2,821 research outputs found
Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements
In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin
Exploring the combinatorial effects of inflammatory stimuli and glucocorticoids on BIRC3 and BIRC2 expression: Differential regulation and functional investigations
Inflammation serves as a crucial innate mechanism in response to injury, infection, or harmful stimuli that activates the immune system and aims to restore homeostasis. Chronic inflammatory disorders like asthma present with ongoing inflammation whereby pulmonary epithelial cells release inflammatory mediators, intensifying airway inflammation, and worsening disease. Synthetic glucocorticoids represent the primary treatment for asthma by utilizing the anti-inflammatory properties of their endogenous counterparts to reduce inflammatory mediators. Their mechanism of action involves binding to the glucocorticoid receptor (GR) in cells and interacting with DNA elements to enhance anti-inflammatory gene expression. However, certain inflammatory genes play protective roles by promoting tissue repair or self-regulatory feedback mechanisms to limit inflammatory signaling. Some of these genes are spared the repressive effects of glucocorticoids and are even augmented, presumably for protection. The BIRC3 gene, while being associated with the inflammatory NF-κB pathway, is a gene upregulated in models of the pulmonary epithelium and in vivo by glucocorticoids. BIRC3, along with its family member BIRC2, may contribute to regulating NF-κB pathways, but also inhibit apoptosis, although their specific roles remain unclear. Consequently, the rationale of glucocorticoid-enhanced BIRC3 remain ambiguous. Given their similarities, BIRC3 and BIRC2 were both investigated, focusing on their expression, regulation, and the interactions between the GR and NF-κB at the gene promoter. These analyses detailed robust expression patterns differentiating BIRC3 and BIRC2 by their response to inflammatory cytokines and glucocorticoids over time. Promoter analysis revealed insights into possible GR and NF-κB interactions to explain the regulation of BIRC3 expression, thus providing an example of GR-NF-κB positive interactions leading to inflammatory gene expression. Further, investigations into the NF-κB pathway as a platform for BIRC2/3 function revealed the established canonical NF-κB pathway may challenge established dogma. This exploratory research highlighted the significance of TAK1 as central kinase within the NF-κB pathway. Moreover, possible redundancy between IKK1 and IKK2, and the presence of additional regulators within canonical NF-κB signaling may exist. While this study provided no greater insights for redundant, or individual roles for BIRC2/3 in NF-κB signaling or apoptosis, it facilitated the development of tools for future investigations
Investigating ADI-PEG20 metabolic therapy to target improved anti-cancer immune responses and outcomes from chimeric antigen receptor T-cell therapy
Amino acid starvation with the asparagine degrading enzyme asparaginase has been a key component of therapy for Acute Lymphoblastic Leukaemia (ALL) since its introduction in the 1960s and 70s, and has contributed to the radical improvement of treatment outcomes during this time, especially in children. However, it is a high toxicity agent and is therefore reduced or omitted from treatment protocols used for some adults, where outcomes lag behind those of paediatric counterparts. Recently, an alternative amino acid starvation approach with arginine degradation has gained attention in solid cancer for tumours lacking the arginine synthesising enzyme Argininosuccinate synthetase 1 (ASS1), with a survival benefit demonstrated as part of multi-agent treatment of mesothelioma, along with a favourable safety profile. We therefore aimed to characterise the scope, functionality and potential of arginine starvation as a low-toxicity addition to ALL therapy, particularly for those patients or phases of treatment where asparaginase is not suitable. Through analysis of large scale transcriptome data we show that large portions of both adult and paediatric B-ALL express non-random, low-levels of ASS1 and this is associated with consistent alterations in a wider network of metabolism related genes. This finding theoretically supports the usage of arginine starvation as therapy for B-ALL, since deficient expression of ASS1 predicts impaired capacity for arginine synthesis and therefore vulnerability to its degradation. Using in vitro cell line models as well as mouse models of primary human B-ALL we then show that pegylated arginine deiminase (ADI-PEG20), the clinical grade arginine degrading enzyme that has reached phase 3 trials in solid tumour oncology, leads to cell cycle arrest, DNA damage and apoptosis with caspase cleavage in those tumours where baseline ASS1 expression is lowest. Furthermore, we show that the effect of ADI-PEG20 can be potentiated when combined with BH3 mimetic agents, along with an additive effect when combined with the standard of care drug dexamethasone. Finally, based on a hypothesised interaction between arginine starvation and the death receptor apoptosis pathway, which is known to be a key mediator of Chimeric Antigen Receptor (CAR)-T cell cytotoxicity, we show the results of an investigation into the potential of ADI-PEG20 to be used as a tumour-priming therapy prior to CAR-T. Using in vitro models, we delineate an exciting effect whereby pre-treatment of ALL blasts with ADI-PEG20 leads to improvement in CAR-T "cytokine efficiency", that we propose to have the potential to generate clinically significant benefits in terms of both treatment efficacy and toxicity management. Collectively, these data strongly support the further development of ADI-PEG20 for ASS1-low ALL, both as a component of pharmacological and immunotherapy treatment paradigms
Inter-individual variation of the human epigenome & applications
Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/
Exploring therapeutic vulnerabilities in tumours with GLI1 oncogene activation
Deregulation of oncogene expression is one of the main drivers in tumorigenesis. Genetic alterations, such as gene amplification and structural variation, or epigenetic mechanisms based on the chemical modification of DNA or histones, facilitate the activation of proto-oncogenes that convey growth and survival advantages to the cells. Previously, our group identified focal amplification of the chromosome arm 12q in 14 of 60 glioblastoma patients (23.3 %) of which 4 patients harboured fusion genes with the oncogene GLI Family Zinc Finger 1 (GLI1).
In this study, I investigated the frequency and structure of GLI1 fusion genes, mechanisms of GLI1 transcriptional activation, GLI1-dependent tumour cell phenotype, and the potential value of GLI1 as a therapeutic target in precision-oncology in glioblastoma and liposarcoma. Initially, I identified GLI1 fusion genes linked with focal amplification on chromosome arm 12q in three independent glioblastoma cohorts (HIPO016, HIPO043, and TCGA-GB). GLI1 fusion genes were associated with high expression of GLI1 and its target genes, such as HHIP, PTCH1, and FOXS1. The boundary of the 12q amplification region often coincided with the GLI1 locus, presumably causing the breakage within the gene and the formation of fusion transcripts. The analysis of sarcoma tumours of the NCT MASTER study revealed high GLI1 expression in subtypes of osteosarcoma and soft tissue sarcoma. In addition, GLI1 fusion genes were found in liposarcoma and leiomyosarcoma. Furthermore, the disruption of a CTCF binding site upstream of the GLI1 locus upregulated the RNA expression of GLI1 and its target genes and increased cell proliferation. These data suggest that fusion-related genetic and epigenetic mechanisms regulate GLI1 expression. To explore its oncogenic function, I conducted phenotypic assays with and without GLI1 suppression and observed a reduction in tumour cell proliferation, anchorage-independent growth and increased apoptosis upon shRNA depletion or inhibition with the GLI1 inhibitor GlaB. The downregulation of several DNA repair pathways upon GLI1 depletion suggested that patients with aberrant GLI1 expression might benefit from combined GLI1 and DNA repair inhibitor therapy. To address this question, I performed a pre-clinical drug combination screen of GLI1 and DNA repair/cell cycle checkpoint inhibitors in glioblastoma and liposarcoma cell lines. In the primary screen, I tested inhibitors individually to identify effective and selective drugs of which the most promising candidates were tested in combination in the subsequent secondary screen. Both glioblastoma and liposarcoma showed high sensitivities to the SHH inhibitor JK184 and the GLI1 inhibitor GlaB. Synergistic effects were observed when GLI1 inhibitors were combined with inhibitors of the ATR/CHK1 axis, i.e., the CHK1 inhibitor LY2606368 or the ATR inhibitor Berzosertib. The independent validation of the screening results in cellular assays showed an increased effect of the combination treatment compared to the single agents on short- and long-term tumour cell proliferation. I furthermore confirmed the reduction in tumour growth upon treatment with GlaB and LY2606368 in a glioblastoma cerebral organoid model.
In conclusion, these data suggest that concurrent targeting of the SHH/GLI1 and ATR/CHK1 axes provides a possible precision-therapy approach for tumours with high GLI1 expression
Examining interactions among SNPs that can explain the prognostic variability in colorectal cancer
Background: Colorectal cancer is a significant medical burden worldwide and in Newfoundland and Labrador. Examining the relationships of SNP interactions with survival outcomes can help identify new prognostic markers for this disease.
Objectives: To examine associations between colorectal cancer survival outcomes and interactions of SNPs from MMP family and VEGF interactome genes using data-reduction methods.
Methods: Two data-reduction software programs, Cox-MDR and GMDR 0.9, were applied to the data of patients from the Newfoundland Familial Colorectal Cancer Registry. Eight datasets were investigated: one for the MMP gene SNPs (201 SNPs), and seven for the VEGF interaction networks (total 1,517 SNPs). Significance of interaction models was assessed using permutation testing. Associations between significant interaction models and clinical outcomes were confirmed using multivariable regression methods.
Results: For the MMP dataset two multi-SNP models and one single-SNP model were identified, while fifteen novel multi-SNP models and thirteen single-SNP models were identified for the VEGF interaction network datasets. All but one of these models were able to distinguish patients based on their outcome risk in multivariable regression models (p-value range: 0.03 – 2.2E-9).
Conclusion: This research demonstrated that novel genetic interactions associated with outcome risk in colorectal cancer can be found using data-reduction methods. This proves the utility of these methods in prognostic research
Protein coding variation in the J:ARC and J:DO outbred laboratory mouse stocks provides a molecular basis for distinct research applications.
Outbred laboratory mice (Mus musculus) are readily available and have high fecundity, making them a popular choice in biomedical research, especially toxicological and pharmacological applications. Direct high throughput genome sequencing (HTS) of these widely used research animals is an important genetic quality control measure that enhances research reproducibility. HTS data have been used to confirm the common origin of outbred stocks and to molecularly define distinct outbred populations. But these data have also revealed unexpected population structure and homozygosity in some populations; genetic features that emerge when outbred stocks are not properly maintained. We used exome sequencing to discover and interrogate protein-coding variation in a newly established population of Swiss-derived outbred stock (J:ARC) that is closely related to other, commonly used CD-1 outbred populations. We used these data to describe the genetic architecture of the J:ARC population including heterozygosity, minor allele frequency, LD decay, and we defined novel, protein-coding sequence variation. These data reveal the expected genetic architecture for a properly maintained outbred stock and provide a basis for the on-going genetic quality control. We also compared these data to protein-coding variation found in a multiparent outbred stock, the Diversity Outbred (J:DO). We found that the more recently derived, multiparent outbred stock has significantly higher interindividual variability, greater overall genetic variation, higher heterozygosity, and fewer novel variants than the Swiss-derived J:ARC stock. However, among the novel variants found in the J:DO stock, significantly more are predicted to be protein-damaging. The fact that individuals from this population can tolerate a higher load of potentially damaging variants highlights the buffering effects of allelic diversity and the differing selective pressures in these stocks. While both outbred stocks offer significant individual heterozygosity, our data provide a molecular basis for their intended applications, where the J:DO are best suited for studies requiring maximum, population-level genetic diversity and power for mapping, while the J:ARC are best suited as a general-purpose outbred stock with robust fecundity, relatively low allelic diversity, and less potential for extreme phenotypic variability
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