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The Effective Contribution of Viral Respiratory Infection to Wheezing Illness
in Hospitalised Young Children
Chisha Teza Sikazwe
BSc (Biomedical Sciences and Molecular biology)
Master of Infectious Diseases
This thesis is presented for the degree of Doctor of Philosophy in Microbiology of
The University of Western Australia
School of Biomedical Sciences
2018
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Thesis declaration
I, Chisha Teza Sikazwe, certify that:
This thesis has been substantially accomplished during enrolment in the degree.
This thesis does not contain material which has been accepted for the award of any
other degree or diploma in my name, in any university or other tertiary institution.
No part of this work will, in the future, be used in a submission in my name, for an y
other degree or diploma in any university or other tertiary institution without the
prior approval of The University of Western Australia and where applicable, any
partner institution responsible for the joint-award of this degree.
This thesis does not contain any material previously published or written by another
person, except where due reference has been made in the text.
The work(s) are not in any way a violation or infringement of any copyright,
trademark, patent, or other rights whatsoever of any person.
Technical assistance was kindly provided by Tom Chung for Cytokine multiplex bead
experiment that is described in Chapter seven of this thesis.
This thesis contains only sole-authored work, some of which has been published
and/or prepared for publication under sole authorship.
XChisha Teza Sikazwe
Date: 25.06.2018
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Abstract
Respiratory tract infections are a leading cause of morbidity and mortality worldwide. Our
emerging understanding of the importance of acute lower respiratory viral infe ction in
early childhood predisposing to chronic inflammatory respiratory disease, coupled with the
complex interplay between virus and host underscores the need for investigations to
understand the clinical manifestation and the interplay between the magnitude of
infection and the corresponding host response. Rhinoviruses, specifically RV -C have been
identified as an important contributor to wheezing illness in paediatric medicine. Though,
little is known about its contribution to infection. Thus, this the sis aims to improve
understanding of the underlying pathophysiological mechanisms in the context of RV -C
wheezing illness through the development of a reliable method of quantifying RV -C load
and characterisation of the host response following infection.
RV-C was the most common virus detected in preschool aged children hospitalised with an
acute asthma exacerbation (Chapter 6). This was also apparent in preschool aged non-
asthmatic children hospitalised with a wheezing illness (chapter 7). Interestingly, i n young
infants under the age of two years hospitalised (Chapter 5) with a wheezing illness, RSV
rather than RV-C was found to be the most common virus detected. Viral load studies
revealed that both RSV and RV-C replicate to significantly higher levels in hospitalised
patients than in non-respiratory disease controls. In asthmatics and non-asthmatic
patients, RV-C induced respiratory wheeze appears to be driven by a Th2 response that is
independent of viral load. The magnitude of the host immune response is more apparent
in children with asthma compared to those without asthma. In addition, RV-C infection in
pre-school aged children with wheeze appears to induce a selective increase in neutrophil
chemokines and a significant increase in neutrophil numbers with a simultaneous increase
in illness severity.
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The findings in this thesis extend the existing knowledge on RV-C mediated wheezing
illness and demonstrate that magnitude of replication does not significantly contribute to
disease outcome. Conversely, it appears that the host immune response for which in part
is driven by a pro-inflammatory, neutrophilic inflammation may play a substantial role in
severity of disease. Host directed therapy targeting neutrophil pathways may prove to be
beneficial in young children infected with RV-C.
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Table of contents
Thesis declaration ......................................................................................................... iii
Abstract........................................................................................................................ iv
List of Figures................................................................................................................. x
List of Tables ................................................................................................................ xii
Acknowledgments....................................................................................................... xiii
AUTHORSHIP DECLARATION: SOLE AUTHOR PUBLICATIONS .................................... xv
Peer reviewed papers and Conference presentations .................................................... xvi
List of Abbreviations................................................................................................... xvii
1 Literature review .................................................................................................... 2
1.1 Introduction ............................................................................................ 3
1.1.1 Respiratory syncytial virus ........................................................................ 6
1.1.2 Rhinoviruses ............................................................................................ 6
1.1.3 Human Metapneumovirus ........................................................................ 8
1.1.4 Influenza virus ......................................................................................... 8
1.1.5 Parainfluenza virus ................................................................................... 9
1.1.6 Adenovirus ............................................................................................ 10
1.1.7 Human Coronaviruses ............................................................................ 10
1.2 Laboratory Diagnosis of Respiratory Infections ................................................... 11
1.2.1 Upper and Lower airway samples............................................................ 12
1.2.2 Virus isolation by cell culture .................................................................. 13
1.2.3 Direct detection of respiratory viruses by immunofluorescence ................ 14
1.2.4 Nucleic Acid Tests .................................................................................. 15
1.3 Viral kinetics of acute respiratory tract infections................................................ 19
1.4 Viral respiratory infection and asthma................................................................ 20
1.5 Innate immune response to viral respiratory infection ........................................ 23
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1.6 Aims of Project ................................................................................................. 26
2 Materials and Methods ......................................................................................... 27
2.1 Sample collection .............................................................................................. 27
2.2 Nucleic acid extraction and viral detection.......................................................... 28
2.3 Design of primers and probes ............................................................................ 29
2.4 Production and quantification of transcribed RNA standards ............................... 31
2.5 Quantitative Real time PCR (Viral load)............................................................... 32
2.6 Digital Droplet PCR ............................................................................................ 33
2.7 PCR reagents .................................................................................................... 34
2.8 Gel Electrophoresis ........................................................................................... 34
2.9 Thermocyclers .................................................................................................. 34
2.10 Computational analysis ..................................................................................... 35
2.11 Multiplex immunoassay..................................................................................... 35
2.12 Quality Control ................................................................................................. 36
2.13 Statistical Analysis ............................................................................................. 36
2.14 Ethics Approval ................................................................................................. 36
3 The design and development of quantitative detection assays for the common
causative viral pathogens of acute lower respiratory tract infection ................................ 38
3.1 Introduction ..................................................................................................... 39
3.2 Samples............................................................................................................ 41
3.3 Results ............................................................................................................. 41
3.4 Discussion......................................................................................................... 55
4 The development of a reliable PCR assay to measure RV-C load in clinical samples ... 61
4.1 Introduction ..................................................................................................... 62
4.2 Samples............................................................................................................ 64
4.3 Results ............................................................................................................. 66
4.4 Discussion......................................................................................................... 75
5. The Quantitative Detection of Respiratory Syncytial Virus in Hospitalized Young South
African Children ........................................................................................................... 78
5.1 Introduction ..................................................................................................... 79
5.2 Samples............................................................................................................ 81
5.3 Results ............................................................................................................. 82
5.3.1 Baseline characteristics .......................................................................... 82
5.3.2 RSV infection and clinical outcome .......................................................... 84
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5.3.3 RSV disease and HIV infection ................................................................. 87
5.3.4 RSV load .................................................................................................... 88
5.4 Discussion......................................................................................................... 91
6 Determinants of acute asthma exacerbation severity following RV-C infection ......... 95
6.1 Introduction ..................................................................................................... 96
6.2 Samples............................................................................................................ 97
6.3 Results ............................................................................................................. 99
6.3.1 Study participants characteristics ............................................................ 99
6.3.2 Virus Detection ...................................................................................... 99
6.3.3 RV-C load..............................................................................................101
6.3.4 Surrogate markers of inflammation........................................................104
6.3.5 Performance of RV-C load, neutrophils and eosinophils in predicting the
severity AAE ........................................................................................................108
6.4 Discussion........................................................................................................109
7. Cytokine profiles in nasal secretions of patients hospitalised with Rhinovirus Species C
associated respiratory wheeze .....................................................................................115
7.1 Introduction ....................................................................................................116
7.2 Samples...........................................................................................................117
7.3 Results ............................................................................................................119
7.3.1 Virus Detections....................................................................................119
7.3.2 Nasal cytokine profiles of wheezing patients following RV-C infection ......124
7.3.3 Relationships between cytokines, RV-C load and clinical outcomes ..........132
7.4 Discussion........................................................................................................135
8. General Discussion and Conclusions......................................................................142
8.1 Introduction .........................................................................................143
8.2 Epidemiology of respiratory viruses in young children under the age of five
years 145
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8.3 Reliable methods of accurately determining viral load in RV-C infected
patients 148
8.4 Viral Determinants of severity of RV-C induced wheezing illness ..............150
8.6 Conclusion .......................................................................................................154
Bibliography ...............................................................................................................156
Appendices .................................................................................................................176
Appendix 1 ..........................................................................................................176
Appendix 2 .................................................................................................................181
Published work completed during PhD .........................................................................181
x
List of Figures
Figure 4.3-1 A BioEdit sequence alignment of primer and probe regions that were targeted
by assays one to four. Sequences of forward primer region (left box), probe region (center
box) and reverse primer region (right box). Identical bases at the same position are
represented by dots whereas capitalized bases indicate mismatches between sequences.
................................................................................................................................... 66
Figure 4.3-2 the algorithm for the determination of RV-C viral load in clinical samples ..... 72
Figure 4.3-3: Box plots of RV-C load in samples from young children presenting to the
Emergency Department with acute wheeze. .................................................................. 74
Figure 5.3-1: Viruses detected in NPAs collected from ALRI cases and NRD controls. RV
(RV-A, RV-B, RV-C), HCoV (OC43, 229E, HKU-1, NL63), HPIV (PIV I-IV), IFV (A/H1N1, A/H3N2,
B and C) ....................................................................................................................... 84
Figure 5.3-2: The distribution of RSV positive and RSV-negative ALRI cases by age. RSV
disease was more prevalent in children within their first year of life. Peak hospitalization
rate was observed in the 0-2 month age group. ............................................................ 86
Figure 5.3-3: Distribution of respiratory viruses detected in HIV infected ALRI patients .... 87
Figure 5.3-4: A box plot comparing RSV load between ALRI cases and NRD controls......... 88
Figure 5.3-5 A box plot comparing RSV load by subtype and clinical diagnosis.................. 89
Figure 5.3-6: A box plot comparing RSV loads in ALRI cases with a viral co-infection [n=12;
RSV with either hAdV (75%, n=9), hCoV (17%, n=2) or RV (8%, n=1)] versus sole RSV
infection (n=15). .......................................................................................................... 90
Figure 6.3-1 A bar graph comparing the frequency (%) of viruses detected in cases and
controls. Rhinovirus-C (RV-C), Rhinovirus-A (RV-A), parainfluenza virus (PIV), respiratory
syncytial virus (RSV), human adenovirus (hAdV), Influenza viruses (IFV), human corona
virus (hCoV) Rhinovirus-B (RV-B) and human metapneumovirus (hMPV) ........................100
Fig 6.3-2: Box plot summarising RV-C load in children hospitalised with an acute asthma
exacerbation (cases) and otherwise healthy individuals with a non-respiratory disease
(controls). Median RV-C load of AAE cases was 2.6 log10 copies/mL higher than that of the
non-respiratory disease control group . ........................................................................102
Figure 6.3-3: A boxplot of RV-C loads for cases(n=21) stratified by disease severity and
controls (n=2). Groups were compared using the Mann-Whitney U test,. median viral load
between the two severity groups did not differ significantly. .........................................103
Figure 6.3-4 Surrogate markers of asthma exacerbation in acute samples from patients
infected with RV-C stratified by illness severity. A) Absolute neutrophil peripheral blood
counts B) absolute eosinophils peripheral blood counts C) Total serum IgE levels counts.
Mann- Whitney U or Kruksall-Wallis test were used for testing on the apporpriate group of
subjects. All data are expressed as box and whisker plots. *:p=0.05 (severe vs non-severe)
** p<0.001 (all AAE cases vs NRD controls) ...................................................................105
Figure 6.3-6 Receiver operator characteristic curves (ROC) of markers of AAE severity. A)
Absolute neutrophil count and b) RV-C load, eosinophil count and serum IgE. ................108
Figure 7.3-1: Virus detection rates from samples of children hospitalised with respiratory
wheeze. RV-rhinoviruses, respiratory syncytial virus (RSV), adenovirus (ADV), human
parainfluenza virus (HPIV), Influenza viruses (IFV) and HBoV (human bocavirus) .............119
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Figure 7.3-2: RV detection rates stratified by species. RV-C was the predominant species
detected in children hospitalised with respiratory wheeze ............................................120
Figure 7.3-3- A comparison of virus detection rates between patients with classified with
asthma compared to those not classified with asthma. .................................................121
Figure 7.3-4: Area under the curve analysis of RV-C load to predict hospitalisation. RV-C
load was poor predictor of hospitalisation with an AUC of 0.5 (95%CI, 0.24-0.74) ...........122
Figure 7.3-5 Levels of IL-4 (a) and IL-13 (b) in nasal secretions of non-respiratory disease
controls, RV-C infected patients with asthma and without asthma. IL-4 and IL-13 were both
significantly elevated in the asthmatic group but IL-13 levels did not differ significantly in
the non asthmatic group compared to controls.* p<0.05. ..............................................125
Figure 7.3-6 Levels of IL-12 (a) and IL-2 (b) in nasal secretions of non-respiratory disease
controls, RV-C infected patients with asthma and without asthma. Levels of IL-2 and IL-12
did not significantly differ when each group (asthmatics and non-asthmatics patients) were
individually compared to controls. ...............................................................................126
Figure 7.3-7 Levels of Interferon (IFN)-γ (a), IFN-λ (b), IFN-α (c), in nasal secretions of
controls, hospitalised RV-C infected patients with asthma and without asthma. IFN-γ was
significantly attenuated in both patient groups compared to controls. The l evels of IFN- λ,
and IFN-α were not significantly different in either patient group compared to controls.
*p<0.05. .....................................................................................................................127
Figure 7.3-8 Levels of IL-1β (a) and IL-6 (b) in nasal secretions of controls, hospitalised RV-C
infected patients with asthma and without asthma. IL-1β was significantly elevated in the
asthma group but not in the non-asthma group compared to healthy controls.*p<0.05. .128
Figure 7.3-9 Levels of IL-8 (a) and IP-10 (b) in nasal secretions of non-respiratory disease
controls, RV-C infected patients with asthma and without asthma. IL-8 and IP-10 were both
significanlty elevated both patient groups comapred to controls.* p<0.05, **p<0.001 ...129
Figure 7.3-10 Levels of IL-10 (a) and IL-17 (b) in nasal secretions of non-respiratory disease
controls, RV-C infected patients with asthma and without asthma. IL-10 and IL-17 were
only significantly elevated in the asthma patient group compared to controls.* p<0.05 ..130
xii
List of Tables Table 1-1: Seasonal and clinical profiles of the commonly detected viruses associated with
acute respiratory tract infection....................................................................................... i
Table 4.3-1 A comparison of RNA transcript concentration and Cq values for the different
RV-C assays.................................................................................................................. 68
Table 4.3-2 the performance of the individual PCR assays for the detection of matched RV -
C RNA transcript........................................................................................................... 69
Table 4.3-3 Intra and Inter assay variability of the four RV-C qRT-PCR assays (Assay 1-4) .. 70
Table 4.3-4 Variation in calculated copy number yield (%) of transcripts 1-4 compared to
the number of probe mismatches ................................................................................. 71
Table 5.3-1 Demographic and clinical details of study participants .................................. 83
Table 6.3-1 Baseline characteristics of children with acute asthma exacerbation (AAE) and
controls ....................................................................................................................... 99
Table 6.3-2: A statistical summary of the risk of being diagnosed with acute asthma
exacerbation follwoing respiratory virus detection........................................................101
Table 7.3-1 Summary of clinical and demographic data of hospitalised children with RV -C
respiratory wheeze .....................................................................................................123
Table 7.3-2 nasal cytokine levels of healthy non-respiratory disease controls, RV-C
infected patients with asthma and without asthma. ....................................................130
Table 7.3-3: An illustration of the relationship between RV-C load and inflammatory
mediator production in the nasal secretions of children with asthma .............................133
Table 7.3-4: An illustration of the relationship between RV-C load and inflammatory
mediator production in the nasal secretions of children without asthma ........................133
Table 7.3-5: Association between cytokine production and hospitalisation of children
hospitalised following RV-C infection............................................................................134
Table 0-1 The performance of the individual PCR assays for the detection of matched RV -C
RNA transcript ............................................................................................................176
Table 0-2 A comparison of RNA transcript concentration and Cq values for the different RV-
C assays ......................................................................................................................177
Table 0-3 Intra and Inter assay variability of the four RV-C qRT-PCR assays (Assay 1-4) ....178
Table 0-4 The RV-C load determinations for patients enrolled in the PREVIEW study. Clinical
samples were tested in triplicate and mean viral load calculated. ..................................179
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Acknowledgments
First and foremost, I want to thank Prof. David Smith, A/Prof. Allison Imrie, Dr. Glenys
Chidlow and Dr. Gerald Harnett. It has been an honour to be your Ph.D. student. You all
have taught me, both consciously and unconsciously, how good research is conducted. I
appreciate all your contributions of time, ideas, and funding to make my Ph.D. experience
productive and stimulating. The joy and enthusiasm you all had for this research was
contagious and motivational for me, even during tough times. I am also thankful for the
excellent example you have provided as successful leaders in your respective fields. Special
thanks to Robyn Thompson for her countless efforts to ensure I had access to everything
(conferences, meetings and training courses) and everyone I needed to accomplish my
objectives.
I would like to thank all the members of the PathWest Molecular Diagnostics Laboratory
for accommodating me into your busy laboratory. You have been a source of friendships as
well as good advice. Many thanks to our collaborators at the Telethon Kids Institute,
especially Professor Peter Le Souef, Dr. Ingrid Laing and Dr Kim Khoo. I would also like to
thank Dr. Abha Chopra at the Institute of Immunology and Infectious diseases for providing
access to their digital PCR instrument.
Last but not the least, I want to thank my partner Emma, my family and my closest friend’s
you guys have been amazing during this time. Mum you have been my biggest supporter
from the get go. Dad thank you for your wise words, my brothers Kapembwa and
Kamando, you are the best brothers and friends one could ever ask for. Jarrad, thank you
for your friendship during this phase it was one where we learnt a lot from each other and
I will forever cherish the times spent at coffee. My dearest Emma, thank you for being so
patient and supportive during this time.
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I gratefully acknowledge the funding sources that made my Ph.D. work possible. I was
funded by University of Western Australia Postgraduate award and University of Western
Australia Top-up Scholarship. Material, consumables and software were made available by
PathWest Laboratory Medicine WA. This research was supported by an Australian
Government Research Training Program (RTP) Scholarship.
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AUTHORSHIP DECLARATION: SOLE AUTHOR PUBLICATIONS
This thesis contains the following sole-authored work that has been published and/or prepared for publication.
Deta ils of the work: Comparison of Droplet Digital RT-PCR to qPCR for the quantitative detection of Respiratory
Syncytia l Virus Chisha T. Sikazwe, Angela Fonceca, Avram Levy, Glenys R. Chidlow, Al lison Imrie, Mark Everard,
David W. Smith. RSV16: 10th International Respiratory syncytial vi rus symposium, Sept 2016, Patagonia,
Argentina. Location in thesis: Chapter Three
Deta ils of the work: Reliable quantification of rhinovirus species C using real-time PCR. Sikazwe CT, Chidlow GR,
Imrie A, Smith DW. J Vi rol Methods. 2016 May 20;235:65-72. doi :10.1016/j.jvi romet.2016.05.014. [Epub ahead
of print] PMID: 27216896. Location in thesis: Chapter Four
Deta ils of the work: The Quantitative Detection of Respiratory Syncytial Virus in Hospitalized Young South
African Children Chisha T. Sikazwe, Al icia A. Annamalay, Glenys R. Chidlow, Salome Abbott, Siew -Kim Khoo,
Joelene Bizzintino, Robin Green, Allison Imrie, Peter LeSouëf, David W. Smith. Submitted to Influenza and other
Respiratory vi ruses. Location in thesis: Chapter Five
Deta ils of the work: Sikazwe CT, Chidlow GR, Imrie A, Smith DW. Determinants of acute asthma exacerbation
severity following RV-C infection. Location in thesis: Chapter Six
Deta ils of the work: Sikazwe CT, Chidlow GR, Imrie A, Smith DW. Cytokine profiles in nasal secretions of
patients hospitalised with Rhinovirus Species C associated respiratory wheeze, Cytokine. (In preparation).
Location in thesis: Chapter Seven
XChisha Teza Sikazwe
Date:
XAllison Imrie
Co-ordinating Supervisor
Date:
25/06/2018
25/06/2018
xvi
Peer reviewed papers and Conference presentations
1 Rachael Lappan; Kara Imbrogno; Chisha Sikazwe; Denise Anderson; Danny Mok;
Harvey Coates; Shyan Vijayasekaran; Paul Bumbak; Christopher Blyth; Sarra Jamieson;
Christopher Peacock A microbiome case-control study on recurrent acute otitis media
identified potentially protective bacterial genera. Microbiome, (In submission)
2 Bjerregaard, A., Laing, I.A., Backer, V., Fally, M., Khoo, S.-K., Chidlow, G., Sikazwe, C.,
Smith, D.W., Le Souëf, P. and Porsbjerg, C. (2016) Clinical characteristics of eosinophilic
asthma exacerbations. Respirology, 22: 295–300. doi: 10.1111/resp.12905.
3 Bjerregaard, A., Laing, I.A., Backer, V., Fally, M., Khoo, S.-K., Chidlow, G., Sikazwe, C.,
Smith, D.W., Le Souëf, P. and Porsbjerg, C. (2017)-High fractional exhaled nitric oxide
and sputum eosinophils are associated with an increased risk of future virus-induced
exacerbations – a prospective cohort study. Clinical and Experimental Allergy
4 The Quantitative Detection of Respiratory Syncytial Virus in Hospitalized Young South
African Children Chisha T. Sikazwe, Alicia A. Annamalay, Glenys R. Chidlow, Salome
Abbott, Siew-Kim Khoo, Joelene Bizzintino, Robin Green, Allison Imrie, Peter LeSouëf,
David W. Smith. Submitted to Influenza and other Respiratory viruses (in submission)
5 Reliable quantification of rhinovirus species C using real -time PCR. Sikazwe CT,
Chidlow GR, Imrie A, Smith DW. J Virol Methods. 2016 May 20;235:65-72.
doi:10.1016/j.jviromet.2016.05.014. [Epub ahead of print] PMID: 27216896
6 Respiratory viruses in young South African children with acute lower respiratory
infections and interactions with HIV. Annamalay AA, Abbott S, Sikazwe CT, Khoo SK,
Bizzintino J, Zhang G, Laing I, Chidlow GR, Smith DW, Gern J, Goldblatt J, Lehmann D,
Green RJ, Le Souëf PN. J Clin Virol. 2016 Aug;81:58-63. doi: 10.1016/j.jcv.2016.06.002.
Epub 2016 Jun 4. PMID: 27317881
7 Comparison of Droplet Digital RT-PCR to qPCR for the quantitative detection of
Respiratory Syncytial Virus Chisha T. Sikazwe, Angela Fonceca, Avram Levy, Glenys R.
Chidlow, Allison Imrie, Mark Everard, David W. Smith. RSV16: 10th International
Respiratory syncytial virus symposium, Sept 2016, Patagonia, Argentina.
8 Reliable Quantification of Rhinovirus Species C using a Taqman Real -time PCR Based
Approach Chisha T Sikazwe · Glenys R. Chidlow · Allison Imrie · David W. Smith. 1st
International Meeting for Respiratory Pathogens, Sep 2015 Singapore.
9 The Relationship between RSV Load and Clinical Disease in South African Children
Chisha T Sikazwe, Glenys R. Chidlow, Allison Imrie, David W Smith. 9th International
Symposium on Respiratory syncytial virus Nov 2014, Cape Town, South Africa
xvii
List of Abbreviations
Abbreviation Definition
µ Micro
µL Microlitre
µM Micromolar
AAE Acute Asthma Exacerbation
ALF Australian Lung Foundation
ARTI Acute Respiratory Tract Infection
BAL Bronchoalveolar Lavage
BHQ Black Hole Quencher
BLAST Basic Local Alignment Search Tool
bp Base Pair
cDNA Complementary DNA
CFT Complement Fixation Test
CMV Cytomegalovirus
COPD Chronic Obstructive Pulmonary Disease
CPE Cytopathic Effect
Cq Cycle Quantification Value
Ct Cycle Threshold
xviii
CXCL- Chemokine Ligand
ddPCR Digital Droplet PCR
DNA Deoxyribonucleic Acid
EIA Enzyme Immunoassay
GINA Global Initiative For Asthma
HAdV Human Adenovirus
HBoV Human Bocavirus
HCoV Human Coronavirus
HMPV Human Metapneumovirus
HPIV Human Parainfluenza virus
GAPDH Glyceraldehyde 3-phosphate dehydrogenase
IF Immunofluorescence
IFAV Influenza A Virus
IFBV Influenza B Virus
IFN- Interferon
IL- Interleukin
LNA Locked Nucleic Acid
LRTI Lower Respiratory Tract Infection
MERS-CoV Middle Eastern Respiratory Syndrome Coronavirus
xix
MGB Minor Groove Binding
mL Milliliter
NA Nasal Aspirate
NAT Nucleic Acid Test
NPA Nasopharyngeal Aspirate
NPS Nasopharyngeal swab
NT Neutralisation Test
NW Nasal Wash
PCR Polymerase Chain Reaction
PMH Princess Margaret Hospital
PWLM PathWest Laboratory Medicine WA
qPCR Quantitative Real Time PCR
RNA Ribonucleic Acid
RSV Respiratory Syncytial Virus
RT-ddPCR Reverse Transcription Digital Droplet PCR
RT-PCR Reverse Transcription PCR
RT-qPCR Reverse Transcription Quantitative Real Time PCR
RV Rhinovirus
SARS-CoV Severe Acute Respiratory Syndrome Coronavirus
xx
Th T Helper Cells
TKI Telethon Kids Institute
TLR Toll Like Receptors
TNF- Tumor Necrosis Factor
URTI Upper Respiratory Tract Infection
UTR Untranslated Region
UWA The University Of Western Australia
VL Viral Load
WHO World Health Organisation
α Alpha
β Beta
2
1 Literature review
1.1 Introduction
Acute respiratory tract infections (ARTIs) contribute substantially to the global burden of
illness from communicable pathogens. ARTIs are a leading cause of morbidity and mortality
accounting for approximately four million deaths per year globally (Bryce et al., 2005).
Children under the age of five years, adults over the age of sixty-five, individuals with an
underlying chronic condition and immunocompromised individuals are population groups
in whom poor outcomes occur following infection (Falsey et al., 2003; Graham and Gibb,
2002; Nair et al., 2011a; Nair et al., 2010; Nair et al., 2013). Estimates of the global burden
of acute respiratory illness indicate that there are vast differences between developed and
developing countries. According to recent reports, ARTIs are the leading cause of childhood
death in developing countries (Nair et al., 2011b). Pneumonia alone accounts for 1.4
million childhood deaths per year in these regions. Conversely, in developed nations
deaths due to ARTIs represent a negligible percentage of total deaths, accounting for less
than two percent of deaths but are predominantly responsible for absenteeism and
enormous financial costs to the healthcare system (Denny Jr., 2001). Reports from the US
estimate that direct financial costs of ARTIs to the healthcare system approach $40 billion
annually. Similarly, in Europe the expenditure on patients with ARTI is over €15 billion. The
direct and indirect cost of ARTIs to the Australian health care system is estimated to be up
to AUD 600 million each year (The Australian Lung Foundation, 2007).
All groups of microbes are capable of establishing an infection in the respiratory tract.
However, viruses predominate as aetiologic agents of ARTIs and can contribute to
respiratory disease either directly through frank infection or indirectly by exacerbating
pre-existing illness (Bafadhel et al., 2011; Bandi et al., 2003; Busse, Lemanske, and Gern,
2010; Hosseini et al., 2015) and increasing the risk of secondary bacterial infection
(Bellinghausen et al., 2016; Ewijk et al., 2007). Influenza virus (IFV), respiratory syncytial
4
virus (RSV), rhinovirus (RV), human metapneumovirus (hMPV), human parainfluenza virus
(hPIV) and human adenovirus (hAdV) are the most prevalent viruses in hospitalised
patients and are all linked to ALRI (Lukšić et al., 2013). The distribution of these viruses is
influenced by season, geographic region and age group (Lukšić et al., 2013). They are at
least 6 families and more than 150 viruses that are associated with ARTI. The clinical and
seasonal profiles of the most common types are listed in Table 1.
Table 1-1: Seasonal and clinical profiles of the commonly detected viruses associated with acute respiratory tract infection
Virus
Incubation period (days)
Seasonality Clinical manifestations Laboratory diagnostic method
Respiratory syncytial virus (RSV) 2-8 Winter bronchiolitis, pneumonia Virus isolation in cell culture, RT-PCR, IF, EIA,
Human metapneumovirus (HMPV) 2-8 Winter to spring bronchiolitis, pneumonia RT-PCR, IF
Rhinovirus (RV) 2 All year round coryza, COPD and asthma exacerbation, bronchiolitis, pneumonia
RT-PCR, NT and Virus isolation in cell culture (for some types)
Coronavirus (HCoV) 2 All year round coryza, pneumonia (rarely in non-SARS or MERS infection)
RT-PCR,ELISA,HA,IF
Influenza virus (IFV) 2-8 Winter Virus isolation in cell culture, RT-PCR, CFT, HI,IF, EIA,NT
Human parainfluenza viruses (HPIV) 2-8 Autumn to winter coryza, croup, bronchitis, bronchiolitis, and pneumonia
Virus isolation in cell culture, RT-PCR, CFT, HI,IF, EIA,NT
Adenovirus (AdV) 5-7 All year round pneumonia, bronchitis, pharyngitis, tonsillitis, and pharyngoconjunctival
fever
RT-PCR, HI, IF,EIA and NT
Human bocavirus (HBoV) 5-7 All year round Coryza, bronchiolitis, pneumonia
RT-PCR
Abbreviations: RT-PCR; reverse transcription PCR, IF; immunofluorescence, EIA; enzyme immunoassay, HA; haemagglutination, NT; neutralisation test, HI; haemagglutination inhibition, CFT; complement fixation
test.
1.1.1 Respiratory syncytial virus
RSV is a member of the paramyxoviridae family and has a single strand negative sense
enveloped RNA genome. RSV genome encodes two non-structural proteins and nine
structural proteins. The attachment (G) and fusion (F) glycoproteins are the
immunodominant antigens of RSV and are essential in infectivity and antigenicity (Fodha et
al., 2007). RSV is considered to be serologically monotypic, but consists of two genetic
subgroups, RSV-A and RSV-B (Papadopoulos et al., 2004). RSV A is more prevalent than RSV
B but there is no consensus on which of the subtypes is more pathogenic.
RSV accounts for 40-50% of all viral infections requiring hospitalisation in young infants
(Nair et al., 2011b). Virtually all children are exposed to RSV before the age of 3 years
(Henderson et al., 2005). Acute bronchiolitis is the typical syndrome associated with RSV
infection in young infants and is characterised by a predominantly neutrophilic pattern of
inflammation and mucus in the airways, which often results in some degree of pulmonary
obstruction. Young infants are predisposed to severe acute bronchiolitis because of the
small diameter of their airways. Congenital heart disease, prematurity, bronchopulmonary
dysplasia or cystic fibrosis are all risk factors for severe infection and possibly death
(PREVENT, 1997).
1.1.2 Rhinoviruses
Rhinoviruses (RVs) are non-enveloped positive sense single stranded RNA viruses
belonging to the family Picornaviridae. They are genetically and antigenically diverse
consisting of three species (A, B and C). Historically, extensive cross neutralisation test
were utilised for the classification of RV-A and B isolates into 100 serotypes but these
isolates (types) are now assigned solely on genomic sequence (McIntyre, Knowles, and
7
Simmonds, 2013). In 2006, utilisation of molecular techniques led to the discovery of RV-C,
which had been entirely unrecognised using traditional rhinovirus detection techniques
(Arden et al., 2006). Cell lines that were conventionally used to isolate rhinovirus are not
permissive to RV-C infection and as a result preclude serotype assignment. Sequence
typing has revealed that RV-C is genetically more diverse than the other two species and
currently consists of 65 distinct genotypes (McIntyre et al., 2013).
Historically, RV-A and RV-B serotypes were stratified into two groups (major and minor) on
the basis of cellular receptor utilisation (intracellular adhesion molecule [major] or low-
density lipoprotein receptor [minor]) whereas the RV-C receptor remained unidentified
until recently. A recent report has shown that the human cadherin-related family member
3 (CDHR3), a member of the cadherin family of transmembrane proteins, facilitates RV -C
attachment and replication (Bochkov et al., 2015). This receptor is highly expressed in the
lower respiratory tract but its biological function remains unknown. A single nucleotide
polymorphism in CDHR3 gene is associated with RV-C wheezing illness in infancy and has
been shown to be risk factor for asthma inception (Bonnelykke et al., 2014).
The spectrum of disease exhibited by RVs range from asymptomatic infections, mild upper
respiratory infections (common cold) to severe or fatal lower respiratory tract infections
(pneumonia and bronchiolitis). RV infection is also associated with the exacerbation of
chronic respiratory conditions such as asthma, cystic fibrosis and other chronic obstructive
pulmonary disorders (Blomqvist et al., 2002; Çalışkan et al., 2013; Camargo et al., 2012;
Choi et al., 2015). In addition, RV induced wheeze in early childhood is associated with an
increased risk of developing asthma later on in life (Jackson et al., 2008a). Recent studies
have also revealed that RV-C species are more closely associated with asthma exacerbation
and illness requiring hospitalization than the other RV species, suggesting that RV-C may be
more pathogenic than RV-A and RV-B (Bizzintino et al., 2011a; Cox et al., 2013). Host and
8
viral factors associated with RV-C pathogenesis and illness severity are yet to be
completely elucidated.
1.1.3 Human Metapneumovirus
HMPV is classified as a negative sense single stranded RNA virus belonging to the
Paramyxoviridae family. Sequence analysis of the fusion (F) and attachment (G) genes has
identified two genotypes namely A and B. Both genotypes may co-circulate but only one
genotype is dominant during an epidemic (Tsukagoshi et al., 2013a; van den Hoogen et al.,
2002).
HMPV is associated with hospitalisation in young children, the elderly and individuals w ith
an underlying chronic condition. It is distributed worldwide and mimics the seasonal
distribution of influenza and RSV in that infection rates tend to peak in winter and early
spring (Kahn, 2003). Much like other respiratory viruses the spectrum of clinical disease
ranges from a mild upper respiratory tract infection to severe pneumonia. Observational
studies report that elderly people infected with hMPV display more severe clinical
symptoms compared to younger patients and those elderly infected with RSV or Influenza
virus (Falsey et al., 2003; Widmer et al., 2012).
1.1.4 Influenza virus
The influenza viruses are a major causative agent of ARTI in both children and adults,
Infection can cause mild to severe illness, and can occasionally lead to death (Pretorius et
al., 2016). Influenza accounts for a substantial burden of ARTI and current estimates
suggest that it is responsible for approximately five million severe cases and between
291,000-646,000 deaths per annum (Iuliano et al., 2018). Three Influenza types (type A,
type B and type C) are known to infect humans. Only the influenza A/H1N1, A/H3N2 and
influenza B viruses are considered as being significant contributors to seasonal influenza.
9
Influenza C virus infection is seasonal but has limited genetic diversity and is not thought to
cause epidemics (Nicholson, Wood, and Zambon, 2003).
Genomes of influenza A and B viruses are composed of eight single-stranded negative
sense RNA segments whereas the influenza C virus is composed of seven segments of
single stranded negative sense (Nicholson et al., 2003).
The genomes of influenza viruses specifically the Influenza A virus (IFAV) regularly undergo
changes under immune pressure. The genes encoding surface glycoproteins
haemagglutinin and neuraminidase may undergo subtle genetic changes (antigenic drift) or
abrupt major changes (antigenic shift).
Influenza therapeutics comes in the form of vaccines and anti -viral compounds. Vaccines
are recommended for annual boosting especially in high risk groups (Fiore, Bridges, and
Cox, 2009; Matsushita et al., 2017). Most Influenza antiviral compounds inhibit viral
replication and release, and can be utilised for treatment and prophylaxis (Dobson et al.,
2015; Jefferson et al., 2014b).However, adverse events reported from use of these
compounds suggested that they should be administered judiciously(Jefferson et al.,
2014a).
1.1.5 Parainfluenza virus
Human parainfluenza viruses (HPIV), of which they are four types (HPIV1-4) are negative
sense RNA viruses of the Paramyxoviridae family. HPIVs typically cause mild, self-limited
upper respiratory tract infections in adults, but can result in severe, life -threatening LRTIs
in immunocompromised patients (Guzmán-Suarez et al., 2012). HPIVs 1-3 are a common
cause of croup syndrome and are among the most commonly detected viruses in
hospitalised young infants after RSV. HPIV-4 is known to cause mild upper respiratory tract
10
infections (Tsukagoshi et al., 2013b). However, due to a lack of epidemiological data on
HPIV-4 the extent of its pathogenicity is largely unknown.
1.1.6 Adenovirus
Human Adenoviruses (HAdV) are a major cause of viral disease in both children and adults
(Lynch, Fishbein, and Echavarria, 2011). HAdV have linear double-stranded DNA genomes
and are classified into seven species (HAdV A to G) and a total of 55 types based on a
combination of serological characteristics and phylogenetic analysis (Robinson et al., 2011).
Certain species are known to cause respiratory illnesses including, acute febrile pharyngitis
(HAdV-B and -C; types 1,3,5,7), pharyngoconjunctival fever (HAdV-B; types 3,7,14), acute
respiratory tract infections (HAdV-B and -E; types 3,4,7,14,21), pneumonia (HAdV-B, -C,
and -E; serotypes 1-4 and 7), pertussis-like syndrome (HAdV-C; type 5) (Demian et al.,
2014). PCR is the method of choice in detecting adenoviruses.
1.1.7 Human Coronaviruses
Human coronaviruses (HCoVs) are enveloped viruses with a single-strand positive sense
RNA genome. Coronaviruses possess the largest known genomes among any of the RNA
viruses (Lau et al., 2013). Six species of HCoVs are known to infect humans; they are
classified into four genera based on proteomic analysis (Lau et al., 2013). HCoV-229E and
HCoV-NL63 belong to the Alphacoronavirus genus, HCoV-OC43 and HCoV-HKU1 belong to
lineage A Betacoronavirus, while severe acute respiratory syndrome associated
coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV)
belong to lineages B and C Betacoronavirus, respectively (Annan et al., 2013). HCoV-OC43,
HCoV-229E, hCoV-HKU1 and hCoV-NL63 are commonly associated with self-limiting mild
upper respiratory tract infections but on some occasions may be causative agents of
severe LRTIs especially in young infants, the elderly, and individuals with compromised
11
immunity (Lu et al., 2012). HCoVs are detected frequently with other respiratory viruses;
the significance of this phenomenon in relation to illness severity is unclear.
In the last decade SARS-CoV and MERS-CoV have been responsible for epidemics around
the world (Cheng et al., 2007). SARS-CoV was identified as the causative agent of the SARS
global epidemic from 2002 to 2003. MERS-CoV was initially reported in Saudi Arabia in
2012 (Zaki et al., 2012). Since then it has caused illness in people in countries with links to
the Arabian Peninsula (de Groot et al., 2013). Both MERS-CoV and SARS-CoV are emerging
zoonotic pathogens that crossed the species barrier with case fatality rates of 35% and
10% respectively (To et al., 2013). Bats appear to be the natural reservoir of both MERS-
CoV and SARS-CoV, transmission to humans occurs via dromedary camels and civets (live
market animals) respectively (Dijkman et al., 2013; Hu et al., 2015; Pfefferle et al., 2009;
Yusof et al., 2015). Nucleic acid tests are the method of choice for the laboratory diagnosis
of HCoVs (Gaunt et al., 2010) and MERS-CoV(Feikin et al., 2015).
1.2 Laboratory Diagnosis of Respiratory Infections
Laboratory diagnostic methods are the cornerstone in accurately attributing pathogen to
clinical presentation. Early detection of the aetiologic agent is pertinent for providing
optimal clinical management, which may include patient isolation, determining
appropriate therapy and/or cessation of inappropriate therapeutic interventions. In
addition, diagnostics are also essential for outbreak detection and response, and public
health surveillance. Characteristics of the ideal diagnostic test include being accurate
(between tests and laboratories), high throughput, cost-effective, suitable for a wide
spectrum of clinical samples, excellent sensitivity and specificity. Current diagnostic tests
fulfil some, but not all of these ideal characteristics.
12
They are several methods that are available for the identification of the aetiologic agent
from patients with an ARTI. The traditional laboratory diagnostic methods such as virus
isolation in cell culture and serology tests perform well but have some inherent limitations
in aspects of sensitivity and/or specificity. Molecular based diagnostic techniques such as
PCR have led to clinicians and scientists re-evaluating the role of certain viruses in disease
outcome. PCR based tests are a relatively new tool in the laboratory diagnostics. These
tests allow for rapid screening of clinical samples for multiple aetiologies with the added
benefit of excellent sensitivity and specificity. Further, these diagnostic approaches
facilitate further understanding of the viral kinetics in respiratory infections as well as the
ability to identify respiratory viruses that have been entirely missed by traditional
diagnostic approaches.
1.2.1 Upper and Lower airway samples
A variety of specimens have been used for directly sampling the respiratory tract including
nasal swabs (NS), nasopharyngeal swabs (NPS), nasal washings (NW), nasal aspirates (NA),
nasopharyngeal aspirates (NPA), sputum, bronchoalveolar lavage (BAL) or biopsies. The
technique utilised to collect a sample is based on clinical presentation and the method to
be utilised to identify the pathogen.
NWs are conventionally used on children and are not well tolerated in adults. NW can be
unpleasant to the patient, in addition, NW can be technically demanding, as the technique
requires the use of a solution such as saline. A NS is commonly taken from the mid-inferior
portion of the inferior turbinates for optimal virus recovery. Though this collection
technique maximises virus recovery, it causes some discomfort to the patient.
Nonetheless, both sampling techniques are suitable for detection of virus by cell culture.
NPA is especially useful for virus culture, antigen detection, and polymerase chain reaction
(PCR) based assays and is collected using a mucus trap, attached to a disposable suction
13
catheter. This technique requires a skilled operator, so it may be unsuitable for widespread
clinical practice. Studies that have compared NS and NPA sampling techniques for
detection by PCR report that the overall sensitivity of nasal swabs was inferior to that of
NPAs, but the authors noted that obtaining a NPA was invasive, uncomfortable and
significantly more distressful than a nasal swab (Heikkinen et al., 2002; Lambert et al.,
2008). With the recent development of flocked nasal swabs and advanced molecular
techniques, the sensitivities have increased to a level comparable to NPAs with the added
advantage of the flocked nasal swab being less painful and more convenient than NPAs, as
no supplementary tools are required (Ortiz de la Tabla et al., 2010). Sputum samples are
more representative of lower respiratory tract and are collected non-invasively. Despite
the aforementioned advantages of sputum collection, practical reasons preclude the
effective collection of sputum from infants and children (Abdullahi et al., 2008; Grant et al.,
2012). Children have difficulty producing sufficient sputum for laboratory evaluation
compared to adults and are more inclined to swallow the specimen than expectorate it
(Grant et al., 2012). Further the viscosity of sputum and the likely presence of
contaminating bacteria as well as the toxic effects sputum can have on cell culture
preclude its widespread use for viral diagnostic purposes (Falsey, Formica, and Walsh,
2012). BAL and lung biopsy specimen are collected directly from lower respiratory tract
sites. Previous studies on MERS-CoV have shown that quantification cycle (Cq) values were
lower in lower respiratory tract samples compared to upper respiratory tract samples and
more accurately reflect the apparent viral kinetics in the lower respiratory tract. However,
these specimen types are usually collected from the severely ill (Falsey et al., 2012) and too
few of these specimens would be available to conduct proper population level studies.
1.2.2 Virus isolation in cell culture
14
Historically, virus isolation by cell culture was the diagnostic method used to screen
respiratory samples for the presence of viruses. Virus isolation in cell culture is regarded as
the "gold standard" to which all other detection methods have been compared since the
isolation of virus in cell culture indicates the presence of an infectious, viable, and
replication competent virus, an occurrence which is unachievable using other detection
techniques (Leland and Ginocchio, 2007). This method is labour intensive, and often
requires prolonged incubation periods (3-14 days) to provide results, limiting its use in the
acute management of patients. A broad range of respiratory viruses can be grown by using
at least 4 cell lines, which includes human epithelial cell lines (HEp-2, A-549, HeLa),
human fibroblast cell lines (HLF, HELF, MCR-5, WI-38) and primary monkey kidney cells
(Denny Jr., 2001). For most respiratory virus applications, the presence of virus is
commonly detected by a characteristic cytopathic effect (CPE) under light microscopy. The
combination of conventional cell culture and commercial viral -antigen detection systems
can be used to accelerate diagnostic turn-around times, since viral antigens are detected in
the cell monolayer by immunofluorescence, usually before a distinct CPE is observable
(Terletskaia-Ladwig et al., 2008). In recent times, more rapid and powerful tools to detect
the presence of virus in clinical samples collected from patients following ARTI have
gradually superseded virus culture techniques.
1.2.3 Direct detection of respiratory viruses by immunofluorescence
Laboratory diagnosis can be performed by direct detection of virus in clinical specimen.
Direct antigen tests assist in the diagnosis of respiratory infection by providing evidence of
the antigen in respiratory specimen. These tests are instrumental in informing pertinent
therapeutic decisions in a short time frame (Gomez et al., 2016). Direct and indirect
immunofluorescence are the two sub-forms of the immunofluorescence test. The direct
test is composed of a virus specific monoclonal antibody that is conjugated directly to
15
fluorescent dye. If the virus is present in the clinical sample the monoclonal antibody
attaches to the targeted viral antigen and the conjugated fluorescent dye can be visualised
under a fluorescent microscope. In the indirect method, two antibodies are used, a specific
primary monoclonal antibody that attaches to the viral antigen and a secondary antibody
labelled with a fluorescent dye to bind to the primary monoclonal antibody. The indirect
method is more sensitive than the direct method because of the signal amplification from
multiple secondary antibodies binding to a single primary antibody.
Antigen detection methods such as the enzyme immunoassay (EIA) are performed by
adding patient specimen onto a surface that is pre-coated with an antibody that captures
the virus specific antigen if present in the specimen. EIA is a highly sensitive assay and
utilises an enzyme system to produce a colour reaction that can be quantified.
1.2.4 Nucleic Acid Tests
Diagnostic laboratories detect respiratory viruses in clinical samples by using molecular
techniques to detect virus genome. PCR based methods are the most favourable and the
most widely used approach for rapid and accurate respiratory virus detection from clinical
specimens. The advent of nucleic acid based tests has led to the identification of viruses
that were entirely missed using conventional diagnostic techniques. Most respiratory
viruses have their genetic information primarily stored in the form of RNA, with the
notable exception of HAdV and HBoV. Amplification of an RNA target requires a reverse
transcription (RT) step in order to convert RNA to cDNA followed by conventional PCR (RT-
PCR). DNA targets do not require an RT step. The repetition of three successive reactions
is the basis of PCR:
1. Denaturation of double stranded DNA (dsDNA) into si ngle stranded DNA (ssDNA)
between 94 and 95⁰C
16
2. Annealing of 2 synthetic oligonucleotides (primers) to each ssDNA at each termini at a
variable temperature ranging from 37-72⁰C
3. Synthesis of new DNA strands; the PCR method util izes a thermostable DNA polymerase
to add nucleotides at the 3' of end of the primers at 68 - 72⁰C.
These three steps entail a single PCR cycle, since each synthesized DNA strand becomes a
template for amplification there is an exponential increase in amplicon at the end of each
cycle. The length of the PCR product is equivalent to that of the two primers plus the
distance separating the two primers. Several variations of PCR are applicable to the
diagnosis of respiratory viral infection including nested PCR, real -time PCR (qPCR) and
multiplex qPCR.
1.2.4.1 Real time PCR (qPCR)
Utilisation of modern molecular techniques has enabled detection of viral nucleic acid
during the exponential phase of the reaction rather than waiting for the endpoint of the
reaction to register detection. The fundamental principle of qPCR is based on the detection
and quantification of fluorescent reporter molecules whose signal can be registered in
each cycle of the PCR. The cycle number when the fluorescence becomes detectable is
referred to as the cycle threshold/quantification value (Ct/Cq), and is proportional to the
logarithm of the initial amount in the clinical sample. Unlike conventional PCR, qPCR does
not require any post PCR amplification manipulations thereby minimizing any issues
related to contamination. However, similar to conventional PCR is the addition of a reverse
transcription step when screening clinical samples for respiratory viruses with an RNA
genome.
A DNA binding fluorescent dye, such as SYBR green represents the simplest method to
detect amplified product using qPCR, given that this dye binds to any double stranded DNA
17
in the reaction. A shortfall of SYBR green chemistry is its lack of sequence specific DNA
binding activity. So this type of chemistry will also bind to non-specific products that the
qPCR reaction may generate and may lead to false positive results. Sequence specific
hydrolysis probes are alternative detection chemistry and mitigate the problem of false
positive results because fluorescence signal is only detected when the probe binds to the
target area. Sequence specific hydrolysis probes are the most widely used and published
detection chemistry available for qPCR. These probes are beneficial because they are
designed to increase both the sensitivity and specificity of the assay. qPCR provides
excellent sensitivity and a wide dynamic range and for this reason can be utilised to
measure viral load in clinical samples.
Viral load determination may increase current understanding of host viral interactions
(Quinn, 2011) and may help predict disease progression (Jartti et al., 2013). Viral load
determination from clinical samples is performed by interpolating the cycle quantification
value (Cq) into a standard curve. The standard curve is constructed by standards of target
nucleic acid that encompass a wide range (5-9 logs) of known concentrations. It is
important to note that there is inherent variability in construction of a standard curve
between batches. As such it is imperative that the reference standards used are scrutinised
thoroughly for precision and reproducibility during the validation process , in order to
understand the limitations of the assays and to provide valid standards for RNA
quantification.
Specimen collection is an important source of variability that may obscure real changes
and consequently unreliable quantification results. The reliability of any PCR-based
quantification experiment can be improved by including an invariant endogenous control
(mRNA that is stably expressed and is not affected by experimental conditions) in the
experiment to correct for sample to sample variation that may arise.
18
1.2.4.2 Multiplex PCR
Multiplex PCR refers to the use of multiple sequence specific primers and probes to detect
multiple targets in a single reaction tube. This type of assay is efficient and economical but
requires substantial assay optimization to ensure optimal amplification of the different
targets if present. The availability of multiplex PCR makes the detection of co-infections
feasible (Franz et al., 2010). Multiplex PCR is an important tool in the diagnostic laboratory
when screening for potential etiologic contributions to disease.
1.2.4.3 Droplet digital PCR
Droplet digital PCR (ddPCR) is a relatively new approach that improves on qPCR by making
external standards unnecessary in viral quantification. In a similar approach to qPCR,
ddPCR involves the detection of template sequence with either a SYBR green or hydrolysis
probe reaction chemistry but quantification is conducted differently. It involves the
generation of a large library of emulsion based droplets (~20,000), also termed partitions
(Markey, Mohr, and Day, 2010). These partitions are generated from sample-reagent
mixtures and are distributed in such a way that there may be either one or zero template
molecules in the each partition (Mojtahedi, Fouquier d'Herouel, and Huang, 2014). This is
the fundamental principle underpinning digital PCR. Further, and in contrast to qPCR the
thermal cycling is performed to endpoint (Markey et al., 2010). The total initial number of
template is obtained by tallying partitions in which the template is detected compared to
the number of partitions in which the reaction is unreactive (Markey et al., 2010). A
Poisson correction is applied to the final copy number to compensate for the possibility
that more than one template molecule may be present in some partitions. ddPCR is highly
sensitive, reproducible, provides enhanced accuracy and precision thus lending itself to a
number of favourable applications such as low copy target detection, quantitative
detection of minor genotypes. Moreover, ddPCR does not rely on factors such as
19
amplification efficiency and arbitrarily assigned threshold values and therefore reduces the
amount of bias in the sample resulting in confident quantification (Hall Sedlak and Jerome,
2014; Huggett et al., 2013).
Recent work from a study comparing ddPCR to qPCR for quantitative detection of
cytomegalovirus (CMV) demonstrated that ddPCR is equivalent for the accurate
quantification of CMV load in clinical samples over a wide dynamic range (Hayden et al.,
2013). Recent work that evaluated ddPCR for influenza vaccine development has
demonstrated a high throughput ddPCR method for very precise and accurate influenza
virus titre quantification (Palatnik de Sousa et al., 2015). The authors also noted several key
issues that are determinants of variability in qPCR were circumvented with the ddPCR
approach.
1.3 Viral kinetics in acute respiratory tract infections
Viral kinetic studies have improved our understanding of the interplay between host and
virus in human immunodeficiency virus (HIV), CMV, hepatitis B and C infections.
Experimental human infection studies on RSV and IFAV have shown the unique ways in
which viral kinetics modulate illness severity and disease course (Bagga et al., 2013;
DeVincenzo et al., 2010; El Saleeby et al., 2011). In addition, viral load studies have shed
light on critical treatment time points used in order to minimise the risk of severe illness.
The recent discovery of novel respiratory pathogens has stimulated investigations into viral
load as a surrogate marker of disease severity in hospitalised patients. For ex ample,
evidence from a recent study conducted in France reports that a high hMPV viral load is an
important predictor of disease severity in young children (Roussy et al., 2014). Indeed,
previous RSV studies indicate that high viral load is associated with symptom severity
(DeVincenzo et al., 2010; El Saleeby et al., 2011; Houben et al., 2010) . Furthermore, a
20
reduction in RSV and influenza viral loads in patients as a consequence of antiviral therapy
has been found to be associated with improved clinical outcomes (Boivin, Coulombe, and
Wat, 2003; Shadman and Wald, 2011). This paradigm of direct virus damage to host tissue
shaping clinical outcome has been supported further by investigations on SARS-CoV and
MERS-CoV (Feikin et al., 2015; Hung et al., 2004; Min et al., 2016). In RV infection, evidence
in the published literature suggests that viral load is predictive of the severity of clinical
illness, given that individuals with a lower respiratory tract infection harbour higher viral
loads that those with an upper respiratory tract infection (Utokaparch et al., 2011). This
finding is further supported by an Italian study that reported that an abundant RV viral
load (>106copies/mL) in the absence of other viral pathogens is strongly associated with
LRTI (Piralla et al., 2012). However, other studies have not reproduced these findings (Jartti
et al., 2008) and thus the relevance of RV viral load in lower respiratory tract infections is
not yet completely understood.
1.4 Viral respiratory infection and asthma
Asthma is one of the most common chronic respiratory inflammatory conditions and a
substantial contributor to morbidity worldwide (GINA, 2017). It is characterised by airway
inflammation, remodelling, reversible airway blockage, and increased airway smooth
muscle tone (Zhao et al., 2002). It can be difficult to diagnose asthma with certainty in
children aged between 0-5 years because there are no standardised diagnostic criteria for
asthma (GINA, 2017). In addition, respiratory infections predominant in childhood such as
bronchiolitis share many clinical features with exacerbations of asthma, including
wheezing, shortness of breath and respiratory distress (Sigurs et al., 2000). Diagnosis of
asthma in children may involve the individual’s history of recurrent wheeze, family history
of atopy and objective investigations that support the diagnosis. Several stimuli can trigger
21
exacerbations of asthma symptoms in young children including smoking, viral infections,
air pollution and environmental allergens (Pelaia, Vatrella, and Maselli, 2012; Riccio et al.,
2012; Saraya et al., 2014; Sykes et al., 2014). The advent of molecular methods has shown
that respiratory viruses are present more commonly in acute asthma than previously
acknowledged, and are detected in up to 85% of asthma exacerbations (Saraya et al.,
2014). RV and RSV are the predominant viruses that appear to be involved with these
exacerbations (Saraya et al., 2014; Sigurs et al., 2000; Soto-Quiros et al., 2012). It is still
not yet clear if infection causes long term changes in the airways which subsequently
increase the risk of developing asthma. An alternative hypothesis is that severe RSV and/or
RV disease (bronchiolitis) may be an early marker of a predisposition for childhood asthma
(Miller et al., 2011; Moore, Stokes, and Hartert, 2013; Moore et al., 2007). The children at
high risk of developing asthma are those who wheeze and develop allergic sensitization
before the age of 2 years (Jackson et al., 2008a; Sigurs et al., 2010; Sigurs et al., 2000). Long
term prospective studies have linked severe RSV disease in young infants to subsequent
recurrent wheeze and asthma in susceptible children (Sigurs et al., 2010). However other
studies do not confirm the association between medically attended RSV disease and
subsequent asthma development (Poorisrisak et al., 2010).
RV infection in young infants is an independent risk factor for subsequent wheeze and
childhood asthma (Jackson et al., 2008a). Several reports have shown that RVs are the
most common virus detected in hospitalised young children with exacerbations of asthma,
suggesting an etiologic role (Liu et al., 2016; Luchsinger et al., 2014; Message and Johnston,
2002; Miller et al., 2011). Children classified as asthmatic are more likely to have poor
outcomes following RV infection compared to normal individuals (Corne et al., 2002). RV-C
appears to be the most commonly detected species in hospitalised asthmatic children with
some studies suggesting that it is also the most pathogenic (Bizzintino et al., 2011a; Cox et
al., 2013; Liu et al., 2016). However, determinants of RV-C associated asthma severity are
22
yet to be clearly defined, and the contribution of RV-C in asthma exacerbation severity is
still not clear.
23
1.5 Innate immune response to viral respiratory infection
In response to infection, germ line encoded receptors (pattern recognition receptors,
PRRs) that are resident on sentinel cells of the immune system sense conserved pathogen-
associated molecular patterns (PAMPs) on microbes (Gommerman and Ng, 2013; Kim and
Lee, 2014; Malmgaard, 2004; Scagnolari et al., 2009). There are three known PRR families
and they include toll-like receptors (TLRs), retinoic acid inducible gene 1 (RIG-1)-like RNA
helicases (RLHs) and nucleotide-binding oligomerization domain (NOD) like receptors
(NLRs). Signalling downstream of each PRR family results in the activation of important
pathways that regulate the expression of chemokines, pro-inflammatory cytokines, type 1
interferon (type 1 IFN) and antimicrobial peptides (Thompson and Locarnini, 2007). RLHs,
TLR 3, 7, 8 and 9 identify viral nucleic acid and induce innate type 1 IFN responses with or
without the production of pro-inflammatory cytokines (IL-1β, IL-6, TNF-α, IL-12, IL-18).
Type 1 IFN is known to block viral replication at an early step in replication and to be very
important for the induction of an anti-viral state against common respiratory viruses
(Scagnolari et al., 2009). Type I IFN deficient mice are more prone to delayed viral
clearance and severe disease following viral challenge. Further, impaired IFN response to
viral infection has been postulated as a pathogenic mechanism for poor outcomes in
asthmatic patients (Baraldo et al., 2012) and MERS-CoV infection (Faure et al., 2014). In
animal models of hMPV infection it has been shown that a TLR 4 mediated inflammatory
response does not facilitate an adaptive immune response important for viral clearance
and protection against reinfection but predicts the progression of clinical disease
(Velayutham et al., 2013). In RSV infection, PRRs correlate with viral load and there is an
increased pulmonary expression of PRRs compared to healthy controls or infants with RV
or hBoV infection (Scagnolari et al., 2009).
24
Different viral infections can elicit different inflammatory responses, and these
inflammatory responses can generally be grouped into T-helper 1 (Th1) and T-helper 2
(Th2) responses. These inflammatory responses are classified based on the type of
chemokines and cytokines produced. Th1 responses are characterised by production of
IFN-γ, IL-1β, IL-2, IL-12, IL-18, and TNF-α. The Th1 response is pro-inflammatory and is
important in the generation of immune responses necessary for the control and clearance
of intracellular pathogens, thus it is the most suitable response to viral infection. The Th2
response on the other hand is characterised by secretion of IL-4, IL-5, IL-6, IL-9, IL-10, and
IL-13. This type of response is involved in antibody production (including IgE) and
eosinophilic inflammation. This response is strongly associated with atopy and protection
against parasitic infection. It has also been demonstrated that Th2 inflammation counter
regulates Th1 mediated responses (Gill et al., 2010). Respiratory viral infections provoke
differing inflammatory profiles. For instance, IFAV can induce the overproduction of Th1
inflammatory mediators and this is associated with poor clinical outcomes. Dysregulated
secretion of TNF-α and IL-1β maybe critical for pathogenicity and may also hinder the
development of an effective adaptive immune response following infection (Han et al.,
2014). In RSV disease, sequential increases of IFN-γ coincide with improved clinical
outcomes and provide support for the protective role of IFN-γ following infection
(Bermejo-Martin et al., 2007a). Conversely, severe acute viral respiratory infection in
infants is associated with augmented Th2 responses (Bermejo-Martin et al., 2007b). In
addition, individuals with an underlying Th2 bias, such as those with an allergic pulmonary
condition, are more likely to have poor clinical outcomes compared with those without an
underlying Th2 bias (Gern et al., 2000; Mahmutovic Persson et al., 2016; Monick et al.,
2007).
Many types of cells have been implicated in the pathogenesis of ARTI and of note is the
quick and robust pulmonary and systemic neutrophil response following infection
25
(Nagarkar et al., 2009; Sugamata et al., 2012). This robust neutrophil response correlates
with disease severity and is mediated primarily by IL-8 (Cortjens et al., 2016). Neutrophilia
is a common response to both bacterial and viral infections. While there are clear
protective roles for neutrophils against bacterial infection, the evidence for an anti-viral
role is less clear. The role of neutrophils has been investigated in RSV infection and they
are believed to play a role in pathogenesis (Cortjens et al., 2016; Linden, 2001;
Mahmutovic Persson et al., 2016). The role of neutrophils in influenza mouse models is
reported to be both protective and pathogenic. Some papers suggest neutrophils can
reduce viral load by phagocytosis of infected cells and trapping of free virus in neutrophil
extracellular traps. However, other reports have demonstrated that neutrophil
myeloperoxidase facilitates acute respiratory distress syndrome following influenza
infection and that mice lacking myeloperoxidase were more competent in reducing viral
load (Sugamata et al., 2012). Neutrophil involvement has also been demonstrated in RV-
induced asthma exacerbations and high counts are observed in the airways of individuals
following fatal exacerbations (Fahy, 2009; Fahy et al., 1995; Linden, 2001). In a RV mouse
model, IL-8 knock-out mice infected with RV showed significantly reduced neutrophil
responses, and demonstrated reduced airway hyper-responsiveness (Nagarkar et al.,
2009). Functional neutrophil activity has been demonstrated to be enhanced in the airways
of asthmatics, particularly during exacerbations, and correlates with reductions in lung
function and increases in symptom score (Proud, 2011). However, the precise contribution
of neutrophils to asthma has yet to be established.
26
1.6 Aims of Project
The general aim of this thesis was to develop reliable quantitative PCR (viral load) assays to
investigate the contribution of certain respiratory viruses to clinical outcome of young
children hospitalised with an acute viral respiratory tract infection.
The specific aims were as follows:
To develop and validate the use of reliable in-house qPCR (viral load) assays for
different respiratory viruses including IFAV, hMPV, RSV-A&B, and RV-C.- Chapter 3
and 4 (RV-C only)
To compare the analytical performance of real time qPCR to digital PCR in the
quantitative measurement of RSV load in clinical samples - Chapter 3
To examine the contribution of RSV to clinical disease in young infants in a high HIV
prevalence setting- Chapter 5
To investigate the contribution of RV-C to disease severity in young children
presenting to emergency department with acute asthma exacerbations - Chapter
6.
To understand the interplay between RV-C infection and the innate immune
response in young children hospitalised with a RV-C associated wheezing illness -
Chapter 7
27
2 Materials and Methods
2.1 Sample collection
Clinical specimens included flocked nasal swabs, per nasal aspirates, sputum and
nasopharyngeal aspirates samples. Swabs were placed in virus transport medium (VTM)
which contains Hanks balanced salt solution, with 50mg gentamicin, 0.1% bovine serum
albumin, 0.1% sodium bicarbonate and 8mM HEPES buffer).
Nasopharyngeal aspirates (NPAs) were collected from 105 ALRI cases and 53 controls from
Pretoria, South Africa between July 2011 and November 2012 (Chapter 5). Children 0-2
years of age hospitalized with an ALRI at the Steve Biko Academic Hospital or Tshwane
District Hospital in Pretoria were enrolled as cases. A diagnosis of pneumonia (respiratory
distress and either chest X-ray changes (e.g. consolidation or effusion), or auscultatory
findings (e.g. crepitations or bronchial breathing) or bronchiolitis (respiratory distress and
at least one of the following; wheeze, chest X-ray changes (e.g. hyperinflation) or
Hoovers’s sign (inward movement of the lower rib cage during inspiration) was determined
by the clinician in charge. Fifty-three age-matched children presenting to the same
hospitals with a non-respiratory illness over the same period were enrolled as controls
(NRD controls). The study protocol was approved by the University of Western Australia
Human Ethics Committee and the University of Pretoria Ethics Committee. Written
informed consent from parent/guardian was obtained prior to participation. NPAs were
stored at -80°C in Pretoria, South Africa until transfer on dry ice to Telethon Kids Institute,
Perth, Australia for processing and long term storage. An aliquot of each sample was
transported on dry ice to PathWest Laboratory Medicine WA, Perth, Australia.
For Chapter 6, flocked nasal swabs were collected from young children presenting with an
acute asthma exacerbation as part of the MAVRIC (Mechanisms of Acute Viral Respiratory
28
Infection in Children) study carried out at Princess Margaret Hospital in Perth, Western
Australia. Flocked nasal swab specimens were also collected from children with no
evidence of respiratory disease. These children were selected to match the acute asthma
exacerbation cases for age, and season of birth. An aliquot of each sample was
transported on dry ice to PathWest Laboratory Medicine WA, Perth, Australia and stored
at -80oC until further use.
Study participants for the cytokine study (Chapter 7) had a flocked swab sample collected
upon inpatient admission. These samples were placed on ice and transported to the
PathWest laboratory as soon as practically possible for storage in -80oC freezer.
2.2 Nucleic acid extraction and viral detection
Total nucleic acids were extracted from 200µL of respiratory specimen using the MagMAX
viral RNA isolation kit (Thermo-Fisher Scientific, Australia) according to the manufacturer’s
instructions. All automated liquid transfer procedures utilised the CAS 1200 instrument
and conductive tips (Corbett Life Science. Australia)
An in-house multiplex respiratory pathogen assay was used to screen study samples for
respiratory pathogens such as influenza A, B and C viruses (FLUAV, FLUBV,FLUCV),
parainfluenza virus I-IV (PIV), RSV-A & B, Coronaviruses (hCoV- HKU1, -NL63, -OC-43, -
229E), human metapneumovirus (hMPV), and human adenoviruses (hAdV) (Chidlow et al.,
2009). Every multiplex PCR run performed included positive (PCR, inhibition and extraction
positive controls) and negative controls (method blanks interspersed by 5 test samples).
Nucleic acid extracts were stored at -80oC. A few minor adjustments were made to the
multiplex PCR real-time assay (Chidlow et al., 2009). Adjustments included the addition of
a 2.5 µM ROX reference dye (ThermoFisher), which was to be used for the 1:10 dilution of
amplicon generated from the enrichment PCR assays. Further, extra primer pairs were
29
included to screen for hMPV (Chidlow et al., 2009). The primer pairs reported by Lee and
colleagues (Lee et al., 2007) were used for RV identification and genotyping. Respective
monoplex quantitative PCR assays were used to confirm any discordant results obtained
from the respiratory multiplex PCR assay. Thermal cycling programmes for the enrichment
PCR assays and the real time multiplex PCR assay were adopted from (Chidlow et al.,
2009).
2.3 Design of primers and probes
Assay oligonucleotides were designed using primer express v3.0 software (primers and
MGB probes) (ThermoFisher Scientific, Australia or the Exiqon microRNA PCR online
service (LNA probes).Primers were synthesised by Integrated DNA Technologies (IDT,
Australia), MGB probes were synthesized by Applied Biosystems (ThermoFisher Scientific,
Australia) and Locked Nucleic Acid (LNA) probes were synthesized by Sigma-Aldrich (Sigma-
Aldrich, Australia). The primers and probes used in the multiplex PCR to screen initially for
viral nucleic acid in respiratory samples were as previously described (Chidlow, 2013;
Chidlow et al., 2009).
Viral load from clinical samples was determined by real-time PCR targeting specific regions
of nucleoprotein gene of RSV (A&B) and HMPV, the matrix gene of IFAV and the
5’untranslated region (UTR) of RV-C (Table 2.1). These sequences were used because they
are highly conserved across all the respective strains investigated. Table 2.1 lists primers
and probes details for IFAV, RSV (A&B), HMPV, RV-C and Glyceraldehyde-3-phosphate
dehydrogenase (GAPDH) assay
30
Table 2.1: Primers and probes used in quantitative real-time PCR assays
aLNA bases are underlined.
Target Target region Oligonucleotide sequence (Position) a
HMPV F (Forward Primer) Nucleoprotein gene 5’-ATCATCAGGYAAYATYCCACAAA-3’ (420 - 442)
HMPV R (Reverse Primer ) 5’- TATTAARGCACCTACACATAATAA-3’ (518 -542)
HMPV (Probe) 5’-FAM-CCTGCGTGGCTGCC-MGBNFQ-3’ (481- 497)
RSV-A F (Forward Primer) Nucleoprotein gene 5’CAACTTCTGTCATCCAGCAAA3’(1117 -1137)
RSV-A R (Reverse Primer) 5’TGCACATCATAATTAGGAGTATCAAT3’ (1166-1191)
RSV-A Probe 5’-FAM-CACCATCCAACGGAGC`3’-BHQ-1 (1140 – 1155)
RSV-B F (Forward Primer) Nucleoprotein gene 5’ATTCAACGTAGTACAGGAGATAATA3’ (1141 - 1165)
RSV-B R (Reverse Primer ) 5’CCACATAGTTTGTTTAGGTGTTT’ (1193 -1214)
RSV-B Probe 5’-FAM-TGACACTCCCAATTAT3’-BHQ-1 (1167 – 1182)
IFAV (Forward Primer) Matrix gene 5’CTTCTAACCGAGGTCGAAACGTA3’ (7-29)
IFAV (Reverse Primer ) 5’-GGTGACAGGATTGGTCTTGTCTTTA--3’ (137-161)
IFAV (Probe) 5’FAM-TCAGGCCCCCTCAAAGCCGAG3’-BHQ-1 (49-69)
RV-C IrlonS (Forward Primer) 5’UTR 5’-GCACTTCTGTTTCCCC-3’ (165 - 180)
RV-C EntA (Reverse Primer) 5’- GCATTCAGGGGCCGGAG-3’ (461 -445)
RV-C Probe 1 5’-FAM-CCTGCGTGGCTGCC-MGBNFQ-3’ (358 - 371)
RV-C Probe 2 5’-FAM-CCCGCGTGGCTGCC3’-BHQ-1 (359 - 372)
RV -C Probe 3 5’-FAM-CCCGCGTGGTGCCC-MGBNFQ-3’ (354 - 367)
RV -C Probe 4 5’ -FAM-CCTGCGTGGTGCCC3’-BHQ-1 (354 – 367)
GAPDH (forward) GAPDH mRNA 5’GAAGGTGAAGGTCGGAGTC3’ (7-25)
GAPDH (reverse) 5’AAATCCCATCACCATCTTC3’ (213-231)
GAPDH probe 5’-FAM-GGCTGAGAACGGGAAGCTTG-MGBNFQ
31
2.4 Production and quantification of transcribed RNA
standards Nucleotide sequences matching a segment of the 5' UTR of RV-C2 [EF077280.1], RV-C6
[EF582387], RV-C51 [JF317015],RV-C25 [JF317013], RSV A [KJ627348]&B [KU950585] NP gene,
HMPV NP gene [AHV79765], IFAV matrix gene [CY056296] were incorporated into individual
plasmid constructs manufactured by Integrated DNA Technologies (IDT, Australia). M13
forward (5′-GTA AAA CGA CGG CCA GT-3′) and reverse (CAG GAA ACA GCT ATG ACC) primers
were used to amplify the target sequence from each individual plasmid construct. The PCR
reaction mix (20µL) contained PCR buffer (Thermo Fisher Scientific Australia Pty Ltd), 2mM
MgCl2, 0.2 mM dNTP, 0.2µM primers (IDT, Australia), 0.5 U AmpliTaq Gold® (ThermoFisher
Scientific, Australia) and cycling conditions were as follows: 10 minutes at 95°C followed by 45
cycles of 30 seconds at 94°C, 45 seconds at 50 °C and 60 seconds at 72°C. Successful
amplification was confirmed by the detection of PCR products by agarose gel electrophoresis.
Post PCR purification was completed using ExoSAP-It reagent (Affymetrix, Ohio, USA) following
the manufacturer’s protocol.
The Megashortscript T7 high yield transcription kit (ThermoFisher Scientific, Australia) was
used to synthesize RNA in vitro. All transcription reactions were completed at 37°C for 16
hours followed by TURBO DNA-free™ DNase treatment, DNAse Removal and MEGAclear™
Transcription Clean-Up (ThermoFisher Scientific, Australia) following the manufacturer’s
instructions. The RNA transcripts were eluted in THE RNA Storage solution (ThermoFisher
Scientific, Australia), and stored in single-use aliquots at -80°C.
The RNA transcript was quantified using the Qubit RNA Broad Range assay on the Qubit 2.0
fluorometer (Life Technologies, USA). To assess the quantification accuracy of the Qubit 2.0
32
fluorometer all RNA transcripts were measured in triplicate. The conversion of RNA
concentration into RNA copies/µL was done with the following formulae:
1. M.W. of ssRNA = (RNA transcript length (bp) x 320.5) + 159.0
2. Number of molecules (copies) per ug ssRNA = Avogadros number (6.022*10^23) *
(M.W. of ssRNA)
3. RNA copies/µL= [RNA transcript concentration as per Qubit * number of molecules
(copies) per ug ssRNA] / (RNA transcript length)
2.5 Quantitative Real time PCR (Viral load)
The qScript XLT One-Step qRT-PCR Toughmix kit (Quanta Biosciences Gaithersburg, USA) was
utilized for the qRT-PCR assays. The 20 μL reaction volume contained 8 μL of template,
forward primer, reverse primer and hydrolysis probe. Thermocycler conditions were as
follows: 10 minutes at 50°C, followed by 3 minute incubation at 95°C then 40 cycles of 95oC for
20 seconds and 60°C for 60 seconds (80 seconds for RV-C assays to accommodate larger
amplicon size) using the Rotor Gene 6000 real-time thermocycler (Qiagen, Australia). All
experiments were performed in triplicate including positive controls and non-template
controls. We determined quantification cycle (Cq) values for each reaction using a manual Cq
threshold of 0.10 on the Rotor Gene 6000 application software.
In all experiments, a standard curve was generated by comparing Cq values and the copy
number. The reaction efficiencies of the assays were calculated according to the equation:
E=10(-1/M)-1, where M is the slope of the standard curve. An eight point standard curve (101 -
108 copies/µL), positive and negative control were mandatory on all verified runs. The viral
load in clinical samples was determined by interpolation of the quantification cycle value into
the appropriate standard curve.
33
The reliability and reproducibility of viral load quantification by RT-qPCR was assessed using
the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE)
guidelines (Bustin et al., 2009). Tenfold dilutions of each cRNA transcript were run in triplicate
to assess intra-assay variation. Inter-assay variation of Cq values was determined by analysing
data from five independent assays. Assay analytical sensitivity and specificity were also
evaluated.
2.6 Digital Droplet PCR
A laboratory developed test for RSV detection was used, together with a QX100 droplet digital
PCR system (Bio-Rad, USA). The ddPCR reaction mixture consisted of 7.5µL of a 5x ddPCR super
mix (Bio-Rad), 3µl of 2X reverse transcriptase, 15mM of DTT, 0.8mM of primers and 0.15mM of
probe mix and 8µL of sample nucleic acid solution in final volume of 20µL. The reaction mix
together with the 70µL of droplet generation oil was loaded into a di sposable plastic cartridge
and placed in the droplet generator (Bio-Rad). Following droplet generation each reaction-
sample mix was transferred to a 96 well PCR plate (BioRad). PCR amplification was performed
on a CFX-96 thermocycler (Bio-Rad) using a thermal profile beginning with a reverse
transcription step of 50oC for 30 minutes, then 95oC for 10 minutes followed by 40 cycles of
95°C for 30 seconds and 55°C for 60 seconds, 1 cycle of 98°C for 10 minutes, and ending at 4°C.
Following amplification, the plate was loaded on the droplet reader (Bio-Rad) and the droplets
from each well of the plate were read automatically at a rate of 32 wells per hour. DdPCR data
were analysed with QuantaSoft analysis software (Bio-Rad), and the quantification of the
target molecule was presented as the number of copies per µl of PCR mixture.
34
2.7 PCR reagents
One step cDNA synthesis and PCR was performed using Superscript III one step RT-PCR system
(Thermofisher, USA) or Quanta qScript XLT one step RT-qPCR tough mix (QuantaBio,USA).
Modifications to the manufacturers instruction included reducing the reaction volume to 20 µL
and the enzyme to 0.5 µL and the addition of iStarTaq DNA polymerase (0.5 units/ reaction)
(Intron Biotechnology, South Korea). Further modification included the addition of 0.9mM of
DTT. Asymmetrical primer concentrations (0.4mM and 0.8mM for forward and reverse primers
respectively) were used for all RV-C assays.
2.8 Gel Electrophoresis
Nucleic acids were mixed with loading buffer and run on a 1 % agarose gel . Electrophoresis
was performed in 1x TAE buffer with a voltage of 200 volts for the time required to obtain
satisfactory migration, typically 30 minutes. DNA was visualised under UV light on a
transilluminator and digitally photographed using LabWorks Image Acquisition and Analysis
Software (UVP Bioimaging Systems).
2.9 Thermocyclers
Conventional PCR assays were performed in either 96 well plates (Axygen, USA) sealed with
Aluminium sealing film (Axygen, USA) or 0.2mL PCR tubes (ThermoFisher, USA) cycled using an
AB 2700 thermocyclers (Applied Biosystems, USA).
Real-time PCR experiments were conducted in either 0.2ml PCR tubes (Axygen, USA), 72 or
100 tube rings (Qiagen, Australia) or 96 well plates (Bio-Rad) or 384 well plates (Axygen, USA).
The rings and plates were sealed with a heat treated film. All experiments were conducted on
35
the Rotorgene6000 thermocycler (Qiagen), the CFX-96 thermocycler (Bio-Rad) or The Applied
Biosystems ViiA 7 system (ThermoFisher).
2.10 Computational analysis
Specificity analysis was conducted using MegaBlast against human, viral and bacterial
sequences from the Genbank nucleotide collection.
Collection of target sequence for computational analysis was conducted using the virus
pathogen resource database (https://www.viprbrc.org/brc/home.spg?decorator=vipr), the
Picornavirus study group website (http://www.picornastudygroup.com/types/enterovirus/hrv-
c.html) and PathWest molecular diagnostics in-house respiratory virus database. Sequence of
the respective viral strains were downloaded and aligned to the appropriate primers and
probe sets. For each set of viral sequences, we determined the corresponding assays
numerical coverage at the strain level.
2.11 Multiplex immunoassay
The multiplex cytokine immunoassay kit was purchased from eBiosciences. Study samples
were sent to the manufacturer for processing. All samples were analysed for IFN-γ, IL-12p70,
IL-13, IL-1β, IL-2, IL-4, IL-5, IL-6, TNF-α, GM-CSF, IL-18, IL-10, IL-17A, IL-21, IL-22, IL-23, IL-27, IL-
9, IFN-α, IL-31, IL-15, IL-1α, IL-1RA, IL-7, TNF-β, Eotaxin, GRO-a, IL-8, IP-10, MCP-1, MIP-1b,
SDF-1 a, RANTES, IL-29 using a human cytokine 34-plex panel (eBiosciences, Australia)
36
2.12 Quality Control
Appropriate personal protective equipment was put on at all times. All equipment was
maintained and serviced at regular intervals. Molecular grade reagents were used at all time.
Prepared reagents were made in large volumes and stored in single use aliquots where
appropriate. Batches of reagents were quality checked before use.
Contamination control measures included a unidirectional work flow and separate rooms with
dedicated equipment. All experiments were performed in triplicate including positive controls
and non-template controls. Detection of glyceraldehyde 3-phosphate dehydrogenase (GAPDH)
mRNA was utilized to ensure adequate specimen collection, RNA extraction and detection of
PCR inhibitors. Good quality samples were considered to be those with GAPDH Cq values
below 31.5. Standard laboratory procedure was followed and validated according to the
National Pathology Accreditation Advisory Council guidelines.
2.13 Statistical Analysis
All statistical analyses were carried out using SPSS statistical software version 16.0 (IBM Inc
Chicago, USA.). Significant differences between groups were evaluated using One Way-ANOVA
test, Chi-square or Fisher’s exact tests. Associations of RSV load with ALRI, RSV subtype or
multiple infections were examined using the Mann-Whitney U test. In all statistical analysis a
p-value <0.05 was considered statistically significant.
2.14 Ethics Approval
The RSV study protocol (chapter 5) was approved by the University of Western Australia
Human Ethics Committee and the University of Pretoria Ethics Committee. Written informed
37
consent from parent/guardian was obtained prior to participation. The RV-C and disease
severity study (Chapter 6) was part of a larger study which was approved by the ethics
committee and Princess Margaret hospital in Perth, Australia where recruitment was
undertaken. Informed consent was obtained before inclusion in the study and the collection of
samples. Ethics approval was also obtained to conduct the study protocol used in chapter 7.
This approval was granted by Princess Margaret Hospital for Children Ethics Committee and
the Research Governance Office.
38
3 The design and development of quantitative detection assays
for the common causative viral pathogens of acute lower
respiratory tract infection
39
3.1 Introduction
Respiratory tract infections are a major cause of childhood morbidity and mortality worldwide
(Walker et al., 2013). Young infants, the elderly and the immunocompromised are population
groups most at risk of severe ALRI. Respiratory syncytial virus (RSV), influenza A viruses (IFAV)
and human metapneumovirus (HMPV) are the most common respiratory viruses identified in
these groups (Ruuskanen et al., 2011). These viruses are strongly associated with a wide range
of clinical manifestations ranging from mild upper respiratory tract infection to severe
pneumonia and death (Chidlow et al., 2012; Cohen et al., 2015b; Jafri et al., 2013; Moyes et al.,
2013; Pretorius et al., 2016; Takeyama et al., 2015a). The role of the virus in determining
disease severity is not clearly understood but several lines of evidence suggest that viral load is
an important contributor (Houben et al., 2010; Jansen et al., 2010; Li et al., 2010; Martin et al.,
2012). Therefore being able to accurately measure the amount of virus during illness may
provide insight into the effective contribution of the virus to disease outcome.
In many diagnostic laboratories reverse transcription quantitative PCR (RT-qPCR) is the gold
standard for the quantitation of respiratory viruses among hospitalized patients with ALRI
(Alsaleh et al., 2014; Chidlow et al., 2009; Do et al., 2010; Gueudin et al., 2003) . RT-qPCR is
preferred to other detection techniques because it provides superior detection sensitivities
and specificities and, of prime importance to this study, is the ability to quantify viral load in
clinical samples. Quantification of viral nucleic acid in clinical samples relies on the
amplification of the target sequence which in turn results in a detectable fluorescence signal
known as the quantification cycle value (Cq) (Niesters, 2001). Sample viral load is determined
by comparing the Cq value to a series of external standards representing serial dilutions of
known copy number. A crucial limitation of this approach is that accuracy of the calibration
40
curve is highly dependent on reaction efficiency, a parameter that can be skewed by inhibitors
(Verhaegen et al., 2016). Droplet digital PCR (ddPCR) on the other hand is relatively new
approach that improves on qPCR by making external standards obsolete in viral quantification
(Huggett, Cowen, and Foy, 2015). In a similar approach to qPCR, ddPCR involves the detection
of template sequence with either a SYBR green or hydrolysis probe reaction chemistry but
quantification is conducted differently. It involves the generation of a large library of emulsion
based droplets (~20,000), also termed partitions (Markey et al., 2010). These partitions are
generated from sample-reagent mixtures and are distributed in such a way that at least a
proportion of them contain no template molecules (this is done in order to se parate and
isolate single molecules). Results are obtained by tallying the number of partitions in which the
template sequence is detected compared to the number of partitions in which there is no
amplification (Huggett et al., 2013).
The genetic diversity observed in the genome of most RNA viruses is a major challenge that
precludes the design of accurate and reliable viral load assays. As such, the aim of this chapter
was to design PCR based quantification assays that provide overall coverage of the known
genetic variability and subsequently evaluate the analytical and clinical performances.
Additionally, to further understand the utility of ddPCR in the clinical virology setting this
chapter thoroughly evaluates the differences in analytical and clinical performances between
ddPCR and RT-qPCR using synthetic RSV RNA and clinical samples from patients following RSV
infection.
41
3.2 Samples
Clinical samples received at PathWest Laboratory Medicine WA were utilised for assay
validation. Specimen types included nasopharyngeal swabs, flocked nasal swabs and sputum.
Nucleic acid was extracted from samples using standard procedure (Chapter 2.2).
3.3 Results
3.3.1 Assay Design
The primer and probes sets of each assay were designed to target a highly conserved region of
the appropriate target sequence. Based on high quality multiple sequence alignment,
primer/probe sets were designed for the quantitative detection of HMPV (NP gene), RSV A and
B (NP gene) and Influenza A (matrix gene). Primer and probe sequence information including
target position for each assay are given in Table 3.1. Each assay consisted of two sequence
specific primers and a hydrolysis probe.
42
Table 3.1 Primers and probes used for respiratory virus quantification assays
Oligonucleotide Oligonucleotide sequence (Position) a
HMPV F (Forward Primer) 5’-ATCATCAGGYAAYATYCCACAAA-3’ (420 - 442)
HMPV R (Reverse Primer ) 5’- TATTAARGCACCTACACATAATAA-3’ (518 -542)
HMPV (Probe) 5’-FAM-CCTGCGTGGCTGCC-MGBNFQ-3’ (481- 497)
RSV-A F (Forward Primer) 5’CAACTTCTGTCATCCAGCAAA-3’(1117 -1137)
RSV-A R (Reverse Primer) 5’TGCACATCATAATTAGGAGTATCAAT-3’ (1166-1191)
RSV-A Probe 5’-FAM-CACCATCCAACGGAGC-3’-BHQ-1 (1140 – 1155)
RSV-B F (Forward Primer) 5’ATTCAACGTAGTACAGGAGATAATA-3’ (1141 - 1165)
RSV-B R (Reverse Primer ) 5’CCACATAGTTTGTTTAGGTGTTT-3’ (1193 -1214)
RSV-B Probe 5’-FAM-TGACACTCCCAATTAT-3’-BHQ-1 (1167 – 1182)
FLUA MAT F (Forward Primer) 5’CTTCTAACCGAGGTCGAAACGTA-3’ (7-29)
FLUA MAT (Reverse Primer ) 5’-GGTGACAGGATTGGTCTTGTCTTTA-3’ (137-161)
FLUA MAT Probe 5’FAM-TCAGGCCCCCTCAAAGCCGAG-3’-BHQ-1 (49-69)
43
3.3.2 In silico coverage analysis
In silico analysis was performed against 60 HMPV nucleocapsid sequences, 80 RSV -A and 75
RSV-B nucleocapsid sequences, and 80 IFAV matrix gene sequences retrieved from GenBank.
For each assay, representative sequences from different geographical regions over the last five
years were selected to provide coverage for most of the sequence diversity observed
worldwide. In silico analysis of the respective viral target sequences revealed negligible
sequence mismatches between target and primers/ probe sets. Subsequently, analytical and
clinical performances of each assay were evaluated to ensure satisfactory laboratory
performance.
3.3.3 The impact of PCR Master mixes on the variability of Cq values
In an attempt to identify which PCR master mix facilitates superior quantification results, a PCR
Master Mix comparison was performed between the Superscript III RT-PCR System and Quanta
qScript XLT one step RT-qPCR tough mix using 10 fold serial dilution of synthetic RNA of the
appropriate viral target. It can be seen in Fig. 3.1 that the two master mixes produced
comparable quantification values (Cq), ranging between 0.1 and 2 per dilution. When
amplification efficiencies of the two master mixes were compared, it was found that
amplification efficiency was consistently higher among assays prepared using the qScript XLT
one step RT-qPCR tough mix compared to assays prepared using Superscript III RT-PCR System
(Fig.3.2). Accordingly, the Quanta qScript XLT one step RT-qPCR tough mix was selected as the
master mix of choice to evaluate the laboratory performance of all the RT-qPCR assays
developed.
44
0
5
10
15
20
25
30
35
40
45
Qu
anti
fica
tio
n c
ycle
val
ue
(C
q)
Standard diluton
qScript XLT
SSIII
A
0
5
10
15
20
25
30
35
40
Qu
anti
fica
tio
n c
ycle
val
ue
(C
q)
Standard diluton
qScript XLT
SSIII
C
0
5
10
15
20
25
30
35
40
45
Qu
anti
fica
tio
n c
ycle
val
ue
(C
q)
Standard diluton
qScript XLT
SSIII
B
0
5
10
15
20
25
30
35
40
Qu
anti
fica
tio
n c
ycle
val
ue
(C
q)
Standard diluton
qScript XLT
SSIII
D
Figure 3.1 A comparison of two commercial master mixes for the quantitative detection of RSV-A (A), IFAV (B), HMPV (C), RSV-B (D). Purified synthetic RNA of each respiratory virus
was prepared and the assays were perfomed in triplicate in serial 10 fold dilutions. For each assay, the data is plotted as the average Cq value for the triplicate samples prepared in the respective mastermixes.
45
Figure 3.2 Comparison of amplification efficiency between qScript XLT mastermix and Superscript III RT-PCR System master mix. The assays prepared in the qScript XLT one step RT-qPCR tough mix generated significantly
higher amplification efficiencies than assays prepared in the Superscript III RT-PCR System (p=0.014).
3.3.4 Analytical Sensitivity
In order to evaluate the analytical performances of the developed quantification assays, it was
necessary to assess linearity, limit of detection, amplification efficiency and goodness of fit.
This experiment was performed using serial dilutions of the appropriate synthetic RNA
transcripts prepared in PCR grade water. The analyses shown in Table 3.2 indicate that the
respiratory quantification assays herein provided excellent amplification efficiencies with
minimal variation between runs, a wide linear dynamic range (>7 orders of magnitude) and
good agreement between theoretical values and experimental values.
88
90
92
94
96
98
100
RSV-A RSV-B IFAV HMPV
Am
plif
icat
ion
eff
icie
ncy
(%
)
Assay
qScript
SSIII
46
Table 3.2 Analytical performance data on IFAV, RSVA, RSVB and HMPV quantitative assays using synthetic R NA
transcript
IFAV RSV-A RSV-B HMPV
Mean ± SD %CV Mean ± SD %CV Mean ± SD %CV Mean ± SD %CV
Slope -3.33 ± 0.04 1.01 -3.35± 0.01 0.06 -3.32 ± 0.02 0.46 -3.31 ± 0.04 1.15
Efficiency 1.00 ± 0.01 1.46 0.99 ± 0.01 0.08 0.99 ± 0.01 0.64 1.01 ± 0.02 1.62
Y-intercept 39.77± 2.86 7.20 41.71 ± 1.57 3.75 40.01 ± 3.20 8.00 38.36 ± 1.33 3.46
Goodness of fit (R2) 0.999 0.01 0.999 0.00 0.999 0.01 0.999 0.00
Range of Linearity 101-108 101-108 101-108 101-108
Limit of Detection
(copies/µL)
7 7 6 4
3.3.5 Analytical specificity
The specificity of each assay was evaluated with nucleic acid extracts from other respiratory
pathogens, including influenza A and B, human respiratory syncytial virus, parainfluenza
viruses 1-4, human adenovirus, human bocavirus, human coronaviruses (HCoV-229E, HCoV-NL-
63, HCoV-OC43, and HCoV-HKU-1), Mycoplasma pneumoniae and Streptococcus pneumoniae.
All assays were non-reactive when tested against these other respiratory pathogens. In
addition, a BLASTn search performed to check the specificity of the primer and probe sets used
in the assays showed no genomic cross-reactivity with other virus families, bacteria or cells.
47
3.3.6 Repeatability and Reproducibility
Assay reproducibility and repeatability were evaluated using four artificial respiratory samples
constructed from high (106), moderate (104 and 102) and low (101) copy number for each viral
type. Repeatability and reproducibility of each quantitative detection assay was evaluated in
triplicate and over five independent experiments respectively. Repeatability which was
defined as the degree of variation between replicates within the same run demonstrated
relatively low coefficient of variation (CV) for all assays(table 3.3). As can be clearly seen from
table 3.3 repeatability was a factor of input copy number. Reproducibility, which was defined
as the degree of variation between runs demonstrated a coefficient of variation of less than
20%, with variability increasing proportionally with dilution of transcript.
48
Table 3.3 Repeatability and reproducibility of quantitative detection assays for HMPV, IFAV, RSV-A and RSV-B
Repeatability a Reproducibility b
RNA target and input target copies
Quantity range (Calculated copies/ reaction)
%CV range
Quantity Mean (Calculated copies/reaction)
%CV
HMPV 106 1270000-1420000 0.50-3.01 1,327,400 3
HMPV 104 10260-12230 0.65- 3.76 13,443 4
HMPV 102 100-140 0.91-4.20 121 7
HMPV 101 11-15 6.09-14.89
15 11
RSV-A 106 1370000-1460000 0.70-2.30 1,435,000 7
RSV-A 104 13500-14200 0.58-2.90 13,898 10
RSV-A 102 135-143 0.89-3.60 139 14
RSV-A 101 12-17 8.03-15.40
15 16
RSV-B 106 1301000 - 1408000 0.65-2.48 1,376,667 5
RSV-B 104 13300-15060 0.48-2.78 14,118 8
RSV-B 102 101-140 0.91-3.74 123 12
RSV-B 101 10-19 5.03-10.06
14 14
IFAV 106 7390000-8180000 0.91-2.63 7807500 6
IFAV 104 75900-83900 1.02-3.05 79848 11
IFAV 102 705-778 1.34-4.83 741 15
IFAV 101 59-83 4.99-11.48
69 18
% CV – Percentage coefficient of variation a Assays were performed in triplicate b Five independent experiments
49
3.3.7 Evaluation of Clinical specimens
Respiratory samples (n=100) were obtained from hospitalised patients with an acute lower
respiratory tract previously screened for a broad range of respiratory pathogens using
standard diagnostic PCRs, that were reactive for either HMPV (n=20), RSV-A (n=20) , RSV-B
(n=20) or IFAV (H1N1[n=20] and H3N2 [n=20]). All samples met the criteria for adequate
sample collection and nucleic acid extraction with GAPDH Cq values of less than 31. Fig.3
clearly shows that each assay is capable of determining a broad range of viral loads in clinical
samples.
Figure 3.3 Box plots of viral loads from patients infected with either IFAV (n=40), HMPV (n=20), RSV-A (n=20),
RSV-B (n=20). Viral load values ranged between 2 log10 copies/mL and 10 log10 copies/mL.
50
3.3.8 Real-time PCR vs Digital droplet PCR
It was of interest to compare the analytical and clinical performance of the current gold
standard for molecular quantification (RT-qPCR) to the relatively new digital droplet PCR (RT-
ddPCR). It was too costly an exercise to test all 4 assays thus the RSV-A assay was selected
arbitrarily for this comparison.
Dilutions of in vitro transcribed RSV-A RNA were tested to evaluate analytical performance
characteristics such as accuracy, limit of detection, linearity, and repeatability. Figure 3.4
shows the relationship between measured RNA concentration and nominal RNA concentration
of the two PCR approaches using in vitro synthesized RSV-A RNA. It can be seen from this
figure that both methods showed a strong linear (>0.98) relationship between predicted and
observed values and that RT-qPCR was slightly more accurate than RT-ddPCR (as determined
by the slope value of the trend line). It was also noted that the quantitative value assigned by
both PCR instruments to the RNA standards across the concentration range was approximately
a 1.0 log10 lower than the expected copy number.
51
Further analytical performance characteristics are illustrated in Fig.3.5. It is evident from these
data that the qPCR assay demonstrated a wider linear dynamic range (spanning more than 6
orders of magnitude) compared to ddPCR assay, which was subject to droplet saturation at
every instance where input RNA concentration was higher than 105 copies. However, the RT-
ddPCR assay demonstrated a lower detection limit compared to the RT-qPCR assay (2 copies vs
7 copies). Importantly for the validity and reliability of the ddPCR data was the clear
discrimination between positive and negative droplets at all measurable RNA input
concentrations, specifically at low concentrations (Fig. 3.5C).
RT-qPCR
y = x - 0.9075
R² = 1
RT-ddPCR y = 0.8843x - 0.4405
R² = 0.9856 0
2
4
6
8
0 2 4 6 8 10
Me
asu
red
RN
A c
on
cen
trat
ion
(lo
g10
co
pie
s/m
L)
Nominal RNA concentration (Log10 copies/mL)
RT-qpcr
RT-ddpcr
Linear (RT-qpcr)
Linear (RT-ddpcr)
Fig. 3.4 A comparison of accuracy between RSV-A RT-qPCR assay and RSV– A RT-ddPCR assay. Each point signifies the mean log10 copy number of duplicate samples. Accuracy was determined using the slope value of the trend line.
52
Figure 3.5 Linear quantitative range of RSV-A assay constructed using serial dilutions of artificially synthesized RSV-A RNA and performed using either the RT-ddPCR assay (A) or RT-qPCR (B). RT-qPCR demonstrated a broader linear dynamic range but ddPCR demonstrated higher analytical sensitivity. RFU (Relative fluorescence units) measured in nanometres. (C) A 2D amplitude plot for varying RNA input concentrations (100-105) on the ddPCR instrument. The red line designates the threshold used to discriminate between clusters of positive and
negative droplets.
B
A
C
53
Repeatability of each method was evaluated using replicates of in vitro RSV RNA transcript at
varying dilutions (104, 103,101,100). It is clear from Fig. 3.6 that the ddPCR assay exhibited
superior precision compared to the qPCR assay, especially at lower concentrations. The high
CV observed at 1 copy (RT-ddPCR) can be attributed to the stochasticity of the platform when
approaching its limit of detection.
Figure 3.6 Precision evaluations between RT-ddPCR and RT-qPCR. %CV was calculated by dividing the standard deviation over the mean of the replicate values. Overall and at each
concentration, RT-ddPCR demonstrated superior precision compared to RT-qPCR.
0
10
20
30
40
50
60
70
1.00E+04 1.00E+03 1.00E+01 1.00E+00
Co
eff
. o
f va
riat
ion
(%
)
RSV-A copies/µL
RT-ddPCR RT-qPCR
54
3.3.9 Clinical evaluation of RT-ddPCR
Finally, to evaluate the clinical applicability of RT-ddPCR, 22 known RSV-A positive clinical
samples were each tested in duplicate with the two PCR methods and compared for positivity
rate and goodness of agreement (Fig 3.7). The analysis showed no difference in positivity rate
between the two methods. However, RT-ddPCR assay could not accurately assign viral copy
number to clinical samples (n=5) with input concentration greater than 105 copies but this was
not a problem with the qPCR assay. Figure 3.7 shows a Bland-Altman plot employed to assess
goodness of agreement between the two platforms. As may be seen from the plot, there was
no significant difference in copy number. The difference values of the measurable samples
(n=17) were within ± 1.96 standard deviations (0.31 log10 copies/mL) of the mean difference.
Further, we found no significant trend in proportions above or below the mean.
Figure 3.7 Bland-Altman plot. Viral loads of known RSV-A positive clinical samples were used for agreement evaluation between the RT-ddPCR and RT-qPCR. Mean differences and the
95% limits of agreement were calculated and are illustrated in the graph above.
55
3.4 Discussion
This study describes the development and validation of accurate and robust qPCR assays that
can be utilized for the quantitative determination of viral load for a range of clinically relevant
viral respiratory pathogens. These viruses are most commonly associated with severe lower
respiratory tract infections and as such it is imperative that the virological factors driving
disease severity are understood. Indeed, recent reports emphasize that viral load maybe a
significant risk factor associated with disease outcome in hospitalized children (DeVincenzo et
al., 2010; El Saleeby et al., 2011; Roussy et al., 2014; To et al., 2010). Thus, the development of
reliable quantification assays provides an essential tool to examine the effective contribution
of the viral pathogen to disease outcome.
The design of primer and probe sets for any given assay should be a cautious approach to
ensure the assays developed are sensitive for the target and specific enough to be non-
reactive to unsuitable genomic material. Moreover, when designing assays it is important to
target highly conserved regions and not regions with a high incidence of mutations; since
mismatches between assay and target sequence may result in grossly inaccurate viral load
measurements (Hoffman et al., 2008; Letowski, Brousseau, and Masson, 2004; Randhawa et
al., 2011; Whiley and Sloots, 2005). For that reason we chose the nucleocapsid gene (HMPV,
RSV-A and RSV-B) and the matrix gene of IFAV as amplification targets. In silico analysis
indicated that the primer and probe sequences of all the assays provided satisfactory
coverage when aligned to the appropriate sequences that represented the known gene tic
variation worldwide over the last five years. Further, in silico analysis indicated that our assays
provided coverage for the known genetic variation within the appropriate target sequence
with minimal mismatches, crucially no mismatches in the probe target region. It has to be
56
acknowledged that In in silico sequence homology between target and assay primer and probe
sets is not an accurate predictor of test performance (Morales and Holben, 2009). Therefore it
was important to evaluate performance of these assays using clinical material. Indeed, the
assays herein demonstrated excellent clinical performance indicating that these assays can be
utilised in a diagnostic setting.
Owing to an increase in the availability of commercial master mix kits, diagnostic laboratories
face the challenge of identifying the most appropriate reagents for their real time PCR
applications. Indeed, it has been demonstrated that different reaction components can affect
assay performance including assay sensitivity and reproducibility (Stephens, Hutchins, and
Dauphin, 2010; West and Sawyer, 2006). This chapter examined two master mixes for use in
the quantitative detection of HMPV, IFAV, RSV-A and B. Comparison of Quanta qScript XLT one
step RT-qPCR tough mix and the Superscript III RT-PCR System, after the application of the
appropriate synthetic RNA to the respective assays revealed no difference between the two
kits in terms of linear dynamic range. This was not surprising as the technical bulletin provided
with each system generally supported this. However, a significant discrepancy in amplification
efficiency was observed between the assays prepared in Quanta qScript XLT and the
superscript III system. We found that Quanta qScript XLT yielded consistently better
amplification efficiencies in comparison to assays prepared in Superscript III RT-PCR System.
One may speculate that the variability in amplification efficiency maybe a result of
thermocycler performance (Stephens et al., 2010), alternatively it may also indicate that
Quanta qScript XLT one step RT-qPCR tough mix facilitates a more efficient cDNA synthesis
compared to Superscript III system. In concordance with our finding is a previous study that
evaluated the laboratory performance of four RT systems and showed that the Superscript III
system was one of the worst performing RT kits in terms RNA-to-cDNA conversion capacity
57
(Levesque-Sergerie et al., 2007). Taken together, this finding underscores the importance of
the reverse transcription step in developing accurate quantification assays. It al so emphasizes
the importance of evaluating PCR master mixes to ensure a suitable selection is made based
on the individual laboratory needs.
All assays demonstrated high amplification efficiencies, broad dynamic range and were non-
reactive when tested against other respiratory pathogens which suggest that all assays can
accurately quantify a wide range of viral loads with remarkably high specificity. Further, the
assays herein demonstrated relatively low intra-assay and inter-assay variation indicating that
data generated from these assays are reliable and reproducible.
Contention still exists about which PCR technology provides accurate and reliable viral load
data. Thus, performance characteristics of ddPCR were compared to qPCR for the quantitative
detection of RSV in clinical samples was evaluated using an already optimized RT-qPCR
protocol adapted for RT-ddPCR. Hayden et al. (2013) showed in their work that qPCR
demonstrated superior sensitivity and less variability across the concentration range for
clinical samples and reference material compared to ddPCR. Yet, in direct contrast to the
Hayden et al. (2013) report are recent studies that indicate that ddPCR demonstrates superior
precision and sensitivity across the measurable concentration range. For example, work
comparing ddPCR to qPCR for quantitative determination of hepatitis B viral load
demonstrated that ddPCR provides improved analytical sensitivity and specificity and as such,
is suitable for hepatitis B viral load determination in clinical samples (Tang et al., 2016). In
other recent work Palatnik de Sousa et al. (2015) evaluated ddPCR for influenza vaccine
development and demonstrated a high throughput ddPCR method for very precise and
accurate influenza virus titre quantification. The authors also noted several key issues that are
58
determinants of variability in qPCR were avoided with the ddPCR approach. Coudray-Meunier
et al. (2016) report that ddPCR provided improved precision and analytical sensitivity and
concluded that digital PCR may have an important tool in human pathogenic virus surveillance
and outbreak investigation and may be beneficial to public health. The finding herein is
consistent with reports that suggest that sample partitioning provides superior sensitivity and
precision compared to qPCR. Our analyses also demonstrated an excellent level of agreement
for viral load values in clinical samples between the two PCR approaches which indicates that
ddPCR can be utilized as an alternative platform for the reliable absolute quantification of RSV
(and possibly other pathogenic respiratory viruses) in clinical samples (Coudray-Meunier et al.,
2016; Hayden et al., 2013; Tang et al., 2016). A major drawback of ddPCR is the finite amount
of droplets (~15,000) that can be generated by the current ddPCR instrument. This drawback
results in a relatively narrow linear dynamic range with a remarkably low upper limit of
quantification (105copies; ~21 Cq) compared to qPCR. Other work report similar inaccurate
measurements when sample copy number exceeds 105 copies (Coudray-Meunier et al., 2016;
Palatnik de Sousa et al., 2015). Indeed, sample dilution to an input concentration that falls
within the linear dynamic range of the ddPCR assay may mitigate this problem (Palatnik de
Sousa et al., 2015). Additionally, RT-ddPCR has relatively larger turnaround times compared to
qPCR (6.5hrs vs 3.5 hrs), thus may need to be streamlined prior to being introduced into the
clinical setting. Nonetheless, the findings of this study showed that ddPCR fulfils most of the
requirements of a reliable molecular quantification tool for the field of clinical virology, with
the added value of avoiding the need for calibrators. Theoretically the high precision and
sensitivity provided by ddPCR lends itself to detection of rare variants in mixed vi ral
populations that may arise from adapting to challenges such as the host immune response and
anti-viral therapeutics. However, to achieve its full potential in the clinical setting ddPCR needs
59
to be further optimized to match the linear dynamic range of RT-qPCR (>9 orders of
magnitude).
There are limitations to acknowledge. Firstly there is a wide range of respiratory samples and
collection methods, which may have an impact on the resulting quantitative analysis.
Secondly, it has previously been shown that mismatch between primer and probe sequences
arising from sequence variability in the target region is an important determinant of inaccurate
viral load. Although a thorough analysis was undertaken to accommodate spatial and temporal
sequence variation for the respective assay target regions. Sequence analysis cannot predict
future point mutations thus ongoing surveillance is required to monitor changes in the target
sequence. Thirdly, accurate quantitative detection of viral target sequence relies on properly
constructed standard curves. This means technical errors in pipetting and preparation of
standards may substantially contribute to inaccuracy. Also it is important to note that optimal
RNA to cDNA conversion is a crucial determinant of accurate quantification measurement
because both RT-ddPCR and RT-qPCR can only measure the amount of DNA that is present.
Developing an accurate and reliable method for viral quantification is essential when
attempting to establish standardized definitions for clinical disease and for therapeutic
response. Pertinent to the correct interpretation of data generated from these assays is a
thorough interrogation of the developed assays prior to implementation in the clinical setting.
It appears that qPCR remains the gold standard for the molecular quantification of viral nucleic
acid in clinical samples but with further instrument optimization ddPCR can provide a useful
alternative.
60
61
4 The development of a reliable PCR assay to measure RV-C load
in clinical samples
62
4.1 Introduction
Rhinoviruses (RV) are a common cause of acute respiratory infection in people of all ages
(Fawkner-Corbett et al., 2015; Puro et al., 2005). RVs are antigenically diverse, thus people can
be infected with different RV types over the course of a lifetime (Cooney, Fox, and Kenny,
1982; Cooney et al., 1975). The spectrum of disease associated with this group of viruses can
range from asymptomatic infection, mild upper respiratory tract infection (common cold) to
severe lower respiratory tract infections which may include bronchiolitis or pneumonia (Choi
et al., 2015; Iwane et al., 2011; Luchsinger et al., 2014). RV has been identified as an important
contributor to acute asthma (Bizzintino et al., 2011) and wheezing illness in young children and
is an important risk factor in the development of asthma later in life (Jackson et al., 2008).
Further, RV exacerbates pre-existing airway diseases such as asthma, cystic fibrosis and
chronic obstructive pulmonary diseases (Camargo et al., 2012; Kennedy et al., 2014;
Luchsinger et al., 2014).
Genome arrangement, capsid properties and conserved sequences are the current basis of RV
species classification, of which there are three recognized species (RV -A, RV-B, and RV-C)
(McIntyre, Knowles, and Simmonds, 2013). RV-C is the most recently described species and to
date there are 55 recognised genotypes. Clinical significance of RV-C is still debated as some
studies report that RV-C causes more severe disease than the other two species (Bizzintino et
al., 2011; Cox et al., 2013; Miller et al., 2009) but others have not found this association (Iwane
et al., 2011; Linsuwanon et al., 2009). Inaccurate quantitative methods complicate the
evaluation of viral factors that may contribute to disease severity (Schibler et al., 2012). Viral
load studies of other viruses have shown that the amount of replicating virus is an important
63
contributor to disease severity (Franz et al., 2010; Jansen et al., 2010), thus an accurate and
reliable method of quantifying RV-C load may be an important tool in understanding the
contribution of RV-C to disease pathogenesis, disease progression and clinical management.
Conventional culture methods used to measure viral load in clinical samples are not suitable
for RV-C types as this virus is non-cultivable using traditional techniques. Molecular methods
have overcome this problem, but the inter-genotype sequence variation within the target
region prevents the design of a single quantitative assay that quantifies all genotypes at equal
efficiencies (Schibler et al., 2012). Furthermore, developing specific primer and probe
combinations for each genotype would be impractical in a diagnostic setting. This study aims
to develop and validate a minimum set of PCR assays required to quantify circulating RV-C
genotypes found in children.
64
4.2 Samples
Nasopharyngeal aspirates NPAs (n = 40) were collected between June 2013 and April 2015
from children presenting to the Emergency Department of Princess Margaret Hospital for
Children in Perth. These samples represented a subset of children with episodic wheeze
enrolled in The Prednisolone Response Evaluation in Viral Induced Episodic Wheeze (PREVIEW)
study. This study was approved by the Princess Margaret Hospital for Children Ethics
Committee (1970/EP).
Total nucleic acids were extracted from 200µL of each respiratory specimen using the
MagMAX viral RNA isolation kit (Chapter 2.2) according to the manufacturer’s instructions.
Detection of the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) reference mRNA
(Gueudin et al., 2003) was utilized to ensure adequate specimen collection, RNA extraction
and removal of PCR inhibitors. Good quality samples were considered those with GAPDH
quantification cycle values (Cq) no higher than 2 standard deviations (6.4) of the mean Cq value
(25.1). Analysis was not performed on samples above the predetermined accepted range.
The primers used in this study were based on previously published primer sequences (Table 1)
which amplify a 296bp region within the 5’ untranslated region (UTR) of RV species (Gama et
al., 1988; Ireland, Kent, and Nicholson, 1993). Rhinovirus species identification was performed
using a published semi nested PCR assay (Ireland et al., 1993) followed by sequencing.
Two hundred and thirty four RV-C 5’ UTR sequences which represented 34/55 of the currently
known RV-C genotypes were evaluated for the design of the minimum amount of probe
sequences required to overcome the inter-genotypic variation within the target region . These
sequences were obtained from our in-house RV-C database (n=218) and the Picornaviridae
65
study group website (n=16) (http://www.picornastudygroup.com). Nucleotide sequence
alignments were analysed using BioEdit Sequence Alignment Editor Version 7.2.5 (Hall, 1999).
The reliability and reproducibility of RV-C viral load quantification by RT-qPCR was assessed
using the Minimum Information for Publication of Quantitative Real -Time PCR Experiments
(MIQE) guidelines (Bustin et al., 2009). Tenfold dilutions of each cRNA transcript were tested in
triplicate to assess intra-assay variation. Inter-assay variation of Cq values was determined by
analysing data from five independent assays.
Using the appropriate RNA transcript for each RV-C assay, a tenfold dilution series of twelve
concentrations was prepared. The second last dilution of the tenfold dilution series was used
to prepare a two-fold dilution series of 10 concentrations. 8µL of each dilution from the two-
fold dilution series was added to a 12 µL PCR reaction mix and run for 50 PCR cycles using the
Rotor Gene 6000 real-time thermocycler (Qiagen, Australia). Twenty-four PCR replicates were
tested at each concentration. Poisson regression analysis was used to determine the limit for a
95% confidence of detection. To evaluate variability these experiments were repeated on five
different occasions.
Analytical specificity was assessed using BLAST searches against other virus families, bacteria
and cell sequences from the Genbank nucleotide collection. In addition, an in -house cross
reactivity panel was used to assess the specificity of our RV-C assays against other respiratory
pathogens.
66
4.3 Results
4.3.1 Validation of real-time PCR assay for RV-C viral load quantification
We designed four assays based on RV-C 5’UTR sequences belonging to the 34 RV-C genotypes
for which 5’UTR sequences were available. All assays used a common primer pair, but with
different specific probe sequences (Fig 4.3-1). In silico analysis demonstrated that the probe
sequence of assay-1 was homologous to the probe target region of 22 of the 34 RV-C
genotypes, while the probe target region of the remaining 12 genotypes aligned completely to
the probe in either assay- 2, -3 or-4 (Appendix 10.1). These assays were unable to be assessed
against the other 21 known genotypes because the 5’UTR sequence information was
unavailable.
Figure 4.3-1 A BioEdit sequence alignment of primer and probe regions that were targeted by assays one to four. Sequences of
forward primer region (left box), probe region (center box) and reverse primer region (right box). Identical bases at the same position are represented by dots whereas capitalized bases indicate mismatches between sequences.
All qPCR assays were optimized for primer concentration and annealing/extension
temperature. The optimal annealing temperature was determined to be 60oC with a
denaturation time of 20 seconds and an annealing/extension time of 80 seconds. The PCR
conditions were selected to produce the maximum fluorescent signal generated after 40
amplification cycles.
Serial 2-fold dilutions of 10 concentrations of each RNA transcript was prepared in PCR-grade
water and tested to determine the limit of detection of the assays. Using Poisson regression
67
analysis, the limit for a 95% probability of detection was estimated to be 1147 copies/mL for
assay-1, and 4765 copies/mL, 1138 copies/mL and 1470 copies/mL respectively for assays 2-4.
Nucleic acid extracts from other respiratory pathogens, including influenza A and B, human
respiratory syncytial virus, human metapneumovirus, parainfluenza viruses 1-4, human
adenovirus, human bocavirus, human coronaviruses (HCoV-229E, HCoV-NL-63, HCoV-OC43,
and HCoV-HKU-1), Mycoplasma pneumoniae and Streptococcus pneumoniae were non-
reactive in each of the RV-C real-time assays. In addition, a BLASTn search performed to check
the specificity of the primer and probe sets used in the assays showed no genomic cross -
reactivity with other virus families, bacteria or cells. However as anticipated there was cross
reactivity with other enterovirus species.
Linearity was assessed in triplicate over five independent experiments, and in all assays it
spanned more than 7 orders of magnitude (Table 4.3-1). All assays demonstrated a strong
linear relationship (r2=>0.995) between Cq values and RNA copy number (Table 4.3-2). All
assays demonstrated amplification efficiencies of more than 95% (Table 4.3-2).
68
Table 4.3-1 A comparison of RNA transcript concentration and Cq values for the different RV-C assays
RV-C Assay-1 RV-C Assay-2 RV-C Assay-3 RV-C Assay-4
RNA transcript
concentration
Mean Cq +/-
SD
Mean Cq +/-
SD
Mean Cq +/-
SD
Mean Cq +/-
SD
100 32.77+/-0.14 33.25+/-1.48 32.66+/-0.31 31.84+/-1.01
101 29.11+/-0.12 28.22+/-0.46 29.62+/-0.06 27.37+/-0.07
102 25.86+/-0.11 25.37+/-0.06 25.95+/-0.07 24.20+/-0.14
103 22.07+/-0.06 22.25+/-0.07 22.52+/-0.04 20.96+/-0.03
104 18.68+/-0.06 18.91+/-0.06 19.08+/-0.08 17.53+/-0.10
105 15.35+/-0.04 15.39+/-0.04 15.52+/-0.11 14.20+/-0.14
106 11.73+/-0.1 13.13+/-0.11 12.05+/-0.08 10.79+/-0.04
107 8.5+/-0.04 8.78+/-0.02 8.55+/-0.02 7.81+/-0.06
108 5.27+/-0.12 5.17+/-0.2 5.19+/-0.16 4.77+/-0.07
% CV – Percentage coefficient variation, Cq- quanti fication cycle va lue , SD- s tandard deviation
69
Table 4.3-2 the performance of the individual PCR assays for the detection of matched RV-C RNA transcript
RV-C Assay-1 RV-C Assay-2 RV-C Assay-3 RV-C Assay-4
Mean +/- SD CV% Mean +/- SD CV% Mean +/- SD CV% Mean +/- SD CV%
Slope (n=) -3.32+/-0.11 3.33 -3.38+/-0.11 3.23 -3.44+/-0.09 2.72 -3.37+/-0.05 1.52
Efficiency 0.98+/-0.05 5.02 0.97+/-0.05 4.68 0.95+/-0.04 3.93 0.97+/-0.03 3.12
Y-intercept 34.00+/-2.46 7.23 38.00+/-2.72 7.17 36.12+/- 1.00 7.55 33.45 +/- 1.59 8.42
Goodness of fit (R2) 0.999 0.11 0.999 0.47 0.999 0.09 0.999 0.08
Range of Linearity 100-108 copies/ml 100-108 copies/ml 100-108 copies/ml 100-108 copies/ml
70
4.3.2 Repeatability and reproducibility
To evaluate repeatability and reproducibility of each assay, dilutions (101,102,104,106) of RNA
transcript were tested in triplicate. Intra-assay %CV of the four RV-C assays ranged from 0.10%
- 8.58% and in most cases variability increased proportionally with dilution (Table 4.3). Inter-
assay variability was evaluated using results from five independent experiments and
demonstrated %CV of less than 15% (Table 4.3).
Table 4.3-3 Intra and Inter assay variability of the four RV-C qRT-PCR assays (Assay 1-4)
Intra-assay variation a
Inter-assay variation b
RNA target and input target copies
Quantity range (Calculated copies/ reaction)
%CV range Quantity Mean (Calculated copies/reaction)
%CV
RV-C1 106 3030000-3560000 0.16-2.39 3480000 6.89
RV-C1 104 30000-36700 0.27-2.33 37500 5.67
RV-C1 102 125-409 0.16-7.07 361 8.92
RV-C1 101 21-40 2.1-7.33 39 5.88
.
RV-C2 106 3560000-4210000 0.22-0.61 3810000 7.50
RV-C2 104 39100-49700 1.70 -2.42 43400 10.56
RV-C2 102 379-405 1.44 -2.75 396 3.04
RV-C2 101 35-42 3.89-5.82 39 7.22
RV-C3 106 4080000-5790000 0.37-2.30 4760000 14.58
RV-C3 104 35700-46200 0.23-1.23 42000 9.26
RV-C3 102 372-470 0.40-8.58 440 9.21
RV-C3 101 42-59 0.93-7.78 47 14.57
RV-C4 106 4020000-4860000 0.10-1.77 4360000 8.22
RV-C4 104 48500-52800 0.28-1.61 50000 4.89
RV-C4 102 423-608 1.07-4.76 525 11.05
RV-C4 101 42-49 1.47-5.16 46 5.36
% CV – Percentage coefficient variation a Assays were performed in tripl icate b five independent experiments
71
4.3.3 Probe mismatch
To demonstrate the need for separate assays this study examined the impact of probe -target
sequence mismatches on viral load. Each RV-C transcript was prepared at seven different
concentrations ranging from 107 to 101 copies/µL with the calculated copy numbers (means of
three experiments) for each transcript expressed as percentages of the copy number obtained
with the perfectly matched RV-C transcript-1. At concentrations between 107 and 102
copies/µL there was minimal (<15%) difference in copy number yield between transcript-1 and
transcript-2. However, at the lowest copy number (101), a single nucleotide mismatch (near
the 5’end) in the probe target region (Fig. 4.3-1) resulted in an inaccurate viral load
determination (Table 4.3-4). Multiple mismatches between the probe and target (transcript-3
and-4) resulted in substantial inaccuracy in RV-C load measurement across the
concentration range (Table 4.3-4).
Table 4.3-4 Variation in calculated copy number yield (%) of transcripts 1-4 compared to the number of
probe mismatches
1Each RV-C transcript was tested at 7 di fferent concentrations ranging from 10
1 – 10
7 copies/ µL in RV Assay-1. The
ca lculated copy numbers (means of three independent experiments) for each transcript i s presented as a percentage of the perfectly matched RV-transcript 1.
Calculated copy number of transcripts 2-4 1
Probe mismatches
107 106 105 104 103 102 101
Transcripts
1 100% 100% 100% 100% 100% 100% 100%
2 95% 90% 76% 72% 90% 87% 28% 1
3 <1% <1% <1% <1% <1% <1% <1% 4
4 7% 10% 12% 5% 10% 3% 3% 3
72
4.3.4 Clinical studies
An algorithm was developed to guide viral load determination for RV-C positive samples (Fig.
4.3-2). Using this algorithm RV-C positive samples (n=40) from children presenting with acute
wheeze were matched to the appropriate assay.
Figure 4.3-2 the algorithm for the determination of RV-C viral load in clinical samples
Sample
Picornavirus PCR
Species Identification by sequence analysis
RV-C matched to appropriate probe (in silico) sequence
Viral Load determined
Detected
RV-C
73
In this group of patients, a total of 23 genotypes were identified with the most commonly
detected genotypes being C-16 (n=5), C-35 (n=3), C-42 (n=3), C-14 (n=3) and C-11 (n=3) (see
Appendix 10.1). In silico analysis demonstrated that assay-1 aligned completely with the target
region of 24/40 (16/23 genotypes) samples. Assays 2-4 were suitable for the remaining
genotypes (n=7) (see appendix 10.1). As can be clearly s seen in Figure 4.3-3, all 40 samples
had there RV-C load determined using one of the assays developed herein. Assay 1 provided
coverage for the majority of samples (27/40, 67.5%) . With assays 2-4 providing coverage for
the remaining samples (Fig.4.3-3). All samples met criteria for adequate sample collection and
nucleic acid extraction with GAPDH Cq values within the accepted range (see Appendix 10.1).
74
Figure 4.3-3: Box plots of RV-C load in samples from young children presenting to the Emergency Department
with acute wheeze.
75
4.4 Discussion
This study presents the development and validation of four qRT-PCR assays that, in
combination are able to accurately and reliably measure the viral load of circulating RV -C
genotypes.
Reports of an association between RV-C infection and severe respiratory disease have been
mixed as some studies have found an association (Bizzintino et al., 2011; Bochkov et al., 2011;
Camargo et al., 2012; Piralla et al., 2009) but others have not (Iwane et al., 2011; Linsuwanon
et al., 2009). Similar to other acute viral respiratory tract infections where a correlation exists
between viral load and disease severity (DeVincenzo et al., 2010; Li et al., 2010; Roussy et al.,
2014; To et al., 2010), it is suspected that RV-C load may also drive disease severity. Inaccurate
quantitative methods complicate the evaluation of viral factors that may contribute to disease
severity (Schibler et al., 2012). Previously published quantitative assays have tried to address
the genetic heterogeneity of RV-C by either using an intercalating dye in place of a specific
hydrolysis probe or adding degenerate bases in either the primer or probe sequence (Bochkov
et al., 2011; Granados et al., 2012). However, accuracy maybe impaired since these techniques
may lead to non-specific amplification and reduced amplification efficiencies of the assays
(Chemidlin Prevost-Boure et al., 2011; Schibler et al., 2012).
In this study, performance evaluation of each assay was conducted in accordance with the
MIQE guidelines (Bustin et al., 2009). A portion in the 5’UTR was chosen as a target for our
assays since previous work has demonstrated that 5’UTR sequence can be used for RV -C
genotypic assignment at almost identical accuracy to the VP4/VP2 and VP1 and with superior
clinical sensitivity (Lee et al., 2012). All assays demonstrated a broad dynamic range, high
sensitivity, efficiency and performance in RV-C viral load determination with both clinical
76
samples and in vitro RNA transcripts. Viral load in our respiratory samples were within the 1x
103 and 1x1012 copies/ mL range which is in concordance with previous publications (Li et al.,
2010; Roussy et al., 2014; Schibler et al., 2012). All assays demonstrated high repeatability and
reproducibility with CV of below 8% and 15% respectively. All assays were nonreactive with a
range of other potential respiratory pathogens but cross-reactivity with other picornavirus
species requires sequencing of the 5’UTR to confirm RV -C identification prior to quantification.
This was also needed to determine the appropriate primer-probe combination.
To accurately measure viral load in clinical samples it is vital that primers and probes are
designed to match the target sequence. Indeed, previously published studies have shown that
the positioning of the mismatch is a crucial determinant of probe binding affinity (Benovoy,
Kwan, and Majewski, 2008; Letowski, Brousseau, and Masson, 2004) which may in turn impact
upon the accuracy of the calculated viral load. Mismatches throughout or near the middle of
the probe target region destabilize hybridization more than those near the ends (Letowski et
al., 2004). Another recent study demonstrated that multiple mismatches in the probe target
region may have a greater impact on accuracy than a single mismatch (Randhawa et al., 2011).
This is consistent with our findings which showed that at most dilutions a single probe
mismatch between assay probe and transcript material had minimal impact on accuracy but
when multiple mismatches were present there was a substantial effect on viral load
measurement across the dilution range. Together, these findings demonstrate the need for
multiple qRT-PCR assays to achieve accurate RV-C loads for the different genotypes. However,
we were able to show that this could be achieved with a small numbe r of assays, requiring
only four different probes to cover the 34 genotypes with known 5’UTR sequences. It is
anticipated that these four assays will cover more of the RV-C genotypes, but that awaits
further sequence data.
77
Fortunately other studies have demonstrated that while a large proportion of RV-C genotypes
circulate simultaneously in various geographical regions worldwide they are dominated by C-1,
C-2, C-6, C-16, C-17, C-18 and C-43 genotypes (Lu et al., 2014; McIntyre et al., 2013). All of
these genotypes were quantified at equal efficiencies in this study, suggesting that our assays
can be used to accurately determine RV-C load of various genotypes from different
geographical regions as well as to properly investigate differences in pathogenesis between
RV-C genotypes. A limitation of the current method is it cannot reveal the presence of a mixed
infection and therefore may not be able to accurately quantify the viral load of each genotype
present.
In conclusion, this study describes a reliable and accurate PCR based method of quantifying
RV-C load in clinical samples containing a wide range of RV-C genotypes. These assays will
provide a reliable tool for investigating the role of RV-C in respiratory illness, and for
evaluating the effectiveness of future antiviral therapies.
78
5. The Quantitative Detection of Respiratory Syncytial Virus
in Hospitalized Young South African Children
79
5.1 Introduction
Acute lower respiratory tract infections (ALRIs) are a leading cause of morbidity and
mortality in infants worldwide. ALRIs add an extensive health and economic burden
especially in developing nations, where 99% of all ALRI-related deaths occur (Denny
Jr., 2001; Nair et al., 2013). Respiratory syncytial virus (RSV) is the predominant
etiological agent of acute lower respiratory tract infection in children under the age of
five years. In children under the age of five years, RSV contributes to approximately 33
million episodes per year (Nair et al., 2010). RSV is estimated to account for 85% of
bronchiolitis cases and 20% of childhood pneumonia cases (Nair et al., 2011b). The
peak age of RSV bronchiolitis is 1—2 months and it is the most common cause of
hospitalization during infancy (Kim, Lee, and Lee, 2000). It is well established that
virtually all children have been exposed to RSV by the age of three years, with the
majority of children being infected in their first year of life. Furthermore between the
ages of one and 12 years-old RSV is associated with more deaths than influenza
(Fleming, Pannell, and Cross, 2005). Several risk factors have been associated with
severe RSV disease including malnutrition (Paynter et al., 2014), immunodeficiency
(Englund, Anderson, and Rhame, 1991), premature birth, and congenital heart and
lung disease (Zhang et al., 2014). Several lines of evidence suggest that severe RSV
bronchiolitis during infancy (Henderson et al., 2005; Schauer et al., 2002; Sigurs et al.,
2010; Wennergren and Kristjánsson, 2001) and early childhood (Sigurs et al., 2000)
may increase the risk of developing asthma.
80
RSV is classified into antigenic subtypes (RSV-A and RSV-B) based on amino acid
sequence composition of the attachment G glycoprotein (Mufson et al., 1985). Both
antigenic subtypes frequently circulate simultaneously but the proportion of infection
due to each differs each season (Akerlind and Norrby, 1986). Although epidemiological
studies have shown that RSV-A dominant seasons are generally associated with more
reports of severe illness (McConnochie et al., 1990) and suggest that RSV subtype may
influence pathogenicity, there are conflicting reports about which subtype is more
pathogenic (Hirsh et al., 2014; Xiang et al., 2013).
The presence of co-infecting pathogens has been suggested as a possible
pathogenicity determinant (Franz et al., 2010). Associations between higher viral loads
and poorer clinical outcomes in patients with RSV and other respiratory infections
have been reported (Bagga et al., 2013; Drews et al., 1997; Foulongne et al., 2006;
Franz et al., 2010; Li et al., 2010; Roussy et al., 2014), but this remains contentious
(Martin et al., 2012; Wang et al., 2014).
Several studies have described the epidemiology of RSV in South Africa (Cohen et al.,
2015a; Cohen et al., 2015c; Cohen et al., 2015d; Pretorius et al., 2013; Tempia et al.,
2015; van Niekerk and Venter, 2011), but none have focused on the importance of
viral load as a potential predictor of clinical outcome among hospitalized patients. We
undertook this study to investigate the relationship between RSV load and clinical
disease among young children (<2 years) in a country with a high HIV prevalence, to
examine the role of RSV subtype and viral co-infections in the pathogenesis of ALRI in
infants.
81
5.2 Samples
Nasopharyngeal aspirates (NPAs) were collected from 105 ALRI cases and 53 controls
from Pretoria, South Africa between July 2011 and November 2012. Children 0-2 years
of age hospitalized with an ALRI at the Steve Biko Academic Hospital or Tshwane
District Hospital in Pretoria were enrolled as cases. A diagnosis of pneumonia
(respiratory distress and either chest X-ray changes (e.g. consolidation or effusion), or
auscultatory findings (e.g. crepitations or bronchial breathing) or bronchiolitis
(respiratory distress and at least one of the following; wheeze, chest X-ray changes
(e.g. hyperinflation) or Hoovers’s sign ( inward movement of the lower rib cage during
inspiration) was determined by the clinician in charge. Fifty-three age-matched
children presenting to the same hospitals with a non-respiratory illness over the same
period were enrolled as controls (NRD controls). The ARCHITECT HIV Ag/Ab Combo
assay (Abbott Diagnostics) was used to screen patients for the presence of human
immunodeficiency virus (HIV). The Amplicor HIV-1 DNA PCR assay (Roche Diagnostics,
Branchburg, NJ) was used to confirm HIV status. At enrolment, baseline characteristics
and clinical symptoms were collected by the physician using a detailed questionnaire.
The study protocol was approved by the University of Western Australia Human Ethics
Committee and the University of Pretoria Ethics Committee. Written informed consent
from parent/guardian was obtained prior to participation.
All RSV positive samples (RSV A/RSV B) by multiplex PCR (Chidlow et al., 2009) were
confirmed and viral load determined using an in-house monoplex RT-qPCR protocol
targeting the nucleoprotein gene as described in chapter 3. RT-qPCR analysis was
performed on the Rotor Gene 6000 cycler (QIAGEN, Australia). All experiments were
82
performed in triplicate including positive controls and non-template controls.
Detection of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA (Gueudin et
al., 2003) was utilized to ensure adequate specimen collection, RNA extraction and
detection of PCR inhibitors. Good quality samples were considered to be those with
GAPDH CT values below 31.5.
5.3 Results
5.3.1 Baseline characteristics
A nasopharyngeal aspirate (NPA) was obtained from 158 children (105 cases and 53
NRD controls). Of the 105 cases, 57 (54%) children were diagnosed with pneumonia
and 48 (46%) children were diagnosed with bronchiolitis (Table 5.3-1).
83
Table 5.3-1 Demographic and clinical details of study participants
ALRI cases (n=105) NRD Controls (n=53) P
RSV-positive (n=27)
RSV-negative (n=78)
RSV-positive (n=9)
RSV-negative (n=44)
Age (wks.) mean ± SD 21 ± 14 32 ± 28 33 ± 26 39 ± 31 0.03#
Gender (%) NS
Male 18 (66.7) 53 (67.9) 5 (55.6) 25 (56.8)
Weight (kg) mean± SD 6.6 ± 2.4 6.4 ± 2.8 6.0 ± 3.1 6.6 ± 2.8 NS¶
Race (%) NS¶
Black 15 (55.6) 68 (87.2) 8 (88.9) 36 (81.8)
White 10 (37) 5 (6.4) 1(11.1) 5 (11.4)
Other 2 (7.4) 5 (6.4) 0 3 (6.8)
HIV status (%) 0.018¶
Positive 0 15^ (19.2) 1 (11.1) 1 (2.3)
Negative 25 (92.6) 60 (76.9) 8 (88.9) 39 (88.6)
Unknown 2 (7.4) 3 (3.8) 0 4 (9.1)
Allergies (%) NS
Yes 0 (0) 3 (2.9) 0 2 (4.5)
No 27 (100) 75 (96.2) 9 (100) 41 (93.2)
Unsure 0 (0) 0 (0) 0 1 (2.3)
Clinical Diagnosis (%) <0.01¶
Pneumonia 7 (25.9) 50 (64) - -
Bronchiolitis 20 (74.1) 28 (36) - -
# Comparison of ages between RSV positive ALRI cases, RSV negative ALRI cases and NRD controls (ANOVA) ¶ Comparisons of proportions between RSV positive ALRI cases vs RSV negative ALRI cases and NRD controls (Fisher’s exact test) ^ No respiratory virus was detected in 67% (10/15) of HIV infected children NS: non-significant, SD - standard deviation.
84
A respiratory virus was detected in 88/105 (84%) ALRI patients compared with 37/53
(70%) NRD controls (p=0.041). Rhinoviruses (51/105, 49%), hAdV (33/105, 31%) and
RSV (27/105, 27%) were the most frequently detected viral pathogens in children with
an ALRI, but were also found in 40%, 28% and 17% of NRD control samples
respectively (Fig. 5.3-1). None of the differences for the individual viruses reached
statistical significance.
Figure 5.3-1: Viruses detected in NPAs collected from ALRI cases and NRD controls. RV (RV-A, RV-B, RV-
C), HCoV (OC43, 229E, HKU-1, NL63), HPIV (PIV I-IV), IFV (A/H1N1, A/H3N2, B and C)
5.3.2 RSV infection and clinical outcome
In patients with ALRI, age was significantly associated with RSV disease (Table 5.3-1),
45% of RSV associated ALRI cases were infected within their first two months of life
(Fig.5.3-2). In addition, children in their first year of life were at a significantly
increased risk of RSV associated ALRI (OR: 9.6, 95%CI: 1.2, 75.7, p=0.011).
85
Twenty of 27 (74%) RSV-positive ALRI cases were classified as bronchiolitis and 7/27
(26%) were classified as pneumonia. RSV was significantly more common in
bronchiolitis cases than pneumonia cases (p<0.001). In non-RSV associated ALRI cases,
50/78 (64%) patients were classified as clinical pneumonia and 28/78 (36%) were
classified as bronchiolitis (Table 5.3-1).
Analysis by antigenic subtype found that RSV-A (18/105, 17%) was more common than
RSV-B (9/105, 9%) in ALRI cases, but RSV-A (8/53, 9%) was also found more often than
RSV-B (1/53, 2%) in NRD controls. Therefore, the proportion of RSV -B detected in
patients with an ALRI compared to that in NRD controls suggest a tendency for RSV-B
to cause more disease than RSV-A, although the association did not reach statistical
significance. However both RSV-A (n=18, 72% vs 28%, p=0.01) and RSV-B (n=9, 71% vs
29%, p=0.04) were significantly more common in bronchiolitis cases than in
pneumonia cases.
86
Figure 5.3-2: The distribution of RSV positive and RSV-negative ALRI cases by age. RSV disease was more
prevalent in children within their first year of life. Peak hospitalization rate was observed in the 0-2 month age group.
87
5.3.3 RSV disease and HIV infection
Interestingly, RSV was significantly less common among HIV infected ALRI patients
compared to HIV uninfected ALRI patients (Table 5.3-1). Clinical pneumonia was the
more likely diagnosis in HIV infected ALRI patients [14/15, (93%) vs 1/15, (7%);
p=0.0013]. As demonstrated in fig. 5.3-3, a respiratory virus was encountered in 33%
(5/15) of cases with parainfluenza viruses being the predominant viral pathogen in this
group of patients.
Figure 5.3-3: Distribution of respiratory viruses detected in HIV infected ALRI patients
hCoV 7% hAdV
7%
hPIV 22%
virus not detected
64%
88
5.3.4 RSV load
To obtain a more accurate estimate of the association between RSV infection and
clinical outcome, RSV load studies were performed. Median RSV load in ALRI patients
(8.2 log10 RSV RNA copies/mL [IQR: 6.3-9.1] n=27) was 1.9 log10 copies higher than in
NRD controls (6.3 log10 RSV RNA copies/mL [IQR: 4.6-7.4] n=9) p=0.031 (figure 5.3-4).
Figure 5.3-4: A box plot comparing RSV load between ALRI cases and NRD controls
This analysis revealed no significant difference in median viral load between ALRI
patients infected with RSV-B compared to those infected with RSV-A (8.2 log10 RSV-B
copies/mL [IQR: 6.4-9.3] vs 8.2 log 10 RSV-A copies/ mL, IQR [6.9-8.6]) (Fig 5.3-5).
Overall RSV load was not associated with a type of ALRI (pneumonia or bronchiolitis),
with the median RSV load being 7.3 log10 copies/mL (IQR: 5.6 – 8.2) in children
diagnosed with pneumonia compared to 8.6 log10 copies/mL (IQR: 6.7 – 9.4) in
children diagnosed with bronchiolitis (Fig.5.3-5).
89
Figure 5.3-5 A box plot comparing RSV load by subtype and clinical diagnosis
Finally, we sought to understand the contribution of viral co-infection to patient
outcome. Viral co-infections were detected in 38/105 (36%) ALRI patient samples and
in 18/53 (34%) NRD control samples with RSV, hAdV or RV being the most frequently
detected viruses in both sample groups. Our analysis revealed a significant association
between viral coinfection and RSV detection in NRD controls (cases: 12/27 (44%) vs
NRD controls: 8/9 (89%) p=0.026). RSV load did not significantly differ among ALRI
patients when RSV was detected alone or in the presence of other viruses (Fig.5.3-6).
90
Figure 5.3-6: A box plot comparing RSV loads in ALRI cases with a viral co-infection [n=12; RSV with either hAdV
(75%, n=9), hCoV (17%, n=2) or RV (8%, n=1)] versus sole RSV infection (n=15).
91
5.4 Discussion
We conducted a study to assess the impact of RSV load on clinical outcomes in a
previously unexamined population of South African children, and where there is a high
prevalence of HIV infection. As in previous studies in other populations we found that
higher RSV loads were associated with clinical illness (El Saleeby et al., 2011; Fodha et
al., 2007; Houben et al., 2010; Utokaparch et al., 2011). This is also consistent with the
suggestion that clinical illness due to RSV infection is a direct virus-mediated
phenomenon (Bagga et al., 2013; DeVincenzo et al., 2010).
Interestingly, we found that RSV and other viral pathogens were found less commonly
in HIV infected children than in non-HIV infected children, and when a viral pathogen
was detected, nearly all of the children had pneumonia rather than bronchiolitis. This
was surprising given that viral pathogens especially RSV contribute substantially to
ALRI in children under five (Nair et al., 2010). However, as most of the HIV-infected
children had pneumonia, the low RSV rates may reflect the high number of alternative
causes in these children. Aetiological studies of pneumonia in HIV infected children
reveal that non-viral pathogens in particular Streptococcus pneumoniae,
Staphylococcus aureus, gram-negative bacteria and Pneumocystis jirovecii
predominantly contribute to hospitalization and death from pneumonia (B-Lajoie et
al., 2016; Gray and Zar, 2010; Lanaspa et al., 2015), presumably related to
immunosuppression (Madhi et al., 2000; Marais et al., 2006; Zash et al., 2016). It is not
clear why RSV-associated ALRI was so uncommon in these children. However we
acknowledge that current findings are limited by sample size and therefore further
investigations are needed to clarify and determine clinical implications.
92
Our analysis revealed that the prevalence of RSV is relatively high in asymptomatic
South African children. Studies in other geographical settings report much lower RSV
prevalence rates (Jartti et al., 2008; Self et al., 2015). However, similar asymptomatic
detection rates (17% and 42%) have been reported in Kenyan children (Matu et al.,
2014; Munywoki et al., 2015) possibly indicating that asymptomatic RSV detection may
be important in the epidemiology of RSV, at least in some populations. We also found
that in asymptomatic individuals, RSV A was more commonly detected and at
comparatively higher titres than RSV-B. RSV-A has been found to shed at higher titres
and for a lengthier period than RSV-B (Takeyama et al., 2015b). Collectively, this may
suggest that the RSV subtype circulating in a population and its association with illness
may be determined by both host characteristics and viral titre.
Differences in disease severity between RSV subtypes are controversial; some studies
have reported that RSV A is more virulent than RSV-B (Hornsleth et al., 1998; Jafri et
al., 2013; Papadopoulos et al., 2004), whilst other studies have reported contrary
results (Fodha et al., 2007; Oliveira et al., 2008; Xiang et al., 2013). Our analysis in
hospitalized cases revealed no significant difference in viral load between the
antigenic subtypes.
The role of viral co-infections in disease outcome remains a controversial topic,
especially in population groups where viral co-infections are common. Some studies
have reported that viral co-infections result in poorer clinical outcome compared to
single respiratory virus infections (Drews et al., 1997; Foulongne et al., 2006; Franz et
al., 2010; Greensill et al., 2003), but this has not been supported in other publications
(Martin et al., 2012; Wang et al., 2014). Since we found that RSV load was related to
93
disease, we expected that if viral co-infections contribute to disease causation, then
the RSV load in co-infected patients (ALRI cases) would be lower than in RSV mono-
infected ALRI cases. This was not the case and RSV load was similar whether RSV was
detected alone or in the presence of other viruses, suggesting that the co-infecting
virus did not contribute to disease. HAdVs were the most common co-infecting virus,
being detected in 75% of the RSV co-infections. HAdVs were frequently detected in
NPA specimens from both our ALRI cases and NRD controls. Previous studies have
found that hAdV pathogenicity may be species-specific (Chidlow et al., 2012) and that
hAdV DNA persists for several weeks following clinical or subclinical infection (Demian
et al., 2014; Robinson et al., 2011). It is therefore possible that any effect of mixed
infections compared to monoinfection may have been masked by a high incidence of
detection of non-pathogenic hAdV species. Overall, additional investigations with
measures of the immunological response in combination with disease severity
indicators will be required to extend our understanding of this phenomenon.
Our study was limited in several ways. First, the small sample size limited our ability to
reach statistical significance for many findings. Second, we only had clinical diagnoses
without detailed data on disease severity. Third, no follow up of the NRD controls was
conducted after enrolment thus we do not know whether some individuals developed
ALRI in the days after sample collection. Fourth, we were unable to consider the role
of bacterial-viral interactions which may have a pertinent role in disease outcome.
Fifth, RSV data was collected over a single season and hence may not be
representative of long term transmission dynamics of RSV in this region. Lastly, studies
of hospitalized children may not reflect wider community impact of this virus.
94
In conclusion, our study shows that RSV is a significant contributor to infant morbidity
in young South African children, and viral load maybe an important predictor of
disease. Although a number of observations in this chapter are confirmatory, this
confirmation is important. The study was conducted in South Africa, a region
previously with little data on the viral kinetics of RSV disease in young children.
Furthermore, it is in a part of the world widely recognised to have a high HIV
prevalence among young children. The findings of this chapter clearly demonstrate
that the causative agents of acute lower respiratory tract infection in young children
infected with HIV are diverse and not isolated to the commonly detected respiratory
viruses in the uninfected HIV population. In addition, our study provides valuable
region and cultural specific data which is pertinent to accurately defining the global
burden of childhood RSV disease. Given the small samples size and the lack of precise
clinical and laboratory data, future study is needed to determine confirm the observed
regional differences and to further describe the importance of regional factors (i.e.
HIV infection, nutritional status, asthma prevalence and bacterial co-infection) on RSV
disease severity.
95
6 Determinants of acute asthma exacerbation severity following
RV-C infection
96
6.1 Introduction
Acute Asthma Exacerbation (AAE) is a leading cause of severe morbidity and hospital ization in
children with airway disease (Murray et al., 2006). AAE is characterized by airway
inflammation, airflow obstruction and airway hyper-responsiveness that results in the rapid
decline in lung function. A substantial recruitment of inflammatory cells to the respiratory
airways is the fundamental basis of airway inflammation. These inflammatory cells and the
products they secrete contribute to airway obstruction in part through plugging of the airways
(Fahy, 2009; Zhao et al., 2002). Respiratory viral infection is the most frequent trigger of AAE
(Johnston et al., 2005a). Clinical and epidemiological findings have shown that viral induced
exacerbations associated with as many as 80% of cases in children (Johnston et al., 2005a).
Other triggers that may result in an AAE include allergens, environmental pollutants, and
occupational irritants. The type of trigger uniquely shapes the pattern of airway infl ammation
during an AAE and this may have implication on clinical management (Berry et al., 2007; Holt
et al., 2010; Leung et al., 2013; Oommen, McNally, and Grigg, 2003; Zhao et al., 2002).
The relationship between respiratory viral infection and exacerbation of asthma disease has
been suspected for a long time, though maybe underestimated. Sensitive molecular diagnostic
methods have substantially improved the detection of respiratory viruses in AAE cases and
consequently reinforced the association between respiratory viral infections and AAE. Viruses
are detected in up to 85% of AAE, the most common type being rhinovirus (RV). RV as a sole
pathogen is detected in approximately 60% of AAE (Denlinger et al., 2011; Jackson et al.,
2008b; Khetsuriani et al., 2008; Miller et al., 2012). In fact, rhinovirus is the most common
virus in near fatal asthma exacerbations. The pathogenesis of AAE triggered by viral infections
is yet to be elucidated. Furthermore, a causal relationship is difficult to establish, since not
97
every asthmatic patient with a viral respiratory tract infection will experience AAE. Further
complicating this are studies that have demonstrated asymptomatic detection of the same
viral types in healthy children (Camargo et al., 2012; Jartti et al., 2008).
Previous studies report that RV-C infected asthmatic patients were more likely to be
hospitalized than patients infected with other RV species (Bizzintino et al., 2011a; Linsuwanon
et al., 2009). The lack of a reliable method to quantify the many genotypes of RV -C has
precluded investigations into the contribution of RV-C on severity of disease. However we
have previously developed (see chapter 4) a comprehensive RV -C quantification assay to
facilitate investigation in to whether severity of AAE is driven by the amount of virus in the
airways.
We hypothesized that RV-C load significantly differs between children with a severe AAE and
non-severe AAE and that the amount RV-C in the airways is driving an inflammatory response
that determines the severity of disease.
6.2 Samples
Participants (n=121) up to the age of 10 were recruited from Princess Margaret Hospital
(Perth, Australia). Of the 121 study participants, ninety-nine presented with an acute asthma
exacerbation of varying severity. Twenty-two otherwise healthy children from the community
matched for age and sex attending clinic formed the healthy control group. All AAE-related ED
encounters during the study period were classified as "mild", "moderate" or "severe",
according clinical practice guidelines (Royal Children's Hospital, 2015) that include vital and
readily available signs and symptoms, including pulse rate, presence of respiratory wheezes,
rales, oxygen saturation, and the use of accessory muscles, measured upon arrival to the ED.
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All medical records of ED and hospital admissions were reviewed for treatment and outcome s.
Blood was collected from each participant for full blood count analysis by clinical staff.
Nucleic acid was extracted from 200µL of clinical samples using automated extraction method
described in chapter 2. Nucleic acid was tested using a multiplex PCR for the common
respiratory pathogens including respiratory syncytial virus, human metapneumovirus, human
parainfluenza viruses, influenza viruses and adenovirus (Chidlow et al., 2009). Rhinovirus
screening and genotyping was performed at the Telethon Kids Institute using previously
published primers (Lee et al., 2012b). The algorithm described in chapter 4 for RV-C viral load
determination was applied to any sample that was picornavirus positive. Viral load
determination was performed on the Rotor Gene 6000 cycler (QIAGEN, Australia). All
experiments were performed in triplicate including positive controls and non-template
controls. Detection of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA (Gueudin
et al., 2003) was utilized to ensure adequate specimen collection, RNA extraction and
detection of PCR inhibitors. Good quality samples were considered to be those with GAPDH CT
values below 31.5.
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6.3 Results
6.3.1 Study participants characteristics
A total of 121 children were enrolled; 99 presented to hospital with an acute asthma
exacerbation (AAE) (cases) and 22 were non-respiratory disease community controls (NRD-
controls). We encountered no statistically significant difference between cases and controls
for age and sex (Table 6.3-1). Furthermore no significant differences in age, sex, atopy status
and history of wheeze were encountered when cases were stratified by RV -C detection. As
can be seen from Table 1, children presenting with acute asthma exacerbation following RV-C
infection were generally atopic (73%). The majority of these children were classified with a
moderate (40%) or severe (48%) asthma exacerbation. Only 4% of cases were admitted to the
ICU, the remaining cases were either discharged at ED (64%) or admitted into an in-patient
ward (28%).
Table 6.3-1 Baseline characteristics of children with acute asthma exacerbation (AAE) and controls
RV-C positive AAE (n=29)
RV-C negative AAE (n=70)
Controls without asthma (n=22)
p-value
Mean age ± SD, yr. 5.1 ± 3.3 4.86 ± 3.7 5 ± 4.2 NS
Male % 56 57% 52 NS
Previous wheezing
episodes (%)
36 31 - NS
Atopic % 73 60 45 NS Definition of abbreviations: SD= standard deviation, yr. = year, atopy determined by skin prick test, NS: not significant. Mann-Whitney U testwas used to test comparisons of continuous variables ¶ Fisher’s exact test was used to test comparisons of categorical variables.
6.3.2 Virus Detection
We examined NPAs of children with acute asthma exacerbation (AAE) symptoms and non-
respiratory disease community controls for the presence of viral pathogens and found that one
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or more viral types were detected in 75% (74/ 99) of samples from children with an AAE and in
50% (11/22) NRD control samples (p=0.03). As can be seen in figure 6.3-1, RVs (47/74, 63%),
parainfluenza virus (HPIV) (11/74, 15%) and respiratory syncytial virus (RSV) (9/74, 12%) were
the most prevalent viruses in patients with AAE (Fig.6.3-1).
Figure 6.3-1 A bar graph comparing the frequency (%) of viruses detected in cases and controls. Rhinovirus-C (RV-C), Rhinovirus-A (RV-A), parainfluenza virus (PIV), respiratory syncytial virus (RSV), human adenovirus (hAdV), Influenza viruses (IFV), human corona virus (hCoV) Rhinovirus-B (RV-B) and human metapneumovirus (hMPV)
Interestingly, RV-C was found to be the most prevalent of the RV species when cases were
stratified by species type. Further, this analysis revealed that study participants infected with
RV-C were 2.6 times more likely to be AAE patients than NRD controls (Table 6.3-2). RSV and
HPIV were the other viruses that may have increased the risk of asthma exacerbation following
infection.
0
5
10
15
20
25
30
35
RV-C RV-A PIV RSV hAdV IFV hCoV RV-B hMPV
Fre
qu
en
cy o
f d
ete
ctio
ns
(%)
Virus
Cases
Controls
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Table 6.3-2: A statistical summary of the risk of being diagnosed with acute asthma exacerbation follwoing
respiratory virus detection
Controls (n=22)
Cases (n=99)
OR (95%CI) p-value
RV-C 3 29 2.6 (0.9-9.6) 0.07
RV-A 4 17 0.9 (0.3-3.1) 0.5
RV-B 0 1 N/A N/A
ADV 0 6 N/A N/A
Influenza 2 4 0.4 (0.07-2.5) 0.3
RSV 1 9 2.1 (0.2-17.5) 0.4
HMPV 2 1 0.2 (0.13-3.6) 0.3
HCOV 3 3 0.2 (0.037-1.1) 0.07
HPIV 2 11 1.3 (0.3-6.1) 0.9
This study also aimed to understand if viral coinfection may be a risk factor of asthma
exacerbation. Overall, the detection rate for viral coinfection was not significantly different
between in NRD controls (5/22, 23%) and AAE cases (14/99, 14%). Further, of the 29 RV-C
infected AAE case, six were identified as coinfections but the combination of viruses detected
did not increase the risk of disease or severity (p>0.1).
6.3.3 RV-C load
In order to gain further understanding on the contribution of RV-C infection on asthma
exacerbation, viral load was determined in 21/29 RV-C infected AAE cases and 2/3 NRD
controls. As clearly demonstrated in figure 6.3-2, viral load was 2.6 log10 copies/mL higher in
children presenting with AAE compared to NRD controls (6.6 log10 copies/mL; IQR: 5.1-8.2 vs
4.0 log10 copies/mL; p=0.0235).
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Fig 6.3-2: Box plot summarising RV-C load in children presenting to emergency department with an acute asthma exacerbation (cases) and otherwise healthy individuals with a non-respiratory disease (controls). Median RV-C load of AAE cases was 2.6 log10 copies/mL higher than that of the non-respiratory disease control group .
In order to investigate the contribution of RV-C load to disease severity, we stratified AAE
cases into mild, moderate and severe groups based on the Royal Children’s Hospital clinical
guidelines (Royal Children's Hospital, 2015). Because of inadequate number of cases in the
mild group, we categorised mild and moderate cases as non- severe. Using this stratification
system we did not observe any significant difference in RV-C load between the severe group
compared to the non-severe group (6.6 log10 copies/mL; IQR: 4.7-8.3 vs 6.5 log10 copies/mL;
IQR: 5.2-8.2, p=0.98) figure 6.3-3).
103
Figure 6.3-3: A boxplot of RV-C loads for cases(n=21) stratified by disease severity and controls (n=2). Groups were compared using the Mann-Whitney U test,. median viral load between the two severity groups did not
differ significantly.
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6.3.4 Surrogate markers of inflammation
Recruitment of inflammatory cells to the airways is central to the cascade of events that result
in AAE. Neutrophil counts and eosinophil counts have previously been shown to be important
markers of illness severity in AAE (Wenzel et al., 1999). Therefore we analysed levels of these
factors to understand their role in illness severity. Our analysis revealed that peripheral blood
neutrophil counts (median: 83% IQR: 67-92% vs 62% IQR: 59-75%, p=0.049) but not eosinophil
counts (median: 0.1% IQR: 0-4% vs 1% IQR: 0.04-2%, P>0.1) were associated with severe
disease (Figure 6.3-4A,B). An elevated serum IgE was observed in patients with severe AAE
compared to patients with non-severe AAE (median: 35 kU/L IQR:5-136 kU/L vs median: 16
kU/L IQR: 3-76 kU/L) but this observation did not reach statistical significance (Figure 6.3-4C).
Despite the association of high neutrophil count and severe AAE following RV -C infection, no
corresponding statistically significant correlation was observed between RV-C load and
neutrophil count.
105
A
*
*
*
Figure 6.3-4 Surrogate markers of asthma exacerbation in acute samples from patients infected with RV-C stratified by illness severity. A) Absolute neutrophil peripheral blood counts B) absolute eosinophils peripheral blood counts C) Total serum IgE levels counts. Mann- Whitney U or Kruksall-Wallis test were used for testing on the apporpriate group of
subjects. All data are expressed as box and whisker plots. *:p=0.05 (severe vs non-severe) ** p<0.001 (all AAE cases vs NRD controls)
106
Figure 6.3-4 B) A comparison of absolute eosinophils peripheral blood counts patients with severe AAE, non-
severe AAE and control group.
B
107
Figure 6.3-4 C) A comparison of total serum IgE levels between severe patients and non- severe patients
C
108
6.3.5 Performance of RV-C load, neutrophils and eosinophils in predicting
the severity AAE
The performance characteristics of RV-C load, absolute neutrophils counts, eosinophil counts
and serum IgE levels as single biomarkers of severe AAE were analysed. The ROC curves of the
four biomarkers are shown in figure 5a and 5b. Area under the curve (AUCs) for RV-C load,
absolute neutrophil count, eosinophil count and serum IgE levels were 0.58 (95%CI; 0.243-
0.912, p=NS), 0.92 (95%CI; 0.81-1.0, p= 0.03), 0.51 (95% CI; 0.1-0.9, p=NS), 0.53 (95%CI; 0.2-
0.88, p=NS) respectively. Neutrophil counts alone were an excellent predictor of severe AAE
(Figure 6.3-5a). Importantly, RV-C load, absolute eosinophil count and serum IgE levels were
not added to the prediction model because of their poor performance as single biomarkers
(Figure 6.3-5b).
Figure 6.3-5 Receiver operator characteristic curves (ROC) of markers of AAE severity. A) Absolute neutrophil count and b) RV-C load, eosinophil count and serum IgE.
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6.4 Discussion
This study revealed that RV is the most common viral agent detected in hospitali sed patients
with an AAE and that RV-C appears to be the predominant of the three species. Furthermore,
the RV-C load in the airways of asthmatic children during an acute exacerbation does not
appear to be associated with illness severity and is a poor predictor of severe AAE. However, a
host response that includes a predominantly neutrophil inflammatory pattern and elevated
levels of IgE was associated with severity of illness.
Several previous studies report viral detection rates of between 62% and 85% in exacerbating
asthmatic children (Heymann et al., 2004; Johnston et al., 2005a; Leung et al., 2013; Rakes et
al., 1999) as opposed to detection rates of 3-12% when asthmatic children were asymptomatic
(Message and Johnston, 2002). Our detection rate of 75% percent falls well within the range of
those studies and suggests that acute respiratory viral infections contribute to the initiation of
AAE. Our analysis also revealed that RV are the predominant viruses detected in children with
AAE thus corroborating other studies that report RV as the major viral trigger of AAE (Johnston
et al., 2005a; Lee et al., 2012a; Monto, 2002).
RV-C was the major RV species in the AAE cases indicating that RV-C strains may have a greater
propensity to trigger AAE than the other two species. Our finding is in concordance with
studies from Australia (Bizzintino et al., 2011c) and Costa Rica (Soto-Quiros et al., 2012) but in
contrast to studies in the United States that suggest RV-A maybe an equally important trigger
of asthma exacerbation (Kennedy et al., 2014; Khetsuriani et al., 2008). Indeed this is an area
in need of further research.
This study is one of the first observational studies to investigate the role of RV -C load in AAE
severity in a paediatric population using a reliable RV-C load quantification technique. Previous
110
studies attempting to address this issue have generalised their findings based on RV -A studies
(Kennedy et al., 2014). However, this approach may not provide an accurate account of the
contribution of RV-C to disease severity. Using the RV-C quantification protocol developed in
chapter four, we found that viral load was not significantly different between severe and non -
severe AAE groups which points towards a limited pathogenic role for the virus in symptom
severity. This finding is in support of previous reports that demonstrate that RV mediated
cytotoxicity does not contribute to illness severity (Kennedy et al., 2014). Interestingly, our
finding is in direct contrast to what we encounter in hospitalized paediatric patients with RSV
bronchiolitis, a disease with similar clinical features to AAE but in which viral load appears to
be the primary driver of disease severity (chapter 4) (Bagga et al., 2013; DeVincenzo et al.,
2010; El Saleeby et al., 2011). Taken together, one may postulate that that the mere presence
of actively replicating RV may be enough to initiate the cascade of inflammatory events. It also
suggests RV-C replication kinetics may not drive asthma symptom severity in a similar manner
to RSV in paediatric bronchiolitis cases.
Serum IgE is a surrogate marker for the activation of allergic inflammation, increased level of
this marker is a characteristic feature in severe AAE (D’Amato et al., 2014; Holt et al., 2010).
Recent studies demonstrate that upon viral infection IgE cross linking on plasmacytoid
dendritic cells from patients with asthma results in an impaired antiviral response (Durrani et
al., 2012; Gill et al., 2010). Elevated levels of IgE have been shown to enhance the expression
of a T-helper 2 (Th2) polarized response and in turn strongly down regulate the expression of
T-helper 1 (Th1) inflammatory response in the asthmatic (Baraldo et al., 2012). This counter-
regulation has been hypothesised to be an important determinant of severe AAE (Durrani et
al., 2012; Gill et al., 2010; Pritchard et al., 2012). Our analysis revealed a trend that suggested
elevated serum IgE levels in RV-C infected patients with severe AAE and potentially points to
111
the existence of a synergistic pathomechanism between viral infection and allergen to
promote severe disease (Murray et al., 2006; Soto-Quiros et al., 2012) however, a lack of
numbers may have prevented us from reaching a statistically meaningful conclusion.
Although an eosinophilic inflammation is a characteristic feature in many asthma phenotypes
it is not associated with viral induced asthma exacerbation. Moreover, RV induced AAE is
strongly associated with a neutrophilic inflammatory pattern in human and animal studies
(Berry et al., 2007; Clarke et al., 2014; Denlinger et al., 2011; Fahy et al., 1995). Thus our
finding of elevated neutrophil counts but not eosinophil counts in children presenting to ED
with AAE fits well with the current understanding of the contribution of inflammatory cells to
virus induced allergic airway disease. This study also revealed an association between
peripheral blood neutrophil count and severe AAE. These findings are in accordance with a
growing body of research demonstrating associations between viral induced severe AAE and
an abundance of neutrophils in peripheral blood and respiratory airways. A previous study
found that several participants who reported upper respiratory tract infection as a trigger for
their AAE at presentation to the emergency department had substantial neutrophilia in their
sputum (25). Another study reported greater than fivefold more neutrophils in the sputum of
patients with RV associated AAE compared to those with RV-associated URTI (Denlinger et al.,
2011) . This relationship was also confirmed in a study where reduction in lung function
correlated strongly with elevated neutrophil counts (Message et al., 2008).
Although we did not investigate pathogenesis in our study, potential mechanisms deserve
comment. Prolonged neutrophil activation in the airways is associated with severe asthma
(Fahy, 2009). Naturally, aged neutrophils spontaneously undergo apoptosis are recognised and
phagocytised by macrophages leading to the resolution of inflammation. However, previous
studies have reported that impaired clearance of aged neutrophils by macrophages may drive
112
a sustained inflammatory environment (Fitzpatrick et al., 2008; Huynh et al., 2005). Moreover,
impaired clearance of these aging neutrophils leads to necrosis with the subsequent loss of
membrane integrity resulting in the release of cytotoxic compounds such as reactive oxygen
species and granule enzymes that contributes to local tissue damage and ultimately airway
obstruction (Cortjens et al., 2016; Li et al., 2009; Obermayer et al., 2014). Another potential
mechanism that may account for neutrophil dominated severe asthma relates to the impaired
production of IL-10 in the lung microenvironment of asthmatics (Kearley et al., 2005). IL-10 is
an anti-inflammatory mediator that effectively suppresses the production of pro-inflammatory
inflammatory mediators and enzymes in activated macrophages, T cells, and
polymorphonuclear cells (Moore et al., 1993). A previous study demonstrated that patients
with severe asthma had significantly lower IL-10 levels compared to controls and patients with
mild disease (Lim et al., 1998). Another study that evaluated the effect of IL-10 producing
interstitial macrophages on allergen-induced asthma in a mouse model demonstrated that
allergen challenged IL-10 deficient mice exhibited severe, neutrophil dominant, lung pathology
compared to wild type mice. Furthermore, transplantation of wild type macrophages reduced
biomarkers of neutrophilic inflammation (Kawano et al., 2016). Clearly, neutrophils play an
important role in the severity of viral induced asthma exacerbation and further study should
focus on the therapeutic implications of targeting these cells.
Our analysis also revealed that absolute neutrophil count (ANC) provided excellent
discrimination of severe AAE from non-severe AAE following RV-C infection. This finding is of
interest given that almost 60% of neutrophilic asthma patients are administered high dose
inhaled corticosteroid therapy or are classified with steroid refractory asthma(Bel et al., 2011).
Addition of absolute neutrophil count to current or future predictive models for severe asthma
113
exacerbation following RV-C infection may facilitate optimised treatment strategies that may
cater to this sub-group of patients to improve clinical outcomes .
There are several limitations to acknowledge first, we did not have a cohort of non-atopic RV-C
infected patients so we could not exclude the effect of atopy independent of RV -C infection on
disease severity. Second, small sample size meant that we could not reach statistical
significance for many of our findings. Thus our data requires confirmation in a larger sample
size. Third, single time point sampling meant it was difficult to ascertain whether exacerbation
severity is a function of viral load. It would be Ideal to sequentially sample participants to
establish possible correlations between symptom severity and RV-C load. Fourth, given that
blood markers may reflect systemic level of inflammation, morbidities external of the
pulmonary environment may skew biomarker concentrations, making it difficult to interpret
the findings. Therefore, the surrogate markers for airway inflammation used in this study may
not be as accurate as analogous biomarkers derived from lower respiratory tract samples.
Fifth, we did not characterise the inflammatory mediators present in the nasal secretions of
our samples meaning we did not comprehensively explore the host's contribution to asthma
severity. This assumes importance because cytokine molecules can sustain and amplify the
inflammatory response through several mechanisms.
In conclusion, RV-C is an important agent in asthma exacerbation but the quantity of virus may
not have a significant influence on the severity of illness. Severe exacerbation induced by RV -C
is associated with a neutrophilic inflammatory pattern, and therapeutic interventions directed
at host related factors may be more important than those directed at controlling viral
replication. Therapeutic strategies such as administering vaccines may yield the greatest
114
health benefit given that respiratory viruses especially rhinoviruses substantially contribute to
the burden of AAE.
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7. Cytokine profiles in nasal secretions of patients
hospitalised with Rhinovirus Species C associated
respiratory wheeze
116
7.1 Introduction
Rhinoviruses are the most frequently detected viruses in pre-school aged children (3-5 years)
hospitalised with respiratory wheeze (Cox et al., 2013; Iwane et al., 2011). RV-C is
disproportionately the most commonly detected of the three rhinovirus species and is of
particular importance because it is strongly associated with asthma related hospitalisations
(Bizzintino et al., 2011a; Cox et al., 2013; Linsuwanon et al., 2009). Acute asthma exacerbations
(AAE) can be severe and are a major contributor to asthma related morbidity. Asthma related
costs pose an enormous financial burden on health resources, with annual cost in Australia
estimated to be more than $700 million (NAC, 2015). Further, RV associated early wheezing
episodes is an independent risk factor for recurrent wheezing and asthma inception (Jackson
et al., 2008b; Saraya et al., 2014).
Factors which contribute to RV-C associated clinical outcome have not been completely
elucidated but appear to have little or no association with viral load (Kennedy et al., 2014)(see
chapter 6). Evidence is accumulating that the host immune response may be the most
important factor in clinical outcome following infection (Holgate et al., 2005; Pritchard et al.,
2012; Quint, 2008). As yet, little data exists on the cytokine profile of hospitalised children
following RV-C infection. Previous studies with other respiratory viruses have shown that
several host cytokines correlate with duration of hospitalisation (Peiris, Hui, and Yen, 2010).
Further, human challenge studies using RV-16 (rhinovirus species A) report differences in
cytokine profiles between asthmatic and non-asthmatic patients suggesting the host immune
response is determined by atopic status (Hansel et al., 2017). However, little data exist on the
differences in cytokine profiles of asthmatic and non-asthmatic patients hospitalised following
RV-C infection. As such, the prime focus of this chapter was to characterise the nasal cytokine
117
profile of hospitalised pre-school aged children with asthma as opposed to those without
asthma following RV-C infection. In addition, we planned to investigate the differences in RV-C
load and clinical outcomes among hospitalised children with asthma compared to hospitalised
non-asthmatic children. Knowing the cytokine profile provides further understanding of
pathogenic mechanisms involved and facilitates the development of better targeted
therapeutic options for the management of RV-C associated disease.
7.2 Samples
Flocked nasal swabs were collected from 605 children between the ages of 24 to 72 months of
age presenting to the Emergency Department at Princess Margaret Hospital with a clinical
diagnosis of acute wheeze. Eligible patients had clinical signs of wheeze on physicial
examination and were deemed clinically to have features in keeping with an acute upper
respiratory tract viral infection (URTI) or a history of URTI symptoms preceding the onset of
wheeze. The flocked nasal swabs were collected in 5mL of virus transport media (VTM)
transported to PWLM and stored at -80ºC pending further use. Samples (n=10) from otherwise
healthy children matched for age and sex were used as controls for nasal cytokines studies. All
samples were collected and tested between 2013 and 2016.
Nucleic acid was extracted from 200µL of clinical samples using automated extraction method
described in chapter 2. Nucleic acid was tested using a multiplex PCR for the common
respiratory pathogens including respiratory syncytial virus, human metapneumovirus, human
parainfluenza viruses, influenza viruses and adenovirus (Chidlow et al., 2009). RV screening
and genotyping was performed at the Telethon Kids Institute using previously published
primers (Lee et al., 2012a). The algorithm described in chapter 4 for RV-C viral load
118
determination was applied to any sample that was RV-C positive. Viral load determination was
performed on the Rotor Gene 6000 cycler (QIAGEN, Australia). All experiments were
performed in triplicate including positive controls and non-template controls. Detection of
glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA (Gueudin et al., 2003) was
utilized to ensure adequate specimen collection, RNA extraction and detection of PCR
inhibitors. Good quality samples were considered to be those with GAPDH CT values below
31.5. Cytokine bead array was used to obtain levels of cytokines from nasal secretions.
119
7.3 Results
7.3.1 Virus Detections
This study enrolled 605 children aged between 24 and 72 months presenting to the emergency
department with respiratory wheeze. Nasal secretions were obtained prior to discharge or
when the patient was admitted into a hospital ward. Respiratory specimens were
subsequently screened for a range of respiratory pathogens using a validate d in-house
respiratory pathogen multiplex PCR panel. Respiratory pathogen detection results are shown
in figure 7.3-1. A respiratory pathogen was detected in 65% (n=393) of samples and as may be
seen from figure 7.3-1 rhinovirus (RV), respiratory syncytial virus (RSV) and adenovirus (ADV)
were the most frequently detected pathogens. RV-C (207/271, 76%) was the predominant
rhinovirus species detected (Fig.7.3-2). In 91% (n=188) of RV-C positive samples, RV-C was the
only pathogen detected.
Figure 7.3-1: Virus detection rates from samples of children hospitalised with respiratory wheeze. RV-
rhinoviruses, respiratory syncytial virus (RSV), adenovirus (ADV), human parainfluenza virus (HPIV), Influenza viruses (IFV) and HBoV (human bocavirus)
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
RV RSV ADV HPIV HCoV HMPV IFV HBoV
viru
s d
ete
ctio
n r
ate
s (%
)
120
Figure 7.3-2: RV detection rates stratified by species. RV-C was the predominant species detected in children
hospitalised with respiratory wheeze
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
RV-A RV-B RV-C
RV
sp
eci
es
de
tect
ion
rat
es
(%)
121
Virus detections were stratified for presence of physician diagnosed asthma in order to
determine whether RV-C is more commonly detected in asthma patients compared to non-
asthma patients. This analysis demonstrated that RV-C was the most common virus detected
in each group and no statistically significant difference in in detection rates was observed
(Fig.7.3-3).
Figure 7.3-3- A comparison of virus detection rates between patients with classified with asthma compared to those not classified with asthma.
122
In this part of this study it was of importance to determine the contribution of RV -C infection
to clinical outcome. Length of hospitalisation was used as a marker of illness severity. Overall,
length of hospitalisation was relatively short with the majority of RV-C infected patients
discharged within a median time 10 hours. Mann-Whitney U test analysis showed that RV-C
load was not associated with hospitalisation given that viral load in the outpatient group
(6.00log10 copies/mL) were not significantly different (p>0.1) compared to the in-patient
group (5.82log10 copies/mL). Further, Area under the curve analysis showed that RV-C load is
not a suitable predictor of hospitalisation (Figure 7.3-4).
Figure 7.3-4: ROC assessment for RV-C load as a predictor of hospitalisation. AUC values shown in the legend (0.5, 95%CI, 0.24-0.74) demonstrated that RV-C load was poor predictor of hospitalisation.
123
Next, we stratified RV-C infected patients by asthma diagnosis to get a better understanding of
the differences following RV-C infection. RV-C infected patients with asthma had a longer
duration of hospitalisation compared to RV-C non-asthmatics but this observation did not
reach statistical significance (Table 7.3-1). Further, RV-C load was evaluated between patients
with asthma and those without asthma in order to compare the replication kinetics of the virus
between these patients groups. As can be clearly seen in Figure 7.3-4 there was no difference
in RV-C load between the two patient groups.
Table 7.3-1 Summary of clinical and demographic data of hospitalised children with RV-C respiratory wheeze
# Comparison of continuous variables (Independent samples T-Test/Mann-Whitney U test) ¶ Comparisons of proportions (Fisher’s exact test) NS: non-significant, SD - standard deviation, IQR: Interquartile range
Asthmatics (n=57) Non-asthmatics (n=134) p-value
Age (mean ± SD) 2.9 ± 1.2 2.8 ± 1 n.s#
Sex (male %) 78 73 n.s ¶
History of eczema (No %) 45 20 n.s ¶
History of hay fever 11 0 n.s ¶
Median RV-C Log10 copies/mL (IQR) 5.8 (3.1 – 6.4) 5.8 (3.8 - 6.8) n.s#
length of stay (median hours, IQR) 7 (1-16) 10 (2-16) n.s#
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7.3.2 Nasal cytokine profiles of wheezing patients following RV-C infection
In order to gain further insight into the host response following RV -C infection, this study
explored the nasal cytokine profiles of RV-C infected children with asthma and without asthma
who were hospitalised with wheezing illness. Samples from 19 RV-C positive patients with
asthma and 15 RV-C positive patients without asthma were randomly selected for further
analyses. All 34 patients were steroid naïve and hospitalised with a RV -C only infection.
Community controls matched for age and sex were included in this part of the study. These
non-respiratory disease healthy controls were screened for respiratory pathogens and four
were detected with RV and one with RSV. These samples were excluded from the final
cytokine analysis.
Overall, our analysis demonstrated that there were no significant differences between the
groups for IL-2, IL-12, IFN- λ, IFN- α, IL-5, TNF- α, MIP-1β, and GM-CSF. When we examined
inflammatory mediators that were common in both the asthma and non-asthma groups
compared to controls, it was found that levels of IL-4, IL-6, IL-8, IL-9, IL-15, IL-27 and IP-10
were significantly elevated (p<0.05) (figures 7.3-5 to 10 and Table 7.3-2). Both groups also
demonstrated similar attenuation of CXCL-1 and the IFN-γ response.
When we investigated the inflammatory mediator changes that were unique to asthma
groups, they showed significantly elevated levels (p < 0.05) of IL-1β, IL-10, IL-13, IL-17 and IL-22
compared to controls. When the same analysis was applied to the non-asthma group, they
only showed significant attenuation of IL-18, IL-21, IL-31, CXCL-12, RANTES and eotaxin (figures
7.3-5 to 10 and Table 7.3-2).
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* *
*
*
*
A B
Figure 7.3-5 Levels of IL-4 (a) and IL-13 (b) in nasal secretions of non-respiratory disease controls, RV-C infected patients with asthma and without asthma. IL-4 and IL-13 were both significantly elevated in the asthmatic group but IL-13 levels did not differ significantly in the non asthmatic group compared to controls.* p<0.05.
126
n.s
n.s
A B
Figure 7.3-6 Levels of IL-12 (a) and IL-2 (b) in nasal secretions of non-respiratory disease controls, RV-C infected patients with asthma and without asthma. Levels of IL-2 and IL-12 did not significantly differ when each group (asthmatics and non-asthmatics patients) were individually compared to controls.
127
*
*
n.s
n.s
A B C
Figure 7.3-7 Levels of Interferon (IFN)-γ (a), IFN-λ (b), IFN-α (c), in nasal secretions of controls, hospitalised RV-C infected patients with asthma and without asthma. IFN-γ was significantly
attenuated in both patient groups compared to controls. The levels of IFN- λ, and IFN-α were not significantly different in either patient group compared to controls. *p<0.05.
128
A B
*
*
*
Figure 7.3-8 Levels of IL-1β (a) and IL-6 (b) in nasal secretions of controls, hospitalised RV-C infected patients with asthma and without asthma. IL-1β was significantly elevated in the asthma group but not in the non-asthma group compared to healthy controls.*p<0.05.
129
*
**
*
*
B A
Figure 7.3-9 Levels of IL-8 (a) and IP-10 (b) in nasal secretions of non-respiratory disease controls, RV-C infected patients with asthma and without asthma. IL-8 and IP-10 were both significanlty elevated both patient groups comapred to controls.* p<0.05, **p<0.001
130
B A
*
*
Figure 7.3-10 Levels of IL-10 (a) and IL-17 (b) in nasal secretions of non-respiratory disease controls, RV-C infected patients with asthma and without asthma. IL-10 and IL-17 were only
significantly elevated in the asthma patient group compared to controls.* p<0.05
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Table 7.3-2 nasal cytokine levels of healthy non-respiratory disease controls, RV-C infected patients with asthma and without asthma.
Participants IL-5 IL-9 IL-15 IL-18 IL-21 IL-22 IL-23 IL-27 IL-31 TNF-α CXCL-12 CXCL-1 MIP-1β RANTES GM-CSF Eotaxin
Ctrls (n=5) 17 0 0 66 89 48 32 0 59 8 390 426 19 8 5 10
IQR 13-16 0-36 0-2 62-78 88-97 45-53 13-34 0-91 56-67 6-9 358-458 344-676 17-26 7-9 5-7 9-17
Asth (n=18) 12 51 10 38 114 157 38 103 77 8 331 193 45 9 7 8
IQR 7-23 20-95 6-19 12-76 32-191 45-389 16-99 25-241 31-147 5-15 124-453 93-315 17-67 3-21 2-15 5-14
Non-Asth (n=15) 14 36 7 22 50 91 16 47 38 5 142 103 25 4 5 6
IQR 10-16 18-87 4-10 13-26 29-81 35-174 15-35 11-107 23-55 5-11 79-228 62-240 10-87 3-5 2-8 5-9
p values
Asth vs Ctrls n.s 0.009 <0.001 n.s n.s 0.048 n.s 0.005 n.s n.s n.s 0.002 n.s n.s n.s n.s
Non-asth vs Ctrls n.s 0.024 <0.001 0.004 0.025 n.s n.s 0.047 0.025 n.s 0.005 0.001 n.s 0.007 n.s 0.004
Asth vs Non-asth n.s n.s 0.049 n.s n.s n.s 0.01 0.031 n.s n.s n.s n.s n.s n.s n.s n.s
Abbreviations: IL-interleukin, TNF-α- Tumour necrosis factor alpha, CXCL- chemokine ligand, MIP- Macrophage Inflammatory protein, RANTES- Regulated on Activation, Normal T Cell Expressed and
Secreted, GM-CSF-Granulocyte-macrophage colony-stimulating factor. Asth-asthmatics, Non.Asth-Non-asthmatics, Ctrls-controls, IQR- interquartile range
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7.3.3 Relationships between cytokines, RV-C load and clinical outcomes
In order to understand whether the level of viral replication influenced the magnitude of
cytokine response, a univariate analysis was conducted separately for asthma and non-
asthma patient groups. For this analysis, we selected inflammatory mediators that
demonstrated significant changes (either attenuated/elevated) compared to controls
(figures 7.3-5 to 10 and Table 7.3-2). Thus, for the asthma group we selected IFN-γ, IL-1β,
IL-4, IL-6, IL-8, IL-9, IL-10, IL-13, IL-15, IL-17, IL-27, IL-22, IP-10, and CXCL-1. Inflammatory
mediators selected to conduct the analysis in the non-asthma group included IFN-γ, IL-4,
IL-6, IL-8, IL-9, IL-15, IL-27 and IP-10, IL-18, IL-21, IL-31, CXCL-12, CXCL-1 RANTES and
eotaxin. The data in table 7.3-3 and 7.3-4 clearly shows weak non-significant relationships
between the selected cytokines and RV-C load for both groups.
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Table 7.3-3: An illustration of the relationship between RV-C load and inflammatory mediator production in the nasal secretions of children with asthma
Table 7.3-4: An illustration of the relationship between RV-C load and inflammatory mediator production in the nasal secretions of children without asthma
IFN-α IL-1β IL-4 IL-6 IL-10 IL-17 IL-9 IL-15 CXCL-1 IL-8 IP-10 IL-22 IL-27
Correlation Coefficient 0.05 -0.03 -0.10 -0.07 0.11 -0.06 -0.02 -0.08 -0.05 0.00 0.02 -0.18 -0.19
p- value 0.85 0.92 0.71 0.79 0.69 0.83 0.93 0.77 0.85 1.00 0.95 0.52 0.47
IFN-α IL-4 IL-6 IL-8 IL-9 IL-15 IP-10 IL-18 IL-21 IL-27 IL-31 Eotaxin CXCL-1 CXCL-12 RANTES
Correlation Coefficient -0.16 0.14 -0.27 -0.01 -0.03 -0.29 -0.18 -0.31 -0.20 0.02 -0.19 -0.15 -0.06 -0.10 -0.42
p-value 0.59 0.64 0.35 0.97 0.92 0.32 0.53 0.28 0.50 0.95 0.51 0.62 0.83 0.74 0.13
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Mann-Whitney U test was used in order to investigate whether hospitalisation was
associated with the aforementioned cytokines for either the asthmatic group or the non-
asthmatic group. As can be seem from table 7.3-5, in either group hospitalisation was not
associated any of the analysed cytokines.
Table 7.3-5: Association between cytokine production and hospitalisation of children hospitalised following RV-C infection
p-value (Asthmatics) p-value (Non-Asthmatics)
IFN-γ 0.26 0.947
IL-4 0.259 0.601
IL-6 0.212 1.00
IL-8 0.26 0.361
IL-9 0.109 0.744
IP-10 0.594 0.896
IL-15 0.191 0.512
IL-27 0.138 0.554
CXCL-1 0.515 0.794
IL-13 0.313 -
IL-1B 0.441 -
IL-17 0.233 -
IL-22 0.26 -
IL-18 - 0.794
IL-21 - 1.00
Eotaxin - 0.946
CXCL-12 - 0.896
RANTES - 0.647
IL-31 - 0.793
- denotes that analysis was not performed in that group.
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7.4 Discussion
Evaluating the contribution and the interplay between viral factors and host response is of
prime importance to our understanding of the pathogenic mechanisms involved in viral
induced respiratory wheeze. The results just described indicate that RV -C is the
predominant virus associated with respiratory illness with wheezing in hospitalised
preschool aged children. However, the amount of that virus does not appear to predict
clinical outcome. In both the asthmatic and non-asthmatic group the nasal cytokine
profiles indicated a Th2-type (allergic) response, which was stronger in the asthmatic
patients, while in both groups there was the same degree of suppression of the Th1-type
(microbicidal) responses. These responses were unrelated to virus load. This indicates that
the pathogenesis of wheeze in all RV-C infected patients was due to an allergic-type
cytokine response, triggered by infection but not driven by ongoing viral replication but by
the underlying tendency of the host to mount an allergic-type response. The nasal
cytokine profiles of RV-C infected patients showed a Th2-type cytokine response.
Furthermore, RV-C infection in asthmatics induces a more intense Th2 cytokine response
compared to infected non-asthmatic patients.
The present results indicate that rhinoviruses are the most important cause of respiratory
wheeze in hospitalised pre-school aged children. The proportion of RV in this group of
children is similar to the range reported in the previous chapter (see chapter five) and
within the range reported in the published literature (Johnston et al., 2005a; Miller et al.,
2007b; Rakes et al., 1999). Interestingly, RV-C as a sole pathogen was the most common
pathogen detected in hospitalised children with respiratory wheeze thus suggesting an
important role for RV-C in both asthma exacerbation and non-asthma respiratory wheeze.
Other studies have reported similar findings in hospitalised patients (Bizzintino et al.,
2011a). In our study, length of hospital stay was used as measure of clinical outcome. Our
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data showed that that length of hospitalization did not statistically significantly differ
between asthmatic patients and non-asthmatic patients following RV-C infection. That was
surprising as the cytokine data indicated that the asthmatic patients had stronger allergic-
type responses. However, the median length of stay was shorter in the non-asthmatic
group (7 hours versus 10 hours) but did not reach statistical significance. Nearly all patients
stayed less than 48 hours, so length of stay measured in hours is probably too crude a
measure of severity. Unfortunately other indicators, such as clinical disease severity
grading, were not available for these patients. However our data does suggest that RV-C
induces a relatively short clinical illness with few severe complications, which is consistent
with a recent Finnish multicentre study, which reported that children with a rhinovirus only
infection had shorter acute clinical course compared to children with RSV only infection
(Hasegawa et al., 2014).
It appears that RV-C load is not a risk factor for hospitalisation, since the findings herein
were unable to demonstrate significantly different RV-C loads between patients that were
hospitalised compared to patients discharged. This finding is in concordance with a recent
study reporting that rhinovirus load does not significantly correlate with short term clinical
outcome (Jartti et al., 2015). Furthermore, area under the curve analysis demonstrated
that RV-C load is not an accurate predictor of poor short term clinical outcome suggesting
that presentation factors other than the level of replication are more accurate predictors
of poor clinical outcome. That supports our findings that RV-C load was not related to the
strength of the Th2-type responses and presumably, the severity of wheeze.
The findings in relation to RV-C are in direct contrast to those found in RSV wheezing illness
in young infants, where viral load is an independent predictor of poor clinical outcomes
(Utokaparch et al., 2011). Altogether, the findings in this thesis suggest that the
mechanism underlying clinical outcome in wheezing illness unlike RSV are not predicated
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on viral replication but are determined by the host immune response and the triggering of
Th2-type cytokine responses.
The results in this chapter show immunological differences between RV-C infected patients
with asthma and without asthma. Among the Th2 cytokines IL-4, IL-9 were significantly
elevated in both patient groups but IL-10 and IL-13 were only significantly elevated in the
asthmatic group. In contrast, in both patient groups the levels of Th1 cytokines were either
significantly attenuated (IFN-y, IL-18) or showed no differences (IL-12, IL-23, IFN-α and IFN-
λ) compared to controls. Altogether, this suggests that RV -C infection promotes the
induction of a host response that is skewed to the Th2-type and away from the antiviral
Th1 type responses (Message et al., 2008; Pritchard et al., 2012). Signature Th2 cytokines
IL-4, IL-9 and IL-13 mediate recruitment of inflammatory cells to the lung, IgE isotype class
switching, upregulation of high affinity IgE receptor on mast cells and basophils, and IgE
dependent mast cell activation which results in the development of immediate allergic
reactions and mucus hypersecretion (Kau and Korenblat, 2014; Kearley et al., 2011).
The ability of the host to mount an effective interferon response typically contributes to
protection against viral illness (Pritchard et al., 2012). Our finding of blunted interferon
responses in asthmatics and non-asthmatics suggests RV-C either directly or indirectly
attenuates interferon production and secretion. It is more likely that the production of
Th2-type cytokines downregulate the induction of antiviral interferons and other Th1-type
cytokines important for the induction of an antiviral response (Machado et al., 2009).
The observation of type 2 response following RV-C infection in pre-school aged children
without asthma raises an interesting point in the context of early viral infection and
childhood asthma. It may simply be that wheeze can be triggered in any child infected with
RV-C, and the pathways for induction of wheeze are the same, irrespective of whether
they have an asthmatic predisposition. However, my finding that the Th2-type responses
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are greater in asthmatic than in non-asthmatic patients, suggest a synergetic relationship
between RV-C and host factors in the genesis of wheeze. It is therefore possible that the
observed skewing towards Th2-type responses following RV-C infection in early childhood
may predispose to later onset of asthma in susceptible children. Especially because
previous reports demonstrate that early life RV ALRI is an independent predictor of
childhood asthma (Lemanske et al., 2005). Alternatively, It could be that children classified
as non-asthmatic in this study may in fact be asthmatic and have not yet been diagnosed.
Diagnosing asthma in children below the age of six years can be difficult because episodic
respiratory symptoms such as wheezing are very common in children below this age, which
includes the children in the studies used in this thesis. Therefore, the observed dominance
of Th2-type response following RV-C infection in “non-asthmatics” may in fact be a marker
of predisposition to asthma. In addition, this finding also highlights the complexity of the
virus-host interactions in the context of asthma and indicates that developing asthma likely
necessitates recurrent infections, a suitable genetic predisposition and allergen exposure
(Ahanchian et al., 2012). Novel therapy that reduces recurrent respiratory viral infection
(specifically RV-C and RSV) which skew host immune response to an allergic phenotype
could help prevent the development of childhood asthma.
My findings also demonstrated elevated levels of IL-10 in patients with asthma but not in
non-asthmatics. Some studies have reported that IL-10 prevents dysregulated
inflammatory processes that cause airway narrowing (Kawano et al., 2016; Message et al.,
2008), but other studies report the contrary suggesting that IL-10 may contribute to
altered airway function (Mäkelä et al., 2000). One may speculate that following RV-C
infection, the elevated levels of IL-10 in patients with asthma but not in non-asthmatic
patients is not indicative of a protective role but one that facilitates Th2-type responses
possibly by promoting IL-4/IL-13 mediated responses (Schopf et al., 2002).
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This analysis also revealed significantly elevated levels of IL-6 and IL-8 in both asthmatic
and non-asthmatic groups. IL-6 is a known pro-inflammatory cytokine that is increased
during symptomatic RV infections, and it promotes the acute phase response in part by
neutrophil activation, as well as stimulating T cell responses and antibody production (Zhu
et al., 1996). IL-8 is a potent chemoattractant of neutrophils, elevated levels of IL- 8
induced by RV infection correlate with increases in bronchial hyper-reactivity and severity
of respiratory symptoms (Zhu et al., 1997). Interestingly, levels of RANTES, eotaxin and IL-5
(all potent chemoattractants of eosinophils) did not significantly differ in either patient
group compared to healthy controls. Altogether these analyses suggest neutrophilic
inflammatory pattern, but not eosinophilic inflammation contributes to disease
pathogenesis following RV-C infection.
Previous studies have associated IL-17 with promoting a Th2-type inflammatory
environment by enhancing production of IL-13 (Jin and Dong, 2013). IL-13 and IL-17 are
reported to function synergistically to regulate the epithelial cell response that controls
mucus production following viral infection (Jin and Dong, 2013). IL-17 also facilitates
neutrophil activation and proliferation in non-eosinophilic asthma in part by enhancing the
production of the pro-inflammatory cytokines IL-6 and IL-8 (Linden, 2001). Animal studies
modelling the interplay in the lung between RV and IL-17 report that IL-17 is detected at
higher levels in the lung of asthmatics compared to healthy controls following RV infection
(Al-Ramli et al., 2009). It is therefore possible that IL-17 may contribute to RV-induced
disease in asthma patients.
In both patient groups RV-C infection was associated with elevated levels of IP-10
compared to controls which is in agreement with other reports (Culley et al., 2006;
Matsumoto and Inoue, 2014; Quint, 2008). IP-10 is a chemoattractant that mediates
inflammatory response by recruitment of circulating leucocytes to the site of
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inflammation. Increased levels of IP-10 are generally considered as a non-specific response
to viral infection (Quint, 2008).
This analysis also demonstrated a significant elevation of IL-1β levels in asthma patients
compared to controls but this was not evident in the children without asthma. IL-1β is a
proinflammatory cytokine that is released following RV infection and contributes to
pathogenesis of respiratory disease by recruitment of inflammatory cells and enhancing
production of IL-8 (Kluijver et al., 2003). Experimental human models of asthma
exacerbations demonstrate that an early rise in IL-1β in respiratory secretions is temporally
associated with clinical symptoms (Kluijver et al., 2003; Liu et al., 2013). However, IL-1β
was not significantly correlated with any of the markers of severity used in this study.
However, it remains possible that levels of IL-1β in children with asthma following RV
infection may be a marker of more severe illness compared to non-asthmatics, and that
the markers used in our studies were not appropriate for detecting this effect.
This study has potential limitations. Firstly, only one time point was used for the
measurement of cytokines and viral load, and although early evaluations are useful for the
acute phase response, they do not permit further insights such as those provided by
sequential measurements. Even though it is tempting to speculate that there is a direct
association between cytokine concentrations and illness severity, the full burden of disease
cannot be solely attributed to this phenomenon because cytokines may behave as markers
of tissue damage, without necessarily contributing to pathology directly.
In summary, RV-C appears to be the predominant pathogen associated with respiratory
wheeze in hospitalised preschool aged children. A short-lived clinical course appears to be
a hallmark of RV-C infection. RV-C load is neither a risk factor nor a reliable marker for
hospitalisation following infection. Further, RV-C infection is more important than the level
of replication because it appears that factors other than viral load drive clinical course. RV -
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C associated respiratory wheeze in hospitalized children is characterized by a dominant
Th2-type inflammatory response. Furthermore, RV-C promotes a more intense cytokine
response in children with asthma compared to children without asthma. The induction of
cytokines that mediate recruitment and activation of neutrophils may be an important
underlying pathogenic mechanism associated with RV-C disease. Thus, potential
therapeutic interventions should be aimed at modulating the host response following
infection.
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8. General Discussion and Conclusions
143
8.1 Introduction
Acute lower respiratory infections are an important cause of morbidi ty and mortality in
children worldwide. Furthermore, respiratory tract infections also deliver an enormous
financial burden on healthcare for which the direct and indirect cost to the Australian
health care system is estimated to be up to AUD 600 million each year (The Australian Lung
Foundation, 2007). Within the last few decades it has become abundantly clear that
viruses are the leading cause of ALRI, yet therapeutic options are limited. In addition to
ALRI, a considerable amount of evidence implicates respiratory viral infection as a major
trigger of asthma exacerbations (Johnston et al., 2005b; Matsumoto and Inoue, 2014;
Message and Johnston, 2002). Further, RV and RSV are implicated in asthma pathogenesis
in children. RSV is the leading cause of lower respiratory tract infection and death in
children below the age of two years old (Nair et al., 2013). Severe RSV bronchiolitis in
infancy is consistently implicated with persistent wheeze to age six (Moore et al., 2013)
and although the picture is still incomplete, the current evidence suggests that severe RSV
bronchiolitis may be an independent risk factor for allergic sensitisation (Wu and Hartert,
2011).
RVs are disproportionately the most common cause of upper respiratory tract infection i n
humans and are now recognised as an important pathogen of the lower respiratory tract.
RV species C especially, has been identified as an important contributor to wheezing illness
in paediatric medicine (Bizzintino et al., 2011b; Cox et al., 2013; Linsuwanon et al., 2009).
Unlike the other RV species that have been extensively studied over the years, RV -C cannot
be grown in conventional cell culture. The lack of a conventional system allowing the
propagation of RV-C in vitro has precluded investigations by traditional virological studies.
This has meant there is little understanding of the pathogenic properties of RV -C. It is
through the use of molecular techniques that all of the currently recognised RV -C
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genotypes have been identified and their diversity established (Khetsuriani et al., 2008;
McIntyre et al., 2013). However, their pathogenic properties are still largely undefined,
and their association with disease severity still yet to be clearly elucidated. It is well
established in other infections that viral load plays an essential role in disease progression
and clinical outcome (Bagga et al., 2013; Feikin et al., 2015; Fleming et al., 2005). For
example, in chapter 5 of this thesis we demonstrated that RSV load is higher in
symptomatic children. In the literature, viral load HMPV is associated with disease severity
in young children (Roussy et al., 2014). Similarly, a recent study conducted in China
demonstrated that high HBoV load is associated with more severe lower respiratory tract
symptoms, longer duration of wheezing and hospitalisation (Deng et al., 2012). The high
genetic variability in the PCR target region of RV-C has in the past impeded the
development of reliable molecular based viral load assays to investigate this association.
Other studies have utilised degenerate bases in the PCR primer and probe sets to
overcome the inter-genotypic variability of RV-C (Granados et al., 2012). However, these
efforts are known to compromise accuracy of measurements (Chemidlin Prevost-Boure et
al., 2011). Thus, this thesis aims to improve understanding of the underlying
pathophysiological mechanisms in the context of RV-C wheezing illness through the
development of a reliable molecular based method for quantifying RV-C load and then the
characterisation of the host response following infection.
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8.2 Epidemiology of respiratory viruses in young
children under the age of five years
RV-C and RSV are the predominant aetiological agents of wheezing illness in young children
(chapter 5-7). Virtually all children are estimated to have been infected by RSV before the
age of three, with bronchiolitis being the predominant clinical manifestation (chapter 5).
RSV predominates as the aetiologic agent of medically attended ALRI in the first year of
life, with peak hospitalisation rates observed in the first two months of life (Bont et al.,
2016; Hasegawa et al., 2014; Henderson et al., 2005)(chapter 5). This finding supports
previous reports in the literature demonstrating that hospitalisation rates for RSV disease
increases with decreasing age, peaking in the first few months of life (Bont et al., 2016).
Indeed, this study also contributes to the global understanding of RSV morbidity
specifically in the first year of life, which is pertinent for future RSV vaccination strategies.
Further, given that the burden of RSV disease has been well described in many high income
settings but data from low income countries is sparse. The findings in chapter 5 come from
a low-middle income country and extend the current knowledge of the global impact of
RSV, and indicate that the inverse relationship between RSV infection and age is a universal
phenomenon in children.
Contrary to the data from many previous epidemiological studies (Camargo et al., 2012;
Jartti et al., 2008; Self et al., 2015), the findings in chapter 5 demonstrated a relatively high
percentage of samples from NRD controls that tested positive for RSV (17%). Indeed this
finding may represent by-stander nucleic acid from previous infection, given that PCR can
detect viral nucleic acid in respiratory secretions even after the resolution of
infection(Camargo et al., 2012). Unfortunately no attempt was made to isolate virus in cell
culture and as such precluded the determination of viability. However, even in the absence
of the viability data, the high rate of detection of RSV-RNA in healthy children suggests that
146
RSV infection is frequent in those communities. Alternatively and biologically plausible,
these data may also suggest individuals with asymptomatic RSV may be an important
transmission pathway in this region. A recent study in Kenya (a region similar to where our
study samples were collected) reported similar findings showing a high percentage (40%)
of asymptomatic RSV detection in the community (Munywoki et al., 2015). It is unknown
whether these frequent asymptomatic cases are a result of unique culturally specific
factors, differences in host immunity, or differences in the infecting RSV strains.
It also appears from this study that RVs are the predominant aetiologic agent of wheezing
illness in preschool aged children, in concordance with previous reports (Miller et al.,
2007a). Chapter 6 and 7 demonstrate that RV-C is the most commonly detected of the RV
species in pre-school aged children (3-5 years old) hospitalised with a wheezing illness or
asthma exacerbation. Table 6.3-2 also indicates that RV-C may be more inherently
pathogenic than RV-A (Unfortunately a lack of numbers for RV-C could not permit this
analysis to draw a reliable condition). Overall, our data suggests that species specific
differences may be important determinants of RV epidemiology (Cox et al., 2013; Liu et al.,
2016). RV-C utilises a different cell surface receptor to RV-A and RV-B for attachment.
Recent studies have identified cadherin-related family member 3 (CDHR3) as the most
likely receptor for RV-C entry and replication (Bochkov et al., 2015). CDHR3 is ubiquitously
expressed in the airway epithelium including areas of the lower respiratory tract.
Interestingly, a variant of the CDHR3 gene which has been associated with wheezing illness
and childhood asthma (Bonnelykke et al., 2014) has also been shown to enhance RV-C
binding and progeny yields in vitro (Bochkov et al., 2015). It is unknown whether the same
relationship exists in vivo and also what implication this may have on the risk of persistent
wheeze and asthma inception.
147
In this thesis, children hospitalised with RV-C infection infrequently required intensive care
treatment and were discharged within 48 hours of admission (chapter 7), supporting other
data that RV-C associated AAE results in a self-limiting illness without serious complications
(Jartti et al., 2015). However, we may have missed more subtle differences in clinical
course that were not reflected in duration of stay or ICU admission rates.
The findings of this thesis also demonstrated that there is an age -related shift in the
epidemiology of medically attended respiratory viral illness. RSV predominates as the most
common aetiologic agent of viral wheeze (bronchiolitis) in the early years of life especially
in the first year of life, which is in agreement with previous reports (Lukšić et al., 2013;
Takeyama et al., 2015a). The findings in chapter 6 and 7 demonstrate that RVs, especially
species C, appear to assume predominance thereafter. An explanation of this observed
shift was not investigated in this study but one may speculate that it is in part driven by a
decline in RSV disease severity as a result of previous exposure in the first years of life, and
also in part by the large number of genetically and likely antigenically distinct RV -C types
capable of infection. Given that RV infection has been reported to be an independent risk
factor for subsequent childhood asthma and decreased lung function (Guilbert et al., 2011;
Tovey et al., 2015) future study should investigate the influence of this sequential infection
in the development of persistent wheeze and asthma onset. It may be that in susceptible
children severe RSV infection primes them for asthma, possibly by interfering with normal
development of the lung (Gern and Busse, 2002) so that subsequent RV-C infection may
permanently shift the host immune response towards a more allergic phenotype.
This thesis also highlighted the difficulty in assessing etiologic contribution of viruses to
clinical disease using qualitative PCR analysis. Chapter 5 and 6, both of which were case
control studies demonstrated a high rate of respiratory virus detections in the non -
respiratory disease control groups. These findings may reflect incidental detection of
148
nucleic acid from a recent previous infection, frequent acute infections, or possibly long
term persistence of infection. As demonstrated in this study, quantitative results may be
helpful in determining the significance of these viruses as a cause of ARTI.
8.3 Reliable methods of accurately determining viral
load in RV-C infected patients
Accurate methods to quantify viral load can be a powerful tool for improving our
understanding of the kinetics and pathogenesis of viral replication (Bagga et al., 2013),
establishment of clinical correlates and evaluating the effectiveness of antiviral therapy
(Boivin et al., 2003). This study, like others has demonstrated the frequency of infections
with RVs (Bruning et al., 2015; Camargo et al., 2012; Piralla et al., 2009), especially RV-C.
Accurately measuring viral loads has been hampered by two major challenges. Firstly, RV-
C is uncultivable in conventional cell culture, so viral load cannot be measured in that way.
Therefore, molecular methods, especially techniques that are capable of accurately
determining viral load in clinical samples become necessary. However, the extensive
variability in the PCR target region of RV-C genotypes presents the second challenge in
developing reliable methods of quantifying RV-C in clinical samples. No single primer probe
pair could be expected to provide accurate quantification of the wide range of RV-C types.
In this study, viral loads assays for RV-C were developed as a tool to obtain an accurate
understanding of the contribution of RV-C to disease.
Given that several RV-C genotypes can circulate concurrently (Chapter 7) (McIntyre et al.,
2013) it was imperative for this project to design an assay that would provide coverage for
all the currently known genotypes. Computational analysis was used to assess the range of
coverage of each assay against sequences representative of the diverse range of RV-C
genotypes. This analysis revealed that four real-time PCR probes were required to
149
overcome the inter-genotypic variation within the target region. This highlights the
importance of computational analysis before proceeding to laboratory evaluations. This
study also demonstrated the importance of target – assay homology on the robustness of
the assay in two respects. Firstly, complete homology between target and probe enhances
the quantitative accuracy of the assay, and secondly, it improves assay specificity.
This study also demonstrated that there are specific factors which must be taken into
account when developing a reliable PCR assay to quantify viral load. Firstly, it was shown
that the reaction conditions provided by the detection reagent is a determinant of
accurate measurement. Our findings indicate that choice of reaction mixture, whether
commercial or in-house, should be evaluated prior to use, as some reaction mixes are
superior to others for the purpose of viral load determination. Secondly, careful primer
and probe optimisation is necessary, and this should also undergo vigorous assessment. In
our study it was necessary to optimise and use asymmetric PCR rather than conventional
PCR conditions to enable better sensitivity and reproducibility. Thirdly, specimen collection
is a source of result variability and may influence the accuracy and the interpretation of the
viral load measurement (Hayden et al., 2012). This is because the quality (amount of cells)
and the quantity (volume per sample) of the different sampling methods vary considerably.
Therefore, the addition of an invariant endogenous control (GAPDH) in the assay was
essential to correct for any sample to sample variation.
The clinical and public health importance of the assays developed in this study is that it is
now possible to accurately and reliably investigate the viral replication kinetics of RV -C. In
so doing these assays will stimulate further discussion and insight into the kinetics and
pathogenesis of RV-C replication. Like RSV (Bagga et al., 2013) and influenza (Boivin et al.,
2003), for which the pattern of viral load indicates when antiviral therapy would be most
150
effective, an accurate understanding of the natural history of RV-C infection will be
important in the development of future therapeutic strategies.
8.4 Viral Determinants of severity of RV-C induced
wheezing illness
Early studies in a range of viruses have shown a relationship between the magnitude of
replication and clinical outcome. For example in HIV infection, a high viral load is
associated with poor clinical outcomes (Attia et al., 2009). In the context of respiratory
viruses, evidence suggests that RSV disease in young children is a virus mediated
phenomenon (chapter 5) and virus load is an independent predictor of disease severity
(Houben et al., 2010; Utokaparch et al., 2011). It appears to be different for RV-C.
Although the clinical manifestations in young children hospitalised following RV -C and RSV
infection are indistinguishable (predominantly a wheezing illness), the findings in this
thesis reveal that the drivers of illness severity may be different. We found that illness
severity in hospitalised children with an exacerbation of asthma or frank whee zing illness is
not associated with RV-C load. This may suggest that a pathogenic process triggered by
infection rather than the ongoing viral replication determines the severity of illness .
Interestingly, while the evidence herein suggests that illness severity is not associated with
viral load, a previous experimental study using RV-A demonstrated that viral load appeared
to drive symptom severity in study participants (Message et al., 2008). Indeed, this may
suggest species specific differences in mechanistic pathways in the pathogenesis of
disease. Taken together, these findings have implications for novel therapy initiatives given
that for a specific type of virus, virus specific interventions may be necessary while on the
other hand and in the context of RV-C these interventions may not be as beneficial to high
risk populations. Indeed, a RV vaccine would have substantial benefits for the community
151
as a whole, especially in individuals with underlying chronic respiratory diseases such as
asthma. However, technical feasibility of composing a vaccine with over 100 or more
serologically distinguishable antigens and also capable of generating a broad long lasting
immune response are the two main challenges that have in the past precluded RV vaccine
development. Encouraging results in recent animal studies has demonstrated that it may
be immunologically possible to develop a polyvalent RV vaccine (Lee et al., 2016). Indeed
an issue that is till yet to be resolved is the reliable propagation RV-C in cell culture. The
viruses used by Lee et al. (2016) were generally RV-A species. As this thesis has shown RV-C
is the most frequently detected virus in medically attended wheezing illness in preschool
aged children and thus any RV vaccine developed should also include antigens that broadly
represent C species.
8.5 Host response following RV-C infection
This thesis also provides an insight into the interplay between RV-C and the host immune
response in hospitalised young children following infection. The findings herei n suggest
that the immune response following RV-C infection shapes illness severity in asthmatic
patients, and most likely in non-asthmatic children (chapter 7). This phenomenon is not
associated with RV-C load and suggests an independent role for immunopathogenesis in
the clinical outcome of RV-C infection. RV-C infection promotes Th2-biased responses in
asthmatic and non-asthmatic children hospitalised with RV-C wheeze, similar to what is
observed in severe RSV bronchiolitis (Zeng et al., 2011). Our work demonstrated that the
dominant Th2 cytokines including IL-4 and IL-13 are more pronounced in asthmatic
children compared to non-asthmatic children; possibly a result of the underlying allergic
pulmonary environment. However, this analysis did not demonstrate any associations
between this pronounced cytokine response and poor clinical outcomes. Other studies
have shown that these cytokines are major mediators of exaggerated airway responses
152
following infection (Mukherjee and Lukacs, 2010; Pala et al., 2002). Future study would
benefit from understanding whether this Th2 biased response relates in any way to
subsequent asthma development later on in life.
RV-C infection induced a robust acute phase neutrophil response but this response was
independent of viral load. Further, increasing neutrophil numbers correlated with disease
severity (chapter 6). Despite neutrophils being important in the engulfment and
subsequent elimination of invading extracellular microorganisms, controversy still
surrounds their role in viral infections. Neutrophils can only be of benefit if they assist in
viral clearance, which in our study does not appear to be the case, as the neutrophil
response was highest in the sickest children but viral load remain unchanged (chapter 6).
Previous reports have also shown that an increase in neutrophil numbers in asthmatic
patients following viral infection corresponds with an increase in symptom score (Louis et
al., 2000). Furthermore, widespread neutrophil infiltration is seen in the lung tissue from
fatal cases of AAE (Wenzel et al., 1999) and RSV LRTI (Johnson et al., 2006). Neutrophilic
inflammation, specifically in asthma patients assumes importance because of the
refractory nature of the neutrophilic asthma phenotype to standard asthma treatment
(Alam et al., 2017). It also assumes importance because it suggests that different triggers
of asthma may induce different inflammatory patterns, which in turn means that
therapeutic interventions may need to be modified according to the response type.
Macrolide antibiotic treatment is a potential intervention for dampening excessive
neutrophilic inflammation following RV-C induced respiratory wheeze (Brusselle and
Pavord, 2017; Gibson et al., 2017). Studies performed with RV-C in this context are
currently scarce but will be necessary for acute treatment and to mitigate long term
asthma like symptoms. Macrolides have demonstrated anti-neutrophilic activities in vitro
models of pulmonary infection and inflammation (Gielen, Johnston, and Edwards, 2010;
Menzel et al., 2016; Tamaoki et al., 1999). Furthermore, in a recent proof of concept study
153
macrolide treatment in RSV induced bronchiolitis demonstrated anti -neutrophilic activity
and consequently better clinical outcomes in comparison to placebo treated patients
(Beigelman et al., 2015). A similar finding has also been reported for influenza infection
(Lee et al., 2017).
Chapter 7 provided insights into the underlying mediators of the observed neutrophil
infiltration. This robust neutrophil infiltration appears to be mediated by the neutrophil
chemoattractant IL-8 (chapter 7), which was observed to be higher in asthma patients
compared to non-asthma patients suggesting a more vigorous inflammatory response
(Alam et al., 2017). It also appears likely that IL-17 may contribute to enhanced neutrophil
recruitment specifically in children with asthma (chapter 7) but its role in clinical outcome
could not be established in this study. In vitro experiments have shown that exacerbation
of neutrophil responses is one of the main consequences of IL-17 secretion following
rhinovirus infection (Wiehler and Proud, 2007). Further, models of rhinovirus induced AAE
have demonstrated that IL-17 secretion is associated with increased airway hyper-
responsiveness (Al-Ramli et al., 2009). Future study should be designed to assess the
therapeutic potential of monoclonal antibody targeting these cytokines in response to RV -
C infection.
One issue that was not investigated in this thesis and is of potential importance is the
contribution the airway microbiome to natural course of vi ral respiratory illness. It is well
established that the presence of the microbiota is pivotal for the development and
maintenance of the host defence. Evidence in the literature suggests that respiratory
commensal bacteria can play both a protective role and a pathological role. For instance,
the presence of a commensal nasopharyngeal microbiota protected mice against RSV -
induced airway hyper-responsiveness (Ni et al., 2012). On the hand a pathological role has
also been described in which certain bacterial pathogens can provoke a strong infection as
154
well as an exaggerated host immune response (Sajjan et al., 2006). The evidence available
suffices to show that the complexity of microbiome interactions in the airways, possibly
contributes to the susceptibility to exacerbations and the natural course of airway
diseases. Future study must consider how RV-C interacts with the airway microbiome to
modulate clinical outcome.
8.6 Conclusion
Overall, RV-C predominates as the most important viral pathogen in preschool aged
children hospitalised with a wheezing illness. The accurate quantification method used to
measure viral load in this project has provided a novel tool for obtaining insight into
replication kinetics of RV-C and enabled further study into its contribution to disease. This
thesis has also demonstrated that the existing platform used to determine viral load has its
limitations and advanced techniques/platforms such as digital PCR may well be used as the
optimal quantification method for viruses with high sequence diversity. Nonetheless, the
accurate method of viral load determination developed in this thesis has demonstrated
that RV-C load does not drive severity of infection; it merely triggers the disease process.
There is strong evidence that RV-C infection is characterised by a strong Th2 biased
response. It appears that the magnitude of neutrophilia within the airways may in part
modulate severity of illness in young children hospitalised following RV-C induced
wheezing illness. Further experimental study is required to understand more fully the
interplay between RV-C and the host immune response in shaping the outcome of RV-C
infection. A better understanding will help guide therapeutic approaches and the
development of new treatment and preventive strategies.
155
156
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Appendices
Appendix 1
Table 0-1 The performance of the individual PCR assays for the detection of matched RV-C RNA transcript
RV-C Assay-1 RV-C Assay-2 RV-C Assay-3 RV-C Assay-4
Mean +/- SD CV% Mean +/- SD CV% Mean +/- SD CV% Mean +/- SD CV%
Slope (n=) -3.32+/-0.11 3.33 -3.38+/-0.11 3.23 -3.44+/-0.09 2.72 -3.37+/-0.05 1.52
Efficiency 0.98+/-0.05 5.02 0.97+/-0.05 4.68 0.95+/-0.04 3.93 0.97+/-0.03 3.12
Y-intercept 34.00+/-2.46 7.23 38.00+/-2.72 7.17 36.12+/- 1.00 7.55 33.45 +/- 1.59 8.42
Goodness of fit (R2) 0.999 0.11 0.999 0.47 0.999 0.09 0.999 0.08
Range of Linearity 100-108 copies/ml 100-108 copies/ml 100-108 copies/ml 100-108 copies/ml
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Table 0-2 A comparison of RNA transcript concentration and Cq values for the different RV-C assays
RV-C Assay-1 RV-C Assay-2 RV-C Assay-3 RV-C Assay-4
RNA transcript
concentration
Mean Cq +/-
SD
Mean Cq +/-
SD
Mean Cq +/-
SD
Mean Cq +/-
SD
100 32.77+/-0.14 33.25+/-1.48 32.66+/-0.31 31.84+/-1.01
101 29.11+/-0.12 28.22+/-0.46 29.62+/-0.06 27.37+/-0.07
102 25.86+/-0.11 25.37+/-0.06 25.95+/-0.07 24.20+/-0.14
103 22.07+/-0.06 22.25+/-0.07 22.52+/-0.04 20.96+/-0.03
104 18.68+/-0.06 18.91+/-0.06 19.08+/-0.08 17.53+/-0.10
105 15.35+/-0.04 15.39+/-0.04 15.52+/-0.11 14.20+/-0.14
106 11.73+/-0.1 13.13+/-0.11 12.05+/-0.08 10.79+/-0.04
107 8.5+/-0.04 8.78+/-0.02 8.55+/-0.02 7.81+/-0.06
108 5.27+/-0.12 5.17+/-0.2 5.19+/-0.16 4.77+/-0.07
% CV – Percentage coefficient variation, Cq- quanti fication cycle va lue, SD- s tandard deviation
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Table 0-3 Intra and Inter assay variability of the four RV-C qRT-PCR assays (Assay 1-4)
Intra -ass ay variation a
Inter-assay variation b
RNA target and input target copies
Quantity range (Calculated copies/ reaction)
%CV range Quantity Mean (Calculated copies/reaction)
%CV
RV-C1 106 3030000-3560000 0.16-2.39 3480000 6.89
RV-C1 104 30000-36700 0.27-2.33 37500. 5.67
RV-C1 102 125-409 0.16-7.07 361. 8.92
RV-C1 101 21-40 2.1-7.33 39 5.88
.
RV-C2 106 3560000-4210000 0.22-0.61 3810000. 7.50
RV-C2 104 39100-49700 1.70 -2.42 43400. 10.56
RV-C2 102 379-405 1.44 -2.75 396. 3.04
RV-C2 101 35-42 3.89-5.82 39 7.22
RV-C3 10
6 4080000-5790000 0.37-2.30 4760000 14.58
RV-C3 104 35700-46200 0.23-1.23 42000 9.26
RV-C3 102 372-470 0.40-8.58 440 9.21
RV-C3 101 42-59 0.93-7.78 47 14.57
RV-C4 106 4020000-4860000 0.10-1.77 4360000 8.22
RV-C4 104 48500-52800 0.28-1.61 50000 4.89
RV-C4 102 423-608 1.07-4.76 525 11.05
RV-C4 101 42-49 1.47-5.16 46 5.36
% CV – Percentage coefficient variation a Assays were performed in triplicate b Five independent experiments
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Table 0-4 The RV-C load determinations for patients enrolled in the PREVIEW study. Clinical samples were tested in triplicate and mean viral load calculated.
Sample ID
RNA copies/mL
SD %CV
Log10 RNA copies/mL
Genotype
Assay
GAPDH Mean Cq
GAPDH SD
1 1.35E+05 4.73E+04
0.35
5.13 C-04 98% 1 30 0.8
2 2.98E+08 4.47E+07
0.15
8.47 C-06 95% 1 24.5 0.1
3 5.33E+09 7.46E+08
0.14
9.73 C-06 95% 1 25.6 0.8
4 3.15E+06 8.19E+05
0.26
6.5 C-08 100%
2 27.7 0.6
5 6.44E+04 1.67E+04
0.26
4.81 C-08 98% 2 27.7 0.3
6 1.06E+07 2.65E+06
0.25
7.02 C-14 96% 3 23 0.3
7 4.20E+07 7.56E+06
0.18
7.62 C-14 96% 3 22.8 0.3
8 3.55E+06 8.17E+05
0.23
6.55 C-14 97% 3 28.8 0.3
9 5.33E+09 8.53E+08
0.16
9.73 C-16 95% 1 27.6 0.6
10 4.28E+06 1.03E+06
0.24
6.63 C-16 94% 1 27.7 0.2
11 2.96E+07 6.51E+06
0.22
7.47 C-16 97% 1 25.4 0.4
12 1.91E+08 3.44E+07
0.18
8.28 C-16 97% 1 27.3 0.5
13 1.76E+08 2.82E+07
0.16
8.25 C-16 98% 1 28.2 0.1
14 3.28E+07 6.56E+06
0.2 7.52 C-23 97% 1 25.6 0.3
15 7.53E+08 1.28E+08
0.17
8.88 C-24 96% 1 21.4 0.6
16 1.98E+06 6.73E+05
0.34
6.3 C-25 97% 1 25.8 0.8
17 5.60E+08 8.40E+07
0.15
8.75 C-25 99% 1 20.2 0.5
18 2.28E+09 3.42E+08
0.15
9.36 C-28 98%)
1 21.6 0.4
19 9.66E+06 1.55E+06
0.16
6.99 C-3 97% 1 26.6 0.4
20 3.48E+05 1.08E+05
0.31
5.54 C-30 99% 1 27 0.4
21 1.99E+03 6.17E+02
0.31
3.3 C-35 96% 4 29.8 0.4
22 1.69E+06 6.76E+05
0.4 6.23 C-35 96% 4 27.3 0.4
23 3.19E+06 8.61E+05
0.27
6.5 C-35 97% 4 25.6 0.3
24 1.25E+07 2.13E+06
0.17
7.1 C-35 97% 4 22.3 1.3
25 3.26E+09 5.87E+08
0.18
9.51 C-38 96% 1 23.7 0.5
26 4.45E+06 1.29E+06
0.29
6.65 C-39 99% 1 24.2 0.1
27 1.04E+05 2.08E+04
0.2 5.02 C-42 95% 2 28.8 0.1
28 7.88E+05 2.99E+05
0.38
5.9 C-42 96% 2 30 0.8
29 4.15E+05 1.66E+04
0.04
5.62 C-42 97% 2 29.9 0.2
30 1.11E+06 3.66E+05
0.33
6.05 C-43 95% 1 28.2 0.4
31 2.65E+07 5.04E+06
0.19
7.42 C-46 96% 3 25.1 0.6
32 1.18E+08 1.53E+07
0.13
8.07 C-46 96% 3 23.5 0.2
33 4.73E+04 1.32E+0 0.2 4.67 C-51 96% 4 24.8 0.8
180
4 8
34 8.51E+08 1.62E+08
0.19
8.93 C-04 99% 1 27.5 0.1
35 1.70E+07 3.74E+06
0.22
7.23 C-19 96% 4 29.8 0.1
36 2.93E+05 7.62E+04
0.26
5.47 C-11 99% 1 24.5 0.6
37 3.29E+05 8.23E+04
0.25
5.52 C-11 99% 1 28.6 0.4
38 1.23E+07 2.34E+06
0.19
7.09 C-11 99% 1 23.2 0.1
39 2.04E+06 4.90E+05
0.24
6.31 C-24 97% 1 25.3 0.2
40 3.95E+03 1.46E+03
0.37
3.6 C-13 96% 1 24.6 0.1
SD- Standard deviation %CV- coefficient of variation GAPDH- Glyceraldehyde 3-phosphate dehydrogenase; internal control
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Appendix 2
Published work completed during PhD
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