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Rett syndrome (RTT) is a devastating neurodevelopmental disorder without effective treatments. Attempts at developing targetted therapies have been relatively unsuccessful, at least in part, because the genotypical and phenotypical variability of the disorder. Therefore, identification of biomarkers of response and patients’ stratification are high priorities. Administration of Insulin-like Growth Factor 1 (IGF-1) and related compounds leads to significant reversal of RTT-like symptoms in preclinical mouse models. However, improvements in corresponding clinical trials have not been consistent. A 20-weeks phase I open label trial of mecasermin (recombinant human IGF-1) in children with RTT demonstrated significant improvements in breathing phenotypes. However, a subsequent randomised controlled phase II trial did not show significant improvements in primary outcomes although two secondary clinical endpoints showed positive changes. To identify molecular biomarkers of response and surrogate endpoints, we used RNA sequencing to measure differential gene expression in whole blood samples of participants in the abovementioned phase I mecasermin trial. When all participants (n = 9) were analysed, gene expression was unchanged during the study (baseline vs. end of treatment, T0–T3). However, when participants were subclassified in terms of breathing phenotype improvement, specifically by their plethysmography-based apnoea index, individuals with moderate-severe apnoea and breathing improvement (Responder group) displayed significantly different transcript profiles compared to the other participants in the study (Mecasermin Study Reference group, MSR). Many of the differentially expressed genes are involved in the regulation of cell cycle processes and immune responses, as well as in IGF-1 signalling and breathing regulation. While the Responder group showed limited gene expression changes in response to mecasermin, the MSR group displayed

This thesis analyses data from genetic and epigenetic studies of brain disorders, in order to establish potential convergences of mechanisms across different conditions. Current research highlights the common symptoms across a wide range of brain disorders. We analyse the properties of the gene regulator: Methyl-CpG binding protein 2 (MeCP2), a chromatin-binding protein and a modulator of gene expression and we establish a DNA binding model: Matrix-GC, to predict MeCP2 targets. We evaluate Matrix-GC’s performance using receiver operating characteristic curves while varying a determinant binding factor: guanine-cytosine nucleotide enrichment (GC content). We show by combining a DNA binding sequence with GC content, that Matrix-GC is able to capture genes bound by MeCP2 better than random chance and binding sequence alone. Matrix-GC is applied to various brain disorders associated with MeCP2, followed by downstream enrichment analysis of molecular pathways and processes. We show three main processes to be under the control of MeCP2 across several brain disorders: neuronal transmission, development, and immunoreactivity. We further validate the performance of Matrix-GC at the single gene level by comparing MeCP2-bound genes with existing high-throughput transcriptome analysis and show that our results are statistically significant. We carry out stringent control analysis by Monte Carlo permutation to strengthen the reliability of our results. We propose the Matrix-GC as an in silico procedure to identify putative MeCP2 target genes and shed light on mechanisms overlapping across different brain disorders. Our method of identifying target genes has broad applications and can be implemented with other proteins that influence gene regulation. Importantly, this research provides a framework for analysing genetic data with statistical rigour which can be applied to downstream gene set analysis.