Neural representations of social and non-social uncertainty in human decision making

The social landscape is filled with an intricate web of species-specific desired objects and course of actions. Humans are highly social animals and, as they navigate this landscape, they need to produce adapted decision-making behaviour. Traditionally social and non-social neural mechanisms affecting choice have been investigated using different approaches. Recently, in an effort to unite these findings, two main theories have been proposed to explain how the brain might encode social and non-social motivational decision-making: the extended common currency and the social valuation specific schema (Ruff & Fehr 2014). One way to test these theories is to directly compare neural activity related to social and non-social decision outcomes within the same experimental setting. Here we address this issue by focusing on the neural substrates of social and non-social forms of uncertainty. Using functional magnetic resonance imaging (fMRI) we directly compared the neural representations of reward and risk prediction and errors (RePE and RiPE) in social and non- social situations using gambling games. We used a trust betting game to vary uncertainty along a social dimension (trustworthiness), and a card game (Preuschoff et al. 2006) to vary uncertainty along a non-social dimension (pure risk). The trust game was designed to maintain the same structure of the card game. In a first study, we exposed a divide between subcortical and cortical regions when comparing the way these regions process social and non-social forms of uncertainty during outcome anticipation. Activity in subcortical regions reflected social and non-social RePE, while activity in cortical regions correlated with social RePE and non-social RiPE. The second study focused on outcome delivery and integrated the concept of RiPE in non-social settings with that of fairness and monetary utility maximisation in social settings. In particular these results corroborate recent models of anterior insula function (Singer et al. 2009; Seth 2013), and expose a possible neural mechanism that weights fairness and uncertainty but not monetary utility. The third study focused on functionally defined regions of the early visual cortex (V1) showing how activity in these areas, traditionally considered only visual, might reflect motivational prediction errors in addition to known perceptual prediction mechanisms (den Ouden et al 2012). On the whole, while our results do not support unilaterally one or the other theory modeling the underlying neural dynamics of social and non-social forms of decision making, they provide a working framework where both general mechanisms might coexist.

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.