Holly_Davies_Dissertation

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The Presence of Prevotella intermedia 17
within the human lung and its relationship
with lung cancer & COPD: a metagenomic
analysis of the human lung microbiome
Student Name: Holly Davies
Student ID: 130023847
Submitted in part candidature for the degree of B.Sc. Biology (Genetics)
Institute of Biological, Environmental and Rural Sciences
Aberystwyth University
Submitted April 2016

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Contents Page
0. Preface
0.1 Declaration 4
0.2 Acknowledgements 5
0.3 Abstract 6
1. Introduction
1.1 Outline and Objectives 7
1.2 Lung cancer & COPD 8
1.2.1 Lung cancer 8
1.2.2 COPD 10
1.3 Prevotella intermedia 17 12
1.3.1 The Prevotella genus 12
1.3.2 Prevotella intermedia 12
1.3.3 Prevotella intermedia 17 13
1.4 Lung microbiome research 13
1.5 Previous work 14
2. Materials and Methodology
2.1 Aims and objectives 15
2.2 Initial analysis 15
2.2.1 Largest contig assembly 15
2.2.2 NCBI Blast search 16
2.2.3 Alignment 16
2.3 Individual samples 16

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2.3.1 Import and sampling 17
2.3.2 De novo assembly 17
2.3.2 Read Mapping 17
3. Results
3.1 NCBI Blast results 18
3.2 NODE alignment 19
3.3 Mapping of individual samples 20
4. Discussion
4.1 Discussion of Results 23
4.1.1 NCBI MegaBlast and NODE alignment 23
4.1.2 Individual sample data 23
4.2 Limitations & implications 24
4.3 Further study 24
4.4 Conclusions 25
5. References 26
6. Word Count 31
7. List of Figures/Tables/Images 32
8. Appendix 33

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0.1 Declaration
Module BR32330
I certify that all material in this paper is the result of my own investigation, except
where indicated, and references used in preparation of the text have been cited. This
paper has not been previously submitted as part of any other assessed module (with the
exception of the project proposal submitted for this paper), or submitted for any other
degree or diploma.
NAME: HOLLY DAVIES
DATE: 13/04/2016

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0.2. Acknowledgements
I would like to take this opportunity to thank Dr Justin Pachebat for the opportunity to be a
part in this research, and for the constant & helpful advice and support throughout this entire
project.
I would also like to thank everyone involved in the MEDLUNG project, specifically Joe
Healey, Simon Cameron and Tom Hitch for providing the background and basis necessary
for me to be able to conduct this research.
Finally, I would like to thank Michael Best and Louise Denny for providing motivation and
support throughout this project, it has been invaluable to me.

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0.3 Abstract
The aim of this project was to analyse the bacterial DNA present in the sputum of lung
cancer and COPD (Chronic Obstructive Pulmonary Disease) patients to further research into
developing a biomarker for these diseases in association with the MEDLUNG Project
(Metabolic Biomarkers for the Detection of Lung cancer) – a multicentre study on behalf of
the National Health Service (NHS). The initial analysis was conducted on an Illumina
metagenome contig assembly of data collected from 30 patients (10 healthy, 10 lung cancer,
10 COPD) using NCBI (National Centre for Biotechnology Information) BLAST (Basic Local
Alignment Search Tool) searches. From this analysis Prevotella intermedia 17 was identified
within the contig assembly.
Prevotella intermedia had previously been found orally in periodontal diseases (Maeda
et al, 1998) periapical periodontitis (Jacinto et al, 2003), and noma (an acute gangrenous
disease) (Bolivar et al, 2012), and also had been found to be associated with cystic fibrosis
(Ulrich et al, 2010) and causing an increased risk of pneumonia in mice (Nagaoka et al, 2014).
Specifically, Prevotella intermedia 17 is a clinical strain of the species that had only been
isolated from the periodontal pocket (Ruan et al, 2015).
This analysis was conducted using CLC Genomics Workbench 8 (CLC bio, 2016) and
included performing a de novo assembly with the initial patient data from the MEDLUNG
collection, and mapping this to the P. intermedia 17 reference genome. From this it was further
found that P. intermedia 17 is indeed found in the lungs, but also that lung cancer and COPD
have a seriously negative effect upon it, reducing it by 85-99% when compared with the healthy
control group.
This study has discovered the presence of Prevotella intermedia 17 in the lungs for the
first time, and also that P. intermedia 17 does have a relationship with both lung cancer and
COPD in humans. This could lead to the development of a new diagnostic test for lung cancer
or COPD, or possibly further the knowledge surrounding these diseases and how they manifest
in the human lung. Developing a new diagnostic test and providing early screening for patients
is vitally important for lung cancer and COPD, as it would have the capacity to save countless
lives by giving more people access to curative treatment at an earlier stage where it can be
effective.

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1. Introduction
1.1 OUTLINE AND OBJECTIVES
The aim of this project was to analyse the bacterial DNA present in the sputum of lung
cancer and COPD (Chronic Obstructive Pulmonary Disease) patients to further research into
developing a biomarker (biological molecule which is specific to said diseases) for these
diseases in association with the MEDLUNG Project (Metabolic Biomarkers for the Detection
of Lung cancer) – a multicentre study on behalf of the National Health Service (NHS). The
initial analysis was conducted on an Illumina metagenome contig assembly of data collected
from 30 patients (10 healthy, 10 lung cancer, 10 COPD) using NCBI (National Centre for
Biotechnology Information) BLAST (Basic Local Alignment Search Tool) searches. From this
analysis Prevotella intermedia 17 was identified within the contig assembly.
Prevotella intermedia had previously been found orally in periodontal diseases (Maeda
et al, 1998) periapical periodontitis (Jacinto et al, 2003), and noma (an acute gangrenous
disease) (Bolivar et al, 2012). Outside of oral diseases, Prevotella intermedia had been found
to be associated with cystic fibrosis (Ulrich et al, 2010) and causing an increased risk of
pneumonia in mice (Nagaoka et al, 2014). Specifically, Prevotella intermedia 17 is a clinical
strain of the species that had only been isolated from the periodontal pocket (Ruan et al, 2015),
with no links to lung cancer/COPD, or even the lungs in general.
From this, the Prevotella intermedia 17 reference genome was aligned with the raw
individual patients’ data to confirm its presence within the lungs, and to determine whether it
is linked to lung cancer and COPD. Hopefully a link between Prevotella intermedia and these
diseases would be established, leading to a new diagnostic test being developed in further
study, ensuring early diagnosis and higher survival rates of lung cancer and COPD sufferers.
Developing a new diagnostic test and providing early screening for patients is vitally
important for lung cancer and COPD, as two-thirds of lung cancer cases are diagnosed at
advanced stages whereby curative treatment becomes unavailable (CancerResearchUK, 2015a)
and COPD is regularly under- and mis-diagnosed (W.H.O., 2015a). If an early diagnostic test
could be developed, it would have the capacity to save countless lives by giving more people
access to early treatment.

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1.2 LUNG CANCER AND COPD
Lung cancer and COPD are among 2 of the most prevalent respiratory tract disorders
(CancerResearchUK, 2015a), both having extremely high morbidity and mortality (Eddy,
1989, Mallia et al., 2007). The most common cause of cancer death in the UK is lung cancer
(CancerResearchUK, 2015b), with COPD causing 6% of deaths globally (W.H.O., 2015a).
These diseases are not mutually exclusive, as a high risk of lung cancer usually equals a high
risk of COPD (Raviv et al, 2011). Hopefully by developing a biomarker for one, it would give
pointers for a biomarker for the other.
1.2.1 LUNG CANCER
Lung cancer is the most common cause of cancer death in the UK, accounting for 22%
of all deaths from cancer, and is the second most common cancer in the UK
(CancerResearchUK, 2015c). Globally, 58% of lung cancer cases occurred in less developed
countries in 2012 (Ferlay et al, 2014), and accounted for 1.59 million deaths (W.H.O., 2014).
In many cases the cause of the disease is clear, with tobacco smoking accounting for
more than 8 out of 10 cases, however other risk factors include exposure to carcinogens and
radiation, air pollution, family history and poor immunity (CancerResearchUK, 2015c).
Ageing is another factor that is involved in the development of lung cancer, which can be down
to an accumulation of the effect of risk factors (overall risk accumulation), however the overall
risk accumulation is then combined with the less effective cellular repair mechanisms as a
person grows older (W.H.O. 2015b). However, the World Health Organisation states that
“more than 30% of cancer deaths could be prevented by modifying or avoiding key risk factors”
(W.H.O. 2015b).
There are many preventative measures currently operating to attempt to reduce the
incidence of lung cancer. The main focus of these are to decrease smoking levels in
populations, but there are also some measures to address the rarer risk factors. Smoking
cessation is the main method of preventing lung cancer, as after 10 years of smoking cessation,
there is a 30-50% reduction in lung cancer mortality risk when compared to persistent smokers
(Fiore et al, 1996) and is helped by government campaigning as seen in Image 1. To help a
person achieve smoking cessation, the Agency for Healthcare Research and Quality (formerly
the Agency for Health Care Policy and Research [AHCPR]) developed a set of clinical
smoking-cessation guidelines for the benefit of both the patient and the health care provider

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(Fiore et al, 1996), including documenting the patient’s tobacco use and the offer of one or
more effective smoking cessation treatments (nicotine-replacement, social support, skills
training/problem solving etc.). Another method of prevention includes the moderating of
occupational exposure to lung carcinogens, such as chromium, arsenic, nickel and asbestos, as
when all considered together, attribute to 9-15% of all lung cancer (Alberg et al, 2007).
Image 1: Government campaign supporting smoking cessation (Parry, 2010)
There are two main classifications of lung cancer; non-small cell and small cell. Non-
small cell lung cancer accounts for approximately 85% of lung cancers and occurs in three
types; adenocarcinoma, squamous cell carcinoma and large cell carcinoma (CancerCare®,
2016). Small cell lung cancer accounts for the remaining 15% of lung cancer incidences, and
tend to grow more quickly than non-small cell tumours (CancerCare®). The most common
symptoms associated with lung cancer are coughing, shortness of breath, fatigue and blood
present in the sputum (CancerResearchUK, 2015c). Other symptoms can include weight loss,
recurrent infections such as bronchitis and pneumonia, and chest pain (American Cancer
Society, 2016). Lung cancer can also produce hormone-like substances which enter the
bloodstream, causing paraneoplastic syndromes in various tissues and organs such as
hypercalcemia (high blood calcium levels), blood clots, gynecomastia (excess breast growth in
men) and various nervous system problems (American Cancer Society, 2016).
Despite all this there is no national screening programming for lung cancer in the UK,
leading to most cases being discovered via x-ray, by which point the cancer is usually too
advanced for curative treatment (CancerResearchUK, 2015c). There are some attempts to
introduce a screening programme into the UK, such as the UK Lung Cancer Screening Trial
(UKLS), which aims to screen people most at risk (e.g. between the age of 50-75) using various

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clinical tests, the most promising being CT scanning, to help diagnose lung cancer earlier
(UKLS, 2012). There are some screening programmes in the US, however they are very
selective in who they screen and also use CT scanning to determine diagnosis (CDC, 2016).
The problem with the current focus on lung cancer screening is that it requires the use of CT
scanning, which exposes the patient to radiation, possibly increasing the risk of cancer
(Brenner, 2003). Cancer Research UK state that the essential criteria for a possible screening
programme is to be simple, quick, relatively inexpensive and not harmful (CancerResearchUK,
2015c), which the current possible screening programmes do not meet, causing harm through
radiation exposure or a possible allergic reaction to the dye used in the CT scan (NHS, 2016).
The discovery of a biomarker for lung cancer could save lives through the development of a
new diagnostic test, detecting lung cancer before it can be seen on a CT scan, whilst also
complying with the essential criteria for a screening programme.
1.2.2 COPD
Chronic Obstructive Pulmonary Disease (COPD) is a lung disease which interferes with
normal breathing via a persistent blockage of airflow. It causes 25000 deaths per year in the
UK and more than 3 million globally in 2012, approximately 6% of all deaths recorded
(W.H.O., 2015a), becoming the third most common cause of death in the world (Lozano et al,
2013). However, these numbers are not an accurate representation of how prevalent COPD is,
with an estimated 24 million people in the US suffering from the disease without even knowing
it (American Lung Association, 2016), pushing for a better diagnosis/screening programme
and more public awareness.
As with lung cancer, the leading cause of COPD is cigarette smoking (NIH, 2013a). As
many as 8 out of 10 COPD-related deaths are caused by smoking (US. Department of Health
and Human Services, 2014), accounting for approximately 5.4 million deaths in 2005 (W.H.O.
2016). There are also other risk factors for COPD, mainly prevalent in low-income countries
(W.H.O. 2016). Exposure to indoor air pollution, mainly caused by the use of biomass fuels
for cooking and heating, is the biggest risk factor in these countries due to inefficient resources
available, with approximately 3 billion people using these methods of heating (W.H.O. 2016).
Other risk factors include exposure to certain types of dust and chemicals at work (e.g. coal
and cadmium) and possibly urban air pollution (not conclusive) (NHS, 2014). The preventative
measures in place for COPD are the same as those for lung cancer, as they both have the same
risk factors.

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The poor airflow associated with COPD is the result of the contributions of two
conditions; emphysema (the breaking down of lung tissue) and obstructive bronchiolitis (small
airways disease) (Vestbo et al, 2013a), which create structural changes within the lungs, as
seen in Image 2. The main symptoms associated with COPD include breathlessness, abnormal
sputum and a chronic cough (W.H.O. 2015a). However, at first, COPD can present no
symptoms, or only mild ones, making early diagnosis difficult (NIH. 2013b).
Image 2: Structural changes in human lungs with COPD (Houghton, 2013)
There is a diagnostic test for COPD called spirometry, which is only considered for
someone over the age of 35-40 who presents with various symptoms and has had a history of
exposure to the risk factors (Vestbo et al, 2013b). Spirometry involves the use of a
bronchodilator (drug to open airways) and works by measuring the amount of airflow
obstruction present (Qaseem et al, 2011). To make a diagnosis, two measurements are made:
the forced expiratory volume in one second (FEV1) (greatest volume of air expelled in one
second), and the forced vital capacity (FVC) (greatest volume of air expelled in one full breath)
(Young & Vincent, 2010). Using these two measurements a FEV1/FVC ratio can be calculated
and compared against medical guidelines (usually a ratio lower than 70% in someone with
COPD-like symptoms) to determine whether or not they have the disease, however this can
lead to an over-diagnosis of COPD in elderly patients (Qaseem et al, 2011). The issue with
spirometry as a diagnostic tool is that using it on people who do not present symptoms of COPD

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has “evidence of uncertain effect, and therefore is currently not recommended” (Vestbo et al,
2013a). Due to this these is no early diagnostic method for people with COPD, therefore by the
time the disease is diagnosed, it is too advanced for curative treatment to be successful
(W.H.O., 2015a). Developing a diagnostic tool based on a biomarker would be highly
beneficial to COPD sufferers as it has the possibility to detect the disease before symptoms
have manifested, making treatment more successful. As with lung cancer, this could be
introduced as a national screening programme to reduce the deaths caused by COPD, as a large
amount of people with COPD are not diagnosed correctly (American Lung Association, 2016).
1.3 PREVOTELLA INTERMEDIA 17
1.3.1. THE PREVOTELLA GENUS
The Prevotella genus is a group of anaerobic gram-negative rod-shaped bacteria most
commonly found in association with periodontal diseases (Maeda et al, 1998). It is classified
among the group of ‘black pigmented bacteria’ due to the formation of smooth and shiny
colonies with black/grey colour when grown on a blood agar plate (Shah & Collins, 1990). The
original classification for these bacteria was Bacteroides melaninogenicus, until it was
reclassified and split into Prevotella melaninogenicus and Prevotella intermedia (Brook,
2015). The Prevotella genus is very versatile, having been found in various areas such as the
oral cavity, upper respiratory tract, urogenital tract (Eiring et al, 1998), rumen and human
faeces (Hayashi et al, 2007). Many species of Prevotella are potential/opportunistic pathogens
(Yunfeng et al, 2015) under a wide range of environments and are known to invade host tissues
(Nadkarni et al, 2012).
1.3.2 PREVOTELLA INTERMEDIA
Due to its isolation from lesions of patients, Prevotella intermedia has been found as a
putative periodontal pathogen, specifically in early periodontitis, advanced periodontitis, and
acute necrotizing ulcerative gingivitis (Haffajee & Socransky, 1994). It has also been found to
invade the human coronary artery endothelial and smooth muscle cells in vitro (Dorn et al,
1999) and has been found in atheromatous plaques (Haraszthy et al, 1998). A significant find
in relation to this study is that “P. intermedia plays a critical role in the complex
pathophysiology of lung disease in patients with cystic fibrosis” when in anaerobic sputum

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plugs (Ulrich et al, 2010). The results of this study could show that this situation is not only
limited to cystic fibrosis patients, but also people suffering from lung cancer and COPD.
1.3.3. PREVOTELLA INTERMEDIA 17
P. intermedia 17 is a strain of P. intermedia clinically isolated from a human
periodontal pocket (Fukushima et al, 1992). It is differentiated from the other strains of P.
intermedia (for example 27 and ATCC 25611) by examining the diameter of fimbriae (curlin
protein appendages carrying adhesins) present on its cell surface (Leung et al, 1989). P.
intermedia presents type C (8nm diameter) fimbriae, unlike other strains of this species (Dorn
et al, 1998). Dorn et al (1998) found that, in terms of the human oral epithelial cell line, P.
intermedia 17 has the ability to invade host cells whereas strain 27 and ATCC 25611 cannot,
and also possesses strong agglutinating activity for human erythrocytes and can bind to human
buccal epithelial cells more avidly than other strains. He further speculates that “the type C
fimbriae could promote invasion by providing a means for the bacteria to attach to the cell
surface” (Dorn et al, 1998). Fan et al (2006) further state that P. intermedia 17 possesses a cell
surface protein with a broad-spectrum extra-cellular-matrix binding ability, which probably
mediates its binding through adhesins. With P. intermedia 17’s ability to do this, it could be
possible that this strain can also invade the cells of human lungs through the epithelial layer
and extracellular matrix present on the alveoli. If this is shown to be true, it would be the first
time this strain has been found in the lungs, and further could present a pathological
relationship with lung cancer/COPD.
1.4 LUNG MICROBIOME RESEARCH
Lung microbiome research is a relatively new method of research in which the bacterial
contents of the human lung are analysed, mostly for the purpose of disease investigation. Many
factors can influence the environment in the lungs, such as oxygen, pH, hydrophobicity,
temperature, salinity, predators, nutrient scarcity and many more (Dickson et al, 2015), factors
which disease can alter very easily. The microbiome of the lungs is determined by three
ecological factors; “microbial immigration into the airways, elimination of microbes from the
airways and the relative reproduction rates of its community members, as determined by
regional growth conditions” (Dickson et al, 2015). During disease these three ecological factors
change, therefore changing the bacteria species present in the lungs. By examining the changes

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in bacteria species, it gives insight into the effects the disease is having on the lungs, and
possibly opens the door to new diagnostic tests and treatments being developed based on it.
Examples of successful lung microbiome projects include; the identification of a core set of
common bacteria found in the lungs of COPD patients (Erb-Downward et al, 2011), the
discovery that certain members of Staphylococcus and Streptococcus are linked to the
progression of idiopathic pulmonary fibrosis (Han et al, 2014), and the discovery that P.
intermedia plays a critical role in the pathophysiology of lung diseases in patients with cystic
fibrosis (Ulrich et al, 2010). Using the techniques set out from these papers and many more,
this study will analyse the microbiome of the lung to identify a bacteria species that possessed
a link to lung cancer or COPD.
1.5 PREVIOUS WORK
Thirty sputum samples were obtained for the MEDLUNG project (10 from healthy
patients, 10 from patients suffering with lung cancer, 10 from patients suffering from COPD)
along with the patients’ medical histories (all data was collected and treated in compliance with
ethical guidelines and confidentially). The genomic DNA was extracted from these samples
and used to create barcoded Illumina sequencing libraries for each individual samples. These
were subsequently paired-end sequences on an Illumina HiSeq2000 platform by Simon
Cameron as part of his PhD (Cameron, 2015). From this a de novo contig assembly was
performed by Tom Hitch (IBERS PhD student), which forms the starting point for this study.

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2. Materials and Methods
2.1 AIMS AND OBJECTIVES
The aims and objectives for this project were to analyse the de novo contig assembly,
provided by Tom Hitch (IBERS PhD student), of the DNA samples obtained by the
MEDLUNG project. The aim of this was to possibly find a bacteria species which possessed a
relationship with lung cancer or COPD, whether that be with its presence or its absence, to
possibly develop a biomarker in future research. The discovery of a successful biomarker for
these diseases could lead to the development of a new diagnostic test for lung cancer or COPD.
An example of how this could happen would be to develop a primer for a biomarker tagged
with fluorescent markers, therefore if this biomarker is present it would be visible under ultra
violet light, meaning that the patient has one of these diseases (depending on the nature of the
biomarker). This would help the global initiative for reducing the suffering from these diseases
by enabling early diagnosis before the symptoms manifest, making curative treatment more
available.
2.2 INITIAL ANALYSIS
For this part of the analysis the metagenome contig assembly produced by Tom Hitch
(IBERS PhD student) was used to discover a bacteria species present within the sputum
samples of the MEDLUNG collection. To conduct this research, the CLC Genomics
Workbench 8 software (CLC bio, 2016) was used.
2.2.1 LARGEST CONTIG ASSEMBLY
The first stage of the initial analysis involved arranging the metagenome contig
assembly by size, from largest number of base pairs (bp), to the smallest. Due to time
constrictions on the project, the 10 largest contigs were chosen to search through as these
represented the largest portion of the contig assembly whilst being within time constraints. The
10 largest contigs were saved as a separate sequence list, then saved as separate sequences to
allow for analysis.

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2.2.2 NCBI BLAST SEARCH
These individual sequences were then subject to a BLAST (Basic Local Alignment
Search Tool) (Altschul et al, 1990) function to identify their individual components by aligning
against reference genomes. For this study, the NCBI (National Centre for Biotechnology
Information) BLAST database was used due to its extensive collection of reference genomes
and genes, and it’s easy to use interface (NCBI, 2016). The individual sequences were run
through the NCBI nucleotide BLAST function, using the MegaBlast algorithm (default
parameters) (Morgulis et al, 2008) which is used for comparing a query sequence to a reference
sequence and is used for sequence identification and intra-species comparison (NCBI, 2015).
It was noticed that Prevotella intermedia 17 had hit 5 of the 10 largest contigs at relatively high
query cover levels, which was unusual as this strain of Prevotella intermedia had not yet been
found in the lungs. Therefore, it was decided to continue with this line of research for the
remainder of this project. Also the MegaBlast search results were saved for these 5 contigs to
reference later.
2.2.3 ALIGNMENT
To display the relation between the 5 largest contigs which hit P. intermedia 17 and the
reference genome itself, CLC genomics workbench 8 was used to create a visual alignment of
these contigs against the reference genome obtained from the NCBI genome database. To do
this the P. intermedia 17 reference genome was imported into CLC using standard import, and
then, using Toolbox > Molecular Biology Tools > Sequencing Data Analysis > Assemble
Sequences to Reference, was used as the reference genome for assembling each of the 5 contigs
to it to display the query cover data obtained from the search results of the MegaBlast. Using
the graphics function in CLC, these alignments were then exported for use in the results.
2.3 INDIVIDUAL SAMPLES
From the discovery of P. intermedia 17 in the metagenome contig assembly, the next
step was to use this reference genome to search through the individual patient samples, obtained
by MEDLUNG, to provide further evidence of the presence of this bacteria and to determine
whether this strain was linked to lung cancer/COPD. This was also performed in CLC genomics
workbench 8. The data consisted of 30 samples (labelled B, C or D depending on which

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collection of patients the samples was from, and numbered 2-11). Each sample consisted of 4
files, 2 lanes each having 2 reads (forward and backward).
2.3.1 IMPORT AND SAMPLING
To start this section of the research, the individual patient data had to be imported into
CLC. It was in [fastq] format, therefore it was imported using the Illumina import function,
ensuring to select the paired read function to merge the 2 read files into one. After this there
was 60 files, 30 samples containing 2 lanes of data each. Then, due to time and computer
restraints, it was decided to sample 500000 reads from each file (according to sample size
calculation, on average needed to exceed ~2000 reads to be significant). To do this in CLC:
Toolbox > NGS Core Tools > Sample Reads, then specified 500000 reads to be sampled.
2.3.2 DE NOVO ASSEMBLY
To merge the 2 ‘lane’ files for each sample and to make further analysis easier, it was
decided to perform de novo assemblies for each sample. In CLC, with the 2 files of the sample
selected, Toolbox > De Novo Sequencing > De Novo Assembly. Default parameters were used
with the exception of mapping the contigs back to the contigs, due to time constraints.
2.3.3 READ MAPPING
Once the de novo assembly had completed, the next stage was to map the assemblies
to the P. intermedia 17 reference genome to identify any reads from the bacteria genome. To
do this the Map Reads to Reference function (default parameters) was used, located in CLC >
Toolbox > NGS Core Tools > Map Reads to Reference, and the P. intermedia 17 genome
selected as the reference. Once all the mapping was completed for a full set of samples i.e. B,
a track list was created of all the mapping graphs, and the maximum graph coverage set at 3
across all sample sets for the purpose of comparison later on. These track lists were then
exported using the graphics function in CLC for comparison later. Furthermore, the number of
reads for each sample set, separated into each individual sample, were plotted as graphs for
easy interpretation as results. It was decided that the track lists of mapping graphs were to be
included in the appendix as they were summarised by the graphs formulated.

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3. Results
3.1 NCBI BLAST RESULTS
The first set of results show the NCBI MegaBlast search results from the initial analysis
of research, indicating the presence of P. intermedia 17 in 5 out of 10 of the largest contigs
(nodes). The full NCBI MegaBlast results are available in appendix (1).
NODE ID Description Max
Score
Total
Score
Query
cover
E
value
Identity Accession
NODE_54069 P. intermedia 17
chromosome II
7491 24979 90% 0.0 83% CP003503.1
chromosome I
3517 10209 40% 0.0 81% CP003502.1
chromosome II
6259 18001 77% 0.0 83% CP003503.1
chromosome II
5068 18113 63% 0.0 81% CP003503.1
chromosome II
2372 5668 33% 0.0 81% CP003503.1
Table 1: NCBI MegaBlast results from inputting the 10 largest contigs (nodes) from the initial
analysis. Only the 5 contigs that hit P. intermedia 17 are displayed, including the chromosome
they hit, the max score, total score, query cover, E value, identity and accession ID.
The 5 nodes input into MegaBlast all hit at above 80% identity, with over 33% query
cover, with an E value of 0. Therefore P. intermedia 17 was significantly found within the 5
largest contigs of initial analysis, validating the search for this bacteria within the individual
samples.
To confirm the presence of this bacteria in the contigs, the P. intermedia reference
genome of the corresponding chromosome found in the MegaBlast results was aligned with
the contigs that returned P. intermedia 17 hits.

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3.2 NODE ALIGNMENT
Figure 1: The alignment of the 5 contigs with the P. intermedia 17 reference genome
corresponding to the chromosomes which hit each individual node. The pink areas of the
coverage graph display the areas which align with the node.
As seen in Figure 1, there is indeed the presence of P. intermedia 17 within the
metagenome contig assembly and therefore present in the lungs of the individual patients.
Some nodes contain more P. intermedia 17 genome than others, with the most conserved being
NODE_54069, containing a 90% query cover and 83% identity to the bacteria genome, and the

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least conserved being NODE_18381, containing a 33% query cover and 81% identity,
displayed in the visual alignment in Figure 1. These results do indeed prove the presence of P.
intermedia 17 within the lungs.
3.3 MAPPING OF INDIVIDUAL SAMPLES
The next stage of the analysis revolved around mapping the P. intermedia 17 genome
to the individual patient data to further support the hypothesis that P. intermedia 17 is present
within the lung and to distinguish any relationship between P. intermedia 17 and lung
cancer/COPD. The full mapping graphs from this part of the analysis are available in the
appendix (2), with individual patient mapping data available in appendix (3).
First looked at was the total number of mapped reads across the three patient groups;
Control, Lung Cancer, and COPD.
Figure 2: The total number of mapped P. intermedia 17 reads across the three patient groups;
Control, Lung Cancer, and COPD. The raw data values are displayed above the data bars.
1622
226
6
0
200
400
600
800
1000
1200
1400
1600
1800
Control Lung Cancer COPD
Numberofmappedreads
Patient Group
Total Number of Mapped P. intermedia 17 Reads

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From Figure 2 it can be said that the highest number of mapped P. intermedia 17 reads
were present in the control group (healthy patients), with the number decreasing significantly
in lung cancer patients, and even further in COPD patients.
To ensure that the trend displayed in Figure 2 was not due to a varying number of
reads/bases in the sample data, the average percentage of mapped reads across the entire
individual patient group was looked at to see if the trend appeared here also.
Figure 3: The average percentage of reads from the individual patient groups that mapped to
P. intermedia 17. The actual percentage is displayed to the left of each marker.
Figure 3 appears to nearly mirror the trend shown in Figure 2 that the amount of P.
intermedia 17 within human sputum is at its highest in healthy people (control group),
decreasing significantly in lung cancer patients, and even further in COPD patients.
It was decided that it would also be useful to look at the distribution of reads among the
two chromosomes in the P. intermedia 17 genome to determine which one is more prevalent
among the mapped reads.
0.756
0.289
0.022
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Average%ofmappedreads
Patient Group
Average % of Reads Mapped to P. intermedia 17

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Figure 4: The distribution of mapped reads in the patient groups. Chromosome 1 is displayed
by the solid black data bars and chromosome 2 is represented by the patterned data bars. The
actual value of number of reads is displayed above the data bars. For COPD (due to the low
numbers) the data bars cannot be seen – 2 represents chromosome 1 and 5 represents
chromosome 2.
From Figure 4 it can be seen that chromosome 2 of P. intermedia 17 appears
significantly more among the individual patient data than chromosome 1, especially in the
control group, where chromosome 2 appears approximately over 4 times more than
chromosome 1. However, this could be due to chromosome 2 being approximately 4 times
longer than chromosome 1. On the other hand, there is also the fact that chromosome 2
appeared in 4 out of the 5 largest contigs, as opposed to chromosome 1 hitting 1 contig, which
could not be affected by sequence length.
299
39
2
1323
187
5
0
200
400
600
800
1000
1200
1400
Numberofmappedreads
Patient Group
Distribution of Mapped Reads in the Patient Groups
Chromosome 1 Chromosome 2

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4. Discussion
4.1 DISCUSSION OF RESULTS
4.1.1 NCBI MEGABLAST AND NODE ALIGNMENT
From the NCBI MegaBlast and the following node alignment it is shown that P.
intermedia 17 is definitely present within the human lung. This is the first time that this strain
of P. intermedia has been located in the human lung and opens the door for various further
studies, such as P. intermedia 17’s molecular relationship with lung cancer/COPD and whether
the decrease in the bacterium is directly caused by the presence of the disease. P. intermedia
has already been found in the human lung in relation to cystic fibrosis (Ulrich et al, 2010) so
it’s not entirely unheard of, however strain 17 has only now been found to be present there
also.
4.1.2 INDIVIDUAL SAMPLE DATA
The individual sample data provides many conclusions towards this study.
Firstly, it further confirms the presence of P. intermedia 17 in the human lung along with the
NCBI MegaBlast and node alignment results. In addition to this these results also show a very
interesting trend between the control group, lung cancer group and COPD group. Many species
of Prevotella are potential/opportunistic pathogens (Yunfeng et al, 2015) under a wide range
of environments and are known to invade host tissues (Nadkarni et al, 2012). From this it would
be expected that if a link was found between P. intermedia 17 and lung cancer/COPD, the trend
displayed would show that the level of bacteria would increase in lung cancer/COPD patients,
as P. intermedia 17 would be a pathogen linked to the diseases (more bacteria presence = higher
chance of disease developing). However, looking at the results of the individual sample data
analysis, the trend is actually opposite in this case. The highest level of P. intermedia 17 was
present in the control group (healthy patients) and decreased by approximately 85% in the lung
cancer patient group, and decreased a further ~15% in the COPD patient group. To ensure this
was not due to variation in sample size between patient groups the average % of mapped reads
out of the whole sample group data was calculated, and the trend from this nearly mirrored the
trend shown in the total mapped reads graph. These trends show that the presence of P.
intermedia decreases when lung cancer or COPD is present in the patient. This could be due to
many factors, direct or indirect. The manifestation of these diseases could directly cause the
death of the P. intermedia 17 cells, for example by phagocytosis or toxin/hormone release. On

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the other hand, they could destroy the P. intermedia cells indirectly, through possibly
increasing the growth/presence of other bacteria species which compete with the P. intermedia,
or through changing the environment in the lungs making it inhabitable for the bacteria. This
would be a question for further study, and could lead to a wider knowledge about the mechanics
of lung cancer and/or COPD in the human lung.
The distribution of mapped reads across the P. intermedia 17 genome was also looked
at to see if lung cancer/COPD affected this. Chromosome 1 of P. intermedia 17 contains
579647 base pairs, and chromosome 2 contains 2119790 base pairs, approximately four times
more. This is reflected in the distribution of mapped reads in the control patient group, with
chromosome 1 having 299 mapped reads and chromosome 2 having 1323 mapped reads. This
is relatively maintained in the other two patient groups with some allowance for standard errors,
therefore the diseases do not affect the viability of chromosome 1 or 2 within the human lung.
4.2 LIMITATIONS & IMPLICATIONS
Time and computer memory/processor deficiency was a large limitation that was
encountered during the research, leading to the sampling of 500000 reads from the individual
data. For example, conducting a de novo assembly on the original data was taking up to 18
hours per file, with some attempts aborting due to disk space and computer memory deficiency,
therefore taking a lot longer than was expected due to the size of the files. To correct this when
using this data in the future, a computer with a very large amount of memory and an excellent
processor would be required to complete genomic analysis of the full individual sample data.
4.3 FURTHER STUDY
There are many various routes that could be followed when conducting further research
from this analysis. An example would be to calculate a minimum threshold of P. intermedia
17 presence within the lung for the two diseases i.e. if a patient falls below this threshold then
further investigation would be required or a diagnosis achieved. For this to work a diagnostic
test would have to be developed. This could be achieved, for example, by developing a
biomarker for P. intermedia 17 and tagging it with a fluorescent marker. When, for example,
mixed with patient’s sputum, the biomarker with fluorescent tag would bind to any P.

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intermedia 17 present, and be visible under an ultra-violet light. The less fluorescence visible,
the higher the patient’s chance of having lung cancer or COPD.
Another route of further study could be researching how the lung cancer/COPD cells
interact with the P. intermedia 17 and cause its reduced prevalence in affected lungs. The
manifestation of these diseases could directly cause the death of the P. intermedia 17 cells, for
example by phagocytosis or toxin/hormone release. On the other hand, they could destroy the
P. intermedia cells indirectly, through possibly increasing the growth/presence of other bacteria
species which compete with the P. intermedia, or through changing the environment in the
lungs making it inhabitable for the bacteria.
Other P. intermedia strains were found in the NCBI MegaBlast results, so researching
whether these appear in the human lung could be another route to follow. Also, investigating
whether P. intermedia 17 is related to any other diseases that predominantly reside in the lungs,
or maybe even whether it has relationships with other types of cancer. Additionally, possibly
investigating whether it has any adverse effects upon the disease itself could be a promising
option.
4.4 CONCLUSIONS
This study has not only discovered the presence of Prevotella intermedia 17 in the lungs
for the first time, it has also discovered that it indeed P. intermedia 17 does have a relationship
with both lung cancer and chronic obstructive pulmonary disorder in humans. This could lead
to the development of a new diagnostic test for lung cancer or COPD, or possibly further the
knowledge surrounding these diseases and how they manifest in the human lung. Developing
a new diagnostic test and providing early screening for patients is vitally important for lung
cancer and COPD, as it would have the capacity to save countless lives by giving more people
access to curative treatment at an early stage where it can be effective.

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6. Word Count
The final word count for this study, excluding the final list of references,
acknowledgements, tables, table of contents, and figure/image legends is:
6692

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7. List of Figures/Tables/Images
Table 1: NCBI MegaBlast Search Results for the 5 largest contigs in relation to P.
intermedia 17
Figure 1: Node alignment of the 5 contigs with the P. intermedia 17 reference
genome
Figure 2: Bar chart displaying the total number of mapped reads found in each of
the patient groups
Figure 3: Line chart displaying the average percentage of mapped reads from the
total genomic data in the patient groups
Figure 4: Bar chart displaying the distribution of mapped reads across the two
chromosomes of the P. intermedia 17 genome for each of the patient
groups
Image 1: Government campaign supporting smoking cessation
Image 2: Structural changes in human lungs with COPD

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8. Appendix
APPENDIX 1 – NCBI MEGABLAST RESULTS (FULL)
NODE_54069
Description Max
Score
Total
Score
Query
Cover
E
value
Identity Accession
Prevotella intermedia DNA.
Complete genome. Strain:
OMA14. Chromosome 1
7413 24943 91% 0.0 83% AP014597.1
Chromosome 2. Complete
genome. Strain: 17-2
7491 24979 90% 0.0 83% AP014925.1
Prevotella intermedia 17
chromosome II. Complete
sequence.
7491 24979 90% 0.0 83% CP003503.1
NODE_28947
Description Max
Score
Total
Score
Query
Cover
E
value
Identity Accession
Prevotella intermedia DNA,
complete genome. Strain:
OMA14. Chromosome II
4071 9268 32% 0.0 83% AP014598.1
chromosome 1. Complete
3517 10209 40% 0.0 81% AP014926.1
chromosome I. Complete
sequence
3517 10209 40% 0.0 81% CP003502.1
OMA14. Chromosome I.
122 122 0% 3e-22 94% AP014597.1
genome. Strain 17-2
121 121 0% 1e-21 94% AP014925.1

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NODE_13609
Description Max
Score
Total
Score
Query
Cover
E
value
Identity Accession
sequence
6259 18001 77% 0.0 83% CP003503.1
6255 17997 77% 0.0 83% AP014925.1
OMA14. Chromosome I
6325 15926 66% 0.0 83% AP014597.1
NODE_12098
Description Max
Score
Total
Score
Query
Cover
E
value
Identity Accession
OMA14. Chromosome I
5265 16356 54% 0.0 82% AP014597.1
5068 18108 63% 0.0 81% AP014952.1
sequence
5068 18113 63% 0.0 81% CP003503.1
NODE_18381
Description Max
Score
Total
Score
Query
Cover
E
value
Identity Accession
2372 5663 33% 0.0 81% AP014925.1
sequence
2372 5668 33% 0.0 81% CP003503.1
OMA14. Chromosome I
2287 3579 20% 0.0 80% AP014597.1

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APPENDIX 2 – MAPPING GRAPHS OF INDIVIDUAL SAMPLE DATA
Blue areas represent areas matching that of the P. intermedia 17 reference genome
SAMPLE B – CHROMOSOME 1

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N.B. B11 is omitted due to no reads being mapped in either chromosome
SAMPLE B – CHROMOSOME 2

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N.B. B11 is omitted due to no reads mapping on either chromosome

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SAMPLE C – CHROMOSOME 1
N.B. C2, 3, 8 are omitted due to no reads mapping on either chromosome

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SAMPLE C – CHROMOSOME 2
N.B. C2, 3, 8 are omitted due to no reads mapping to either chromosome

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SAMPLE D – CHROMOSOME 1
N.B. D2, 4, 8, 10, 11 omitted due to no reads mapped for either chromosome.

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SAMPLE D – CHROMOSOME 2
N.B. D2, 4, 8, 10, 11 omitted due to no reads mapped for either chromosome.

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APPENDIX 3 – INDIVIDUAL PATIENT MAPPING DATA
SAMPLE B
B2
B3
B4
B5

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B10
SAMPLE C
C4
C5
C6

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SAMPLE D
D3
D5
D6
D7

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