The metabolic and epigenetic effects of the isocitrate dehydrogenase-1 mutation in glioma

Cancer cells have been noted to have an altered metabolic phenotype for over ninety years. In the presence of oxygen, differentiated cells predominately utilise the tricarboxylic acid (TCA) cycle and oxidative phosphorylation to efficiently produce energy and the metabolites necessary for protein and lipid synthesis. However, in hypoxia, this process is altered and cells switch to a higher rate of glycolysis and lactate production to maintain their energy and metabolic needs. In cancer cells, glycolysis is maintained at a high rate, even in the presence of oxygen; a term described as “aerobic glycolysis”. Tumour cells are rapidly dividing and have a much greater need for anabolism compared to normal differentiated cells. Rapid glucose metabolism enables faster ATP production as well as a greater redistribution of carbons to nucleotide, protein, and fatty acid synthesis, thus maximising cell growth. Recently, other metabolic changes, driven by mutations in genes related to the TCA cycle, indicate an alternative role for metabolism in cancer, the “oncometabolite”. This is where a particular metabolite builds up within the cell and contributes to the tumorigenic process. One of these genes is isocitrate dehydrogenase (IDH) IDH is an enzyme that forms part of the tricarboxylic acid (TCA) cycle and converts isocitrate to α-ketoglutarate (α-KG). It exists in three isoforms; IDH1, IDH2 and IDH3 with the former present in the cytoplasm and the latter two in the mitochondria. Point mutations have been identified in the IDH1 and IDH2 genes in glioma which result in a gain of function by converting α-KG to 2-hydroxyglutarate (2HG), an oncometabolite. 2HG acts as a competitive inhibitor of the α-KG dependent dioxygenases, a superfamily of enzymes that are involved in numerous cellular processes such as DNA and histone demethylation. It was hypothesised that the IDH1 mutation would result in other metabolic changes in the cell other than 2HG production, and could potentially identify pathways which could be targeted for therapeutic treatment. In addition, 2HG can act as a potential competitive inhibitor of α-KG dependent dioxygenases, so it was hypothesised that there would be an effect on histone methylation. This may alter gene expression and provide a mechanism for tumourogenesis and potentially identify further therapeutic targets. Metabolic analysis of clinical tumour samples identified changes associated with the IDH1 mutation, which included a reduction in α-KG and an increase in GABA, in addition to the increase in 2HG. This was replicated in several cell models, where 13C labelled metabolomics was also used to identify a possible increase in metabolic flux from glutamate to GABA, as well as from α-KG to 2HG. This may provide a mechanism whereby the cell can bypass the IDH1 mutation as GABA can be metabolised to succinate in the mitochondria by GABA transaminase via the GABA shunt. JMJ histone demethylases are a subset of the α-KG dependent dioxygenases, and are involved in removing methyl groups from histone tails. Changes in histone methylation are associated with changes in gene expression depending on the site and extent of chemical modification. To identify whether the increase in 2HG and fall in α-KG was associated with inhibition of histone demethylases a histone methylation screen was used. The IDH1 mutation was associated with an increase in methylation of H3K4, which is associated with gene activation. ChiP and RNA sequencing identified an increase in H3K4me3 at the transcription start site of the GABRB3 subunit, resulting in an increase in gene expression. The GABRB3 subunit forms part of the GABA-A receptor, a chloride channel, which on activation can reduce cell proliferation. The IDH1 mutation was associated with an increase in GABA and GABRB3 subunit of the GABA-A receptor. This raises the possibility of GABA transaminase as a potential therapeutic target. Inhibition of this enzyme could reduce GABA metabolism, potentially reducing any beneficial effect of the GABA shunt in IDH1 mutant tumours, and increasing activation of the GABA-A receptor by increasing the concentration of GABA in the brain. This in turn may reduce cell proliferation, and could be achieved by using Vigabatrin, a GABA transaminase inhibitor licensed for use in epilepsy.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.