3,544 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

    Neuromodulatory effects on early visual signal processing

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    Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    Differential gene expression and potential regulatory network of fatty acid biosynthesis during fruit and leaf development in yellowhorn (Xanthoceras sorbifolium), an oil-producing tree with significant deployment values

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    Xanthoceras sorbifolium (yellowhorn) is a woody oil plant with super stress resistance and excellent oil characteristics. The yellowhorn oil can be used as biofuel and edible oil with high nutritional and medicinal value. However, genetic studies on yellowhorn are just in the beginning, and fundamental biological questions regarding its very long-chain fatty acid (VLCFA) biosynthesis pathway remain largely unknown. In this study, we reconstructed the VLCFA biosynthesis pathway and annotated 137 genes encoding relevant enzymes. We identified four oleosin genes that package triacylglycerols (TAGs) and are specifically expressed in fruits, likely playing key roles in yellowhorn oil production. Especially, by examining time-ordered gene co-expression network (TO-GCN) constructed from fruit and leaf developments, we identified key enzymatic genes and potential regulatory transcription factors involved in VLCFA synthesis. In fruits, we further inferred a hierarchical regulatory network with MYB-related (XS03G0296800) and B3 (XS02G0057600) transcription factors as top-tier regulators, providing clues into factors controlling carbon flux into fatty acids. Our results offer new insights into key genes and transcriptional regulators governing fatty acid production in yellowhorn, laying the foundation for efforts to optimize oil content and fatty acid composition. Moreover, the gene expression patterns and putative regulatory relationships identified here will inform metabolic engineering and molecular breeding approaches tailored to meet biofuel and bioproduct demands

    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

    An integrated single-cell analysis of human adrenal cortex development

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    The adrenal glands synthesize and release essential steroid hormones such as cortisol and aldosterone, but many aspects of human adrenal gland development are not well understood. Here, we combined single-cell and bulk RNA sequencing, spatial transcriptomics, IHC, and micro-focus computed tomography to investigate key aspects of adrenal development in the first 20 weeks of gestation. We demonstrate rapid adrenal growth and vascularization, with more cell division in the outer definitive zone (DZ). Steroidogenic pathways favored androgen synthesis in the central fetal zone, but DZ capacity to synthesize cortisol and aldosterone developed with time. Core transcriptional regulators were identified, with localized expression of HOPX (also known as Hop homeobox/homeobox-only protein) in the DZ. Potential ligand-receptor interactions between mesenchyme and adrenal cortex were seen (e.g., RSPO3/LGR4). Growth-promoting imprinted genes were enriched in the developing cortex (e.g., IGF2, PEG3). These findings reveal aspects of human adrenal development and have clinical implications for understanding primary adrenal insufficiency and related postnatal adrenal disorders, such as adrenal tumor development, steroid disorders, and neonatal stress

    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

    Immune Mechanisms of Microbial Cancer Therapy

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    Neutrophils are responsible for protection from microbial infections. But they can also infiltrate solid tumours, and their presence within tumours is linked with cancer growth and poorer prognosis. However, this view oversimplifies their relationship with tumours, as recent research suggests that neutrophils also have anti-tumour properties. In addition, neutrophils possess considerable functional plasticity, and physiological and pathological factors can alter their function. In this study, we examined how neutrophil plasticity could be exploited to shift the tumour immune microenvironment to favour the inhibition of cancer growth. We achieved this by injecting inactivated Staphylococcus aureus (S. aureus) bioparticles into tumours. As a result, the TME (TME) was altered by the rapid influx of activated neutrophils that had acquired a pathogen killing effector phenotype. When examined using two-photon microscopy, S. aureus bioparticle activated neutrophils within tumours had increased motility and interacted with tumour cells. Repeated administration of S. aureus bioparticle therapy maintained a neutrophil activating environment within tumours. This led to neutrophil-dependent inhibition of tumour growth, highlighting an important role for microbe activated neutrophils in achieving cancer control. S. aureus bioparticle treatment also enhanced CD8+ T cell responses within tumours and draining lymph nodes. Notably, mice treated with our microbial therapy were protected from cancer recurrence, which suggests that a long-lasting protective immune response was elicited by the treatment. In keeping with these results, we found that S. aureus bioparticle treatment had a synergistic tumour inhibiting effect when combined with systemic checkpoint inhibitor therapy. In conclusion, we demonstrated that neutrophils are critical for microbe based cancer immunotherapy. We leveraged neutrophil plasticity and their rapid tissue infiltration in response to S. aureus bioparticle induced inflammation to alter the TME and control tumour growth. This body of research supports a model for developing neutrophil based immunotherapy using microbial bioparticles and its application with existing clinical cancer therapies
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