Fecal Microbial Composition in Relation to Diet and Body Mass Index

by

Wen Su

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Nutritional Sciences

University of Toronto

© Copyright by Wen Su 2012

ii

Fecal Microbial Composition in Relation to Diet and Body Mass

Index

Wen Su

Master of Science

Department of Nutritional Sciences University of Toronto

2012

Abstract Emerging evidence from animal and human studies has indicated a potential link between a

certain pattern of gut microbiota and adiposity. In the current study, the relationship between gut

microbial composition and BMI was further investigated in human with the incorporation of

macronutrient consumption and quantification of methanogenic Archaea. BMI was found to be

negatively associated with the amount of fecal Bacteroides/Prevotella, E. coli and total bacteria.

The intake of carbohydrate and dietary fiber were negatively associated with the amount of C.

coccoides, and polyunsaturated fat intake was inversely correlated with the amount of fecal C.

leptum, Bacteroides/Prevotella, and total bacteria. Fecal Archaea measured by breath CH4 was

positively associated with the amount of fecal E.coli but negatively associated with C. coccoides

and log Firmicutes/Bacteroidetes ratio. In conclusion, our findings suggest that fecal bacterial

composition is associated with BMI, diet and fecal Archaea.

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Acknowledgments Firstly, I would like to express my sincere gratitude to my supervisor, Dr. Elena Comelli, for her

persistent help and invaluable guidance along every stage of this Masters project. I am grateful

for her knowledgeable, insight, and inspiration, and it is my greatest fortune to have her as my

mentor both in science and in life, and will be indebted to her forever for her support.

Special thanks to my co-supervisor, Dr. Thomas Wolever, for his unfailing support and

tremendous insights towards this project, and I benefited considerably from his feedback.

Also, I want to extend my gratefulness to my advisory committee member, Dr. Ahmed El-

Sohemy for his expertise and encouragement during my graduate studies.

Thanks to all members (past or present) of the Comelli lab who supported me in this project:

Angela Wang, Christopher Villa, Natasha Singh, Raha Jahani, and Andrea Glenn.

I also want to thank Judyln Fernandes, Sari Rosenbloom, and Kervan Rivera-Rufner for their

collaboration and help.

Finally, I would like to take this opportunity to thank my family and friends. This thesis would

not have been possible without their constant love and support.

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Table of Contents Abstract ........................................................................................................................................... ii

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

List of Abbreviation ....................................................................................................................... ix

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

1 Introduction ................................................................................................................................ 2

Chapter 2 Background .................................................................................................................... 4

2 Background ................................................................................................................................ 5

2.1 Gut microbiota .................................................................................................................... 5

2.1.1 Gut microbiota diversity ......................................................................................... 5

2.1.1.1 Dominant and sub-dominant groups of intestinal bacteria ....................... 6

2.1.1.2 Acquisition of the gut microbiota in early life ......................................... 7

2.1.1.3 Archaea ..................................................................................................... 8

2.1.2 Metabolic activity of gut microbiota ....................................................................... 9

2.1.2.1 Diet and gut microbiota ............................................................................ 9

2.1.2.2 Products of colonic fermentation ............................................................ 13

2.1.2.3 Gut microbiota impacts on energy homeostasis ..................................... 15

2.1.2.3.1 Obesity epidemic .................................................................................... 15

2.1.2.3.2 Treatment of obesity ............................................................................... 16

2.1.2.3.3 Environmental factors involved in obesity ............................................. 17

2.1.2.3.4 Animal studies ........................................................................................ 18

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2.1.2.3.5 Clinical studies ....................................................................................... 19

2.1.2.3.5.1 Role of Bacteria in obesity ..................................................................... 19

2.1.2.3.5.2 Role of Archaea in obesity ..................................................................... 21

2.1.2.3.6 Gut microbiota and host energy balance ................................................ 25

2.1.2.4 Gut microbiota influences on other host functions ................................. 27

2.2 Summary and rationale ..................................................................................................... 27

Chapter 3 Hypothesis and objectives ............................................................................................ 30

3 Hypothesis and objectives ........................................................................................................ 31

3.1 Hypothesis ......................................................................................................................... 31

3.2 Objectives ......................................................................................................................... 31

Chapter 4 Materials and methods ................................................................................................. 32

4 Materials and methods ............................................................................................................. 33

4.1.1 Subjects and study design ..................................................................................... 33

4.1.2 Enumeration of fecal microbes ............................................................................. 34

4.1.2.1 Fecal DNA extraction ............................................................................. 34

4.1.2.2 Quantitative analysis of fecal microbial composition ............................ 34

4.1.3 Diet analysis .......................................................................................................... 38

4.1.4 Data Analysis ........................................................................................................ 39

Chapter 5 Results .......................................................................................................................... 40

5 Results ...................................................................................................................................... 41

5.1 Group comparison ............................................................................................................. 41

5.1.1 Subject characteristics ........................................................................................... 41

5.1.1.1 Subject characteristics based on BMI ..................................................... 42

5.1.1.2 Subject characteristics based on the presence of Archaea ...................... 44

5.1.1.3 Subject characteristics based on the presence of breath CH4 ................. 46

vi

5.1.2 Energy and macronutrient intake .......................................................................... 47

5.1.2.1 Energy and macronutrient intake based on BMI .................................... 47

5.1.2.2 Energy and macronutrient intake based on the presence of Archaea ..... 47

5.1.2.3 Energy and macronutrient intake based on the presence of breath CH4 ......................................................................................................... 48

5.1.3 Fecal microbial composition ................................................................................. 52

5.1.3.1 Fecal microbial composition based on BMI ........................................... 52

5.1.3.2 Fecal microbial composition based on the presence of Archaea ............ 54

5.1.3.3 Fecal microbial composition based on the presence of breath CH4 ....... 56

5.2 Correlation analysis .......................................................................................................... 58

5.2.1 Fecal microbial composition vs. demographic and anthropometric parameters ... 58

5.2.2 Fecal microbial composition vs. dietary intake .................................................... 61

5.2.3 3 Anthropometric parameters vs. dietary intake ................................................... 63

5.2.4 Other correlations .................................................................................................. 65

Chapter 6 Discussion .................................................................................................................... 69

6 Discussion ................................................................................................................................ 70

6.1 Conclusion ........................................................................................................................ 78

6.2 Limitation and future research .......................................................................................... 79

6.3 Significance ....................................................................................................................... 80

References or Bibliography .......................................................................................................... 81

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List of Tables Table 1. Studies investigating the role of diet in relation to gut microbiota 12

Table 2a. Fecal microbiota and BMI: Intervention studies 22

Table 2b. Fecal microbiota and BMI: Cross-sectional studies 23

Table 3. List of specific 16S rRNA gene targeted primers and probes used in this study 36

Table 4. Specificity of primer and probe sets for qPCR 37

Table 5. Bacteria and grow condition used in this study 38

Table 6. Characteristics of the studied subjects 41

Table 7. Energy and macronutrient intake: Lean, overweight, and obese subjects 49

Table 8. Energy and macronutrient intake: Arch+ and Arch- subjects 50

Table 9. Energy and macronutrient intake: CH4+ and CH4- subjects 51

Table 10. Multiple linear regression: microbiology data and breath CH4 and H2 66

Table 11. Multiple linear regression: dietary intake and microbiology data 67

Table 12. Multiple linear regression: dietary intake and breath CH4 and H2 68

viii

List of Figures Figure 1. Chemical structure of 3 major short chain fatty acids found in human feces. 14

Figure 2. Demographic and anthropometric comparison among lean, overweight, and obese

subjects. 43

Figure 3. Demographic and anthropometric comparison between Arch+ and Arch- subjects. 45

Figure 4. Demographic and anthropometric comparison between CH4+ and CH4- subjects. 46

Figure 5. Fecal microbial composition comparison among lean, overweight, and obese subjects.

53

Figure 6. Fecal microbial composition comparison between Arch+ and Arch- subjects. 55

Figure 7. Fecal microbial composition comparison between CH4+ and CH4- subjects. 57

Figure 8. Association between fecal microbial composition and BMI. 59

Figure 9. Association between fecal microbial composition and breath CH4. 60

Figure 10. Association between macronutrient intakes and fecal microbial composition. 62

Figure 11. Association between macronutrient intakes and BMI or breath CH4. 64

Figure 12. Association between age and breath CH4. 65

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List of Abbreviation AAC- Acetyl-CoA carboxylase

AMPK- AMP-activated protein kinase

Arch-- Subjects without detectable amount of Archaea

Arch+- Subjects with detectable amount of Archaea

BMI- Body mass index

CFUs- Colony forming units

CH4+- Subjects with detectable amount of breath CH4

CH4--Subjects without detectable amount of breath CH4

ChREBP- Carbohydrate responsive element binding protein

DM- Drosophila melanogaster

F/B- Firmicutes/Bacteroidetes

FAS- Fatty acid synthase

Fiaf- Fasting-induced adipose factor

FISH- Fluorescence in situ hybridization

GF- Germ-free

GI- Gastrointestinal

GRP- G-protein coupled receptor

HMG-CoA- 3-hydroxy-3-methylglutaryl-CoA

LAGB- Laparoscopic adjustable gastric banding

x

LPL- Lipoprotein lipase

LPS- Lipopolysaccharides

PUFAs- Polyunsaturated fatty acids

qPCR- Quantitative PCR

RYGB- Roux-en-Y Gastric Bypass

SCFAs- Short chain fatty acids

SRB- Sulfate-reducing bacteria

SREBP-1c- Sterol regulatory element binding protein-1c

TLR- Toll-like receptor

Yrs- Years

1

Chapter 1 Introduction

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1 Introduction The human gastrointestinal (GI) tract is populated by more than 1014 microorganisms. The

number of bacterial cells per gram of luminal content increases along the length of GI tract, and

reaches the highest level in the proximal region of the colon where substrate availability is the

greatest. Residing in one of the most metabolically active regions of the human body, the gut

microbiota influence a wide array of host biological events including intestinal development,

epithelial function, immune response, and nutrient processing. In particular, during colonic

fermentation, the gut microbiota extract energy from the otherwise indigestible part of human

diet such as dietary fibers and resistant starches, and at the same time, generate short chain fatty

acids (SCFAs) and gases. SCFAs not only provide a source of energy to host, but are also

involved in a variety of physiological responses such as assisting cholesterol synthesis in the

adipose tissue, promoting gluconeogenesis in the liver, and modulating gut hormones to limit

weight gain. These suggest an important role of gut microbiota in obesity.

Obesity is an emergent epidemic, and recognized as one of the biggest global public health

challenges not only because of its rapidly growing rate, but also because of its increasing

prevalence. In Canada, according to the World Health Organization, more than 60% of adults

over the age of 20 years are either overweight or obese. This poses a significant public health

concern in Canada due to its associated co-morbidities. For example, more than 8000 deaths in

Canada were linked to obesity in 2004. In 2006, the direct cost associated with obesity was

approximately 6 billion annually [1]. The fundamental cause of excessive weight gain in obese

people is the positive imbalance between energy intake and expenditure.

Evidence raised from in vitro, and human studies has suggested a link between the gut microbial

composition and obesity. Through the use of gnotobiotic animals, it has been found that the

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presence and absence of gut microbiota is associated with remarkable difference in terms of body

fat content, and that Archaea may be an important player in fat deposition processes [2][3]. On

the other hand, clinical studies have shown that obesity is associated with an abnormal fecal

microbial composition, and an altered Firmicutes to Bacteroidetes (F/B) ratio [4][5][6].

Overall, these studies suggest an association between the gut microbiota and body weight

regulation. However, it is worth pointing out that the results from subsequent human studies are

not always in accordance with each other. Therefore, the working hypothesis of this study was

that fecal microbial composition correlates with body mass index (BMI), diet and Archaea. To

address this hypothesis, this study investigated the dominant bacterial species in relation to

dietary intake, and Archaea in a study population with different BMIs.

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Chapter 2 Background

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2 Background

2.1 Gut microbiota

2.1.1 Gut microbiota diversity

The human GI tract is populated by an immense number of microorganisms collectively known

as the gut microbiota. This microbial community is composed of at least 1014 residents from 500-

1000 species encompassing all three domains of life (Archaea, Bacteria and Eukarya), whose

collective genome, commonly referred to as the microbiome, contains 100 times more genes than

the human genome [7]. Along the cephalo-caudal axis of human GI tract, there is a substantial

increase of the gut microbiota concentration from the stomach to the colon, and a shift of

microbial composition from aerobes to anaerobes. Depending on whether a microorganism is

able to establish itself within a given niche in the GI tract, the gut microbiota can be divided into

two distinct groups: resident bacteria and transient, non-resident bacteria. The colonization of the

microorganisms in the GI tract is controlled by a number of factors such as gastric, pancreatic

and biliary secretions, pH, oxygen, transit time, peristalsis, redox potential, mucin secretion,

bacterial adhesion, diet, and nutrient availability [8][9]. Evidences from classic culture-

dependent studies have indicated that the bacterial count is higher in the distal portion of the

intestine, which is predominantly populated by anaerobic bacteria, than in the proximal portion

of the intestine, where most aerobic and facultative aerobic bacteria are found [10][11]. Indeed,

in the distal portion of the GI tract and in particular in the colon, which are characterized by slow

turnover and high redox potential and a relatively high concentration of SCFAs, microorganisms

reach the highest density with up to 1011 to 1012 colony forming units (CFUs) of bacteria per mL

of luminal content [12][9].

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2.1.1.1 Dominant and sub-dominant groups of intestinal bacteria

Dominant and sub-dominant groups of bacteria are defined according to their relative presence in

the entire fecal bacteria. A dominant group of bacteria represents ≥1% of total fecal bacteria,

whereas a sub-dominant group represents less than 1% [13]. Metagenomic studies have showed

that Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria are the 4 main phyla found in

human colon [14]. Harmsen et al. and Lay et al. have shown 80-90% of fecal bacteria in healthy

adults can be detected with fluorescence in situ hybridization (FISH), with 54-75% being Gram-

positive bacteria [15][16]. Clostridium leptum (C. leptum) group and Clostridium coccoides-

Eubacterium rectale (C. coccoides) group are the Gram-positive bacterial groups found in higher

numbers in human feces, and they represent 21.1-25.2% and 22.7-28.0% of total bacteria, while

Bacteroides-Prevotella group is the most abundant Gram-negative bacterial group accounting for

8.5-28.0% of total bacteria [17]. In line with this, Eckburg et al. found that in human colon,

Firmicutes and Bacteroidetes make up more than 90% of all bacterial phylotypes [14].

Actinobacteria such as Bifidobacteria are another group of dominant bacteria commonly found in

feces, and Proteobacteria such as Enterobacteria are widely considered a sub-dominant group

[13][18]. Interestingly, although one’s gut microbiota varies at the species taxonomic level, it is

not the case at the phylum level [14]. In a recently published paper, three distinct human gut

microbiome enterotypes have been identified from the analysis of fecal samples obtained from

39 volunteers which can be used to classify human beings [19]. Enterotype 1 is characterized by

high level of Bacteroides and co-occurring Parabacteroides. Enterotype 2 has an enriched

number of Prevotella and co-occurring Desulfovibrio. Whereas, enterotype 3 is the most

common type characterized by a high level of Ruminococcus and co-occurring Akkermansia.

This finding suggested the stratified nature of gut intestinal microbiota variation other than

continuous. Interestingly, the 3 enterotypes are mostly species driven, and independent of BMI,

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age, or gender, while long-term dietary pattern is strongly associated with alternative enterotype

states [20]. Despite the relative abundance at phyla level, most people share a gut microbial

profile comprised of 50 to 100 core species, and a core microbiome consisting of more than 6000

functional gene groups [21].

2.1.1.2 Acquisition of the gut microbiota in early life

The GI tract of a fetus in uterus is considered sterile. The first exposure of a naturally delivered

infant to microbes takes place in the vaginal canal and continues through adulthood. The

acquisition of the gut microbiota is a rapid process influenced by a series of elements such as,

mode of delivery, diet, gestational age, drug treatment, health status, and surrounding

environment [22][23]. A study conducted by Pettker et al. showed that a detectable amount of

microbes are present not only in the amniotic fluid and placenta from the mothers, but also in

umbilical cord blood of healthy neonates, suggesting that maternal microbiota may contribute to

the first set of residents in GI tract of the new born [24]. The mode of delivery is an important

factor which shapes composition of the infant microbiota [23][25][26]. For instance, in contrast

to vaginally born infants, newborns delivered by caesarean section have a different enteric

microbiota characterized by delayed colonization and lower number of strict anaerobes such as

Bacteroides [27][23]. In addition, the feeding regime is a potent factor in the determination of

gut microbiota in infants. Formula-fed newborns have a broader spectrum of microorganisms

than those who are breast-fed [28]. Furthermore, preterm infants have been reported to have an

altered developmental scheme of enteric microbes characterized by increased pathogenic

bacterial species accompanied by delayed beneficial bacterial colonization [29]. In general,

during the first 3 weeks of life, the GI tract of infants is colonized by a significant number of

facultative bacterial species such as Escherichia coli (E. coli) and streptococci which in turn

create a reduced environment favoring the establishment of strictly anaerobic bacterial strains

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including Bacteroides, Bifidobacterium, and Clostridium later on [30][31]. Weaning represents

an important stage during such transition when a carbohydrate-rich diet is introduced to replace

high-fat milk-based diet supporting the establishment of carbohydrate-fermenting bacteria [32].

The intestinal microbiota undergoes a continuous regulated shaping until two years of age when

it resembles that of an adult which is considered to be relative stable over time [33].

2.1.1.3 Archaea

Intestinal Archaea are methanogens, utilizing various substrates such as CO2, H2, acetate, and

methylamines for methanogenesis [34][35]. Methanobrevibacter smithii (M. smithii) is the

predominant archaeon in the human gut [14][36][37][38]. It was first identified from the human

intestinal contents through microbiological, physiological, and immunological methods [39]. It is

the most dominant methanogenic species in human GI tract, and can comprise up to 10% of all

anaerobes residing in the colon of healthy individuals [40][41]. Interestingly, despite dietary

change and the wide use of antibiotics, the percentage of methane excretors (36.4%) in the

population, and the average concentration of methane (16.6 ppm), is close to what were

measured 35 years ago, 33.6% and 15.2 ppm respectively [42]. This is partly due to the fact that

methanogens belonging to the genus Methanobrevibacter have not been found in the human food

chain, and the distribution frequency of methane producers is related to geographical and cultural

origins. [43]. Typically, the acquisition of the methanogens starts in childern after 27 months of

age with the incidence of methanogenic Archaea host gradually increasing to 40% at 3 years and

60% at 5 years [44]. The distribution of methanogens also shows spatial difference within the

human intestine. For methanogens carriers, the distal region of the colon is heavily populated by

methanogens reaching up to 12% of total anaerobes in contrast to only 0.003% in the proximal

colon [45]. Methanogens are known to interact with other bacteria in the colon. For example,

methanogens and sulfate-reducing bacteria (SRB) coexist in the colon, and compete for H2. Up

9

regulating the level of SRB by increasing dietary sulfate is able to limit the number of

methanogens, and lower breath methane [46]. In addition, since methanogens are able to

generate methane by utilizing the H2, they essentially remove the product of polysaccharide

fermentation, and promote the growth of H2 producing microbes such as Bacteroides

thetaiotaomicron (B. thetaiotaomicron). Indeed, Samuel et al found that the co-colonization of

germ-free (GF) mice with only M. smithii and B. thetaiotaomicron significantly enhanced the

serum acetate level and liver triglycerides compared to GF mice colonized with either B.

thetaiotaomicron or M. smithii alone [3]. However, no difference was observed when M. smithii

was co-colonized with Desulfovibrio piger which is a SRB. Moreover, although the isolation of

certain Archaea species is currently not feasible, evidence based on molecular analysis supports

the existence of other groups of Archaea such as Methanosarcina, Thermoplasma,

Crenarchaeota and halophilic Archaea in the human GI tract [47]. Therefore, the level of

Archaea is of great importance in the examination of gut microbiota with which they interact.

2.1.2 Metabolic activity of gut microbiota

The human large intestine is about 150 cm in length with a corresponding surface area of 1300

cm2, and one can have somewhere between 58g and 980g of contents within this region [48]. In a

typical person consuming a western diet, bacteria represent 40-55% of fecal solid matter. The gut

microbiota is considered by many as a true organ in the human body performing functions we

have not evolved ourselves.

2.1.2.1 Diet and gut microbiota

Diet is considered the most important factor shaping the gut microbiota composition. Most

nutrients available for colonic fermentation are the leftover from the digestion occurring in the

upper GI tract. Unlike fat which undergoes more complete digestion, 20-60g of available

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carbohydrate, and 5-20g of protein are estimated to pass onto the colon on a daily basis

[49][50][51]. Among all sources of fermentation, resistant starches are considered the major

substrates for colonic fermentation followed by dietary fiber, unabsorbed sugars, and modified

cellulose [52]. Furthermore, non-dietary components such as mucus, digestion enzymes, and

salvaged host cell and bacterial cells are additional substrates for colonic fermentation [53]. For

the purpose of this thesis, effects of diet are discussed more in detail.

Diet can affect the gut microbial composition by modulating the substrate availability for colonic

fermentation. Although a link exists between diet and the gut microbiota composition, a few

intervention studies are available showing association between diets and gut microbial

composition (Table 1). It has been found that despite the type of calorie-restricted diet, the

number of Bacteriodetes is positively correlated with the amount of weight loss [4]. In particular,

the combination of low-calorie diet and exercise has been reported to reduce the counts of C.

coccoides, C. histolyticum, B. longum and B. adolescentis, but to increase the concentration of B.

fragilis, Lactobacilli and Bacteroides [54][55]. In addition, it has been shown that glycated pea

protein promotes the growth of gut commensal bacteria such as lactobacilli and bifidobacteria

[56]. Higher intakes of resistant starch and dietary fiber have been shown to promote the growth

of Firmicutes such as Ruminococcus, E.rectale-C. coccoides, F. prausnitzii and Roseburia

[57][58]. Moreover, high dietary fiber intake has also been associated with increased presence of

beneficial bacteria groups such as Bifidobacteria and Lactobacilli [58]. For instance, inulin, a

prebiotic, is a common food additive known for its bifidogenic property in human [59][60].

Furthermore, when taking enterotype into consideration, carbohydrate-based diet has been linked

to enterotypes 2 characterized by high level of Prevotella, while western diet with high animal

protein and saturated fat consumption is associated with enterotype 1 dominated by Bacteroides

[20]. Such finding paralleled a recent paper where it was found that European microbiome was

11

dominated by taxa typical of Bacteroides enterotype, whereas African microbiome was

dominated by the Prevotella enterotype [61].

On the other hand, micronutrients can also affect the gut microbiota. Dietary selenium has been

shown to impact on the intestinal microorganism by increasing their overall diversity and

exerting differential effects on the bacterial colonization of specific taxonomic groups [62]. In a

long-term randomized control study in African children, iron fortification has been shown to

result in lower level of fecal lactobacilli and higher level of fecal enterobacteria [63]. Vitamins

have also been suggested to influence gut microbial composition. For example, a clinical study

composed of African American and Caucasian American subjects has suggested that different

intake of vitamins such as vitamin A, C, and D was associated with distinct fecal microbial

compositions between the two groups [6]. In animal studies, rats fed with a vitamin A-deficient

diet had an altered microbiota characterized by an elevated total number of bacteria in the GI

tract, a proportional decrease in Lactobacillus spp. and the simultaneous increased appearance of

E. coli strain [64]. Mice with vitamin D deficiency were found to have a 50-fold increase of

bacteria in the colonic tissue compared to mice on vitamin D sufficient diet [65].

In summary, intervention studies over the last few years have indicated potential link between

the intake of specific macronutrients and the composition/function of the gut microbiota.

However, there is lack of cross-sectional studies showing the association between multiple

macronutrients and gut microbiota. Such research is needed to understand the interplay between

different macronutrients and the microbiota.

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Table 1. Studies investigating the role of diet in relation to gut microbiota

Diet Subject character Bacteria increased Bacteria decreased Refs

Low caloric & fat restricted

Obese Bacteroidetes Firmicutes [4]

Low caloric & carbohydrate restricted

Obese Bacteroidetes Firmicutes [4]

Low carbohydrate & high protein

Overweight

Oscillibacter valerigens Roseburia, E. rectal [57]

Obese Bifidobacterium [66]

Obese Roseburia, E. rectal [67][57]

High resistant starch Overweight Ruminococcus bromii, Oscillibacter valerigens, Roseburia and E. rectale

[57]

Calorie restriction & exercise

Overweight Adolescents

B. fragilis, Lactobacillus C. coccoides, B.longum, B. adolescentis

[54]

Obese Adolescents

Bacteroides C. histolyticum, E. rectal-C. coccoides [55]

- Subjects are adults unless otherwise indicated

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2.1.2.2 Products of colonic fermentation

Due to their relatively high abundance of substrates in the proximal colon, carbohydrates, as the

more energetically efficient choice to generate ATP, are preferentially fermented [68].

Consequently, the proximal region of the colon is predominantly populated by saccharolytic

species. As the available carbohydrates drop along the length of the colon, the colonic microbiota

gradually changes into a more proteolytic, methanogenic and sulphate-reducing phenotype

[49][69]. Proteolysis is common in the distal part of colon where low substrate availability and

neutral pH are persistent [69]. In contrast to the benign end products of carbohydrate

fermentation, some of metabolites formed through proteolytic fermentation are potential toxins

[70]. The endpoint of protein fermentation is the generation of SCFAs and branched-chain fatty

acids together with the production of potentially toxic compounds such as ammonia, amines,

phenols, thiols, and indoles [71][72]. While some of the metabolites are utilized by the bacteria

as a nitrogen source, others such as phenols and indoles are metabolized by hepatic enzymes and

excreted in urine [70].

It is estimated that colonic fermentation gives rise to up to 10% of energy absorbed by human

body [73]. SCFAs are a group of organic fatty acids comprised by no more than 6 carbon atoms,

and they are considered the principal anions and the major products of colonic fermentation

[74][75][49]. The production of SCFAs is influenced by a number of factors including but not

limited to nutrient availability, regional microbiota composition, transit time, and pH

[76][77][78]. Depending on the types of substrates available for fermentation, bacteria in

proximal colon consume soluble carbohydrates which give rise to linear SCFAs together with H2

and CO2 [79][80]. Instead of linear SCFAs, bacteria in the distal colon generate branched

14

SCFAs, H2, CO2, CH4, phenols, and amines by proteolytic fermentation [80]. Population survey

data has shown that acetate, butyrate, and propionate are the 3 major SCFAs (Figure 1) found in

human feces with acetate being the most abundant (60% of total SCFA) followed by propionate

(20%) and butyrate (20%) [81]. They are efficiently absorbed through primarily simple diffusion

or ion exchange in the colon, and only 5-10% is excreted in the feces [82][83].

Acetic acid Propionic acid Butyric acid

Figure 1. Chemical structure of 3 major short chain fatty acids found in human feces.

Acetate is the major SCFA present in the colon. It is less metabolised, and rapidly absorbed and

transported to liver [76]. Acetate is important for cholesterol synthesis. In the systemic

circulation, acetic acid is converted by the cytosolic acetyl-CoA synthetase of adipose tissue and

mammary gland to acetyl-CoA which is utilized for fatty acid synthesis.

Propionate can be found in a few naturally occurring foods, and it is also available as a food

preservative due to its anti-fungal and anti-bacteria effect [84][85]. However, the main source of

propionate comes from microbial fermentation in the colon [86]. Compared to acetate,

propionate is more readily absorbed and utilized in the human liver, and therefore, colonic

propionate reaches systemic circulation to a lesser extent [87][88]. It is a substrate for hepatic

15

gluconeogenesis, and exhibits hypocholesterolemic effect by inhibiting cholesterol synthesis in

hepatic tissue [89]. Indeed, studies using animal models have suggested that propionate inhibits

both 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthase which catalyzes the reaction of

acetoacetyl-CoA to HMG-CoA, and HMG-CoA reductase which catalyzes a rate limiting step of

the conversion from HMG-CoA to mevalonic acid in the mevalonate pathway for the production

of cholesterol and other isoprenoids [90].

Butyrate is preferred over propionate, acetate, glucose, and glutamine as the main energy source

of colonic epithelial cells [76][81][91]. It is often considered the most important SCFA in

colonocyte metabolism with up to 90% of butyrate metabolized by colonocytes [76]. Evidence

from animal and cell line studies has pointed out the anti-proliferative effect of butyrate, and its

protective role against colorectal adenoma and cancer [92]. However, the underlying mechanism

of butyrate anti-carcinogenetic action is not clear. Although acetate and propionate have also

been shown to provoke apoptosis, they are less effective than butyrate [93][94].

What is common to all three major SCFAs is that they all exert trophic effect in the small and

large intestine, and have been shown to stimulate epithelial cell proliferation and differentiation

in vivo [95].

2.1.2.3 Gut microbiota impacts on energy homeostasis

2.1.2.3.1 Obesity epidemic

Obesity is defined as a BMI of 30 or higher [96], and it can be further categorized into Class I

(30 ≤ BMI < 35), Class II (35 ≤ BMI < 40), and Class III (BMI ≥ 40) [97]. Obesity is a very

common metabolic condition in developed countries, and it has reached epidemic proportions

16

[98]. According to the Canadian Community Health Survey conducted in 2004, 23.4% of

Canadian adults between the age of 20 and 64 are considered obese, and in addition, 35.4% are

overweight (25≤BMI<30) [99].

Obesity is known for its detrimental effect on the quality of life [100]. A recent report estimated

that 10% premature mortality among Canadian adults from age 20 to 64 years is directly linked

to obesity [101]. Obesity is also accompanied by a low-grade chronic inflammation state

characterized by a systematic increase level of CRP and circulating cytokines including TNF-α

and IL-6 [102][103]. Indeed, being obese or overweight is a well-established risk factor for a

number of associated morbidities including Type 2 diabetes, cardiovascular diseases, cancer,

nonalcoholic fatty liver disease, and other disorders [104]. In Canada, the direct cost associated

with obesity was approximately 6 billion in 2006, which represented 4.1% of total health

spending in 2006 [1].

2.1.2.3.2 Treatment of obesity

The fundamental cause of obesity is the long-term imbalance among the energy intake,

expenditure, and storage, which in turn contributes to weight gain in the form of growing fat

deposit [105]. The basic treatment of obesity targets the persistent positive energy imbalance by

putting obese people on calorie-restricted diets together with increasing physical activity [106].

Unfortunately, long-term adherence towards the lifestyle intervention is a common problem in

such approach because compliance is not only difficult to maintain, but also hard to assess [107].

Moreover, effect from such remedy is usually transient, and most patients tend to regain part, if

not all, of their lost weight once the treatment is discontinued [108]. On the other hand,

pharmaceutical treatment of obesity has been disappointing because of its limited utility in long-

17

term weight loss, compared to diet and exercise [109][110]. Orlistat (Xenical, Roche), which

blocks fat absorption in the body, is the only FDA approved long-term anti-obesity medication

currently available on the market, while other medications have been withdrawn due to safety

concerns. Moreover, Orlistat is considered only modestly efficacious in terms of weight loss, and

known for its side effect such as steatorrhea [111]. In the case of morbid obesity (BMI>40),

bariatric surgery is performed, when the appropriate conservative approaches do not result in

substantial weight loss. Complications may occur after such invasive approaches including but

not limited to gastric dumping syndrome, incisional hernia, infections and pneumonia

[112][113]. For example, among frequently performed surgical managements of obesity,

laparoscopic adjustable gastric banding (LAGB) is considered medium efficacy with a lower rate

of overall and major complications [114]. However, patients undergo LAGB may experience

symptoms such as pouch enlargement, band slip, and port breakage.

2.1.2.3.3 Environmental factors involved in obesity

A twin study from a total of 15,017 monozygotic and dizygotic twin pairs has shown that genetic

variation is significantly positively correlated with the prevalence of obesity [115]. Although

one’s predisposition towards excessive weight gain is genetically determined, the role of

environmental components certainly adds another layer of complexity to the current obesity

epidemic [116]. Factors such as the accessibility of energy-dense foods, time spent on physical

activities, and the ease of technology advance in modern life, all are considered the building

blocks of the obesity labyrinth. For instance, the availability of green space is linked to physical

activities; therefore, it also links to obesity. However, the results from evaluating the association

between obesity and green space have been inconsistent, and conclusion has not yet been reached

18

[117]. Other studies suggested that suboptimal micronutrient status such as calcium or dairy

intake may also contribute to positive energy metabolism [118]. Nevertheless, the results from

several clinical trials of dietary components in relation to weight management are far from

conclusive [119]. Enlightened by the recent finding on gut microbiota and obesity, an essential

role of intestinal microbial community in energy and metabolic homeostasis has been proposed

[120-124].

2.1.2.3.4 Animal studies

Gut microbiota is an important environmental factor that contributes to the development of

metabolic syndrome and obesity [2]. Backhed et al. were the first to show that 8 to 10 week old

conventional mice have 40% higher body fat content and 47% higher gonadal fat content than

their GF counterparts despite lower food consumption. In other words, GF mice are protected

against diet-induced obesity. Besides, the obese phenotype of the conventional microbiota was

also shown to be transmissible, as it induced 60% increase in body fat mass and development of

insulin resistance upon transplantation in GF recipient animals. The same was true, at an even

greater extent, when the transplanted gut microbiota was harvested from genetically obese

(ob/ob) mice [125].

On the other hand, obesity is also associated with an altered gut microbiota composition. A study

using 5,088 bacterial 16S rRNA gene sequences showed that compared to lean mice, obese

animals had a 50% reduction in the amount of Bacteroidetes and a proportional increase in the

Firmicutes [126]. In addition, a recent study has shown that diet-induced obesity in mice resulted

in an increased proportion of a single uncultured clade within the Mollicutes class of Firmicutes,

which was diminished by subsequent dietary intervention to restrict the weight gain [127].

19

Studies from both Backhed et al. and Ley et al. imply the potential effect of manipulating gut

microbial community to regulate energy homeostasis in obese animals. Then the question arises,

what about the intrinsic difference of gut microbiota in humans in comparison to mice, and what

is the relevance of the animal studies towards human conditions? Although the majority of

species of mouse gut microbiota is specific to mice, mice and humans do share similar gut

microbiota structure at the division level, with predominantly Firmicutes and Bacteroidetes

[126].

2.1.2.3.5 Clinical studies

2.1.2.3.5.1 Role of Bacteria in obesity

In a pioneer study in 2006, the relation between fat storage and microbiota composition in 12

obese people who were put on a weight-loss program for a year was investigated [4]. Obese

individuals were found to carry significantly more Firmicutes and less Bacteroidetes than the

lean control participants. Furthermore, after weight-loss, the ratio of Bacteroidetes and

Firmicutes increased, and the abundance of Bacteroidetes in obese participants increased from

approximately 3% to approximately 15% [4]. This withstanding, this study could not explain

why obese people have a higher proportion of Firmicutes. While this study has the merit of being

the first to show a connection between microbiota and obesity, the findings presented were not

consistently confirmed in subsequent research from other groups (Table 2a). For instance, a

clinical study compared the fecal microbial composition over a 4 week period between a group

of obese and non-obese subjects undergoing a weight maintenance diet and group of obese

subjects undergoing a weight-loss diet has shown a significant dietary-dependent reduction of

butyrate producing Firmicutes in the feces for those subjects put on a weight-loss diet [128].

20

However, neither a difference in the percentage of Bacteroidetes between the obese and non-

obese subjects, nor a change of the proportion of Bacteroidetes before or after weight-loss diet

was detected. In other words, the author was unable to identify a relation between BMI and

altered proportion of Bacteroidetes and Firmicutes in their obese cohort.

Results from cross-sectional studies are even more mixed (Table 2b). According to Brignardello

et al., compared to lean subjects, the levels of C. coccoides and Bacteroides were decreased in

obese subjects. However, in contrast to its finding, Collado et al. reported an increased level of

Bacteroides in obese subject, whereas, Schwiertz et al. found an higher number of C. leptum, C.

coccoides and Bacteroides in both lean and obese subjects than overweight subjects. Other

groups reported no difference between lean and obese people in terms of these three bacterial

species [129][130]. Faecalibacterium prausnitzii (F. prausnitzii) is a member of the Firmicutes,

and a lower level of F. prausnitzii has been associated with an increased low-grade inflammation

state in obesity and diabetes [131]. However, the role of F. praunitzii is not clear based on the

contradicting results from two studies. Balamurugan et al. claimed that obesity is associated with

increased F. praunitzii level, whereas Brignardello et al. had the opposite conclusion [130][132].

In summary, previous studies have provided interesting observations indicating possible

correlation between prevalence of bacterial groups and host weight, yet discrepancies were

frequently found among these studies. Such discrepancies could have been due to different

methodologies used between the different researchers or, more likely, as a result of the inherent

differences among the participants in each study (eg., different geographical region, population,

gender, diet, definition of obesity, etc.). As such, further studies with large sample size based on

21

a geographically defined heterogenous population/diet habits will be of interest to bridge

between these population-dependent results.

2.1.2.3.5.2 Role of Archaea in obesity

In the context of Archaea, in the obese subject, Brignardello et al. reported a decreased level of

M. smithii in obesity, while Zhang et al. declared a higher level of M. smithii in both the lean and

obese people in contrast to post-gastric bypass (PGB) patients [132][133]. Others reported that

there is no difference between the obese patient and normal people in terms of their Archaea

level [129].

One limitation commonly found in studies of the role of Archaea in obesity lies in the usually

small sample. For example, the study by Zhang et al. recruited only 9 subjects. We have reported

detectable breath methane in only 39% of 100 participants in a recent study, suggesting that

findings by Zhang et al. could have been due to chance alone. As such, further studies with a

more prudent sample size are needed in order for a consensus on this issue.

22

Table 2a. Fecal microbiota and to BMI: Intervention studies

- Subjects are adults unless otherwise indicated -↑, increase; ↓, decrease -RYGB: Roux-en-Y Gastric Bypass

Sample characteristic

Intervention Methodology Finding Refs Before After

12 obese 2 normal weight

Low-calorie diets (52 wks) ♦ Fat-restricted (30%) ♦ Carb-restricted (25%)

16S rRNA gene sequencing

Obese people have fewer Bacteroidetes & more Firmicutes

↑F/B ↑Bacteroidetes correlated with percentage of weight loss

[4]

30 obese

Before & after RYGB qPCR ↓Bacteroides/Prevotella in obese ↓F. prausnitzii in diabetes

↑Bacteroides/Prevotella & E. coli ↓Bifidobacterium, Lactobacillus /Leuconostoc/Predicoccus ↑F. prausnitzii

[131]

36 overweight adolescents

Calorie-restricted diet (10-40% reduction) Increased physical activity (15 to 23 kcal/kg of body weight per week) (10 weeks)

qPCR ↓C. coccoides, B. longum, B. adolescentis ↑Bacteroides fragilis, Lactobacillus

[54]

33 obese 14 non-obese

23 out of 33 obese subjects undergo weight loss regimes (< 2031.5 Kcal per day) (8 weeks)

FISH No relation between BMI and the proportion of Bacteroides For obese subjects in weight loss diets ♦ No change in the percentage of

Bacteroidetes Diet-dependent reductions in a group of butyrate producing Firmicutes

[128]

23

Table 2b. Fecal microbiota and BMI: Cross-sectional studies

Sample characteristic Methodology Finding Refs 20 obese 9 anorexia 20 normal weight

qPCR Similar Firmicutes data in all groups ↑Lactobacillus in some obese subjects ↑M. smithii in anorexia patients

[129]

33 obese 35 overweight 30 normal weight

qPCR ↑F/B ratio in overweight & obese subjects [5]

3 obese 3 post-gastric bypass 3 normal weight

16S rRNA gene sequencing qPCR

↓Firmicutes, proportional ↑ Gammaproteobacteria in PGB ↑H2-producing Prevotellaceae in the obese ↑Archaea in obese than in normal weight or PGB

[133]

13 obese 11 normal weight

G+C profiling ↓Bacteroides, C. coccoides, F. prausnitzii, Bifidobacterium in obese subject ↑low G+C Clostridium cluster, some species of Lactobacillus in obesity

[132]

Children 15 obese 13 normal weight

qPCR Higher level of F. prausnitzii in obese than in non-obese children [130]

Children 25 overweight & obese 24 normal weight

FISH qPCR

Higher bifidobacterial number in normal than in overweight during infancy.` Greater number of Staphycococcus aureus in children becoming overweight than in children remaining normal weight

[134]

52 African American 46 Caucasian American

Comet assay BMI was not associated with proportions of Bacteroides or Firmicutes [6]

24

Table 2b. Continued

- Subjects are adults or otherwise indicated -↑, increase; ↓, decrease -qPCR: Quantitative PCR -FISH: Fluorescence in situ hybridization

Sample characteristic Methodology Finding Refs Pregnant women 16 overweight 34 normal weight

qPCR ↓Bifidobacterium & Bacteroides number in overweight compared to controls ↑Staphylococcus, Enterobacteriaceae & E. coli in overweight compared to controls

[135]

18 overweight women 36 normal weight

FISH qPCR

↑Bacteroides & Staphylococcus in overweight Higher concentration of Bacteroides, Clostridium & Staphylococcus correlated with mother weight before pregnancy ↑Bacteroides was associated with excessive weight gain over pregnancy

[136]

25

2.1.2.3.6 Gut microbiota and host energy balance

Several mechanisms have been proposed to explain the links between composition of the gut

microbial community and energy homeostasis. The first pathway was suggested by Jeffrey I.

Gordon’s group. In a study focusing on developmental regulation of intestinal angiogenesis in

mice, they found adult GF mice had arrested capillary network formation in the small intestinal

villi, and such process could be revived and completed within 10 days once the GF mice were

colonized with a complete microbiota derived from conventional mice, thereby improving

nutrient absorption in the small intestine [137].

The second pathway involves energy extraction from fermentable food components, which in

turn gives rise to SCFAs, and eventually, leads to de novo hepatic lipogenesis. This can be

achieved through the expression of several mediators such as acetyl-CoA carboxylase (ACC) and

fatty acid synthase (FAS) [138]. Both ACC and FAS are the downstream targets for

carbohydrate responsive element binding protein (ChREBP) and sterol regulatory element

binding protein-1c (SREBP-1c) [139]. According to a study performed by Backhed and

colleagues, conventionalized GF mice have an increased hepatic ChREBP mRNA and SREBP-

1c mRNA expression [2]. The liver has several ways of dealing with enhanced delivery of

calories, and one of them is to relocate them in the form of fat deposit in the peripheral tissues.

However, up-regulated ChREBP and SREBP-1c are not the only players. Lipoprotein lipase

(LPL) is a key enzyme responsible for the hydrolysis of circulating triglycerides to free fatty

acids [140]. Enhanced LPL activity results in increased cellular uptake of fatty acids and

triglyceride accumulation in adipose tissue. Fasting-induced adipose factor (Fiaf) is a circulating

inhibitor of LPL. It has been show that conventionalization of adult GF mice leads to suppression

26

of Fiaf expression in ileum, and therefore fat is deposited as a consequence of liberated LPL

activity [2].

A third mechanism has been proposed to explain the resistance to diet-induced obesity in GF

mice, which was shown to be associated with elevated level of skeletal muscle and liver

phosphorylated AMP-activated protein kinase (AMPK) and its downstream targets involving fat

oxidation [141][98]. Therefore, gut microbiota colonization causes the down regulation of

AMPK related fat oxidation in liver and skeletal muscle.

The fourth mechanism involves SCFAs as the substrates for a group of G-protein coupled

receptor (GPR) namely GPR41 and GRP 43 [142]. A study by Samuel et al. has shown that

compared to wild type littermates, GPR41 knock out mice are associated with a specific pattern

of gut microbiota expression, and increased resistance to diet-induced obesity [143]. Another

study looking into GRP43 knock out mice draws a similar conclusion [144].

Low-grade inflammation is a common characteristic of obese people. A recent study explored

the gut microbiota composition in relation to bariatric surgery-induced weight loss in obese

patients [131], and found that increased numbers of the Firmicute F. prausnitzii are directly

linked to the reduction in low-grade inflammation state in obesity and diabetes, independently of

dietary intake patterns. In other words, reduced numbers of F. prausnitzii correlate with the

establishment of a low-grade inflammatory state in obese and diabetic patients. Others suggest

that an antibiotic-induced reduction of endotoxins such as lipopolysaccharides (LPS) leads to

reduced glucose intolerance, weight gain, fat disposition, oxidative stress, and inflammation

[145][142]. In line with this, a recent study showed that in mice gut Bacteroides-derived LPS

correlates with metabolic endotoxemia in mice on a high-fat diet for 4 weeks [146]. Interestingly,

the same authors demonstrate a role of toll-like receptor 4 (TLR4) as a mediator in this process,

27

highlighting a role for bacterial-sensing molecules expressed on intestinal epithelial cells.

Consistently with this, Vijay-Kumar and colleagues recently found that conventionalization of

wild-type GF mice conventionalized with gut microbiota harvested from TLR5-deficient mice

conferred many features resembling metabolic syndrome to the hosts [147]

2.1.2.4 Gut microbiota influences on other host functions

Gut microbiota has been also found to impact on various aspects of host function other than

nutrient processing and energy homeostasis. For example, a healthy gut microbiota has been

associated with increased longevity in humans, worms and insects by modulating the host

immune system, influencing systemic metabolic effects, and limiting the colonization and

growth of pathogenic species [148]. Surprisingly, the mating preference of Drosophila

melanogaster (DM) is also affected by gut microbiota [149]. This study employed DM feeding

on molasses based and starch based medium has shown that molasses-fed flies were more likely

to mate with other molasses-fed flies, and similar mating pattern was also observed among

starch-fed flies. However, antibiotic treatment was able to abolish the mating preference,

indicating that the commensal bacteria is responsible for the establishing such preference. What

is more, gut microbiota is also responsible for mammalian brain development and behaviour

[150]. Compared to GF mice, specific pathogen free mice with a normal gut microbiota have

reduced motor activity and increased anxiety, suggesting that the colonization of the gut

microbiota alters the signalling pathway such as second messenger and long-term synaptic

potential in regions of brain dedicating to motor control and anxiety.

2.2 Summary and rationale The human gut microbiota has been recognized as an important metabolic organ whose

composition and function are driven by the force of continuous competition and selection. A

28

number of factors such as dietary habits, age, and race have been shown to contribute to the

variation in the makeup of intestinal microbiota between individuals. Accordingly, the exact

functions provided by such a “super organ” are still poorly understood partially due to its great

inter-individual difference at the species level. The gut microbiota possesses the enzymes that are

essential for the catabolism of the indigestible component of the human diet (i.e. dietary fiber).

Indeed, evidence from recent animal and human studies suggests a role for the gut microbiota as

an environmental factor impacting host energy homeostasis. However, no consensus has been

reached in the literature on the characterization of microbial dysbiosis in the feces of overweight

and obese population as compared to their lean counterparts. Although the fundamental cause of

obesity is a positive energy balance, it results from a complicated interaction between

environmental and genetic factors with the former playing a more decisive role. Overall, current

literature suggests a link between adiposity, diet and a certain pattern of gut microbiota.

However, a ratio of Firmicutes/Bacteroidetes alone is often not sufficient to describe the tie

between obesity and gut microbial composition. For example, Archaea which is the sole methane

producer in the human gut has been shown to make up to 10% of total gut microbiota, yet despite

being recognized as potential contributor to determination of host weight, evidence on the

direction of the influence of Archaea is often contradictory, and further studies with larger

samples size are warranted. Furthermore, relationship between prevalence of certain bacterial

groups and host adiposity requires further investigation to bridge the discrepancies frequently

observed in published results. Finally, as discussed above, the effects of the interplay between

multiple macronutrients in shaping the composition and function of gut microbiota remains

unclear; whereas published studies usually were mostly intervention studies which primarily

focused on either the types of diet or specific macronutrient. Therefore, a cross sectional study

featuring multiple macronutrients and gut microbial composition is of great interest. As such,

29

this study was initiated with the hypothesis that the observed link between obesity and gut

microbial composition is likely a result of interactions among bacterial composition, diet and

Archaea.

30

Chapter 3 Hypothesis and objectives

31

3 Hypothesis and objectives

3.1 Hypothesis Fecal microbial composition correlates with BMI, diet and Archaea.

3.2 Objectives 1) To quantify dominant and sub-dominant microbial groups in the feces of lean, overweight

and obese subjects

2) To determine the association among macronutrient intake, fecal microbial composition, and

BMI

3) To investigate whether fecal Archaea correlate with microbial composition in the feces

32

Chapter 4 Materials and methods

33

4 Materials and methods 4.1.1 Subjects and study design

Ninety-nine males and non-pregnant females over the age of 17 years were recruited by

advertisement in the form of posters on the University of Toronto campus. Subjects were

excluded from the study if any of the following conditions were present: diabetes, BMI<18.0 or

>39.9, use of any oral hypoglycaemic agent, insulin sensitizer or insulin, use of antibiotics within

the last 3 months, GI diseases, motility disorder or malabsorption, liver or kidney disease or

major medical or surgical event within 6 months, high fiber intake (>30 g/day) or other abnormal

dietary pattern, and unwilling or unable to give informed consent and/or comply with study

protocol. This study was approved by Research Ethics Board, University of Toronto, and all

subjects gave written informed consent to participate in the study. Prior to the study, eligible

individuals were screened for their medical history and therapeutic agents use, and were asked to

visit the laboratory twice at any convenient time of their choice. At the first visit, subjects were

asked to provide demographic parameters such as height, weight, waist circumference, blood

pressure, two breath samples and other routine biochemistry parameters. In addition, they were

also given instructions on how to compile a 3-day dietary record and collect stool samples while

maintaining usual diet and activities. At the second visit, subjects returned with 3-day dietary

record, stool samples and provided two more breath samples. Freshly passed stools from

participants were gathered in a plastic container and placed on dry ice until delivery which

occurred within 24 hours from defecation. Once the stool sample was received in the lab, it was

homogenized using a stomacher, and stored in aliquots at -20 °C until further analysis.

Easy Sampler TM with tube holder (Quintron Instrument Company, Milwaukee, WI) was used for

collecting breath samples. Breath methane and hydrogen were measured by gas chromatography

34

(Quintron Microlyzer, Model SC, Milwaukee, WI) while calibrating simultaneously with room

air, as previously described [151]. In the current study, the detection limit is 2 ppm for breath

CH4 and H2, and subjects with no detectable breath CH4 and H2 were assigned a value of 0.

The participants were categorised as lean, overweight or obese based on BMI < 25 kg/m2, 25

kg/m2 ≤ BMI < 30 kg/m2, and BMI ≥ 30 kg/m2, respectively.

4.1.2 Enumeration of fecal microbes

4.1.2.1 Fecal DNA extraction

Total DNA was extracted from 80 mg of stool with E.Z.N.A.® Stool DNA Isolation Kit

(OMEGA Bio-tek, Norcross, GA) according to the manufacturer’s protocol modified to include

an additional lysozyme digestion step at 37°C for 30 minutes before the proteinase digestion step

and eluted in 100 µl of UltraPure™ DNase/RNase-Free Distilled Water (Life Technologies Inc.,

Burlington, ON). DNA concentration (µg/µl) and purity (OD260/280, 260/230) were assessed by

Thermoscientific’s Nanodrop 1000 spectrophotometer (Nanodrop Technologies, Wilmington,

DE) and DNA samples were stored at -20 °C until processing.

4.1.2.2 Quantitative analysis of fecal microbial composition

Fecal bacteria and Archaea were enumerated in triplicates by quantitative real-time PCR using

50 ng of DNA for the specific TaqMan® custom assay or SYBR green assay, in conjunction

with the TaqMan® Gene Expression Master Mix (Applied Biosystems, Foster City, CA) or

Power SYBR Green Master Mix (Applied Biosystems, Foster City, CA), respectively, and a

7900 HT fast real-time PCR system equipped with a 384 wells block (Applied Biosystems,

Foster City, CA). Primer and probe sets and their corresponding efficiency and detection limits

for the bacteria genera and/or groups of interest are listed in Table 3. Numbers of microbial cells

in each sample were calculated by interpolation using pre-constructed standard curves, and were

35

expressed as log number of cell counts per gram of wet feces and log number of 16S rRNA gene

copies per gram of wet feces for bacteria and Archaea, respectively. In addition, F/B ratio was

calculated by dividing the sum of absolute amounts of C. coccoides and C. leptum by the

absolute amount of Bacteroides/Prevotella. The specificity of primer and probe sets were tested

using DNA from pure bacterial cultures (Table 4). In addition, a non-template control was used

in all qPCR runs. In all cases, only the DNA from target species successfully generated

amplification product.

36

Table 3. List of specific 16S rRNA gene targeted primers and probes used in this study

Target Primers and Probes (5’ - 3’)

Detection limit (cells/ng of DNA)

Efficiency Refs Lower Upper Total Bacteria Forward: CGGTGAATACGTTCCCGG 8.07E+02 2.52E+06 100.75% [13] Reverse: TACGGCTACCTTGTTACGACTT Probe: CTTGTACACACCGCCCGTC

Bacteroides/Prevotella Forward: CCTTCGATGGATAGGGGTT 1.25E+01 1.25E+07 93.71% [13] Reverse: CACGCTACTTGGCTGGTTCAG Probe: AAGGTCCCCCACATTG

C. coccoides Forward: GACGCCGCGTGAAGGA 3.09E+00 3.09E+06 99.93% [13] Reverse: AGCCCCAGCCTTTCACATC Probe: CGGTACCTGACTAAGAAG

C. leptum Forward: CCTTCCGTGCCGSAGTTA 1.97E+02 1.97E+07 100.78% [13] Reverse: GAATTAAACCACATACTCCACTGCTT Probe: CACAATAAGTAATCCACC

Bifidobacteria Forward: CGGGTGAGTAATGCGTGACC 5.12E-01 5.12E+06 94.83% [13] Reverse: TGATAGGACGCGACCCCA Probe: CTCCTGGAAACGGGTG

E. coli Forward: CATGCCGCGTGTATGAAGAA 1.82E+00 1.82E+06 100.70% [13] Reverse: CGGGTAACGTCAATGAGCAAA

Archaea Forward: ATTAGATACCCGGGTAGTCC 2.51E+02 2.51E+10 100.52% [133] Reverse: GCCATGCACCTCCTCT Probe: AGGAATTGGCGGGGGAGCAC

-Efficiency was calculated using the equation: Efficiency = -1+10(-1/slope)

37

Table 4. Specificity of primer and probe sets for qPCR

Strain Primer sets Total Bacteria C. coccoides C. leptum

Bacteroides /Prevotella Bifidobacteria E. coli Archaea

Total bacteria + - - - - - -

C. coccoides + + - - - - -

C. leptum + - + - - - -

Bacteroides/Prevotella + - - + - - -

Bifidobacteria + - - - + - -

E. coli + - - - - + -

Archaea - - - - - - + -+, positive; -, negative

38

The standard curves for each TaqMan® custom assay and SYBR green custom assay were

obtained through 9 serial 10-fold dilutions of pure bacterial DNA extracted from a given pure

bacterial culture. The detection limit for each primer used in the study was calculated based on

the upper and lower limit of the linearity range of the corresponding standard curve (Table 3). In

the case of Archaea, the standard curve was obtained from serial dilutions prepared from plasmid

DNA, as previously described [152]. Bacteria and culture conditions used in this study are listed

in Table 5. Pure M. smithii (ATCC 35061) DNA was obtained from DSMZ, Braunschweig,

Germany.

Table 5. Bacteria and grow condition used in this study

Species Medium Conditions Bacteroides thetaiotaomicron ATCC 29148

PRAS cooked meat glucose medium (Remel, Lenexa, KS)

48-72 hrs 37 °C Anaerobiosis

Clostridium coccoides ATCC 29236

PRAS cooked meat glucose medium (Remel, Lenexa, KS)

48-72 hrs 37 °C Anaerobiosis

Clostridium leptum ATCC 29065

Reinforced Clostridal Medium, 10 g/L maltose (Oxoid, Nepean, ON)

4-7 days 37 °C Anaerobiosis

Bifidobacterium longum NCC2705

MRS Broth, HCL-cysteine 0.05% (Oxoid, Nepean, ON)

24-48 hrs 37 °C Anaerobiosis

4.1.3 Diet analysis

Dietary records were analyzed using The Food Processor SQL, Version 10.6.3 (ESHA Research;

Salem, OR).

39

4.1.4 Data Analysis

All data collected was first tested for normality using Shapiro-Wilk test. The choices of

parametric or non-parametric statistical tests were based on the normality test results using SPSS

(SPSS, Inc., Somers, NY). The differences in terms of microbiology data, demographic and

anthropometric parameters, and macronutrient intake among the groups that are classified

according to BMI, the presence of Archaea, or breath CH4, were tested using GraphPad Prism

(GraphPad Software, Inc., La Jolla, CA). One-way ANOVA, Kruskal-Wallis test, t-test, or

Mann-Whitney test was applied based on the distribution and nature of data sets. Chi-square test

was applied to compare the distributions of gender and ethnicity among the groups using SPSS.

GraphPad Prism was also used for examining the association among the counts of specific

microbial group or count-derived ratio, demographic and anthropometric data, and macronutrient

intake using Pearson’s correlation test. Multiple linear regression analysis (SPSS) was used to

assess whether the observed associations persist after excluding the confounder effect of BMI,

age and gender. All the graphs presented in this thesis were made using GraphPad Prism. For all

statistical tests, p<0.05 was considered significant.

40

Chapter 5 Results

The recruitment of study subjects and the collection of their demographic data,

anthropometric data, dietary record, and collection of fecal samples were done by

Judlyn Fernandes, and Sari Rosenbloom.

Part of data from objective 3 is included in a manuscript under submission.

41

5 Results

5.1 Group comparison

5.1.1 Subject characteristics

In total, 99 subjects were recruited in the study, 46 males and 53 females (Table 6). The age

range for the participants was from 18 to 67 years old, and the range for BMI was from 18.14 to

37.79 kg/m2. The subjects were from different ethnic backgrounds with more than half of them

being Caucasians. In addition, approximately, 89% (88/99) and 26% (26/99) subjects had

detectable amount of breath H2 and CH4, respectively, and 19 subjects had detectable amount of

both breath H2 and CH4. The ranges of breath H2 and CH4 in detectable subjects were found to

vary from 0.3 to 53.8 ppm and 0.5 to 94.3 ppm, respectively.

Table 6. Characteristics of the studied subjects

Total subjects N=99 Age (Yrs) 18-67

BMI (kg/m2) 18.14-37.79

Gender (Male:Female) 46:53

Breath CH4 (ppm) 0.5-94.3 (n=26)

Breath H2 (ppm) 0.3-53.8 (n=88)

Ethnicity (Caucasian:Asian:Black:Spanish) 51:40:5:3

-Data are shown as ranges except for gender and Ethnicity which are shown as counts

42

5.1.1.1 Subject characteristics based on BMI

To determine the relationship between subject BMI and other characteristics, the 99 subjects

were separated into 3 groups with 55 people in the lean group, 26 people in overweight group

and 18 people in the obese group. The 3 groups were matching for gender and ethnicity. Kruskal-

Wallis test was used to test whether the age, breath CH4 and H2 were different among the groups.

As shown in Figure 2, the age of obese group was significantly higher than that of lean group.

However, there were no significant differences in breath H2 or CH4 among the groups or groups

containing only subjects with detectable amount of breath H2 or CH4.

43

Figure 2. Demographic and anthropometric comparison among lean, overweight, and

obese subjects. * and *** denote significance at p<0.05 and p<0.001 levels, respectively.

44

5.1.1.2 Subject characteristics based on the presence of Archaea

To assess whether specific subject characteristics are affected by the presence of Archaea, the 99

subjects were divided into 2 groups based on the presence (Arch+) or the absence of Archaea

(Arch-). The Arch+ and Arch- groups included 37 and 62 subjects, respectively, and were also

matching for gender and ethnicity. Mann-Whitney test revealed that, compared to Arch- groups,

Arch+ subjects had a significantly higher level of breath CH4 when including subjects with non-

detectable breath CH4 (Figure 3). No difference was found in age, BMI, and breath H2 between

the groups.

45

Age

Arch+ Arch-0

20

40

60

80

(n=37) (n=62)

yrs

BMI

Arch+ Arch-0

10

20

30

40

(n=37) (n=62)

kg/m

2

CH4

Arch+ Arch-0

50

100

150

***

p<0.0001

(n=37) (n=62)

ppm

H2

Arch+ Arch-0

20

40

60

(n=37) (n=62)

ppm

CH4 (detectable only)

Arch+ Arch-0

50

100

150

(n=20) (n=6)

ppm

H2 (detectable only)

Arch+ Arch-0

20

40

60

(n=31) (n=57)

ppm

a)

d)c)

b)

f)e)

Figure 3. Demographic and anthropometric comparison between Arch+ and Arch-

subjects. *** denotes significance at p<0.001 level.

46

5.1.1.3 Subject characteristics based on the presence of breath CH4

To assess the relationship between subject breath CH4 and other characteristics, subjects were

categorized as methane producers (CH4+) or non-producers (CH4-) based on whether he or she

had detectable amount of breath CH4 (i.e higher than 2 ppm). Of 99 subjects, 26 were CH4+, and

73 were identified as CH4-. CH4- subjects had a significantly higher breath H2 concentration than

CH4+ subjects including or excluding subjects without detectable H2 (Figure 4). CH4+ and CH4-

subjects were matching for their gender and ethnicity, and no difference was found in terms of

age and BMI between those two groups.

Age

CH4+ CH4-0

20

40

60

80

(n=26) (n=73)

yrs

BMI

CH4+ CH4-0

10

20

30

40

(n=26) (n=73)

kg/m

2

H2

CH4+ CH4-0

20

40

60

80 ***p=0.0008

(n=26) (n=73)

ppm

H2 (detectable only)

CH4+ CH4-0

20

40

60

80 *p=0.045

(n=19) (n=69)

ppm

a)

d)c)

b)

Figure 4. Demographic and anthropometric comparison between CH4+ and CH4- subjects.

* and *** denote significance at p<0.05 and p<0.001 levels, respectively.

47

5.1.2 Energy and macronutrient intake

Totally, 97 dietary records were collected from the participants (subject No. 62 had his dietary

record misplaced; subject No. 73 is considered an outlier for his dietary data). The intake of total

energy, percentage of total energy intake from carbohydrate, protein and fat, and other

macronutrients such as carbohydrates, protein, fat, sugar, dietary fiber, soluble fiber, saturate fat,

and polyunsaturated fat were included for dietary analysis.

5.1.2.1 Energy and macronutrient intake based on BMI

To evaluate the relationship between BMI and energy/macronutrient intake, total energy intake,

percentage of total energy intake from carbohydrate, protein and fat, and macronutrient intake

were compared among the 3 BMI groups by Kruskal-Wallis test or one-way ANOVA according

to normality test. The total energy intake among lean, overweight and obese groups was not

significantly different from each other (Table 7). Moreover, carbohydrate, fat, sugar, dietary

fiber, soluble fiber, saturate fat, polyunsaturated fat, and cholesterol intakes were not different

among the groups. Nevertheless, obese groups had a higher intake of protein compared to lean or

overweight groups. However, the percentages of total energy intake from protein as well as from

carbohydrate and fat were similar in the 3 BMI groups (Table 7).

5.1.2.2 Energy and macronutrient intake based on the presence of Archaea

To assess the relationship between Archaea concentration and energy/macronutrient intake, total

energy intake, percentage of total energy intake from carbohydrate, protein and fat, and

macronutrient intake were compared between Arch+ and Arch- groups by Mann-Whitney test or

t-test based on the normality of the distribution. Total energy intake, percentage of total energy

48

intake from carbohydrate, protein and fat as well as macronutrient intakes were not significantly

different from each other (Table 8).

5.1.2.3 Energy and macronutrient intake based on the presence of breath CH4

Next, to determine if there is an association between breath CH4 levels and energy/macronutrient

intake, energy and dietary intake were compared between CH4+ and CH4- groups. Based on the

normality test, Mann-Whitney test or t-test was used. CH4+ subjects had significant higher level

of dietary fiber intake than subjects in CH4- (Table 9). Other dietary parameters were not

significantly different between the groups.

49

Table 7. Energy and macronutrient intake: Lean, overweight, and obese subjects

Lean (n=53)

Overweight (n=26)

Obese (n=18)

p-value1 p-value2

Total energy intake (Kcals) 2066±93 1966±106 2265±168 0.2325

Carbohydrate (g) 260.99±14.25 238.09±15.36 262.22±21.91 0.6284

Protein (g) 84.50±4.76a 77.23±4.51a 105.01±8.61b 0.0188*

Fat (g) 76.00±3.86 78.39±5.53 88.51±9.15 0.2928

Sugar (g) 88.83±6.05 86.13±8.98 90.80±13.50 0.9777

Dietary fiber (g) 21.52±1.77 20.18±1.36 20.68±1.70 0.6913

Soluble fiber (g) 0.57±0.09 0.87±0.16 0.74±0.20 0.1692

Saturated fat (g) 24.14±1.49 25.48±2.36 29.66±3.50 0.3994

Polyunsaturated fat (g) 10.67±0.81 11.96±1.56 12.72±1.66 0.3411

% of total energy intake -carbohydrate

50.34±0.01 48.48±0.02 46.23±0.02 0.2252

% of total energy intake -protein

16.46±0.01 16.10±0.01 19.41±0.01 0.1884

% of total energy intake- fat 33.19±0.01 35.42±0.02 34.37±0.02 0.5134

-Values are Mean ± SEM -* donates significance at p<0.05 level -1Kruskal-Wallis test -2One-way ANOVA

50

Table 8. Energy and macronutrient intake: Arch+ and Arch- subjects

Arch+ (n=36)

Arch- (n=61)

p-value1 p-value2

Total energy intake (Kcals) 1988±94 2127±91 0.4502

Carbohydrate (g) 234.32±12.33 267.22±13.56 0.1325

Protein (g) 80.75±5.25 89.59±4.47 0.2411

Fat (g) 80.91±5.31 77.78±3.88 0.5708

Sugar (g) 83.40±8.11 91.42±5.96 0.4418

Dietary fiber (g) 19.60±1.08 21.83±1.61 0.9643

Soluble fiber (g) 0.61±0.15 0.72±0.09 0.1505

Saturated fat (g) 27.75±2.32 2421±1.41 0.1730

Polyunsaturated fat (g) 10.90±1.10 11.67±0.88 0.6012

% of total energy intake -carbohydrate

47.51±1.46 50.01±1.05 0.1952

% of total energy intake -protein

16.50±0.76 17.15±0.61 0.4135

% of total energy intake- fat 35.99±1.33 32.84±0.87 0.0529

-Values are Mean ± SEM -1t- test -2Mann-Whitney test

51

Table 9. Energy and macronutrient intake: CH4+ and CH4- subjects

CH4+ (n=25)

CH4- (n=72)

p-value1 p-value2

Total energy intake (Kcals) 2012±128 2098±78 0.5748

Carbohydrate (g) 247.17±19.48 257.86±11.29 0.6343

Protein (g) 82.04±7.00 87.81±3.92 0.1480

Fat (g) 77.25±6.45 79.50±3.56 0.7530

Sugar (g) 82.51±10.78 90.51±5.26 0.2625

Dietary fiber (g) 23.87±1.88 20.03±1.30 0.0202*

Soluble fiber (g) 0.68±0.16 0.68±0.09 0.7099

Saturated fat (g) 25.02±2.55 25.67±1.41 0.8163

Polyunsaturated fat (g) 11.86±1.62 11.22±0.74 0.8930

% of total energy intake -carbohydrate

48.90±2.06 49.16±0.91 0.8965

% of total energy intake -protein

16.72±0.94 16.97±0.55 0.8037

% of total energy intake- fat 34.39±1.77 33.87±0.80 0.7623

-Values are Mean ± SEM -* donates significance at p<0.05 level -1t- test -2Mann-Whitney test

52

5.1.3 Fecal microbial composition

All 99 subjects had quantifiable C. coccoides (7.50-10.18 log cell counts/g of wet feces), C.

leptum (7.61-10.49 log cell counts/g of wet feces) and Bacteroides/Prevotella (5.61-9.84 log cell

counts/g of wet feces) corresponding to 0.29% to 72.72%, 0.28% to 55.05% and 0.002% to

29.26% of total bacteria, respectively. 95 and 86 subjects had quantifiable number of

Bifidobacteria (5.01-10.64 log cell counts/g of wet feces) corresponding to less than 0.00% to

82.19% of total bacteria and E. coli (3.81-10.42 log cell counts/g of wet feces) corresponding to

less than 0.00% to 2.04% of total bacteria, respectively. Archaea were detected in 37 subjects

(6.29-10.24 log copies/g of wet feces).

5.1.3.1 Fecal microbial composition based on BMI

According to normality test, Kruskal-Wallis test or one-way ANOVA were chosen over the other

to analyze microbiology data. There was no difference in terms of amount of specific microbial

groups quantified in the study, nor the log F/B ratio among the 3 BMI groups (Figure 5).

53

Figure 5. Fecal microbial composition comparison among lean, overweight, and obese

subjects. 1 indicates the use of one-way ANOVA instead of Kruskal-Wallis test.

54

5.1.3.2 Fecal microbial composition based on the presence of Archaea

Either Mann-Whitney or t-test was performed for data analysis. When Arch+ and Arch- groups

were compared, there were significantly higher levels of C. leptum and total bacteria in the

Arch+ group (Figure 6). On the other hand, the amount of other microbes and log F/B ratio were

similar between the groups.

55

C. coccoides

Arch+ Arch-7

8

9

10

11

(n=37) (n=62)

log

cell c

ount

s/g

of w

et fe

ces

C. leptum

Arch+ Arch-7

8

9

10

11

12 *p=0.024

(n=37) (n=62)

log

cell c

ount

s/g

of w

et fe

ces

Bacteroides/Prevotella

Arch+ Arch-4

6

8

10

12

(n=37) (n=62)

log

cell c

ount

s/g

of w

et fe

ces

Bifidobacteria

Arch+ Arch-0

5

10

15

(n=36) (n=59)lo

g ce

ll cou

nts/

g of

wet

fece

s

E. coli 1

Arch+ Arch-0

5

10

15

(n=34) (n=52)

log

cell c

ount

s/g

of w

et fe

ces

Total Bacteria

Arch+ Arch-8

9

10

11

12 *p=0.016

(n=37) (n=62)

log

cell c

ount

s/g

of w

et fe

ces

log F/B ratio1

Arch+ Arch--1

0

1

2

3

4

5

(n=37) (n=62)

log

F/B

ratio

a)

f)

d)

b)

g)

e)

c)

Figure 6. Fecal microbial composition comparison between Arch+ and Arch- subjects. 1

indicates the use of t-test instead of Mann-Whitney test. * denotes significance at p<0.05 level.

56

5.1.3.3 Fecal microbial composition based on the presence of breath CH4

When only the presence of breath CH4 was considered, CH4+ subjects had significant higher

level of Archaea than CH4- participants (Figure 7). The bacterial composition was not

significantly different between the groups.

57

C. coccoides

CH4+ CH4-7

8

9

10

11

(n=26) (n=73)

log

cell c

ount

s/g

of w

et fe

ces

C. leptum

CH4+ CH4-7

8

9

10

11

12

(n=26) (n=73)

log

cell c

ount

s/g

of w

et fe

ces

Bacteroides/Prevotella

CH4+ CH4-4

6

8

10

12

(n=26) (n=73)

log

cell c

ount

s/g

of w

et fe

ces

Bifidobacteria

CH4+ CH4-0

5

10

15

(n=25) (n=70)lo

g ce

ll cou

nts/

g of

wet

fece

s

E. coli 1

CH4+ CH4-0

5

10

15

(n=23) (n=63)

log

cell c

ount

s/g

of w

et fe

ces

Total Bacteria

CH4+ CH4-8

9

10

11

12

(n=26) (n=73)

log

cell c

ount

s/g

of w

et fe

ces

log F/B ratio1

CH4+ CH4--1

0

1

2

3

4

5

(n=26) (n=73)

log

F/B

ratio

Archaea

CH4+ CH4-

6

8

10

12*

p=0.0084

(n=20) (n=17)

log

copi

es/g

of w

et fe

ces

a)

f)

d)

b)

g)

e)

c)

h)

Figure 7. Fecal microbial composition comparison between CH4+ and CH4- subjects. 1

indicates the use of t-test instead of Mann-Whitney test. * denotes significance at p<0.05 level.

58

5.2 Correlation analysis Next, to assess the association among fecal microbial composition, demographic data,

anthropometric data, and dietary intake, Pearson’s r was used for correlation analyses. In

addition, multiple regression analysis was applied to assess the strength of relationship after

adjusting for confounding factors such as BMI, age, and gender.

5.2.1 Fecal microbial composition vs. demographic and anthropometric parameters

BMI was inversely correlated with the counts of Bacteroides/Prevotella and Total Bacteria

(Figure 8). Also, there was a significant negative association between BMI and E. coli counts for

the 86 subjects with detectable amount of E. coli. When correlating breath CH4 with

microbiology data (n=26), breath CH4 was found to be negatively correlated with C. coccoides

and log F/B ratio, and positively correlated with the amount E.coli and fecal Archaea (Figure 9).

In particular, breath CH4 was found to be positive correlated with the amount of fecal Archaea

after adjusting for BMI, age and gender (Table 10).

59

Figure 8. Association between fecal microbial composition and BMI.

60

C. coccoides vs. Breath CH4

0 20 40 60 80 1007

8

9

10

11

(n=26)

r=-0.5268, p=0.0057

ppm

log

cell

coun

ts/g

of w

et fe

ces

E. coli vs. Breath CH4

0 20 40 60 80 1000

2

4

6

8

10

(n=23)

r=0.4478, p=0.0321

ppm

log

cell

coun

ts/g

of w

et fe

ces

log F/B vs. Breath CH4

20 40 60 80 100

-1

0

1

2

3

(n=26)

r=-0.4244, p=0.0307

ppm

log

F/B

rat

io

Archaea vs. Breath CH4

0 20 40 60 80 1005

6

7

8

9

10

11

(n=20)

r=0.4514, p=0.0457

ppm

log

copi

es/g

of w

et fe

ces

a)

d)

b)

c)

Figure 9. Association between fecal microbial composition and breath CH4.

61

5.2.2 Fecal microbial composition vs. dietary intake

There were significant negative correlations between carbohydrate, dietary fiber intake and C.

coccoides group independently of BMI, age and gender (Figure 10) (Table 11). For C. leptum

group, there was a significant negative correlation with polyunsaturated fatty acids (PUFAs)

intake, which was not significant after adjusting for BMI, age and gender (Table 11). The counts

of Bacteroides/Prevotella phylum and total bacteria were negatively associated with PUFAs

intake, but were no longer significant after adjusting for BMI, age and gender (Table 11). In the

case of Bifidobacteria and E. coli, they were not significant association with any of the

macronutrients.

62

Polyunsaturated fat intake vs.Bacteroides/Prevotella

4 6 8 10 120

10

20

30

40

(n=97)

r = -0.2372, p=0.0193

log cell counts/g of wet feces

Gra

ms

Polyunsaturated fat intake vs.C. leptum

7 8 9 10 11 120

10

20

30

40 r = -0.2011, p=0.0482

(n=97)log cell counts/g of wet feces

Gra

ms

Polyunsaturated fat intake vs.Total bacteria

8 9 10 11 120

10

20

30

40 r = -0.2080, p=0.0409

(n=97)log cell counts/g of wet feces

Gra

ms

Carbohydrate intake vs.C. coccoides

7 8 9 10 110

100

200

300

400

500 r = -0.2380, p=0.0189

(n=97)log cell counts/g of wet feces

Gra

ms

Dietary fiber intake vs.C. coccoides

7 8 9 10 110

20

40

60 r = -0.2918, p=0.0037

(n=97)log cell counts/g of wet feces

Gra

ms

a)

d)

b)

e)

c)

Figure 10. Association between macronutrient intakes and fecal microbial composition.

63

5.2.3 3 Anthropometric parameters vs. dietary intake

There were significantly positive correlations between total energy intake, protein intake, fat

intake, saturate fat intake and BMI (Figure 11). In addition, a positive association was observed

between breath CH4 and dietary fiber consumption with or without adjusting for BMI, age and

gender (Table 12).

64

Dietary fiber intake vs.Breath CH4

0 20 40 60 80 1000

20

40

60

(n=25)

r = 0.5156, p=0.0083

ppm

Gra

ms

Total energy intake

0 10 20 30 400

1000

2000

3000

4000

5000 r = 0.2046, p=0.044

(n=97)BMI

Kca

l

Protein intake

0 10 20 30 400

50

100

150

200

250 r = 0.2695, p=0.0076

(n=97)BMI

Gra

ms

Fat intake

0 10 20 30 400

50

100

150

200

250 r = 0.2214, p=0.0293

(n=97)BMI

Gra

ms

Saturated fat intake

0 10 20 30 400

20

40

60

80 r = 0.2429, p=0.0165

(n=97)BMI

Gra

ms

a)

d)

b)

e)

c)

Figure 11. Association between macronutrient intakes and BMI or breath CH4.

65

5.2.4 Other correlations

There was a positive correlation between breath CH4 and the age of participants (Figure 12).

Age vs. Breath CH4

0 20 40 60 80 1000

20

40

60

80

(n=26)

r=0.5097, p=0.0078

ppm

yrs

Figure 12. Association between age and breath CH4.

66

Table 10. Multiple linear regression: microbiology data and breath CH4 and H2

-Values are expressed as standardized coefficients Beta, and significance -Controlling for Age, BMI and sex -* denotes significance at p<0.05 level

Dependent variables C. coccoides C. leptum Bacteroides /Prevotella Bifidobacteria E. coli Archaea All bac log F/B

CH4 -.139, .153 -.054, .573 .023, .814 .053, .574 .173, .067 .305, .001* .065, .502 -.081, .393

H2 .158, .136 .057, .585 -.069, .510 .110, .283 .001, .992 -.137, .183 .050, .636 .127, .221

67

Table 11. Multiple linear regression: dietary intake and microbiology data

Dependent variables Total E Carb Protein Fat DT_fiber Sat_fat Poly_fat % Carb % Protein % Fat

C. coccoides -.166, .103 -.214, .030* -.121, .255 -.027, .789 -.242, .014* .019, .856 -.068, .500 -.150, .135 .028, .773 .149, .130

C. leptum -.043, .679 -.057, .576 -.061, .575 .008, .940 -.016, .876 .065, .534 -.155, .126 .000, .998 -.068, .504 .042, .675

Bacteroides/Prevotella -.028, .789 -.001, .989 -.100, .355 -.020, .852 -.023, .821 .032, .763 -.197, .052 .107, .295 -.076, .458 -.073, .471

Bifidobacteria -.076, .481 -.019, .855 -.077, .494 -.120, .265 .029, .784 -.091, .370 -.120, .253 .070, .504 .001, .992 -.079, .445

E. coli .014, .895 -.026, .803 .153, .157 .001, .990 -.082, .422 -.039, .711 .056, .586 -.059, .565 .156, .126 -.027, .792

Archaea -.075, .705 -.092, .636 -.117, .554 .003, .987 .210, .266 .100, .596 -.100, .592 -.019, .917 -.030, .867 .038, .834

All bac -.096, .354 -.092, .363 -.079, .466 -.059, .571 -.101, .315 .007, .949 -.173, .087 .020, .847 -.001, .996 -.023, .823

log F/B -.018, .861 -.067, .516 .079, .470 .018, .862 -.041, .689 .001, .992 .142, .168 -.164, .112 .090, .383 .127, .211 -Values are expressed as standardized coefficients Beta, and significance -Controlling for Age, BMI and sex -* denotes significance at p<0.05 level - Total E: total energy; Carb: carbohydrate; DT_fiber: dietary fiber; Sat_fat: saturated fat; poly_fat: polyunsaturated fat %Carb: % of total energy from carbohydrate; % Protein: % of total energy from protein; % Fat: % of total energy from fat

68

Table 12. Multiple linear regression: dietary intake and breath CH4 and H2

-Values are expressed as standardized coefficients Beta, and significance -Controlling for Age, BMI and sex -* denotes significance at p<0.05 level - Total E: total energy; Carb: carbohydrate; DT_fiber: dietary fiber; Sat_fat: saturated fat; poly_fat: polyunsaturated fat %Carb: % of total energy from carbohydrate; % Protein: % of total energy from protein; % Fat: % of total energy from fat

Dependent variables Total E Carb Protein Fat DT_fiber Sat_fat Poly_fat % Carb % Protein % Fat

CH4 -.006, .952 .021, .821 .030, .765 -.058, .546 .221, 0.016* -.038, .697 -.070, .445 .063, .504 .046, .625 -.098, .290

H2 .061, .564 .056, .583 .046, .673 .041, .700 .037, .715 .056, .605 .003, .978 .054, .605 -.008, .938 -.055, .586

69

Chapter 6 Discussion

70

6 Discussion This cross-sectional study investigated the relative importance of demographic and

anthropometric measurements, energy and macronutrient intake and fecal microbial composition

in adults. In particular, this analysis focused on the impact of BMI, the intakes of energy and

macronutrient, and the amount of Archaea on fecal microbial profiles.

BMI is considered to be one of the most important factors modulating the gut microbial

composition [153]. In the current study, 99 subjects were categorized into lean, overweight and

obese groups based on BMI, and then, demographic and anthropometric measurements,

microbiology data, energy and macronutrient intake were compared among the 3 groups. Data

indicated that the obese people were significantly older than the lean subjects enrolled in the

study. Similar finding has been reported in a number of publications [154][155][156]. This

suggests that weight changes are closely linked to age-related events such as sedentary life style,

muscle loss and decreased metabolic activities [157][158].

A number of early studies looked into the connection between BMI, skin fold measurements,

waist to hip ratio and/or waist circumference and energy intake, and suggested the potential role

of macronutrients (carbohydrate, protein and fat) in the development of obesity in adulthood

([159][160][161]. In this study, in addition to carbohydrate, protein and fat, subjects’ intakes of

sugar, dietary fiber, soluble fiber, saturate fat, polyunsaturated fat, cholesterol were also

evaluated as part of macronutrient analysis. While the total energy intakes among the 3 BMI

groups were similar to each other, which is consistent with published literature [162][163], there

was a positive correlation between total energy intake and BMI in the study, suggesting that

people with higher calorie intake tend to also have higher BMI. Moreover, no evidence of

significant associations between the percentage energy intake from carbohydrate, protein or fat

71

and BMI was found, which is in accordance with previous reports [164]. In the current study, the

3 BMI groups had similar intake of macronutrients except for protein intake that was

significantly higher in the obese group compared to lean or overweight subjects. This is

expected, given that moderately elevated protein intake increases thermogenesis, satiety, and

retains fat-free mass, which may be part of the weight management some of these obese

participants are subject to [165]. On the other hand, it has been reported that total dietary fat

intake including saturated fatty acids, and PUFAs is positively associated with BMI [166][167].

In line with this, data from current study demonstrated that there was a significant positive

correlation between total energy, protein, fat, or saturated fat intake and BMI suggesting that

total calorie intake and the energy density of the food are of great importance in one’s weight

status. Furthermore, it has been shown that obesity is related to low-grade inflammation possibly

caused by elevated level of LPS. In animal studies it was demonstrated that compared to control,

the plasma concentration of endotoxin in 4 week high-fat-fed mice was 2 to 3 times higher

compared to control, which was accompanied by reduced number of C. coccoides group and the

anti-inflammatory Bifidobacteria, suggesting an increased proportion of LPS-containing

bacterial population in the gut [146]. Also, it has been shown that consuming Western diet

characterized by high fat content for 1 month period led to a 71% increase in plasma levels of

endotoxin activity measured using a specially designed monoclonal antibody targeting both

soluble and bound LPS [168]. Such rise in endotoxin activity could result from either an altered

intestinal epithelial barrier function or a change in gut microbial composition; both can be caused

by the consumption of Western diet. Consistently, it was found in the current study that there

was a positive correlation between BMI and fat intake, potentially as a result of elevated LPS in

response to high fat intake, which in turn leads to higher BMI. In accordance with mice studies,

similar findings have been observed in humans as reported in intervention study [169] and

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population-based cross-sectional survey [170]. Increased levels of inflammatory makers such as

TNF-α and IL-6 could impair insulin sensitivity by suppressing insulin signal transduction that in

turn leads to higher blood sugar level and increased fat production [171][172].

While current study suggested that being lean, overweight or obese based on BMI cut-offs does

not necessarily transform into differences in the levels of fecal C. coccoides, C. leptum,

Bacteroides/Prevotella, Bifidobacteria, total bacteria, log F/B ratio, and Archaea, this study

indicated that the amount of Bacteroides/Prevotella and total bacteria present in the feces are

inversely correlated with BMI. Numbers of Firmicutes and Bacteroidetes as the two most

predominant groups of bacteria found in human gut have been shown to be linked to weight

changes in both human and animal models. It has been suggested that obese humans and animals

have higher percentage of their gut bacteria coming from Firmicutes counterbalanced by relative

less bacteria from the phylum of Bacteroidetes. Indeed, the proportion of Firmicutes tended to

decrease overtime when the patient went through weight loss program [4]. On the other hand, in

adults, the proportion of Bacteroidetes was found to be significantly higher in lean than obese

subjects by using qPCR and 16S rRNA gene sequencing [129][173]. A decrease of Bacteroides

caused by the death of such predominant Gram-negative bacteria has been proposed to be pro-

inflammatory by releasing endotoxin in the gut. LPS is an endotoxin and a primary constituent of

the outer membrane of Gram-negative bacteria, which is mobilized into the gut upon the death of

these bacterial cells [174]. Once LPS goes into intestinal capillaries, it binds to

lipopolysaccharide-binding protein in serum and exerts pro-inflammatory effect through TLR4

dependent cascade. Nevertheless, circulating levels of LPS were not measured in the current

study, and should be evaluated in future studies. Though based on the current study, it can be

speculated that high fat intake damages the intestinal environment making it unfavorable for

bacteria.

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Since the interaction between Firmicutes and Bacteroidetes has been suggested to play an

important role in weight changes, the log F/B ratio provides a way to evaluate Firmicutes and

Bacteroidetes simultaneously, and it is a maker of dysbiosis. In this study, the log F/B ratio was

not significantly different among the 3 BMI groups, which is in line with the finding of other

groups [6][128], but in disagreement with others [4][5]. The contradiction could rise from the

difference in methodology (qPCR vs 16S rRNA gene based sequencing) and the nature of study

population. Also, such ratio has been found to be diet-related. Ley et al. has shown that F/B ratio

was positively associated with the changes in body weight (%) regardless of the type of low

calorie diets [4]. In addition, a comparative study of gut microbiota of 1 to 6 year old from two

different regions has indicated that, rural African kids consuming diet characterized by high fiber

content had a significantly increased proportion of Bacteroidetes and a decreased proportion of

Firmicutes in contrast to that of European kids following a western diet [61]. What’s more, the

F/B ratio has also been shown to be age-related with 0.4 in the infants, 10.9 in the adults and 0.6

in the elders [175].

Although the amount of fecal E. coli was also not significantly different among the three BMI

groups, this study has shown that BMI is negatively associated with the level of E. coli. In

contrast, Santacruz et al. reported an increased E. coli levels in overweight pregnant women

(BMI>25 kg/m2) [135]. The discrepancy can be partially explained by the distinct study designs

and subjects of interest (pregnant vs. general population) analyzed in each study. Given its gram-

negative property, a reduced level of E. coli caused by its death may relate to low-grade

inflammatory status in overweight and obese subjects via a similar mechanism as Gram-negative

Bacteroidetes.

74

Given that age and protein intake are higher among the obese subjects in current study, a partial

correlation analysis that controlled for age and protein intake was applied to see whether the

association of BMI with other variables is still valid. After adjusting for age and protein intake,

BMI was found to be negatively associated with the C. coccoides, E. coli counts and breath CH4

concentration. C. coccoides group together with C. leptum group are the two most important

butyrate-producing bacteria clusters present in human fecal samples [176][177] Butyrate is a

SCFA derived from colonic fermentation of dietary fiber by the gut bacteria. It has been

suggested to be beneficial in both intestinal and extra-intestinal conditions. In particular, butyrate

is considered as an anti-inflammatory agent, and has the ability to alleviate oxidative stress.

Since obesity is associated with a low-grade inflammatory state, decrease level of C. coccoides

could lead to less butyrate being produced which may in turn contribute to the inflammatory

state. The negative association observed over BMI and breath CH4 is opposite to what has been

reported by other group [178]. However, there were major limitations for the cited study. In

contrast to current study, the observed positive association by Basseri et al. was based on a small

sample size with only obese subjects whose BMI range from 30.3 to 57.2 kg/m2, and 12 out of

the total 58 participants were methane-positive based on the criteria implemented in the study.

Moreover, the subjects in that study were all seeking surgical or medical option for weight loss,

and may not be a good proxy for general obese population as well as the general public.

Dietary habits, especially calorie and macronutrient intakes, are important environmental factors

that modulates the diversity and density of human gut microbiota. While most animal studies

support a positive correlation between carbohydrate intake or dietary fiber intake and C.

coccoides after adjusting for energy intake, finding from current study suggested otherwise

75

[179][180]. An increased proportion of C. coccoides has been associated with increased BMI,

weight, body fat, fat mass percentage, serum triglycerides, and decreased high-density

lipoprotein HDL [181]. In this study, higher carbohydrate or dietary fiber intake were associated

with lower counts of C. coccoides, and the observed relationships were valid even after adjusting

for BMI, age and sex. Interestingly, an in vitro three-stage colonic model has shown that high

level of fiber intake is associated with up-regulated level of C. coccoides in the vessel 1 that is

designed to resemble the proximal colon [58]. The different observation generated from the

current study and study done by Shen et al. could result from the fact that the bacterial content of

proximal colon content is different from the bacterial content of stools. On the other hand, a

comparison study investigating the fecal microbial composition in vegetarians and omnivores

has implicated that the type of diet rather than specific macronutrient component may be a more

relevant approach to formulate relationship with specific microbial groups [182]. Moreover, it

has been shown that long-term dietary pattern correlate with enterotypes clustering in a way that

animal protein/fat and carbohydrates were associated with Bacteroides enterotype and Prevotella

enterotype, respectively [183]. This study was not designed to quantify Bacteroides and

Prevotella separately; therefore, no conclusion can be drawn in the context of enterotypes.

Interestingly, data generated from current study demonstrated a number of negative associations

between C. leptum, total bacteria, Bacteroides/Prevotella and PUFAs intake. PUFAs are fatty

acids containing more than one double bond and considered to be beneficial in both human and

animal health. Most of the genes present in gut microbiome do not normally engage in fatty acid

metabolism [184]. However, there are reports indicating that PUFAs affect the gut microbiota. In

vitro studies have revealed that modulatory effect of PUFAs concentrations in the growth and

adhesion of different Lactobacillus strains. A concentration of 10-40 μg/ml of PUFAs stunned

the growth and adhesion of several lactobacilli, whereas 5μg/ml of PUFAs promoted the growth

76

and adhesion to mucus of Lactobacillis casei Shirota (L. casei Shirota) [185]. The observed link

suggested that dietary fatty acids can alter the fatty acid composition of intestinal wall, and

modify the attachment site to promote or inhibit the colonization of indigenous microbiota. In

addition, high monounsaturated fatty acids intake has been associated with decreased total

bacteria, but did not affect individual bacterial groups, in a metabolic syndrome prone population

[186].

The third objective of this study was to assess if the presence of Archaea is a determinant of fecal

microbial composition. Fecal Archaea are methanogens, and utilize the H2 produced by colonic

fermentation to generate CH4 that is excreted as flatus or diffuses into portal vein and is excreted

in breath. M. smithii is the dominant species of Archaea found in human feces [187]. Animal

studies suggest that methanogenic Archaea could indirectly promote the energy extraction from

the food in the colon by improving colonic fermentation through their ability of utilizing H2 in

the gut [3]. Out of 99 subjects, 37 and 62 of them were Arch+ and Arch-, respectively. In

contrast to the study of Zhang et al. which had a small sample size of only 3 subjects in each

group, and found that normal weight subjects had no detectable amount of fecal Archaea, the

percentage of Arch+ subjects in this study was not significantly different among the lean,

overweight and obese groups [133]. Most bacteria groups were not affected by the presence of

Archaea except for the counts of C. leptum and total bacteria. The two groups did not differ in

any of the demographic and anthropometric measurement, total energy and macronutrient

intakes, except for breath CH4 which was higher in the Arch+. This is in line with Archaea being

the only methane producer in the human gut. Moreover, the increased presence of Archaea was

positively associated with breath CH4 level that stands after adjusting for BMI, age and gender.

77

Compared to 46% of Arch+ subjects did not have detectable breath CH4 in the study, only 23%

of CH4+ did not have detectable Archaea supporting that qPCR quantification as a more sensitive

tool to detect Archaea than breath CH4 measurement. Moreover, the presence of oral Archaea

can also confound with the breath CH4 measurement weakening the connection between fecal

Archaea and breath CH4.

In this study, 26 subjects has detectable amount of breath CH4 (CH4+), and the rest are without

detectable breath CH4 (CH4-). The CH4+ and CH4- subjects were roughly matched for everything

except that CH4+ group had low concentration of breath H2, higher amount of Archaea, and

higher dietary fiber intake. CH4+ subjects are expected to have a lower concentration of breath

H2 given that H2 is utilized as a reducing agent to reduce CO2 to CH4. Moreover, in accordance

with the finding by Fernandes et al., the concentration of breath CH4 in this study was also

positively associated with dietary fiber intake [188]. The positive relationship stands even after

adjusting for BMI, age and gender. Interestingly, the level of Bacteroides has been shown to be

elevated by dietary fiber, thus high dietary fiber intake could indirectly impact the breath CH4

production of Archaea by providing more substrate for methanogensis [189]. In addition, there

was a clear positive correlation between breath CH4 and age. Such association has been

confirmed in a number of published studies [190][191]. Human infants are devoid of any

detectable breath CH4, and gradually obtaining the breath CH4 level of adult at age of 10 [192].

Also, it has been indicated that the percentage of breath CH4 producers in the population

increases throughout adulthood [191]. Such association can be in part explained by the changes

in gastrointestinal tract function over time: as we age, breath CH4 excretion is likely enhanced by

decreased colonic transit time and increased lactose intolerance [193]. Current study also

suggested a linkage among breath CH4 and log F/B ratio such that increased level of breath CH4

was inversely associated with log F/B ratio. In line with this finding, Samuel et al. have shown

78

that compared to colonization with M. smithii or B. thetaiotamicron alone, GF mice colonization

with both M. smithii and B. thetaiotamicron enhanced the representation of both species in the

cecum and distal region of the colon [3]. In addition, we found that E. coli was positively

associated with breath CH4, perhaps because an increased level of E. coli provides more

substrate (CO2, H2) for methanogenesis by Archaea in the gut. Also, C. coccoides was found to

inversely correlate with breath CH4 concentration. Unfortunately, no clear explanation can be

given at this point. However, what really came to one’s attention is that most associations

observed with breath CH4 were not readily explained by the corresponding Archaea level(s). It

has been shown that the concentration of subject’s Archaea in the colon has to exceed 108 cells

per gram of dry weight feces to have detectable amount of breath CH4 [194]. One explanation is

that the concentration of Archaea becomes physiologically relevant to human only when it is

higher than a certain threshold that may vary from one to another. Further studies are needed to

investigate this aspect.

6.1 Conclusion 1) Although the abundance of dominant and sub-dominant microbial groups was not different

among lean, overweight and obese subjects, BMI was negatively associated with the C.

coccoides, E. coli counts and breath CH4 concentration after controlling for age and protein

intake.

2) The total energy and micronutrient intake were similar among the 3 BMI groups but obese

subjects had higher intake of protein. Increased presence of C. coccoides was inversely

associated with carbohydrate and dietary fiber intake, whereas increased PUFAs intake was

inversely associated with the abundance of C. leptum, Bacteroides/Prevotella and total bacteria.

79

Although the cause of such observation was not directly addressed in the study, it suggests a

potential role of macronutrients in the modulation of gut microbiota.

3) The number of C. leptum and total bacteria were higher in Arch+ groups than Arch- subjects.

However, the abundance of fecal Archaea was not associated with the gut microbial composition

at genus or higher taxonomic level. On the other hand, breath CH4, not fecal Archaea, was useful

in evaluating the relationship between intestinal methanogenesis activity and its impact on gut

microbial composition.

6.2 Limitation and future research There are limitations to the current study that can be reconciled in future research.

Firstly, SCFAs are the major product of colonic fermentation. However, neither the absorption of

SCFAs, nor SCFAs in the feces were measured. Those data can help towards a more insightful

understanding of factors involved in colonic fermentation. It will be interesting to see how

changes in the absorption or production of SCFAs are related to dietary intake and gut

microbiota, thereby further substantiating the argument that dietary intake can influence

physiological function of host through metabolic product generated by gut microbiota.

Secondly, the current study did not measure fecal energy loss, which can be used to derive true

energy intake by subtracting fecal energy loss from total energy intake. This may help

strengthen the power of the study to detect energy-intake-related changes in gut microbiota.

80

Lastly, in the current study, the number of bacteria was quantified based on wet weight of feces.

Since the water content of feces varies among individuals, this may introduce potential error

when calculating bacterial counts from qPCR data.

6.3 Significance The obesity epidemic has become increasingly prevalent in Canada, and represents a huge

burden to the Canadian healthcare system with approximately 3.96 (CDN) billion spending each

year [195]. Emerging knowledge suggests more of an active role of gut microbial community in

shaping the host’s overall health. Animal studies have provided insight and interest in the

subject, but the ultimate measures in humans are urgently needed. To our knowledge, this is the

first cross-sectional study conducted in Canadian population comprised of a group of people

living in the same area with a very broad ethnicity, genetic background and various diet types.

Overall, this study contributes to the knowledge of interplay between macronutrient intakes, gut

microbial composition and adiposity, and may assist in the design of dietary intervention to

better substantiate the effect of weight-control regime.

81

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