Effects of different antibiotic treatment lengths

on gut microbiota and development of resistance

: a PCR and qPCR approach

Ahmed Nahil Yasen Ek

Master's thesis at the section for Microbiology Master degree in pharmacy

45 credits

Department of Pharmacy

The Faculty of Mathematics and Natural Sciences

UNIVERSITY OF OSLO

June 2020

II

III

Effects of different antibiotic treatment lengths

on gut microbiota and development of resistance

: a PCR and qPCR approach

IV

© Author

2020

Effects of different antibiotic treatment lengths on gut microbiota and development of

resistance: a PCR and qPCR approach

Ahmed Nahil Yasen Ek

http://www.duo.uio.no/

Print Center, Universitetet i Oslo

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Acknowledgments

First, I would like deeply to thank my supervisors Professor Hanne Cecilie Winther-Larsen and

Postdoctoral researcher Katrine Lekang for their constructive help and guidance through all the

way on my master thesis. I am very grateful for them to allow me to be one of their group and

refresh my knowledge after 15 years since my graduation form home country, and that they

make it possible for me to accomplish this thesis and complete authorities requirements to be a

pharmacist in Norway.

I want also to thank all the people in ZEB for their help and kindness, specially Beata Mohebi,

Elia Ciani, Sarah Finke and Truls Rasmussen. They had kindly answered my questions and

provided a good learning atmosphere.

Last but not least, I would like to thank my parents who always supported me all over the years.

They will be proud to hear that I submit this thesis and wish to be able to be with me and

celebrate the postgraduation moments. In addition, I want to thank my lovely wife, Nawar who

did the most of the job at home and with a great patience took care of our 2 lovely kids in the

period that I was away from them in Oslo. She made sure that I have a good atmosphere to

work on this project and complete it.

Thank you,

Author

Ahmed Ek

Oslo, June 2020

VI

Abstract

Antibiotics are precious weapons in fighting infectious diseases. However, the prolonged use,

overuse and incorrect use of antibiotics resulted in crucial consequences. Such consequences

include development of antibiotic resistance also among the host microbiota, intestinal

colonization by opportunistic bacteria, permanent or transient loss of microbial diversity and

permanent or transient loss of some microbial species. These consequences depend on the type

of antibiotic, narrow or broad spectrum, concentrations that reach the gut microbiota and the

susceptibility of the bacterial species. The gut microbiota has a key role in many physiological

and pathological processes, and changes of microbiota can for example be detected by the

changes in ratio between the two domain species, Firmicutes and Bacteroidetes. The use of

antibiotics is considered to be one of the factors that has permanent or transient consequences

on the microbiota. However, on the effect of different lengths of antibiotic exposure on

microbiota and resistance development, little is known. To study this more closely, the

alteration of gut microbiota in mice was examined after treatment with the broad spectrum

antibiotic amoxicillin for 3, 7 and 14 days. In this thesis, a fecal sample was collected from the

mice prior to amoxicillin exposure and on day 25 on the experiment’s timeline. Genomic DNA

was extracted and analyzed by PCR and quantitative PCR using universal bacterial and phyla-

specific primers. Preliminary analysis can indicate that there seem to be a significant increase

in Firmicutes:Bacteroidetes ratio after 3 days of amoxicillin exposure, while no significant

differences were detected for the other treatment durations. The initial results can be interpreted

as the ability of the gut microbiota to recover after cessation of antibiotic exposure. During the

trial, the presence of the resistance gene blaTEM was observed both prior to antibiotic exposure

and on day 25 in the experiment’s timeline for the different lengths of antibiotic exposure.

Interestingly the PCR results showed higher intensity of the blaTEM gene in the group treated

with 14 days amoxicillin compared to shorter treatment lengths of 3 and 7 days. From these

preliminary data, more samples need to be analyzed to be able to draw a final conclusion about

the effect of antibiotic treatment lengths on the gut microbiome and prevalence of resistance

genes. These studies should also include the day of antibiotic termination and equal interval

after antibiotic termination.

VII

Table of Contents

1 Introduction 1

1.1 Antibiotics ....................................................................................................................1

1.1.1 Antibiotics mechanism of action ............................................................................3

1.2 Antibiotics usage ......................................................................................................5

1.3 Antibiotics resistance development ...........................................................................7

1.4 Mechanism of antibiotics resistance..........................................................................9

1.5 Intestinal Microbiota .............................................................................................. 12

1.5.1 Bacteroidetes........................................................................................................ 14

1.5.2 Firmicutes ............................................................................................................ 15

1.5.3 Firmicutes: Bacteroidetes Ratio ............................................................................ 15

1.6 Antibiotics effect on the microbiota ...................................................................... 16

1.7 Some possible methods for studying antimicrobial resistance ................................. 17

2 Aims of the study………………………………………………………………………...19

3 Materials……………………………………….………………………………………....20

3.1 Primers targeting specific genes .................................................................................. 20

3.2 Positive controls for PCR and qPCR ........................................................................... 20

3.3 Genomic DNA extraction Kits ................................................................................... 21

3.4 Solutions prepared in the laboratory ............................................................................ 21

3.5 Preparation of agarose gel used in electophoresis of DNA ........................................... 22

3.6 Gene Ruler 1 kb DNA ladder ..................................................................................... 22

4 Methods…………………….……………………………………………………………..23

4.1 Animal experiment ..................................................................................................... 23

4.2 Genomic DNA extraction from mouce stool samples .................................................. 23

4.3 Genomic DNA extraction from positive control samples ............................................ 24

4.3.1 Gram negative bacteria ......................................................................................... 24

4.3.2 Gram positive bacteria ......................................................................................... 24

4.3.3 Fungus ................................................................................................................. 25

4.3.4 Algae ................................................................................................................... 25

4.4 Quantification of DNA................................................................................................ 26

4.5 Polymerase chain reaction (PCR) ................................................................................ 26

VIII

4.6 Agarose gel electophoresis ......................................................................................... 27

4.7 Quantitative polymerase chain reaction (qPCR) ......................................................... 28

4.8 Firmicutes:Bacteroidetes ratio ..................................................................................... 30

4.9 The verification of DNA for new extracted stool samples by PCR ............................. 30

4.10 Statistical analysis…………………………………………………………………….31

5 Results………….…………………………………………………………………………32

5.1 Literature search to identify amoxicillin resistance genes ............................................ 32

5.2 Verification of positive controls by PCR .................................................................... 32

5.3 Testing of primers and positive controls by qPCR ....................................................... 34

5.4 Testing of diluted positive controls by qPCR .............................................................. 35

5.5 qPCR assessment of Bacteroidetes and Firmicutes in mouse stool samples prior and

after antibiotic treatment ................................................................................................... 35

5.6 qPCR detection of blaTEM gene in mouse stool samples prior and after antibiotic

treatment .............................................................................................................................. 38

5.7 The verification of DNA from new extracted stool samples by PCR ........................... 39

5.8 The PCR detection of blaTEM gene in the new extracted stool samples……….……….40

5.9 The PCR amplification of fungal ITS region in mice stool samples for restriction

analysis ............................................................................................................................. 41

5.10 The PCR amplification of 16S rRNA gene in mice stool samples for restriction

analysis ................................................................................................................................ 42

6. Discussion…….………… ……………………………………………………………….44

6.1 Different antibiotic treatment lengths and effects on the gut microbiota ..................... 44

6.2 Does longer antibiotic treatments increase development of resistance? ........................ 47

6.3 Antibiotic treatment and the effect on eukaryotic and yeast colonization ..................... 50

6.4 Mthodological and experimental considerations .......................................................... 51

6.4.1 Verification of positive controls by PCR .............................................................. 51

6.4.2 Testing of primers and positive controls by qPCR ................................................ 51

6.4.3 Testing of diluted positive controls by qPCR ........................................................ 52

6.4.4 The PCR amplification of fungal ITS region in mice stool samples for restriction

analysis ......................................................................................................................... 52

6.4.5 The PCR amplification of 16S rRNA gene in mice stool samples for restriction

analysis ......................................................................................................................... 53

6.4.6 Sources of error .................................................................................................... 54

7. Conclusion…….………………………………………………….…………………...….55

IX

8. Future perspectives…………………………………………….………………………...56

References……………………………………………………………………………………57

Appendix A…………………………………………………………………………………..67

Appendix B…………………………………………………………………………………..69

Appendix C………………………………………………………………………………..…71

Appendix D………………………………………………………………………………..…72

Appendix E………………………………………………………………………………...…74

Appendix F:…………………………………………………………………………………..76

Appendix G…………………………..……………………………………………………….77

Appendix H….………………………………………………………………………………..79

Appendix I ….……………………….…………………………………………………….….81

Appendix J….………………………………………………………………………….……..83

1

1. Introduction

1.1 Antibiotics

The discovery of antibiotics in the last century was one of the most revolutionary weapons

against microbial infections. Antibiotics are substances that can kill the bacterial species in

question or slow down its growth which gives the chance for the immune system to deal with

the infection. The first generations of antibiotics were originally produced by microorganism

like fungi such as Penicillium1 which produced the penicillin, or bacteria such as Streptomyces,

which produced streptomycin2. Antibiotics have been developed further and can be categorized

according to manufacturing method as natural, semisynthetic and fully synthetic antibiotics. In

the natural product, the manufacturing process happens in a large-scale fermentation of bacteria

or fungi. The natural antibiotics is isolated from the fermentation broth by chromatographic

techniques such as ion exchange chromatography and liquid-liquid extraction.3,4 Semisynthetic

antibiotics are manufactured by a chemical synthesis of a natural product as starting material.

Examples of such antibiotics are doxycycline5 and tigecycline.6 The synthetic antibiotic is

manufactured by fully synthetic process such as for the antibiotic eravacycline.5,7 See Figure 1

for examples of the different categories of antibiotics based on their manufacturing method.

Figure 1 shows the different structures of tetracycline products and derivative according the synthesis process as

natural, semisynthetic and fully synthetic.5

The development of semisynthetic and synthetic antibiotics helps to produce antibiotics in a

larger scale, develop the safety of antibiotics and reduce its side effect, enhance its activity,

absorption, penetration and lipid dissolvability, overcome bacterial resistance, and to prepare

prodrug that become active metabolite at the active site.7

2

Another way to classify the antibiotics is according to their activity against bacterial species.

Bacteria are categorized using Hans Christian Gram method to Gram positive and Gram

negative bacteria, based on the structural differences in their cell walls.8 Gram positive bacteria

are surrounded by a thick cell wall that consists of many layers of peptidoglycans and this is

why it retains violet staining.9 Gram negative bacteria has thin layer of cell wall which is

surrounded with a lipid bilayer called outer membrane (OM). This OM consists of

lipopolysaccharides and works as permeability barrier.9 In addition, the OM has proteins called

outer membrane proteins that has important role in the transport across the OM.9 Some

antibiotics will work against a limited number of bacterial species or against a few numbers of

Gram positive or negative bacteria. These antibiotics are called narrow spectrum antibiotics10.

Others will kill or inhibit a wild range of bacteria species and is referred to as broad-spectrum

antibiotics10. Broad spectrum antibiotics can kill more pathogenic and non-pathogenic bacteria

than narrow spectrum. This to a higher degree can alter the host microbiome.11

3

1.1.1 Antibiotics mechanism of action

Antibiotics can be classified into different classes according to their mechanism of action. These

mechanisms include inhibition of cell wall synthesis, disruption of cell membrane, inhibition

of protein synthesis, inhibition of DNA synthesis and inhibition of bacterial metabolism (Figure

2).

Figure 2 shows the different targets of antibiotics, mechanism of action and some example antibiotics for each

class.12

A) Cell wall synthesis inhibitor

The cell wall that surround the bacteria consists of peptidoglycan, a type of long sugar

polymer. These peptidoglycans (PG) are crosslinked with each other through D-alanyl-

alanine adjacent glycans, with the use of enzyme called penicillin binding protein13. The

disruption of this crosslink can lead to bacterial lysis because of the weakening of the

bacterial cell wall. The most known antibiotics in this class are β-lactam antibiotics14

and glycopeptides13. The first generation β-lactam antibiotic was narrow spectrum and

used against Gram positive bacteria. However later generations are developed to

4

increase the spectrum of activity or to overcome certain resistance mechanism.15 As an

example is Amoxicillin which is considered as broad spectrum β-lactam antibiotic.15

B) Protein synthesis inhibitor

The main component in bacteria that is responsible for protein synthesis is the ribosome.

The bacterial ribosome is consisting of two main parts, the 30S and 50S subunits16.

By targeting the 30S and 50S subunits of bacterial ribosome, this class of antibiotic can

inhibit protein synthesis. Examples of an antibiotic group that inhibit 30S ribosomal

subunit are aminoglycosides16 and tetracyclines17, while examples for those antibiotic

classes that inhibits 50S subunit are macrolides18, chloramphenicol19 and

oxazolidinones20. The high affinity for these antibiotics for bacterial ribosomes across

all the bacteria species is the reason for them being classified to have broad spectrum

activity.21,22

C) DNA synthesis inhibitor

Quinolones are the main class in this group. They act by inhibiting the bacterial enzyme

gyrase which spilt the double stranded DNA and so inhibiting DNA replication23.

Quinolones antibiotics are considered as broad-spectrum antibiotics that work against

both Gram negative and Gram positive bacteria.24

D) Cell membrane inhibitor

This type of antibiotic works by disturbing the integrity of cell membrane through

interrupting with lipopolysaccharides in cell membrane. This results in increasing the

membrane permeability and leakage of cell content. Example for this group is

polymyxin25. Polymyxin is mainly used to treat infections caused by Gram negative

bacteria such as Acinetobacter baumannii and Pseudomonas aeruginosa as a last resort

antibiotic.26

E) Bacterial metabolism inhibitor

These antibiotics work by inhibiting the enzymes responsible for folic acid metabolism

which in necessary for the bacterial life cycle. Examples of antibiotics that inhibit these

processes are sulfonamides and trimethoprim16. Sulfonamides were widely used against

both Gram positive and Gram negative organisms. Nowadays their use is limited to

urinary tract infection in combination with trimethoprim.27

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1.2 Antibiotics usage

Antibiotics are considered as life-saving drugs that started with the discovery of penicillin in

1928.28 They are used now days both for humans and animals to treat or prevent infectious

diseases29. In humans, antibiotics are used for treatment of infectious diseases such as

pneumonia, gonorrhea and tuberculosis.30 They are also used in the treatment of infected

wounds as well as prophylaxis of infection in open wounds.31 In addition, antibiotics made

many medical procedures possible such as organ transplants, open heart surgery and cancer

treatment.32 As cancer treatments often suppress patient’s immune system and make them more

susceptible to infections, the use of antibiotics becomes important to prevent or treat infection.

Antibiotics are used in the treatment of animal stock from infection such as Staphylococcus,

and Pasteurella multocida in poultry.33 Moreover, antibiotics are used in larger scale for

metaphylaxis (administration of antibiotic to all the animal in the farm when perceived to be in

contact with some animals that is diagnosed with disease),33 and prophylaxis (administration of

antibiotic to all animals to prevent disease when risk is established).33 In addition, antibiotics

were used as growth promoter for animal production.34 The theory behind the use of antibiotics

as growth promotor is not fully understood, but it can be explained as the use of sub therapeutic

dose of antibiotic can reduce density of microbiota, promote more favorable GIT microbial

balance for weight gain and/or reduce sub clinical infections.35 Such use was banned in the

European Union and New Zealand but still in used in Brazil and China.36

The consumption of antibiotics in animals is more than double the human use. It was estimated

that 6703 tonnes of antibiotics active ingredients37 was used for animal in 31 EU land in

comparison to 3858 tonnes used for medical purpose according to the European center for

Disease Control and European Medicine Agency surveillance report in 2017.38,40 On the other

hand in the USA, antibiotics used in animal farming accounted for 70% of the total consumption

of antibiotics in 2014.39 The sales of antibiotics in Norway for animal production was around

6.2 tonnes in 2017, compared to 10.3 tonnes in Sweden, 95.2 tonnes in Denmark, 751.6 tonnes

in Poland and 1067.7 tonnes in Italy.40 The β-lactam antibiotics count for more than 50 % of

the total consumption in Norway.41 The antibiotics consumption for animal and human use in

Norway is given in Table 1.

6

Table 1 shows the development of antibiotics consumption for both animal and medical use in 2001, 2010, 2017

and 2018 in Norway.42

Antibiotics use

/Year

2001 2010 2017 2018

Animal use/Kg

active

ingredients

5694 Kg 6347 Kg 5587 Kg 5167 Kg

Human use/

DDD a)

16,8 DDD/ 1000

inhabitants per

day

19,7 DDD/ 1000

inhabitants per

day

13,8 DDD/ 1000

inhabitants per

day

12,9 DDD/ 1000

inhabitants per

day a) DDD is defined daily dose

1n 2018, the consumption of antibiotics for animal use was reduced by 7,5% in comparison to

2017. While the consumption of antibiotics for human use was decreased by 3 % in compare to

2017.42 The health department in Norway is working to reduce the consumption of antibiotics

by 30% compared to 2012 by 2020. For hospitals, the target is a 30% reduction in the use of

broad-spectrum antibiotics by 2020.43 This target and the consumption of antibiotics in the

previous years is illustrated in Figure 3.

Figure 3 Total human sales of antibacterial agents for systemic use and for respiratory tract infections (amoxicillin,

phenoxymethylpenicillin, macrolides and doxycycline) in Norway in 2012-2018 measured in Defined Daily Dose

(DDD) per 1000 inhabitants per day. The goal according to national plan is reduction by 30% in 2020.43

Globally, the consumption of the top seven antibiotic classes for human use was around 70

billion individual doses in the world in 201044. While the consumption was around 54 billion

individual doses 10 years before, in 2000.44 Researchers estimated that the antibiotics

7

consumption will increase by 202% to 128 billion DDDs by 2030 if the countries continued

with their antibiotics use polices.45

1.3. Antibiotic resistance development

Antibiotic resistance is defined as the ability of bacteria to adapt genetic changes that reduce or

remove the effect of antibiotic on bacteria.46 However, some bacterial species such as

Pseudomonas aeruginosa and Mycobacterium tuberculosis are naturally resistant to several

antibiotics.47,48 Antimicrobial resistance is a growing and challenging global problem. The

world health organization (WHO) has announced that one of the main health concerns in the

21s Century is antibiotic resistance.49 Antibiotics resistant infections has claimed the life of at

least 50,000 individuals every year in the European union and the USA only.50 Worldwide, it

causes the death of 700,000 per year and it has been estimated to reach 10 million deaths every

year by 2050.51 Nowadays it is reported that least 2,000,000 infections by antibiotics resistant

microorganisms in the USA yearly.46 This costs the American healthcare system around $21 to

$34 billion dollar and more than 8 million additional hospitalization days.49

One of the factors that has led to this crisis of antibiotic resistance is the extensive use and

misuse of antibiotics.52 As the extended spectrum beta lactamase (ESBL) enterobacteria are

generally resistant to quinolones, carbapenems are the last resort of treatment. However the

excess use of the carbapenems to treat ESBL expressing bacteria resulted in global emergence

of carbapenems resistance.53 Mainly the over- and misuse of broad spectrum antibiotics

increase the resistance problem.54 A study conducted in India shows that the pediatric dentist

and pediatric medical practitioner prescribed more than 70% amoxicillin as first choice of

option for children, and the duration of treatment prescribed were 5 days and 3 days

respectively.55 This indicates the preference of empirical prescription rather than the

recommended guidelines. Another study from Korea evaluated the use of broad spectrum

antibiotics such as 3rd and 4th generation cephalosporines, beta lactam/beta lactamase inhibitors

and fluoroquinolones, and antibiotics used against multidrug resistance pathogens such as

carbapenems, tigecycline, polymyxin, glycopeptides, and oxazolidonone in the period from

2004 tom 2012.56 It showed the increase of broad spectrum antibiotics consumption by 10%

and by 70% for the antibiotics used against multidrug resistance pathogens, despite the

reduction of total antibiotics consumption by 15% in the same period.56

8

According to Korean surveillance study, the methicillin resistant Staphylococcus aureus

consists of 60% of S. aureus infections in hospitals, and the presence of imipenem-resistant

Acinetobacter baumannii increased from 20% to 62% between 2007 and 2013.57 Moreover, it

has been observed some strain of A. baumannii that is resistant to all antibiotics used

clinically.58 This indicates that the world is approaching a post-antibiotic era, where these drugs

are useless against the bacteria. The increased use of broad spectrum antibiotics is a main risk

factor for the emergence of resistance.59

Another factor that need to be considered is the wide use of antibiotics as prophylaxis and

growth promoter in food animal and agriculture.60 This may increase the risk of transmitting of

the resistance zoonotic bacteria either from direct infection from food contaminated product or

by transfer of genetic elements like plasmid to gut bacteria.61 It has been shown an increase in

presence of the tetracycline resistance gene tet(A), tet(B), tet(O) after the use of oxytetracycline

in pigs farm.62 Another study showed the presence of methicillin resistant S. aureus in raw food

of animal origin in Denmark.63 In Norway, it was found in 2012 that 31% of retail chicken meat

was contaminated with extended spectrum cephalosporin resistant E. coli.64 This high

prevalence of ESC-resistant E. coli raised concern, and the Norwegian poultry industry

introduced measures to limit the occurrence. This resulted in successful reduction in the E. coli

isolates from broilers displayed resistance to the third generation cephalosporins to a prevalence

below 1.3% in 2018. 65

9

1.4 Mechanism of antibiotic resistance

Bacteria have developed many mechanisms to overcome the effect of antibiotics. These

mechanisms include modification of drug molecule, reduction of drug accumulation, alteration

of target site and alteration of metabolic process. These processes are summarized in Figure 4.

Figure 4 shows mechanism of resistance of both Gram positive and Gram negative bacteria and examples of the

antibiotic in concern.66

A) Modification of the drug molecule

This mechanism is well recognized for the bacteria that are resistant for β-lactam and

aminoglycoside antibiotics. By producing specific enzymes, the bacteria can destroy the

antibiotic molecule or modify its structure. E. coli can for example hydrolyze the

penicillin by breaking the β-lactam ring with the use of β-lactamase enzyme67. On the

other hand, enzymes such as aminoglycoside modifying enzymes can change the

structure of aminoglycosides antibiotics by adding phosphor, acetyl or adenyl group68.

This will reduce the affinity of the antibiotic to the target site.

10

B) Reduction of drug accumulation

This include both reducing the penetration of the antibiotic into the cell and flushing out

antibiotic from the bacteria. By changing the permeability of the outer membrane, the

bacteria can reduce the intracellular concentration of antibiotics like some types of β-

lactams, tetracyclines and fluoroquinolones thereby reducing their effect69. On the other

hands, with the presence of efflux pumps the bacteria can thrush out antibiotics like

tetracycline and macrolides and limit its action70,71.

C) Alteration of the target site

By modifying the target site, the antibiotic will not match their original target site and

will no longer be able to work against the bacteria. Example for this mechanism are

modifying of penicillin binding protein which can result in penicillin resistance72,

changing the lipid A in lipopolysaccharides in the outer membrane of Gram negative

bacteria which can result in polymyxin resistance73, and adding methyl group to the

ribosome which result in chloramphenicol resistance74.

D) Alteration of the metabolic process

Such actions for alteration of metabolic processes can be exemplified by sulfonamide

resistant bacteria that turn to use preformed folic acid instead of para aminobenzoic acid

which is inhibited by sulfonamides. This allow the bacteria to survive75. Another

method to counteract the effect of sulfonamide is increasing the number of the enzyme

which is target by sulfonamide. This will reduce the concentration of antibiotic so that

it is no longer enough for killing the bacterial.76

The bacterial genomes can evolve and acquire the necessary genes to overcome the benefits

antibiotic administration by two methods. The first is gene mutation that is considered not the

most common method. The second is through acquiring resistance gens from DNA fragments

by a process called horizontal gene transfer (HGT)77. There are three main mechanisms of HGT:

transformation, transduction and conjugation.78 These mechanisms are illustrated in Figure 5.79

Transformation is an active mechanism in which free DNA fragments, typically from a dead

cell, are taken up into the cell from the surrounding environment.80 Transduction is a type of

DNA transfer from one cell to another through the use of bacteriophage.81 Conjugation is the

transfer of genetic element in form of plasmid. A physical contact between two cells is

established, forming a bridge that allows the transfer of DNA.82 It has been shown that transfer

11

of antibiotic resistance gene in the intestine of human and animal occur by conjugative method

and plasmid trasfer.83

Figure 5 illustrates mechanisms of horizontal gene transfer; transduction, conjugation and transformation.79

Example for horizontally transferred genes that is internationally distributed are the genes for

β-lactamase enzymes. The archetypical plasmid encoded β-lactamase TEM (blaTEM) is one of

the most common gene responsible for this enzyme in this familie84. Another example which is

known since 1990s is the extended-spectrum β-lactamase CTX-M(blaCTX-M). This enzyme is

able to hydrolyze cephalosporins at a significant level85. A rapidly expanding list of β-lactam–

hydrolyzing enzymes for which the number of unique protein sequences has surpassed 2100.86

12

1.5 Intestinal Microbiota

The term microbiota and microbiome are frequently used and sometime interchangeably.

However intestinal microbiota is referred to the microorganisms that live in the intestinal track

of their host and are key player in the host physiology and pathology,87 whereas these microbes

and their genetic content is defined as microbiome.88 This is a rich and diverse community that

consist of billions of bacteria which belong to different species.89,90 Each millimeter of the large

intestine considered of around 1011 microbial cell compared to 108 microbial cell in the small

intestine.91 Other body sites such as the mouth, nose, skin and vagina have also its own

microbiota.92

The bacterial species in the intestine belong typically to Bacteroidetes (Prevotella,

Porphyromonas), Firmicutes (Clostridium, Ruminococcus, and Eubacteria), Actinobacteria

(Bifidobacterium) and Proteobacteria phyla.93 Enterobacteriaceae, Streptococcus, and

Lactobacillus are found in smaller amount.94 The diversity and abundance of these phyla differs

from one person to another,95 and even through the stages of life.96 As the microbiota has low

density in the first days after birth and mostly has Enterobacteriaceae phylum,97 the growth of

Bifidobacterium increase and become the domain bacterium in the first months of life.97 The

diversity increases when the solid food is introduced after 6 months of life and an adult-like

microbiota starts to develop which is dominated by Bacteroidetes and Firmicutes.97 It was

reported changes in the Firmicutes:Bacteroidetes ratio in different life stages, in which infants

(3 weeks to 10 months) recorded 0,4 ratio compared to 10,9 in adults (25-45 years) and 0,6 in

the elderly group above 70 years.98 The proportion of Bacteroidetes and Firmicutes within

individual composition ranges from 3% to 92% and 7% to 97% respectively in an elderly

group.99 This individual extraordinary variation was mainly due to elderly group are more

subjected to antibiotics courses, variation in general health status and diet.99

The human microbiota is important for our health and wellbeing, and participates in vital

immunological and physiological processes such as energy metabolism and homeostasis,

vitamins synthesis, endocrine signaling, prevention of colonization and regulation of immune

function.100 The gut microbiota is responsible for fermentation of starch and dietary fibers and

producing acetate, butyrate and propionate, a type of short chain fatty acids (SCFAs).101 These

bacterial metabolites involve in multiple cellular and regulatory process.102 Propionate is

mainly produced by Bacteroidetes, butyrate by Firmicutes, and acetate by most gut

13

anaerobes.103 Butyrate is the main energy source for the epithelial cells,104 and plays important

role in the intestinal barrier maintenance.105 Butyrate stimulates the production of mucin,

antimicrobial peptides, and tight-junction proteins.105

Butyrate, propionate and acetate seem to regulate hepatic glucose and lipid homeostasis in an

adenosine monophosphate activated protein kinase dependent way involving peroxisome

proliferator activated receptor-γ regulated effects on gluconeogenesis and lipogenesis.106 This

is why disruption in the microbiota is related to obesity and type 2 diabetes.107 A recent studies

using 16S DNA sequencing of fecal samples from obese and lean humans and mice, reveals

that obese people has decreased microbiota diversity and lower proportions of Bacteroidetes

compared to their lean counterparts that has a proportional increase in Bacteroidetes.108,109 It

has been also reported increased abundance of Firmicutes in obese mice compared to their

counterparts,110 and a higher proportion of Actinobacteria in obese subjects.111 This can be

explained with more efficient energy generation in the form of shot chain fatty acid from the

diet that contributes to weight gain.112 In addition, butyrate demonstrated potential role in

immune regulation by inhibiting nuclear factor kappa β (NF-κΒ) activation in macrophages in

ulcerative colitis,113 and inhibiting histone deacetylation in acute myeloid leukemia.114 Also

another study shows the potential role of butyrate and propionate in the regulatory of T cell

production and inhibition of histone deacetylation.115 NF-κΒ is a transcription factor that helps

in the control of a plethora of normal cellular processes, which includes inflammatory and

immune responses. Histone deacetylation inhibition is involved in specific inflammatory

signaling pathways and epigenetic mechanism.116

Another inflammatory disease that has been linked with changes in the microbiota is

inflammatory bowel disease. The gut microbiota shows reduced bacterial diversity and loss of

butyrate producing bacteria like Roseburia hominis and Faecalibacterium prausnitzii.117 The

same loss of diversity was observed in other autoimmune disease, such as psioaritic arthritis.

This suggests a relationship between the microbiota and its metabolites in immune regulatory

process.118

Moreover, the gut microbiota has also been shown to be involved in the biosynthesis of many

essential B vitamins including biotin, riboflavin, cobalamin, nicotinic acid, folic acid,

pyrodixine, thiamine and pantothenic acid, as well as biosynthesis of vitamin K.119 Vitamin K

can be obtained from the diet or from the bacterial synthesis in the gut. Some bacteria are known

to synthesize naphthoquinones which considered the main precursor in all forms of vitamin

14

K.120 Examples of these bacteria are Bacteroides, Veillonella, and Enterobacter.121 Cobalamin,

which is known as vitamin B12, is particularly obtained by anaerobes such as Lactobacillus.122

Finally, the host microbiota also has a protective role against the opportunistic pathogens and

prevent overgrowth of pathogenic members in a process called resistance colonization.123 The

microbiota will compete for the nutrition and the colonization sites, and direct inhibition of

pathogens by production of antimicrobial substances like thuricin CD.124 Thuricin CD is an

example of bacteriocin, ribosome produced peptide, that is produced by Bacillus thuringiensis

and is capable of killing a wide range of C. difficile isolates.125 It was reported that C. difficile

infection is associated with decreased microbiota diversity and increased in opportunistic

pathogens.126 Also the community diversity and richness were significantly lower in the

microbiota of patients with methicillin-resistant S. aureus (MRSA) compared to individual

without.127

The two phyla, Bacteroidetes and Firmicutes, that count for more than 90% of the bacteria that

colonized the colon128, are discussed below:

1.5.1 Bacteroidetes

The Bacteroidetes phylum is the largest phylum of Gram negative bacteria in the gut

microbiota. The obligately anaerobic genus Bacteroide and other two genera Prevotella,

Porphyromonas are the most commonly encountered in the western gut microbiota.129 They

grow and live exclusively in the gut suggesting strong adaptation of this environment.130

Bacteroidetes is recognized for its ability to degrade glycan and dozen of indigestible plant

derived polysaccharide,131 producing products like short chain fatty acids that can provide 10%

of daily calories from a fiber rich diet.132 Most Bacteroidetes that lives in the intestine do not

cause diseases, with one exception: the Enterotoxigenic B. fragilis. This bacterium can produce

toxin causing colitis and can promote colon tumorigenesis.133 Besides, Bacteroides species have

the highest resistance rate among all anaerobic pathogens and can adapt most antibiotic

resistance mechanisms.134

15

1.5.2 Firmicutes

The Firmicutes phylum is the largest phylum of Gram positive spore forming bacteria. They

consist of both obligate and facultative anaerobic bacteria. The most common class in this

phylum is Clostridia which is colonized between the mucosal folds and promotes epithelial

health.135 Certain species also produce butyrate through fermentation process. They can induce

colonic T cell that helps in the homeostasis as discussed above. Certain classes of Clostridia

can cause serious disease including members of C. tetani and C. difficile.136

Another class that belong to Firmicutes phylum is the Bacilli class. The most known and

clinically relevant pathogens in this class are the Streptococcus and Enterococcus species.

Although they are found in a low level but in case of microbiota disturbance, they can cause

serious infections specially in the hospitals like septicemia, bacteremia, endocarditis, intra-

abdominal and intra-pelvic infections.137,138

1.5.3 Firmicutes:Bacteroidetes ratio

The Firmicutes:Bacteroidetes ratio is a metric method that helps in proposing the challenges in

gut microbiota in both mouse and human.139 It has been shown by many studies that

Firmicutes:Bacteroidetes ratio is correlated with Irritable bowel Syndrome,140 obesity and other

disease.128 For patients with Irritable bowel Syndrome, a relatively consistent changes was

noticed in fecal microbiota including increased Firmicutes, reduced Bacteroidetes, and

increased Firmicutes:Bacteroidetes ratio.140 An increased ratio of Firmicutes to Bacteroidetes

was observed in the gut microbiota in overweight and obese people.128 On the other hand, a case

study with 16 children with type 1 diabetes showed significantly decrease in

Firmicutes:Bacteroidetes ratio compared to healthy children.141 Another decrease in the ratio

was found in patients with chronic pancreatitis.142

It has been described lower Firmicutes:Bacteroidetes ratio in patients with systemic lupus

erythematosus, a type of autoimmune disease. Conversely, Firmicutes are increased in

rheumatoid arthritis patients.143 Gut dysbiosis in the form of an increased

Firmicutes:Bacteroidetes ratio has been connected to patients with autism spectrum disorder144

and hypertension.145 It has been found that the administration of antibiotic minocycline has

affected the blood pressure levels and reduced Firmicutes:Bacteroidetes ratio.145

16

1.6 Antibiotics effect on the microbiota

Antibiotics use is continued to be curative and prophylactic treatment with an increase in the

last years. Many studies show that the prolonged use, overuse and incorrect use of antibiotics

resulted in crucial consequences. Such consequences include development of antibiotic

resistance also among the host microbiota146, intestinal colonization by opportunistic

bacteria147, permanent or transient loss of microbial diversity148, permanent or transient loss of

some microbial species149, prolonged infection period and the risk for infection reoccurrence.

These consequences depend on the type of antibiotic, narrow or broad spectrum, concentrations

that reach the gut microbiota and the susceptibility of the bacterial species. Studies show that

use of macrolides can result in disorder in microbiota that persist over 2 years150. Similar pattern

is shown with administration of amoxicillin which also affect the diversity and the composition

of gut flora151. Another study shows that the diversity and composition of microbiota affected

significantly during amoxicillin therapy but become small and insignificant after 27 days after

treatment was started152. For instance, the taxonomic diversity and richness of the human

microbiota was decreased after the administration of ciprofloxacin. It took 4 weeks after the

end of treatment to closely resemble the composition of bacterial community, but several taxa

failed to come to normal state within 6 months.153

Many studies showed relationship between use of the antibiotics in early life and dysbiosis,

disturbance of gut microbial community and which in its turn is correlated with diabetes and

obesity154. It was found that the administration of antibiotics for children before 6 months of

age was related to the development of asthma in these children who have no family history of

asthma.155 Another study showed the use of antibiotics in the first year of life increase the risk

of early childhood asthma, and the odds doubled when 5 or more antibiotics courses were

received.156 A Finnish study showed the increase risk of asthma in early life use of Macrolides

is associated with disturbance in intestinal microbiota.157 This included decrease in

Actinobacteria and increase in Gram negative Bacteroidetes and Proteobacteria.157 Penicillin

users did not have a distinctly different phyle composition. The gut microbiota recovered within

6-12 months after penicillin treatment compared to even more than 2 years after macrolides

treatment.157

17

1.7 Some possible methods for studying antimicrobial resistance

Antibiotic resistance and susceptibility can be studied mainly by culture dependent methods

using agar or liquid media.158 Broth dilution tests was one of the earliest method to check

microbial susceptibility.159 This involves preparing dilution of antibiotic in a two fold

concentrations in a liquid growth medium which is divided in separate compartment. The

bacteria sample is added and incubated over the night. The turbidity will indicate bacteria

growth and the minimum concentration of antibiotic to get a clear solution is called the

minimum inhibitory concentration (MIC).160 Another method is culturing the bacteria in agar

plates and adding paper disk which impregnated in fixed concentration of different antibiotics.

A method called disk diffusion test or Kirby-Bauer Test (Figure 6).161

Figure 6. Illustration of the disc diffusion method. The antibiotic will diffuse from the disk and inhibit the growth

of bacteria. According to the value of diameter around each disk, the bacteria is categorized as susceptible,

intermediate, or resistant.161

The culture method is a time consuming method and it is often difficult to get sensitive values

from the gastric track.162 As a large fraction of the bacteria in gastro intestinal track (GIT)

system is not culturable which has made earlier studies of their impact more complicated and

challenging.163 A modern genotypic method of testing the interaction between the host

microbiota and antibiotics which helped in understanding this complex ecosystem are

molecular microbial analysis. This method works by detecting specific phylogenetic marker

genes, such as 16S rRNA. Commonly, the 16S rRNA is amplified by polymerase chain reaction

(PCR) using universal primers targeting a broad spectrum of prokaryotes, and further the

resulting fragments are sequenced by amplicon sequencing, e.g. Illumina MIseq.164

18

The polymerase chain reaction (PCR) is a method used to copy DNA fragments. It employs a

DNA polymerase enzyme in addition to suitable designed primer that is complementary to the

DNA sequence of interest at a known temperature.165 One of the polymerase chain reaction

(PCR) techniques which is widely used in clinical microbiology is quantitative PCR (qPCR).166

This method is used to detect DNA amplification in real time by using fluorescence.167 The

amount of the fluorescence detected during each run is directly proportional to the amount of

the DNA amplified. SYBR green dye, a non-specific fluorescent DNA dye,167 is used as

detector in qPCR reaction. The dye binds to the double stranded DNA leading to increase in the

fluorescence intensity of the complex, and then being detected and registered along the duration

of 40 cycle. The point at which the intensity of fluorescent DNA is at detectible level that

correspond to the number of template DNA in the sample is called Threshold cycle, CT.168 This

value can be used in relative or absolute quantifications.169 qPCR allows rapid detection of

different microorganism like bacteria, virus, fungus and parasites and directly from clinical

samples.170 By sequencing of 16S rRNA gene which is only found in bacteria, the detection of

bacteria and its concentration in a fecal sample can be measured.171

Another, even more advanced method is shotgun metagenomic sequencing. This allows

sequencing of small segments of DNA after random fragmentation process of the sample and

then reassembling the whole bacterial genomes without the use of specific primer.164,172 Using

this technology, it is possible to detect e.g. resistance genes, mechanism of resistance and even

mutations.173,174 It allows also researcher to evaluate bacterial diversity and detect the

abundance of different microbes in various environments. 173,174 The limitation for using

shotgun sequencing is that it is more expensive and that the bioinformatic platform used to

analyze such data requires higher level of training and access to such platforms.176

19

2. Aims of study

Antibiotics are precious weapons in fighting infectious diseases. However, the prolonged use

of antibiotic can alter the composition of gut microbiota as well as increase the emergence of

antibiotic resistance. The gut microbiota has a key role in many physiological and pathological

processes, and disruption of microbiota can be both transient and persistent. In addition, the

antibiotic resistance genes can be raised and harbored by the gut microbiota.

In this thesis, the goal is to study the effects of short- and long- treatments of amoxicillin on

murine gut microbiota, using a molecular approach. The specific sub-goals are to investigate if:

1- longer exposure to amoxicillin will result in higher bacterial changes in the gut

microbiota in the form of Firmicutes:Bacteroidetes ratios.

2- longer exposure to amoxicillin, specially the group with 14 days treatment, will results

in higher abundance of resistance genes.

3- longer exposure to amoxicillin, specially the group treated for 14 days, will increase the

eukaryotic and fungal colonization in the gut microbiota.

20

3. Materials

3.1. Primers targeting specific genes.

To analyze the disturbance of gut microbiota after antibiotic treatment, a group of specific

primers were used in the PCR and qPCR assay. These primers were obtained by literature

searches. The use of these primers contributed to analyze the two major phyla in the microbiota,

the total genomic bacterial content, the presence of resistance gene, the presence of fungal

region or eukaryotic ribosomal gene. These primers are listed in Table 2.

Table 2: Primers used for PCR and qPCR in the study and its annealing temperature.

Primer a) Gruppe Annealing temperature Sequences Reference

BlaTem-a F Bacteria 55 ºC 5’-ATG AGT ATT CAA CAT TTC CG -3’ (176)

BlaTem-a R Bacteria 55 ºC 5’- CCA ATG CTT AAT CAG TGA GG-3’ (176)

BlaTem-b F Bacteria 61 ºC 5’-AGTGCTGCCATAACCATGAGTG- 3’ (177)

BlaTem-b R Bacteria 61 ºC 5’-CTGACTCCCCGTCGTGTAGATA -3’ (177)

16S 1542 R Bacteria 55 ºC 5′-AAGGAGGTGATCCAGCCGCA -3′ (178)

16S 8 F Bacteria 55 ºC 5′-AGAGTTTGATCCTGGCTCAG-3′ (178)

16S 338 F Bacteria 55 ºC 5′-ACTCCTRCGGGAGGCAGCAG‐3′ (179)

16S 27 F Bacteria 55 ºC 5′-AGAGTTTGATCMTGGCTCAG-3′ (178)

16S 1492 R Bacteria 55 ºC 5′-GGTTACCTTGTTACGACTT 3′ (178)

16S Univ F Bacteria 56 ºC 5’-AGAGTTTGATCATGGCTCAG-3’ (180)

16S Univ R Bacteria 56 ºC 5’-ACCGCGACTGCTGCTGGCAC-3’ (180)

Bac960 F Bacteria 60 ºC 5’-GTTTAATTCGATGATACGCGAG-3’ (183)

Bac1100 R Bacteria 60 ºC 5’-TTAASCCGACACCTCACGG-3’ (183)

Firm934F Bacteria 60 ºC 5’-GGAGYATGTGGTTTAATTCGAAGCA-3’ (182)

Firm1060R Bacteria 60 ºC 5’-AGCTGACGACAACCATGCAC-3’ (182)

Euk_NSR399F Archaea 60 ºC 5’-TCTCAGGCTCCYTCTCCGG-3’ (181)

18S-67ar R

ITS1

ITS2

Archaea

Fungus

Fungus

60 ºC

60 ºC

60 ºC

5’-AAGCCATGCATGYCTAAGTATMA-3’

5′-CTTGGTCATTTAGAGGAAGTAA-3′

5’- GCTGCGTTCTTCATCGATGC -3’

(181)

(181)

(181)

a) F: forward primer, R: revers primer

3. 2 Positive controls for PCR and qPCR.

The positive control is an DNA template that is carrying the target gene. Under optimal

condition, the sample will be detected. It is usually used to verify optimal conditions and to

detect potential contamination. If the positive control does not work, further optimization is

required e.g. adjusting annealing or extension temperature, adjusting master mix ingredients,

or that the primer set is not compatible with the desired sequence. A list of the bacteria used as

21

positive controls is shown in Table 3. Escherichia coli was used as positive control for both

blaTEM and 16S rRNA. Bacillus subtilis sample was used as a positive control for primers

targeting the 16S rRNA in Firmicutes (Firm 16S rRNA). While Bacteroides thetaiotaomicron

was used as positive control for 16S rRNA in Bacteroidetes (Bac 16S rRNA).

Table 3. The Bacteria used as positive controls in detecting the targeted genes in the study.

Phylum Bacteria used Target gene

Proteobacteria Escherichia coli blaTEM, 16S rRNA

Firmicutes Bacillus subtilis Firm 16S rRNA

Bacteroidetes Bacteroides thetaiotaomicron Bac 16S rRNA

3.3 Genomic DNA extraction Kits

Genomic DNA was extracted from the positive control samples and the mice faecal samples

using kits listed in Table 4.

Table 4 List of kits used in the study for extraction of DNA. The extraction protocols are attached in Appendix

A and B.

Name Product number Manufacturer

QIAamp DNA purification kit from Tissue 51304 QIAGEN

QIAamp Fast DNA Stool minikit 51604 QIAGEN

3.4 Solutions prepared in the laboratory.

Lysozyme Solution (20 mg/mL lysozyme, 20 mM Tris-HCl PH 8, 2 mM EDTA, 1,2 % Triton).

This solution was be used as a part in the extraction method of DNA from Gram positive

Bacteroidetes. 40 mg lysozyme powder was dissolved in 40 μL 1M Tris-HCl, 8 μL 0,5M

22

EDTA, 24 μL Triton X100 and then 1888 μL RNA free water was added. The solution was

mixed until it became clear. The solution was prepared the same day of extraction procedure.

3.5 Preparations of agarose gel used in electrophoresis of DNA

1,5% agarose gel was prepared by taking 0,75 agarose powder and dissolving it with 50 mL

1xTAE buffer (40 mM Tris-HCl PH 8, 1 mM EDTA, 40mM Acetate) with the use of heating

in the microwave for 40-60 seconds. When the powder was dissolved, the solution was cooled

down until round 50 ºC and 5 μL of GelRed nucleic acid stain was added and mixed. The

resulting solution was poured into a dish and a comb introducing wells in the agarose gel was

adjusted over it. The dish was left to solidify in room temperature.

3.6 Gene Ruler 1 kb DNA ladder

The Gene Ruler 1 kb DNA Ladder (Thermo fisher) was applied in one of the lanes on each side

of agarose gel plates before gel electrophoresis. This DNA ladder consists of 14 DNA fragment

in the range of 250 bp to 10000 bp. This helps to estimate the size of DNA samples examined

and approximate quantification. A Figure showing the different sizes of DNA fragment in the

Gene DNA ladder is illustrated in Appendix C.

23

4 Methods

4.1 Animal experiment

20 mice, aged 6-8 weeks, were used in this experiment. Mice were placed in 4 groups; each

group consisted of 5 mice. Group A, a control group which did not get any antibiotic treatment.

Group B, C and D were treated with antibiotic for 3, 7 and 14 days respectively. The antibiotic

used was amoxicillin, a broad-spectrum antibiotic which is globally used to treat upper and

lower respiratory tracts infections, urinary tract infection and gastric tract infections.

Amoxicillin stock powder (100 mg/ml, Sandoz) were diluted in sterile distilled water according

to the manufacturer’s recommendations. The daily dosage was 32 mg/kg/day, based on the

assumption that each mouse drinks 4 mL per day and weighs approximately 25 g. Fecal samples

were collected from day 0 (prior to antibiotic treatment) and every fourth day during the

experiment on day 1, 5, 9, 13, 17, 21, 25, 29, 33 and 37. Each collected sample was mixed with

RNA later and frozen immediately in -80 ºC until the day of analysis. The mouse experiment

was conducted by Katrine Lekang.

4.2 Genomic DNA extraction from mouse stool samples

In this study, fecal sample from each mouse was analyzed before antibiotic treatment on day 0

(sample nr 1) and on day 25 (sample nr 7) after the start with antibiotic treatment in groups B,

C and D. This means that the seventh sample appears to be 22 days from the last antibiotic

dosage for group B, 18 days from the last antibiotic dosage for group C, 11 days from the last

antibiotic dosage for group D.

Genomic DNA was extracted from fecal samples collected at day 0 and day 25, from all five

replicate mice in groups A, B, C, D using QIAamp fast DNA Stool Mini Kit (QIAGEN,

Appendix A) with following modifications: (in step 1; the frozen stool pellet was spun down

and the RNA later was removed, in step 2; the sample was vortexed for 10 minutes after adding

1 ml inhibitEX Buffer, in step 3; the sample was heated for 5 min at 70 ºC and then vortexed

for 1 minute and continued as described in manufacturer protocol in Appendix B). The resulting

DNA extracts were stored at -80 ºC until further use.

24

4.3 Genomic DNA extraction from positive control samples

The positive controls in this experiment were included in the setup to verify optimal conditions

and to detect potential contamination. Genomic DNA for the positive controls used were

extracted by the following methods:

4.3.1: Gram negative bacteria

QIAamp fast DNA Tissue Kit (QIAGEN, Appendix B) was used to extract genomic DNA from

E. coli liquid culture. This culture was obtained from the Norwegian Veterinary Institute (with

a kind gift from Marianne Sunde) and the E. coli sample contained the resistance gene blaTEM.

The extraction started with taking 200 uL from the liquid culture and mixed with 20 uL

Proteinase K and 200 uL Buffer ATL and incubated for 10 minutes at 56 ºC. 200 uL Buffer AL

was added and vortexed for 1 minute at step 2. The rest of the protocol was performed as

described in manufacturer protocol in Appendix B. The extracted DNA was used as a positive

control for both the blaTEM and 16S rRNA in the PCR and qPCR.

A frozen sample culture of B. thetaiotaomicron was obtained from the Department of

Biosciences (IBV) in UiO (with a kind gift from Eric De Muinck ) and further cultured in anoxic

liquid media in the microbiology laboratory at Department of Pharmacy in UiO. This cultured

sample was centrifuged, and the bacteria pellets was first treated with 180 uL lysozyme solution

(20mg/mL lysozyme, 20mM Tris-HCl PH8, 2 mM EDTA, 1,2% Triton) which was prepared

as described in step 3.4. Then the sample was incubated for 30 minutes at 37 ºC before starting

the extractions with QIAamp kit (QIAGEN) with following modification: 200 uL Buffer AL

and 20 uL Proteinase K were added to the sample and was incubated for 30 minutes at 56 ºC

and then for further 15 minutes at 95 ºC. The sample was then centrifuge for 30 seconds before

continuing with the protocol from step 4 as described in Appendix B. The extracted DNA was

used as a positive control for Bacteroidetes.

4.3.2: Gram positive bacteria

QIAamp fast DNA Tissue Kit (QIAGEN, Appendix B) was used to extract genomic DNA from

the B. subtilis sample. A frozen sample culture was obtained from our own culture collection

(retrieved by Sarah Finke) and cultured in Lysogeny broth liquid medium (10g/L tryptone, 5g/L

yeast extract, 10g/L NaCl) at the Department of Pharmacy. This cultured sample was

25

centrifuged, and the bacteria pellets was first treated with 180 uL lysozyme solution (20mg/mL

lysozyme, 20mM Tris-HCl PH8, 2 mM EDTA, 1,2% Triton) which was prepared as described

in step 3.4. Then the sample was incubated for 30 minutes at 37 ºC before starting the extractions

with QIAamp kit (QIAGEN) with following modification: 200 uL Buffer AL and 20 uL

Proteinase K were added to the sample and was incubated for 30 minutes at 56 ºC and then for

further 15 minutes at 95 ºC. The sample was then centrifuge for 30 seconds before continuing

with the protocol from step 4 as described in Appendix B. The extracted DNA was used as a

positive control for Firmicutes.

4.3.3: Fungus

QIAamp fast DNA Tissue Kit (QIAGEN, Appendix B) was used to extract genomic DNA from

the yeast Candida albicans. This sample was cultured in sabouraud agar dish (40 g/L dextrose,

10 g/L peptone) by Truls Rasmussen. The instruction for extraction was done with the following

modifications; yeast from agar dish was scraped and mixed together with 20 uL Proteinase K

and 200 uL Buffer ATL and was incubated for 10 minutes at 56 ºC. 200 uL Buffer AL was

added and vortexed for 1 minute and the procedure was continued as described in manufacturer

protocol in Appendix B. This sample was used in verification of fungus in the mouse samples

using both PCR and qPCR.

4.3.4: Algae

Three liquid cultures of the algae Isochrysis galbana, Dunaliella tertiolecta, and Tetraselmis

suecica were provided from the Norwegian Culture Collection of Algae (NORCC). 1 mL from

each culture was used for the extractions using the QIAamp fast DNA Tissue Kit (QIAGEN,

Appendix B) with the following modification; each 1 mL liquid sample was centrifuged and

algae pellets was incubated for 10 minutes at 56 ºC together with 20 uL Proteinase K and 200

uL Buffer ATL. 200 uL Buffer AL was also added and vortexed for 1 minute and then the

procedure was continued as described in manufacturer protocol in Appendix B. The DNA was

further used as positive control for the 18S rRNA gene.

26

4.4 Quantification of DNA

Extracted DNA was quantified by using NanoDrop™ Lite Spectrophotometer (Thermo

Scientific). 1 µL of each sample was placed directly on the measurement pedestal and

concentration was measured. Extraction buffer served as a blank. In addition, the A260/A280

ratio was measured. This ratio provides a rough indication of purity of nucleic acid samples as

well as insight regarding the type of nucleic acid (DNA or RNA). In a pure DNA sample, the

A260/A280 ratio lies around 1.8-2.1. In case of protein contamination, a reduction of this ratio

is detected, while in case of RNA contamination, an increase of this ratio is detected.184

4.5 Polymerase chain reaction (PCR)

PCR was used to verify the necessary positive control and to check the compatibility of the

primers. Master mix for 10 reactions, (each reaction is 24 μL), was prepared for each primer

set according to the protocols in Table 5. Bovine serum albumin (BSA) was added in some of

the reactions if the positive PCR products were not detected in the initial experiment. BSA is a

small globular protein that is used to increase PCR yields from low purity templates and prevent

adhesion of enzymes to the reaction tubes and surfaces. The addition of BSA may therefore

have a positive effect on the PCR reaction.

Table 5 shows the ingredients of the master mix for 10 reactions used in testing bacteria, fungus and eukaryotes

primers.

Ingredient Concentration Volumes without BSA Volumes with BSA

RNase Fri water a) - 186.25 μL 180 μL

DyNAzyme Buffer b) 10x 25 μL 25 μL

dNTPs e) 10 mM 2,5 μL 2,5 μL

Forward primer c) 10 μM 12,5 μL 12,5 μL

Reverse Primer c) 10 μM 12,5 μL 12,5 μL

DyNAzyme b) 2 U/μL 1,25 μL 1,25 μL

BSA d) 100x - 6,25 μL

a) Qiagen, b) Thermo Fischer, c) Fermentas, d) New England Biolab, e) Sigma

27

1 μL of template DNA sample was added to 24 μL from PCR master mix in Eppendorf tubes.

The tubes were centrifuged for 30 seconds to make sure that the template sample was mixed

with the master mix. Then, the tubes were placed in GeneAmp*PCR system 2700 (Applied

Biosystems). The PCR-program set up used is shown in Table 6. The main PCR conditions

were as follows: initial denaturation at 95 °C for 5 minutes followed by 25-35 cycles of

denaturation at 95 °C for 30 seconds, annealing temperature °C for 30 seconds and extension

at 72°C for 1 minute followed by a final extension step at 72°C for 10 minutes and held at 4°C.

Annealing temperature was used according to the primer’s compatibility and manual of use

(Table 2).

Table 6. Illustration of the PCR program set up used in the trail.

Program Set up Main PCR set up Dollive Set up

Initialization 95 °C 5 minutes 94 °C 5 minutes

Degradation 95 °C 30 seconds 94 °C 45 seconds

Annealing X 30 seconds X 45 seconds

Elongation 72 °C 1 minute 72 °C 1,5 minute

Final elongation 72 °C 5 minutes 72 °C 10 minutes

Final hold 4 °C Indefinite 4 °C Indefinite

Number of cycles a) 25-35 cycle 35 cycle

a) Each cycle consists of degradation, annealing and elongation.

4.6 Agarose gel electrophoresis

PCR products were verified through gel electrophoresis using 1.5% agarosegel prepared

according to point 2.9.1. For sample preparations 5 μL of each sample was mixed with 1 μL

DNA loading dye and loaded on the gel. One lane was loaded with 1 Kb DNA ladder from

Thermo Scientific. The gel was covered with TAE buffer and electrical power on 80 V for 30

minutes was applied. The gel was analyzed in a BIO-RAD Gel Doc XR+.

28

4.7 Quantitative polymerase chain reaction (qPCR)

qPCR was used to identify the presence of antibiotic resistance genes, the presence of the

bacterial gene (16S rRNA), or gene sequences present in specific bacterial phyla such as 16S

rRNA in Bacteroidetes (Bac 16S rRNA) or 16S rRNA in Firmicutes phyla (Firm 16S rRNA).

Other examples could be gene specific for eukaryotes (the 18S rRNA gene that is equivalent

for the prokaryotic 16S rRNA gene) and marker specific for fungal species such as the internal

transcribed spacer (ITS) region. This method was used to detect quantitative concentration of

genetic elements in the DNA templates. A Master mix was prepared for X reactions by mixing

reverse primer, forward primer and SYBR Green PCR Master Mix according to Table 7.

Table 7 shows the ingredients of one reaction mix for qPCR.

Description/item Concentration Volume per

10 μL reaction mix

Volume per

20 μL reaction mix

Reverse primer a) 10 μM 1 μL 1 μL

Forward primer a) 10 μM 1 μL 1 μL

SYBR Green Master Mix b) 2x 5 μL 10 μL

DNA template per reaction 3 μL 8 μL

a) Fermentas, b) Applied Biosystems

For quality assessment, one sample with a positive control was included to make sure that the

primers work, and one negative control with RNase free water instead of DNA template was

also used to detect the presence of potential contaminations. A single- and multi-channel

electronic pipette E4XLS (Rainin) was used to make sure that the volume of template was the

same for all samples. The pipetting was conducted in a 96 well plate as shown in Figure 8. Each

well had 7 μL master mix and 3 μL DNA template. A full plate was covered with optical

adhesive cover and centrifuged for 2 minutes at 1000 x G. Then the plate was run in Applied

Biosystems 7000 qPCR system. This instrument is generally used for laboratory educational

courses and takes 2,5 hours for each experiment performed. The setting of the qPCR run is

shown in Figure 7.

29

Figure 7. Illustration of the qPCR program set up used in the experiment. The annealing temperature was adjusted

according to the primer manufacturer reference.

Primers used for qPCR test were the same which were used in PCR test. Their annealing

temperature are stated in Table 2.

Three technical replicates were used for each sample, where every half plate was filled with

one technical replicate. One primer set was tested with the samples from different mouse in

different treatment period. For each primer set, 1,5 plate was used. A 96 well set up is illustrated

in Figure 8. E. coli sample with 16S rRNA universal primer was included in all the plates to

ensure that the conditions was the same for all experiment.

30

Figure 8. A 96 well plate set up for qPCR. A1-A5 are biological replicates for control group. B1-B5 are biological

replicates for 3 days treatment. C1-C5 are biological replicates for 7 days treatment. D1-D5 are biological

replicates for 14 days treatment. FS1 is sample before treatment. FS7 is sample after treatment. NTC is negative

control.

4.8 Firmicutes:Bacteroidetes ratio

The Firmicutes and Bacteroidetes DNA level were quantified by real time PCR.139 The amount

detected is presented as CT value and the results is presented as a ratio between the Firmicutes

and Bacteroidetes for each sample.139

4.9 The verification of DNA for new extracted stool samples by

PCR.

As the DNA samples given by Katrine Lekang were limited, a new batch of genomic DNA

samples were extracted as described in step 4.2. In addition, a mechanical crashing of the stool

pallet was applied with the use of pipette. Then the extracted DNA was quantified by

NanoDrop™ Lite Spectrophotometer (Thermo Scientific) and the r aw result are

attached in Appendix E.

The forty samples were also verified using PCR and gel electrophoresis as per step 4.5 and 4.6

respectively without the use of primer set and at temperature 55 ºC.

31

4.10 Statistical analysis

Data were expressed as mean ± standard error of mean (SEM). The results of the threshold

values for both blaTEM, 16S rRNA, Firm 16S rRNA and Bac 16S rRNA genes were used for

data normalization and presented in ratios between blaTEM and 16S rRNA genes, and between

Firm 16S rRNA and Bac 16S rRNA genes. Statistical analysis was performed using Mann–

Whitney test and presented by GraphPad Prism software. Differences were considered

significant when p < 0.05.

32

5. Results

To assess the effect of antibiotic treatment lengths on the microbiota, an experimental approach

was used to test the effect of amoxicillin on the composition of the largest bacterial phyla in

murine gut microbiota. Moreover, the presence and development of resistance genes was

investigated for the various treatment lengths.

5.1 Literature search to identify amoxicillin resistance genes.

The mice in the experiment was treated with amoxicillin so the first task was to identify the

most likely predominant antibiotic resistance gene or genes to study the potential resistance

development. To do this, science related databases like PubMed, Embase and Scopus were used

in literature searches. A number of resistance genes are described in the literature for

amoxicillin.176,177,185,186,187,188 From the searched literature, the most predominant β-lactamase

resistance genes were identified to be blaTEM, blaSHV, blaOXA and blaCTX-M.1,188 The blaTEM gene

is one of the most frequently detected plasmid transferred resistance genes.189 It is widely spread

resistance gene in the environment and associated with Enterobacteriaceae.37 This gene was

chosen as start and two primers sett were tested; (BlaTem-a F/ BlaTem-a R) 176 and (BlaTem-

b F/ BlaTem-b R)177 (Table 2).

5.2 Verification of positive controls by PCR.

In order to assure that the primer sets both for the blaTEM gene and the other genes that was of

interest in this study listed in Table 2 (BlaTem-a F/ BlaTem-a R), (BlaTem-b F/ BlaTem-b R),

(Firm934F/Firm1060R), (Bac960F/Bac1100R), (16SUnivF/16SUniv R), (Euk_NSR399F/18S-

67ar R) and (ITS1/ITS2), were compatible with the positive controls for the studied genes

(blaTEM, Firm 16S rRNA, Bac 16S rRNA, 16S rRNA, 18S rRNA and fungal ITS region), a PCR

were conducted as described in 4.5 first without using BSA as additive. DNA extracted from

the bacteria E. coli, B. subtilis, B. thetaiotaomicron, the fungus C. albicans, and the algae I.

galbana, D. tertiolecta, and T. suecica were used as positive controls. The resulting PCR

products were verified using gel electrophoresis (Figure 9).

33

A

B

C

D

Figure 9. Images of the gels from the gel electrophoresis of the PCR primer verification. The presence of positive

band for the primers used for both of; A) Bac960F/Bac1100R and (Firm934F/Firm1060R) primer sets when B.

thetaiotaomicron and B. subtilis were tested as positive control for Bacteroidetes 16S rRNA and Firmicutes 16S

rRNA respectively, B) BlaTem-b F/ BlaTem-b R primer set when E. coli was tested as positive control for

resistance gene blaTEM, C) 16S Univ primer set when E. coli was tested as positive control for 16S rRNA gene, and

D) 18S primer set did not show any appearance in the first 5 lanes when tested algae species; I. galbana (2 lanes),

D. tertiolecta (2 lanes), and T. suecica (1 lane) respectively. The ITS primer set showed a positive band when the

fungus C. albicans sample was tested as positive control in the last 3 lanes. NTC is a non template control (negative

control).

As showed in Figure 9, a positive match was obtained for all the primers except in one of the

BlaTem primer set (BlaTem-a F/ BlaTem-a R) and 18S primer which was negative. The PCR

test for 18S primer was repeated with the use of Dollive protocol as in Table 6, where denaturing

step, annealing step and extension steps was more elongated. Unfortunately, the results were

34

still negative for all the three algae samples. Therefore, testing of 18S rRNA gene was

postponed for later investigation.

5.3 Testing of primers and positive controls by qPCR

After verification that the primer set function in the positive controls with PCR, a new test was

performed with the same primer sets for qPCR analysis. This was done in order to investigate

the presence and relative quantity of a gene in the samples. The DNA template used for positive

controls for the various genes were similar to what is described for PCR above. The threshold

values detected was between 10 and 16 cycle for the 16S rRNA, Bac 16S rRNA, Firm 16S

rRNA, and blaTEM genes as shown in Figure 10. These values indicate a god quantity of these

genes in the positive control samples. These positive controls will be the standard for further

testing of the presence of the corresponding genes from the mice stool samples. During the

qPCR, the fungal ITS region was not detected although it was positive for regular PCR (see

results chapter 5.2.1 above). Another qPCR was set up with the use of higher amount of DNA

templet (8 μL) in a larger reaction volume (20 μL). A Ct value of 29,54 was finally detected

after cycle nr 30 in the positive fungal sample as shown in Figure 10. This indicated an

extremely low quantity of fungal ITS region in the positive control sample, or that the qPCR

for the ITS region need further optimization.

35

A

B

Figure 10 illustrates the threshold values detected for A) 16S rRNA, Bac rRNA, Firm rRNA, and blaTEM positive

controls which appear early after 10 to 16 cycles B) ITS positive control that appear late after 30 cycles when a

large amount of DNA template was used.

5.4 Testing of diluted positive controls by qPCR

5 μL of the positive controls was diluted in ratio 1:5, 1:10 and 1:20 and were tested by qPCR.

These diluted samples were positively detected in all ratios. As the amount of genomic DNA

samples extracted by Katrine Lekang from mice stool were limited, a dilution process for some

of the samples was done in ratios: 1:2, 1:3 and 1:4. The dilution will make the concentration

more similar across the samples and increase the sensitivity of the qPCR. The DNA

concentration before and after dilution is shown in Appendix D. Finally, 60 μL of genomic

DNA from each stool sample was provided for the experiments.

5.5 qPCR assessment of Bacteroidetes and Firmicutes in mouse

stool samples prior and after antibiotic treatment

The extracted genomic DNA samples before and after antibiotic treatment were tested using

the Firmicutes 16S rRNA primers (Firm934F/Firm1060R) and Bacteroidetes 16S rRNA

primers (Bac960F/Bac1100R). In the initial part of the experiment a Roche qPCR instrument

was used only for testing the positive control of E. coli. The plan was to use this instrument in

36

performing all the qPCR tests in the experiment. However, this instrument broke down, and the

alternative was to perform all the tests experiment using an Applied Biosystems qPCR

instrument. The results of the threshold values for both of Bacteroidetes and Firmicutes are

presented in Figure 11.

Figure 11. shows the mean CT values for Firm 16S rRNA and Bac 16S rRNA genes for the different treatment

groups; the control group A (black), the 3 days treatment group B (Green), 7 days treatment group C (red) and 14

days treatment group D (blue).

From the threshold values, the Firmicutes:Bacteroidetes ratio was calculated and the according

results are presented in Figure 12. The results reveal statistically significant increase in the

Firmicutes:Bacteroidetes ratio in the group B which had been exposed to amoxicillin for 3 days

(Mann–Whitney test; P = 0.0079). No significant differences were detected in the ratio for

group C with 7 days exposure (Mann–Whitney test; P = 0.4603) and group D with 14 days

exposure to amoxicillin was observed (Mann–Whitney test; P = 0.3413). It is expected that the

animals should have the same initial Firmicutes:Bacteroidetes ratio on day 0. Precautions

should therefore be taken in the interpretations of the results from the 3 days treatment group.

37

Figure 12 Firmicutes:Bacteroidetes ratio for the four groups; Group A = control group, group B = Treated with

3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14 days amoxicillin.

Individual black circles refer to the individual animals in each group. Day 0 and Day 25 refer to the day of

sample taking; before treatment and on day 25 after start of exposure to amoxicillin.

38

5.6 qPCR detection of blaTEM gene in mouse stool samples prior and

after antibiotic treatment.

The extracted genomic DNA samples (isolated by Katrine Lekang) were tested using the

BlaTem-b primers (BlaTem-b F/BlaTem-b R) and the 16S rRNA primers (16S Univ F/16S

Univ R) by Applied Biosystems qPCR system. The threshold values using BlaTem-b primers

were detected for all biological replicates except one biological replicate (B3) where no value

was detected neither before nor after exposure of amoxicillin in the 3 technical replicates.

The threshold values for both blaTEM and 16S rRNA genes were used directly to calculate the

relative abundance of blaTEM (ratio blaTEM/16S rRNA)37 and are presented in Figure 13. The

higher the relative abundance value, the lower the relative presence of resistance gene. The

relative abundance of blaTEM is not statistically significant for group A (control, without

treatment), group B (treated with 3 days amoxicillin), group C (treated with 7 days amoxicillin)

and group D (treated with 14 days amoxicillin).

Figure 13 shows relative abundance ratio of blaTEM gene in the various groups. Group A = control group, group

B = Treated with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14 days

amoxicillin. Day 0 and Day 25 refer to the days of sample taking; before treatment and on day 25 after start of

treatment.

39

5.7 The verification of DNA for new extracted stool samples by

PCR.

To perform further analysis, a new batch of mice fecal samples was extracted and verified using

PCR and gel electrophoresis as per step 4.9. The results were shown in Figure 14, and reveal

low amount of DNA in A1, A2 and A5 samples, and high amount of DNA in B4, B5, C1 in the

samples taken on day 0. The reason that samples A1, A2 and A5 shows lower intensity

compared to the other samples can be due to the absence of mechanical crushing of stool pallet

for these samples during the extraction process. Samples A2, C1, C3, C4, C5, D2, and D4 had

higher amount of DNA than the other samples taken on day 25 of the experimental timeline.

This variation in concentration of DNA extracted is not uncommon and can be related to the

size of the stool pellet and technical aspects in the extraction method.

Figure 14 illustrates the intensity of band that reflects the amount of DNA in the samples. Group A = control

group, group B = Treated with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated

with 14 days amoxicillin. Numbering refers to the individual animals in each group. Day 0 and Day 25 refer to the

days of sample taking; before treatment and on day 25 after start of treatment.

40

3 of the samples that were collected on day 0 (B4, B5 and C1) and 7 of the samples that were

collected on day 25 (A2, C1, C3, C4, C5, D2 and D4) (showed in Figure 14) had higher

intensity of the DNA positive band. Therefore, these samples were diluted in a ratio 1:2

before further analysis. The dilution will make the concentration more similar across the

samples.

5.8 The PCR detection of blaTEM gene in the new extracted stool

samples

The new extracted sample after dilution were analyzed using PCR and BlaTem-b primer set

(BlaTem-b F/BlaTem-b R) without BSA (as described in step 4.5). The amplified product was

verified using gel electrophoresis and shown in Figure 15. The results reveal more intense bands

of DNA in group D after treatment with antibiotic compared to before. Two biological

replicates from group B (B2, B4) exhibit positive DNA bands of the resistance gene after

treatment compare to only one from group C (C5). Positive PCR products of the resistance gene

was also detected in some of the mice even before the exposure to antibiotic at day 0 (A3, A4,

A5, B3, B5, C2, C4, C5 and D3). However, these bands had in general lower intensity on the

DNA agarose gel electrophoresis compared to the mice that had been exposed to longer

treatment lengths (D1, D2, D2, D3 and D5).

41

Figure 15 illustrates agarose gel electrophoresis where the intensity of band that reflects the amount of resistance

gene, blaTEM gene in the samples using primer set (BlaTem-b F/BlaTem-b R). Group A = control group, group B

= Treated with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14 days

amoxicillin. Numbering refers to the individual animals in each group. Day 0 and Day 25 refer to the day of sample

taking; before treatment and on day 25 after start of treatment.

5.9 The PCR amplification of fungal ITS region in mice stool

samples for restriction analysis.

As the qPCR experiment was difficult to achieve for the fungal ITS region, a PCR trial was

performed for the 40 murine stool samples as described in step 4.5. The goal was to obtain

positive PCR products that can be further treated with restriction enzymes and resulting DNA

fragments that can then be verified using gel electrophoresis. The primer set (ITS1/ITS2) was

used with the annealing temperature described in Table 2. The PCR was first performed without

BSA. Positive band was not detected for the genomic DNA stool samples except for the positive

control (results not shown). The PCR was repeated with BSA in order to increase detection

sensitivity and detect positive products in the samples. Unfortunately, the positive band was not

detected in the PCR reaction with BSA even for the positive control (results not shown). The

42

experiment was repeated for 5 of the samples by manipulating the annealing temperatures;

either higher (65 ºC) or lower (48 ºC and 56 ºC) annealing temperatures. The results were still

negative (no band detected), and the presence of primer dimer was observed in the lower

temperature (48 ºC). Because of only negative results the gel is not shown.

5.10 The PCR amplification of 16S rRNA gene in mice stool

samples for restriction analysis.

As the PCR amplification products were difficult to achieve for the fungal ITS region, the same

experiment was performed for 16S rRNA gene. Due to the need for large fragments of DNA

amplification for restriction analysis, the use of new 16S rRNA primer sets was essential. The

first primer set used was (16S 27F/16S 1492R). The PCR products were verified by gel

electrophoresis and the results presented in Figure 16 reveal positive band only in some of the

samples in the groups B, C and D on day 25. The presence of weak band can be seen in two of

the samples on day 25 from group B (B1 and B2), two of the samples on day 25 from group C

(C1 and C2), and two of the samples on day 25 from group D (D2 and D3). The positive band

was more intense on two of the samples on day 25 from group C (C4 and C5).

43

Figure 16 illustrates an agarose gel where the intensity of band that reflects the presence of amplification products

of DNA in some of samples from group B, C and D on day 25 with the use of 16S rRNA primer set (16S 27F/16S

1492R). These amplicons har the size of 1500 bp which considered large fragment of 16S rRNA gene. Group A =

control group, group B = Treated with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D =

Treated with 14 days amoxicillin. Numbering refers to the individual animals in each group. Day 0 and Day 25

refer to the day of sample taking; before treatment and on day 25 after start of treatment.

The second primer set used for PCR for the 16S rRNA gene (16S 8F/16S 1542R) and (16S

338F/1542R) was also tested. This was performed in order to get positive PCR products both

on day 0 and day 25 to be able to perform further restriction analysis. The presence of PCR

products was verified by DNA agarose gel electrophoresis. Unfortunately, no band were

obtained (results not shown).

44

6. Discussion

6.1 Different antibiotic treatment lengths and effects on the gut

microbiota

As gut microbiota participates in many vital immunological and physiological processes such

as energy metabolism and homeostasis, vitamins synthesis, endocrine signaling, prevention of

colonization and regulation of immune function,100 and as the Bacteroidetes and Firmicutes,

counts for more than 90% of the bacteria that colonized the colon,128 the

Firmicutes:Bacteroidetes ratio becomes a potential marker that helps in proposing the

challenges in human gut microbiota.139 This ratio has shown correlation with the different

inflammatory and metabolic disorders.140,128

In the current study, the threshold values for both of Bacteroidetes and Firmicutes were

measured by qPCR for different groups of mice that were treated with different antibiotic

lengths, 3, 7, and 14 days prior and after treatment on day 25. The threshold values were used

to calculate the Firmicutes:Bacteroidetes ratio which is presented in Figure 12. The

Firmicutes:Bacteroidetes ratio for the 4 groups were between 0,85 and 1,05 as the mice aged

from 6 to 8 weeks. The Firmicutes:Bacteroidetes ratio showed significant increase in the group

B (that was treated with 3 days amoxicillin) compared to prior treatment. However, the

Firmicutes:Bacteroidetes ratio prior treatment on day 0 for group B was significantly lower than

the other groups A, C and D on day 0. This may be due to technical error. To confirm the results,

a new qPCR is recommended before any conclusions can be drawn from the significance of

this result.

In another similar study in animals, the effect of a single dose of lincomycine, chlortetracycline

and amoxicillin was assessed in swine.190 It was reported an increase in the abundance of

Firmicutes and an increase in the Firmicutes:Bacteroidetes ratio by day 12 after antibiotics

administration.190 This can indicate either that the effect of short treatment of antibiotics is

associated with increase in the Firmicutes:Bacteroidetes ratio, or that the effect of 3 antibiotics

together can results in prolong effect on microbiota even 12 days after antibiotics cessation.

45

Of interest it can be noted that such an increase in Firmicutes:Bacteroidetes ratio has been

reported in human studies to be associated to a group of health conditions and diseases like

obesity128, Irritable bowel Syndrome140 and patients with autism spectrum disorder.144

In the present study, there was no significant difference between the Firmicutes:Bacteroidetes

ratios for group C (that was treated with 7 days amoxicillin) and group D (that was treated with

14 days amoxicillin) after treatment on day 25, compared to pre-treatment. The fecal samples

that was obtained after treatment (on day 25 of the experiment), were taken 18 and 11 days

from the last exposure to antibiotic for groups C and D respectively.

The effect of 7 days treatment with amoxicillin has previously been studied in the gut of

pregnant rats.191 The samples were taken from the intestinal sections on the last day of treatment

after all the rats were sacrificed.191 The results showed reduction in Firmicutes and

Bacteroidetes.191 The samples in our study were collected at least 11 days after the last exposure

and can not be compared to this study where the samples were collected at the day of antibiotic

termination. The effect of antibiotic treatment for more than 3 days, for example 5 and 7 days

has been shown in other studies.152,192 One of these studies reported that the microbiota had

greater reduction in diversity on individuals who received 7 days amoxicillin compared to those

who received 3 days azithromycin.192 Here the comparison was between two different type of

antibiotics and it would be interesting to compare the effect of amoxicillin on day 3 for group

B, day 7 for group C and day 14 for group D in our study. Another study showed that chickens

which received 5 days treatment of amoxicillin had significant effect on the microbiota on the

6th day after start of exposure, but this effect disappeared on day 16 after start of treatment.152

As there was no significant difference in the current study between the Firmicutes:Bacteroidetes

ratios for group C (that was treated with 7 days amoxicillin) and group D (that was treated with

14 days amoxicillin) on day 25 after start of treatment compared to pre-treatment, you could

assume that the two main phyla in microbiota had recovered to pre-exposure levels after the

use of antibiotics. It is therefore important to analyze the fecal samples collected on the day of

amoxicillin termination for each group to be able to compare the results to the post exposure

ones. This can confirm our theory about microbiota recovery in the current study.

A recent study showed a treatment with amoxicillin for 2 and 3 weeks reshaped the microbiota

and reduced the Firmicutes:Bacteroidetes ratio in genetically hypertensive rats.193 Amoxicillin

treated rats had lower blood pressure compared to the untreated rats.193 The researcher found a

decrease of Veillonellaceae, which are succinate producing bacteria and belong to the phylum

46

Firmicutes, in the amoxicillin treated group. Elevated plasma succinate was reported to have

connection with hypertension and lower serum level of succinate was observed in the rats

treated with amoxicillin.193 All these studies had measured the Firmicutes:Bacteroidetes ratio

either during the antibiotic exposure or at the end of exposure, while in present study, the

Firmicutes:Bacteroidetes ratio was measured after antibiotic exposure.

Although other studies shows that the diversity of microbiota can recovered to the pretreatment

states, some antibiotic like macrolides can results in disorder in human microbiota that persist

over 2 years.150 The administration of ciprofloxacin was found to decrease the taxonomic

diversity of the human microbiota, but the composition of bacterial community was closely

resembled after 4 weeks from last antibiotic exposure. Unfortunately, several taxa failed to

come to normal state within 6 months.153

As amoxicillin is a β-lactam antibiotic that affects both Gram negative and Gram positive

bacteria by inhibiting an essential enzyme for bacterial cell wall synthesis, it is necessary to

perform further analysis of the fecal samples that was collected on the day of antibiotic

termination and 21 days from the last antibiotic exposure for both group C and D. This will

give the opportunity to see how the microbiota of each group would look like at the end of

treatment and after 21 days from the last antibiotic exposure as well as the direct comparison

of the short and long course of amoxicillin on microbiota. Another thing is to investigate the

bacterial taxa and diversity through 16S rRNA gene sequencing process which can detect the

different changes that happened though-out the experiment.

47

6.2 Does longer antibiotic treatments increase development of

resistance?

As the antibiotic used in the current study was amoxicillin which is a β-lactam antibiotic, it was

important to choose a resistance gene that is responsible for the production of β-lactamase

enzymes. The archetypical plasmid encoded β-lactamase TEM (blaTEM) is one of the most

common gene responsible for this enzyme in this family84. The blaTEM gene is a frequently

detected resistance gene.189 It is spreading widely in the environment and associated with

Enterobacteriaceae.37

The 16S rRNA gene was analyzed to estimate bacterial abundance. The blaTEM gene was

normalized to 16S rRNA in each of the samples and presented in Figure 13. The relative

abundance ratio (ratio blaTEM/16S rRNA) was calculated and statistically tested using Mann–

Whitney test. The results reveal no significant difference between A, B, C and D groups treated

with 0, 3, 7 and 14 days amoxicillin.

The effect of one single dose of amoxicillin, lincomycin and chlortetracycline given to 24

pregnant swine was reported in a study. An increase in the abundance of antibiotic resistance

genes was detected by day 3 after antibiotics administration, but then a decrease in the

abundance by day 12 after antibiotics administration was seen.190 The presence of β-lactam

resistance genes; blaTEM, blaCTX and blaOXA were detected before and after treatment.190 The

present of resistance gene blaTEM in our study was also detected prior and after treatment.

However, the fecal samples right after antibiotic termination was not tested in our study, so it

was not possible to compare the effect of different length of antibiotic treatment at antibiotic

cessation. All the post treatment samples were collected at least 11 days from the last antibiotic

exposure. Further investigation will be conducted to confirm if the abundance of resistance gene

was recovered to pre treatment level or if the abundance was at the same level throughout the

experiment.

A recent study assessing the effect of different concentrations and durations of amoxicillin (5,

10, 20, and 30 mg/kg body weight) once a day for 5 days and twice a day for 2.5 day on S.

aureus using tissue cage infection model, reveals that the emergence of resistant bacteria

increased with time and was dose dependent in the same time interval.194 The leading genes in

S. aureus which had role in resistance development were femX, factor C essential for methicillin

48

resistance and mecA, methicillin determinant A.194 It would be interesting to test the abundance

of resistance gene on day 3, day 7 and day 14 for group B, C and D respectively in the current

study to confirm the results of the previous study.

Two studies confirm our results that it is possible to have the resistance gene before and after

treatment. The first recent study illustrated the development of resistance in the microbiome of

children with acute otitis media treated with penicillin V for 5 days. Metagenomic sequencing

data for the fecal samples shows the presence of seven antibiotic resistance genes at the

baseline, 5 resistance genes at the end of treatment (day 5) and 21 resistance genes at day 30.195

The second study was a shotgun metagenomic analysis that had been done to monitor the effect

of 7 days administration of oxytetracycline on the microbiota and resistance development in

fecal samples collected from pigs. The data showed significant increase in the richness of

antibiotic resistance genes (including blaTEM) to 41 genes compared to control group and this

richness decreased to 17 genes two week after the termination of antibiotic.196 The presence of

resistance gene was tested 2-3 week after antibiotic termination. In the current study, not all the

fecal samples were tested after equal interval from the last antibiotic exposure. This can alter

the possibility to check the effect of different length on the intensity of resistance gene. In

addition, only one resistance gene was tested in our experiment and this could not confirm the

presence of more than one resistance gene after different treatment lengths. On contrast, a study

tested the presence of blaTEM and other antibiotic resistance genes using fecal metagenomic data

obtained from 5 pigs treated with amoxicillin for 7 days. The blaTEM gene was observed on day

4 during administration of antibiotic and disappeared after 2 days from antibiotic termination

(day 9).197

The PCR results of the new extracted fecal DNA samples in our study showed the presence of

blaTEM in nine out the twenty (9/20) samples in both prior and after amoxicillin exposure. This

confirmed the previous results of qPCR (obtained from the first extracted DNA batch) that the

presence of blaTEM gene can be observed both prior and after amoxicillin exposure although

two different fecal DNA sample batches were tested in each experiment. This agreed with other

studies mentioned early.190,195 However, the intensity of the positive band was stronger in group

D, treated with amoxicillin for 14 days, indicating a higher presence of resistance gene in group

D. Although the intensity of positive bands was stronger in the group D exposed for 14 days

amoxicillin, samples were taken 11 days after the last exposure to amoxicillin compared to 18

and 22 days for group C and B. This could be the reason that the positive bands of blaTEM were

49

poorly detected or not detected on the other groups as the resistance gene went to baseline

level.196, 197 Testing the samples on the day of antibiotic termination and 21 days after antibiotic

termination could confirm if the abundance of the resistance gene was affected with the length

of treatment. Another consideration could be that most of the studies tested the presence of

antibiotic genes either during antibiotic treatment or directly after antibiotic cessation, while in

our study the effect of antibiotic treatment was measured at least 11 days after antibiotic

termination. Some of the previous studies showed that most of antibiotic resistance genes

recovered to pre treatment levels after only a few days from last antibiotic exposure. It will be

an interesting follow up to the present study to further test the fecal samples obtained directly

after last antibiotic exposure for each group, testing the post treatment effect on equal interval

after the last antibiotic exposure and testing the presence of other resistance genes like blaOXA

and blaCTX.

50

6.3 Antibiotic treatment and the effect on eukaryotic and yeast

colonization.

Since antibiotic exposure have be shown to alter gut microbiota, which has an important role

in the protection against opportunistic pathogens, and so increase the risk of opportunistic

bacterial and fungal infections, the abundance of yeast organism was tested in current study

using PCR. The results showed no positive detection in any of the samples before and after

antibiotic exposure. This is on contrast to study that showed fungal increase of 25-40s fold

during the treatment with antibiotic cocktail (ampicillin, neomycin, vancomycin, and

metronidazole) for 2 weeks that declined back to their original abundance one week after

termination of antibiotic.181 The reason that no fungal presence was found could be due to either

technical consideration in the extraction method used, the use of only amoxicillin compared to

the effect of 4 antibiotics that had been used at the same time for long period, or that the samples

in our study were collected at least 11 days after the last antibiotic exposure and so the fungal

abundance were restored. Therefore, a further investigation is needed taking into account the

considerations of the extraction method and the time of sampling.

51

6.4 Methodological and experimental considerations

6.4.1 Verification of positive controls by PCR.

The positive controls for both blaTEM, 16S rRNA, Bac 16S rRNA and Firm 16S rRNA were

successfully verified using PCR amplification and gel electrophoresis as presented in Figure 9

A, B and C. The region that was used in detecting the fungi presence was internal transcribed

spacer (ITS) region. The fungal ITS region was also successfully verified by PCR. The algae

samples; I. galbana, D. tertiolecta, and T. suecica did not give any positive band after PCR

amplification and gel electrophoresis. The reason could be either that the 18S primer set used

was not compatible as some strains may have sequence variations and thus do not match the

primers, or that the master mix was not suitable for this reaction. That is why it was decided to

postpone testing of 18S rRNA gene for later investigations.

6.4.2 Testing of primers and positive controls by qPCR

Testing of the positive controls through qPCR was successful. The threshold cycle was between

10 and 16 cycle for the bacteria E. coli, B. subtilis and B. thetaiotaomicron. These samples were

used as a control for blaTEM resistance gene, 16S rRNA gene, Firm 16S rRNA and Bac 16S

rRNA respectively. The fungal ITS region was not detected by qPCR although it was detected

by PCR amplification. A new qPCR protocol was used with increasing the DNA template and

reaction volume. A Ct value of 29,54 was finally detected after cycle nr 29 in the positive C.

albicans fungal sample. One may speculate that the lack of positive results for the fungal ITS

region could be due to low amount of DNA in the used positive sample, the DNA quantified by

Nanodrop was 31,1 ng/µL which in theory should be adequate and even quite high. On the other

hand, the 260:280 ratio was 1,37. This indicates contamination of the sample and the presence

of proteins or solvent. This can affect the work of SYBR green master mix and primer set. This

could be the reason for weak detection of fungal ITS region by qPCR. Therefore, further

investigation about the proper detection of fungal ITS region is needed before using the limited

amount of extracted fecal samples available. A suggestion to overcome this is purifying the

DNA in the positive sample used for fungus detection.

52

6.4.3 Testing of diluted positive controls by qPCR

This step was important as we had a limited volume of the extracted genomic DNA from the

mice fecal samples. By testing the positive controls that represents blaTEM, 16S rRNA, Firm

16S rRNA and Bac 16S rRNA in diluted ratio 1:5, 1:10 and 1:20, and checking that the positive

controls is still being detected by qPCR, gave the possibility to test the genomic extracted DNA

from the fecal samples for the 4 mentioned genes. The dilution process was done for some of

the samples in a ratio 1:2, 1:3 and 1:4. This depended on the concentration of DNA in the

samples after quantification by the NanoDrop™ Lite Spectrophotometer and the

available volume of each. The dilution will make the concentration more similar across the

samples and increase the sensitivity of the qPCR. The new concentrations were in the range

from 1,7 ng to 9,2 ng/µL (Appendix D).

The dilution process was also performed for some of the samples from the new extracted stool

samples batch. The samples (B4, B5 and C1) collected on day 0 and the samples (A2, C1, C3,

C4, C5, D2 and D4) collected on day 25 of the experiment timeline showed higher intensity of

positive band after agarose gel verification as seen in Figure 14. This indicated higher

concentration of DNA and therefore these samples were diluted in a ratio 1:2. The dilution will

make the concentration more similar across the samples and increase the sensitivity of the PCR

reaction needed for restriction analysis. The new concentrations were in the range from 2,5 ng

to 17 ng/µL (Appendix E).

6.4.4 The PCR amplification of fungal ITS region in mice stool samples for

restriction analysis.

The goal of this part was to detect the present of fungal growth in mouse gut during the different

treatment lengths of amoxicillin and try to find a pattern of fungal communities by later

restriction analysis. Unfortunately, it was not detected any positive result in all the samples

before and after antibiotic exposure. In a previous study it was mentioned that fungal increase

of 25-40s fold during the treatment with antibiotic cocktail for 2 weeks and that this declined

back to their original abundance one week after termination of antibiotic.181 In this study, the

DNA extraction from mice stool pellets used in that study was different. The DNA extraction

was done using bead beating and high incubation temperature in enough time to facilitate the

lysis of fungal cells.181 This suggests repeating the experiment and taking in consideration the

53

extraction procedure and to analyze samples from the last day of antibiotic exposure in addition

to 22 days after exposure. Another explanation is that the mice microbiota did not have enough

fungal species to be detected.

6.4.5 The PCR amplification of 16S rRNA gene in mice stool samples for

restriction analysis.

To study the diversity of bacteria species before and after different antibiotic lengths, a PCR

was performed using two different primer sets and the positive products would be used further

in a restriction analysis to find the genetic variation between samples. The primer sets had not

been used previously. Also, a restriction enzyme analysis generally needs longer amplicon

fragments than those used in qPCR. Amplicons larger than 1000 bp may need extra time during

the extension step to be completed.

Although none of the primer sets resulted in DNA positive bands with the required quality

neither for prior nor after amoxicillin exposure, some positive bands were noticed using the

primer set (16S 27F/16S 1492R) as shown in Figure 16. These positive bands were detected

only on the groups treated with amoxicillin. This can indicate a kind of alteration of the

microbiota. A further investigation and optimization of the protocol can be interesting.

54

6.4.6. Sources of error

The following points could affect the results of this thesis:

1 – The genomic DNA concentrations differed between each sample in both the old and new

extracted fecal mice sample. This could be prevented by normalizing each DNA sample by

adding nuclease free water to have a final concentration of 1 ng/µL. This will result in addition

to more available volume of sample for experiment and help in easier comparison between the

group results with each other.

2- In assessment of Firmicutes gene using qPCR, most of the samples had 3 detected threshold

values representing 3 technical replicates except 5 samples that had only one detected threshold

value and 2 samples that had 2 detected threshold values. This could be due to missing to add

the DNA template in the plate at the template was not mixed with the SYBR green master mix.

3- The mean Firmicutes:Bacteroidetes ratio for group B at baseline (day 0) was significantly

lower than Firmicutes:Bacteroidetes ratio on day 0 for group A, C and D. This can be related

to the previous error.

4- In assessment of blaTEM gene using qPCR, most of the samples had 3 detected threshold

values representing 3 technical replicates except (11/40) sample that had only 1 detected

threshold value and (5/40) samples that had 2 detected threshold values. This could be due to

missing to add the DNA template in the plate or at the template was not mixed with the SYBR

green master mix.

5- The 260/280 ratio for the new extracted fecal DNA batch were higher than 2,2 in 27 of the

40 extracted samples. This can indicate less purity than the first used fecal DNA sample.

55

7. Conclusion

In summary, the current results could not conclude an effect of amoxicillin on the mice gut

microbiota after different treatment lengths using Firmicutes:Bacteroidetes ratio. However, the

data was collected in different days after the termination of antibiotic. A significant increase in

Firmicutes:Bacteroidetes ratio after 3 days exposure should be confirmed by repeating the

experiment. As studies reported that the composition of gut microbiota can recover after

cessation of antibiotic exposure, the present study did not show any significant difference in

Firmicutes:Bacteroidetes ratio prior and post antibiotic exposure. Further testing of

Firmicutes:Bacteroidetes ratio on the day of antibiotic termination could confirm if the

microbiota had recovered to pretreatment level after 21 days of antibiotic termination. The

presence of resistance gene blaTEM was observed both prior and after antibiotic exposure in the

different antibiotic lengths. The qPCR results did not confirm the relationship between the

length of treatment and the prevalence of resistance gene. However, the PCR results showed

higher presence of resistance gene blaTEM in group D treated with 14 days amoxicillin. This can

be further investigated. The presence of yeast was not confirmed after the different exposure of

amoxicillin.

56

8. Further perspectives.

To increase our knowledge about the effect of different lengths of amoxicillin treatment on gut

microbiota through Firmicutes:Bacteroidetes ratio, and the development of resistance gene

blaTEM at the day of antibiotic termination, a further PCR and qPCR should be performed using

the mice fecal samples collected from the last day of antibiotic exposure and 21 days post last

antibiotic administration. The results should be compared to prior antibiotic administration.

This can help in understanding the dynamic of gut microbiota and compare the changes in

Firmicutes:Bacteroidetes ratio between the different groups prior, after and post antibiotic

exposure. The presence of other resistance genes like blaOXA and blaCTX can be tested at the

same time. The presence of these genes was reported together with blaTEM on different levels

in other studies.

The colonization of fungus in microbiota after different lengths of treatment with amoxicillin

can be studied by try testing a new methods of DNA extraction that can ensure lysing of fungal

cells in the marine samples, verification of the DNA by PCR followed by further restriction

analysis and quantification by qPCR at different sampling days; before treatment, at antibiotic

termination and 21 days post termination.

57

Literature list

1. Lobanovska, M. and G. Pilla, Penicillin's Discovery and Antibiotic Resistance: Lessons

for the Future? Yale J Biol Med, 2017. 90(1): p. 135-145.

2. Peck, R.L., C.E. Hoffhine, Jr., and K. Folkers, Streptomyces antibiotics;

dihydrostreptomycin. J Am Chem Soc, 1946. 68: p. 1390.

3. Fedeniuk, R.W. and P.J. Shand, Theory and methodology of antibiotic extraction from

biomatrices. J Chromatogr A, 1998. 812(1-2): p. 3-15.

4. Pereira, J.F., et al., Extraction of tetracycline from fermentation broth using aqueous

two-phase systems composed of polyethylene glycol and cholinium-based salts. 2013.

48(4): p. 716-722.

5. Liu, F. and A.G. Myers, Development of a platform for the discovery and practical

synthesis of new tetracycline antibiotics. Curr Opin Chem Biol, 2016. 32: p. 48-57.

6. Stein, G.E. and W.A. Craig, Tigecycline: a critical analysis. Clin Infect Dis, 2006.

43(4): p. 518-24.

7. Wright, P.M., I.B. Seiple, and A.G. Myers, The evolving role of chemical synthesis in

antibacterial drug discovery. Angew Chem Int Ed Engl, 2014. 53(34): p. 8840-69.

8. Weinstein, L. and R.B. Brown, Colonization, suprainfection and superinfection: major

microbiologic and clinical problems. Mt Sinai J Med, 1977. 44(1): p. 100-12.

9. Silhavy, T.J., D. Kahne, and S. Walker, The bacterial cell envelope. Cold Spring Harb

Perspect Biol, 2010. 2(5): p. a000414.

10. Sarpong, E.M. and G.E. Miller, Narrow- and Broad-Spectrum Antibiotic Use among

U.S. Children. Health Serv Res, 2015. 50(3): p. 830-46.

11. Langdon, A., N. Crook, and G. Dantas, The effects of antibiotics on the microbiome

throughout development and alternative approaches for therapeutic modulation.

Genome Med, 2016. 8(1): p. 39.

12. Etebu, E. and I.J.I.J.A.M.B.R. Arikekpar, Antibiotics: Classification and mechanisms

of action with emphasis on molecular perspectives. 2016. 4(2016): p. 90-101.

13. Kahne, D., et al., Glycopeptide and lipoglycopeptide antibiotics. Chem Rev, 2005.

105(2): p. 425-48.

14. Džidić, S., et al., Antibiotic resistance mechanisms in bacteria: biochemical and genetic

aspects. 2008. 46(1).

15. Bush, K. and P.A. Bradford, β-Lactams and β-Lactamase Inhibitors: An Overview. Cold

Spring Harb Perspect Med, 2016. 6(8).

16. Yoneyama, H. and R. Katsumata, Antibiotic resistance in bacteria and its future for

novel antibiotic development. Biosci Biotechnol Biochem, 2006. 70(5): p. 1060-75.

17. Grossman, T.H., Tetracycline Antibiotics and Resistance. Cold Spring Harb Perspect

Med, 2016. 6(4): p. a025387.

18. Vázquez-Laslop, N. and A.S. Mankin, How Macrolide Antibiotics Work. Trends

Biochem Sci, 2018. 43(9): p. 668-684.

19. Marks, J., et al., Context-specific inhibition of translation by ribosomal antibiotics

targeting the peptidyl transferase center. Proc Natl Acad Sci U S A, 2016. 113(43): p.

12150-12155.

20. Long, K.S. and B. Vester, Resistance to linezolid caused by modifications at its binding

site on the ribosome. Antimicrob Agents Chemother, 2012. 56(2): p. 603-12.

58

21. Krause, K.M., et al., Aminoglycosides: An Overview. Cold Spring Harb Perspect Med,

2016. 6(6)

22. Dinos, G.P., The macrolide antibiotic renaissance. Br J Pharmacol, 2017. 174(18): p.

2967-2983.

23. Gutierrez, A., J.M. Stokes, and I. Matic, Our Evolving Understanding of the Mechanism

of Quinolones. Antibiotics (Basel), 2018. 7(2).

24. Pham, T.D.M., Z.M. Ziora, and M.A.T. Blaskovich, Quinolone antibiotics.

Medchemcomm, 2019. 10(10): p. 1719-1739.

25. Gai, Z., et al., Molecular Mechanisms of Colistin-Induced Nephrotoxicity. Molecules,

2019. 24(3).

26. Vaara, M., Polymyxin Derivatives that Sensitize Gram-Negative Bacteria to Other

Antibiotics. Molecules, 2019. 24(2)

27. Gleckman, R., S. Alvarez, and D.W. Joubert, Drug therapy reviews: trimethoprim-

sulfamethoxazole. Am J Hosp Pharm, 1979. 36(7): p. 893-906.

28. Singh, S.B. and J.F. Barrett, Empirical antibacterial drug discovery--foundation in

natural products. Biochem Pharmacol, 2006. 71(7): p. 1006-15.

29. Pagel, S.W. and P. Gautier, Use of antimicrobial agents in livestock. Rev Sci Tech,

2012. 31(1): p. 145-88

30. Powers, J.H., Antimicrobial drug development--the past, the present, and the future.

Clin Microbiol Infect, 2004. 10 Suppl 4: p. 23-31.

31. Negut, I., V. Grumezescu, and A.M. Grumezescu, Treatment Strategies for Infected

Wounds. Molecules, 2018. 23(9).

32. Hutchings, M.I., A.W. Truman, and B. Wilkinson, Antibiotics: past, present and future.

Curr Opin Microbiol, 2019. 51: p. 72-80.

33. Walia, K., et al., Understanding policy dilemmas around antibiotic use in food animals

& offering potential solutions. Indian J Med Res, 2019. 149(2): p. 107-118.

34. Burow, E., et al., Oral antimicrobials increase antimicrobial resistance in porcine E.

coli--a systematic review. Prev Vet Med, 2014. 113(4): p. 364-75

35. Gaskins, H.R., C.T. Collier, and D.B. Anderson, Antibiotics as growth promotants:

mode of action. Anim Biotechnol, 2002. 13(1): p. 29-42.

36. Price, L.B., B.J. Koch, and B.A. Hungate, Ominous projections for global antibiotic use

in food-animal production. Proc Natl Acad Sci U S A, 2015. 112(18): p. 5554-5.

37. Narciso-da-Rocha, C., et al., blaTEM and vanA as indicator genes of antibiotic

resistance contamination in a hospital–urban wastewater treatment plant system.

Journal of Global Antimicrobial Resistance, 2014. 2(4): p. 309-315.

38. Summary of the latest data on antibiotic consumption in the European Union ESAC-

Net surveillance data November 2017.

39. O’Neill, J.J.T.r.o.a.r., Antimicrobials in agriculture and the environment: reducing

unnecessary use and waste. 2015: p. 1-44.

40. ESVAC, 2017. Sales of veterinary antimicrobial agents in 31 European countries in

2017 Ninth ESVAC report. Accessed: https://www.ema.europa.eu/en/veterinary-

regulatory/overview/antimicrobial-resistance/european-surveillance-veterinary-

antimicrobial-consumption-esvac

41. Norway. NORM/NORM-VET, 2017. Usage of Antimicrobial Agents and Occurrence

of Antimicrobial Resistance in Norway. ISSN 1502-2307/1890-9965

42. NORM/NORM-VET, 2017. Usage of Antimicrobial Agents and Occurrence of

Antimicrobial Resistance in Norway. ISSN 1502-2307/1890-9965

59

43. Helsedirektoratet, 2018. Handlingsplan mot antibiotikaresistens i helsetjenesten 2016–

2020 – Statusrapporter. Opened https://www.helsedirektoratet.no/rapporter/

44. Van Boeckel, T.P., et al., Global antibiotic consumption 2000 to 2010: an analysis of

national pharmaceutical sales data. Lancet Infect Dis, 2014. 14(8): p. 742-750.

45. Klein, E.Y., et al., Global increase and geographic convergence in antibiotic

consumption between 2000 and 2015. Proc Natl Acad Sci U S A, 2018. 115(15): p.

E3463-e3470.

46. Centers for Disease Control and Prevention (CDC) Antibiotic resistance threats in the

United States, 2013. 2013 Retrieved from http:

http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf.

47. Mielko, K.A., et al., Metabolomic studies of Pseudomonas aeruginosa. World J

Microbiol Biotechnol, 2019. 35(11): p. 178.

48. Ren, H. and J. Liu, AsnB is involved in natural resistance of Mycobacterium smegmatis

to multiple drugs. Antimicrob Agents Chemother, 2006. 50(1): p. 250-5.

49. World Health Organization [Internet]. Antimicrobial resistance: Global report on

surveillance 2014. Geneva: WHO; June 2014. Available from:

http://apps.who.int/iris/bitstream/10665/112642/1/9789241564748_eng.pdf?ua=1.

50. O’Neill, J.J.R.A.R., Antimicrobial resistance: tackling a crisis for the health and wealth

of nations. 2014. 20: p. 1-16.

51. Tagliabue, A. and R. Rappuoli, Changing Priorities in Vaccinology: Antibiotic

Resistance Moving to the Top. Front Immunol, 2018. 9: p. 1068

52. Ferber, D., Antibiotic resistance. Superbugs on the hoof? Science, 2000. 288(5467): p.

792-4.

53. Livermore, D.M., Fourteen years in resistance. Int J Antimicrob Agents, 2012. 39(4):

p. 283-94.

54. Oberoi, S.S., et al., Antibiotics in dental practice: how justified are we. Int Dent J, 2015.

65(1): p. 4-10.

55. Konde, S., et al., Antibiotic overusage and resistance: A cross-sectional survey among

pediatric dentists. J Indian Soc Pedod Prev Dent, 2016. 34(2): p. 145-51.

56. Kim, B., et al., Trends and correlation between antibiotic usage and resistance pattern

among hospitalized patients at university hospitals in Korea, 2004 to 2012: A

nationwide multicenter study. Medicine (Baltimore), 2018. 97(51): p. e13719.

57. Ryu, S.J.J.o.g.a.r., The new Korean action plan for containment of antimicrobial

resistance. 2017. 8: p. 70-73.

58. Boucher, H.W., et al., Bad bugs, no drugs: no ESKAPE! An update from the Infectious

Diseases Society of America. Clin Infect Dis, 2009. 48(1): p. 1-12.

59. Davies, J. and D. Davies, Origins and evolution of antibiotic resistance. Microbiol Mol

Biol Rev, 2010. 74(3): p. 417-33.

60. Landers, T.F., et al., A review of antibiotic use in food animals: perspective, policy, and

potential. Public Health Rep, 2012. 127(1): p. 4-22.

61. Lazarus, B., et al., Do human extraintestinal Escherichia coli infections resistant to

expanded-spectrum cephalosporins originate from food-producing animals? A

systematic review. Clin Infect Dis, 2015. 60(3): p. 439-52.

62. Græsbøll, K., et al., Effect of tetracycline treatment regimens on antibiotic resistance

gene selection over time in nursery pigs. BMC Microbiol, 2019. 19(1): p. 269.

63. Sieber, R.N., et al., Drivers and Dynamics of Methicillin-Resistant Livestock-Associated

Staphylococcus aureus CC398 in Pigs and Humans in Denmark. mBio, 2018. 9(6).

60

64. Berg, E.S., et al., Norwegian patients and retail chicken meat share cephalosporin-

resistant Escherichia coli and IncK/bla(CMY-2) resistance plasmids. Clin Microbiol

Infect, 2017. 23(6): p. 407.e9-407.e15.

65. NORM/NORM-VET 2018. Usage of Antimicrobial Agents and Occurrence of

Antimicrobial Resistance in Norway. Tromsø / Oslo 2010. ISSN: 1502- 2307 (print) /

1890-9965 (electronic)

66. Liu, Y., et al., Nonribosomal antibacterial peptides that target multidrug-resistant

bacteria. Nat Prod Rep, 2019. 36(4): p. 573-592.

67. Raczynska, J.E., et al., A close look onto structural models and primary ligands of

metallo-β-lactamases. Drug Resist Updat, 2018. 40: p. 1-12.

68. Ramirez, M.S. and M.E. Tolmasky, Aminoglycoside modifying enzymes. Drug Resist

Updat, 2010. 13(6): p. 151-71.

69. Pagès, J.M., C.E. James, and M. Winterhalter, The porin and the permeating antibiotic:

a selective diffusion barrier in Gram-negative bacteria. Nat Rev Microbiol, 2008.

6(12): p. 893-903.

70. Linkevicius, M., L. Sandegren, and D.I. Andersson, Potential of Tetracycline

Resistance Proteins To Evolve Tigecycline Resistance. Antimicrob Agents Chemother,

2016. 60(2): p. 789-96.

71. Ross, J.I., et al., Inducible erythromycin resistance in staphylococci is encoded by a

member of the ATP-binding transport super-gene family. Mol Microbiol, 1990. 4(7): p.

1207-14.

72. Lim, D. and N.C. Strynadka, Structural basis for the beta lactam resistance of PBP2a

from methicillin-resistant Staphylococcus aureus. Nat Struct Biol, 2002. 9(11): p. 870-

6.

73. Carattoli, A., et al., Novel plasmid-mediated colistin resistance mcr-4 gene in

Salmonella and Escherichia coli, Italy 2013, Spain and Belgium, 2015 to 2016. Euro

Surveill, 2017. 22(31).

74. Kehrenberg, C., et al., A new mechanism for chloramphenicol, florfenicol and

clindamycin resistance: methylation of 23S ribosomal RNA at A2503. Mol Microbiol,

2005. 57(4): p. 1064-73.

75. Henry, R.J., THE MODE OF ACTION OF SULFONAMIDES. Bacteriol Rev, 1943.

7(4): p. 175-262.

76. Flensburg, J. and O. Sköld, Massive overproduction of dihydrofolate reductase in

bacteria as a response to the use of trimethoprim. Eur J Biochem, 1987. 162(3): p. 473-

6.

77. Munita, J.M. and C.A. Arias, Mechanisms of Antibiotic Resistance. Microbiol Spectr,

2016. 4(2).

78. Oliveira, P.H., et al., The chromosomal organization of horizontal gene transfer in

bacteria. Nat Commun, 2017. 8(1): p. 841.

79. Blokesch, M., Protocols for Visualizing Horizontal Gene Transfer in Gram-Negative

Bacteria Through Natural Competence, in Hydrocarbon and Lipid Microbiology

Protocols. 2015, Springer. p. 189-204.

80. Mell, J.C. and R.J. Redfield, Natural competence and the evolution of DNA uptake

specificity. J Bacteriol, 2014. 196(8): p. 1471-83.

81. Daubin, V. and G.J. Szöllősi, Horizontal Gene Transfer and the History of Life. Cold

Spring Harb Perspect Biol, 2016. 8(4): p. a018036.

82. Lerminiaux, N.A. and A.D.S. Cameron, Horizontal transfer of antibiotic resistance

genes in clinical environments. Can J Microbiol, 2019. 65(1): p. 34-44.

61

83. Shoemaker, N.B., et al., Evidence for extensive resistance gene transfer among

Bacteroides spp. and among Bacteroides and other genera in the human colon. Appl

Environ Microbiol, 2001. 67(2): p. 561-8.

84. Barlow, M. and B.G. Hall, Phylogenetic analysis shows that the OXA beta-lactamase

genes have been on plasmids for millions of years. J Mol Evol, 2002. 55(3): p. 314-21.

85. Livermore, D.M., et al., CTX-M: changing the face of ESBLs in Europe. J Antimicrob

Chemother, 2007. 59(2): p. 165-74

86. Logan, L.K. and R.A. Weinstein, The Epidemiology of Carbapenem-Resistant

Enterobacteriaceae: The Impact and Evolution of a Global Menace. J Infect Dis, 2017.

215(suppl_1): p. S28-s36.

87. Mangiola, F., et al., Gut microbiota in autism and mood disorders. World J

Gastroenterol, 2016. 22(1): p. 361-8.

88. Ursell, L.K., et al., Defining the human microbiome. Nutr Rev, 2012. 70 Suppl 1(Suppl

1): p. S38-44.

89. Sender, R., S. Fuchs, and R. Milo, Are We Really Vastly Outnumbered? Revisiting the

Ratio of Bacterial to Host Cells in Humans. Cell, 2016. 164(3): p. 337-40.

90. Claesson, M.J., et al., Comparative analysis of pyrosequencing and a phylogenetic

microarray for exploring microbial community structures in the human distal intestine.

PLoS One, 2009. 4(8): p. e6669.

91. Walter, J. and R. Ley, The human gut microbiome: ecology and recent evolutionary

changes. Annu Rev Microbiol, 2011. 65: p. 411-29.

92. Huse, S.M., et al., A core human microbiome as viewed through 16S rRNA sequence

clusters. PLoS One, 2012. 7(6): p. e34242.

93. Lloyd-Price, J., G. Abu-Ali, and C. Huttenhower, The healthy human microbiome.

Genome Med, 2016. 8(1): p. 51.

94. Dominguez-Bello, M.G., et al., Role of the microbiome in human development. Gut,

2019. 68(6): p. 1108-1114.

95. Nature. Structure, function and diversity of the healthy human microbiome. Nature,

2012. 486(7402): p. 207-14.

96. Mohajeri, M.H., et al., The role of the microbiome for human health: from basic science

to clinical applications. Eur J Nutr, 2018. 57(Suppl 1): p. 1-14.

97. Bezirtzoglou, E., The intestinal microflora during the first weeks of life. Anaerobe,

1997. 3(2-3): p. 173-7.

98. Mariat, D., et al., The Firmicutes/Bacteroidetes ratio of the human microbiota changes

with age. BMC Microbiol, 2009. 9: p. 123.

99. Claesson, M.J., et al., Composition, variability, and temporal stability of the intestinal

microbiota of the elderly. Proc Natl Acad Sci U S A, 2011. 108 Suppl 1(Suppl 1): p.

4586-91.

100. Nicholson, J.K., et al., Host-gut microbiota metabolic interactions. Science,

2012. 336(6086): p. 1262-7.

101. Flint, H.J., et al., The role of the gut microbiota in nutrition and health. Nat Rev

Gastroenterol Hepatol, 2012. 9(10): p. 577-89.

102. Kasubuchi, M., et al., Dietary gut microbial metabolites, short-chain fatty acids,

and host metabolic regulation. Nutrients, 2015. 7(4): p. 2839-49.

103. Louis, P. and H.J. Flint, Formation of propionate and butyrate by the human

colonic microbiota. Environ Microbiol, 2017. 19(1): p. 29-41.

104. Roediger, W.E., Role of anaerobic bacteria in the metabolic welfare of the

colonic mucosa in man. Gut, 1980. 21(9): p. 793-8.

62

105. Rivière, A., et al., Bifidobacteria and Butyrate-Producing Colon Bacteria:

Importance and Strategies for Their Stimulation in the Human Gut. Front Microbiol,

2016. 7: p. 979.

106. Den Besten, G., et al., Short-Chain Fatty Acids Protect Against High-Fat Diet-

Induced Obesity via a PPARγ-Dependent Switch From Lipogenesis to Fat Oxidation.

Diabetes, 2015. 64(7): p. 2398-408.

107. Hansen, T.H., et al., The gut microbiome in cardio-metabolic health. Genome

Med, 2015. 7(1): p. 33.

108. Santacruz, A., et al., Interplay between weight loss and gut microbiota

composition in overweight adolescents. Obesity (Silver Spring), 2009. 17(10): p. 1906-

15.

109. Kieler, I.N., et al., Overweight and the feline gut microbiome - a pilot study. J

Anim Physiol Anim Nutr (Berl), 2016. 100(3): p. 478-84.

110. Ley, R.E., et al., Obesity alters gut microbial ecology. Proc Natl Acad Sci U S

A, 2005. 102(31): p. 11070-5.

111. Turnbaugh, P.J., et al., A core gut microbiome in obese and lean twins. Nature,

2009. 457(7228): p. 480-4.

112. Turnbaugh, P.J., et al., An obesity-associated gut microbiome with increased

capacity for energy harvest. Nature, 2006. 444(7122): p. 1027-31.

113. Lührs, H., et al., Butyrate inhibits NF-kappaB activation in lamina propria

macrophages of patients with ulcerative colitis. Scand J Gastroenterol, 2002. 37(4): p.

458-66.

114. Maeda, T., et al., Up-regulation of costimulatory/adhesion molecules by histone

deacetylase inhibitors in acute myeloid leukemia cells. Blood, 2000. 96(12): p. 3847-

56.

115. Arpaia, N., et al., Metabolites produced by commensal bacteria promote

peripheral regulatory T-cell generation. Nature, 2013. 504(7480): p. 451-5.

116. Akimova, T., et al., Histone/protein deacetylases and T-cell immune responses.

Blood, 2012. 119(11): p. 2443-51

117. Machiels, K., et al., A decrease of the butyrate-producing species Roseburia

hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative

colitis. Gut, 2014. 63(8): p. 1275-83.

118. Scher, J.U., et al., Decreased bacterial diversity characterizes the altered gut

microbiota in patients with psoriatic arthritis, resembling dysbiosis in inflammatory

bowel disease. Arthritis Rheumatol, 2015. 67(1): p. 128-39.

119. LeBlanc, J.G., et al., Bacteria as vitamin suppliers to their host: a gut microbiota

perspective. Curr Opin Biotechnol, 2013. 24(2): p. 160-8.

120. Walther, B., et al., Menaquinones, bacteria, and the food supply: the relevance

of dairy and fermented food products to vitamin K requirements. Adv Nutr, 2013. 4(4):

p. 463-73.

121. Aydin, S., Can vitamin K synthesis altered by dysbiosis of microbiota be blamed

in the etiopathogenesis of venous thrombosis? Biosci Microbiota Food Health, 2017.

36(3): p. 73-74.

122. LeBlanc, J.G., et al., Bacteria as vitamin suppliers to their host: a gut microbiota

perspective. Curr Opin Biotechnol, 2013. 24(2): p. 160-8.

123. Lawley, T.D. and A.W. Walker, Intestinal colonization resistance. Immunology,

2013. 138(1): p. 1-11.

124. Rolhion, N. and B. Chassaing, When pathogenic bacteria meet the intestinal

microbiota. Philos Trans R Soc Lond B Biol Sci, 2016. 371(1707).

63

125. Rea, M.C., et al., Effect of broad- and narrow-spectrum antimicrobials on

Clostridium difficile and microbial diversity in a model of the distal colon. Proc Natl

Acad Sci U S A, 2011. 108 Suppl 1(Suppl 1): p. 4639-44.

126. Gu, S., et al., Identification of key taxa that favor intestinal colonization of

Clostridium difficile in an adult Chinese population. Microbes Infect, 2016. 18(1): p.

30-8.

127. Zhao, J., et al., Reduction in fecal microbiota diversity and short-chain fatty acid

producers in Methicillin-resistant Staphylococcus aureus infected individuals as

revealed by PacBio single molecule, real-time sequencing technology. Eur J Clin

Microbiol Infect Dis, 2017. 36(8): p. 1463-1472.

128. Ley, R.E., et al., Microbial ecology: human gut microbes associated with

obesity. Nature, 2006. 444(7122): p. 1022-3.

129. Johnson, E.L., et al., Microbiome and metabolic disease: revisiting the bacterial

phylum Bacteroidetes. J Mol Med (Berl), 2017. 95(1): p. 1-8.

130. Ley, R.E., et al., Evolution of mammals and their gut microbes. Science, 2008.

320(5883): p. 1647-51.

131. Salyers, A.A., et al., Fermentation of mucin and plant polysaccharides by strains

of Bacteroides from the human colon. Appl Environ Microbiol, 1977. 33(2): p. 319-22.

132. McNeil, N.I., The contribution of the large intestine to energy supplies in man.

Am J Clin Nutr, 1984. 39(2): p. 338-42.

133. Wu, S., et al., A human colonic commensal promotes colon tumorigenesis via

activation of T helper type 17 T cell responses. Nat Med, 2009. 15(9): p. 1016-22

134. Salyers, A.A., A. Gupta, and Y. Wang, Human intestinal bacteria as reservoirs

for antibiotic resistance genes. Trends Microbiol, 2004. 12(9): p. 412-6.

135. Nava, G.M., H.J. Friedrichsen, and T.S. Stappenbeck, Spatial organization of

intestinal microbiota in the mouse ascending colon. Isme j, 2011. 5(4): p. 627-38.

136. Collins, M.D., et al., The phylogeny of the genus Clostridium: proposal of five

new genera and eleven new species combinations. Int J Syst Bacteriol, 1994. 44(4): p.

812-26.

137. Raza, T., et al., Vancomycin resistant Enterococci: A brief review. J Pak Med

Assoc, 2018. 68(5): p. 768-772.

138. Leung, T.N., K.L. Hon, and A.K. Leung, Group A Streptococcus disease in

Hong Kong children: an overview. Hong Kong Med J, 2018. 24(6): p. 593-601.

139. Hills, R.D., Jr., et al., Gut Microbiome: Profound Implications for Diet and

Disease. Nutrients, 2019. 11(7).

140. Duan, R., et al., Alterations of Gut Microbiota in Patients With Irritable Bowel

Syndrome Based on 16S rRNA-Targeted Sequencing: A Systematic Review. Clin Transl

Gastroenterol, 2019. 10(2): p. e00012.

141. Murri, M., et al., Gut microbiota in children with type 1 diabetes differs from

that in healthy children: a case-control study. BMC Med, 2013. 11: p. 46.

142. Jandhyala, S.M., et al., Altered intestinal microbiota in patients with chronic

pancreatitis: implications in diabetes and metabolic abnormalities. Sci Rep, 2017. 7: p.

43640.

143. Calabresi, E., et al., One year in review 2018: pathogenesis of rheumatoid

arthritis. Clin Exp Rheumatol, 2018. 36(2): p. 175-184.

144. Strati, F., et al., New evidences on the altered gut microbiota in autism spectrum

disorders. Microbiome, 2017. 5(1): p. 24.

145. Yang, T., et al., Gut dysbiosis is linked to hypertension. Hypertension, 2015.

65(6): p. 1331-40.

64

146. Sullivan, A., C. Edlund, and C.E. Nord, Effect of antimicrobial agents on the

ecological balance of human microflora. Lancet Infect Dis, 2001. 1(2): p. 101-14.

147. Lewis, B.B., et al., Loss of Microbiota-Mediated Colonization Resistance to

Clostridium difficile Infection With Oral Vancomycin Compared With Metronidazole. J

Infect Dis, 2015. 212(10): p. 1656-65.

148. Duan, Y., et al., Gut resistomes, microbiota and antibiotic residues in Chinese

patients undergoing antibiotic administration and healthy individuals. Sci Total

Environ, 2020. 705: p. 135674.

149. Isaac, S., et al., Short- and long-term effects of oral vancomycin on the human

intestinal microbiota. J Antimicrob Chemother, 2017. 72(1): p. 128-136.

150. Pilmis, B., A. Le Monnier, and J.R. Zahar, Gut Microbiota, Antibiotic Therapy

and Antimicrobial Resistance: A Narrative Review. Microorganisms, 2020. 8(2).

151. Brismar, B., C. Edlund, and C.E. Nord, Impact of cefpodoxime proxetil and

amoxicillin on the normal oral and intestinal microflora. Eur J Clin Microbiol Infect

Dis, 1993. 12(9): p. 714-9.

152. Wisselink, H.J., et al., Antibiotics in 16-day-old broilers temporarily affect

microbial and immune parameters in the gut. Poult Sci, 2017. 96(9): p. 3068-3078.

153. Dethlefsen, L., et al., The pervasive effects of an antibiotic on the human gut

microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol, 2008. 6(11): p. e280.

154. Lazar, V., et al., Gut Microbiota, Host Organism, and Diet Trialogue in Diabetes

and Obesity. Front Nutr, 2019. 6: p. 21.

155. Risnes, K.R., et al., Antibiotic exposure by 6 months and asthma and allergy at

6 years: Findings in a cohort of 1,401 US children. Am J Epidemiol, 2011. 173(3): p.

310-8.

156. Ong, M.S., D.T. Umetsu, and K.D. Mandl, Consequences of antibiotics and

infections in infancy: bugs, drugs, and wheezing. Ann Allergy Asthma Immunol, 2014.

112(5): p. 441-445.e1.

157. Korpela, K., et al., Intestinal microbiome is related to lifetime antibiotic use in

Finnish pre-school children. Nat Commun, 2016. 7: p. 10410.

158. Metcalf, T., et al., Evaluation of the GeneXpert MTB/RIF in patients with

presumptive tuberculous meningitis. PLoS One, 2018. 13(6): p. e0198695.

159. Ericsson, H.M. and J.C. Sherris, Antibiotic sensitivity testing. Report of an

international collaborative study. Acta Pathol Microbiol Scand B Microbiol Immunol,

1971. 217: p. Suppl 217:1+.

160. Syal, K., et al., Current and emerging techniques for antibiotic susceptibility

tests. Theranostics, 2017. 7(7): p. 1795-1805.

161. Bauer, A.W., et al., Antibiotic susceptibility testing by a standardized single disk

method. Tech Bull Regist Med Technol, 1966. 36(3): p. 49-52.

162. Chisholm, S.A. and R.J. Owen, Application of polymerase chain reaction-based

assays for rapid identification and antibiotic resistance screening of Helicobacter

pylori in gastric biopsies. Diagn Microbiol Infect Dis, 2008. 61(1): p. 67-71.

163. Wayne, P.J.C.d.M.-A.C.d.M.-S.C. and L.S. Institute, Clinical and Laboratory

Standards Institute: Performance standards for antimicrobial disk susceptibility tests;

approved standard. 2009.

164. Frickmann, H., W.O. Masanta, and A.E. Zautner, Emerging rapid resistance

testing methods for clinical microbiology laboratories and their potential impact on

patient management. Biomed Res Int, 2014. 2014: p. 375681.

65

165. Saiki, R.K., et al., Enzymatic amplification of beta-globin genomic sequences

and restriction site analysis for diagnosis of sickle cell anemia. Science, 1985.

230(4732): p. 1350-4.

166. Espy, M.J., et al., Real-time PCR in clinical microbiology: applications for

routine laboratory testing. Clin Microbiol Rev, 2006. 19(1): p. 165-256.

167. Higuchi, R., et al., Simultaneous amplification and detection of specific DNA

sequences. Biotechnology (N Y), 1992. 10(4): p. 413-7.

168. Yang, S. and R.E. Rothman, PCR-based diagnostics for infectious diseases:

uses, limitations, and future applications in acute-care settings. Lancet Infect Dis, 2004.

4(6): p. 337-48

169. Kralik, P. and M. Ricchi, A Basic Guide to Real Time PCR in Microbial

Diagnostics: Definitions, Parameters, and Everything. Front Microbiol, 2017. 8: p. 108.

170. Patel, R., New Developments in Clinical Bacteriology Laboratories. Mayo Clin

Proc, 2016. 91(10): p. 1448-1459.

171. Shrestha, N.K., et al., Heart valve culture and sequencing to identify the infective

endocarditis pathogen in surgically treated patients. Ann Thorac Surg, 2015. 99(1): p.

33-7.

172. Mellmann, A., et al., Prospective genomic characterization of the German

enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation

sequencing technology. PLoS One, 2011. 6(7): p. e22751.

173. Livermore, D.M. and J. Wain, Revolutionising bacteriology to improve

treatment outcomes and antibiotic stewardship. Infect Chemother, 2013. 45(1): p. 1-10.

174. Lupo, A., et al., Non-phenotypic tests to detect and characterize antibiotic

resistance mechanisms in Enterobacteriaceae. Diagn Microbiol Infect Dis, 2013. 77(3):

p. 179-94.

175. Sherry, N.L., et al., Outbreak investigation using high-throughput genome

sequencing within a diagnostic microbiology laboratory. J Clin Microbiol, 2013. 51(5):

p. 1396-401.

176. Šunta, U., et al., Faecal indicator bacteria and antibiotic-resistant β-lactamase

producing Escherichia coli in blackwater: a pilot study. Arh Hig Rada Toksikol, 2019.

70(2): p. 140-148.

177. Osińska, A., et al., Quantitative occurrence of antibiotic resistance genes among

bacterial populations from wastewater treatment plants using activated sludge. 2019.

9(3): p. 387.

178. Galkiewicz, J.P. and C.A. Kellogg, Cross-kingdom amplification using bacteria-

specific primers: complications for studies of coral microbial ecology. Appl Environ

Microbiol, 2008. 74(24): p. 7828-31.

179. Liu, J.H., et al., Comparative studies of the composition of bacterial microbiota

associated with the ruminal content, ruminal epithelium and in the faeces of lactating

dairy cows. Microb Biotechnol, 2016. 9(2): p. 257-68.

180. Gutiérrez, A., et al., Cytokine association with bacterial DNA in serum of

patients with inflammatory bowel disease. Inflamm Bowel Dis, 2009. 15(4): p. 508-14.

181. Dollive, S., et al., Fungi of the murine gut: episodic variation and proliferation

during antibiotic treatment. PLoS One, 2013. 8(8): p. e71806.

182. Guo, X., et al., Development of a real-time PCR method for Firmicutes and

Bacteroidetes in faeces and its application to quantify intestinal population of obese

and lean pigs. Lett Appl Microbiol, 2008. 47(5): p. 367-73.

183. Takemori, H., et al., Mouse model of metformin-induced diarrhea. 2020. 8(1): p.

e000898.

66

184. Koetsier, G. and E.J.I.I. Cantor, MA, USA, A Practical Guide to Analyzing

Nucleic Acid Concentration and Purity with Microvolume Spectrophotometers. New

England BioLabs. 2019

185. Di Conza, J.A., et al., β-lactamases produced by amoxicillin-clavulanate-

resistant enterobacteria isolated in Buenos Aires, Argentina: a new blaTEM gene. Rev

Argent Microbiol, 2014. 46(3): p. 210-7.

186. Nawaz, Z., et al., Molecular identification of blaCTX-M and blaTEM genes

among multi-drug resistant Enteropathogenic Escherichia coli isolated from children.

Pak J Pharm Sci, 2019. 32(3 (Supplementary)): p. 1215-1218.

187. Ehlers, M.M., et al., Detection of blaSHV, blaTEM and blaCTX-M antibiotic

resistance genes in randomly selected bacterial pathogens from the Steve Biko

Academic Hospital. FEMS Immunology & Medical Microbiology, 2009. 56(3): p. 191-

196.

188. Graham, D.W., et al., Appearance of β-lactam Resistance Genes in Agricultural

Soils and Clinical Isolates over the 20 th Century. 2016. 6: p. 21550.

189. Rodriguez-Mozaz, S., et al., Occurrence of antibiotics and antibiotic resistance

genes in hospital and urban wastewaters and their impact on the receiving river. Water

Research, 2015. 69: p. 234-242.

190. Sun, J., et al., Development of aminoglycoside and β-lactamase resistance

among intestinal microbiota of swine treated with lincomycin, chlortetracycline, and

amoxicillin. Frontiers in microbiology, 2014. 5: p. 580-580.

191. Khan, I., et al., Metagenomic Analysis of Antibiotic-Induced Changes in Gut

Microbiota in a Pregnant Rat Model. Frontiers in pharmacology, 2016. 7: p. 104-104.

192. Abeles, S.R., et al., Microbial diversity in individuals and their household

contacts following typical antibiotic courses. Microbiome, 2016. 4(1): p. 39-39.

193. Galla, S., et al., Exposure to Amoxicillin in Early Life Is Associated With

Changes in Gut Microbiota and Reduction in Blood Pressure: Findings From a Study

on Rat Dams and Offspring. Journal of the American Heart Association, 2020. 9(2): p.

e014373-e014373.

194. Yao, Q., et al., Amoxicillin Administration Regimen and Resistance Mechanisms

of Staphylococcus aureus Established in Tissue Cage Infection Model. Frontiers in

microbiology, 2019. 10: p. 1638-1638.

195. Sturød, K., et al., Impact of narrow-spectrum penicillin V on the oral and faecal

resistome in a young child treated for otitis media. J Glob Antimicrob Resist, 2020. 20:

p. 290-297.

196. Ghanbari, M., et al., The dynamics of the antibiotic resistome in the feces of

freshly weaned pigs following therapeutic administration of oxytetracycline. Sci Rep,

2019. 9(1): p. 4062.

197. Connelly, S., et al., Distinct consequences of amoxicillin and ertapenem

exposure in the porcine gut microbiome. Anaerobe, 2018. 53: p. 82-93.

67

Appendices

Appendix A

This is the manufacturer protocol for extraction of fecal samples using QIAamp Fast DNA

Stool Mini Kit (QIAGEN).

68

69

Appendix B

This is the manufacturer protocol for the extraction of genomic DNA from the positive

control samples using QIAamp Fast DNA Stool Mini Kit (QIAGEN).

70

71

Appendix C

GeneRuler 1 kb DNA ladder (Thermo Fisher). This ladder was used in all the gel

electrophoresis as a DNA size marker to identify the size of DNA templates.

72

Appendix D

The row data for the quantification of the genomic DNA in the mice stool samples extracted by

Katrine Lekang using NanoDrop™ Lite Spectrophotometer (Thermo Scientific). The

extracted DNA was quantified by using 1 µL of each sample. Some of the samples were diluted

and the concentration after dilution is mentioned below. The sample name indicates which

group, and at which round the sample was taken. Group A = control group, group B = Treated

with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14

days amoxicillin. Numbering refers to the individual animals in each group. FS1 is fecal sample

taken first while FS7 is fecal sample taken on the 7th round of sampling.

Sample

Concentration

ng/µL

260:280

ratio

Diluted

Concentration after

dilution

A1FS1 6,7 1,7 OK 6,7

A2FS1 24,2 1,9 1:4 6,05

A3FS1 4,5 1,9 1:2 2,25

A4FS1 10,6 2,25 1:3 3,53

A5FS1 3,1 2,5 OK 3,1

B1FS1 6,3 1,98 1:2 3,15

B2FS1 10,5 1,94 1:2 5,25

B3FS1 23,9 2,11 1:3 7,97

B4FS1 24,9 1,94 1:3 8,3

B5FS1 10,3 1,94 1:2 5,15

C1FS1 9,8 1,9 1:2 4,9

C2FS1 12,1 2,13 1:3 4,03

C3FS1 3,8 1,84 1:2 1,9

C4FS1 3,3 2 1:2 1,65

C5FS1 8,7 1,89 1:3 2,9

D1FS1 20,5 2 1:4 5,125

D2FS1 3,7 1,9 1:2 1,85

D3FS1 16,8 1,92 1:3 5,6

D4FS1 4,3 2,07 OK 4,3

D5FS1 3,7 2,18 OK 3,7

A1FS7 8 1,8 1:2 4

A2FS7 23,8 1,86 1:4 5,95

A3FS7 9,7 1,81 1:3 3,23

A4FS7 3,4 2,03 1:2 1,7

A5FS7 5,2 1,91 1:2 2,6

B1FS7 3,7 2,04 1:2 1,85

B2FS7 12,7 1,96 1:3 4,23

B3FS7 7,6 1,92 1:2 3,8

B4FS7 22,4 2,52 1:3 7,47

73

B5FS7 6,5 1,98 1:2 3,25

C1FS7 21,5 2,01 1:4 5,38

C2FS7 11 2,12 1:3 3,67

C3FS7 8,1 1,91 1:2 4,05

C4FS7 18,5 2,23 1:3 6,17

C5FS7 36,8 2,21 1:4 9,2

D1FS7 8,5 2,29 1:2 4,25

D2FS7 20,5 2,02 1:4 5,13

D3FS7 4,4 1,85 1:2 2,2

D4FS7 29,8 2,09 1:4 7,45

D5FS7 6,9 1,91 1:2 3,45

74

Appendix E

The row data for the quantification of the genomic DNA in the mice stool samples extracted by

Ahmed Ek using NanoDrop™ Lite Spectrophotometer (Thermo Scientific). The

extracted DNA was quantified by using 1 µL of each sample. Some of the samples were diluted

and the concentration after dilution is mentioned below. The sample name indicates which

group, and at which round the sample was taken. Group A = control group, group B = Treated

with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14

days amoxicillin. Numbering refers to the individual animals in each group. FS1 is fecal sample

taken first while FS7 is fecal sample taken on the 7th round of sampling.

Sample

Concentration

ng/µL

260:280

ratio Diluted

Concentration after

dilution

A1FS1 4,9 4,16 OK 4,9

A2FS1 9,3 4,48 OK 9,3

A3FS1 5 2,42 OK 5

A4FS1 8,3 1,98 OK 8,3

A5FS1 2,5 2,43 OK 2,5

B1FS1 13,5 3,06 OK 13,5

B2FS1 16,1 2,88 OK 16,1

B3FS1 16,6 2,11 OK 16,6

B4FS1 16,7 2,7 1:2 8,35

B5FS1 16,4 2,14 1:2 8,2

C1FS1 24 2,47 1:2 12

C2FS1 9,2 2,85 OK 9,2

C3FS1 17 2,08 OK 17

C4FS1 9,5 1,88 OK 9,5

C5FS1 7,1 2,38 OK 7,1

D1FS1 13 2,67 OK 13

D2FS1 9,9 2,64 OK 9,9

D3FS1 13,7 2,34 OK 13,7

D4FS1 12 2,71 OK 12

D5FS1 8,2 3,66 OK 8,2

A1FS7 11,4 2,06 OK 11,4

A2FS7 23,9 2,1 1:2 11,95

A3FS7 8,3 2,73 OK 8,3

A4FS7 4,5 2,94 OK 4,5

A5FS7 9,4 1,94 OK 9,4

B1FS7 5,8 2,69 OK 5,8

B2FS7 13,2 2,57 OK 13,2

B3FS7 13 2,57 OK 13

B4FS7 13,7 2,53 OK 13,7

75

B5FS7 6,4 3,26 OK 6,4

C1FS7 19,3 1,97 1:2 9,65

C2FS7 9,8 2,28 OK 9,8

C3FS7 23 2,29 1:2 11,5

C4FS7 21,2 2,34 1:2 10,6

C5FS7 18,9 1,89 1:2 9,45

D1FS7 5 2 OK 5

D2FS7 19,8 1,99 1:2 9,9

D3FS7 8,9 2,49 OK 8,9

D4FS7 16 2,1 1:2 8

D5FS7 9,1 2,5 OK 9,1

76

Appendix F

The row data for the quantification of the genomic DNA in the samples used as positive control

extracted by Ahmed Ek using NanoDrop™ Lite Spectrophotometer (Thermo

Scientific). The extracted DNA was quantified by using 1 µL of each sample.

Positive Control samples

Concentration DNA in

ng/µL

Purity in

260:280 ratio

Isochrysis galbana sample 5,2 2,3

Dunaliella tertiolecta sample 4 2,2

Tetraselmis suecica sample 2,7 2,27

Fungus sample 1 31,2 1,37

Firmicutes Sample 1 20,2 2,21

Firmicutes Sample 2 18,5 2,15

Firmicutes Sample 3 15,8 2,16

Bacteroides sample 1 40 2,03

Bacteroides sample 2 20,6 2,14

Bacteroides sample 3 16,6 2,22

E. coli sample 1 8,5 1,93

77

Appendix G

The raw data of the threshold values of the different mice group studied using 16S rRNA

universal primer set (16S Univ F/16S Univ R). The sample name indicates which group, and at

which round the sample was taken. Group A = control group, group B = Treated with 3 days

amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14 days

amoxicillin. Numbering refers to the individual animals in each group. FS1 is fecal sample

taken first while FS7 is fecal sample taken on the 7th round of sampling.

Sample Rep. 1 Rep. 2 Rep. 3 Avg. Sample Rep. 1 Rep. 2 Rep. 3 Avg

A1FS1 16,19 13,80 13,31 14,43 A1FS7 11,60 12,22 11,38 11,73

A2FS1 12,63 12,69 12,44 12,59 A2FS7 12,26 12,84 12,66 12,59

A3FS1 13,65 13,09 12,75 13,16 A3FS7 12,09 12,08 11,44 11,87

A4FS1 13,15 13,43 12,55 13,04 A4FS7 13,84 14,10 13,22 13,72

A5FS1 14,83 13,98 12,80 13,87 A5FS7 13,45 13,32 13,07 13,28

B1FS1 13,83 12,48 12,20 12,84 B1FS7 13,79 13,63 13,19 13,54

B2FS1 12,04 13,02 11,97 12,34 B2FS7 12,66 12,96 12,03 12,55

B3FS1 18,41 13,51 12,19 14,70 B3FS7 13,36 12,28 11,37 12,34

B4FS1 11,67 12,09 10,86 11,54 B4FS7 12,62 13,32 12,24 12,73

B5FS1 12,64 13,05 14,74 13,48 B5FS7 12,50 12,87 12,19 12,52

C1FS1 12,25 12,38 12,15 12,26 C1FS7 12,41 12,55 12,19 12,38

C2FS1 12,21 12,19 11,71 12,04 C2FS7 12,57 12,56 12,01 12,38

C3FS1 14,68 14,64 14,11 14,48 C3FS7 13,42 13,04 12,55 13,00

C4FS1 14,85 14,56 14,15 14,52 C4FS7 14,00 14,10 13,54 13,88

C5FS1 13,09 13,05 12,53 12,89 C5FS7 11,65 11,66 11,01 11,44

D1FS1 12,95 13,22 11,66 12,61 D1FS7 13,25 13,36 12,19 12,93

D2FS1 13,27 13,28 13,34 13,30 D2FS7 11,38 18,15 14,74 14,76

D3FS1 12,71 12,36 12,17 12,41 D3FS7 13,52 Undet 13,17 13,35

D4FS1 13,96 13,12 13,04 13,37 D4FS7 10,77 11,16 10,18 10,70

D5FS1 14,99 14,48 14,04 14,50 D5FS7 13,54 22,49 12,86 16,30

78

Positive 15,71 15,27 16,62 E2.

15.87

Positive 16,84 14,14 18,02 E1.

16,33

NTC 30,41 30,18 32,90 31,16 E-Coli 12,41 12,36 11,07 11,95

NTC 30,56 31,23 33,67 31,82 E-Coli 11,82 26,92 13,18 17,31

79

Appendix H

The raw data of the threshold values of the different mice group studied using 16S rRNA primer

set for Firmicutes (Firm934 F/Firm1060 R). The sample name indicates which group, and at

which round the sample was taken. Group A = control group, group B = Treated with 3 days

amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14 days

amoxicillin. Numbering refers to the individual animals in each group. FS1 is fecal sample

taken first while FS7 is fecal sample taken on the 7th round of sampling.

Sample Rep. 1 Rep. 2 Rep. 3 Avg. Sample Rep. 1 Rep. 2 Rep. 3 Avg

A1FS1 36,94 Undet 13,81 15,38 A1FS7 Undet Undet 13,52 13,52

A2FS1 17,15 14,57 12,36 14,69 A2FS7 15,16 15,46 11,21 13,94

A3FS1 15,31 13,94 13,86 14,37 A3FS7 12,33 12,33 11,55 12,07

A4FS1 16,88 14,99 12,59 14,82 A4FS7 13,48 13,42 14,23 13,71

A5FS1 15,25 14,49 14,28 14,67 A5FS7 16,81 16,03 13,99 15,61

B1FS1 16,45 15,28 12,34 14,69 B1FS7 15,24 15,31 13,58 14,71

B2FS1 14,57 13,10 12,16 13,28 B2FS7 15,57 15,53 12,09 14,40

B3FS1 Undet 15,29 15,64 15,47 B3FS7 15,63 14,12 14,05 14,60

B4FS1 Undet Undet 11,13 11,13 B4FS7 Undet Undet 12,40 12,40

B5FS1 13,52 13,13 11,48 12,71 B5FS7 14,75 14,40 13,50 14,22

C1FS1 13,86 13,49 12,22 13,19 C1FS7 14,69 14,96 12,40 14,02

C2FS1 14,31 13,85 11,60 13,25 C2FS7 13,47 13,38 12,98 13,28

C3FS1 14,11 13,11 14,48 13,90 C3FS7 15,60 14,32 13,46 14,46

C4FS1 16,65 16,74 15,18 16,19 C4FS7 15,17 14,54 14,57 14,76

C5FS1 17,06 17,02 13,83 15,97 C5FS7 16,47 15,82 11,00 14,43

D1FS1 17,44 17,00 11,92 15,45 D1FS7 13,70 12,82 14.33 13,62

D2FS1 Undet Undet 14,76 14,76 D2FS7 Undet Undet 11,31 11,31

D3FS1 15,52 15,46 12,14 14,37 D3FS7 12,74 14,89 14,29 13,97

D4FS1 12,78 12,95 14,31 13,35 D4FS7 16,09 16,45 10,34 14,29

D5FS1 15,24 15,49 15,36 15,36 D5FS7 11,65 12,50 13,30 12,48

80

Positiv 16,27 15,88 10,54 14,23 Positive 14,55 15,13

Positiv 11,56 11,67 10,78 11,34 NTC 30,10 Undet.

NTC 12,52 12,14 31,04 18,57 E-Coli Undet Undet 12,76 12,76

NTC 36,20 35,30 31,53 34,34 E-Coli 15,67 11,75 13,02 13,48

81

Appendix I

The raw data of the threshold values of the different mice group studied using primer set

(BlaTem-b F/BlaTem-b R) that targeted blaTEM gene. The sample name indicates which group,

and at which round the sample was taken. Group A = control group, group B = Treated with 3

days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated with 14 days

amoxicillin. Numbering refers to the individual animals in each group. FS1 is fecal sample

taken first while FS7 is fecal sample taken on the 7th round of sampling.

Sample Rep. 1 Rep. 2 Rep. 3 Avg. Sample Rep. 1 Rep. 2 Rep. 3 Avg

A1FS1 Undet Undet 36,20 36,20 A1FS7 33,00 33,29 31,72 32,67

A2FS1 35,48 33,80 32,21 33,83 A2FS7 34,56 35,16 34,84 34,85

A3FS1 33,65 32,98 33,77 33,47 A3FS7 34,46 38,02 34,39 35,62

A4FS1 33,27 34,56 31,22 33,02 A4FS7 34,99 32,58 31,49 33,02

A5FS1 36,17 33,24 30,78 33,40 A5FS7 35,25 Undet Undet 35,25

B1FS1 35,80 34,13 33,68 34,54 B1FS7 undet 33,38 Undet 33,38

B2FS1 Undet 33,67 32,60 33,14 B2FS7 38,16 Undet 36,88 37,52

B3FS1 Undet Undet Undet Undet B3FS7 Undet Undet Undet Undet

B4FS1 Undet Undet 33,19 33,19 B4FS7 Undet 36,91 38,21 37,56

B5FS1 Undet Undet 33,39 33,39 B5FS7 Undet 39,63 Undet 39,63

C1FS1 33,84 34,35 36,92 35,04 C1FS7 30,51 32,88 30,15 31,18

C2FS1 33,70 32,65 32,37 32,91 C2FS7 31,56 32,13 29,61 31,10

C3FS1 32,44 31,51 31,59 31,85 C3FS7 Undet 35,68 Undet 35,68

C4FS1 33,56 33,65 31,97 33,06 C4FS7 30,69 30,81 30,48 30,66

C5FS1 Undet Undet 33,98 33,98 C5FS7 33,87 32,16 32,01 32,68

D1FS1 Undet Undet 33,76 33,76 D1FS7 33,80 32,27 30,32 32,13

D2FS1 34,53 Undet 32,57 33,55 D2FS7 31,90 Undet 29,86 30,88

D3FS1 Undet 34,46 Undet 34,46 D3FS7 32,99 36,41 31,51 33,64

D4FS1 31,82 31,34 31,94 31,70 D4FS7 32,38 31,91 33,55 32,61

D5FS1 30,21 29,83 29,43 29,82 D5FS7 Undet Undet 35,27 35,27

82

Positiv 18,08 16,01 21,43 E1

18,51

Positiv 17,34 16,48 23,46 E2

19,09

NTC Undet Undet Undet undet E-Coli 12,17 19,62 11,31 14,37

NTC Undet Undet Undet Undet E-Coli 14,46 Undet 10,68 12,57

83

Appendix J

The raw data of the threshold values of the different mice group studied using primer set

(Bac960 F/Bac1100 R) that targeted 16S rRNA for Bacteroidetes. The sample name indicates

which group, and at which round the sample was taken. Group A = control group, group B =

Treated with 3 days amoxicillin, group C = Treated with 7 days amoxicillin, group D = Treated

with 14 days amoxicillin. Numbering refers to the individual animals in each group. FS1 is

fecal sample taken first while FS7 is fecal sample taken on the 7th round of sampling.

Sample Rep. 1 Rep. 2 Rep. 3 Avg. Sample Rep. 1 Rep. 2 Rep. 3 Avg

A1FS1 15,69 13,16 13,89 14,43 A1FS7 12,03 11,94 11,07 11,68

A2FS1 14,08 13,51 14,17 13,80 A2FS7 13,34 12,99 12,72 13,02

A3FS1 14,55 13,06 12,97 13,81 A3FS7 13,91 13,34 13,29 13,51

A4FS1 16,39 14,87 15,22 15,63 A4FS7 14,12 13,80 13,33 13,75

A5FS1 14,48 12,55 13,12 13,52 A5FS7 13,27 12,71 12,78 12,92

B1FS1 16,49 14,30 13,56 14,78 B1FS7 15,02 14,21 13,81 14,35

B2FS1 14,19 13,12 12,27 13,19 B2FS7 14,73 14,10 13,97 14,27

B3FS1 18,50 14,79 16,04 16,44 B3FS7 11,92 11,71 11,87 11,83

B4FS1 13,68 13,06 13,01 13,25 B4FS7 13,03 13,15 13,05 13,08

B5FS1 17,42 16,60 16,11 16,71 B5FS7 12,78 12,26 12,07 12,37

C1FS1 12,64 12,19 11,49 12,11 C1FS7 14,04 13,77 12,65 13,49

C2FS1 13,20 12,99 12,74 12,98 C2FS7 12,28 12,28 12,47 12,34

C3FS1 14,78 14,46 14,02 14,42 C3FS7 12,84 12,52 12,86 12,74

C4FS1 15,11 14,53 12,33 13,99 C4FS7 14,23 14,18 14,43 14,28

C5FS1 13,00 12,75 12,24 12,66 C5FS7 14,51 14,20 13,32 14,01

D1FS1 16,13 15,90 15,59 15,87 D1FS7 14,00 13,63 12,77 13,47

D2FS1 13,66 13,52 12,78 13,32 D2FS7 15,86 16,31 15,29 15,82

D3FS1 13,12 13,25 13,66 13,34 D3FS7 13,63 13,56 12,54 13,24

D4FS1 13,08 12,77 12,33 12,73 D4FS7 11,19 13,16 13,22 12,52

D5FS1 14,98 14,45 14,00 14,48 D5FS7 12,40 12,37 12,47 12,41

84

Positiv 10,22 9,95 9,65 9,94

Positiv 10,21 10,25 10,23

NTC 31,43 32,93 31,08 31,81 E-Coli 11,82 12,02 11,15 11,66

NTC 32,19 32,81 32,79 32,60 E-Coli 13,12 24,63

Bubble

11,98