Developing an index of dietary estimation of fermentable carbohydrate to allow dietary analysis for epidemiological studies

It has been proposed that long-term consumption of diets rich in non-digestible carbohydrates (NDCs), such as cereals, fruit and vegetables might protect against several chronic diseases, however, it has been difficult to fully establish their impact on health in epidemiology studies. The wide range properties of the different NDCs may dilution their impact when they are combined in one category for statistical comparisons in correlations or multivariate analysis. Several mechanisms have been suggested to explain the protective effects of NDCs, including increased stool bulk, dilution of carcinogens in the colonic lumen, reduced transit time, lowering pH, and bacterial fermentation to short chain fatty acids (SCFA) in the colon. However, it is very difficult to measure SCFA in humans in vivo with any accuracy, so epidemiological studies on the impact of SCFA are not feasible. Most studies use dietary fibre (DF) or Non-Starch Polysaccharides (NSP) intake to estimate the levels, but not all fibres or NSP are equally fermentable. It has been proposed that long-term consumption of diets rich in non-digestible carbohydrates (NDCs), such as cereals, fruit and vegetables might protect against several chronic diseases, however, it has been difficult to fully establish their impact on health in epidemiology studies. The wide range properties of the different NDCs may dilution their impact when they are combined in one category for statistical comparisons in correlations or multivariate analysis. Several mechanisms have been suggested to explain the protective effects of NDCs, including increased stool bulk, dilution of carcinogens in the colonic lumen, reduced transit time, lowering pH, and bacterial fermentation to short chain fatty acids (SCFA) in the colon. However, it is very difficult to measure SCFA in humans in vivo with any accuracy, so epidemiological studies on the impact of SCFA are not feasible. Most studies use dietary fibre (DF) or Non-Starch Polysaccharides (NSP) intake to estimate the levels, but not all fibres or NSP are equally fermentable. The first aim of this thesis was the development of the equations used to estimate the amount of FC that reaches the human colon and is fermented fully to SCFA by the colonic bacteria. Therefore, several studies were examined for evidence to determine the different percentages of each type of NDCs that should be included in the final model, based on how much NDCs entered the colon intact and also to what extent they were fermented to SCFA in vivo. Our model equations are FC-DF or NSP$ 1: 100 % Soluble + 10 % insoluble + 100 % NDOs¥ + 5 % TS** FC-DF or NSP 2: 100 % Soluble + 50 % insoluble + 100 % NDOs + 5 % TS FC-DF* or NSP 3: 100 % Soluble + 10 % insoluble + 100 % NDOs + 10 % TS FC-DF or NSP 4: 100 % Soluble + 50 % insoluble + 100 % NDOs + 10 % TS *DF: Dietary fibre; **TS: Total starch; $NSP: non-starch polysaccharide; ¥NDOs: non-digestible oligosaccharide The second study of this thesis aimed to examine all four predicted FC-DF and FC-NSP equations developed, to estimate FC from dietary records against urinary colonic NDCs fermentation biomarkers. The main finding of a cross-sectional comparison of habitual diet with urinary excretion of SCFA products, showed weak but significant correlation between the 24 h urinary excretion of SCFA and acetate with the estimated FC-DF 4 and FC-NSP 4 when considering all of the study participants (n = 122). Similar correlations were observed with the data for valid participants (n = 78). It was also observed that FC-DF and FC-NSP had positive correlations with 24 h urinary acetate and SCFA compared with DF and NSP alone. Hence, it could be hypothesised that using the developed index to estimate FC in the diet form dietary records, might predict SCFA production in the colon in vivo in humans. The next study in this thesis aimed to validate the FC equations developed using in vitro models of small intestinal digestion and human colon fermentation. The main findings in these in vitro studies were that there were several strong agreements between the amounts of SCFA produced after actual in vitro fermentation of single fibre and different mixtures of NDCs, and those predicted by the estimated FC from our developed equation FC-DF 4. These results which demonstrated a strong relationship between SCFA production in vitro from a range of fermentations of single fibres and mixtures of NDCs and that from the predicted FC equation, support the use of the FC equation for estimation of FC from dietary records. Therefore, we can conclude that the newly developed predicted equations have been deemed a valid and practical tool to assess SCFA productions for in vitro fermentation.

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.