A METAGENOMIC ANALYSIS OF THE RESPIRATORY …

I

A METAGENOMIC ANALYSIS OF THE RESPIRATORY

MICROBIOTA OF BIRDS

MUHAMMAD ZUBAIR SHABBIR

(2005-VA-166)

A THESIS SUBMITTED IN THE PARTIAL FULFILLMENT OF THE

REQUIREMENT FOR THE DEGREE

OF

DOCTOR OF PHILOSOPHY

IN

MICROBIOLOGY

UNIVERSITY OF VETERINARY AND ANIMAL SCIENCES,

LAHORE – PAKISTAN

2013

II

To

The Controller of Examinations,

University of Veterinary and Animal Sciences,

Lahore.

We, the Supervisory Committee, certify that the contents and form of the thesis, submitted by

Muhammad Zubair Shabbir, have been found satisfactory and recommend that it be

processed for the evaluation by the External Examiner(s) for the award of the degree.

SUPERVISOR: ______________________________________

Prof. Dr. Masood Rabbani (Izaz-i Fazeelat)

Co-SUPERVISOR: _______________________________________

Prof. Dr. Eric Thomas Harvill

MEMBER: _______________________________________

Prof. Dr. Khushi Muhammad

MEMBER: _______________________________________

Prof. Dr. Muhammad Younus Rana

III

I would like to give all my praises and humblest thanks to the Most Gracious, Merciful and

Almighty Allah, who granted me the potential and ability to contribute this material to the

existing knowledge in the fields of microbiology and bioinformatics. I offer my humblest

thanks from the core of my heart to the Holy Prophet Muhammad (S.A.W.), who is forever a

torch of guidance and knowledge for humanity as a whole.

This thesis has been completed as a collaborative research project between PennState

University (PSU), USA and University of Veterinary and Animal Science (UVAS), Pakistan,

entitled “A metagenomic approach to the unbiased identification of pathogens endemic to

Pakistan”, funded by the Defense Threat Reduction Agency (DTRA), USA. I am thankful to

this donor agency for providing the financial support for the described work.

Of all the individuals involved directly or indirectly in this work, I would especially

like to express my deep sense of gratitude to Dr. Eric Thomas Harvill, Professor of

Microbiology and Infectious Diseases, PSU, State College, USA. Thank you, Eric, for your

unconditional scientific support, your inspiring attitude toward learning and writing, and the

number of meetings and discussions in your office during the execution of this project and

thesis work. I would also like to thank Dr. Harvill’s graduate student, JiHye Park, at PSU,

USA, for providing guidance during the initial phases of this study. I am thankful to Deb

Groove from the Genome Sequencing Facility at the Huck Institute of the Life Sciences,

Penn State University, for sharing theoretical and practical advice pertaining to advanced

sequencing techniques. Thanks to my major supervisor, Prof. Dr. Masood Rabbani,

Director, University Diagnostic Lab, UVAS, Lahore, and the members of my supervisory

committee, Prof. Dr. Khushi Muhammad, Chairman, Department of Microbiology and

Prof. Dr. Muhammad Younus Rana, Principal, CV&AS, Jhang, for their help and

facilitation of this work. Moreover, I owe many thanks to the staff of all concerned

laboratories for their help during this research.

I have no words to thank Prof. Dr. Tahir Yaqub, Director, Quality Operations Lab /

Institute of Biochemistry and Biotechnology, who always allowed me to use his laboratory

facilities. I am also grateful to my parents and family for their support and encouragement.

Finally, as is customary, all mistakes left uncorrected are entirely mine.

(MUHAMMAD ZUBAIR SHABBIR)

IV

TABLE OF CONTENTS

PAGE NO. TITLE

I Title of Thesis

III Acknowledgements

IV Table of Contents

V List of Tables

VI List of Figures

S. NO. CHAPTERS PAGE NO.

1.

Introduction 1

2.

Review of Literature 4

3.

Materials and Methods 16

4.

Results 26

5.

Discussion 69

6.

Summary 79

7.

Literature Cited 81

V

LIST OF TABLES

TABLE

NO.

TITLE PAGE

NO.

1 Prevalence of bacterial infections among birds in Pakistan 12

2

454-sequencing reads for the selected flocks in each management

system

21

3

454-sequencing reads obtained from houbara bustard (Chlamydotis

undulata) and ostrich (Struthio camelus)

23

4

Brief history, geographical location, and individual clinical sample

details (16S reads and subsequent diversity) for the free range system

flocks

30

5

Brief history, geographical location, and individual clinical sample

details (16S reads and subsequent diversity) for the open house system

flock

36

6

Brief history, geographical location and individual clinical sample detail

(16S reads and subsequent diversity) for the controlled house system

flocks

43

VI

LIST OF FIGURES

FIGURE

NO. TITLE

PAGE

NO.

1 Culture-based analysis of the respiratory microbiome in clinically

diseased and healthy birds

9

2

The hypervariable regions in the 16S rRNA gene evaluated in this

study

21

3

Percentage of sequences retrieved from clinically diseased and

healthy birds in each management system

23

4

Taxonomy rarefaction plot of the T-BALs from birds in the free

range system at the genera node

31

4a

NCBI taxonomic content-based hierarchical clustering of clinically

diseased and healthy birds in the free range system collapsed at the

genera node

31

4b

Comparative visualization of the abundances of phyla in clinically

diseased and healthy birds in the free range system using the NCBI

taxonomy database

32

4c

Relative abundances of bacterial families identified in clinically

diseased and healthy birds in the free range system using the

taxonomy database available at NCBI

32

4d

Relative abundances of bacterial genera identified in clinically

diseased and healthy birds in the free range system using the

taxonomy database available at NCBI

34

4e 16S rRNA sequence (V1 – V5)-based identification of bacterial 34

VII

species corresponding to the NCBI database in clinically diseased

and healthy birds from the free range system

5

Taxonomy rarefaction plot of the T-BALs from birds in the open

house system at the genera node

36

5a


diseased and healthy birds in the open house system collapsed at the

genera node

37

5b


diseased and healthy birds in the open house system using the NCBI

taxonomy database

37

5c

Comparative visualization of the abundances of bacterial families in

clinically diseased and healthy birds in the open house system using

the taxonomy database available at NCBI

39

5d

Relative abundances of bacterial genera in clinically diseased and

healthy birds in the open house system using the taxonomy database

available at NCBI

41

5e

16S rRNA sequence (V1 – V5)-based identification of bacterial

species corresponding to NCBI database in clinically diseased and

healthy birds from open house system

41

6

Taxonomy rarefaction plot of the T-BALs from birds in the

controlled house system at the genera node

44

6a


diseased and healthy birds in the controlled house system collapsed

at the genera node

44

6b Comparative visualization of the abundances of phyla in clinically 46

VIII

diseased and healthy birds in the controlled house system using the

NCBI taxonomy database

6c

Relative visualization of the abundances of bacterial families in

clinically diseased and healthy birds in the controlled house system

using the taxonomy database available at NCBI

46

6d

Relative abundances of bacterial genera in clinically diseased and

healthy birds in the controlled house system using the taxonomy

database available at NCBI

48

6e


species corresponding to NCBI database in clinically diseased and

healthy birds from controlled house system

49

7

Taxonomy rarefaction plot of the T-BALs from clinically diseased

birds at the genera node

51

8

Taxonomy rarefaction plot of the T-BALs from clinically healthy

birds at the genera node

51

7a


diseased birds collapsed at the genera node

52

8a


healthy birds collapsed at the genera node

52

7b


diseased birds using the NCBI taxonomy database

54

7c


clinically diseased birds using the taxonomy database available at

NCBI

54

7d Relative abundances of bacterial genera in clinically diseased birds 55

IX


7e

Individual variation in bacterial genera identified from clinically

diseased birds corresponding to NCBI taxonomy

56

7f


species in clinically diseased birds corresponding to NCBI database

56

7g

Individual variation in 16S rRNA sequence (V1 – V5)-based

identification of bacterial species in clinically diseased birds

corresponding to NCBI taxonomy database

57

8b


healthy birds using the NCBI taxonomy database

59

8c


clinically healthy birds using the taxonomy database available at

NCBI

59

8d

Relative abundances of bacterial genera in clinically healthy birds


60

8e

Individual variation in bacterial genera identified from clinically

healthy birds corresponding to NCBI taxonomy database

60

8f


species in clinically healthy birds corresponding to NCBI database

61

8g

Individual variation in 16S rRNA sequence (V1 – V5)-based

identification of bacterial species in clinically healthy birds

corresponding to NCBI taxonomy database

61

9

Taxonomy rarefaction plot for the analyzed T-BAL sample from a

houbara bustard (Chlamydotis undulata) at the genera node

64

9a Relative abundance of bacterial phyla, families, and genera identified 64

X

in a houbara bustard (Chlamydotis undulata) using the taxonomy

database available at NCBI

9b

16S rRNA sequence (V1 – V5)-based phylogenetic analysis of

taxonomic content for the houbara bustard (Chlamydotis undulata)

corresponding to the NCBI database

65

10

Taxonomy rarefaction plot for the analyzed T-BAL sample from

ostrich (Struthio camelus) at the genera node

67

10a

Relative abundance of bacterial families and genera identified in

ostrich (Struthio camelus) using the taxonomy database available at

NCBI

67

10b

16S rRNA sequence (V1 – V5)-based phylogenetic analysis of

taxonomic contents in ostrich (Struthio camelus) corresponding to

NCBI taxonomy database

68

1

CHAPTER 1

INTRODUCTION

Every day, the respiratory system of birds encounters a large number of microbes from the

environment. However, little is known about the respiratory microbiota of the birds in health

and disease (Smibert et al., 1957; Silvanose et al., 2001; Nehme et al., 2005). Based on the

results of culture-dependent techniques, the lower respiratory tract and lungs of birds have

generally been considered sterile in the absence of clinical disease (Pecora, 1963). The nature

of an organism, its role as a primary or secondary pathogen, the host immune response, and

several management factors (ammonia level, humidity, etc.) determine which invading

organism will adhere to the mucosa and produce a clinical outcome (Glisson, 1998; van

Empel and Hafez, 1999; Pan et al., 2012). Clinically, it is difficult to diagnose potential

pathogens because a wide range of bacteria can present similar symptoms. To date, the

isolation and phenotypic identification of a number of genera and microorganisms (either

alone or in synergy with other organisms) have permitted the identification of the bacterial

causes of respiratory infection in birds around the globe (Vandamme et al., 1994; Travers,

1996; Van Empel and Hafez, 1999; Zorman-Rojs et al., 2000; El-Sukhon et al., 2002; Hafez,

2002; Chin et al., 2003; Canal et al., 2005; Murthy et al., 2008; Pan et al., 2012). In Pakistan,

a number of bacterial species, such as Haemophilus paragallinarum (Akhter et al., 2001;

Hasan et al., 2002; Siddique et al., 2012), Escherichia coli (Hasan et al., 2002; Mustafa et al.,

2005), Mycoplasma and Salmonella spp. (Mustafa et al., 2005; Lateef et al., 2006),

Staphylococcus, Streptococcus, and Bacillus spp. (Lateef et al., 2006), Pasteurella multocida

(Zahoor and Siddique, 2011), and Mycoplasma synoviae (Ehtisham-ul-Haque et al., 2011),

have been isolated and identified with varying prevalence in birds raised in free range, open

house, and/or controlled house systems. Apart from the small number of organisms identified

(< 1%) that have been cultured using prior knowledge of standard microbiological techniques

INTRODUCTION

2

and genome based identification procedures (Schuster, 2008), in most cases, the etiology

remains unknown because of limited diagnostic and culturing facilities. When faced with an

outbreak, empirical treatment with a broad range of antibiotics further complicates the

isolation and identification of the candidate pathogen. Together, compared to advanced

culture independent sequencing techniques such as pyrosequencing, this provides a partial

representation or identification of the airway microbiota and diminutive prospects for

determining or identifying the association of novel organisms/pathogens with clinical

outcomes.

Metagenomics is a powerful tool for analyzing bacterial communities directly at the

nucleic acid level and requires no culturing, cloning, or prior knowledge of the organism in a

given sample (Schuster, 2008; Pereira et al., 2010). Because more than 99.8% of organisms

cannot be cultured, the hypervariable region of the 16S rRNA gene, the sequence of which is

species-specific, can be used as an alternative to the phenotypic identification of bacteria.

Recent advances in next-generation sequencing techniques (e.g., pyrosequencing) have

permitted determination of the structure and variety within communities and have led to the

reclassification of bacteria into new genera or species (Weisburg et al., 1991; Brett et al.,

1998; Schuster, 2008; Pereira et al., 2010). Notably, compared to the varying and time-

consuming conventional microbiological procedures used for many known pathogens that

further complicate accurate and prompt diagnoses, metagenomics is directly applicable to

clinical samples and can lead to the identification of any known as well as novel pathogen

using a common procedure (Nakamura et al., 2008). Using these techniques, a number of

novel organisms have been identified in recent years (Wylie et al., 2012; Eckburg et al.,

2005). Furthermore, with the vast and ever-increasing amount of genomic information

available in public databases, there is increased potential for identifying novel pathogens or

associating pathogens with clinical outcomes using these approaches.

INTRODUCTION

3

In birds, recent studies have focused largely on the culture-independent identification

of the bacterial community in the gastrointestinal tract (Pedroso et al., 2006; Rothrock et al.,

2008). Nevertheless, there is a paucity of information regarding the culture-independent

analysis of respiratory microbiota, particularly with respect to 1) clinical respiratory

symptoms that are not correlated with known bacterial or viral infections and 2) the different

management practices used to raise the birds. Obtaining basic information regarding normal

and disease-causing microbiota is helpful for understanding the nature of the bacteria in the

environment, monitoring endemic infections in a particular geographical area, investigating

emerging novel or known pathogens, improving clinical and molecular diagnostics, and

determining the association of certain pathogens and/or commensals with clinical status.

Ultimately, this information can be used to devise health and disease control strategies for

birds. This study gains importance when one considers birds as a reservoir or mixing vessel

for a number of emerging pathogens of significance to public health (Waldenstrom et al.,

2003; Abulreesh et al., 2007).

Therefore, a comparative metagenomic analysis of the respiratory system of clinically

diseased and healthy birds reared under different management systems has been performed.

The approach used (454-sequencing or pyrosequencing) was unbiased and culture-

independent, relying on the amplification of 16S rRNA, which is highly conserved across

different species of bacteria and archaea (prokaryotes) (Weisburg et al., 1991; Brett et al.,

1998; Clarridge, 2004; Streit and Schmitz, 2004; Schuster, 2008; Pereira et al., 2010).

Aims of the Thesis:

1. To identify differences in the microbiota of commercial and rural poultry reared under

different management practices

2. To identify differences in the microbiota of clinically diseased and healthy birds

3. 16S rRNA based identification of the bacterial flora in the respiratory tracts of birds

4

CHAPTER 2

REVIEW OF LITERATURE

2.1. MANAGEMENT SYSTEMS USED TO RAISE COMMERCIAL AND FREE RANGE

BIRDS; A BRIEF COMPARISON

Poultry is a low-input and valuable source of food throughout the world. Most poultry is

produced in developing countries that are situated in the hot climate zone (between 25-30

latitude north and south of the equator), in which the economy is based largely on agriculture,

livestock, and livestock products. Different systems, such as free range, open house, and

controlled house are used to raise birds for meat and egg production. Collectively, open and

controlled house farming are considered commercial poultry farming and are widely accepted for

the intense production of poultry with rapid and efficient output. In many countries in the Far

East and South East, such as Pakistan, India, Bangladesh, and Sri Lanka, approximately 90% of

birds are maintained on floor and deep litter systems. In most farms in the region, the floors are

cemented concrete, which facilitates washing and disinfection. Because natural ventilation

promotes heat loss, open house farming is more appropriate than closed house farming under hot

climatic conditions. A locally modified version of controlled environment houses (tunnel type) is

practiced in a number of countries, including India and Pakistan. However, cooling pads are

currently used in most farms in Pakistan. By contrast, free range poultry are raised on a very

small scale in village areas alongside livestock. Compared with intensive farming, free range

farming requires very low input and is a common practice in almost all developing countries. In

Pakistan, controlled house farming is used for broilers and breeders, open house farming is used

for layers, and free range farming is used for layers for both meat and eggs. Birds with a genetic


5

makeup that improves meat and egg production are reared in both controlled and open house

sheds, whereas free range birds are not as productive.

A well-planned vaccination and treatment schedule, along with regular monitoring of the

flock, is followed in commercial poultry production. Compared to commercial poultry, the

genetic makeup of breeds used in free range systems renders them resistant to diseases through

natural selection. Within the flock in a free range system, there is a relatively lower risk of

disease transmission because of the lower stock number. The continuous supply of fresh

environmental air along with sunlight may aid the cycling and/or denaturing of environmental

bacteria. However, the diverse environment and exposure of free range birds to humans,

livestock, and wildlife provides an opportunity for harboring a number of commensals and

potential pathogenic organisms, which plays an important role in the epizootology of a number

of infectious diseases. Usually, free range birds carry potential pathogens in a sub-clinical form,

which can result in widespread outbreaks in commercial poultry populations by transmission

through various sources, such as humans, vehicles, etc. The most recent example of this scenario

is the isolation and characterization of the virulent Newcastle disease virus (vNDV) from

commercial poultry; a large number of outbreaks occurred in many areas of the Punjab province,

Pakistan. The virus was similar to that isolated from free range system birds in which there was

no outbreak and the clinical form of the disease was not observed (Munir et al., 2012). In

contrast to the use of antibiotics when faced with an outbreak in commercial poultry, birds in

free range systems generally remain naïve to antibiotic-resistant bacteria.

2.2. MANAGEMENT FACTORS AND BIRD’S HEALTH

Each of the three management systems (controlled, free range and open house) have several

advantages and disadvantages in terms of management practices, cost, exposure and response to


6

bacterial or viral infectious diseases, non-infectious factors (such as ammonia, morbidity,

mortality, and, above all, profitability), and acceptability to humans as consumers. There are

many hazards associated with over-crowding and hygiene that can cause health problems in birds

and humans. In addition, several factors in each management system augment further disease

occurrence or spread (Van Empel et al., 1996; Glisson, 1998; van Empel and Hafez, 1999;

Murthy et al., 2008; Pan et al., 2012).

In addition to infectious diseases, high temperature and humidity during the summer

months result in heavy losses in terms of production and mortality. Under conditions of high

temperature and humidity combined with improper aeration, particularly in controlled houses,

bacteria on the farm decompose organic matter and generate ammonia. The increased ammonia

levels make the birds vulnerable to tracheitis and other respiratory problems increases the risk of

secondary bacterial infections, such as E. coli (Estevez and Angel, 2002). Ammonia production

in commercial poultry farms results in the deciliation of the tracheal mucosa and the excessive

release of mucus, which promotes tracheal colonization by bacteria and subsequent disease

production (Van Empel and Hafez, 1996; Estevez and Angel, 2002).

The most serious outcomes are often associated with these secondary bacterial infections.

For example, several bacterial pathogens, such as Bordetella avium and Ornithobacterium

rhinotracheale, induce susceptibility of the host to disease (Van Empel and Hafez, 1996; Murthy

et al., 2008). Furthermore, under field conditions, many factors such as stress, high stock density,

poor ventilation, the presence of other bacteria, or high ammonia levels, aggravate bacterial

infections such as O. rhinotracheale infections (Van Empel et al., 1996; Murthy et al., 2008). In

addition, Laubscher et al. (2000) isolated yeast microflora from the air and soil in poultry houses,


7

wet feed, and bird droppings and suggested that, under environmental stress such as a decrease in

temperature, yeast may emerge as the dominant spoilage organism.

2.3. NORMAL AND PATHOGENIC RESPIRATORY MICROBIOTA; EFFECT ON

BIRD’S HEALTH

Microorganisms are ubiquitous and continuously interact with each other and with animals,

including humans. Each anatomical feature of the body creates its own environment that favors

certain organisms over others, promoting the selective growth of certain organisms in certain

parts of the body, which are collectively considered the normal microbiota of each anatomical

part (Talaskova et al., 2004). This indigenous normal flora is relatively stable and usually

interacts competitively with environmental pathogens to prevent their colonization of the host

and the development of infectious diseases. Many factors, such as immunological problems,

chemotherapy, or exposure to microorganisms from the environment, can potentially affect or

disrupt the ecological balance of these organisms in the host. Even the movement of the

organism from one anatomical region to another can make the organism opportunistic or

pathogenic, either alone or in association with resident or foreign bacteria (Levinson and Jawetz,

2000).

Understanding and enumerating the normal respiratory bacterial flora is helpful for

differentiating resident from invaders or pathogenic organisms. This information is also

important during the course of a disease or under conditions of stress because certain bacterial

species that are part of the normal microbiota can become opportunistic, resulting in a clinical

outcome. For example, irrespective of the management system on a farm, birds regularly ingest

or inhale E. coli from the dust on the ground or in the air. However, normal defense mechanisms

in the birds prevent the complex respiratory disease infections that are usually caused by these


8

bacteria. Nevertheless, immunosuppression or damage to the respiratory mucosa caused by

virulent respiratory tract infections, such as Newcastle disease virus (NDV), infectious bronchitis

virus (IBV), and Mycoplasma gallisepticum can facilitate the establishment of E. coli infection

(Huq, 2002). Unfortunately, the respiratory microbiota of birds has not been investigated widely.

Only a small number of studies, using known culture-dependent techniques, have investigated

the microbiota in healthy birds with respect to their relationship with disease (Gibbs, 1931; Price,

1957; Poornima and Upadhye, 1995) or their ability to facilitate viral pathogen-induced

respiratory disease (Byrum and Slemons, 1995; Van Empel and Hafez, 1996). In a sample of ten

birds, Gibbs (1931) reported Micrococcus pyogenes var. albus, Escherichia communior, Sarcina

lutea, Alcaligenes bronchiosepticus, and Spirochetes as normal inhabitants of the respiratory

tract. Price (1957) found that members of the genus Micrococcus were most prevalent in the nose

and isolated a number of species of the genera Bacillus, Streptococcus, and Corynebacterium.

Smibert et al. (1957) determined the frequency of isolation of different genera and groups of

bacteria from various anatomical divisions of the respiratory system of birds. In most instances,

the air sacs were found to be free from any type of bacteria. In general, Streptococcus,

Micrococcus, and Corynebacterium were isolated most frequently from the different anatomical

regions of the respiratory system of chickens. An abundance of Streptococcus, Micrococcus,

Lactobacillus, and Corynebacterium was found in the nose and trachea, whereas Micrococcus,

Lactobacillus, Corynebacterium, and PPLO-like organisms were observed in the sinus and lungs.

In another study, Byrum and Slemons (1995) described proteolytic bacteria in the respiratory

tract of normal white leghorn layer and turkey birds reared under cage and pasture. The

proteolytic bacteria were isolated and investigated for their ability to facilitate Orthomyxovirus

infection. The organisms found were Staphylococcus aureus, S. epidermidis, S. xylosus, S. sciuri,


9

S. hyicus, Falvobacterium spp., and Vibrio alginolyticus. Similarly, Nehme et al. (2005)

enumerated the bacteria in the upper respiratory tract of layers and broilers raised under different

management systems, free range and industrial. Comparing both systems, significant differences

were not observed in the total bacterial count, the Staphylococcus count, and the psychrophilic

count. Significant differences were observed in the coliform count, whereas Clostridium

perfringens was isolated from the birds raised in each of the management systems. The lack of

significant differences in the Staphylococcus count, coliform count, and psychrophilic count was

attributed to the birds in the different management systems having the same tracheal receptors,

which facilitate colonization with similar bacteria when living in close proximity. The significant

difference in the coliform count was determined to be due to differences in the feed formulation

for the layers and broilers, which could have affected the levels of coliforms shedding in the

litter and the subsequent inhalation by the birds. Although there was some similarity in the

organisms found in the respiratory tract in each study described, the differences in the microbiota

could be due to environmental factors and the geography of each study area.

Poornima and Upadhye (1995) described the predominance of gram-positive bacteria

over gram-negative bacteria in the respiratory tracts of apparently healthy birds. The study

identified 64 bacterial isolates, 54 of which were gram-positive bacteria.

Figure 1: Culture-based analysis of the respiratory microbiome in clinically diseased and healthy

birds (Poorniya and Upadhey, 1995)


10

Among the gram-positive bacteria, there was a higher proportion of Staphylococcus

epidermidis, followed by Bacillus subtilis, Staphylococcus aureus, B. megaterium,

Corynebacterium xerosis, and Micrococcus spp. The gram-negative organisms were Escherichia

coli, Citrobacter freundii, and Enterobacter aerogenes. By contrast, gram-negative bacteria

predominated in the diseased birds, and compared with the healthy birds, there was more

diversity in the type and number of bacteria. Of the 238 isolates in the diseased birds, 233 were

gram-negative and 105 were gram-positive bacteria. E. coli was the predominant gram-negative

bacterium in the diseased birds, whereas Staphylococcus aureus was the predominant gram-

positive bacterium. The results clearly demonstrated that there was a significant alteration in the

type and number of bacteria in diseased birds compared to healthy birds (Figure 1).

2.4. BACTERIAL PATHOGENS ASSOCIATED WITH RESPIRATORY DISEASES IN

BIRDS

On a daily basis, the respiratory systems of birds are exposed to a large number of microbes such

as viruses, bacteria, and fungi. Respiratory tract infections are among the most frequent and most

dangerous health problems in poultry because inhalation directly exposes the normal flora to the

environment. Whenever the indigenous microflora is disrupted or suppressed, pathogenic

microorganisms can easily develop and grow, resulting in microbial pathologies. In the absence

of respiratory disease, the lungs and lower respiratory tract are considered sterile (Pecora, 1963);

however, this may not be the case following exposure to known and/or previously unknown

organisms that can cause disease. Although the intensity or number of outbreaks may vary in

each management system, a direct consequence of increased production in any of the rearing

system is the increased risk of infection, particularly respiratory infection, which affects

subsequent outcomes in terms of high morbidity, mortality, and a decrease in production.


11

Among infectious organisms, bacterial pathogens play an important role as a primary

infectious organism or a secondary infectious organism (in combination with other bacteria or

viruses as the primary infectious organism). Soon after infection with the infectious bronchitis

virus, the air sacs of birds become colonized with E. coli, illustrating the potential for bacteria to

act as secondary infectious agents (Glisson, 1998). Similarly, in respiratory disease, the isolation

of more than one pathogen, such as E. coli, Ornithobacterium rhinotracheale, Pasteurella

multocida, and Haemophilus paragallinarum, indicates a concomitant infection and increases the

severity of the infection (De Rosa et al., 1996; Murthy et al., 2008). Several microorganisms,

such as Pasteurella multocida, P. gallinarum, P. haemolytica, P. anatipestifer, Ornithobacterium

rhinotracheale, Bordetella avium, E. coli and H. paragallinarum, have been isolated and

identified in different species of birds worldwide (Joubert et al., 1999; Van Empel and Hafez,

1999; El-Sukhon et al., 2002; Hafez, 2002; Chin et al., 2003). Murthy et al. (2008) isolated and

identified E. coli (51.9%), Ornithobacterium rhinotracheale (34.6%), P. multocida (9.6%), and

H. paragallinarum (3.8%) in respiratory outbreaks in birds. In addition, O. rhinotracheale was

identified as one of the emerging causative agents of respiratory disease either singly or

concurrently with other bacteria, such as H. paragallinarum. Similarly, a varying prevalence of a

number of pathogens associated with respiratory tract infections in birds raised under different

management systems has been identified in Pakistan (Table 1). These pathogens include H.

paragallinarum (Akhter et al., 2001; Hasan et al., 2002; Siddique et al., 2012), E. coli (Hasan et

al., 2002), Mycoplasma and Salmonella spp. (Mustafa et al., 2005; Lateef et al., 2006),

Staphylococcus, Streptococcus, and Bacillus spp. (Lateef et al., 2006), P. multocida (Zahoor and

Siddique, 2011), Mycoplasma synoviae (Ehtisham-ul-Haque et al., 2011; Alam et al., 2012), and

Mycoplasma gallisepticum (Hanif and Najeeb, 2007; Islam et al., 2011; Alam et al., 2012).


12

Table 1: Prevalence of bacterial infections among birds in Pakistan

No. Region in

Pakistan

Type of

bird

Management

system

Technique

and analysis

performed

Causative

agent

Prevalence

(%)

References

1 Arifwala,

Punjab

province

White

Leghorn

layer

Semi

controlled

house farm

Isolation and

identification

of bacteria

Haemophillus

paragallinarum

- Akhtar et

al., 2001

2 Punjab

province

Broiler

flock

Open house

farm

Isolation and

identification

of bacteria

Haemophilus

paragallinarum

and E. coli

- Hasan et

al., 2002

3 Sheikhupura,

Punjab

province

Rural layer Open house

farm

Isolation and

identification

of bacteria

E. coli,

Mycoplasma

spp.,

Haemophilus

paragallinarum

and Salmonella

spp.

(5, 7, 8.33

and 6.33)

Mustafa

and Ali,

2005

4 Punjab

province

Partridge

Open house

farm

Isolation and

identification

of bacteria

Salmonella,

Escherichia,

Staphylococcus,

Bacillus, and

Streptococcus

spp.

- Lateef et

al., 2006

5 Faisalabad,

Punjab

province

Layer Open house

farm

Isolation and

identification

of bacteria

Pasteurella

multocida

13.95 Zahoor and

Siddique,

2006

6 Punjab

province

Breeder Controlled

house farm

Polymerase

chain reaction

Mycoplasma

spp.

9.0 Hanif and

Najeeb,

2007

7 Lahore,

Punjab

province

Broiler Controlled

house farm

Polymerase

chain reaction

Mycoplasma

gallispeticum

46.0 Islam et al.,

2011

8 Punjab

province

Broiler Controlled

house farm

Polymerase

chain reaction

Mycoplasma

synoviae

23.52 Ehtisham

ul Haq et

al., 2011

and Haque

et al., 2011

9 Punjab

province

Commercial

layer

Open house

and semi-

controlled

house farm

Antibody

determination

through plate

agglutination

test

Mycoplasma

gallisepticum

49.01 Mukhtar et

al., 2012

10 Sindh

province

Layers and

broilers

Open house

farm

Dot-ELISA Mycoplasma

gallisepticum

15.5 Alam et al.,

2012

11 Punjab

province

Layer,

broiler and

breeder

flocks

Open and

controlled

house farm

Multiplex

PCR

Avibacterium

paragallinarum

and E. coli

(5.7 and

24.4)

Siddique et

al., 2012


13

2.6. METAGENOMICS: AN OPPORTUNITY TO DETERMINE THE ASSOCIATION

OF NOVEL PATHOGENS WITH RESPIRATORY DISEASES IN BIRDS

Scientific advancements have brought radical changes to the world today; however, despite the

use of advanced diagnostic techniques and research facilities, fewer than 1% of all organisms are

known and culturable (Schuster, 2008). Known organisms have been isolated and identified

based largely on prior knowledge or with the use of standard microbiological techniques or

known genome-based identification procedures. Therefore, in most cases, the etiology of disease

remains unknown due to the limitations of diagnostic tools and culturing facilities. The co-

occurrence of certain microorganisms (e.g., O. rhinotracheale) with other respiratory pathogens

causes them to be overgrown by other bacteria, such as E. coli (Charlton et al., 1993; De Rosa et

al., 1996), rendering such pathogens difficult to culture and diagnose in the lab using routine

methods. Efforts to isolate and identify new pathogens associated with particular diseases are

further complicated by the irrational and empirical use of broad-spectrum antibiotics. Therefore,

the genomes of most microbes cannot be analyzed because more than half of the known bacterial

phyla have no cultured representatives. Archaeal kingdoms are also dominated by uncultured

members. This problem can be addressed through the use of culture-independent techniques that

involve the analysis of microbial DNA extracted directly from a given clinical sample,

representing entire bacterial communities (Schuster, 2008; Pereira et al., 2010).

Since the introduction of the culture-independent analysis of bacterial communities based

on 16S rRNA gene sequences by Pace et al. (1986), unprecedented advances in the analyses of

genome heterogeneity and evolution in diverse environmental communities have been made;

these techniques have enabled the phylogenetic and functional gene analysis of exponentially

greater number of species than can be viewed in the petri dish. rRNA genes can be considered


14

evolutionary chronometers (Woese, 1987), and 5S and 16S sequence diversity in environmental

samples can be analyzed directly without culturing (Pace et al., 1986). Pace et al. (1986) were the

first to propose the cloning of microbial DNA; however, Schmidt et al. (1991) were the first to

report cloning in a phage vector. In the next major advance in the field of metagenomics, Healy

et al. (1995) constructed a metagenomic library of microbial DNA derived from dried grasses.

The construction of libraries of microbial DNAs extracted from the soil produced results similar

to those obtained from seawater (Rondon et al., 2000). Metagenomic studies in the Sargasso Sea

(August et al., 2000), water drained from acid mines (Baker et al., 2003) and soils, and sunken

whale skeletons (Barns et al., 1994) have used the shotgun sequencing approach to sample the

genomic content of these varied environments. In addition to facilitating the assessment of

diversity, 16S rRNA-based identification also provides substantial insights into how an organism

might be cultured by clustering unknown species with its closest ancestors. Stein et al. (1996)

constructed a 40-kb clone of the 16S rRNA gene from an archaeon isolated from seawater that

had never been cultured.

The requirement of specific nutrients, surface, temperature, pressure, atmospheric gas

composition, metabolic products, growth rate, or any other parameter can cause a bacterium to

be recalcitrant to culture in vitro (Simu and Hagstrom, 2004). Testing myriad conditions by

focusing on critical variables is challenging and laborious and can only succeed if there is a

sufficient quantitative assay available to determine whether the organism of interest has been

enriched under a specific set of conditions. Fluorescent tag-labeled nucleic acid probes have

facilitated the assessment of the growth requirements for members of the genus Pelagibacter,

which represent more than one-third of the prokaryotic cells on the surface of the ocean but are

challenging to culture (Rappe et al., 2002). Similarly, Acidobacteria have been found in


15

abundance in various soils, and Acidobacteria sequences represent 20 to 30% of the 16S rRNAs

retrieved from soil microbial DNA analyses. However, until recently, only three members of this

genus had been cultured (Smit et al., 2001).

Recent advances in next-generation sequencing techniques, e.g., barcoded

pyrosequencing coupled with bio-informatics tools such as MOTHUR (Schloss et al., 2009),

MEGAN (“MEtaGenomeANalyzer”) (Huson et al., 2011), and METAGENassist (Arndt et al.,

2012), have enhanced the popularity of metagenomics studies (Schuster, 2008). Large datasets

containing hundreds of thousands of 16S RNA fragments may be generated, enabling the

simultaneous analysis of bacterial communities with enormous species diversity in a given

environment, e.g., soil, ocean water, humans, and animals. To date, more than 106 16S rRNA

gene sequences have been deposited in public repositories such as GenBank, and the number of

these sequences doubles every two years (http://www.arb-

silva.de/news/view/2009/03/27/editorial/). More than 52 phyla comprised predominantly of

uncultured organisms have been delineated (Rappe et al., 2002), and the species identification

process is ongoing.

In this study, the genetic diversity and richness of bacterial populations in clinically

diseased and healthy birds originating from different management systems has been analyzed

using a 16S rRNA gene sequence (V1 – V5, ~ 1000 bp)-based 454 platform (pyrosequencing)

followed by sequence alignment with reference database sequences (16S Ribosomal Database

Project, RDP) and homology-based exploration of the taxonomic database available at NCBI.

http://www.arb-silva.de/news/view/2009/03/27/editorial/

http://www.arb-silva.de/news/view/2009/03/27/editorial/

16

CHAPTER 3

MATERIALS AND METHODS

3.1. SAMPLING; INCLUSIVE FLOCKS AND RELEVANT DETAILS

The purpose of this case control study was to differentiate the respiratory microbiota of

clinically diseased and healthy birds raised under different management systems and to

conduct 16S rRNA-based identification of the bacterial species in accordance with the

sequence information available in the NCBI taxonomy database. Tracheo-broncho alveolar

lavage (T-BAL) samples were collected aseptically from flocks in each rearing system

representing free range birds (layers, n = 3), open house birds (layers, n = 3), and controlled

house birds (broiler breeder, n = 3), originating from different districts of the Punjab

province, Pakistan. The study flocks in each management system were infected with

respiratory disease and displayed clinical symptoms, such as sneezing, coughing, nasal and

ocular discharge, neck stretching, and respiratory rales. However, the outbreak/disease

situation was not known with respect to the predicted viral or bacterial infection, and there

was no history of antibiotic treatment before sampling. In each flock, equal T-BAL samples

were collected aseptically from morbid/recently dead (n = 4) and apparently healthy birds (n

= 4). The sampling procedure involved personal visits to the farms where the outbreaks

occurred (reported by field veterinarians) and the transport of the birds by the farmers to the

University Diagnostic Laboratory at the University of Veterinary and Animal Sciences,

Lahore for postmortem, lab-based disease diagnostics and/or consultation (the lab is

internationally accredited for ISO 17025:2005).

Because wild birds, e.g., houbara bustard (Chlamydotis undulata), are considered a

source of several bacterial pathogens of significance to public health and ostrich (Struthio

camelus) farming is emerging as a domesticated poultry farm industry in developing

countries particularly Pakistan, T-BAL (n = 1 each) samples from houbara bustard and


17

ostrich who died of respiratory disease were also included. Given the endangered status of the

houbara bustard, it was not possible to obtain additional birds for this analysis, and future

studies involving these birds will be severely limited. The houbara bustard is included on the

“IUCN red list of threatened species (http://www.iucnredlist.org/)”, largely because of over-

hunting and habitat loss to grazing. Further, we were not able to convince the owners to let us

use the birds as healthy controls. Nevertheless, the culture-independent analysis of the

respiratory microbiome of the clinically diseased houbara bustard and ostrich birds will aid in

the understanding of the potential pathogens harbored in the birds as well as disease

diagnostics, control, and management.

Because of the relatively high cost associated with metagenomic studies, this study

focused on the quality rather than the quantity of the samples. After the careful selection of

the flocks included in the study, the presence of enough genomic DNA (5 ng total volume)

was verified before further processing. Detailed information was recorded for each

outbreak/clinical disease, such as the location of the farm, farmer name and contact details,

age of the flock, sex and breed (if known) of the bird, history of previous disease outbreaks,

antibiotic treatments, and any prophylactic measures used.

3.2. COLLECTION OF TRACHEO-BRONCHO ALVEOLAR LAVAGE (T-BAL)

The International Animal Care and Use Committee (IACUC), USA, and the Ethical Research

Committee of University of Veterinary and Animal Sciences, Lahore, Pakistan, approved the

protocol used for the collection of the T-BAL samples. The sample collection procedure was

performed as aseptically as possible using personal protective equipment (PPE).

Approximately 500 µL to 1.2 mL of respiratory lavage was collected from each bird using a

sterile catheter tip disposable syringe (STAR, Jiangsu Kanghua, China), which was attached

to a sterile pipette tip that was adjusted according to the tracheal opening. The collected


18

lavage was transferred to a sterile 1.5-mL microfuge tube (Eppendorf, Germany) and stored

at -80°C until further processed for genomic DNA extraction.

The apparently healthy and morbid birds were euthanized by intravenous injection of

potassium chloride (KCl) at a concentration of 1 to 2 mEq/kg of body weight (Merck,

Germany) (Raghav et al., 2011). The birds were placed in a dorsal recumbent position, and a

ventral midline incision was made from the angle of the mandible to the thoracic inlet. The

surrounding fascia was removed from the trachea, and the trachea was cut transversely at the

mid-cervical region. A 60-mL catheter tip disposable syringe (STAR, Jiangsu Kanghua,

Medical Equipment Co., Ltd., China) attached to a sterile pipette tip was placed into the

trachea to form an airtight seal. While pushing back the plunger of the 60-mL catheter

syringe, air was withdrawn and negative pressure or a vacuum was created in the respiratory

airway of the bird (visualized by the partial collapse of the extra-thoracic portion of the

trachea). The vacuum-created airway was blocked by placing the trachea between the index

finger and thumb. Using another catheter syringe placed in the trachea, while carefully

maintaining the vacuum, approximately 3 – 5 mL of sterile phosphate buffered saline (PBS)

was added. The bird was rocked/rolled from side to side to allow the fluid to reach and make

contact with all parts of the respiratory tract. Finally, the T-BAL was withdrawn slowly until

partial collapse of the trachea was observed again.

3.3. GENOMIC DNA EXTRACTION AND MEASUREMENT

The collected lavage was centrifuged at 14000×g for 10 minutes, and the pelleted bacteria

were resuspended in 300 µL – 500 µL sterile PBS. The resuspended solution was used for

genome extraction (gDNA) using a commercially available BiOstic® FFPE Tissue DNA

Isolation Kit (Mobio, USA) according to the manufacturer’s instructions, with minor

modifications as optimized in the Harvill lab at the Millennium Sciences Complex,

Pennsylvania State University, USA. The modification involves a longer incubation of the


19

lavage sample with the lysis and protease solutions (FP1, FP2 and FP3) at 55°C (Thermostat;

Eppendorf, Germany); an overnight (16 hour) incubation was performed instead of the 1 hour

incubation recommended by the manufacturer. All procedures were carried out inside a

sterile BSL-2 biohazard safety cabinet (BH-EN 2004-5 CE, Italy) at the University

Diagnostic Laboratory and Quality Operations Laboratory at UVAS, Lahore.

The extracted genome (gDNA) was shipped via FedEx to the Harvill lab at W-213

Millennium Science Complex, Penn State University PA, State College 16801, USA. A

Nanodrop spectrophotometer (Thermo Scientific, USA) was used to evaluate the quality and

concentration of each of gDNA sample. The gDNA samples with concentrations of at least 5

ng/µL and 260/280 and 260/230 ratios >1.80 were considered of good quality and processed

further. The samples were sent to genomic core facility of The Huck Institute of Life

Sciences at Penn State University (Chandalee Laboratory), and the purity and quality of the

genomic DNA was tested again on both Bioanalyzer and Qubit flourometer (Invitrogen,

USA) instruments, both of which are considered more sensitive than the Nanodrop for the

analysis of double-stranded DNA. Although higher quality and concentrations of gDNA/µL

are considered ideal, a total of 5 ng double stranded gDNA is sufficient for further analysis

by 454 sequencing (Roche Diagnostics, USA).

3.4. PREPARATION OF PCR AMPLICONS (dsDNA) AND 454-SEQUENCING

One-way read amplicons (Lib-L) were prepared using bar-coded fusion primers and the 27F

(5´AGAGTTTGATCMTGGCTCAG 3´) and 907R (5´TACGGGAGGCAGCAG 3´) 16S

primers (Figure 2) (forward: CCATCTCATCCCTGCGTGTCTCCGACTCAG-MID-

AGTTTGATCMTGGCTCAG and reverse:

CCTATCCCCTGTGTGCCTTGGCAGTCTCAG-TACGGGAGGCAGCAG). PCR (25 µL)

was performed with the 27F and 907R primers using 5 pmoles of the forward and reverse

primers, 5 to 10 ng of ds DNA, 5 nmoles of each dNTP, 0.25 µL of Taq (Fast Start High


20

Fidelity PCR system, Roche, Indianapolis, IN), and 2.5 µL of the 10X buffer solution

supplied with the enzyme. The samples were denatured at 94°C for 3 minutes, followed by 35

cycles of 94°C for 15 sec, 55°C for 45 seconds, and 72°C for 60 seconds and a final

extension at 72°C for 8 min (Gene AMP PCR System 9700; Applied Biosystems, Foster

City, CA). The PCR products (approximately 1,000 bp) were separated on agarose gels and

were extracted and purified using Agencourt AMPure technology (Beckman Coulter, Brea,

CA). This technique is important for the removal of undesirable shorter-length DNA

fragments and improved sequencing yield. After purification, the products were quantified

using both Qubit flourometer (Lifetech, Carlsbad, CA) and qPCR using the KAPA

Biosystems library quantification kit (Kapa Biosystems, Woburn, MA). The products were

pooled based on the molar amounts, separated on a 1% agarose gel and extracted. After

purification, using the QIAquick PCR purification kit (Qiagen,Valencia, CA), the quality and

quantity of the samples were assessed again using a DNA 7500 LabChip on an Agilent 2100

Bioanalyzer (Agilent Technologies, Santa Clara, CA) and Qubit flourometer quantification.

Pyrosequencing on a 454/Roche GS FLX+ instrument using titanium chemistry (Roche

Diagnostics, Indianapolis, IN) was performed in accordance with the manufacturer’s

instructions. The 16S rRNA genomic region (V1 – V5) was amplified using fusion primers

containing the sequences to amplify the 16S, adaptor, key and MID sequences for the FLX+

sequencing.

3.5. SEQUENCE PROCESSING

The GS-FLX-Titanium sequencer data was generated (.sff file) with the GS Amplicon

software package (Roche, Branford, CT). In total, 296,811 raw sequences were processed,

including 278,040 reads from clinically diseased (142,904) and healthy birds (135,136) from

the different management systems, with an average read of 9,930 per sample (Table 2), as

well as 18,771 reads from the houbara bustard and the ostrich (Table 3).


21

Figure 2: The hypervariable regions in the 16S rRNA gene evaluated in this study. The

hypervariable regions are shown in different colors, and the conserved regions (C1–C9) are

shown in yellow. The arrows indicate the genomic sequences amplified and analyzed using

primers 27F and 907R (V1 – V5; ~ 1000 bp).

Table 2: 454-sequencing reads for the selected flocks in each management system

No. Type of

bird

Clinical

status

Samples

sequenced

@gDNA

(ng/µL)

No. of

reads

Average

reads/sample

1 Layer

Healthy 3 23.4 – 112 39,996 13,332

Diseased 3 43.0 – 145 29,731 9,910

2 Layer

Healthy 3 34.8 – 83.9 32,730 10,910

Disease 3 14.0 – 41.4 43,236 14,412

3 Layer

Healthy 2 14.1 – 24.7 12,987 6,494

Diseased 2 59.2 -104 20,488 10,244

4

Broiler

breeder

Healthy 3 6.06 – 136 13,735 4,578

Diseased 3 4.67 – 62.7 26,915 8,972

5

Broiler

breeder

Healthy 3 18.1 – 155 38,687 12,896

Diseased 3 21.1 – 89.3 19,535 6,512

Total 28 4.67 – 155 278,040 9,930

Free range farm; Open house farm; Controlled house farm; @

ds DNA concentration

measured with Qubit flourometer


22

The percentage of sequences retrieved from diseased and healthy birds from each

management system is shown in Figure 3. Briefly, the primer sequences and barcodes were

removed from each raw sequence, and the sequences were screened based on the quality

score from 454 sequencing using the MOTHUR software platform v. 1.20.0. (Schloss et al.,

2009). Reads containing more than 8 bases in a row or exhibiting an average quality score

less than 25 were trimmed, and the individual bird sequence data were split using the

split.groups command.

The pairwise sequence alignment tool BLASTN

(http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE

=Download) was used to align sequences against the Ribosomal Database Project (RDP)

“Bacteria + Archeaea (isolates only)” database (www.microgator.org/taxcollector/), which

contains approximately 164,517 almost full-length 16S rRNA sequence reads. The sequence

alignment was performed using the BLAST run and tabular output format with the 100 best

hits in the background.

3.6 SEQUENCE ANALYSIS USING MEtaGenomeANalyzer (MEGAN)

MEGAN is a standalone analysis tool for short read metagenomic data (Huson et al., 2011)

that uses homology-based methods to bin sequence reads, provides interactive exploration of

multiple datasets using NCBI taxonomy with unique names and IDs for more than 568,000

taxa, including approximately 287,000 eukaryota, 28,000 bacteria, and 62,000 viruses. The

aligned sequence data was imported from BLAST (.rma files) and analyzed using the default

LCA (lowest common ancestor) parameter setting (min support: minimum number of reads

that must be assigned to a taxon t 5; min score: a threshold for the score that an alignment

must achieve to be considered 35; top percent: to retain only those hits for a given read r

whose scores lie within a given percentage of the highest score involving r 10; and min

complexity: 0.44).

http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download



http://www.microgator.org/taxcollector/


23

Table 3: 454-sequencing reads obtained from houbara bustard (Chlamydotis undulata) and

ostrich (Struthio camelus)

No Type of bird

Clinical

status

Samples

sequenced

@gDNA

(ng/µL)

No. of

reads

1

Houbara

bustard

Diseased 1 61.8 8,950

2 Ostrich Diseased 1 19.0 9,821

Total 2 19.0 – 61.8 18,771

Free range farm; Open house farm; @

ds DNA concentration measured with Qubit

flourometer

Figure 3: Percentage of sequences retrieved from clinically diseased and healthy birds in each

management system


24

To produce comparative views of the different taxonomic classification nodes,

multiple datasets of clinically diseased and healthy birds grouped together within the open

house, free range and controlled house systems were opened simultaneously in a single

comparative analysis document. The comparative view of the taxonomic classification as

phyla, family, genera, and species was visualized in the form of a tree, pie chart, or heat map

in which the sizes of the nodes were scaled logarithmically to represent the number of reads.

The abundance of each taxonomic node in the lavage samples was determined, including the

number of reads summarized and assigned at that particular node and its descendants.

Using the LCA approach with a minimum threshold of 5 reads, every read retrieved

from each lavage was assigned to a taxon. If the read aligned very specifically to only a

single taxon, it was assigned to that taxon. The less specifically a read hit a taxa, the higher

up in the taxonomy it was placed. In short, this approach is inherently conservative and is

more prone to error toward the non-informative assignment of reads (to high-level nodes in

the taxonomy) than toward false positive assignments (placing reads from one species into

the nodes of another species). In short, the 16S reads will not be assigned to any of the

bacterial species unless more than one read is hit in the reference database. Conversely, using

a simple LCA algorithm, if a read has a significant match to sequences in more than one

species, MEGAN will assign the read to the “Lowest Common Ancestor (LCA)”.

Because the maximum diversity was observed at the genera node, it was selected to

compute the matrix distance, rarefaction curve, and diversity indices using absolute counts of

the reads in each dataset. The matrix distance of the datasets, comprised of clinically diseased

and healthy birds within each management system, was calculated using Goodall’s ecological

index and UPGMA algorithm, a simple version of UniFrac and Euclidean distances. The

distance matrix was visualized by hierarchical clustering in which the diseased and healthy

bird’s lavage were clustered closer or separately based on taxonomic content within each


25

management system, the assigned genera node. Goodall’s index assigns more importance to

the likeliness or similarity for reads corresponding to rare genera in data than for common or

more abundant genera. Similarly, comparative taxonomic content richness as well as

diversity in a given lavage sample (clinically diseased vs. healthy) were plotted in the form of

a rarefaction curve that operates by repeatedly sampling subsets from a set of reads and

computing the number of leaves to which taxa have been assigned. Furthermore, the

biodiversity and richness of the flocks from each management system were evaluated using

the Shannon-Weiner diversity index (SWDI) and the Simpson-Reciprocal diversity index

(SRDI).

26

CHAPTER 4

RESULTS

The primary objective of this study was to evaluate 1) the differences in the bacterial

communities in clinically diseased and healthy birds; 2) the different rearing systems, such as

free range, open house and controlled house; and 3) the 16S rRNA-based identification of

species according to the reads recovered from each group of birds by comparison to

sequences in the NCBI taxonomy database. Because pyrosequencing involves high costs and

requires high-quality double-stranded genomic DNA (gDNA), of the 9 study flocks, only 5

flocks and 28 gDNA samples were chosen for 16S-based metagenomic analysis. The

included flocks represented each management system and were comprised of layers (free

range system, n = 2), layers (open house farms, n = 1), and broiler breeders (controlled house

farm, n = 2). In addition, gDNA (n = 1 each) extracted from the T-BAL of a houbara bustard

and a young ostrich was also processed and analyzed. Therefore, in total, 30 T-BALs were

analyzed and described in this study.

4.1. DIFFERENCES IN MICROBIOTA IN FREE RANGE, OPEN HOUSE, AND

CONTROLLED HOUSE FARMS

Almost all of the reads, which were retrieved through rigorous quality-controlled sequencing

and chimera sequence filtering, were summarized correctly or were assigned to bacteria and

its descendants in taxonomic ranking. The abundance of each taxonomic node (phyla, family,

genera, and species) to the corresponding clinical status of the bird (diseased or healthy) was

determined by the assigned or summarized reads at that particular node and its descendants.

The rarefaction plot-based taxonomy richness as well as hierarchal clustering was determined

at the genera node; however, there could be a number of differences in the richness and

taxonomic content-related visualization when using the node above the genera. For example,

of the 6,988 reads corresponding to 3 phyla (Proteobacteria, Firmicutes and Actinobacteria)

RESULTS

27

and 3 families (Halomonadaceae, Enterobacteriaceae and Pasteurellaceae), only one genus

(Kushneria) was identified in the T-BAL of sample FAH2; therefore, it was plotted low in the

rarefaction plot. Only 22 reads were summarized at the genera node, whereas the rest of the

reads were either assigned or summarized at the taxonomy node above genera. This means

that when using the family or phyla node for taxonomic relatedness, the rarefaction curve for

this sample may plot higher compared with other clinical samples (T-BAL’s reads) with a

number of identified families or phyla less than 3.

Overall, the rarefaction curve within each management system indicated higher

diversity and richness in the clinically healthy birds than in the diseased birds. Furthermore,

the observation of the curve parallel to the x-axis in nearly all of the clinical samples

indicated that much of the diversity was captured using 454-sequencing with 15,000 – 25,000

reads/run. For the biodiversity indices, in general, the higher the value, the higher the

diversity and richness present in a given sample. However, a high Shannon-Wiener diversity

index value does not necessarily correspond to richness but to uniformity, whereas a high

Simpson-Reciprocal diversity index value corresponds to both diversity and richness in

taxonomic content. The values for each clinical sample within each management system are

shown in the tables, and in the majority of cases, the values were higher in the clinically

healthy birds than in the diseased birds.

4.1.1. FREE RANGE REARING OF BIRDS; HISTORY AND OTHER DETAILS

Of the three flocks sampled, only two flocks with T-BAL (n = 12) samples from diseased and

healthy birds (three of each) were included. A higher number of reads were recovered from

diseased birds than from healthy birds (75,966 vs. 69,727, respectively). On average, a higher

number of reads per sample (12,661) was found in clinically diseased birds compared with

healthy birds (11,621) (Table 4).

RESULTS

28

4.1.1.1. CULTURE-INDEPENDENT ANALYSIS OF CLINICALLY DISEASED AND

HEALTHY BIRDS IN FREE RANGE SYSTEM

Overall, in both groups, the sequenced reads were summarized and/or assigned to 8 phyla, 55

families, 59 genera, and 24 characterized bacterial species with reference to the NCBI

database. The microbiota harbored by the clinically healthy birds comprised 8 phyla, 51

families, 48 genera, and 15 species, whereas 8 phyla, 43 families, 46 genera, and 19 species

were identified in the diseased birds. Relative to the healthy birds, less taxonomic diversity

was observed in the diseased birds as indicated by the rarefaction plot. The data suggest an

abundance of similar taxa in the diseased birds at the genera node (Figure 4). A number of

individual birds from both flocks with different clinical statuses exhibited similar taxonomic

contents; however, a variation in taxonomic relatedness was observed in each sample as

indicated by the hierarchical clustering (Figure 4a).

Of the total 16S rRNA reads summarized at the phylum level, including those

assigned to descendants in the clinically diseased and healthy birds (53,981 and 38,797,

respectively), Proteobacteria was the most abundant (41,185 reads) and represented 76.29%

of the total bacterial communities present in the clinically diseased birds, followed by

Tenericutes (9,098, 16.85%), Firmicutes (1,859, 3.44%), Actinobacteria (1,285, 2.4%),

Bacteroidetes (514, 0.95%), Fusobacteria (22, 0.04%), Acidobacteria (9, 0.02%), and

Cyanobacteria (9, 0.02%). Similarly, Proteobacteria (33,532, 86.42%) was found in greater

abundance than Firmicutes (1,932, 4.97%), Actinobacteria (1,433, 3.69%), Bacteroidetes

(1,137, 2.93%), Tenericutes (335, 0.86%), Fusobacteria (316, 0.81%), Cyanobacteria (106,

0.27%), and Chlamydiae/Verrucomicrobia (6, 0.015%) in the clinically healthy birds.

Acidobacteria and the Chlamydiae/Verrucomicrobia group were found exclusively in the

diseased and healthy birds, respectively (Figure 4b). The microbiota harbored by clinically

healthy birds was more diverse, exhibiting identified reads corresponding to all families (n =

55), with the exception of Pseudonocardiaceae, Enterococcaceae, Chromatiaceae, and

RESULTS

29

Acidaminococcaceae. Of the total reads summarized/assigned at the family node (n = 35075),

Pasteurellaceae (28,275, 80.61%) was dominant, followed by Pseudomonadaceae (1,558,

4.44%), Moraxellaceae (1,321, 3.76%), Streptococcaceae (708, 2.02%),

Porphyromonadaceae (410, 1.17%), Mycoplasmataceae (334, 0.95%), and others (2,469,

7.03%). Similarly, in the clinically diseased birds (42,643), the dominant families were

Enterobacteriaceae (15,634, 36.66%), followed by Mycoplasmataceae (9,098, 21.34%),

Moraxellaceae (9,065, 21.26%), Pseudomonadaceae (4,493, 10.54%), Pasteurellaceae

(2,012, 4.72%), Staphylococcaceae (362, 0.85%), and others (1,979, 4.64%) (Figure 4c).

In total, 59 genera were identified from the clinically diseased (n = 46) and healthy (n

= 48) birds, and the majority were common to both groups. Of the total reads

assigned/summarized at the genera node (30,334), a higher number of reads were identified

from the clinically diseased birds (24,572, 81.0%) than from the healthy birds (5,762,

18.99%). The most abundant genera identified in the clinically diseased birds were

Mycoplasma (9,098, 37.02%), followed by Acinetobacter (9,065, 36.89%), Pseudomonas

(4,471, 18.19%), Streptococcus (341, 1.38%), Staphylococcus (278, 1.13%), and others

(1,319, 5.36%).

RESULTS

30

Table 4: Brief history, geographical location, and individual clinical sample details (16S

reads and subsequent diversity) for the free range system flocks

&Flock

ID Brief history

#GC

*Sample

ID

No. of

reads

α - diversity

indices ΩSWDI

δSRDI

FA

A flock of Desi layers (n = 1700)

aged 36 weeks with poor

management practices expressed

clinical symptoms such as dyspnea,

tracheal rales, fever, nasal

discharge, occasional neck

stretching and diarrhea in some

birds. A total of 140 animals died

within two days of disease onset.

Necropsy revealed tracheal

hemorrhage, lung congestion,

enlarged liver, spleenomegaly,

hemorrhages of the thigh muscles

and nephritis.

31°3

2′5

9″N

74°2

0′3

7″E

(Lah

ore

)

FAH1 19,882 2.286 2.577

FAH2 6,988 0.000 1.000

FAH3 13,126 3.792 8.453

FAD1 12,574 4.141 11.857

FAD2 11,069 0.240 1.049

FAD3 9,087 1.216 1.936

FB

A layer flock (n = 350) of

approximately 37 weeks in age

suffered from a respiratory disease

with viscous nasal and ocular

discharge, swollen heads, increased

body temperature, and dyspnea.

The disease was associated with

morbidity in 50% of the flock and

25 fatalities before sampling.

Necropsy revealed hemorrhages in

the trachea and cecal tonsils,

hemorrhage and white nodules in

the lungs, spleenomegaly, and

nephritis.

31°2

1′5

2″N

and 7

2°5

9′4

0″E

(Fai

sala

bad

)

FBHI 15,153 0.657 1.392

FBH2 7,054 0.682 1.421

FBH3 7,524 0.510 1.252

FBD1 12,964 1.093 1.980

FBD2 17,385 0.948 1.777

FBD3 12,887 0.105 1.024

&F = free range system, A and B = flock number assigned;

#geographical co-ordinate;

*H =

healthy, D = diseased; Ω

SWDI = Shannon-Weiner diversity index; δSRDI = Simpson-

Reciprocal diversity index

RESULTS

31

Figure 4: Taxonomy rarefaction plot of the T-BALs from birds in the free range system at the

genera node. The plot represents the percentage of reads present in a given sample

corresponding to leaves in the NCBI taxonomy database.

Figure 4a: NCBI taxonomic content-based hierarchical clustering of clinically diseased and

healthy birds in the free range system collapsed at the genera node. The distance between

taxonomic contents in each sample was calculated using the Goodall normalization method

with the UPGMA algorithm.

RESULTS

32

Figure 4b: Comparative visualization of the abundances of phyla in clinically diseased and

healthy birds in the free range system using the NCBI taxonomy database.

Figure 4c: Relative abundances of bacterial families identified in clinically diseased and

healthy birds in the free range system using the taxonomy database available at NCBI.

RESULTS

33

Similarly, Pseudomonas (1,558, 27.03%), Acinetobacter (1,319, 22.89%),

Streptococcus (648, 11.24%), Porphyromonas (341, 5.91%), Mycoplasma (334, 5.79%),

Campylobacter (262, 4.54%), and others (1,300, 22.56%) were identified in healthy birds

(Figure 4d). In total, 24 bacterial species were identified, a number of which were common to

both groups; 19 were detected in clinically diseased birds, whereas 15 were detected in

healthy birds. With respect to the number of reads assigned to the species node (999),

Mycoplasma synoviae (799, 79.97%) was the most abundant bacterial species, followed by

Turicibacter sanguinis (21, 2.10%), Pseudomonas veronii (20, 2.0%), Acinetobacter sp. N15

(19, 1.90%), and others (140, 14.01%). Similarly, in healthy birds, with respect to the number

of reads assigned to the species node (470), Kushneria sp. Z35 (182, 38.72%) was the most

abundant, followed by Mycoplasma gallinaceum (146, 31.06%), Mycoplasma gallinarum

(35, 7.44%), Ornithobacterium rhinotracheale (24, 5.10%), and others (83, 17.65%). None of

the bacterial species identified from clinically healthy birds in flock FB were analogous to

species in the NCBI database. Acinetobacter sp. N15, Pseudomonas vernoii, Shigella flexneri,

Anaerococcus octavius, Clostridium sp. TM-40, Enterococcus cecorum, Actinomyces

graevenitizii, Phascolaractobacterium faecium, and Porphyromonas catoniae were found

only in diseased birds. Similarly, Barnisiella viscericola, Alkalibacterium iburiense,

Veilonella sp. oral taxon 780, Avibacterium votantium, and Mycoplasma gallinaceum were

found only in healthy birds (Figure 4e).

RESULTS

34

Figure 4d: Relative abundances of bacterial genera identified in clinically diseased and

healthy birds in the free range system using the taxonomy database available at NCBI.

Figure 4e: 16S rRNA sequence (V1 – V5)-based identification of bacterial species

corresponding to the NCBI database in clinically diseased and healthy birds from the free

range system.

RESULTS

35

4.1.2. OPEN HOUSE REARING OF BIRDS; HISTORY AND OTHER DETAILS

Of the three flocks sampled, T-BAL (n = 2 each) from clinically diseased and healthy birds

from only one flock were included. Of the total reads recovered (33,475), more 16S rRNA

reads were obtained from clinically diseased birds than healthy birds (20,488 vs. 12,987,

respectively). On average, a higher number of reads (10,244) was found per sample from

clinically diseased birds than apparently healthy birds (6,493) (Table 5).


HEALTHY BIRDS IN AN OPEN HOUSE SYSTEM

Overall, the retrieved sequence reads were summarized and/or assigned to 5 phyla, 15

families, 17 genera, and 5 bacterial species. A higher number of reads were retrieved from the

diseased birds; however, greater diversity and richness were observed in the clinically healthy

birds, as indicated by the rarefaction plot (Figure 5). The clinically healthy birds were found

to harbor sequence reads corresponding to 5 phyla, 14 families, 15 genera, and 3 species. By

contrast, the microbiota of the diseased birds comprised only 4 phyla, 8 families, 7 genera,

and 2 species. As indicated in the hierarchical clustering at the genera node, the microbiota of

the diseased birds was closer in taxonomic content than that harbored by healthy birds

(Figure 5a).

Of the 16S rRNA reads summarized/assigned at the phyla node in the clinically

diseased and healthy birds (7,403 and 8,563, respectively), Proteobacteria was the most

abundant and represented 90.53% and 97.43%, respectively, of the total bacterial

communities present in the T-BAL samples. In the clinically diseased birds, Proteobacteria

(6,702, 90.53%) was the most abundant phylum, followed by Bacteroidetes (687, 9.28%),

Firmicutes (9, 0.12%), and Tenericutes (5, 0.07%).

RESULTS

36


reads and subsequent diversity) for the open house system flock

&Flock

ID Clinical History

#GC

*Sample

ID

No. of

Reads

α – diversity ΩSWDI

δSRDI

OC

A 55-week-old Leghorn flock (n =

7,500) encountered a respiratory

disease outbreak showing nasal

discharge, reddish-black combs,

swollen heads, increased body

temperature, and dyspnea.

Morbidity was 90 – 95%, whereas

mortality was approximately7%.

Necropsy demonstrated tracheal

hemorrhage, lung congestion, and

nephritis. Worm infestation was also

present. N

31

077.4

93 a

nd

E074

026.0

95

OCH1 5,379 2.387 3.711

OCH2 7,608 1.919 2.854

OCD1 10,722 1.211 2.103

OCD2 9,766 1.764 2.893

&O = open house system, C = flock number assigned;


*H =

healthy, D = diseased; Ω

SWDI = Shannon-Weiner diversity index; δSRDI = Simpson-


Figure 5: Taxonomy rarefaction plot of the T-BALs from birds in the open house system at

the genera node. The plot represents the percentage of reads present in a given sample


RESULTS

37


healthy birds in the open house system collapsed at the genera node. The distance between

taxonomic contents in each sample was calculated using the Goodall normalization method



healthy birds in the open house system using the NCBI taxonomy database.

RESULTS

38

Similarly, in healthy birds, the highest number of 16S rRNA corresponded to Proteobacteria

(8343, 97.43%), followed by Firmicutes (116, 1.35%), Bacteroidetes (74, 0.86%) Tenericutes

(18, 0.21%), and Actinobacteria (12, 0.14%) (Figure 5b).

The number of reads assigned/summarized at the family node was higher in the

clinically healthy birds (2,360) than in the diseased birds (2,089). With the exception of

Sphingobacteriaceae, all families identified in the open house system were observed in the

healthy birds. The reads analogous to Moraxellaceae (935, 39.61%) were more frequent

among healthy birds, followed by Pseudomonaceae (372, 15.76%), Shewanellaceae (333,

14.11%), Pasteurellaceae (260, 11.01%), Enterobacteriaceae (221, 9.36%),

Flavobacteriaceae (60, 2.54%), and others (179, 7.58%). Similarly, among the dominant

families identified in the clinically diseased birds, Shewanellaceae (837, 40.06%) was the

most abundant, followed by Sphingobacteriaceae (662, 31.68%), Pseudomonacae (337,

16.13%), and Comamonadaceae (227, 10.86%) and others (26, 1.24%) (Figure 5c). With

respect to the 16S reads at the genera node (1,941), Shewanella (837, 43.12%) was dominant

in the diseased group, followed by Sphingobacterium (623, 32.09%), Pseudomonas (337,

17.36%), Comamonas (127, 6.54%), and others (17, 0.87%). Pseudomonas (371, 30.33%),

however, was the most abundant genus in clinically healthy birds followed by Shewanella

(333, 27.22%), Acinetobacter (202, 16.51%), Psychrobacter (69, 5.64%), and others (248,

20.27%). Mycoplasma, Pseudomonas, Shewanella, and Bacteroides were common to both

groups of birds; however, members of the genera Ornithobacterium, Rimerella,

Enterococcus, Streptococcus, Psychrobacter, Acinetobacter, Shigella, Klebsiella,

Aeromonas, and Veilonella were found exclusively in the clinically healthy birds (Figure 5d).

RESULTS

39

Figure 5c: Comparative visualization of the abundances of bacterial families in clinically

diseased and healthy birds in the open house system using the taxonomy database available at

NCBI.

RESULTS

40

Only five bacterial species were identified in the open house system flock that

includes Shigella flexneri, Ornithobacterium rhinotracheale, Myroides spp. MY15,

Mycoplasma gallinaceum and Enterococcus cecorum. One T-BAL sample from the diseased

birds did not contain any 16S reads corresponding to bacterial species in the NCBI taxonomy

database. Myroides spp. MY15 (7, 58.33%) and Mycoplasma gallinaceum (5, 41.66%) were

exclusive to the diseased birds, whereas, Ornithobacterium rhinotracheale (52, 37.68%),

Enterococcus cecorum (52, 37.68%), and Shigella flexneri (34, 24.63%) were found only in

the apparently healthy birds (Figure 5e).

RESULTS

41

Figure 5d: Relative abundances of bacterial genera in clinically diseased and healthy birds in

the open house system using the taxonomy database available at NCBI.


corresponding to NCBI database in clinically diseased and healthy birds from open house

system

RESULTS

42

4.1.3. CONTROLLED HOUSE REARING OF BIRDS; HISTORY AND OTHER

DETAILS

Of the three sample flocks, two flocks with equal numbers of T-BAL samples from diseased

and healthy birds (n = 3 each) were included. Overall, 98,872 reads were obtained from birds

from the controlled house system. A higher number of 16S rRNA sequences were retrieved

from clinically diseased birds than from healthy birds (65,602 vs. 33,270). On average, a

higher number of reads (10,934) per sample were obtained from the clinically diseased birds

than from the healthy birds (5,545) (Table 6).


HEALTHY BIRDS IN A CONTROLLED HOUSE SYSTEM

In general, the reads from both groups of birds were summarized and/or assigned to 10 phyla,

53 families, 66 genera, and 33 bacterial species. Compared to the diseased birds, more

taxonomic diversity was observed in the clinically healthy birds, as indicated by the

rarefaction plot and the increased number of taxon nodes represented. The microbiota

harbored by the clinically healthy birds represented 10 phyla, 50 families, 58 genera, and 16

species. By contrast, the microbiota of the clinically diseased birds comprised 6 phyla, 27

families, 31 genera, and 24 species (Figure 6). As indicated in the hierarchical clustering at

the genera node, a similarity in the taxonomic content of the diseased birds in flocks D and E

and of the healthy birds in flock E was observed. However, the taxonomic content of the

healthy birds in flock D differed from those in flock E (Figure 6a).

RESULTS

43


reads and subsequent diversity) for the controlled house system flocks

&Flock

ID Clinical History

#GC

*Sample

ID

No. of

Reads

α – diversity ΩSWDI

ΩSWDI

CD

A 66-week-old breeder flock (n =

125,000) suffered from respiratory

disease with clinical symptoms,

such as sneezing and watery nasal

discharge. Increased morbidity

(60 – 80%) and mortality (12% to

15%) were observed within two

weeks. Egg production and

hatchability was decreased to 45%

and 30%.

N

31

038.8

22 a

nd

E073

053.1

50

CDH1 1,004 0.000 1.000

CDH2 6,556 2.848 4.036

CDH3 6,175 2.762 3.566

CDD1 9,939 1.302 2.031

CDD2 8,918 0.220 1.052

CDD3 8,058 0.276 1.064

CE

A 38-weeks-oldbreeder flock (n =

30,000) contracted respiratory

disease with clinical symptoms

that included an increase in body

temperature, nasal discharge,

gargling sounds, and neck

paralysis at the terminal stage of

the disease. Morbidity was high

(70%), and within 5 days of onset,

approximately 5,000 of the birds

died. 31°4

2′5

4″N

and 7

3°5

9′0

6″E

CEH1 10,882 3.157 5.023

CEH2 1,501 2.239 3.204

CEH3 7,152 2.226 2.429

CED1 7,603 0.354 1.096

CED2 9.094 0.253 1.057

CED3 21,990 0.451 1.112

&C = controlled house system, D and E = flock number assigned;


*H = healthy, D = diseased;

ΩSWDI = Shannon-Weiner diversity index;

δSRDI = Simpson-


RESULTS

44

Figure 6: Taxonomy rarefaction plot of the T-BALs from birds in the controlled house system

at the genera node. The plot represents the percentage of reads present in a given sample



healthy birds in the controlled house system collapsed at the genera node. The distances

between branches in each sample were calculated using the Goodall normalization method


RESULTS

45

In the clinically diseased birds, of the 16S rRNA reads summarized at the phyla node,

including those assigned to its descendants, Tenericutes (34,807, 54.49%) was the most

abundant, followed by Firmicutes (17,992, 28.17%), Proteobacteria (10,095, 15.80%),

Bacteroidetes (522, 0.82%), Actinobacteria (374, 0.58%), and Cyanobacteria (77, 0.12%).

By contrast, Proteobacteria (20,269, 77.98%) was most abundant in the clinically healthy

birds, followed by Firmicutes (2,609, 10.03%), Bacteroidetes (1,691, 6.50%), Actinobacteria

(1261, 4.85%), Tenericutes (72, 0.27%), Fusobacteria (56, 0.22%), Cyanobacteria (9,

0.03%), Deinococcus-Thermus (9, 0.03%), Chloroflexi (8, 0.03%), and Verrucomicrobia (6,

0.02%). Reads corresponding to Verrucomicrobia, Fusobacteria, Deinococcus-Thermus, and

Chloroflexi were not identified or found from clinically diseased birds (Figure 6b).

Reads corresponding to all families (n = 53) were identified in clinically healthy birds,

with the exception of Micromonosporaceae, Paenibacillceae, and Burkholderiaceae.

Enterobacteriaceae (7,318, 36.10%) was the most abundant family, followed by

Pseudomonadaceae (3,600, 17.76%), Alcaligenaceae (2,198, 10.84%), Staphylococcaceae

(1,676, 8.26%), Pasteurellaceae (1,160, 5.72%), Bacteroidaceae (983, 4.85%), Neisseriaceae

(518, 2.55%), and others (2,816, 13.89%). Similarly, of the reads corresponding to 27

families identified in the clinically diseased birds, the most abundant families were

Mycoplasmataceae (34,805, 62.83%), Pseudomonadaceae (7,142, 12.89%), Enterococcaceae

(5,534, 9.99%), Lactobacillaceae (3,738, 6.74%), Enterobacteriaceae (1,275, 2.30%),

Streptococcaceae (632, 1.14%), Pasteurellaceae (629, 1.14%), Flavobacteriaceae (398,

0.72%), and others (1242, 2.24%) (Figure 6c).

16S reads corresponding to 31 and 58 genera were identified in clinically diseased

and healthy birds, respectively. Mycoplasma was found in abundance in the diseased birds

(34,805, 65.41%), followed by Pseudomonas (7,136, 13.41%), Enterococcus (5,519, 10.37%)

RESULTS

46


healthy birds in the controlled house system using the NCBI taxonomy database.

Figure 6c: Relative visualization of the abundances of bacterial families in clinically diseased

and healthy birds in the controlled house system using the taxonomy database available at

NCBI.

RESULTS

47

Lactobacillus (3,699, 6.95%), Streptococcus (632, 1.18%), Collinsella (269, 0.50%),

Staphylococcus (208, 0.39%), Klebsiella (184, 0.35%), and others (754, 1.42%). Similarly, in

the healthy birds, the highest number of reads corresponded to Pseudomonas (3,555,

37.40%), followed by Staphylococcus (1,526, 16.05%), Bacteroides (983, 10.34%),

Klebsiella (865, 9.10%), Aeromonas (479, 5.04%), Acinetobacter (291, 3.06%), and others

(1,804, 18.98%) (Figure 6d).

Of the 24 bacterial species identified in the diseased birds, Mycoplasma synoviae

(5,222, 46.09%) was the most abundant, followed by Enterococcus cecorum (5,128, 45.26%),

Collinsella aerofaciens (269, 2.37%), Streptococcus alactolyticus (191, 1.68%),

Staphylococcus aureus (93, 0.82%), Eubacterium yurii (90, 0.79%), and others (336, 2.96%).

Of the 16 bacterial species identified in the healthy birds, Staphylococcus aureus (712,

54.43%) was the most abundant, followed by Enterococcus cecorum (203, 15.51%),

Sphingobacterium sp. EQH22 (94, 7.18%), Ornithobacterium rhinotracheale (90, 6.88%),

Salinicoccus carnicancri (75, 5.73%), and others (134, 10.24%). Micromonosporaceae sp.

SB1-35, Bacillus sp. PeC11, Brevibacillus agri, Abiotrophia defective, Lactobacillus agilis,

Lactobacillus coleohominis, Lactobacillus intermedius, Lactobacillus pontis, Lactobacillus

secaliphilus, Lactobacillus sp. oral taxon 052, Streptococcus alactolyticus, Streptococcus

pluranimalium, Methylibacterium hispanicum, Mycoplasma arginini, and Mycoplasma

synoviae were found exclusively in diseased birds. By contrast Alcaligenes sp. badwp,

Marinomonas protea, Myroides sp. MY15, Ornithobacterium rhinotracheale,

Sphingobacterium sp. EQH22, Salinicoccus carnicancri, Clostridium sp. TM-40,

Eubacterium sp. Pei061, and Phascolaractobacterium faecium were found only in apparently

healthy birds (Figure 6e).

RESULTS

48

Figure 6d: Relative abundances of bacterial genera in clinically diseased and healthy birds in

the controlled house system using the taxonomy database available at NCBI.

RESULTS

49


corresponding to NCBI database in clinically diseased and healthy birds from controlled

house system.

RESULTS

50

4.2. METAGENOMIC ANALYSIS OF THE RESPIRATORY MICROBIOTA OF

CLINICALLY DISEASED AND HEALTHY BIRDS

Of all the five flocks sampled comprising of equal number of T-BAL from diseased and

healthy birds (n = 14 each), a total of 278,140 16S rRNA read were retrieved. The diseased

birds contained more 16S rRNA reads than the healthy birds (162,056 vs. 116,084). On

average, a higher number of reads per sample was found in the clinically diseased birds than

in the apparently healthy birds (11,575 vs. 8,291) (Table 1). Overall, the reads were

summarized and/or assigned to 11 phyla, 66 families, 96 genera, and 50 bacterial species. In

contrast to the diseased birds, the microbiota harbored by the apparently healthy birds was

more diverse in taxonomic content; 10 phyla, 65 families, 85 genera, and 30 species were

identified. The microbiota of the clinically diseased birds was comprised of 08 phyla, 52

families, 65 genera, and 42 species (Figures 7 and 8). The hierarchical clustering at the

genera node revealed similarities in the taxonomic content among few of the diseased and

healthy birds; however, individual variations in T-BALs taken from each flock were observed

(Figures 7a and 8a). Seven phyla were common to both groups of birds. Acidobacteria was

absent from the healthy birds, whereas the diseased birds were devoid of any read

corresponding to Verrucomicrobia, Chloroflexi, and Deinococcus-Thermus.

RESULTS

51

Figure 7: Taxonomy rarefaction plot of the T-BALs from clinically diseased birds at the



Figure 8: Taxonomy rarefaction plot of the T-BALs from clinically healthy birds at the



RESULTS

52

Figure 7a: NCBI taxonomic content-based hierarchical clustering of clinically diseased birds

collapsed at the genera node. The distances between branches in each sample were calculated

using the Goodall normalization method with the UPGMA algorithm.

Figure 8a: NCBI taxonomic content-based hierarchical clustering of clinically healthy birds

collapsed at the genera node. The distances between branches in each sample were calculated

using the Goodall normalization method using the UPGMA algorithm.

RESULTS

53

4.2.1. CULTURE-INDEPENDENT ANALYSIS OF THE RESPIRATORY

MICROBIOTA OF CLINICALLY DISEASED BIRDS

Based on the 16S rRNA reads summarized at the phyla node, including those assigned to its

descendants, Proteobacteria (57,982, 46.29%) was the most abundant bacterial phyla,

followed by Tenericutes (43,910, 35.05%), Firmicutes (19,860, 15.85%), Bacteroidetes

(1,723, 1.37%), Actinobacteria (1,659, 1.32%), Cyanobacteria (86, 0.07%), Fusobacteria

(22, 0.02%), and Acidobacteria (9, 0.007%) (Figure 7b). Together, Proteobacteria (46.3%)

and Firmicutes (35.5%) constituted approximately 81.4% of the total reads summarized at the

phyla node. The most abundant families identified were Mycoplasmataceae (43,908,

43.80%), followed by Enterobacteriaceae (16,909, 16.86%), Pseudomonadaceae (11,972,

11.94%), Moraxellaceae (9,259, 9.23%), Enterococcaceae (5,544, 5.53%) Lactobacillaceae

(3,919, 3.90%), Pasteurellaceae (2,641, 2.63%), and others (6,090, 6.07%) (Figure 7c).

Similarly, with respect to genera (n = 65), the most abundant were Mycoplasma (43,908,

55.08%), Pseudomonas (11,944, 14.98%), Acinetobacter (9,246, 11.59%), Enterococcus

(5,529, 6.93%), Lactobacillus (3,880, 4.86%), Shewanella (837, 1.05%), Sphingobacterium

(623, 0.78%), and others (3,748, 4.70%) (Figures 7d and 7e). Among the bacterial species

identified (n = 42), the dominant species were Mycoplasma synoviae (6021, 48.49%),

followed by Enterococcus cecorum (5138, 41.38%), Collinsella aerofaciens (269, 2.16%),

Staphylococcus aureus (93, 0.75%), Streptococcus alactolyticus (91, 0.73%), Eubacterium

yurii (90, 0.72%), Enterobacter cloacae (81, 0.65%), and others (633, 5.09%). The bacterial

species that were found in more than one sample included Enterococcus cecorum,

Mycoplasma synoviae, Mycoplasma arginini, Staphylococcus aureus, Eubacterium yurii,

Enterobacter cloacae, Escherichia coli, Acinetobacter sp. N15, Brevibacillus agri, and

Clostridium sp. TM-40 (Figures 7f and 7g).

RESULTS

54

Figure 7b: Comparative visualization of the abundances of phyla in clinically diseased birds

using the NCBI taxonomy database.

Figure 7c: Relative visualization of the abundances of bacterial families in clinically diseased

birds using the taxonomy database available at NCBI.

RESULTS

55

Figure 7d: Relative abundances of bacterial genera in clinically diseased birds using the

taxonomy database available at NCBI.

RESULTS

56

Figure 7e: Individual variation in bacterial genera identified from clinically diseased birds

corresponding to NCBI taxonomy.

Figure 7f: 16S rRNA sequence (V1 – V5)-based identification of bacterial species in

clinically diseased birds corresponding to NCBI database.

RESULTS

57

Figure 7g: Individual variation in 16S rRNA sequence (V1 – V5)-based identification of

bacterial species in clinically diseased birds corresponding to NCBI taxonomy database.

RESULTS

58

4.2.2. CULTURE-INDEPENDENT ANALYSIS OF THE RESPIRATORY

MICROBIOTA OF CLINICALLY HEALTHY BIRDS

Of the phyla identified (n = 10), Proteobacteria (62,144, 84.86%) was the most abundant,

followed by Firmicutes (4,657, 6.35%), Bacteroidetes (2,902, 3.96%), Actinobacteria (2,701,

3.68%), Tenericutes (425, 0.58%), Fusobacteria (372, 0.50%), Deinococcus-Thermus (9,

0.012%), Chloroflexi (8, 0.011%), and Verrucomicrobia (6, 0.008%) (Figure 8b). The

dominant families identified were Pasteurellaceae (29,695, 51.46%), Enterobacteriaceae

(7,599, 13.16%), Pseudomonadaceae (5,530, 9.58%), Moraxellaceae (2,615, 4.53%),

Alcaligenaceae (2,278, 3.94%), Staphylococcaceae (1,804, 3.12%), Bacteroidaceae (1,053,

1.82%), Streptococcaceae (748, 1.29%), Flavobacteriaceae (654, 1.13%), Neisseriaceae

(582, 1.00%), Aeromonadaceae (517, 0.89%), Mycoplasmataceae (415, 0.72%), and others

(4,214, 7.30%) (Figure 8c). Similarly, the total reads identified (16,478) corresponded to 85

genera, the most abundant of which were Pseudomonas (5,484, 33.28%), Acinetobacter

(1,812, 10.99%), Staphylococcus (1,646, 9.98%), Bacteroides (1,053, 6.39%), Klebsiella

(882, 5.35%), Streptococcus (688, 4.17%), Aeromonas (517, 3.13%), Mycoplasma (415,

2.51%), Corynebacterium (360, 2.18%), Shewanella (359, 2.17%), and others (3,262,

19.79%) (Figures 8d and 8e). Among the reads (1,916) corresponding to species in the NCBI

database, the most abundant were Staphylococcus aureus (712, 37.16%), Enterobacter

cloacae (203, 10.59%), Kushneria sp. Z35 (182, 9.49%), Ornithobacterium rhinotracheale

(166, 8.66%), Mycoplasma gallinaceum (155, 8.08%), Sphingobacterium sp. EQH22 (94,

4.91%), and others (404, 21.08%). The bacterial species present in more than one lavage

sample were Alcaligenes sp. badwp, Enterobacter cloacae, Enterococcus cecorum,

Kushneria sp. Z35, Shigella flexneri, Ornithobacterium rhinotracheale, Sphingobacterium sp.

EQH22, Atopostipes suicloacalis, Escherichia coli, Salinicoccus carnicancri, and

Mycoplasma gallinaceum (Figures 8f and 8g).

RESULTS

59

Figure 8b: Comparative visualization of the abundances of phyla in clinically healthy birds

using the NCBI taxonomy database.

Figure 8c: Relative visualization of the abundances of bacterial families in clinically healthy

birds using the taxonomy database available at NCBI.

RESULTS

60

Figure 8d: Relative abundances of bacterial genera in clinically healthy birds using the

taxonomy database available at NCBI.

Figure 8e: Individual variation in bacterial genera identified from clinically healthy birds

corresponding to NCBI taxonomy database.

RESULTS

61

Figure 8f: 16S rRNA sequence (V1 – V5)-based identification of bacterial species in

clinically healthy birds corresponding to NCBI database.

Figure 8g: Individual variation in 16S rRNA sequence (V1 – V5)-based identification of

bacterial species in clinically healthy birds corresponding to NCBI taxonomy database.

RESULTS

62

4.3. COMPARISON OF 16S READ-BASED IDENTIFICATION OF BACTERIAL

SPECIES IN CLINICALLY DISEASED AND HEALTHY BIRDS

From the total reads recovered from the diseased and healthy birds, a total of 50 bacterial

species were identified. A total of 42 and 30 bacterial species were identified in the clinically

diseased and healthy birds, respectively (Figures 7f and 8f). The bacterial species common to

both groups of birds were Bacteroides acidofaciens, Chrysobacterium hispanicum,

Collinsella aerofaciens, Enterobacter cloacae, Enterococcus cecorum, Escherichia coli,

Helococcus kunzii, Kushneria sp. Z35, Lactobacillus vaginalis, Mycoplasma gallinaceum,

Mycoplasma gallinarum, Mycoplasma synoviae, Myroides sp. MY15, Ornithobacterium

rhinotracheale, Phascolaractobacterium faecium, Propionobacterium granulosum,

Ruminococcus bromii, Shigella flexneri, Staphylococcus aureus, Turicibacter sanguinis, and

Eubacterium yurii. The following species were identified exclusively in the clinically healthy

birds: Alcaligenes sp. badwp, Alkalibacterium iburiense, Atopostipes suicloacalis,

Avibacterium volantium, Barnisiella viscericola, Marinomonas protea, Salinicoccus

carnicancri, Sphingobacterium sp. EQH22, and Veilonella sp. oral taxon. Similarly, the

bacterial species identified exclusively in the diseased birds were Streptococcus

pluranimalium, Streptococcus alactolyticus, Pseudomonas veronii, Porphyromonas catoniae,

Mycoplasma arginini, Micromonospora sp. SB1-35, Methylobacterium hispanicum,

Lactobacillus sp. oral taxon 052, Lactobacillus secaliphilus, Lactobacillus pontis,

Lactobacillus intermedius, Lactobacillus coleohominis, Lactobacillus agilis, Eubacterium sp.

Pei061, Clostridium sp. TM-40, Brevibacillus agri, Bacillus sp. PeC11, Anaerococcus

octavius, Actinomyces gravenitizii, Acinetobacter sp. N15, and Abiotrophia defectiva.

RESULTS

63

4.4. CULTURE-INDEPENDENT ANALYSIS OF THE RESPIRATORY

MICROBIOTA OF A WILD HOUBARA BUSTARD (Chlamydotis undulata)

DNA was extracted from a T-BAL sample from a recently deceased houbara bustard bird,

16S sequences were amplified and sequenced, and 8,950 high-quality reads were identified.

The rarefaction analysis suggested that the taxonomic diversity was largely captured at this

relatively modest sequencing depth (Figure 9). Of the total reads, 5,442 corresponded to the

following 5 phyla: Proteobacteria (2,563, 47.1%), Bacteroidetes (1,519, 27.9%),

Fusobacteria (772, 14.2%), Firmicutes (402, 7.4%), and Actinobacteria (186, 3.42%).

Proteobacteria and Bacteriodetes predominated and constituted 75% of the sequence reads

summarized at the phyla node.

The reads that collapsed to lower taxonomic nodes included 15 families and 14

genera. Of the 4,972 reads that were assigned/summarized to the family node, the dominant

families were Flavobacteriaceae (1,317, 26.5%), Enterobacteriaceae (1,315, 26.4%),

Fusobacteriaceae (771, 15.5%), Aeromonadaceae (555, 11.2%), and Pseudomonadaceae

(294, 5.9%). Similarly, at the genera node, the 3,128 reads were identified as predominantly

Myroides (1185, 37.9%), Aeromonas (555, 17.7%), Fusobacterium (434, 13.9%),

Pseudomonas (294, 9.4%), Bacteroides (154, 4.9%), and Proteus (108, 3.5%) (Figure 9a).

Many reads could not be assigned to the species level because the top BLAST hits contained

heterogeneous taxonomic lineages. Among the reads assigned at the species level, most were

Myroides spp. MY15 (1,054), followed by Collinsella aerofaciens (181), Kurthia zopfii (55),

Enterococcus cecorum (9), and Bacteroides fragilis (8) (Figure 9b).

RESULTS

64

Figure 9: Taxonomy rarefaction plot for the analyzed T-BAL sample from a houbara bustard

(Chlamydotis undulata) at the genera node. The plot represents the percentage of reads in a

given sample that correspond to leaves in the NCBI taxonomy database.

Figure 9a: Relative abundance of bacterial phyla, families, and genera identified in a houbara

bustard (Chlamydotis undulata) using the taxonomy database available at NCBI.

RESULTS

65

Figure 9b: 16S rRNA sequence (V1 – V5)-based phylogenetic analysis of taxonomic content

for the houbara bustard (Chlamydotis undulata) corresponding to the NCBI database.

RESULTS

66

4.5. CULTURE-INDEPENDENT ANALYSIS OF THE RESPIRATORY

MICROBIOAT OF AN OSTRICH (Struthio camelus)

In total, 9,821 reads were obtained from gDNA (19.0 ng/µL) extracted from an ostrich that

were designated to the bacteria domain and its descendants. The taxonomic diversity within

the T-BAL sample was captured with a modest sequencing depth of 15, 000 – 25,000

reads/run as indicated by the rarefaction analysis between the sequence retrieved and the

associated taxonomy leaves at the level of genera (Figure 10). Collapsing the sequence file

(.rma) to the taxonomic node identified reads corresponding to 2 phyla, 6 orders, 7 families,

and 8 genera. Of the total reads, 8,253 matched 2 phyla, Proteobacteria (8,028, 97.3%) and

Bacteroidetes (225, 2.7%). Based on the sequences summarized at the family node (1,263),

the dominant family was Shewanellaceae (531, 42.0%), followed by Moraxellaceae (196,

15.5%), Aeromonadaceae (179, 14.2%), and Pseudomonacae (153, 12.1%). Similarly, among

the sequence reads summarized at the genera node (1,231), the most abundant read was

Shewanella (531, 43.1%), followed by Acinetobacter (196, 15.9%), Aeromonas (179, 14.5%),

Pseudomonas (153, 12.4%), and Sphingobacterium (89, 7.2%) (Figure 10a). Because the top

BLAST hits contained heterogeneous taxonomic lineages, among the reads assigned to the

species level, only one bacterial specie named Myroides spp. MY15 (44) was identified,

which belongs to the phylum Bacteroidetes (Figure 10b).

RESULTS

67

Figure 10: Taxonomy rarefaction plot for the analyzed T-BAL sample from ostrich (Struthio

camelus) at the genera node. The plot represents the percentage of reads present in a given

sample that corresponded to leaves in the NCBI taxonomy database.

Figure 10a: Relative abundance of bacterial families and genera identified in ostrich (Struthio

camelus) using the taxonomy database available at NCBI.

RESULTS

68

Figure 10b: 16S rRNA sequence (V1 – V5)-based phylogenetic analysis of taxonomic

contents in ostrich (Struthio camelus) corresponding to NCBI taxonomy database.

69

CHAPTER 5

DISCUSSION

Respiratory diseases are the most common and devastating bacterial diseases worldwide.

Effective vaccination and subsequent serological monitoring have been successfully

implemented against some of the infectious diseases; however, in developing countries such

as Pakistan, bacterial diseases are diagnosed based on clinical signs and are treated

empirically, largely because of the limited availability of culturing facilities. A key

component of poultry development policies worldwide is effective disease control and

effective management; therefore, extensive knowledge of the normal and diseased flora in a

given system is required. Typically, limited culturing procedures exclude the normal flora

present in the respiratory tract or are restricted to analyzing only potential pulmonary

pathogens. This is the first study to reveal the unbiased molecular identification of bacterial

communities present collectively in the trachea and lungs of domestic poultry within different

management systems and in wild birds. Considering the potential of birds to harbor and

transmit pathogens of significance to public health, the outcome of this study is of great

interest. In this study, 454-pyrosequencing has been used to investigate respiratory disease

outbreaks in birds that were suspected to be caused by previously unknown bacteria. This

study identified a number of new organisms and pathogens that were not previously

associated with the respiratory system of birds in either clinically diseased or health

situations, including those of public health significance.

The collection of appropriate samples that reflects the system compartment and the

subsequent analysis is the principal challenge for defining the respiratory microbiome.

Traditional culture-based studies have indicated that the lower respiratory tract is a sterile

compartment (Pecora, 1963); however, recent molecular analyses have demonstrated

otherwise. Using broncho-alveolar lavage, Erb-Downward et al. (2011) reported the

DISCUSSION

70

distribution of characteristic organisms in the lower respiratory tract in humans. Although

transient rather than independent communities with indistinguishable structures, Charlson et

al. (2011) described that the 16S rRNA bacterial population in the lower respiratory tract in

humans largely reflects upper respiratory tract organisms. Similarly, the culture-based

identification of a number of these bacterial species has reported variations in the upper and

lower respiratory tract of birds, such as in the nose, sinus, trachea, lungs, and air sacs, under

different poultry management systems (Smibert et al., 1957). Using these data and

establishing the scope of the study to determine the respiratory microbiota of normal and

diseased birds irrespective of typical respiratory tract compartments, T-BALs were collected

and analyzed using 454-pyrosequencing of the 16S rRNA gene (V1 – V5, ~ 1,000bp).

Among the hypervariable regions of the 16S gene, V2 (nucleotides 137 - 242), V3

(nucleotides 433 - 497), and V6 (nucleotides 986 - 1043) are generally considered to exhibit

the maximum heterogeneity; therefore, these regions provide the maximum differentiation

among the bacterial community (Chakarvorty et al., 2007).

Summarizing almost all of the reads to the corresponding bacterial domain and its

subsequent descendants is indicative of the rigorous quality control and filtering of the

chimeric sequences. A number of metagenomic studies have reported a higher proportion of

unclassified bacteria (Daly et al., 2001; Shephered et al., 2011); however, these studies lack

rigorous high-quality sequencing processes. Although significant advancements have been

made in sequence processing tools in recent years, an abundance of unclassified bacteria in a

collection of retrieved sequences is unlikely but not impossible. In an analysis of the fecal

microbiota of horses (n = 16), despite high-quality sequence processing, Costa et al. (2012)

derived 17.6% and 8.6% unclassified sequences from healthy horses and horses with colitis.

However, when compared with the NCBI BLAST nucleotide collection (nr/nt), many of these

sequences corresponded to various uncultured bacteria. This observation raised concerns

DISCUSSION

71

about the ability of intensive sequencing methods to assign taxonomic rank accurately

because these methods have an assignment rate that remains lower than that produced using

traditional sequence analysis of the full-length 16S rRNA gene. With continuous

improvements in advanced sequencing technologies, the differences in the sequence read

lengths achieved by the different sequencing approaches will likely narrow gradually (Nam et

al., 2011) and eventually disappear.

Culture-independent analysis indicates the potential susceptibility of birds to a diverse

microbial community involving a variety of phyla, families, genera, and well-characterized

bacterial species. Although not all reads can be delineated to the lowest taxonomic ranking

but are classified to the genera level or above, a number of new species have been identified

that were previously unknown in birds. A number of gram-positive and gram-negative

bacterial species belonging predominantly to the phyla Proteobacteria, Firmicutes,

Tenericutes, Actinobacteria, Bacteroidetes, and Chlamydia/Verrucomicrobia have been

reported in the respiratory systems of domestic and wild birds (Charlton et al., 1993; Byrum

and Selmons, 1995; De-Rosa et al., 1996; Joubert et al.,1999; Van Empel and Hafez, 1999;

Akhter et al., 2001; Silvanose et al., 2001; El-Sukhon et al., 2002; Hafez, 2002; Hasan et al.,

2002; Chin et al., 2003; Mustafa et al., 2005; Lateef et al., 2006, Murthy et al., 2008;

Ehtisham-ul-Haque et al., 2011; Zahoor and Siddique, 2011; Siddique et al., 2012). To the

best of our knowledge, there is no information available regarding bacterial species from the

phyla Fusobacteria, Acidobacteria, Chloroflexi, Cyanobacteria, and Deinococcus-Thermus

in the respiratory systems of birds. This lack of information is simply because to date, only

culture-dependent techniques have been used for the identification of microbes. Because

conventional culturing procedures are applicable to less than 1% of organisms (Schuster,

2008), it is very likely that bacteria that are difficult to culture and/or are non-viable will be

overlooked. In previous studies, a 100- to 1,000-fold increase in species richness and

DISCUSSION

72

diversity of bio-aerosol constituents has been reported when assessing 16S sequences

compared with analyses using traditional culturing techniques (Brodie et al., 2007; Nehme et

al., 2008). The association of a number of microorganisms, such as Ornithobacterium

rhinotracheale, with other respiratory pathogens causes them to be overgrown by other

bacteria (e.g., E. coli) (Charlton et al., 1993; De Rosa et al., 1996) and contributes to the

difficulties associated with the culture and diagnosis of these pathogens in the lab using

routine methods. This phenomenon could explain the detection of unidentified microbes of

the phyla Fusobacteria, Acidobacteria, Chloroflexi, Cyanobacteria, and Deinococcus-

Thermus, which were found in relatively low proportions as indicated by the logarithmic

scale or the number of reads in flocks from different management systems. The irrational and

empirical use of broad-spectrum antibiotics further complicates the isolation and

identification of new pathogens associated with a particular disease. These culture-dependent

approaches underscore the identification of bacteria; therefore, procedures involving culture-

independent analysis are essential to determine the comprehensive bacterial diversity in a

given system.

In the diseased birds, the identification of bacteria that differ from previously known

pathogens reflects the care taken when selecting the flocks/outbreaks used in this study.

Although further clinico-pathological studies to investigate the cause versus the clinical

outcome will be required, compared to other studies, the organisms identified in this study as

having a high relative abundance may represent potential pathogens involved in disease. Of

the few pathogens known worldwide and identified in this study, many have not been

described as primary or secondary infectious agents in birds, such as Acinetobacter sp. N15,

which was identified in the diseased birds in the free range system. Well-known pathogens

isolated previously in birds include Haemophilus paragallinarum (De-Rosa et al., 1996;

Akhter et al., 2001; Hasan et al., 2002; Murthy et al., 2008; Siddique et al., 2012), E. coli

DISCUSSION

73

(Charlton et al., 1993; De Rosa et al., 1996; Hasan et al., 2002; Mustafa et al., 2005; Murthy

et al., 2008), Ornithobacterium rhinotracheale (Murthy et al., 2008), Mycoplasma and

Salmonella spp. (Mustafa et al., 2005; Lateef et al., 2006), Staphylococcus, Streptococcus

and Bacillus spp. (Byrum and Selmons, 1995; Lateef et al., 2006), Pasteurella multocida

(Murthy et al., 2008; Zahoor and Siddique, 2011), Mycoplasma synoviae (Ehtisham-ul-Haque

et al., 2011), Pasteurella gallinarum, Pasteurella haemolytica, Pasteurella anatipestifer, and

Bordetella avium (Joubert et al.,1999; Van Empel and Hafez, 1999; El-Sukhon et al., 2002;

Hafez, 2002; Chin et al., 2003).

Usually, respiratory disease is a complex, multi-factorial phenomenon in which the

infectious organisms involved and their subsequent effects in terms of clinical disease in

different management systems and hosts are relatively complex until examined in detail, both

in vitro and in vivo. Based on the relative abundance of an organism in the total community in

flocks with different management systems, in diseased birds, the bacteria that were identified

alone or in combination with two or more other pathogens may represent the primary

pathogen or a pathogen involved in a concomitant infection. For example, the identification

of Acinetobacter sp. N15 and Mycoplasma synoviae as the most abundant organisms in one

of the study flocks in the free range and controlled house systems could indicate that these

bacteria are the primary infectious agents. Similarly, the presence of more than one organism,

including Escherichia coli, Pseudomonas vernoii, Shigella flexneri, Clostridium sp. TM-40,

Ornithobacterium rhinotracheale, Mycoplasma synoviae, and Actinomyces gravenitizii, in the

clinically diseased birds in the free range system is indicative of a concomitant infection. The

presence of and/or exposure to opportunistic and/or pathogenic bacteria associated with

varying degrees of clinical outcomes as the primary infectious agent or the secondary

infectious agent in combination with viruses aggravates respiratory infection (Murthy et al.,

2008; Van Empel and Hafez, 1999; Pan et al., 2012). In an experimental study, Van Empel et

DISCUSSION

74

al. (1996) determined that the prior administration of avian viruses aggravates

Ornithobacterium rhinotracheale infection in turkeys and chickens. Under field conditions,

the presence of protease-producing bacteria of the phyla Firmicutes and Proteobacteria in the

upper respiratory tract in birds is involved in the cleavage of avian influenza (AI) virus

hemagglutinin molecules and the subsequent increase in the severity of Al infections in

poultry (Byrum and Selmons, 1995). Soon after the exposure of birds to the infectious

bronchitis virus, colonization of the air sacs with E. coli also represents an example of a

bacterium as a secondary infectious agent (Glisson, 1998). In respiratory disease, the

isolation of more than one pathogen, such as E. coli, Ornithobacterium rhinotracheale,

Pasteurella multocida, and Haemophilus paragallinarum, indicates a concomitant infection

in birds that increases the severity of the infection (De Rosa et al., 1996; Murthy et al., 2008).

Similarly, post infection with Haemophilus paragallinarum, unique clinical presentations

such as arthritis and septicemia, were found to be complicated by other pathogens, such as

Mycoplasma gallisepticum, M. synoviae, Pasteurella spp., Salmonella spp., and the infectious

bronchitis virus, in broiler and layer flocks (Sandoval et al., 1994).

The reads recovered from the lavages were not delineated to the lowest taxonomic

rank; the species level. This is because despite the enormous size of the microbial world, only

approximately 6,000 bacterial species have been named (Kuever et al., 2005), and most of

species are represented by only a few genes in public databases. The results indicated that the

clinically healthy birds harbored more diversity and taxonomic richness than the diseased

birds when collapsed at higher taxonomic nodes. The predominance of gram-negative

bacteria in both groups of birds is similar to previous culture-dependent analyses by Poorniya

and Upadhey (1995); however, the diversity among the groups was different. Poorniya and

Upadhey (1995) observed more diversity in the microbial community in the diseased birds

than in the healthy birds. Bacterial communities are relatively conserved at the phyla level;

DISCUSSION

75

however, at lower taxonomic levels, differences exist in phylotypes as well in abundance

between birds and flocks under different management systems. While determining the

bacterial diversity of bioaerosols from cage-housed and floor-housed poultry operations using

PCR-DGGE (denaturing gradient gel electrophoresis), Just et al. (2011) demonstrated that,

regardless of barn type, clustering analysis with respect to area and personal bacterial profiles

revealed less than 10% similarity. Similarly, Nam et al. (2011) described differences in gut

microbial communities between different geographical areas (Korea, US, Japan, and China).

The differences within the Korean population were relatively small; however, inter-individual

differences were observed at different time periods during the analysis. A number of

differences in each type of bird management system, such as the housing and environmental

conditions, high stock density, poor ventilation, exposure to humans and animals, airborne

bacterial and endotoxin concentrations, use of antibiotics, dust and ammonia levels, any type

of stress, etc., determines whether an organism will persist alone or in combination with other

bacteria or viruses and the subsequent clinical outcome. Whyte (1993) and Just et al. (2011)

determined that the type and level of bacteria depend upon management practices including

temperature, relative humidity, and litter type. 16S rRNA-based DGGE profiles obtained

from barns with similar characteristics, such as above- or below-average relative humidity,

above- or below-average temperature, and paper or straw litter, have been found to cluster

together (Kirychuk et al., 2010). Similarly, post-antibiotic treatment alterations in chicken

intestinal microbiota have been demonstrated through PCR-DGGE (Pedroso et al., 2006;

Gong et al., 2008). The identification of more than one candidate pathogen involved in each

management system, particularly in controlled and open house systems, could be attributed to

the development of resistance to antimicrobial agents. Generally, in Pakistan, irrational use of

antibiotics to treat clinical symptoms is practiced, without the aid of laboratory facilities for

the identification of bacteria and the corresponding drug of choice. Ryll et al. (1996) and El-

DISCUSSION

76

Sukhon et al. (2002) attributed the isolation of various pathogens from the respiratory tracts

of birds to the development of resistance from the frequent indiscriminate and uncontrolled

use of antibiotics, particularly against E. coli and O. rhinotracheale. In houses where the

ventilation system is poor and the ammonia levels are high, the resultant damage to the

respiratory tract renders the birds vulnerable to respiratory pathogens (Nehme et al., 2005).

Because the sampling was performed during the winter season at different geographical

regions within the Punjab province and there were variations in the operations and practices

of each management system, these characteristics may be responsible, in part, for the

bacterial diversity observed between and within flocks.

Regardless of the management type and bird species, and despite the limited

taxonomic ranking of the retrieved 16S sequences, a number of new bacterial species

previously unknown to be associated with respiratory system were identified in this study. Of

these, the dominant species found in the free range, open house, and controlled house poultry

and in the wild houbara bustard and ostrich were Acinetobacter sp. N15, Kushneria Z35,

Clostridium sp. TM-40, Pseudomonas veronii, Shigella flexneri, Turicibacter sanguinis;

Shigella flexnerii, Myroide ssp. MY15, Enterococcus cecorum; Streptococcus alactolyticus,

Sphingobacterium sp. EQH22, Myroides spp. MY15, Lactobacillus spp., Marinomonas

protea, Methylobacterium hispanicum, Helcoccus kunzii, Brevibacillus agri, Alcaligenes sp.

badwp, Phascolaractobacterium faecium, Enterobacter cloacae, Enterococcus cecorum,

Myroides spp. MY15, Kurthia Zopfii, Enterococcus cecorum, Collinsella aerofaciens, and

Bacteroides fragilis. The identification of a number of organisms that were common to all

birds studied indicates a similarity in the receptor in the respiratory system. The clinical

importance of these bacteria has not been established, and their role in the respiratory

microbiome of birds is unknown; however, a number of these bacteria are associated with

infectious diseases in humans and other species. For example, Myroides spp. MY15 is a gram-

DISCUSSION

77

negative bacterium within the family Flavobacteriaceae and has been recently isolated as a

novel organism in human saliva in China, a country that shares a border with Pakistan (Yan

et al., 2012). Members of the genus Myroides have been isolated from clinical sources, soil,

and seawater and are considered opportunistic pathogens (Yan et al., 2012). Collinsella

aerofaciens is a gram-positive bacillus that is abundant in the human intestine (Kageyama et

al., 2000) and has been associated with human intestinal disorders (Swidsinski et al., 2002).

Bacteroides fragilis, a penicillin-resistant bacterium associated with intestinal disorders, has

been isolated from the intestine of several mammals and birds (Avila-Campos et al., 1990;

Garcia et al., 2012). Enterococcus cecorum, associated with arthritis and osteomyelitis, is

another intestinal microbe that has been isolated from birds (Boerlin et al., 2012). Species of

the genera Enterococcus are gram-positive bacteria that inhabit the alimentary tract in

humans and are associated with nosocomial pathogenicity and resistance to glycopeptide

antibiotics (Fisher and Philip, 2009). Kurthia zopfii, however, has been isolated from various

sources, such as meat, wastewater, milk, feces, and abattoir air, as well as at altitudes

exceeding 3,000 meters (Stackerbrandt et al., 2006). Kushneria sp. Z35T, proposed as

Kushneria sinocarnis sp. nov, is a gram-negative, aerobic, and moderately halophilic

bacterium of the family Halomonadaceae that has been isolated from traditional Chinese

cured meat produced in Wuhan (Zou and Wang, 2010). Although several biotic and abiotic

factors are involved in the spread of pathogenic organisms, birds also serve as an important

source and transmitter of bacteria from one geographical area to another (Bailey et al., 2000;

Hubalek et al., 2004; Benskin et al., 2009). Together with the extensive exposure, mobility,

and migratory habits of certain birds, numerous avian species have been implicated as a

source of infection for humans, domestic animals, and other wildlife (Craven et al. 2000). A

number of bacterial pathogens of importance to public health, including Anaplasma

phygocytophilum, Borrelia burgdorferi, Pasteurella multocida, Compylobacter jejuni,

DISCUSSION

78

Clostridium botulinum, Pseudomonas aeruginosa, and Mycobacterium avium, have been

associated with birds as biological and/or mechanical carriers (Sambyal and Baxi, 1980;

Bailey et al., 2000; Hubalek et al., 2004; Abulreesh et al., 2007).

Conclusions and future prospects:

Culture-independent analysis has revealed a far more diverse consortium of bacteria

in the respiratory airways of birds than previously known, including bacteria of significance

to public health. Phylogenetic profiling has indicated differences in the phylotypes in

clinically diseased and healthy birds originating from different management systems;

however, functional and clinico-pathological studies will be needed to ascertain or establish

links between causes versus clinical outcome. Furthermore, base upon study outcomes, the

future prospect could be the determination of novel organisms that has been identified in

relatively higher taxonomic nodes (phyla, families and genera), however, did not delineate to

lowest taxonomic node (species). In this regards, culturing of such organisms, subsequent

16S rRNA based sequencing and phylogeny with closely related organisms in database will

be of much potential.

79

CHAPTER 6

SUMMARY

The respiratory systems of birds are susceptible to and are a reservoir for numerous bacterial

species, including those of significance to public health. A number of bacteria, either as

primary or secondary infectious agents, have been associated with respiratory outbreaks in

poultry and subsequent losses worldwide. A key component of a poultry development policy

is the proper diagnosis and control of infectious diseases, which requires substantial

knowledge of the microbiome in diseased and healthy birds. Because only a small proportion

(< 1%) of organisms are culturable, limited as well as highly variable and time-consuming

conventional microbiological procedures have typically excluded the normal flora present in

the respiratory tract or have restricted the analysis to potential pulmonary pathogens. This

limitation provides only a partial representation of the airway microbiota of birds and has

little potential for determining or discovering novel organisms/pathogens and their

association with clinical outcomes. Using the hypervariable region of the 16S rRNA gene,

culture-independent techniques such as 454-pyrosequencing, can provide species-specific

sequences of any bacteria in a given clinical sample. This approach has identified a number

of novel bacterial species in recent years.

Based on the quality and quantity of the double-stranded gDNA, a total of 30 T-BAL

samples including houbara bustard and ostrich, were collected from equal numbers of

clinically diseased and healthy birds originating from flocks within different management

systems, including free range, open house, and controlled house. Using 454 bar-coded

pyrosequencing, the hypervariable regions of the 16S rRNA gene corresponding to V1 – V5

(~ 1,000 bp) were sequenced. Of the high-quality reads obtained (296,811) using the

MOTHUR platform, the sequences were processed for sequence alignment with the 16S RDP

SUMMARY

80

database via BLASTn, and subsequent taxonomic analysis through MEGAN programs using

a homology-based method to bin sequence reads.

Almost all of the read were classified to the bacterial domain and its subsequent

descendants. The birds were shown to be susceptible to a diverse microbial community

belonging to a variety of phyla, families, genera, and well-characterized bacterial species.

The bacterial communities were relatively conserved at the phylum level; however, at lower

taxonomic levels, differences were observed in the phylotypes and abundance between the

clinically diseased and healthy birds as well as between different management systems. The

biodiversity and richness in the taxonomic content was higher in the clinically healthy birds

compared with the diseased birds, as indicated by the rarefaction plot and the Shannon-

Wiener and Simpson-Reciprocal diversity indices. Regardless of the management type, bird

species, and health status, a number of new bacterial species were identified. Although the

clinical importance of these bacteria as part of the respiratory microbiome of birds has not

been established, a number of these bacterial species have been found to be associated with

infectious diseases in humans and other species.

The interactions of bacterial species with one another and, potentially, with the birds

themselves provide a fascinating avenue for continued research. Further clinico-pathological

studies will be needed to establish the links between causes versus effects. This information

may help us gain insight into the ecological roles of these bacterial species and their potential

co-evolution with birds.

81

CHAPTER 7

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