<p> Fragile X Syndrome (FXS) is the most common inherited disease of mental impairment, typically caused by a mutation in the Fragile X mental retardation 1 (FMRJ) gene. The clinical features are thought to result from abnormal neurobiology due to a lack of the Fragile X mental retardation protein (FMRP). Previously, it was thought that FMRP was confined exclusively to neurons; however, our laboratory recently discovered that astrocytes also express FMRP. Consequently, it is possible that astrocytes also suffer abnormalities as a result of a lack of FMRP. Astrocytes play integral roles in the development and maintenance of communication in the central nervous system. Therefore, it is now important to determine the contribution of astrocytes to the abnormal neuronal phenotype seen in FXS. In these experiments, neurons and astrocytes were independently isolated from wild type (WT) or FMRJ null mice and grown in a coculture. Neurons were evaluated using immunocytochemistry in combination with computer-aided morphometric and synaptic protein analyses. The findings presented here provide convincing evidence that Fragile X astrocytes contribute to the abnormal neurobiology seen in FXS . Fragile X astrocytes alter the dendrite morphology and excitatory synaptic protein expression of WT neurons in culture; and, importantly, when Fragile X neurons are grown with WT astrocytes these changes are prevented. Interestingly, the Fragile X astrocytes appear to act by causing a delay in development; even WT neurons grown in the presence of Fragile X astrocytes, that displayed an abnormal phenotype at 7 days in culture, exhibited nearly normal dendrite morphology and expression of excitatory synapses at 21 days. Furthermore, the results suggest that the dendritic abnormalities induced by the Fragile X astrocytes specifically target neurons with a spiny stellate morphology. This research establishes a role for astrocytes in the development of the abnormal neurobiology seen in FXS, and as such, the results presented here have significant implications for Fragile X research. The novel prospect that astrocytes are key contributing components in the development of FXS provides an exciting new direction for investigations into the mechanisms underlying FXS, with many unexplored avenues for potential treatment strategies. </p> / Thesis / Doctor of Philosophy (PhD)

Recent progress in fundamental biological sciences and medicine has considerably increased the quantity ofdata that can be studied and processed. The main limitation now is not retrieving data, but rather extractinguseful biological insights from the large datasets accumulated. More recent advances have provided detailedhigh-density data regarding metabolism (metabolomics) and protein expression (proteomics). Clearly, no single analytic methods, can provide a comprehensive understanding. Rather, the ability to link available datatogether in a coherent manner is required to obtain a complete view. The improving application of MachineLearning (ML) techniques provides the means to make continuous progress in processing complex data sets.A brief discussion is offered on the advantages of ML, the state-of-the-art in Deep Learning (DL) for proteinpredictions and the importance of ML in biological data processing. Noise stemming from incorrect classification or arbitrary/ambiguous labelling of data may arise when ML techniques are applied to large datasets. Furthermore, the stochasticity of biological systems needs to be considered for correctly evaluating theoutputs. Here we show the potential of a workflow to respond biological questions taking into consideration aperturbation of the biological data.  For controlling the applicability of models and maximizing the predictivity, in silico filtering schemescan usefully be applied as an “Ockham’s razor” before using any ML technique. After reviewing differentDL approaches for protein prediction purposes, this work shows that a computational approach in filteringsteps is a valuable tool for proteins classification when biological features are not fully annotated or reviewed.The in silico approach has identified putative proline transporters in fungi and plants as well as carotenoidbiosynthetic gene products in the plant family Brassicaceae. The proposed method is suitable for extractingfeatures of classification and then maximizing the use of a DL approach.