Exploring the role of miR-34a in regulating adipose inflammation during obesity

Background: Obesity is not a new disease, with roots that can be traced back to 400 BC. However, with the staggering increase in individuals that are overweight and obese since the 1980s, now over a quarter of individuals in Europe and the Americas are classed as obese. This presents a global health problem that needs to be addressed with novel therapies. It is now well accepted that obesity is a chronic, low-grade inflammatory condition that could predispose individuals to a number of comorbidities. Obesity is associated with cardiovascular diseases (CVDs) and type 2 diabetes (T2D) as part of “the metabolic syndrome,” and as first identified by Dr Vauge, central distribution of white adipose tissue (WAT) is an important risk factor in the development of these diseases. Subsequently, visceral WAT (vWAT) was shown to be an important factor in this association with CVDs and T2D, and increasing inflammation. As the obese WAT expands, mainly through hypertrophy, there is an increase in inflammation that recruits numerous immune cells to the tissue that further exacerbate this inflammation, causing local and systemic inflammatory and metabolic effects. One of the main types of immune cell involved in this pathogenic process is pro-inflammatory M1 adipose tissue macrophages (ATMs). MicroRNAs (miRNAs) are a species of small RNAs that post-transcriptionally regulate gene expression by targeting gene mRNA, causing its degradation or translational repression. These miRNAs are promiscuous, regulating numerous genes and pathways involved in a disease, making them useful therapeutic targets, but also difficult to study. miR-34a has been shown to increase in the serum, liver, pancreas, and subcutaneous (sc)WAT of patients with obesity, non- alcoholic fatty liver disease (NAFLD) and T2D. Additionally, miR-34a has been shown to regulate a number of metabolic and inflammatory genes in numerous cell types, including those in macrophages. However, the role of miR-34a in regulating vWAT metabolism and inflammation is poorly understood. Hypothesis: miR-34a is dysregulated in the adipose tissue during obesity, causing dysregulation of metabolic and inflammatory pathways in adipocytes and ATMs that contribute to adipose inflammation and obesity’s comorbidities, particularly T2D. Method/Results: The role of miR-34a in adipose inflammation was investigated using a murine miR-34a-/- diet-induced obesity model, and primary in vitro models of adipocyte differentiation and inflammatory bone marrow-derived macrophages (BMDMs). miR-34a was shown to be ubiquitously expressed throughout the murine epididymal (e)WAT of obese high-fat diet (HFD)-fed WT mice and ob/ob mice, as well as omental WAT from patients with obesity. Additionally, miR-34a transcripts were increased in the liver and brown adipose tissue (BAT) of ob/ob and HFD-fed WT mice, compared to WT controls. When miR-34a-/- mice were fed HFD ad libitum for 24 weeks they were significantly heavier than their WT counterparts by the end of the study. Ex vivo examinations showed that miR-34a-/- eWAT had a smaller adipocyte area on chow, which significantly increased to WT levels during HFD-feeding. Additionally, miR-34a-/- eWAT showed basal increases in cholesterol and fatty acid metabolism genes Cd36, Hmgcr, Lxrα, Pgc1α, and Fasn. miR-34a-/- iBAT showed basal reductions in Cebpα and Cebpβ, with increased Pgc1α expression during HFD- feeding. The miR-34a-/- liver additionally showed increased basal transcript expression of Pgc1α, suggesting miR-34a may broadly regulate PGC1α. Accompanying the ex vivo changes in cholesterol and fatty acid metabolism genes, in vitro miR-34a-/- white adipocytes showed increased lipid content. An F4/80high macrophage population was identified in HFD-fed miR-34a-/- eWAT, with increased Il-10 transcripts and serum IL-5 protein. Following these ex vivo observations, BMDMs from WT mice upregulated miR-34a expression in response to TNFα stimulation. Additionally, miR-34a-/- BMDMs showed an ablated CXCL1 response to TNFα. Conclusion: These findings suggest miR-34a has a multi-factorial role in controlling a susceptibility to obesity, by regulating inflammatory and metabolic pathways, potentially through regulation of PGC1α.

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.

Filling in gaps of Drosophila melanogaster urate degradation metabolic pathway using metabolomics approaches: towards the core metabolome of the fruit fly

The primary goal of systems biology is to integrate complex omics data, and data obtained from traditional experimental studies in order to provide a holistic understanding of organismal function. One way of achieving this aim is to generate genome-scale metabolic models (GEMs), which contain information on all metabolites, enzyme-coding genes, and biochemical reactions in a biological system. Drosophila melanogaster GEM has not been reconstructed to date. Constraint-free genome-wide metabolic model of the fruit fly has been reconstructed in our lab, identifying gaps, where no enzyme was identified and metabolites were either only produced or consume. The main focus of the work presented in this thesis was to develop a pipeline for efficient gap filling using metabolomics approaches combined with standard reverse genetics methods, using 5-hydroxyisourate hydrolase (5-HIUH) as an example. 5-HIUH plays a role in urate degradation pathway. Inability to degrade urate can lead to inborn errors of metabolism (IEMs) in humans, including hyperuricemia. Based on sequence analysis Drosophila CG30016 gene was hypothesised to encode 5- HIUH. CG30016 knockout flies were examined to identify Malpighian tubules phenotype, and shortened lifespan might reflect kidney disorders in hyperuricemia in humans. Moreover, LC-MS analysis of mutant tubules revealed that CG30016 is involved in purine metabolism, and specifically urate degradation pathway. However, the exact role of the gene has not been identified, and the complete method for gap filling has not been developed. Nevertheless, thanks to the work presented here, we are a step closer towards the development of a gap-filling pipeline in Drosophila melanogaster GEM. Importantly, the areas that require further optimisation were identified and are the focus of future research. Moreover, LC-MS analysis confirmed that tubules rather than the whole fly were more suitable for metabolomics analysis of purine metabolism. Previously, Dow/Davies lab has generated the most complete tissue-specific transcriptomic atlas for Drosophila – FlyAtlas.org, which provides data on gene expression across multiple tissues of adult fly and larva. FlyAtlas revealed that transcripts of many genes are enriched in specific Drosophila tissues, and that it is possible to deduce the functions of individual tissues within the fly. Based on FlyAtlas data, it has become clear that the fly (like other metazoan species) must be considered as a set of tissues, each 2 with its own distinct transcriptional and functional profile. Moreover, it revealed that for about 30% of the genome, reverse genetic methods (i.e. mutation in an unknown gene followed by observation of phenotype) are only useful if specific tissues are investigated. Based on the FlyAtlas findings, we aimed to build a primary tissue-specific metabolome of the fruit fly, in order to establish whether different Drosophila tissues have different metabolomes and if they correspond to tissue-specific transcriptome of the fruit fly (FlyAtlas.org). Different fly tissues have been dissected and their metabolome elucidated using LC-MS. The results confirmed that tissue metabolomes differ significantly from each other and from the whole fly, and that some of these differences can be correlated to the tissue function. The results illustrate the need to study individual tissues as well as the whole organism. It is clear that some metabolites that play an important role in a given tissue might not be detected in the whole fly sample because their abundance is much lower in comparison to other metabolites present in all tissues, which prevent the detection of the tissue-specific compound.