Applying Bioinformatic Tools to Better Understand Eye Diseases by Vikrant Singh M.Sc. Bioinformatics (Jamia Millia Islamia- Central University, India) M. Engg Bio-Medical Engineering (Anhalt University of Applied Sciences and Martin Luther University, Germany) Menzies Institute for Medical Research Submitted in fulfilment of the requirements for the Doctor of Philosophy University of Tasmania October 2019
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Applying Bioinformatic Tools to Better Understand Eye Diseases
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Applying Bioinformatic Tools to Better Understand Eye Diseases
by
Vikrant Singh
M.Sc. Bioinformatics (Jamia Millia Islamia- Central University, India)
M. Engg Bio-Medical Engineering (Anhalt University of Applied Sciences and Martin
Luther University, Germany)
Menzies Institute for Medical Research
Submitted in fulfilment of the requirements for the Doctor of Philosophy
University of Tasmania
October 2019
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Declarations
“The thesis contains no material which has been accepted for the a degree or diploma by the
University or any other institution, except by way of background information and duly
acknowledged in the thesis, and to the best of my knowledge and belief no material previously
published or written by another person except where due acknowledgement is made in the text
of the thesis, nor does the thesis contain any material that infringes copyright.”
This thesis may be made available for loan and limited copying in accordance with the
Copyright Act 1968.
Student Signature: Vikrant Singh
Primary Supervisor Signature:
Alex Hewitt
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Acknowledgements
“No one who achieves success does so without acknowledging the help of others. The wise
and confident acknowledge this help with gratitude.” – Alfred North Whitehead
One of the pleasant aspects of writing an acknowledgement is the opportunity to thank all those
who have contributed to it. Unfortunately, the list of expression of gratitude- no matter how
extensive is always incomplete and inadequate. This acknowledgement is no exception.
First of all, I wish to express my sincere gratitude to my supervisors Prof. Alex Hewitt and
Assoc/Prof. Anthony Cook who gave me the chance to complete my PhD under their esteemed
supervision. Without your unconditional support and guidance, I am unable to complete my
thesis on time. Because of your inspiring guidance, motivation, positive criticism, continuous
encouragement and constant supervision, this work could be brought to its present shape.
Prof. Alex Hewitt provided excellent scientific guidance that helped me to expand my
knowledge about the use of bioinformatic applications to analyse the omics datasets. I want to
extend my special thanks for being so patience and understanding. You helped me to think and
work beyond my capabilities. You gave me opportunity to work on different projects that
helped me to develop my scientific aptitude. My words are enough to express my gratitude and
respect towards you. I will always be grateful to you. I want to express my special thanks to
my co-supervisor Assoc/Prof. Anthony Cook. Your support, guidance, positive feedback and
short-term discussion kept me motivated and helped me a lot during my PhD candidature.
My special thanks to Dr Guei-Sheung Liu for allowing me to work with you on a project that
helped me a lot to understand the biological concept of retinal neovascularisation along with
the computational analysis. I am very thankful for your motivation and long discussion we had
during my PhD. Other special thanks to my graduate research co-ordinators Prof. Heinrich
Korner and Prof. Wendy Oddy for your help and support in and out during my PhD
candidature. Other Special thanks to Prof. Alice Pebay and Dr. Duncan Crombie from the
University of Melbourne for Providing the differentiated cell lines for virtual karyotyping.
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“Oldies are the goldies”: This goes for not-so-long-gone lab mates – Fan Li, Sloan Wang whose
presence is missed for sure. I appreciate every moment of rising and fall we spent during my
PhD. Science can’t be carried out by a single man. I would like to thank Qi Wang, Peter Lu,
Peter Tran and Mohd Khairul Nizam for engaging into the short and long discussions and
always keeping the spirit high. I also appreciate the time spent in discussing captivating advises
about the future. Canteen mates are always reminded you to have lunch on time. I can’t thank
enough to Ankit Gupta, always look after me when I was not around. Your support and
encouragement can’t be replaced by anything.
Someone honestly said – “Where you go, whatever you do, your family is always there to
support and love you”. I never realised that I had come that far but, this would not be possible
without the immense love from my family. I know my Dadu (Grandfather) and Dadi
(Grandmother) must be very proud of me in heaven. My journey wouldn’t be possible with the
unconditional love and support from my Maa (Mother) and Paa (Father). Your continuous
motivation and encouragement helped me a lot to reach my goal. I am very thankful for you to
teaching me a useful lesson of my life, both socially and economically. I also thank my brother
Yug for always guarding my back in every situation.
I would also like to express my not so formal thankfulness to my friend, Mr Karan Pater for
being there and make me laugh in the weird situations, making it worse but the best time of my
life. Bharat Sir, special thanks to you for continuous motivation and helping me in a tough
situation. Prasoon Thakur, my bioinformatics buddy, thank you for having a long discussion
with me about problem-solving.
In the end, I would like to acknowledge my wonderful wife, Soniya Sharma who is there with
me since always. She is an eternal part of my past, present and future. I don’t want to thank
you but congratulate her for completing this together with hand in hand. Being a PhD student,
it is very tough sometimes to manage things, but you always backed me and helped me
throughout our PhD journey. Also, thank you for the beautiful gift I ever had as our babies
(Sovik Singh and Nivika Singh). Thank you for being there always!! Last but not least, I would
like to thank the almighty GOD for being there somewhere around me with all positive energy.
Vikrant Singh
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Abstract
The highly specialized cells of the eye function in concert to produce clear vision, one of the
most valued senses. Visual impairment or blindness can occur as a result of disease or trauma.
Age-related macular degeneration, glaucoma, cataracts and diabetic retinopathy are common
causes of vision loss in older individuals. This thesis explores the bioinformatics approaches
based on the central dogma as a model for exploring the experimental models for human eye
diseases including quality control of stem cells to detect chromosomal abnormalities;
epigenetic age prediction of ocular tissues; identification of novel genes in Clustered Regularly
Interspaced Short Palindromic Repeats (CRISPR) knockout screening in uveal melanoma; and
RNA-Seq analysis to understand the molecular mechanisms in oxygen-induced retinopathy
and differentiation of dental pulp mesenchymal stem cells into trabecular meshwork cells.
In a virtual karyotyping study, I investigated the utility of a low-density genome-wide SNP
array for the karyotypic assessment of human pluripotent stem cells (hPSCs) using the Illumina
Infinium HumanCore BeadChip. Specifically, the resolution of these arrays in detecting
chromosomal aberrations and their ability to identify clonal variations was determined. It was
shown that the SNP array can detect chromosomal abnormalities when at least 25% of the cell
population is aberrant. Our data demonstrate that an array-based karyotyping offers an
economical and robust sampling method, in the genomic resolution, compared with standard
cytogenetic karyotyping of hPSCs. This approach could provide a reliable, rapid and cost-
effective assessment of hPSCs clonality and virtual karyotype for large-scale generation and
maintenance of hPSCs.
To investigate the effects of aging among different tissues, we calculated the DNA methylation
age of whole peripheral blood and ocular tissue from the same individual and compared it with
the person’s chronological age. We found significant differences between chronological and
epigenetic ages (p<0.048). Our study showed that there is a significant difference (mean = 44.4
years) between chronological and epigenetic age in neurosensory retinal tissue and the same
pattern identified by various tools.
Through a CRISPR knockout screening study, we used the Human GeCKOv2 pooled library
in OCM1 cell lines (uveal melanoma) to identify novel genes that are associated with
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tumorigenesis using the CRISPRAnalyzeR tool on next-generation sequencing data. Overall,
from our analysis, we found 15 genes that have relatively low expression in the passage 12 as
compared to the passage 0 and are associated with the metabolic process, cellular process,
primary metabolic process, cellular metabolic process, biological process and organic
substance metabolic process. Our study shows that these genes are crucial for cell proliferation;
however, further in-vitro and in-vivo validation is required.
In RNA-Seq studies, we investigated the miRNA expression in oxygen-induced retinopathy
(OIR) rat models through next-generation sequencing (RNA-Seq) data. Our RNA-Seq data
suggested that the expression of miR-143 dysregulated and mediated the regulatory networks,
leading to retinal neovascularization. Furthermore, miR-143 can influence cellular motion and
cell-matrix interaction during vascular formation. We also found that miR-126, miR-150 is
significantly down-regulated and directly involved in retinal neovascularization. We also
investigated the propensity of mesenchymal stem cells (MSC) populations (dental pulp-derived
MSCs) to differentiate into trabecular meshwork (TM) cells. We performed RNA sequencing
under two conditions: control (DPMSCs) and treated (with growth factors to differentiate into
the TM cells). Overall, we found over 8000 genes that are statistically significant and have an
association with tissue development, extracellular matrix development and related pathways,
and demonstrated the ability of DPMSCs to differentiate into TM cells. Using bioinformatics
techniques to develop models of blinding eye diseases can lead to new and better treatments to
preserve vision.
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Publications
• Maciej Daniszewski, Quan Nguyen, Hun S. Chy, Vikrant Singh, Duncan E. Crombie,
Tejal Kulkarni, Helena H. Liang, Priyadharshini Sivakumaran, Grace E. Lidgerwood,
Damián Hernández, Alison Conquest, Louise A. Rooney, Sophie Chevalier, Stacey B.
Andersen, Anne Senabouth, James C. Vickers, David A. Mackey, Jamie E. Craig,
Andrew L. Laslett, Alex W. Hewitt, Joseph E. Powell, Alice Pébay,"Single-Cell
Profiling Identifies Key Pathways Expressed by iPSCs Cultured in Different
• Fan Li, Vikrant Singh, Vivienne Lu, Jinying Chen, Sandy S.C. Hung, Joanne L.
Dickinson, Phillippa Taberlay, Anthony L. Cook, Alex W. Hewitt, Guei-Sheung Liu
"A genome-wide crispr/cas9 screen to identify novel therapeutic targets for uveal
melanoma", Clinical and Experimental Ophthalmology2018;46 (Suppl 1) Pages 21-
35
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Table of Contents Declarations ...................................................................................................................... ii
Abstract .............................................................................................................................. v
Publications ..................................................................................................................... vii
Abstract Publications ..................................................................................................... viii
TIME Telomerase-immortalized microvascular endothelial cell
TM Trabecular meshwork
tpm Transcript per million
UM Uveal melanoma
UTR Untranslated region
UV Ultraviolet
VEGF Vascular endothelial growth factor
WCP Whole chromosome paints
ZFN Zinc finger nuclease
Chapter 1 Introduction
1.1 Structure of the eye
The eyes are one of the most complex organs in the human body. To enable optimal vision, all
structures within the eye must function properly in order to refract and focus light onto the
retina. The eye has three layers: outer, middle and inner (Figure 1.1). The outer layer, also
known as the fibrous coat, consists of the sclera and the cornea. The cornea is the transparent
layer in front of the iris, pupil and lens and maintains the shape of the eye. It transmits light to
the inner eye and also protects it from infection and structural damage. The middle layer, or
uveal tract, consists of the choroid, iris and ciliary body. The iris controls the size of the pupil
and therefore the amount of light entering the eye and reaching the retina.
Figure 1.1 Structure of the eye. Outer layer (sclera and cornea), middle layer (choroid, iris and ciliary body) and the inner layer (macula fovea and optic disc)1.
The ciliary body controls the shape of the lens and the production of aqueous humour whereas
the choroid provides nutrients and oxygen to the retinal layer. The inner layer of the retina is a
complex layer of neurons that capture and process light (Willoughby et al., 2010).
1 Image Source: Rhcastilhos. And Jmarchn
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1.2 Eye diseases
Normal eye function can be affected by minor issues that resolve quickly, such as
conjunctivitis, or more serious conditions, e.g. glaucoma, that can lead to visual impairment.
With many eye diseases, there may be no early symptoms and they can only be diagnosed after
the disease reaches an advanced stage. Unfortunately, advanced blinding diseases are typically
less amenable to the treatment.
1.2.1 Blinding Mendelian Disease of the RPE
Many blinding diseases occur due to inherited degeneration of the retinal pigment epithelium
(RPE). Although specific mutations responsible for the majority of inherited retinal dystrophies
have been identified, all these conditions are currently untreatable (De Roach et al., 2013). Best
Disease, Doyne Honeycomb Retinal Dystrophies, and Sorsby Fundus Dystrophy are clinically
well-defined but remain poorly characterized in terms of their cellular pathophysiology and the
underlying mechanisms that lead to RPE degeneration. Currently, there are no treatments for
these blinding dystrophies, and as such, they are excellent candidates for disease modelling
and genetic correction.
Best disease (OMIM #153700) is an autosomal disorder of RPE that causes secondary
photoreceptor degeneration within the macula and concomitant loss of central vision, usually
beginning in the second to third decade of life (Blodi and Stone, 1990, Nordstrom and
Barkman, 1977). One of the earliest clinical manifestations of Best Disease is the appearance
of a yellowish vitelliform lesion located in the subretinal space of macula (Boon et al., 2009,
Mohler and Fine, 1981, Xiao et al., 2010). Best Disease is caused by mutations in the RPE
gene BESTROPHIN1 (BEST1), which, through mechanism(s) that remain unclear, leads to the
accumulation of subretinal fluid and auto fluorescent waste products from shed photoreceptor
outer segments (POS) (Singh et al., 2013).
Doyne Honeycomb Retinal Dystrophies (or Malattia Leventinese; OMIM #126600) is an
inherited autosomal dominant maculopathy that results in progressive and irreversible visual
loss (Zhang et al., 2014a). It is characterized by a radial pattern of many laminar drusen (a lipid
waste deposition between the RPE and Bruch’s membrane in the outer retina) that accumulate
beneath the RPE at the posterior pole (Zhang et al., 2014a). It is caused by mutations in the
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epidermal growth factor-containing fibulin-like extracellular matrix protein 1 (EFEMP1) gene.
In this disease, drusen develop during early adulthood. The build-up of lipofuscin and
coalescence of large drusen form a honeycomb-like pattern within the macula, causing disease
progression (Stone et al., 1999).
Sorsby Fundus Dystrophy (OMIM #136900) is a rare, autosomal-dominant retinal dystrophy
caused by a missense mutation in the tissue inhibitor of metalloproteinases-3 (TIMP3) gene
(Szental et al., 2009). ). Clinically it is characterized by early-onset (<45 years of age) bilateral
loss of central vision loss due to RPE atrophy and subretinal neovascularization at the macula
(Szental et al., 2009).
1.2.2 Glaucoma
Glaucoma involves chronic neurodegeneration of the optic nerve and is a leading cause of
blindness (Blomdahl et al., 1997, Munier et al., 1998, McKinnon, 2003). Glaucoma is a
heterogeneous group of ocular disorders, with primary open-angle glaucoma (POAG) the most
common subset (Hewitt et al., 2006). POAG is defined as progressive excavation of the optic
disc with corresponding visual fields defects and an open iridotrabecular angle(Quigley,
2011a). POAG is challenging to diagnose in the early stages because the clinical features can
be subtle. The disease is characterized by the death of retinal ganglion cells (RGC) due to
axonal degeneration, resulting in visual impairment (Yasuda et al., 2014). However, despite an
active genetic component, the precise molecular drivers of POAG pathology are not known.
Currently there is no definitive treatment (Quigley, 2011a), although intraocular pressure
(IOP)-lowering medications and/or surgery can delay progression.
Globally, it is estimated that 60 million people have glaucomatous optic neuropathy and 8.4
million people are blind as a result of glaucoma (Cook and Foster, 2012). By 2020 these
numbers are set to increase to 80 million and 11.2 million, respectively (Cook and Foster,
2012). Extensive population-based epidemiology studies revealed that the prevalence of
POAG in Australia is approximately 3% in people aged over 50 years, rising to 10% by age 90
(Mitchell et al., 1996). Various comprehensive examinations are used to detect glaucoma:
• Tonometry
• Visual field test
• Pachymetry
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• Gonioscopy
1.2.3 Age-Related Macular Degeneration
Age-related macular degeneration (AMD) is a neurodegenerative disease that affects the
central part of retina — the macula. The disease is categorized into early, intermediate and
advanced stages based on the severity and underlying pathology, including choroidal
neovascularization (Pennington and DeAngelis, 2016). AMD is further classified as dry AMD
or wet AMD. Dry AMD is more common and is related to the drusen (yellow deposition) in
the macula; this leads to loss of macula function and blurred vision. The signs of dry AMD
cannot be observed easily and it is particularly difficult to detect if it affects only one eye. Wet
AMD accounts for about 15% of cases. In wet AMD, blood vessels located under the macula
start leaking, which causes the retina to deform. Wet AMD can progress rapidly and can be
very severe, degrading vision quickly (Parmet et al., 2006). Clinical features of AMD include:
• Aging: increasing age is a consistent risk factor for both dry and wet AMD. In the
United States, the incidence of AMD was reported as 0.3% in people aged between 50-
59 years and 16% in those aged 80 years and older (Friedman et al., 2004).
• Genetics: The most common genetic variation for AMD is the long arm of
chromosome 1 (1q31). It is a common amino acid variation (tyrosine with histidine at
402bp amino acid) reported at CFH (Complement factor H) gene (Hageman et al.,
2005, Klein et al., 2005, Haines et al., 2005). The CFH gene is highly implicated in the
development of AMD, with an odds ratio of 2.4–4.6 for heterozygous patients and 3.3–
7.4 for homozygous patients (Losonczy et al., 2011).
Nominally, 5% of blindness worldwide is due to AMD (Pascolini and Mariotti, 2012). By 2020
it is estimated that 196 million people will have AMD, and this will increase up to 288 million
by 2040 (Wong et al., 2014). The global cost for AMD is around $343 billion, including $255
billion in health care (Gordois et al., 2010). According to a report published by the Australian
Institute of Health and Welfare in 2005, nearly 3.1% of the population aged 55 or more have
AMD and 10.1% of Australians aged >80 have drusen in one of their eyes, with men and
women equally affected. The early and intermediate stages of AMD are asymptomatic. AMD
can only be detected by specific eye exams:
• Amsler grid
• Dilated eye exam
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• Fluorescein angiography
• Optical coherence tomography
• Visual acuity test
There is no definitive treatment for dry AMD, but several combinations of nutritional
supplements have been shown to impact drusen (Krishnadev et al., 2010).
• Vitamin C (500 mg)
• Vitamin E (400 IU)
• Lutein (10 mg)
• Zeaxanthin (2 mg)
• Zinc (80 mg)
• Copper (2 mg)
For treating wet AMD, anti-vascular endothelial growth factor (VEGF) drugs can be used to
reduce abnormal blood pressure in the retina (Yorston, 2014).
1.2.4 Diabetic Retinopathy
Individuals with diabetes often experience diabetic retinopathy (DR); indeed, DR is the leading
cause of vision loss among working adults (Cheung et al., 2010). Ongoing hyperglycaemia
causes the blood vessels in the retina to bleed, distorting vision (Mustafa et al., 2018). DR
classified into four stages, which are as follows:
● Mild nonproliferative retinopathy: This first stage of DR is also known as
background retinopathy. Microaneurysms (tiny bulges) on the blood vessels in the
retina occur.
● Moderate nonproliferative retinopathy: This is the second stage of DR, also known
as pre-proliferative retinopathy. At this stage, the retina swells and is unable to carry
the blood, resulting in a change in the physical appearance of the eye. Diabetic macular
edema (DME) due to deposition of blood and fluid in the macula occurs. This affects
sight as the macula is directly involved in direct vision.
● Severe nonproliferative retinopathy: At this stage, the blood vessels are blocked,
(macular ischemia). Floaters can be visible and vision is blurry.
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● Proliferative diabetic retinopathy (PDR): This advanced stage is caused by
neovascularization. Thin and weak vessels are formed and frequently bleed because of
scar tissue, which can be responsible for retinal detachment and permanent vision loss.
The main risk factors associated with the DR are the duration of diabetes and degree of
glycaemic control, with other conditions such as hypertension, pregnancy and elevated serum
lipid levels also playing a role. It is estimated that, in 2015, nearly 2.6 million people were
visually impaired as a result of DR and it is expected that this number will increase to 3.2
million by 2020 (Leasher et al., 2016, Flaxman et al., 2017).
In Australia, the prevalence of DR is 29.1% (McKay et al., 2000). Ophthalmoscopy, with or
without dilating the eye, can be used to screen for DR by detecting microaneurysms (de Carlo
et al., 2015). Fluorescein angiography detects the vascular changes in the inner and outer
retinal blood barrier and thus is a more sensitive, although more costly, option than
ophthalmoscopy for diagnosing DR (Kwiterovich et al., 1991). It is crucial that individuals
with DR maintain blood glucose levels within target range. Treatments for later stage disease
include laser therapy, eye injections (ranibizumab and aflibercept) and eye surgery (removal
of vitreous humour).
1.2.5 Cataract
Cataracts are caused by aging of the crystalline lens; the lens becomes cloudy or even opaque
so light does not pass through and vision is blurry. Lifestyle factors such as smoking and
exposure to the sunlight and long-term oral corticosteroid therapy also contribute to cataracts
(Allen and Vasavada, 2006). Cataract can be diagnosed using various eye examinations such
as a slit-lamp exam, retinal exam, refraction and visual acuity test.
Just over half (51%) of the global blindness burden — 20 million people afflicted — is due to
cataract (World Health Organization report -(report)). Although cataract can be treated by
surgery, surgical services are inadequate in developing countries.
1.2.6 Uveal Melanoma
Uveal melanoma (UM) is the most common intraocular malignant tumour in adults and it has
a high rate of fatal metastasis. UM emerges from the melanocytes in the choroid, ciliary body
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or the iris (also known as uveal tract) (Chang et al., 1998). The cause of UM is not entirely
understood. Sun exposure is considered the most critical risk factor for cutaneous melanoma;
however, ultraviolet (UV) radiation also plays a role in the development of UM. The iris is
unprotected from visible light and UV radiation, whereas the ciliary body and choroid are less
directly exposed. Nonetheless, around 85% of UM emerges in the choroid, 10% in the ciliary
body, and the remaining 5% in the iris, demonstrating an inverse association between exposure
to sunlight and the manifestation of UM (Weis et al., 2006). Notably, welding as an occupation
(artificial source of UV radiation) is associated with UM (Vajdic et al., 2002). Additionally,
light iris colour and skin pigmentation and the inability to tan are other factors that correlate
with increased risk for UM (Wakamatsu et al., 2008).
Genetic variants such as rs12913832, rs1129038 and rs916977 on chromosome 15q13.1, which
contribute to eye and skin pigmentation, are correlated with UM (Ferguson et al., 2016) and
mutation at BAP1 (BRAC1-associated protein 1) gene is also found to be an inclined element
for UM (Harbour et al., 2010). The most common symptoms of UM are:
• blurred vision
• visual field defect
• ocular irritation
• photopsia (flashing of lights)
• floaters
• Metamorphopsia
The incidence of UM varies depending upon sex, race and geographical location, with the rate
in men 30% higher than in women (McLaughlin et al., 2005). In the United States, around 5.1
people in 1 million are diagnosed with UM (Singh et al., 2011) whereas, in Europe, especially
in Denmark and Norway, the rate is more than 8 people in 1 million. In Spain and Italy, 2
people in 1 million are diagnosed with UM (Virgili et al., 2007). In contrast, the incidence
among the Asian population is 0.39 per million (Hu et al., 2005).
UM is diagnosed primarily by clinical examination, including slit-lamp biomicroscopy,
indirect ophthalmoscopy and ancillary diagnostic testing such as ocular ultrasonography
(Jovanovic et al., 2013b). Patients with posterior UM are mostly treated by plaque radiation
therapy or enucleation. Local radiotherapy such as 106 ruthenium, 125 iodine brachytherapy,
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stereotactic radiosurgery or photo beam therapy are other treatment options for UM (Nathan et
al., 2015).
1.2.3 Bioinformatics application in eye disease
Bioinformatics is an interdisciplinary field that uses mathematics, computer science and
statistics to solve complex biology problems. Various bioinformatics approaches can be
applied in order to discern the underlying genetics of the eye:
● Quality Control in stem cells for genomic aberrations.
● Epigenetic age analysis.
● CRISPR screening to identify essential knockout genes.
● RNA-Seq analysis.
1.2.3.1 Quality Control in Stem Cells Genomic Integrity
iPSCs are equivalent to a human embryonic stem cell in that they have the ability to self-renew
and to differentiate into all types of embryonic germ layers (Takahashi and Yamanaka, 2006,
Takahashi et al., 2007, Yu et al., 2007). Induced pluripotent stem cells (iPSCs) show great
promise in clinical applications because of their potential use in personalised cell therapy or
disease modelling (Simara et al., 2013). To develop the best disease model, it is essential to
have a stable and intact genome. For reasons not entirely understood, iPSCs tend to accumulate
genomic aberrations and lack the functionality of repair mechanisms (Weissbein et al., 2014).
Genomic instability in human pluripotent stem cells (hPSC) was first identified in the early
2000s when karyotyping abnormalities such as trisomy of the chromosome were noted (Cowan
et al., 2004, Draper et al., 2004). Other chromosomal abnormalities observed in hPSCs include
duplication and deletions in genes or point mutations (International Stem Cell et al., 2011,
Taapken et al., 2011). Single nucleotide polymorphism (SNP) genotyping arrays can detect
these abnormalities. It is reported that iPSCs have a higher number of copy number variations
(CNV) than hPSCs (Laurent et al., 2011, Martins-Taylor et al., 2011). These variations in
iPSCs seem less evident, but recently it was found that somatic mosaicism in fibroblast cultures
causes CNVs among human and murine iPSCs (Bock et al., 2011, Hussein et al., 2011).
Because each member of a colony of iPSCs is a clonal offspring of single programmed somatic
cells, the variation present in the parent’s cells is amplified (Peterson and Loring, 2014). The
genomic aberration in stem cell lines can be identified using traditional methods such as G-
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Banding or Fluorescence in-situ hybridisation (FISH) or using computational analysis on
genotyping arrays (Illumina and Axiom). Using comparative genomic hybridization (CGH)
array, (Chin et al., 2009) analysed the CNVs of three fibroblast lines and the hiPSCs that were
derived from the fibroblasts and unique CNVs were identified in each Human induced
pluripotent stem cells (hiPSC) (Chin et al., 2009).
1.2.3.1.1 Traditional Karyotyping
Karyotyping is a cytogenetic process of putting together all the chromosomes of an organism
to provide a genome-wide print of that individual. It is prepared by the standard procedure
following graded staining to reveal the structure of each chromosome and can detect anomalies
up to several megabase pairs or more (O'Connor, 2008). Several methods are used to detect
chromosomal abnormalities:
G-banding (Giemsa Banding)
G-banding is also known as Giemsa banding. It is a classic staining method that employs
various chemical and enzymatic treatments for chromosome preparations (Figure 1.2). The
main advantage of using this method is that permanent slides can be prepared and studied under
a standard light microscope. In this method, Giemsa stain correlates with the intense banding
fluorescent regions. Trypsin is used for the pre-treatment of a chromosome before Giemsa
staining; other stains, including the Wright stain and the Leishman stain, provide the same
pattern as Giemsa stain (Charleen M Moore and Robert G Best, 2001).
Figure 1.2 G-banding showing metaphase arranged in the standard karyotype format.
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Fluorescence in-situ hybridisation (FISH)
FISH is the technique used to determine the chromosomal position of a deoxyribonucleic acid
(DNA) or Ribonucleic acid (RNA) probe. This cytogenetic technique was developed and first
used in the early 1980s (Pinkel et al., 1988).
Figure 1.3: Fluorescence in-situ hybridisation (FISH) -Normal fibroblast cell line showing normal chromosomes 11 positive for WCP11 (spectrum orange) and normal chromosomes 15 positive for WCP 15 (spectrum green).
In this technique, fluorescent DNA probes are attached to the complementary region of the
chromosome and emit signals that are later visualized by using probes to detect abnormalities
in the chromosome (Amann and Fuchs, 2008) (Figure 1.3).
FISH is much faster than other traditional methods for metaphase karyotyping analysis
(Sujobert et al., 2013). Specific chromosomal abnormalities such as amplification,
translocation or deletion can be reported and sketched (Ratan et al., 2017).
1.2.3.1.2 Virtual karyotyping
Virtual karyotyping is a sophisticated technique that determines karyotype from analysing a
DNA probe, which is isolated and recorded (Wang et al., 2002). It detects genomic aberrations
at high resolution compared to the traditional method that identifies abnormalities at a low
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resolution based on Comparative genomic hybridization (CGH) (Shinawi and Cheung, 2008).
The main methods used for virtual karyotyping are as follows:
• CGH-arrays
• SNP genotyping arrays
CGH-arrays: Array-based comparative genomic hybridisation is also known as molecular
karyotyping. This method is developed to detect CNV that is undetectable using traditional
methods. CNV occurs due to the loss of function or amplification of a genomic sequence. In
CGH array, genomic DNA is extracted from peripheral blood lymphocytes or skin fibroblasts
and labelled with fluorescent dye (generally Cy3-labelled dCTP). The patient DNA is labelled
with another fluorescent dye (Cy5-labelled dCTP) with the control genomic DNA co-
hybridised for a selected set of pre-determined genomic fragments (Vermeesch et al., 2005).
The amount of Cy3 and Cy5 fluorescent emission is interpreted as normal or not. For example,
if the fluorescent intensity of Cy3 is high in the sample, then there is duplication in the sample
whereas if Cy5 fluorescent is high, a deletion is present (de Ravel et al., 2007). CGH provides
resolution more than 100-1000 times higher than traditional methods and also detects small
genomic aberrations of up to a few hundred base pairs and thus is useful for detecting
syndromes involving microdeletions or microduplications. Moreover, CGH arrays allow us to
understand the relationship between the disease and its underlying genetic defect.
SNP arrays: Single nucleotide polymorphism (SNP) array is used to detect polymorphisms
within the population. SNP is the typical type of genetic variant and presents as a different
single nucleotide within the genomic sequence. The basic principle of SNP arrays is the same
as DNA microarrays. SNP arrays work mainly on three components: immobilizing allele-
specific oligonucleotides (ASO), labelling a genomic sequence with fluorescent dyes and a
system used for recording hybridization system (LaFramboise, 2009). The ASO probes are
selected based on the sequencing representation of an individual. In the SNP arrays, two probes
are used for the SNP positions for both alleles (Rapley et al., 2004 ). SNP microarrays have
come to play an essential role since the human genome map was published. SNP array is a
potent tool to study genomic variation and for the virtual karyotyping using various
computational tools by determining the CNV from each SNP from the array and also to align
the chromosome order (Sato-Otsubo et al., 2012).Various commercial technologies such as
Affymetrix SNP genotyping and Illumina genotyping platforms are used to study CNVs.
29
1.2.3.1.3 Tools for detecting Copy number variations
CNV is discrepancy in the number of copies of a gene between individuals in the form of gain
or loss of genetic material; CNV can lead to the genetic imbalance. SNP is the standard variant
observed in the population (>1%). CNV can be described as amplification, insertion or deletion
at the specific genomic location (Thapar and Cooper, 2013). Various computational methods
used to determine the CNV in genomic samples are shown in Table 1.1.
Table 1.1: Various computational tools for CNV detection in whole-genome genotyping and next-generation sequencing data.
Software Algorithm Published URL Programming
language
Citations
PennCNV
(Wang et
al., 2007)
Hidden
Markov Model
(HMM)
2007 http://penncnv.open
bioinformatics.org/
en/latest/
C++ 1402
QuantiSNP
(Colella et
al., 2007)
Objective
Bayes Hidden-
Markov Model
2007 https://sites.google.
com/site/quantisnp/
MATLAB 586
BCFtools
CNV
(Danecek et
al., 2016)
Hidden
Markov Model
(HMM)
2016 https://samtools.git
hub.io/bcftools/how
tos/cnv-calling.html
Perl 7
CHAT (Li
and Li,
2014)
Markov Chain
Monte Carlo
(MCMC)
2014 https://sourceforge.
net/p/clonalhetanal
ysistool/wiki/CHA
T/
R 39
CNVcaller
(Wang et
al., 2017b)
Read Depth
Algorithm
2017 https://github.com/J
iangYuLab/CNVca
ller
Perl/Python 9
CNVhap
(Coin et al.,
2010)
chromosome-
wide
2010 https://bioinformati
cshome.com/tools/c
nv/descriptions/cnv
Hap.html
Java 49
30
Software Algorithm Published URL Programming
language
Citations
haplotype
model
ADTEx
(Amarasing
he et al.,
2014)
Hidden
Markov Model
(HMM)
2014 https://sourceforge.
net/projects/adtex/
R/Python 61
PICNIC
(Greenman
et al., 2010)
Hidden
Markov Model
(HMM)
2010 https://www.sanger
.ac.uk/science/tools
/picnic
MATLAB 169
1.2.3.2 DNA Methylation
1.2.3.2.1 Background
Epigenetics is the study of genomic modification that affects gene expression without altering
the genomic sequence. In the 1970s, DNA methylation was considered to be a transcriptional
regulatory mechanism that was maintained during cell division (Riggs, 1975). Over time, the
definition of epigenetics has evolved from the cellular level to the molecular level. With recent
advancements in next-generation sequencing technologies, epigenetics has expanded to
include the study of genome-wide chromatin modification that does not change the genomic
sequence including DNA methylation, histone modification, chromatin accessibility and
microRNA regulations (Sarda and Hannenhalli, 2014).
Figure 1.4: DNA methylation (Martin and Fry, 2018).
31
1.2.3.2.2 DNA methylation
DNA methylation occurs when a methyl group is added to the 5th carbon of a cytosine residue
that is linked by a phosphate to a 5CpG dinucleotide by DNA methyltransferase (DNMT1,
DNMT3A and DNMT3B), thus resulting in 5-methylcytosine as shown in Figure 1.4
(Ballestar, 2011). DNA methylation is involved in transcriptional regulation. Most of the
genome does not have CpG sites, but a collection of CpG sites (known as a CpG island) occurs
commonly on the promoter region of housekeeping genes. However, these CpG islands do not
undergo methylation, resulting in gene activation Methylated CpG promoters are related to low
gene expressions, and CpG islands shores usually have low density, located 2kb from the CpG
islands. These CpG shores demonstrate tissue-specific methylation with gene silencing
whereas exonic methylation have active expression. Outside the shore region, the genomic
region has a lower frequency of CpG sites, which are methylated. These CpG promoters and
enhancers are regulated by tissue-specific genes (Irizarry et al., 2009). At present, DNA
methylation (CpG sites) have been identified as a useful probe in age-associated studies.
1.2.3.2.3 DNA methylation age
Aging is an irreversible biological process associated with a decline in physical function and
an increase in the rate of neurodegenerative and cardiovascular disease and cancer (Sen et al.,
2016). The aging process involves changes at a molecular and cellular level, including
epigenetic alterations, telomere abrasion or cellular agedness (Lopez-Otin et al., 2013).
Epigenetic modifications such as DNA methylation, histone modification and non-coding
RNA with the change in DNA methylation are related to the aging process (Jones et al., 2015,
Bruce Richardson, 2003). DNA methylation age (DNAm age) can be used as an epigenetic
biomarker for the aging process. DNAm age of various human tissue and cell type can be
predicted by using several tools as shown in Table 2 (Horvath, 2013) (Hannum et al., 2013).
Horvath Method for DNAm Age prediction
• Data from Illumina 27K or Illumina 450K array platform used for analysis.
• Elastic net regression model used for selecting 353 CpGs for DNAm age analysis with
the glmnet R package ((Friedman et al., 2010).
● WGCNA R package (Langfelder et al., 2013) used for meta-analysis to measure pure
age effects.
● The tool is implemented in R.
32
Hannum Method for DNAm Age prediction
• Illumina Infinium HumanMethylation450 array was used to extract the methylation
fraction values.
• 71 CpGs for DNAm age analysis.
• Elastic Net algorithm used in aging model implemented in a glmnet R package
(Friedman et al., 2010).
• Missing values claimed by the ten nearest marker approach using impute R package
(Troyanskaya et al., 2001).
• The tool is implemented in R.
1.2.4.1 CRISPR Cas 9
1.2.4.1.1 Background
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a naturally occurring
anti-viral defence mechanism identified in many prokaryotic species such as bacteria and
archaea. The CRISPR loci appear on prokaryotic DNA and have been characterised as having
identical palindromic repeats of 21-40 base pairs in length (Jansen et al., 2002, Mojica et al.,
2000). The CRISPR-associated protein 9 (Cas9 protein) derived from type II CRISPR emerged
as a powerful tool for genomic engineering. An RNA-guided DNA endonuclease, Cas9 can
easily be programmed to alter a specific site with a slight change in the guided RNA sequence
(Wang et al., 2016). The Cas9 protein is able to search out and cleave the target DNA in both
natural and artificial CRISPR/Cas systems. It has six domains: REC I, REC II, Bridge Helix,
PAM Interacting, HNH and RuvC (Figure 1.5) (Jinek et al., 2014, Nishimasu et al., 2014).
33
Figure 1.5: Cas9 protein domains and schematics and Crystal structure from PDB2. The Rec I domain is the most prominent domain and binds to the guide RNA, whereas the
HNH and RuvC domains are responsible for the cleavage activity and cut single-strand DNA.
These domains are highly homologous and are also found in other proteins as well (Jinek et
al., 2014, Nishimasu et al., 2014). However, it is reported that Cas9 protein remains inactive
in the absence of guide RNA (Jinek et al., 2014).
Cas9 protein searches for the target DNA with the help of protospacer adjacent motif (PAM)
sequence (two or three base sequence located in the complementary region to the guide RNA)
(Sternberg et al., 2014).
1.2.4.1.2 CRISPR Cas9 genome editing
The initial step of CRISPR/Cas9 genome editing is to modify the genomic locus by a double-
stranded break (DSB). In mammalian cells, two types of methods are used for detection and
repair: homology-directed repair (HDR) and non-homologous end joining (NHEJ) (Vartak and
Raghavan, 2015). The HDR functions only in the replicative phase of the cell cycle by using a
homologous template to repair the missing genetic information. NHEJ can occur anywhere in
the cell cycle and works by ligating the broken DNA using chemicals based on an independent
template with the introduction of indels (small insertions and deletions mutations) of various
lengths (Vartak and Raghavan, 2015). The position of indels in the NHEJ is very crucial; if it
occurs in the coding region, this could result in a frameshift and lead to a premature stop codon
with a change in the protein function (Figure 1.6).
Figure 1.6: Schematic picture of genome editing via the CRISPR/Cas9 system3. The CRISPR/Cas type II system from Streptococcus pyogenes has been widely used for
genome editing. It is employed with the aid of Cas9 nuclease and a short guide RNA (sgRNA),
which is an integration of crRNA (contains genome-targeting sequence) and tracrRNA in the
cell. Various strategies, such as lentiviral transduction (Kabadi et al., 2014) and cas9-guide
RNA ribonucleoprotein complex, can be used for delivering sgRNA and cas9 into the cells
(Lin et al., 2014). The sgRNA library can be delivered by viral vectors such as lentivirus,
retrovirus or adeno-associated virus (AAV). The AAV vector does not integrate into the host
genome, whereas lentivirus and retrovirus integrate into the host genome. Thus, the retrovirus
delivery method works for dividing cells whereas AAV works non-dividing cells (Joung et al.,
2017a). In terms of capacity, the insert size of AAV is much smaller compared to retrovirus
or lentivirus (Joung et al., 2017a). Zinc finger nuclease (ZFN) and transcription activator-like
effector nuclease (TALEN) lentiviral-based delivery methods are widely used in genomic-
editing technologies. TALENs can effectively deliver as mRNA by using lentiviral delivery
method containing inactivated reverse transcriptase, and this method was adopted by the
CRISPR/Cas9 system (El-Aneed, 2004). Type II CRISPR/Cas9 genome editing has been used
edit various cells and organisms in vivo and in vitro including human (Mali et al., 2013),
bacteria (Pyne et al., 2015), zebrafish (Hwang et al., 2013), mice (Hall et al., 2018), rat (Back
et al., 2019), rabbits (Kawano and Honda, 2017) and others.
3 Source: AcceGen R&D Team
35
1.2.4.1.3 CRISPR Screening
CRISPR Screening is a technique used for uncovering the genes that are responsible for
specific biological functions. With the advent of next-generation screening technologies,
functional phenotypes can be identified and essential genes determined using loss-of-function
and gain-of-function inquiries. Generally, genetic pooled screens are used for targeting a large
number of genes under the suitable condition for one gene agitated per cell. A cellular
population can be interrogated to uncover unanswered biological questions for a particular
disease, with enrichment or depletion of cells as per the environment. Genetic screening not
only facilitates identification of genetic variants but also helps discover target for therapeutic
intervention (Bartha et al., 2018). Genetic screening can be carried out by two methods:
● Array: siRNAs, shRNAs and sgRNA separately brought into cells.
● Pooled: a mixture of shRNA/sgRNA introduced into cells simultaneously with specific
barcoding.
In array format screening, targeting reagent is synthesised separately and is delivered by
transfection after analysis has been done on the readout of each well (Shalem et al., 2015,
Agrotis and Ketteler, 2015). In pooled array, targeting reagents are sourced efficiently through
Insilco oligonucleotide libraries and transfection has been done on plasmid libraries that cloned
for pooled screening. In pooled screening, the number of transgenes is limited during
transfection to ensure that each cell has only one sgRNA, resulting in low multiplicity of
infection (MOI) and the readout can be analysed by next-generation sequencing (Shalem et al.,
2015). Pooled screening is less expensive but more time-consuming compared to array screens.
However, it is relatively easier to apply long cultures in pooled screening compared to the array
format because eventually a smaller culture volume is required; a pooled approach is
commonly used with the CRISPR/Cas9 system (Wang et al., 2014, Koike-Yusa et al., 2014,
Zhou et al., 2014). Pooled screening array can be further divided into two categories:
● Negative Selection: the primary goal of negative selection is to find the sgRNA that is
depleted during selection, which corresponds to cell apoptosis.
● Positive Selection: the primary goal of positive selection is to find the sgRNA that
enriched during the selection resulting in cell proliferation.
Several tools have been developed for analysing CRISPR knockout screening data or other
screening data (Table 1.2).
36
Table 1.2: Tools available for CRISPR screening data analysis.
Software Algorithm Published URL Programming
Language
Citation
MAGeCK
(Li et al.,
2014)
Mean/variance modelling and robust rank aggregation
(Figure 2.1B). The chromosome abnormalities observed included a derivative chromosome 1
that resulted from a translocation between the long arms of chromosomes 1 and 15, which
46
resulted in gain of 15q from band 15q15 to 15qter. Two translocations were also observed: one
between the short arm of chromosome 4 and the long arm of chromosome 8 and the other
between the short arm of chromosome 15 and the long arm of chromosome 22. These appeared
to be balanced cytogenetically; however, small losses or gains of material are outside the limit
of detection of conventional chromosome analysis. There was a derivative chromosome 11 that
resulted from a translocation between the short arm of chromosome 1 and the long arm of
chromosome 11. This resulted in loss of 11q from band 11q23 to 11qter.
Figure 2.1: G-banding and FISH analysis: A) Normal male fibroblast cell line BCL6 with karyotype 46, XY. B) Karyotype of cell line BCL6 7+5 with karyotype 45,X,-Y,der(1)t(1;15)(p34.1;q15),t(4;8)(p14;q24),der(11)t(1;11)(p34;q23),t(15;22)(p11.2;q13). C) Normal Cell line whole chromosome painting (WCP) showing Chr11 positive with spectrum orange and Chr15 positive with spectrum green. D) Chromosome painting, WCP#11 (orange) and WCP#15 (green) showing duplication at Chromosome 15 and deletion at chromosome 11.
FISH analysis using whole chromosome paints (WCP) showed normal chromosome 11
showing positive WCP11 in spectrum orange, whereas the derivative of chromosome 11
showed a portion of the chromosome that is positive for WCP11, with the remaining material
A B
C D
47
negative. Chromosome 15 showed positive WCP15 in spectrum green and the derivative
chromosome 15 involved in the translocation with chromosome 22 with a portion of the
chromosome being negative for the WCP15 and at last derivative chromosome 1 showing
positivity for the WCP15. Normal cell line WCP showing chromosomes 11 positive for WCP
11 in spectrum orange and chromosome 15 positive for WCP 15 in spectrum green as shown
in Figure 2.1C. Whole chromosome paints (WCP) 11 (spectrum orange) and 15 (spectrum
green) shown in Figure 2.1D. The derivative chromosome 1 is positive for WCP 15, consistent
with the conventional chromosome analysis and virtual karyotyping which showed a gain of
15q from band 15q15. The derivative chromosome 11 showed partial hybridization with WCP
11, with the distal q arm negative. This is also consistent with the conventional cytogenetics
and virtual karyotyping, which showed deletion of 11q from 11q23.
Further CNVs were assessed by using three programs: PennCNV, QuantiSNP, BCFtools CNV
and aneuploid genome proportion was calculated using CHAT. In these CNV readings, the
regions of the genome that are aberrant in terms of copy numbers were identified by using two
different measurements: BAF and LRR. Briefly, normal BAF values are 0, 0.5 or 1 and any
variation from these numbers will indicate abnormalities in copy numbers. Similarly, the
normal LRR value is 0 and any variation from 0 indicates an abnormality in copy numbers. To
notify the aberration, we selected the CNV across the sample by 10 contiguous SNPs or 1mb
spanning length. The CNV events recorded in all the samples are shown in Figure 2.2 (where
(-) no aberrations and (+) aberrations) with the change in genomic proportion and CNV event
in all samples shown in Supplementary Table 2.1 with genomic proportion shown in
Supplementary Table 2.2.
48
Figure 2.2: Comparison table of CNV events detected by various tools with aneuploid genome proportion (AGP). Pie chart represent the percentage of aberrations in the sample (0,10,15,20,25,30,35,40,45,50 and 100).
The results showed both variations of BAF and LRR in chromosome 1, 11and 15. The same
genomic aberrations were detected by both traditional methods and with the genotyping arrays.
Further BAF/LRR plots were drawn to show the higher resolution of CNV events at which
position these events were reported as shown in Figure 2.3(All samples BAF/LRR Plots are in
Supplementary Figures 2.1). From our workflow, we are able to detect the aberrations in cell
lines with in at least 25% abnormalities.
MBE-01878
MBE-01878-CL6-7+5
QuantiSNP PennCNV BCFtools CHAT (AGP)
---- ---- ----- 0.05
---- ---- ----- 0.75
-+-- ---- ----- 0.23
---- ---- ----- 0.99
+-+- ---+ -+--- 0.28
--+- ---+ -+--- 0.3
+-++ --++ ++-++ 0.37
---- --++ -+--- 0.41
---- --++ -+-+- 0.49
++++ -+++ +++++ 0.74
++++ ++++ +++++ 0.97
49
Figure 2.3: BAF/LRR Plots A) Cell line with 0% genomic aberration (no CNV events). B) Cell line with 100% genomic aberrations (including CNV events - duplication at chromosome 1, deletion at chromosome 11 and duplication at chromosome 15).
This data suggest that the assay is at least as sensitive than a G-banding to detect single cell
variability within samples. It thus establishes that this assay is reliable for testing of
chromosomal abnormalities in cells. Hence, we used CNV analysis for the assessment of
chromosomal stability of hPSCs using established lines.
UK Biobank genotype data were analysed by using Axiom Analysis suite (Inc;, 2019) where
raw CEL format file were used to generate the PennCNV formatted output files for all samples
by using Axion Long Format Export Tools (Inc, 2019) and CNV events are detected by using
PennCNV. Furthermore, we have also compared the CNV events on the same samples which
are genotyped by using different platforms (Illumina and UK Biobank) for detecting genomic
aberrations are shown in Supplementary Table 2.3.
A B
50
2.4 Discussion We demonstrated that a low-resolution CNV array-based karyotyping offers an economical
and robust sampling method, both in the number of cells studied and genomic resolution,
compared with standard cytogenetic karyotyping genomic resolution for aberration detection.
Our data also showed that this method provides sufficient resolution to detect changes not
found through G-banding, which is regularly used for assessing genetic stability in hPSCs. In
Australia, these arrays are approximately one-third to one-quarter of the price of a standard
karyotype analysis. Together with the higher resolution than G-banding assay, this method
provides a clear advantage as it allows for a more economical and more efficient screening of
genetic rearrangements in hPSCs. This could be beneficial for allocating financial resources
for large-scale generation and maintenance of large repositories of iPSCs, as currently initiated
by consortia worldwide.
The virtual karyotype described has the advantage of generating more in-depth data than the
current mainstream assay for checking hPSC genetic stability (G-banding); it allows for DNA
fingerprinting of samples, thus permitting direct coding and tracing of samples; and it allows
population structure/outlier detection based on genome-wide profile.
There are, however, some important caveats to our findings. First, balanced translocations are
not readily identifiable using SNP-based virtual karyotyping. Second, despite DNA being
extracted from many more cells than could be visualized by cytogenetic karyotyping, we
hypothesise that virtual karyotyping would only be robust after a set number of passages,
assuming that most chromosomal changes/aberrations occur during reprogramming.
hPSCs must be characterized prior to using them in vitro or in vivo to understand fundamental
mechanisms or model disease. The field is moving from boutique experiments towards larger
scale studies, involving hundreds of cell lines. In this context, it is obvious that standard
characterization of hPSCs will have to evolve from time-consuming assessment of cells by
PCR, immunochemistry or even teratoma formation, to more high-throughput economical
assays. Such an approach already exists for some aspects of characterizing hPSCs. In particular,
pluripotency can now be assessed using high-throughput embryoid body assay TaqMan
scorecard (TaqMan hPSC Scorecard™ assay, Life Tech) in place of standard
immunochemistry for markers of the three germ layers. It is clear from our data that virtual
karyotyping with low-density SNP array is a natural complement to this approach. With
51
ongoing reductions in costs, the eventual incorporation of next-generation sequencing into the
large-scale profiling of iPSCs will only improve the resolution of CNV assessment. In
summary, we have confirmed that virtual karyotyping on a low-density SNP array is a good
addition to the evolving panels of assays for the economical and high-throughput assessment
of iPSCs.
52
Chapter 3 DNA methylation Age
3.1 Introduction
Many eye diseases manifest due to inherited degeneration of the retinal pigment epithelium;
despite the identification of specific mutations causing the majority of inherited retinal
dystrophies, all these conditions are currently untreatable (De Roach et al., 2013). Genome-
wide association studies (Sheffield and Stone, 2011) have identified genetic causes of ocular
diseases but genetic variants remain poorly understood.
Epigenetic variation refers to genetic modifications that are not due to a change in the DNA
sequence (Petronis, 2010) this can be influenced by environment interactions including DNA
methylation, post-translational histone modifications and chromatin remodelling (Gibney and
Nolan, 2010). DNA methylation (cytosine-5 methylation within CpG dinucleotide) is a
common and well-studied epigenetic mechanism because it is highly variable across different
cell types. All tissues of an organism are affected by aging and the accumulated cellular
changes (Garinis et al., 2008). Recent studies showed that age can predict DNA methylation
levels in specific tissues (Bocklandt et al., 2011, Hannum et al., 2013). A biomarker of aging,
also referred to as the epigenetic clock, DNA methylation levels can be used to calculate the
DNA methylation age (DNAm age) (Horvath, 2013).
The aim of this study was to determine the DNAm age of the optic nerve, RPE/choroid and
neurosensory retina tissue from the eye and peripheral whole blood from the same individual.
In addition, we also compared chronological age and DNAm age between ocular tissue and
blood.
3.2 Methods
3.2.1 DNAm data collection and processing
Whole blood from the subclavian vein and whole eyes were obtained post-mortem. Specimens
were taken from eight donors (all male) with no known ophthalmic disease. Blood samples
were collected in anticoagulant (EDTA)-containing tubes. All ocular tissue was collected and
53
stored within 12 hours’ post-mortem. The vitreous body was discarded by circumferential pars
plana incision. The neurosensory retina was removed and the RPE and choroid were dissected
from the scleral wall. Dura matter was separated from the anterior segment of the optic nerve
before storage. Dissected ocular tissue was stored in QIAGEN Allprotect Tissue Reagent
(QIAGEN, Hiden, Germany) and DNA extraction was subsequently performed using the
QIAGEN DNeasy Blood & Tissue Kit (QIAGEN). Following bisulfite conversion using the
Australia), samples were hybridized to Illumina Infinium HumanMethylation450 (Illumina
Inc, San Diego, CA, USA) BeadChips (HM450K) according to the manufacturer's protocols.
Array processing and Beadstudio analysis was conducted through the Australian Genome
Research Facility (Melbourne, VIC, Australia). This study was approved by the human
research ethics committee of the University of Western Australia (RA/4/1/4805).
3.2.2 DNAm age estimation by using epigenetic clock
The epigenetic clock is a prediction method used for calculating age based on DNA
methylation levels of 353 CpGs dinucleotides. DNAm (Predicted age) can be correlated with
chronological age in various tissue and cell types (Horvath et al., 2015) using a DNAm age
calculator tool (Horvath, 2013). It defines DNAm age using a penalized regression model on
Illumina 450K and 27K platform. The beta values were obtained from the raw data in IDAT
format from 450k array using minfi (R package) (Aryee et al., 2014). Normalization and pre-
processing were included in the tool to improve the accuracy, specifically the median error
(Horvath, 2013). Meta-Analysis R function in the WGCNA R package (Langfelder et al., 2013)
was used to measure the pure age effects irrespective of the tissue type. We also predicted the
epigenetic age by another method developed by Hannum et al (2013) (Hannum et al., 2013).
This method is based on the 71 CpGs dinucleotides and uses an elastic net regression model
on the DNA methylation levels from the whole blood (Hannum et al., 2013). We used the
online version of the DNA methylation age calculator (Horvath, 2017) for calculating
epigenetic age based on Hannum et al (2013) using advanced analysis for blood data. An online
version of the DNA methylation age calculator is also available at (Horvath, 2017). The DNAm
age was also calculated by using Enpred and BLUpred predictor developed by Qzhang (Zhang
et al., 2019). Source code for reproducing results and figures of the paper is available on GitHub
(https://github.com/hewittlab/Ocular_mDNA_age).
54
3.3 Results
The mean (SD) age of the eight male donors was 60.6 (11.3) years. In total, we analysed 29
samples. We studied 485,512 CpGs that were present on the Illumina 450K platform. The
Horvath method found a linear relationship between the DNAm age and chronological age in
blood and ocular tissues, with a correlation of 0.37 (p<0.048) and standard error of 18 years
(Figure 3.1A) whereas the Hannum method predicted with a correlation of 0.32 (p<0.091) and
standard error of 26 years (Figure 3.2). The correlations between the chronological and DNA
methylation age are reported in Table 3.1. There was a significant association between the
measures. The correlation coefficient was strongest in neurosensory retina (r2 = 0.93,
p<0.00081).
55
Figure 3.1. (A)Chronological age versus DNAm age across all sample data set (r = 0.37, p< 0.048 and standard error = 18 years). Mean epigenetic age difference (y- axis) versus different tissues in (B) respectively. The bar plots also report a p-value from a non-parametric group comparison test (Kruskal Wallis test). Each bar plot depicts the mean value and 1 standard error. (C) Post mortem interval versus DNAm age across all sample data set (r = -0.15, p<0.44). Table 3.1: chronological age correlation with epigenetic age acceleration
Tissue Type Correlation P-value
Peripheral Blood 0.86 0.013
Optic Nerve 0.87 0.011
RPE/Choroid 0.83 0.021
Neurosensory Retina 0.93 0.00081
10 20 30 40 50 60 70 80
1020
3040
5060
7080
Horvath cor=0.37, p=0.048
Chronological Age
DNAm
Age H
orvath
Blood
Neurosensory Retina
Optic Nerve
RPE/Choriod
Blood Optic Nerve RPE/Choroid Neurosensory Retina
p = 3e−05
Tissues
Differe
nce in
epigen
etic an
d chro
nologi
cal ag
e(year
s)−60
−40−20
020
2 4 6 8 10 12
−60−40
−200
20
C cor=−0.15, p=0.44
Post Mortem Interval
Age A
ccelera
tion Di
fferenc
e
b
a
c
56
Figure 3.2: Hannum Method (A) Chronological age versus DNAm age across all sample data set (r = 0.32, p< 0.091 and standard error = 26 years). Mean epigenetic age difference (y- axis) versus different tissues in (B) respectively. The bar plots also report a p-value from a non-parametric group comparison test (Kruskal Wallis test). Each bar plot depicts the mean value and 1 standard error. (C) Post mortem interval versus DNAm age across all sample data set (r = 0.031, p<0.87).
Surprisingly, in neurosensory retinal tissue we found a significant age difference of 44.4 years
between the DNAm age (mean = 16.1 years) and chronological age (mean = 60.6 years) (Figure
3.1B). The mean DNAm age of the RPE/Choroid tissue was 31.8 years, which was also
significantly different from the mean chronological age of 60.4 years, and the age difference
of optic nerve tissue was 11.3 years. We did not find any significant age difference in the blood
tissue as compared to the DNAm age (mean = 58.1 years) and chronological age (mean = 59.8
years). We tested the DNAm with all causes of mortality across the samples. Respiratory failure
had the maximum age acceleration difference (mean = 38 years with overall p<0.47). In
10 20 30 40 50 60 70 80
2040
6080
100120
140
Hannum cor=0.32, p=0.091
Chronological Age
DNAm
Age H
annum
Blood
Neurosensory Retina
Optic Nerve
RPE/Choriod
Blood Optic Nerve RPE/Choroid Neurosensory Retina
p = 2.5e−05
Tissues
Differe
nce in e
pigeneti
c and ch
ronolog
ical age
(years)
−60−40
−200
2040
60
2 4 6 8 10 12
−60−40
−200
2040
60
C cor=0.031, p=0.87
Post Mortem Interval
Age Ac
celerati
on Diff
erence
b
a
c
57
addition, we found no significant relationship between the measure of age acceleration
difference and post-mortem interval (p<0.44) (Figure 3.1C).
Figure 3.3: Qzhang Method (a) Chronological age versus DNAm age across all sample data set by using Enpred Algorithm and mean epigenetic age difference (y- axis) versus different tissues in respectively. The bar plots also report a p-value from a non-parametric group comparison. Each bar plot depicts the mean value and 1 standard error. (b) Chronological age versus DNAm age across all sample data set by using BLUpred and mean epigenetic age difference (y- axis) versus different tissues in respectively. The bar plots also report a p-value from a non-parametric group comparison. Each bar plot depicts the mean value and 1 standard error. The DNAm age was also predicted by Qzhang method. Best linear unbiased prediction and
Elastic net prediction model were used, and a similar pattern was also recognised in our dataset.
By the following predictor, we have found the mean age difference of 63 years (r= 0.25, p<
0.0000001667) (Figure 3.3a) in the neurosensory retina by Enpred predictor whereas 51 years
(r=72.9, p< 0.0000009404) (Figure 3.3b) mean difference were found by BLUpred method.
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3.4 Discussion
Tissue aging is a complex and dynamic process that compromises biological function and
increases susceptibility to disease and death. The aging process is still poorly understood
(Berdasco and Esteller, 2012) and some body tissues may be more prone to the effects of aging
than others. It is therefore valuable to understand the epigenetic contribution to age-dependent
changes in gene expression. During aging and the pathogenesis of ocular disease, changes in
chromatin lead to the dysregulation of gene expression. Retinal diseases such as glaucoma
(McDonnell et al., 2014) , an optic neuropathy that occurs due to loss of RGC, are influenced
by genetic mutation and aging (Leske et al., 1995). Multiple epigenome-wide studies (EWASs)
have identified chronological aging-associated changes in the DNA methylome (Rakyan et al.,
2010, Marttila et al., 2015, Yuan et al., 2015). Age-related alteration in the DNAm has been
reported previously (Hernandez et al., 2011, Maegawa et al., 2010, Gronniger et al., 2010).
DNAm age has been validated across different complex tissues and organs including brain,
breast, kidney, liver and lung (Horvath, 2013) and can be compared to chronological age.
Individual cells acquire a specialized gene expression profile specific to their tissue type and
function and further changes in the chromatin landscape are required in various tissue types
such as the lens and retina (Otteson, 2011). Understanding the tissue-specific differential
methylation pattern can help tease out underlying mechanisms of tissue-specific processes
(Ollikainen et al., 2010).
Age-related macular degeneration (AMD) causes irreversible central vision loss and its
pathogenesis involves a complex interaction of both genetic and environmental risk factors
(Ding et al., 2009). The role of epigenetics in AMD is poorly understood. Alterations in
epigenetic pattern may be the link between environmental factors and gene expression for the
development of disease. Our data shows that DNAm age is highly correlated (>0.80) with
chronological age in ocular tissue but that the DNAm age of peripheral blood differs to that of
ocular tissue. The main limitation of this work is that it is not yet completely understood what
biomarker of aging is measured and what represents noise and epigenetic drift (Teschendorff
et al., 2013).
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Chapter 4 CRISPR Screening for uveal melanoma
4.1 Introduction
Uveal melanoma (UM) (OMIM #155720) is a rare form of a cancer of the eyes and is the most
common intraocular malignancy in adults. It arises primarily from melanocytes in the choroid
(80%), iris (5%) or the ciliary body (15%) (which is collectively known as uveal tract, one of
the inner layers of eye) (Chang et al., 1998). In the Unites States, the incidence of UM is 5.1
people in 1 million (Singh et al., 2011) compared to more than 8 people in 1 million in Norway
and Denmark and 2 people in 1 million in Spain and Italy (Virgili et al., 2007). The most
common symptoms of UM are blurred vision, floaters, photopsia, metamorphopsia, a dark spot
on the iris, change in the shape and size of pupil and change in the position of the eyeball in
the eye socket. Sun exposure is a risk factor (Singh et al., 2004). UM can be detected using slit
lamp biomicroscopy and indirect ophthalmoscopy and ancillary diagnostic testing such as
ocular ultrasonography (Jovanovic et al., 2013a). Most UM (small- or medium-sized tumours)
are treated by brachytherapy, teletherapy and dosimetry (Stannard et al., 2013). Regardless of
the treatment modality, nearly 45% of patients die from metastases (Kujala et al., 2003), with
90% of metastases involving the liver (Bedikian et al., 1995). Metastases also occur
sporadically in lung and bone (Gragoudas, 2006, Collaborative Ocular Melanoma Study, 2001,
Diener-West et al., 2004, Landreville et al., 2008).
The molecular profile of UM comprises various genomic aberrations and somatic gene
alterations. The most common genomic aberrations are monosomy 3, 1p loss, 1q gain, 6q loss,
6p gain, 8p loss and 8q gain (Damato et al., 2010). Monosomy 3 is observed in nearly 50% of
tumours and is associated with metastatic disease (Thomas et al., 2012). There is no proven
standard care for the patients who developed metastatic disease. Dacarbazine, a
chemotherapeutic option for cutaneous melanoma but has limited activity in UM (Dummer et
al., 2012, Carvajal et al., 2014, Robert et al., 2013). Other chemotherapy including
temozolomide, cisplatin, treosulfan, fotemustine have been investigated in UM but results have
been discouraging (Pereira et al., 2013, Spagnolo et al., 2013, Augsburger et al., 2009).
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a feature of the
bacterial immune system. The CRISPR-associated protein 9 (Cas9) derived from the CRISPR
60
type II system has emerged as an effective genomic engineering tool. As an RNA-directed
DNA endonuclease, Cas9 can be readily programmed to target and modify a specific site with
a slight shift in the directed RNA sequence, and this technique can be used for genome editing,
control, biochemical and structural studies (Wang et al., 2016). Genomic screening for a
phenotype of interest can be undertaken using custom- or ready-made guided RNA libraries
(Joung et al., 2017b). We used a Genome-Scale CRISPR Knockout GeCKOv2 (lentiCRISPR
v2.0 and lentiGuide-Puro) pooled library (provided by Feng Zhang, Catalogue# 1000000048)
that targets around 19050 genes throughout the whole human genome, with a total of 1233411
sgRNAs pooled together (6 gRNAs per targeted gene, control sgRNAs designed not to target
the genome, and sgRNAs targeting miRNA). This pooled knockout screen experiment is akin
to performing tens of thousands of small experiments simultaneously, creating thousands of
knockouts at once. The cells with key genes that are knocked out will die with these related
guide RNAs depleted from the population. By identifying the missing guide RNAs, we can
determine which genes are crucial for the initiation and proliferation of UM.
The lentiCRISPRv2 library was amplified and packaged with lentivirus. The UM cell line
OCM-1 was transduced with lentiCRISPRv2 library and puromycin was added to maintain a
low Multiplicity of infection (MOI) to ensure each cell received only one sgRNA. A large
volume of transfected OCM-1 cells was maintained and cells were collected at two time points:
once immediately after transduction, and finally after 12 passages. Genomic DNA was
extracted, and the presence of the guide RNA library was identified using next-generation deep
sequencing. The web-based tools CRISPRAnalyzeR and gProfiler were then employed for
bioinformatics and functional analysis. The aim of the following study is to identify the
essential genes essential for the proliferation and survival of uveal melanoma via a forward
Figure 4.1: a) Scatterplot showing distribution of sgRNA read count in UMP0 and UMP12. b) Boxplot with outliers for all samples. c) Distribution plot of all samples with normalised read count.
4 The Gini Index of the read count distribution indicates more evenness of the count distribution.
a b
c
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The read count data were further accessed and sgRNAs with less than 100 read counts were
calculated in P0 and P12. In our data, 0.45% of P0 reads had less than 100 read counts whereas
3.9% of P12 reads had less than 100 read counts. The number of sgRNAs with less than 10
read counts in P12 compared to P0 and only 0.16% sgRNAs were identified (Figure 4.1a).
From the initial screening, the sgRNAs with read counts less than 100 in P12 compared to P0
with read counts more than 100 were used and 56 genes were identified with low expression
in 3 or more sgRNAs in P12 and considered as possible hits (Table 4.2).
Table 4.2 List of genes with missing 3 or more sgRNA in UMP12 as compared to UMP0.
Figure4.2: a) Scatterplot showing significant gene using MAGeCK, b) Scatterplot showing significant gene using sgRSEA, c) Scatterplot showing significant gene using DESeq2, d) Scatterplot showing significant gene using EdgeR.
a b
dc
66
A number of overlapping genes were identified (Figure 4.3a). The final selection for
identifying hits was done using MAGeCK and DESeq2 algorithms and 15 genes were
identified as hit candidates (Figure 4.3b). Almost all sgRNA associated with genes had low
expression in Passage 12 compared to the Passage 0 (Table 4.3) (Supplementary Figure 4.1).
Figure 4.3 a) Venn diagram showing the overlap genes between the different algorithms. b) overlapping genes with final selected algorithms MAGeCK and DESeq2. Table 4.3: List of final gene subset select based on MAGeCK and DESeq2 algorithms.
Genes Total sgRNA in P0 and P12 Number of sgRNA with low
expression in P12
WASH1 6 6
SLC3A2 6 6
ABT1 6 6
NDUFB10 6 6
RPL35 6 5
COQ2 6 6
LSM11 6 6
KATNB1 6 6
UBL5 6 6
MRPL22 6 4
HIST2H4A 6 6
HTRA2 6 6
SPDYE5 4 4
CCNA2 6 6
POLR3K 6 6
a b
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The final selected subset genes were used for the functional analysis and significant gene
ontology pathways were identified. The detailed pathways interactions are shown in Figure
4.4.
Figure 4.4: Pathway analysis showing statistically significant pathways by using gProfiler and visualisation using enrichment map.
Functional analysis suggested the major pathways with the most intersects were: metabolic
process and organic substance metabolic process (Supplementary Table 4.5).
4.4 Discussion
Cancer is a malignant disease generally caused by genetic alterations from activation of
innate/acquired mutations and epigenetic alterations that lead to functional abnormality and
initiate the process of tumorigenesis (Hanahan and Weinberg, 2000). High-throughput
sequencing platforms can provide useful insight into understanding various mutated genes.
However, genes that are involved in the initial step of tumorigenesis remain elusive (Hanahan
and Weinberg, 2000). The CRISPR/Cas9 screening method for annotation of gene function has
uncovered various mutations that are involved in the origin of cancer or in the progression into
biological process
metabolism
cellular component organization
cellular metabolic process
rna metabolic process
cellular protein complex
ribonucleoprotein complex biogenesis
protein targeting
ribosome assembly
protein complex assembly
organic cyclic compound
primary metabolic process
small subunit processome
metabolic process
organic substance biosynthetic
organic substance metabolic
cellular process cellular nitrogen compound
cellular biosynthetic processgene expression
biosynthetic process
nitrogen compound transport
intracellular protein transport
cellular component biogenesis
cellular macromolecule metabolic
organelle organization
protein complex subunit
rna processingcellular component organization
ribosome biogenesis
68
metastasis (Luo, 2016). Several oncogenes have already been uncovered, e.g. KRAS and
PIK3CA are fatal hits in the colorectal cancer cell line DLD-1 and HCT116, BCR and ABL are
fatal hits in the chronic myelogenous leukaemia cell line KBM7 that harbours BCR-ABL
translocation (Wang et al., 2014). UM is an intraocular malignant tumour with a high potential
for lethal metastasis and a low mutational load compared to cutaneous melanoma (Luo, 2016).
However, currently there are no effective therapies for treating UM and most clinical trials are
only Phase I or II (Yang et al., 2018). Hence, it is crucial to set up genome-wide CRISPR/Cas9
loss-of-function screening to identifying the novel genes that underpin the development,
growth, and survival of the cancer cell lines (Shalem et al., 2014).
In screening loss-of-function analysis, we found 15 genes as possible hits. One of the possible
target genes SLC3A2 (Solute carrier family 3 member 2; also known as CD98hc) is a
transmembrane protein and exists as a heavy chain of heterodimer with large neutral amino
acid transporter LAT1 in cells (Estrach et al., 2014). SLC3A2 overexpression widely occurs in
different cancer cells such as skin squamous cell carcinoma (Estrach et al., 2014), Gastric
cancer (Wang et al., 2017a) and osteosarcoma (Zhu et al., 2017). The consumption of L- type
transporters (LATs) including L-leucine into the cells, signalling and protein synthesis were
stimulating in mammalian targets. Different L-type amino acid transporters are active in
different stage of prostate cancer and are responsible for the cell growth (Wang et al., 2013).
Another possible target gene COQ2 (Coenzyme Q2, Polyprenyl Transferase) encodes for the
biosynthesis of COQ (ubiquinone), an electron and proton carrier in the mitochondrial
respiratory chain and a lipid-soluble antioxidant [RefSeq 2009]. A deficiency in coenzyme Q10
related to the COQ2 gene has been associated with retinopathy in some patients (Desbats et al.,
2016) and a homozygous mutation visual dysfunction trait related to rod-cone retinopathy has
also been observed (Quinzii et al., 2014). RPL35 and POLR3K are differentially expressed in
the breast cancer MCF-7 and MDA-MB-231 breast tumour cell lines and are associated with
the translation- and transcription-related molecular pathways (Satih et al., 2010). CCNA2
(CyclinA2) is a highly conserved member of the cyclin family and functions as a regulator in
the cell cycle [RefSeq 2016]. CCNA2 is identified as a prognosis biomarker for estrogen-
positive breast cancer and tamoxifen resistance (Gao et al., 2014).
69
Figure 4.5: Expression level (transcripts per million- tpm) of final selected gene on TCGA datasets.
We further assessed the tumour subgroup expression on our hit candidates using the online
portal ULCAN (Chandrashekar et al., 2017) to check the expression level on different stages
based on genomic data from The Cancer Genomic Atlas (TCGA) (Cancer Genome Atlas
Research et al., 2013) and found that all genes had relative fair expression (tpm) on the UM
genomic dataset as shown in Figure 4.5 whereas WASH1 gene expression was not found.
Overall, our selected candidate genes in the CRISPR/cas9 knockout screening analysis showed
lower expression in UM OCM_P12 as compared to the P0, suggesting these genes have a
crucial role in the cell growth and development of UM cell lines. However, further invitro and
in vivo analysis are required to validate our findings.
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Chapter 5a RNA-Seq Analysis for Retinal neovascularization in miRNAs
5a.1 Introduction
Retinal neovascularization is a sight-threatening condition that occurs in ischemic retinopathies
including retinopathy of prematurity (ROP), central retinal vein occlusion (CRVO) and
proliferative retinopathy (PDR) (Vinekar et al., 2016, Jonas et al., 2017, Smith and Steel, 2015).
Angiogenesis influences the neovascularization in various conditions such as hypoxia,
ischemia or inflammation (Campochiaro et al., 2016). Pathological angiogenesis in the retina
has to be monitored carefully as it may lead to visual impairment (Bressler, 2009). Vascular
endothelial growth factor (VEGF) is a crucial regulator of retinal angiogenesis and impacts
endothelial cell migration and proliferation after hypoxia (Bressler, 2009). VEGF inhibitors
are effective in some but not all patients, indicating that other pathways might play a crucial
role as well. Moreover, VEGF inhibitors such as ranibizumab and aflibercept are very
expensive and are associated with some adverse effects (Ross et al., 2016). Therefore, new
treatments for retinal neovascularization are needed.
MicroRNAs (miRNAs) are a small class of endogenous non-coding RNA 21-25 nucleotide
sequences in length that act by complementary base pairing at the 3’ untranslated region (3
UTR) of targeted mRNA, resulting in translation repression or degradation of targeted mRNA
(McClelland and Kantharidis, 2014). Major biological processes such as cell growth, cell death,
development and cell differentiation are regulated by the miRNAs (Raghunath and Perumal,
2015). miRNAs are an important modulator of blood vessel formation and function. Various
retinal miRNAs such as miRNA-150 (miR-150), miRNA-126 (miR-126) and miRNA-155
(miR-155) have been shown to be dysregulated in animal models of retinal neovascularization,
although study results have been inconsistent (Liu et al., 2015, Bai et al., 2011, Yan et al.,
2015). miR-143 have been shown involve in regulating actin actin-related protein subunits
smooth muscle and TM cells (Li et al., 2017).
We performed next-generation sequencing (NGS) RNA-Seq in vivo and in vitro in an OIR
(Oxygen-induced retinopathy) Rat model to identify novel genes associated with retinal
neovascularization. miR-143, miR-126 and miR-150 were evaluated in human endothelial cells
(telomerase-immortalized microvascular endothelial cell [TIME], human dermal
microvascular endothelial cells [HMEC] and human umbilical vein endothelial cell [HUVEC])
to assess for any impact of dysregulation involved in retinal neovascularization.
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5a.2 methods
In-vivo and In-vitro experiment for OIR models was performed “Jiang-Hui
Wang” and “Jinying Chen” and full details are in Supplementary Method 3.
5a.2.1 Sequencing
The libraries of cDNA from the biological samples were sequenced using an Illumina HiSeq-
2500 RNA-Seq platform as 100 bp single end at the Australian Genome Research Facility
(AGRF, Melbourne, VIC, Australia). HiSeq Control software (HCS) v2.268 and Real time
analysis (RTA) v1.18.66.3 were used. The Fastq data were generated using the Illumina
bcl2fastq 2.20.0.422 pipeline.
5a.2.2 Dataset We used two datasets:
● Two groups from rat retina including OIR scrambled RNA and OIR-miR143 with two
biological replicates were sequenced.
● Four groups of mRNA samples from human endothelial cells (telomerase-
immortalized microvascular endothelial cell [TIME], human dermal microvascular
endothelial cell [HMEC] and human umbilical vein endothelial cell [HUVEC])
including Control, miR126, miR143, miR150 with three biological replicates were
sequenced.
The raw data were deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible
through the GEO accession number XXXXXXXX (Data submission in progress).
5a.2.3 Quality Control The raw Fastq files were obtained by the Illumina HiSeq 2500. Initially the quality of the Fastq
files were assessed by the FASTQC and it was confirmed that all the reads had phred score >
28. FASTQC is a quality control tool that generates interactive reports including basic
statistics, adapter content information and base quality score for reads in a Fastq file (Andrews,
2010).
5a.2.4 RNA-Sequencing Analysis
Kallisto (v0.46.0) is a new approach to quantify the gene expression abundance from an RNA-
Seq data. It employs the novel concept of pseudoalignment for determining the compatibility
of reads with target, with no requirement for alignment. The Kallisto pseudoalignment
72
approach finds potential transcripts using K-mers in the reads. In addition to pseudoalignment,
the expectation-maximum (EM) algorithm is performed on a likelihood based on equivalence
class. This allows the EM algorithm to scale in complexity to the number of equivalence classes
rather than number of reads. It builds the indices for quantification that are useful for assessing
the reliability of abundance estimates produced by the bootstrap samples (Bray et al., 2016).
Sleuth (Pimentel et al., 2017) is a program that implements a statistical algorithm for
differential expression analysis on transcript abundance which is quantified using Kallisto. It
has been designed for exploring differentially expressed data using the shiny application in
RStudio (Team, 2015) and ggplots (Wickham, 2016) an R package were used for generating
plots. Heatmaps were generated using ClustVis (Metsalu and Vilo, 2015).
5a.2.5 Network and Pathway Analysis
Ingenuity Pathway Analysis Software (IPA: (Ingenuity Systems, Redwood City, CA, USA,
http://www.ingenuity.com/) is a web-based application used for solving various biological
problems in system biology. Functional analysis of genes and their networks were used in the
analysis and the significance of any gene function in a network was denoted by p-value less
than 0.05. The right-tailed Fisher Exact Test was used to calculate the p-value.
5a.3 Results
The statistics of mapped reads from the RNA-Seq data are shown in Table 5a.1 for all the
samples and more than 70% of reads were mapped to the reference genome. OIR Scrambled
and OIR miR143 were mapped to the Rattus norvegicus genome assembly (rn6) whereas
Control and other miRNA samples were mapped to the Homosapiens genome assembly (hg38)
and transcript abundance was estimated with bootstrapping the samples 100 times. The
quantified transcript abundance was used for calculating differential gene expression across all
the conditions using Sleuth. Table 5a.1: Statistics of mapped reads from samples OIR Scrambled, OIR-miR143, miR126, miR143, miR150 and control summary table.
Sample Mapped Reads Total Reads Mapping Percentage Bootstraps
OIR Scrambled_1 12931393 18041628 71.68% 100
OIR Scrambled_2 13747121 18864406 72.87% 100
OIR miR143_1 13477391 17694969 76.17% 100
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OIR miR143_2 12070325 16723655 72.18% 100
Control_2 19376283 20703074 93.59% 100
Control_3 18464997 19807112 93.22% 100
Control_4 19125654 20485604 93.36% 100
miR126_2 17182109 18561094 92.57% 100
miR126_3 16252186 17647104 92.1% 100
miR126_4 16390376 17607238 93.09% 100
miR143_2 17676654 18985807 93.1% 100
miR143_3 17802910 19086343 93.28% 100
miR143_4 16881020 17908706 94.26% 100
miR150_2 19324683 20806718 92.88% 100
miR150_3 18433277 19883336 92.71% 100
miR150_4 17616278 19099779 92.23% 100
Figure 5a.1: a) Correlation plot between the OIR Scrambled and OIR-miR143 sample. b) Heatmap showing the expression (tpm) of statistically significant (adjusted pValue<0.05) gene found by using Sleuth. c) Volcano plot highlighting statistically significant (adjusted pValue<0.05) between OIR Scrambled and OIR-miR143 sample.
74
Figure 5a.2: a) Correlation plot between Control and miR126 sample. b) Volcano plot highlighting statistically significant (adjusted pValue<0.05) between Control and miR126 sample along with highlighted genes that are related to angiogenesis. c) Heatmap showing the expression (tpm) of statistically significant (adjusted pValue<0.05) gene found by using Sleuth.
Figure 5a.3: a) Correlation plot between Control and miR143 sample. b) Volcano plot highlighting statistically significant (adjusted pValue<0.05) between Control and miR143 sample along with highlighted genes that are related to angiogenesis. c) Heatmap showing the expression (tpm) of statistically significant (adjusted pValue<0.05) gene found by using Sleuth.
75
Figure 5a.4: a) Correlation plot between Control and miR150 sample. b) Volcano plot highlighting statistically significant (adjusted pValue<0.05) between Control and miR150 sample along with highlighted genes that are related to angiogenesis. c) Heatmap showing the expression (tpm) of statistically significant (adjusted pValue<0.05) gene found by using Sleuth.
Overall, from our analysis we found 122 genes were differentially expressed (upregulated and
downregulated genes) in OIR Scrambled and OIR-miR143 based on adjusted p<0.05 as shown
in Figure 5a.1 (Supplementary table 5a.1), 1163 genes were differentially expressed
(upregulated and downregulated genes) in Control and miR126 based on adjusted p<0.01 as
shown in Figure 5a.2 (Supplementary table 5a.2), 445 genes were differentially expressed
(upregulated and downregulated genes) in Control and miR143 based on adjusted p<0.01 as
shown in Figure 5a.3 (Supplementary table 5a.3) and 192 genes were differentially expressed
(upregulated and downregulated genes) in Control and miR150 based on adjusted p<0.01 as
shown in Figure 5a.4 (Supplementary Table 5a.4).
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Table 5a.2: List of top pathways from IPA in miR126, miR143, miR150 and OIR miR143. miRNA Diseases or Functions Annotation p-value # Molecules miR126 Development of vasculature 7.27E-17 149 miR126 Endothelial cell development 2.86E-09 60 miR126 Proliferation of endothelial cells 3.34E-07 50 miR126 Migration of endothelial cells 4.6E-14 68 miR126 Cell movement of endothelial cells 1.18E-13 71 miR126 Cell movement of endothelial cell lines 1.45E-07 24 miR126 Migration of endothelial cell lines 4.56E-07 20 miR126 Migration of vascular endothelial cells 0.00000138 30 miR126 Movement of vascular endothelial cells 0.00000581 31 miR126 Angiogenesis 9.7E-17 145 miR126 Vasculogenesis 7.4E-13 114 miR126 Morphogenesis of blood vessel 0.0000118 14 miR126 Formation of blood vessel 0.0000324 26 miR126 Development of endothelial tissue 9.86E-10 62 miR126 Formation of endothelial tube 0.0000166 14 miR126 Permeability of vascular system 0.000016 25 miR143 Development of vasculature 0.00000016 58 miR143 Endothelial cell development 0.000255 23 miR143 Cell proliferation of vascular endothelial cells 0.000863 13 miR143 Proliferation of endothelial cells 0.0018 19 miR143 Migration of vascular endothelial cells 0.00000968 16 miR143 Migration of endothelial cells 0.000277 22 miR143 Migration of microvascular endothelial cells 0.000451 6 miR143 Angiogenesis 2.63E-07 56 miR143 Vasculogenesis 0.00127 37 miR143 Permeability of vascular system 0.00011 13 miR150 Development of vasculature 0.00000972 29 miR150 Vascularization 0.00185 8 miR150 Binding of endothelial cells 0.000251 9 miR150 Adhesion of endothelial cells 0.00426 6 miR150 Endothelial cell development 0.000432 13 miR150 Proliferation of endothelial cells 0.00514 10 miR150 Migration of vascular endothelial cells 0.0000198 10 miR150 Cell movement of endothelial cells 0.0000432 15 miR150 Migration of endothelial cells 0.0000641 14 miR150 Vasculogenesis 0.00000652 25 miR150 Angiogenesis 0.000014 28 miR150 Vascularization of absolute anatomical region 0.00144 6 miR150 Formation of blood vessel 0.00207 7 miR150 Permeability of vascular system 0.00553 6
OIR miR143 Tubulation of vascular endothelial cells 0.0000264 6
Ingenuity Pathway Analysis Software (IPA) was employed to identify the statistically
significant biological pathways associated with the differential expressed genes across all the
samples. All the significant genes from the groups were used as input for pathway analysis by
IPA and major pathways are shown in Table 5a.2. Angiogenesis and vascularization were
associated with all groups.
5a.4 Discussion
Ischemic retinopathies are the leading cause of visual impairment in working adults and are
characterized by microvascular degeneration and preretinal neovascularization (Desjarlais et
al., 2019). Neovascularization is implicated in tractional retinal detachment and vitreous
haemorrhage in patients with diabetes. Retinal hypoxia is the underlying cause of retinal
neovascularization and fundus biomicroscopy has revealed that large vessels create new
irregular vascular networks on the retinal surface or on the vitreous(Group, 1991) (B.A., 2008).
miRNAs expression profile in oxygen-induced retinopathy (OIR) was used to identify
dysregulation that might contribute to the pathological processes of retinal neovascularization
(Wang et al., 2017c, Liu et al., 2016, Shen et al., 2008). Several in vivo and in vitro studies
suggested that miRNA is a crucial regulator of organ development and homeostasis (Vidigal
and Ventura, 2015). Human retina is difficult to sample for analysis. Therefore, animal models
are commonly used to investigate the role of miRNA expression in retinal neovascularization.
miRNA profiling expression in human specimens is emerging as a tool for disease
characterisation and identifying novel biomarkers or therapeutic targets for treatment and
diagnosis (Lu et al., 2005).
Various studies have already identified miRNAs that are downregulated in the retinas of OIR
in mice models including miR-126 (Bai et al., 2011), miR-218 (Han S, 2016), miR-410 (Chen
et al., 2014), miR-150 (Liu et al., 2015) and miR-129-5p (Liu et al., 2016). In the present study,
miR-143, miR-126 and miR-150 were down-regulated in the OIR rats at Passage 14 compared
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with those in the normoxic rats. Studies have shown that mending endothelial-specific miR-
126 and miR-150 in retina can reduce the neovascularization in OIR mice models through
directly targeting pro-angiogenesis genes (Liu et al., 2015, Bai et al., 2011). miR-143 is down-
regulated in diseases with angiogenesis, notably cancers such as colon and lung cancer (Calin
and Croce, 2006).
Overall, we found novel genes that are downregulated in the OIR rat model and associated with
angiogenesis and vascularization in miR-143. Moreover, we observed the same pattern with
miR-126 and miR150. Identifying these novel genes will allow us to develop new therapeutic
targets and approaches for addressing retinal neovascularization.
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Chapter 5b RNA-Seq Analysis for differentiating DPMSCs into TM cells
5b.1 Introduction
Glaucoma is a leading cause of irreversible blindness (Blomdahl et al., 1997, Munier et al.,
1998). The disease is characterized by the death of retinal ganglion cells (RGC) due to axonal
degeneration, resulting in visual impairment (Yasuda et al., 2014). Globally, 60 million people
are estimated to have glaucomatous optic neuropathy, and 8.4 million people are blind as a
result of glaucoma (Cook and Foster, 2012). Elevated intraocular pressure (IOP) is associated
with the development of glaucoma and IOP is dependent upon the flow of aqueous humour
(Langenberg et al., 2008, Quigley, 2011b). One of the functions of the specialized trabecular
meshwork (TM) tissue located at the anterior chamber angle of the eye is to regulate aqueous
humour drainage through the trabecular outflow pathway and hence balance IOP within the
eye (Gasiorowski and Russell, 2009, Du et al., 2013, Paulavičiūtė-Baikštienė et al., 2013,
Langenberg et al., 2008). Imbalance in the outflow of aqueous humour promotes pathological
IOP and later leads to the death of RGC (Paulavičiūtė-Baikštienė et al., 2013, Kwon et al.,
2009). It is not completely understood how dysfunction of TM is involved in glaucoma (Stamer
and Acott, 2012). Various animal models of glaucoma have been developed (Gasiorowski and
Russell, 2009, Weinreb and Lindsey, 2005, Kokotas et al., 2012, Gelatt, 1977, Zhang et al.,
2003, Tripathi et al., 1994, Flugel-Koch et al., 2004, Caballero et al., 2003, Bradley et al.,
2001). however, patient-specific disease cells provide more information than animal models
(Stamer et al., 1995, Gasiorowski and Russell, 2009). Human disease models can be derived
from pluripotent stem cells (HSCs), which could differentiate into TM cells.
Currently, mesenchymal stem cells (MSCs) can be directly differentiated from PSCs
(Pluripotent Stem Cells) (Menendez et al., 2013), but the conditions required to differentiate
MSCs into TM cells are still unknown. We differentiated dental pulp-derived MSCs
(DPMSCs) into TM cells using various growth factors such as retinoic acid (RA),
(transforming growth factor-β) (TGF-β2), BMP4 and examined the process under two
conditions (control and treated). The aim of this study is to examine the ability of dental pulp-
derived MSCs to differentiate into TM like cells by using Next-generation sequencing
bioinformatics analysis.
80
5b.2 Methods
Differentiation Protocol was performed “Nicola McDonald” and full details
are in Supplementary Method 4.
5b.2.1 Sequencing
The libraries of cDNA from the biological samples were sequenced using an Illumina HiSeq-
2500 RNA-Seq platform as 150 bp single end at the Australian Genome Research Facility
(AGRF, Melbourne, VIC, Australia). HiSeq Control Software (HCS) v2.268 and Real-time
analysis (RTA) v1.18.66.3 were used to perform the image in real-time. The Fastq data were
generated using the Illumina bcl2fastq 2.20.0.422 pipeline.
5b.2.2 Dataset
Two groups of DPMSCs (control and treated) with two replicates were sequenced. The raw
data have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible
through the GEO accession number XXXXXXXX (Data submission in progress).
5b.2.3 Quality Control Described in Chapter 5a. 5b.2.4 RNA-Seq Analysis
Described in Chapter 5a. 5b.2.5 Functional Enrichment Analysis
gProfiler (Reimand et al., 2016) was used for the functional enrichment analysis to identify the
statistically significant pathways for the selected genes, including gene ontology and other
gene-related functions.
5b.3 Results The statistics of mapped reads from the RNA-Seq data are shown in Table 5b.1 for all the
samples. More than 82% of reads were mapped to the Homosapiens (hg38) reference genome,
and transcript abundance was estimated with bootstrapping the samples 100 times. The
81
quantified transcript abundance was used for calculating differential gene expression across all
the conditions using Sleuth. Table 5b.1: Statistics of mapped reads from Control and treated sample with all replicates.
Figure 5b.1: a) Heatmap showing the expression (tpm) of statistically significant (adjusted pValue<0.01) gene found by using Sleuth. b) Volcano plot highlighting statistically significant (adjusted pValue<0.01) between control and treated condition. c) Read distribution plot across all samples. d) Statistically significant pathways by using gProfiler.
(GO:0048762) and endodermal cell differentiation (GO:0035987). Other pathways such as
signaling by TGF-beta family members (REAC:R-HSA-9006936), signaling by TGF-beta
Receptor Complex (REAC:R-HSA-170834) and downregulation of TGF-beta receptor
signaling (REAC:R-HSA-2173788) were also found, which further confirms that the DPMSCs
differentiated into the TM cells. Overall, our results indicate that the growth factor is essential
for the differentiation of DPMSCs into TM cells. Thus, this method of developing models of
eye disease, such as glaucoma, can help reveal the underlying cellular and molecular
mechanisms.
Overall, I have implemented various Bioinformatics approaches to understand the different
aspect of eye disease models including quality control of iPSCs to detect the genomic
aberrations in high-resolution as compared to the traditional method, Predicting DNA
methylation age of ocular tissue compared to biological age, knock-out gene identification by
CRISPR screening method and RNA-Seq analysis to find the role of miR-143 in the OIR rat
model and Differentiation of DPMSCs into TM like cells. In future it would be interesting to
compare the DNA methylation age prediction in knowing ocular disease patients and to
compare with the existing result to unfold how epigenetic age plays a crucial role in ocular
tissues in healthy and disease conditions. Also, it would be interesting to perform the single
cell analysis to better understand differentiated cell lines and how closely related they are
related to TM like cells. However, the sensitivity and efficacy of the pipeline can be tested for
qualitative analysis.
87
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Appendix
Method 1 iPSCs Differentiation
Ethics Experimental work performed in this study was approved by the Human Research Ethics
committees of the University of Melbourne (0605017, 0829937) and Royal Victorian Eye and
Ear Hospital (11/1031H) in accordance with the requirements of the National Health &
Medical Research Council of Australia and conformed with the Declarations of Helsinki.
Informed consent was obtained from all participants prior to sample collection.
Fibroblast Isolation and culture
Primary human dermal fibroblasts were isolated from skin punch biopsy specimens using
standard techniques (15). Human fibroblasts were cultured in DMEM with high glucose, 10%
fetal bovine serum (FBS), 100 U/mL Penicillin and 100 µg/mL streptomycin (all medium
components and supplements were purchased from Life Technologies, unless otherwise
noted).
hPSC lines, maintenance and passaging
The newly generated iPSC lines MT1 and BD1, the hESC line H9 (2), the abnormal hESC line
BG01V (49, +12, +17, XXY) (17, 18) and the iPSC lines FA3, FA4 (16) and the iPSC lines
FA3, FA4 (16) were maintained undifferentiated using mitotically inactivated mouse (for MT1,
H9 and BG01V) or human foetal skin fibroblasts (WS1, ATCC CRL-1502 for FA3, FA4) in
Knockout Serum Replacement (KSR) medium containing 20 ng/mL FGF2 (Millipore) as
described in (16).
iPSC Reprogramming and Selection of Primary iPSC Clones
Fibroblasts were reprogrammed to iPSCs with non-integrating episomal vectors containing 6
reprogramming factors (OCT-4, SOX2, KLF4, L-MYC, LIN28, and shRNA for p53) as
110
described previously (Piao et al. 2014; Hernández et al. 2016). 60,000 fibroblasts were
transfected with 1 µg each of pCXLE-hOCT4-shP53, pCXLE-hSK and pCXLE-hUL plasmids
(plasmids gifts from Prof. S. Yamanaka; Addgene plasmids #27077, 27078, 27080) using the
nucleofection kit for primary fibroblasts (Lonza) and nucleofected using the Nucleofector 2b
Device (Lonza, Program T-016). Cells were cultivated in fibroblast medium for 6 more days
and then trypsinized and 1 x 105 cells were plated onto a 10 cm dish with MEF feeders. Media
was changed to iPSC culture media the following day. iPSC colonies start appearing around
Day 17-30 from transfection and were manually dissected and expanded for further
characterisation. Cell lines generated were named MT1 and BD1, followed by clone (CL)
number.
Multi-lineage Differentiation of iPSCs
Neuroectodermal differentiation was carried out as described in (21). Embryoid bodies (EBs)
at day 7 were plated on gelatinized dishes to differentiate for 7-14 days cultured in 20% KSR
media without bFGF supplementation. Cells were fixed with 4% paraformaldehyde
(Proscitech) and permeabilized and blocked with 10% normal goat serum (Life Tech.)
containing 0.1% Triton X-100 (Sigma-Aldrich).
Immunocytochemistry
Cells were immunostained using the following antibodies: mouse anti-OCT3/4 (Santa Cruz