Paediatrics (RCH) - Theses
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ItemThe innate immune mechanisms underlying the off-target effects of Bacillus Calmette-Guérin vaccine in infants( 2022-12)Bacillus Calmette Guerin vaccine (BCG) is administered to over 100 million newborn infants globally each year with the aim of protecting against tuberculosis. In addition to its specific anti tuberculous effects, BCG vaccine has off target effects that protect against a broad range of non mycobacterial infections in both children and adults. Immunological studies have implicated trained immunity in the mechanism underlying the off target effects of BCG vaccine in adults. Trained immunity is a de facto innate immune memory underpinned by the functional epigenetic and metabolic reprogramming of innate immune cells, such as monocytes. While much research has focused on elucidating the immunological mechanisms underlying the off target effects of BCG in adults, few studies to date have investigated the immunological mechanisms in children. Using samples from a well characterised sub cohort of 130 infants from MIS BAIR, a randomised trial of neonatal BCG vaccination in Australia, the projects in this thesis aim to characterise the phenotypic, functional, and epigenetic effects of neonatal BCG vaccination on the infant immune system in the first year of life. Chapter 2 of this thesis sought to better understand the role of vaccination and natural infection in shaping the human epigenome by a systematic review of the literature. This review identified a paucity of paediatric studies and highlighted a need for further research investigating the epigenetic mechanisms underlying the off target effects of BCG vaccine in children. This knowledge gap is addressed by the subsequent projects of this thesis. Immunological studies in infants are hampered by the ethical and logistical challenges of obtaining blood samples from this age group. Optimisation experiments detailed in Chapter 3 led to the refinement of laboratory methods that generate high dimensional immunological data from small volume infant blood samples containing as few as 3 million peripheral blood mononuclear cells (PBMC). Multi parameter flow cytometry was used to comprehensively profile and sort cell populations of interest for downstream analysis. The monocyte fraction was then used for parallel DNA methylation analysis and ex vivo heterologous stimulation assays. This analysis pipeline produced rich normative data on baseline immune cell profiles and monocyte cytokine responses in BCG naive infants. Subsequent studies in Chapters 4 and 5 found that BCG vaccinated infants have increased proportions of circulating classical and non classical monocytes, and activated regulatory CD4 T cells, compared to BCG naive infants at 13 months of age. Monocytes from BCG vaccinated infants also have an attenuated inflammatory response following heterologous stimulation and retain a DNA methylation signature of early life BCG exposure. Genes associated with this signature are involved in interferon signalling pathways and viral immunity. In summary, the studies in this thesis demonstrate that neonatal BCG vaccination induces durable changes in circulating immune cell profiles, monocyte cytokine responses and DNA methylation remodelling that persist over the first year of life. These changes are different to those observed in adults and strengthen evidence that immune responses to BCG vaccination are age dependent. Collectively, these findings contribute to a better understanding of the immunological mechanisms underlying the off target effects of BCG vaccine in infants that will inform the development of new immunomodulatory therapies and guide global vaccine policy.
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ItemEpigenetic Markers As A Predictor Of Lung Disease Severity In Cystic Fibrosis( 2020)Cystic fibrosis (CF) is a multisystem disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Lung disease which is the major source of morbidity and mortality in CF, begins in infancy and persists and progresses in the first years of life. The most sensitive measure of CF lung disease is computed tomography (CT) and the majority of children show evidence of bronchiectasis, which represents irreversible structural lung disease, by five years of age. The rate with which lung disease develops and progresses varies greatly between people with CF. Accurate prediction of future lung disease severity in people with CF would facilitate significant improvements in clinical care, as well as provide helpful information to people with CF and their families, health authorities and CF researchers. It has been demonstrated that CF lung disease severity is determined by a combination of non-CFTR genetic and environmental factors. However, no robust predictive biomarkers have been developed. Epigenetic mechanisms are those which regulate gene expression but do not alter the DNA sequence itself. DNA methylation is the most widely studied epigenetic mechanism, and an individual’s DNA methylation profile is determined by their underlying genotype and environmental exposures (including in utero). DNA methylation has been studied as a predictive biomarker in other childhood diseases, and given it is determined by the same factors which determine CF lung disease severity, is well positioned to act as a biomarker in CF. Despite this, DNA methylation based biomarkers of future lung disease severity have not been explored in CF. The thesis presents a series of projects which aimed to identify DNA methylation based biomarkers of future CF lung disease severity. The projects in this thesis utilised biospecimens and clinical data from the world leading Australian Respiratory Early Surveillance Team for Cystic Fibrosis (AREST CF) cohort. In order to better understand previous attempts at identifying predictive biomarkers, Chapter Two and Three describe reviews of the evidence regarding non-CFTR genetic modifiers of CF lung disease and DNA methylation based biomarkers of future health outcomes in children. The reviews highlight the importance of appropriate study design when trying to identify predictive biomarkers, which then informed design of the subsequent epigenome wide association study (EWAS). Chapters Four and Five describe the development of methods that were necessary for subsequent EWAS. In Chapter Four protocols for cryopreservation, and flow cytometry based phenotyping and sorting of specific cell populations were developed and validated for use with paediatric bronchoalveolar lavage (BAL). These methods were then used in Chapter Five to obtain purified populations of the common cell types in BAL, which were then used to develop reference epigenomes for these cell types. These reference epigenomes allowed adjustment for cell composition in the subsequent EWAS, which is a crucial step in DNA methylation studies involving samples with multiple cell types. Chapters Six and Seven describe the EWAS studies that attempted to identify predictive biomarkers. Chapter Six involved genome wide DNA methylation profiling of BAL collected at six years of age, and assessed the ability to predict the presence or absence of bronchiectasis at nine years of age. No predictive biomarkers were identified. Chapter Seven involved genome wide DNA methylation profiling of BAL collected at one year of age, and assessed the ability to predict the presence or absence of bronchiectasis at five years of age. Seven predictive biomarkers were identified, which when assessed using area under the receiver operator curve analysis had extremely good performance as predictive biomarkers. Several of the predictive biomarkers were related to genes that are relevant to CF lung disease pathophysiology and hence may represent therapeutic targets. By following best practice for EWAS, and in particular using tissue specific samples, adjusting for cell composition and assessing outcome using the gold standard measure, this thesis succeeded in identifying DNA methylation based biomarkers of future lung disease severity in CF. If these biomarkers are validated in external cohorts they could dramatically improve the care of children with CF around the world. N.B. There are two files that have been submitted separate to this thesis document as supplementary files. They have been highlighted as appropriate in the thesis. In addition, the code used for statistical analysis is available at: https://atlassian.petermac.org.au/bitbucket/pages/OS/paed-cf- methylation/master/browse/docs/index.html
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ItemInvestigating the DNA methylation profiles of children with oligoarticular juvenile idiopathic arthritis (JIA)( 2019)Juvenile idiopathic arthritis (JIA) is a complex autoimmune disease affecting children aged between 6 months and 16 years. JIA represents a group of 7 subtypes of disease, with the most common being oligoarticular JIA (oJIA). Despite a prevalence of up to 1 in 400, rates similar to those in T1D, JIA research is relatively sparse. Research into disease pathogenesis has largely focussed on genetic risk factors, and has also identified CD4+ T-cells as likely to mediate the autoimmune process. However, research is particularly needed regarding diagnosis and prognosis of disease and its outcomes. Currently, diagnosis is almost entirely dependent on clinical observation and history, with little in the way of biomarkers to classify patients or to guide clinical management. Epigenetics represent biological modifications to DNA and chromatin that control gene expression and chromatin structure. DNA methylation is perhaps the most accessible modification available for study, and is known to modulate immune cell function particularly amongst CD4+ T-cell subsets. A number of autoimmune diseases have reported significant DNAm associations, and have also provided intriguing data on the potential of DNAm to predict clinical outcomes. This study hypothesised that DNAm is important in oJIA pathogenesis, and potentially provides a biological basis for the diagnosis and prognosis of disease. This study utilised CD4+ T-cells and a case-control study design to analyse the associations between DNAm and oJIA, with data generated from the Illumina Infinium HumanMethylation450 BeadChip array. Cases were matched with controls according to age and sex. Further, cases were subtyped according to current diagnostic criteria and had active disease, both of which attempted to ensure all cases were clinically homogeneous. The first aim was to profile DNAm in oJIA cases compared to controls. Processing of data through analysis pipelines resulted in high quality data. Differential methylation analysis suggested that oJIA cases and controls could be segregated in cluster analysis using DNAm data, despite no genome wide significant hits being produced. Immune system pathways analysis suggested the top hits were relevant to disease, being enriched for receptor binding of cytokines such as IL6, IL17 as well as MHC class II. In addition, a number of top ranking probes were enriched within cell death and survival functions. Indeed, gene expression data suggested genes within those pathways were also correlated with DNAm. Technical validation of a selection of probes was highly successful, with all probes validating. A small replication study, however, was not able to reproduce these findings. Of particular note, a wide distribution of DNAm values was observed for many of the validated probes. Since technical validation was so successful, this DNAm heterogeneity potentially derived from sample group heterogeneity, which may well have played a part in difficulties replicating data. Therefore, biological sources of heterogeneity were explored in chapter 5, focussing primarily on the genetic associations with DNAm. Probes utilised for technical validation were analysed for genetic associations associating with either mean or variable DNAm. Both analyses suggested that the most robust associations were for known mQTLs and enhancer SNPs. Indeed, DNAm differences according to genotype were up to 13% and 27% for 2 probes analysed, representing a many-fold difference over case-control differences (typically approximately 5%). Combined with an intermediate level of minor allele frequency for many of these robustly associated SNPs, these mQTLs represent a likely source of biological variation contributing to oJIA DNAm variation. These minor allele frequencies increase the likelihood of inadvertent sampling bias, potentially resulting in difficulties in replicating DNAm data. Deeper analysis provided some initial indication that these mQTLs may also be potential oJIA risk loci, with the most significant associations again coming from known mQTL or enhancer SNPs. This also suggested DNAm data may well identify regions of interest for genetic risk loci discovery. The final chapter hypothesised that sources of potential clinical heterogeneity not captured within current classification criteria may well lead to DNAm heterogeneity, as could recognised subgroups within oJIA. Of primary focus, age of disease diagnosis was assessed for associations with DNAm. This study found that case-control analyses of older diagnosed samples (greater than or equal to 6 years) resulted in case-control clustering using far fewer probes. Indeed, the reduction of probes required for clustering was more pronounced in the analysis of younger diagnosed samples (less than 6 years of age), and also resulted in a genome wide significant hit. These subgroups represented 2 highly divergent populations, since top ranking probes from each subgroup had virtually no overlapping probes. This data suggested that age subgroups in oJIA represent sources of sample heterogeneity, leading to DNAm heterogeneity. Technical validation for a large majority of the select probes from the younger-diagnosed analysis was also successful. However, a small replication study could not reproduce these initial findings. In light of the potential for mQTLs to have pronounced effects on DNAm, as explored in chapter 5, larger replication groups (or, indeed, discovery groups) will likely be needed to mitigate the risk of sampling error to enable reproduction of findings. OJIA heterogeneity was also explored by looking at known subgroups, Persistent vs Extended disease. A number of oJIA cases would go on to develop extended disease, and the possibility existed for DNAm signatures to identify these cases prior to disease extension. This was indeed the case, with an exploratory analysis suggesting a number of probes can cluster persistent cases from extended-to-be cases. Further, these probes were able to produce a highly sensitive and specific test to predict disease extension, thereby providing a proof of principle for a prediction test using DNAm data. This study is the largest case-control analysis of JIA DNAm to date, and provided insights into the potential for DNAm to identify pathogenic pathways, identify sources of oJIA heterogeneity, and opened the possibility for biological markers of disease to be used in clinical management. The findings regarding the pronounced effect of mQTLs on DNAm also suggest that genetics is a large source of DNAm variability, far larger than group differences typically found in a complex diseases (such as oJIA). The identification of subgroup specific differences, even with a clinically homogeneous subtype, warrants further investigation to explore potential differences in pathogenesis between age groups and the use of DNAm as biomarkers for classification or disease management.
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ItemAn epigenomic and omics approach to neurodevelopmental disorders( 2020)Neurodevelopmental disorders such as cerebral palsy (CP) and epilepsy are some of the most prevalent childhood neurological disorders caused by damage to the growth and development of the brain. Early life environments predispose children to later health outcomes evidenced by the developmental origins of health and disease (DOHaD) phenomenon. Epigenetics, which refers to modifications of DNA without change in DNA sequence, is one way by which environmental exposures may contribute to development of disease. DNA methylation, arguably the most highly studied epigenetic mark, has been correlated with early life environmental exposures and have implications in both disease mechanisms as well as clinical biomarkers of neurodevelopmental diseases. These modifications most likely originate in utero, in line with the DOHaD hypothesis. The study of monozygotic (MZ) twins, in which genetics, age, sex, parental factors and shared environment are controlled for, helps in distinguishing the extent of effect of genetics and environment. Discordance for neurodevelopmental disorders has been recorded in MZ twins indicating a potential role of non-shared factors in disease risk. The aim of this PhD was to utilise the discordant MZ twin model to understand epigenetic changes associated with neurodevelopmental disorders. Genome-wide DNA methylation was measured within MZ twin cohorts discordant for CP or epilepsy using Illumina’s Infinium HumanMethylation450 and EPIC arrays. Statistical and bioinformatics pipelines were applied to evaluate the association of DNA methylation data to disease phenotypes. As detailed in Chapter Three of this thesis, DNA methylation analysis of CP-discordant twin pairs provides the first evidence that environmentally mediated differential methylation in genes involved in known processes such as hypoxia and inflammation, and processes such as cell adhesion, may contribute to the development of CP. As detailed in Chapter Four, an epigenome-wide analysis of epilepsy discordant MZ twin pairs revealed distinct patterns of DNA methylation within subtypes of epilepsies of unknown cause. Differentially methylated genes within epilepsy subtypes included those with a role in metabolic pathways, voltage-gated channel signalling and neurotransmitter processes. This research paves the way for future larger studies, as understanding DNA methylation profiles associated with neurodevelopmental disorders, may facilitate biomarkers for earlier diagnosis. This could lead to possible intervention strategies for patients suffering from a broad spectrum of disorders. Analysing epigenetic data from disease discordant twins provides an elegant study design and has the power to explore non-shared environmental factors that further refine models of disease mechanisms and biomarkers. The findings of this thesis suggest that epigenetic factors may play a role regulating biological pathways that underlie neurodevelopmental disorders, some of which arise as early as the prenatal period. Replication in other larger and similar cohorts of discordant twin pairs may provide novel targets for biomarker development, thereby allowing for early interventions and helping the health of children.
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ItemThe epigenetic landscape of paediatric acute myeloid leukaemia( 2019)Paediatric acute myeloid leukaemia (AML) is a cancer of the blood and bone marrow. It is currently one of the leading causes of cancer-related mortality in children. While induction therapy is largely successful in achieving patient remission, the relatively high mortality rate is driven by the large genetic heterogeneity of AML and recurrence of disease. Disease relapse rate is higher than other childhood leukaemias, is fast acting and often chemotherapy resistant. While much of the genetic contribution to disease has been described, there is still a component of AML pathogenesis that has yet to be discovered. Many of the genetic lesions found in adult AML directly affect epigenetic modifying genes, however this is not the case in children. Despite this, previous research has shown vast epigenetic alteration in paediatric AML. As such it is possible that some of the unexplained pathogenesis in childhood AML can be elucidated by modulation of gene activity via aberrant changes in the most widely studied epigenetic process, DNA methylation (DNAm). Few studies have comprehensively interrogated the DNA methylome of paediatric AML, nor has the prognostic utility or biomarker potential of DNAm been explored. In this study, we explored the global methylation profile of paediatric AML in comparison to non-leukaemic controls and subtype-dependant and independent biomarkers of disease that may have functional relevance. Furthermore, we described DNAm signatures with potential prognostic utility, to accurately identify predisposition to relapse at diagnosis. Genome-wide DNAm was interrogated via the HumanMethylation450 BeadChip Array (HM450K) on a cohort comprising of 128 archival and fresh bone marrow tissue sourced from multiple hospitals around Australia. This data was then combined with the TARGET AML cohort comprising of a further 231 bone marrow samples. Targeted replication and validation of findings was undertaken on a reduced cohort using SEQUENOM MassArray EpiTYPER. Bioinformatic and machine learning analyses were undertaken in R. The findings revealed subtype-independent genome-wide average methylation (GWAM) to be increased in diagnostic samples compared to non-leukaemic controls. This was further verified by differences in the global methylation proxy genes known as LINE1 and Alu. Deeper interrogation of these differences demonstrated wide-spread differential methylation in previously implicated genes in AML pathogenesis including WT1 and DGKG, both of which were validated in an independent cohort. Other genes identified to be differentially methylated included ZSCAN1, REC8 and IRX1. Subtype analysis validated previous studies showing inv(16)-specific differential methylation in MN1 and MEIS1. Finally, DNAm was used as the primary feature for a machine learning model designed to predict patient relapse at diagnosis. The final model achieved an area under the curve (AUC) of 94% with correct identification of 91% of all cases involved (F-measure=0.914). To date, this study represents the largest and most comprehensive insight into aberrant DNAm in paediatric AML. Results have increased our understanding of genes that are differentially methylated and highlight the potential utility of DNAm as a future prognostic biomarker. It is anticipated that these findings will serve as a foundation for future functional studies aimed at delivering truly personalised treatment regimens for children with AML.