutes
Ruminococcaceae
Oscillospira
Clostridiaceae
Clostridium
Lachnospiraceae
Veillonellaceae
Bacteroidetes
Bacteroidaceae
5-7N15
Rikenellaceae[Paraprevotellaceae]
CF231[Prevotella]
RF16
S24-7
Prevotellaceae
PrevotellaPorphyromonadaceae
p-2534-18B5
Proteobacteria
Succinivibrionaceae
Succinivibrio
Tenericutes
Spirochaetes
Spirochaetaceae
Treponema

Mean
46.0
22.7
1.98
7.18
6.72
7.13
1.02
45.1
10.6
7.74
5.94
5.59
3.87
1.20
2.86
2.22
1.27
1.27
1.14
1.08
2.90
2.19
1.44
1.99
1.68
1.64
1.64

CV
0.163
0.318
0.192
0.355
0.365
0.395
0.921
0.121
0.157
0.257
0.229
0.270
0.348
0.576
0.912
0.797
1.423
1.423
0.698
1.239
1.312
1.682
1.900
0.452
1.041
1.066
1.067

Ruminococcaceae and Christensenellaceae varied between farms as well as countries(Table 4.4). Ruminococcaceae, the greatest population in our data, is known as an active plantdegrader, whereas the function of Christensenellaceae is still poorly understood. Goodrich etal. (2014) reported that this family is a heritable taxon in humans and is associated with ahealthy state but not with the diet, and often co-occurs with Methanobacteriaceae. In our study,
Christensenellaceae and Methanobacteriaceae accounted for similar relative abundances insamples from Japan and China.
Fiber-degrading Ruminococcaceae showed a positive correlation (p < 0.01) with
Christensenellaceae (r2 = 0.638), indicating a negative correlation with Succinivibrionaceae (r2= 0.590) and Prevotellaceae (r2 = 0.567). Ruminococcaceae also showed positive correlations

54

with the other six families, i.e. BS11 (r2 = 0.381), Verrucomicrobiaceae (r2 = 0.346),
Mogibacteriaceae (r2 = 0.540), RFP12 (r2 = 0.327), Desulfovibrionaceae (r2 = 0.312), andMethanobacteriaceae (r2 = 0.358). Although the functions of these six families are yet
unknown, these taxa could be involved in fiber-degrading activity. Meanwhile, although
Lachnospiraceae is known as a fiber-degrading family, a negative correlation with
Ruminococcaceae (r2 = 0.360; p < 0.01) was observed in this study.
In conclusion, rather than diet (silage) microbiota, feeding management andgeographical location may contribute greatly to shaping the gut microbiota of silage-fed dairy
cows. Although variations between farms and countries were seen, neither highly variable nor
stable families of microbiota in feces samples were affected by silage microbiota. An interesting
cow-to-cow variation for concentrate-associated taxa was demonstrated in the same farm; twocows showed high abundance of Succinivibrionaceae and Prevotellaceae, whereas another cow
had a high abundance of Porphyromonadaceae. Similarly, although diets with similar forageto-concentrate were offered, cows under separate feeding had greater abundance of concentrateassociated families than those under TMR feeding. These findings help to envisage the effects
of feeding management and diet formulation on gut microbiota of silage-fed dairy cows.
Because assessment of rumen microbiota was not within the scope of this study, further
investigation is necessary to warrant the relationship between diet microbiota, gut microbiota,
and the health and productivity of dairy cows.

55

CHAPTER 5
MICROBIAL COMMUNITY IN THE GASTROINTESTINAL TRACT OF
GOATS FED WHOLE CROP CORN SILAGE
5.1. Abstract
Gut microbial communities were examined across the gastrointestinal tract using goatmodel. All goats were fed corn silage as daily meal. Silage-associated Lactobacillus community
had no influence on gut-associated Lactobacillus communities across the gastrointestinal tractof goats. In gut-associated samples, composition of mucosa-associated Lactobacillus
community was more stable than the one of digesta samples, but relative abundance of thiscommunity was rather regionally fluctuated. Total microbial population and its component
bacterial

population(Bacteroides-Prevotella-Porphyromonas,

Enterobacteriaceae,

Ruminococcus albus, Faecalibacterium prausnitzii, Fibrobacter succinogenes) were varied in
number across the tract for both digesta and mucosa samples. Throughout the tract, abomasumassociated microbiota indicated the greater differences in distribution between digesta andmucosa samples, while no difference was seen between digesta- and mucosa-associated small
intestine microbiota. Proportion of Bacteroides-Prevotella-Porphyromonas, F. succinogenes
and mucosa-associated R. albus showed significant variation across the tract, which all
described their highest proportion in abomasum. Overall, this is the first study providing insight
into the relation of silage-associated Lactobacillus community to the gut-associated ones
throughout the gastrointestinal tract of goat. Digesta- and mucosa-associated microbiota wererevealed intra-variation across different segments of the tract.
5.2. Introduction
Gastrointestinal tract of animal is the natural habitat for a dense and dynamic microbialcommunity. This resident microbiota has been recognized to provide benefits to the animals,
especially for ruminants with their symbiotic bacteria that fermenting plant materials into
nutrient sources available for the host. But a substantial part of this resident microbiota is still
to be discovered. Theoretically, this population has been contributed by three subpopulations,
including digesta-associated, mucosa-associated, and food-associated microbiota (Cheng et al.
1979; Li et al. 2011). Comparing to mucosa- and food-associated population, digesta-associatedpopulation has intensively been studied targeting to rumen and feces microbiota, due to the easy
and convenient sampling. However, recent studies have raised concern about intra-variation ofgut microbiota in different segments across the gastrointestinal tract (de Oliveira et al. 2013).

56

It is indicated that gut microbiota varied across the tract in abundance as well as in microbial
composition. Understanding about this fluctuation of gut microbiota is still limited (de Oliveira
et al. 2013; Mao et al. 2015; Zeng et al. 2017). And, the contribution of mucosa-associatedpopulation to this dynamics is further restrictedly comprehended despite its crucial role to thehost (Van den Abbeele et al. 2011).
Corn silage has been reported to harbor a rich source of Lactobacillaceae, which
promoting to the ensiling process (Ni et al. 2017; Tang et al. 2017). The consumption of thissilage-microbiota is considered to propose probiotic effect for the ruminants if these
Lactobacillaceae could comply a satisfactory survivability throughout the gastrointestinal tract.
In this study, we examined gut microbial communities across the gastrointestinal tract
using goat model. All goats were fed corn silage as daily meal. Lactobacillus community was
examined to understand how this community inhabited throughout the gastrointestinal tract andif silage-associated Lactobacillus community had influence on gut-associated Lactobacillus
communities. We also investigated the digesta- and mucosa- associated microbiota to satisfy
our own knowledge about their variation in different segments across the gastrointestinal tract
as well as the differences in distribution between these two populations.
5.3. Materials and methods
Animals and sample collection – This study used four female goats (body weight: 35.6 ± 3.3kg). All goats were fed hay exclusively before experiment. The experimental diets were
formulated to meet the energy requirement of the goats, containing 50% concentrates, 10%alfalfa hay, 40% whole crop corn silage on a DM basis. Goats were fed at 09:00 and 17:00
everyday during 14 days of the experimental period. Goats were kept in stalls and fed ad libitum
to assure 10% orts, and they were given free access to fresh water during the trial.
Feces samples were collected at day 0 to compare feces microbiota before and after
examination. On the day 14, the goats were sacrificed. Mucosa and digesta samples werecollected from the rumen, abomasum, small intestine, cecum and rectum. Digesta samples ofrumen, abomasum, cecum, and rectum were separately collected from each opened segment,
and transferred to sterilized tube kept on ice. In case of small intestine, digesta sample wasachieved by washing up the content of a 2-cm-in-length-small-intestine using sterilized saline
water. The resulted liquid was collected into sterilized tube kept on ice. For mucosa samples, 2
cm2-mucosa-tissue of each segment (except for rumen) was rinsed with sterilized saline waterto eliminate contamination from the digesta. Sterilized glass slide was used to scrape the surfaceof mucosa tissue. The collected sample containing the mucosa-associated bacteria was washed57

up by sterilized saline water and transferred into sterilized tube kept on ice. For rumen sample,
after rinsing with sterilized saline water, the lining of the rumen wall harboring papillae was
detached and directly transferred into sterilized tube. In addition, whole crop corn silage wascollected. All of the resulted samples were then transferred to laboratory and stored at -30oC
prior to DNA extraction.
DNA extraction –All of the liquid samples were centrifuged shortly to achieve pellets. Bacterial
DNA was extracted from achieved pellets (for liquid samples) and 200 mg of frozen samples(for digesta samples) using repeated bead beating plus column method (Yu and Morrison,2004). DNA concentration of each sample was determined by Invitrogen Qubit 2.0 Fluorometerwith Qubit® dsDNA BR Assay Kits (Life Technologies Ltd., Paisley, UK). DNA samples wasstored at -30oC for further examination.
PCR – DGGE analysis – Bacterial DNA was subject to PCR using primers Lab159f (5’-GGA
AACAGRTGCTAATACCG-3’) and Lab617r (5’-CACCGCTACACATGGAG-3’) fortargeted Lactobacillus community (Heilig et al. 2002). Nested PCR were employed with
universal primers GC357f (5’-CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGG
GGGGCCTACGGAGGCAGCAG-3’)

and

517r

(5’-ATTACCGCGGCTGCTGG-3’).

Resulted PCR products were separated by DGGE by using the DCode Universal Mutation
Detection System (Bio-Rad Laboratory Inc.). Polyacrylamide gels consisted of 8% (v/v)
polyacrylamide. Denaturing gradient (Urea and Formamide) of 25 - 50% was used for the
separation of the generated. Electrophoresis was performed for 8 h at 150 V in TAE buffer at aconstant temperature of 60°C. Gels were stained with SYBR Green, observed by UVtransilluminator and photographed.
Species identification – PCR amplicons (with 357f (without GC clamp) and 517r primers)
resulted from excised bands on DGGE gel were purified using FastGene® Gel/PCR Extraction
Kits (Nippon Genetics Co., Ltd., Japan) according to the manufacturer’s instructions, inserted
in to pTAC–1 vector and cloned into Escherichia coli strain DH5α competent cells(DynaExpress TA Cloning Kit; BioDynamics Laboratory Inc., Tokyo, Japan). Positive whiteclones are randomly selected and confirmed for the presence of expected size inserts by PCR.
Subsequently, positive colonies were sub-cultured into LB broth media. Extracted plasmids
should be employed for a new PCR amplification followed by purification. To enhance thequality of sequencing, the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied
Biosystems) was used according to the manufacturer’s instructions. DNA sequences wereanalyzed using an ABI PRISM 3130 sequencer (Applied Biosystems Inc., Foster City, Calif.,
U.S.A.). The Basic Local Alignment Search Tool (BLAST) program andGenBank databases

58were used to determine the closest relatives of partial 16S rRNA gene sequences. Unknown
sequences that shared greater than 98% identity with a sequence in the BLAST database wereconsidered as identified.
Quantitative PCR – A master mix for total quantification (357f (5’-ACGGGGGGCCTACGGAGGCAGCAG-3’) and 517r (5’-ATTACCGCGGCTGCTGG-3’)), BacteroidesPrevotella-Porphyromonas population (BPP fwd (5’-GGTGTCGGCTTAAGTGCCAT-3’)
and BPP rev (5’-CGGA(C/T)GTAAGGGCCGTGC-3’)), Clostridium cluster I&II population(Cl-perf fwd (5’-ATGCAAGTCGAGCGA(G/T)G-3’) and Cl-perf rev (5’-TATGCGGTATTAATCT(C/T)CCTTT-3’)), Lactobacillus population (Lact fwd (5’-AGCAGTAGGGAATCTTCCA-3’) and Lact rev (5’-CACCGCTACACATGGAG-3’)), Faecalibacterium prausnitzii(Fprau-2 fwd (5’- CCCTTCAGTGCCGCAGT-3’) and Fprau-2 rev (5’- GTCGCAGGATGTCAAGAC-3’) ((Rinttilä et al. 2004), Enterobacteriaceae population (Eco1457 fwd (5’-CATTG
ACGTTACCCGCAGAAGAAGC-3’) and Eco1652 rev (5’-CTCTACGAGACTCAAGCTT
GC-3’)), Ruminococcus albus population (Ralb561 fwd (5’-CAGGTGTGAAATTTAGGG
GC-3’) and Ralb807 rev (5’-GTCAGTCCCCCCAC ACCTAG-3’)) (Bartosh et al. 2004),
Fibrobacter succinogenes (Fibsuc fwd (5′-GGTATGGGATGAGCTTGC-3′) and Fibsuc rev(5′-GCCTGCCCCTGAACTATC-3′)) (Tajima et al. 2001) was prepared for each wellcontained following reagents: 2 x KAPA SYBR FAST qPCR Master Mix Universal (Kapa
Biosystem Inc.) that contained integrated antibody-mediated hot start, SYBR Green I
fluorescent dye, MgCl2, dNTPs, forward and reverse primers, and distilled water to a total of
23 µl per well. To these, 2 µl of each sample or standard was added, and the plates were briefly
spun down and placed in MiniOpticon Real-Time PCR Detection System (Bio-Rad Laboratory,Inc.) for analysis. Amplification programme involved an initial denaturation step at 95oC for 5min, followed by 35 cycles of denaturation at 95oC for 15s, primer annealing at optimal
temperatures for each target bacterial group for 20 s, extension at 72oC for 30 s and an additional
incubation step at 80-85oC for 30s to collect the fluorescent data (Rinttilä et al. 2004).
Statistical analysis – Binary matrix resulted from DGGE banding patterns of Lactobacillus
group was created to describe the presence or absence of individual band in all lanes. Resulted
binary matrix was engaged to Principal Coordinates Analysis (PCO) to demonstrate similarityand differences between gut segments and mucosa vs. digesta samples using Primer version 7
with Permanova + add-on software (Primer-E, Plymouth Marine Laboratory, and Plymouth,
UK). Quantitative PCR data were subjected to one-way analysis of variance. Tukey’smultiple
range test was performed using the JMP software (version 11; SAS Institute, Tokyo, Japan) to
examine the differences between segments and samples. test. All data are presented as means

59with their pooled standard error.
Table 5.1. Proportion of Lactobacillus group, R. albus, F. prausnitzii, Enterobacteriaceae,
Bacteroides–Prevotella–Porphyromonas group, F. succinogenes in digesta (D) and mucosa-adhered
microbiota (M) of different sampling sites across the gastrointestinal tract of goat. Means with different
subscripts are considered statistically different (p<0.05). §Sample type effect on bacterial population.
†
Regional effect on bacterial population.

D

0.00090.0038

Small
Intestine
21.2467

M

0.00320.01130.06410.02300.0259

#

0.018

D

0.00850.00990.01390.00260.00340.01040.005

M0.0037b0.0496*a0.0065b0.0080b

0.0358
*

a

#

0.006

Regions Rumen
Lactobacillus

Abomasum

0.0001

Feces
0 day
0.0012

8.66

Cecum

Rectum0.0014SEP
value†

P value§
Ruminococcus
albus
P value§
Faecalibacteriumprausnitzii
P value

P value§
Baceteriode –Prevotella–Porphyromonas
P value§
Fibrobacter
succinogenes
P valueD

0.1471

0.09410.21870.18470.2541

0.49290.093

M0.5201*0.2526*

0.52880.23260.2824

#

0.152

D

0.00040.00080.03290.09680.4436

0.05490.101

M0.0044*0.0137*0.03920.04350.0734

#

0.020

D

8.2227ab19.264a0.0569b6.196ab7.295ab2.731b

3.185

*

M

5.1325b13.260a0.0654b1.5842b*1.4479b
*

#

1.614

*

D

1.0361ab

1.4701a0.6704ab

0.0442b0.0226b0.0225b

0.249

*

M

b

a

b

b

b

#

1.564

*

§

Enterobacteriaceae§*

5.1972*26.461*

0.5236

1.5863*4.5321*5.4. Results
5.4.1. Habitation of Lactobacillus community from silage throughout the gastrointestinal
tract of goat
The Lactobacillus community was detected in silage samples and in all segments of thegastrointestinal tract regardless of sample type. Lactobacillus population was found as high as
2.91 x 1010 copies/g on average in silage samples. But this population revealed a lower
abundance in digesta- (1.36 x 109 copies/g) and mucosa-associated (7.97 x 107 copies/cm2)
samples. Mucosa-associated Lactobacillus population showed significant higher number insmall intestine comparing to other segments, while digesta-associated Lactobacillus population
was rather stable across the tract (Fig. 5.3C).
DGGE profile and species identification of Lactobacillus community revealed that thesilage-associated Lactobacillus community was dominated by L. acetotolerans, L.
parafarraginis, L. buchneri, and L. kefiri, while gut-associated Lactobacillus was belonged to60

L. johnsonii, L. helviticus, L. acetotolerans, L. buchneri, L. crispatus, and many unidentified
bacterium clone (Fig. 5.1).
PCoA profile indicated that silage-associated Lactobacillus community was different
from gut-associated ones. Mucosa-associated samples were distributed closely into one group,
indicating that the mucosa-associated Lactobacillus community was similar between different
segments of the gastrointestinal tract. In general, digesta-associated Lactobacillus samples were
not statistically similar to each other, but these samples were allocated unrelatedly from themucosa-associated ones (except for small intestine samples). Feces samples collected at day-0
showed independent grouping pattern with other gut-associated samples collected at day-14(Fig. 5.2).

Figure 5.1. DGGE banding patterns and species identification of Lactobacillus group of silage sample(S) and digesta (d) and mucosa-associated samples (m) across gastrointestinal tract of goat including
rectum (RE, RE-0: Feces sample collected before examining period), cecum (CE), small intestine (SI),abomasum (AB), rumen (RU).

61

Silage
Fe 0
RectumCecum
Small intestine
Abomasum

Digesta

Rumen

5

MucosaPCO2 (15.3% of total variation)

10

0

-5
-10

-5

0
5
PCO1 (24% of total variation)

10

Figure 5.2. Principal Coordinate Analysis (PCoA) profile of Lactobacillus group based on DGGE
banding pattern of digesta and mucosa-associated microbiota across the gastrointestinal tract of 4 goatsand silage samples.

5.4.2. Microbial population changing across the gastrointestinal tract of goat
Total bacterial population showed different trends of distribution in digesta and mucosa
samples. In digesta samples, total population appeared to be stable in different segments acrossthe gastrointestinal tract, except for small intestine. Small intestine exposed the smallest
bacterial population with two order lower in comparison to other segments. The opposite trend
was observed in mucosa-associated samples. Small intestine samples indicated the mostabundant population (Fig. 5.3A).
In digesta samples, population of component bacterial groups also appeared to be lowest
in small intestine, except for Lactobacillus and Enterobacteriaceae group. BacteroidesPrevotella-Porphyromonas group, R. albus, and F. succinogenes population showed theirhighest number in forestomach (rumen and abomasum). After a decline in number of small
intestine, in large intestine (cecum and rectum) Bacteroides-Prevotella-Porphyromonas group
and R. albus regained their number as great as their performance in forestomach, while F.
succinogenes maintained the low number from small to large intestine. Enterobacteriaceae
population was found smallest in abomasum and gradually increased their number to the largest
population in rectum. F. prausnitzii showed a gradual decreased trend from rumen to small
intestine and increased again to reach the highest population in rectum (Fig. 5.3).

62

In mucosa samples, across the gastrointestinal tract, the greatest number of several
component bacterial groups was found in small intestine, such as Lactobacillus,Enterobacteriaceae, R. albus, and F. prausnitzii. The opposite pattern was observed for
Bacteroidetes-Prevotella-Porphyromonas group and F. succinogenes, that showed smallest
number in small intestine (Fig. 5.3).
5.4.3. Comparative evaluation of digesta- and mucosa-associated microbial population
across the gastrointestinal tract of goat
The different sampling method was employed for digesta and mucosa samples, hence,
the representing unit was also different. It’s infeasible to make direct comparison between
digesta (copies/g) and mucosa (copies/cm2) samples for every segment across thegastrointestinal tract. Therefore, proportion of component bacterial group was calculated inorder to earn a synchronized evaluation. The proportion of Lactobacillus, BacteroidesPrevotella-Porphyromonas, Enterobacteriaceace, R. albus, F. prausnitzii, F. succinogenes ofdigesta and mucosa samples across the gastrointestinal tract were presented in the Table 1.
Lactobacillus, Enterobacteriaceae, F. prausnitzii did not revealed any fluctuation in their
proportion across the gastrointestinal tract regardless of sample type. Bacteroides-PrevotellaPorphyromonas, F. succinogenes and mucosa-associated R. albus showed significant variation
across the tract, which all described their highest proportion in abomasum (Table 5.1).
When comparing between digest and their corresponding mucosa samples, abomasum
was the segment that showed several differences between these two microbiota, referring to R.
albus, F. prausnitzii, F. succinogenes, and Enterobacteriaceae population. Rumen indicated
the greater proportion of mucosa-associated F. prausnitzii, F. succinogenes, andEnterobacteriaceae than those of digesta samples. Whereas, cecum and rectum revealed thegreater proportion of digesta-associated Bacteroides-Prevotella-Porphyromonas and F.
succinogenes. But R. albus exposed its higher proportion in mucosa-associated microbiota ofrectum (Table 5.1).

63

11

d

x

8

x
xy

y

Fe 0

RectumAbomasum

Rumen

Cecum

y

7
6

bcd

cd

Small
intestine

9

Fe 0

Rectum

Cecum

Small
intestine
x

x

xy

9

y

y

8

Fe 0

RectumFibrobacter succinogenes population
13

ab

a

12

11

bc
x

xy
10

9

yz

yz

b

bc

c

z

Fe 0

abc

ab

10

d

F

a

12

bc

10

7

Fe 0

RectumCecum

Enterobacteriaceae population
13

a

c

RectumE

Small
intestine

5

Abomasum

6

bc

11

Cecum

7

y

y

y

ab

Cecum

xy

12

Small
intestinex8

Faecalibacterium prausnitzii population

13

Small
intestine

10

xy

xy

6
5

Log 16S rRNA gene copy number

11

x

xy

y
7

D

12

9

8

Abomasum

Rectum

Cecum

Small
intestine

Abomasum

Lactobacillus population

13

Rumen

Bacteroides-Prevotella-Porphyromonas
population

G
14

a

a

a

a

13

a

Digesta
Mucosa

12
11

x

xy
b

z

yz

Rectum

Cecum

Small
intestine

8

Rumen

9

Fe 0

x

10

AbomasumLog 16S rRNA gene copy number

y

y

9

Fe 0

y
11

ab

bc

c

Abomasum

xy

10

Rumen

12

C

Log 16S rRNA gene copy number

x

a

ab

ab

Rumen

b

11

Rumen

13

Log 16S rRNA gene copy number

14

10

Log 16S rRNA gene copy number

a

a

a

Ruminococcus albus population

Ba

Log 16S rRNA gene copy numberaAbomasum

Total population

15

Rumen

Log 16S rRNA gene copy number

A

Figure 5.3. Relative abundance of total bacteria (A), R. albus (B), Lactobacillus group (C), F.
prausnitzii (D), Enterobacteriaceae (E), F. succinogenes (F), Bacteroides–Prevotella–Porphyromonas
group (G) population of digesta- (log 16S rRNA gene copies/g) and mucosa-associated (log 16S rRNA
gene copies/cm2) samples across the gastrointestinal tract of goat. Superscripts with different charactersabove their whiskers are considered statistically different (a, b, and c for digesta samples and x, y, andz for mucosa samples, p<0.05).

64

5.5. Discussion
Symbiotic gut microbiota colonizing the gastrointestinal of the animal have long beenrecognized to provide crucial benefits to the host. However, limited studies have been
conducted to characterize the microbial community inhabiting in different segments across thegastrointestinal tract (de Oliveira et al. 2013; Gu et al. 2013; Mao et al. 2015; Zeng et al. 2017).
In general, this symbiotic gut microbiota has been contributed by three subpopulations,
including digesta-associated, mucosa-associated, and food-associated microbiota. These three
microbial population has been supposed to interact with each other across the gastrointestinal
tract (Cheng et al. 1979; Li et al. 2011). Hence, in the current study, microbiota of digesta-,mucosa- and feed-associated samples were examined using goat model for understanding about
the dynamic and relation between these three microbiota across the gastrointestinal tract.
In silage, where lactic acid fermentation usually occurs, a rich source of lactic acidbacteria (LAB) could be found. The consumption of this rich LAB-silage-microbiota could
supposed to a probiosis effect accumulation for the ruminants if these LAB could comply asatisfactory survivability and compatibility throughout the gastrointestinal tract. In this study,
corn silage, which has been overwhelmed by Lactobacillaceae (Ni et al. 2017; Tang et al.
2017), was provided to goats. Hence, Lactobacillus community, the most important taxa
contributing to the Lactobacillaceae, was examined for the structure and number in silage, indigesta and mucosa samples across the gastrointestinal tract. It is indicated that there had been
variation in distribution of this community between different sampling sites and sampling types.
There were changes appeared in Lactobacillus group composition before and after
examination based on PCoA profile. The 0-day-Lactobacillus community in feces was
separated from the other 14-day-Lactobacillus communities (Fig. 5.2). However, all of the gutassociated samples showed a different pattern from silage-associated ones, that indicated silageassociated Lactobacillus community had no influence on gut-associated Lactobacillus
communities across the gastrointestinal tract. Only L. acetotolerans was found in common
between silage samples and all gut microbiota samples. Rather than silage-associatedmicrobiota, changing in diet formulation from 100% fiber to 50% fiber and 50% concentrate
was assumed to have effects on gut-associated microbiota.
Mucosa-associated Lactobacillus composition was more stable and differentiated fromdigesta-associated Lactobacillus community throughout the gastrointestinal tract, except forsmall intestine-associated samples that was similar between digesta and mucosa samples (Fig.
5.2). This finding is consistent with a study of Li et al. (2012) that suggested the tissueassociated bacteria may be less dynamic compared to the content-associated bacteria using

65

PCR-DGGE analysis (Li et al. 2012). However, although composition of mucosa-associated
Lactobacillus community is stable across the gastrointestinal tract, relative abundance is rather
regionally fluctuated (Fig. 5.3C). When comparing between digesta- and their corresponding
mucosa-associated samples, proportion of mucosa-associated Lactobacillus was numericallyhigher than that of digesta-associated microbiota. Small intestine seemed to be the preferable
habitat for Lactobacillus, which indicated the rather high proportion of this community
comparing to other segments (difference is not significant).
Total bacterial quantification and several component bacterial populations were
examined for all samples from digesta and mucosa samples across the gastrointestinal tract(Fig. 5.3). The lower number of small intestine-associated samples could be supposed to related
to the different sample collecting comparing to other digesta samples. Small DNA yield andconsequent small number of quantitative copies could be derived from the smaller amount ofdigesta samples from small intestine. The trend of variation in abundance of total population indigesta seemed to be contributed mainly by Bacteroides-Prevotella-Porphyromonas group, R.
albus, and F. prausnitzii population (Fig. 5.3A, B, D, G). In our study, Bacteroides andPrevotella, the two main genera composing to phylum Bacteroidetes, showed their highpopulation in the rumen and abumasum, that is consistent with other reports which indicatedBacteroidetes was the most abundant phylum in the forestomach of bovines (Mao et al. 2015;
Peng et al. 2015). However, the increase of Bacteroides and Prevotella population in cecum
and rectum of our study was opposite to finding of Mao et al. (2015), who implied that thesegenera would decline their abundance into neglectable level comparing to the one inforestomach of bovines, especially for digesta samples (Mao et al. 2015). The finding that
number of dominant fibrolytic bacteria, R. albus and F. succinogenes, were consistently highin the rumen and abomasum, is in agreement with report of Zeng et al. (2015) in bovines. Zenget al. (2017) using Illumina Miseq sequencing revealed that Fibrobacter was discovered onlyin the forestomach, and Faecalibacterium was determined only in large intestine (Zeng et al.
2017). Whereas, F. succinogenes and F. prausnitzii was detected at meaningful level in all
segments of gastrointestinal tract of our goats (Fig. 5.3D, F). The different findings could be
attributed to difference in techniques, among which qPCR has been considered more sensitivein discover the minor population than robust Miseq sequencing. The other possible reason could
be explained by different host animals. In mucosa samples, except for Bacteroides-PrevotellaPorphyromonas and F. succinogenes population, small intestine showed a greater number ofbacteria than other segments across the gastrointestinal tract. The cause of the increase in66

bacterial density in the small intestine is not clear, and further studies are needed to clarify thisconcerning.
Principally, the distribution of bacteria in digesta and mucosa tissues is different.
Digesta-associated bacteria are freely inhabiting in the lumen of gastrointestinal tract, whilemucosa-associated bacteria are only adhered on the surface of the mucosa tissue. Despite thedifferent distribution of these two microbiota, several reports had tried to equalize the sourceof sampling when comparing digesta- and mucosa-associated microbiota (Li et al. 2012; Maoet al. 2015). The equal volume or weight of sample could not provide a comparative evaluation
for these microbial population. Hence, to reflect the actual density of the two microbiota,
different units were used to represent data. The shortcoming is the difficulties of making crosscomparison to other reports and also between digesta and mucosa samples in our own study.
Proportion of several component bacterial populations were calculated in order to have asynchronized comparison (Table 5.1). There were several differences in microbial proportion
between digesta and mucosa samples across the gastrointestinal tract. Abomasum was thesegment that showed large variation between digesta and mucosa samples, while small
intestine-associated digesta and mucosa were not differentiated in their microbial proportion(Table 5.1). F. prausnitzii, a well-known butyrate producer and a putative immune regulator at
microbiota-host interface, was reported to be consistently and abundantly present in themucosa-associated microbiota of the healthy object (Duncan et al. 2002; Soko et al. 2008;
Willing et al. 2009; Van den Abbeele et al. 2011). The greater abundance of F. prausnitzii inmucosa-associated microbiota was confirmed in this study, significantly in rumen andabomasum (Table 5.1). The presence of F. prausnitzii in mucosa-associated microbiota
supported to their ability of producing butyrate close to epithelium, enhancing the butyratebioavailability for proliferating epithelium for the host animal (Van den Abbeele et al. 2011).
Fibrolytic F. succinogenes and R. albus indicated their higher proportion of mucosa-associasted
microbiota in several segments across the gastrointestinal tract. This finding was opposed to a
study of Li et al (2012) that phyla Fibrobacteres was only detected in the bovine rumen digesta
but not in rumen mucosa community (Li et al. 2012), while Malmuthuge et al. (2014) discorved
Fibrobacter in both mucosa- and digesta-associated community of preweaned calves(Malmuthuge et al. 2014).
In conclusion, the study showed that silage-associated Lactobacillus community had noinfluence on gut-associated Lactobacillus communities across the gastrointestinal tract of goats.
Composition of mucosa-associated Lactobacillus community is more stable than the one ofdigesta samples across the gastrointestinal tract, but relative abundance is rather regionally67

fluctuated. Total microbial population and its component bacterial population were varied in
number across the gastrointestinal tract of goats for both digesta and mucosa samples.
Throughout the tract, abomasum-associated microbiota indicated the greater differences inbacterial composition between digesta and mucosa samples, while no difference was seen indigesta- and mucosa-associated small intestine microbiota.

68

CHAPTER 6
GENERAL CONCLUSION
The divergence of gut microbiota in ruminants is affected by many factors including
genotype, aging, farm management, geographical locations, and diet formulation. Likewise,
differences can be seen even when animals consume the same diet and show similar
physiological profiles. Because microbial activity and metabolism in the gut is a key to secure
health and productivity of ruminants, understanding robustness and resilience of the gut
microbiota is of great importance. Therefore, in this thesis, three experiments were carried out
to examine variability, stability, and adaptability of gut microbiota in relation to feeding andnutritional management.
In early life of Holstein heifers, weaning, with the transition of diet from milk to solid
feed, showed a substantial effect on the gastrointestinal microbiota composition in all bacterial
taxa examinned, whereas no changes were observed thereafter. The composition of
Lactobacillus group changed over the growing stage, while maintaining high similarity between
individual heifers. These findings indicated that weaning had a marked influence on the gut
microbiota especially in the composition. Management during this transition period may be
critical in shaping gut microbiota, whereas silage microbiota would have no effects.
In lactating cows, rather than diet (silage) microbiota, feeding management andgeographical location may contribute greatly to shaping the gut microbiota of silage-fed dairy
cows. Although fecal microbiota composition was similar in most samples, relative abundancesof several families varied with regard to farms, countries and feeding managements. In addition,
cow-to-cow variation for concentrate-associated families was also found in the same farm.
Lactobacillaceae were dominant in all silages, while in feces, the predominant families were
Ruminococcaceae, Bacteroidaceae, Clostridiaceae, Lachnospiraceae, and Rikenellaceae.
Lactobacillaceae of corn silage appeared to be eliminated in the gastrointestinal tract. Thesefindings help to envisage the effects of feeding management and diet formulation on gut
microbiota of silage-fed dairy cows.
Across the gastrointestinal tract of ruminants, regardless of digesta- and mucosaassociated samples, the bacterial populations varied across gut segments and between bacterial
taxa. In digesta-associated samples, the populations of total bacteria. Lactobacillus spp. insilage had no influence on gut-associated Lactobacillus spp. Difference of Lactobacillus
composition between digesta- and mucosa-associated samples was shown regardless of gut

69

segments. These results indicated dynamics of bacterial populations in the gastrointestinal tract
and concrete differences between digesta- and mucosa-associated microbiota.
In general, diet formulation and feeding management showed substantial influence on
shaping the gut microbiota in ruminants, whereas food-borne bacteria like silage lactic acidbacteria had little ability of modulation. Weaning had a great impact regardless of bacterial taxa
and difference between total mixed ration feeding and separate feeding had effects on the
proportions of concentrate- and fiber-associated bacteria. Variations with regard to farms,
countries, and individual cows were also seen in relative abundances of several bacterialfamilies. Differences between gut segments and between digesta- and mucosa-associatedsamples were distinctive. Because most of the findings to date are derived from rectum samples,
there is still a large room for improvement on understanding the roles and functions of gut
microbiota in ruminants.

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