1,031 research outputs found

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Tracing Evolution of Gene Transfer Agents Using Comparative Genomics

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    The accumulating evidence suggest that viruses and their components can be domesticated by their hosts, equipping them with convenient molecular toolkits for various functions. One of such domesticated system is Gene Transfer Agents (GTAs) that are produced by some bacteria and archaea. GTAs morphologically resemble small phage-like particles and contain random fragments of their host genome. They are produced only by a small fraction of the microbial population and are released through a lysis of the host cell. Bioinformatic analyses suggest that GTAs are especially abundant in the taxonomic class of Alphaproteobacteria, where they are vertically inherited and evolve as a part of their host genomes. In this work, we extensively analyze evolutionary patterns of alphaproteobacterial GTAs using comparative genomics, phylogenomics and machine learning methods. We initially develop an algorithm that validate the wide presence of GTA elements in alphaproteobacterial genomes, where they are generally mistaken for prophages due to their homology. Furthermore, we demonstrate that GTAs evolve under the selection that reduces the energetic cost of their production, indicating their importance for the conditions of the nutrient depletion. The genome-wide screenings of translational selection and coevolution signatures highlight the significance of GTAs as a stress-response adaptation for the horizontal gene transfer, revealing a set of previously unknown genes that could play a role in the GTA cycle. As production of GTAs leads to the host death, their maintenance is likely to be under a kin or group level selection. By combining our findings with accumulated body of knowledge, this work proposes a conceptual model illustrating the role of GTAs in bacterial populations and their persistence for hundreds of millions of years of evolution

    Evaluating Symbolic AI as a Tool to Understand Cell Signalling

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    The diverse and highly complex nature of modern phosphoproteomics research produces a high volume of data. Chemical phosphoproteomics especially, is amenable to a variety of analytical approaches. In this thesis we evaluate novel Symbolic AI based algorithms as potential tools in the analysis of cell signalling. Initially we developed a first order deductive, logic-based model. This allowed us to identify previously unreported inhibitor-kinase relationships which could offer novel therapeutic targets for further investigation. Following this we made use of the probabilistic reasoning of ProbLog to augment the aforementioned Prolog based model with an intuitively calculated degree of belief. This allowed us to rank previous associations while also further increasing our confidence in already established predictions. Finally we applied our methodology to a Saccharomyces cerevisiae gene perturbation, phosphoproteomics dataset. In this context we were able to confirm the majority of ground truths, i.e. gene deletions as having taken place as intended. For the remaining deletions, again using a purely symbolic based approach we were able to provide predictions on the rewiring of kinase based signalling networks following kinase encoding gene deletions. The explainable, human readable and white-box nature of this approach were highlighted, however its brittleness due to missing, inconsistent or conflicting background knowledge was also examined

    Genetics of Scarring

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    Investigating the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric disorders

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    Autism spectrum disorder (ASD) and schizophrenia (SCZ) are two neuropsychiatric conditions with variable times of onset and are influenced by both genetic and environmental factors. Genome-wide association studies (GWASs) have led to the identification of numerous genetic loci common to both these disorders, however our understanding remains far from complete, with many clinical cases without a genetic cause. While increasing the statistical power of genome-wide association studies (GWASs) to find additional risk variants could rule-in or rule out rare cases of ASD and SCZ, this presently remains a difficult task. Furthermore, the biological functions for genetic susceptibility loci remains poorly understood, particularly for more-recent discoveries of loci devoid of gene bodies. On the other hand, recent biotechnological developments have made it possible to conduct high-resolution experimental measurements of the three-dimensional architecture of the genome, including enhancer-promoter interactions (EPIs). Such data have been used to connect GWAS risk variants to their potential target genes which, in turn, provide insights into underlying molecular mechanisms and cellular processes. The functions of enhancer-promoter interactions in controlling gene expression programmes is crucial to how implicated genes mediate neurological function and disease. Yet, knowledge on enhancer-promoter interactions remains to be used in conjunction with GWAS data, particularly on such data from specific brain cell types, which may be useful to uncover the biological underpinnings of psychiatric conditions. This thesis examines the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric conditions. In Chapter 2, I report on the identification of significant chromosomal interactions from studies of brain Hi-C data generated from neuronal and glial cells, with the goal to investigate the impact of EPIs genome-wide, as well as to provide a template for an in-depth understanding of how EPIs impact transcriptional regulation. In the Chapter 3, I discuss a novel approach integrating Activity by Contact (ABC) and gene set enrichment analyses of GWAS data in two steps. In the first step, ABC is used to predict enhancer-gene regulatory interactions in a given cell type (e.g., glial cells, neurons). Secondly, Hi-C coupled multi-marker analysis of genomic annotation (H-MAGMA) is used to assign the SNPs located in the regulatory regions identified by ABC to each gene and calculate gene-level association p-values. I applied this novel framework (ABC-HMAGMA) to GWAS data from SCZ and ASD, to identify novel SCZ and ASD trait-associated genes and molecular pathways. In Chapter 4, I have evaluated a potential novel mechanism for the regulation of enhancer activity within cells. I hypothesized that, in addition to its known roles in DNA replication and transcription, Topoisomerase I may regulate enhancer activity in brain cells. To test this hypothesis, I employed RNA-seq and transient transcriptome sequencing (TT-seq) data, a method that enriches for short-lived enhancer derived RNAs. These data showed that Topoisomerase I inhibition leads to significant changes in eRNA expression and offers evidence that such changes are relevant to the homeostatic functions for Top 1 in cellular gene expression regulation
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