3,851 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Therapeutic and prognostic strategies in neuroblastoma : exploring nuclear hormone receptors, MYC targets, and DIAPH3

    Get PDF
    Neuroblastoma (NB) is a pediatric cancer derived from the cells of neural crest origin that form the sympathoadrenal system. Typically, the tumor cells migrate along the spinal cord and spread to the chest, neck, and/or abdomen. Different clinical behaviors are observed in this disease: some tumors spontaneously regress without treatment, while others are highly aggressive and resistant to current therapies. Approximately 40% of high-risk NB patients have MYCN amplification while 10% have MYC (i.e. encoding c-MYC) overexpression. These patients have undifferentiated tumors with a poor prognosis. Our group previously found that the expression and activation of nuclear hormone receptors (NHRs) estrogen receptor alpha (ERα) by 17-β-estradiol (E2), and the glucocorticoid receptor (GR) by dexamethasone (DEX), could trigger differentiation by disrupting the regulation of the miR-17 ~ 92 microRNA cluster by MYCN. In paper I, we sought to investigate whether the simultaneous activation of both ERα and GR has a more beneficial effect compared to the activation of either ERα or GR alone. We examined cell survival, alterations in cell shape as indicated by neurite extension, variations in metabolic pathways, accumulation of lipid droplets, and performed xenograft experiments. Our findings revealed that the simultaneous activation of GR and ERα, compared to their single activation, led to reduced viability and a more robust differentiation. This dual activation also caused changes in glycolysis and oxidative phosphorylation, increased lipid droplet accumulation, and decreased aggressiveness in mouse models. The triple activation with an additional activation of the retinoic acid receptor using all trans-retinoic acid (ATRA), amplified the differentiation phenotype. Bulk-sequencing analysis showed that patients with high levels of NHRs are related to favorable survival and clinical outcome. In summary, our data suggest that combination activation of these NHRs could be a potential differentiation induction treatment. Paper II investigates target genes of c-MYC and MYCN to explore if it is possible to obtain a better prognosis prediction using the expression of this group of genes, instead of the expression of MYC and/or MYCN alone. In addition, we analyzed if there are different prediction power capabilities between c-MYC and MYCN target genes, and their different role during sympathoadrenal development. We screened lists of target genes by using comprehensive approaches, including differential expression analysis between clinical risk groups, INSS stages, MYCN amplification status, progression status; Univariate Cox regression analysis to select the target genes related to prognosis prediction power, and protein interaction network analysis to select genes that share a meaningful biology function. Following the training and validation of (LASSO) regression prediction models in three different patient cohorts (SEQC, Kocak, and Versteeg), we found that a risk score computed on c-MYC/MYCN target genes with prognostic value, could effectively classify patients in groups with different survival probabilities. The high-risk group of patients exhibited unfavorable clinical outcomes and low survival rates. Further, single cell RNA sequencing analysis revealed that c-MYC and MYCN targets have different expression patterns during sympathoadrenal development. Notably, genes linked to adverse outcomes were predominantly expressed in sympathoblasts in comparison to chromaffin cells. In summary, our research provides new insights into the importance of c-MYC/MYCN target genes during sympathoadrenal development and their value in predicting patient outcome. In paper III we studied the function of one member of the formin protein family involved in cytoskeleton modulation: Diaphanous Related Formin 3 (DIAPH3). We found that high DIAPH3 expression in NB tumors are associated with MYCN amplification, higher stage, risk, progression and negative clinical outcome. Elevated DIAPH3 expression was also found in specific cells during mouse sympathoadrenal development and in progenitor cells of the post- natal human adrenal gland. Furthermore, the knockdown of DIAPH3 resulted in a slight decrease in cell growth and cell cycle arrest. Our study suggests that DIAPH3 could be a promising target for new therapeutic strategies

    A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

    Get PDF
    The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning

    Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022

    Get PDF
    In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet

    Climate Change and Critical Agrarian Studies

    Full text link
    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Emergence of number sense through the integration of multimodal information: developmental learning insights from neural network models

    Get PDF
    IntroductionAssociating multimodal information is essential for human cognitive abilities including mathematical skills. Multimodal learning has also attracted attention in the field of machine learning, and it has been suggested that the acquisition of better latent representation plays an important role in enhancing task performance. This study aimed to explore the impact of multimodal learning on representation, and to understand the relationship between multimodal representation and the development of mathematical skills.MethodsWe employed a multimodal deep neural network as the computational model for multimodal associations in the brain. We compared the representations of numerical information, that is, handwritten digits and images containing a variable number of geometric figures learned through single- and multimodal methods. Next, we evaluated whether these representations were beneficial for downstream arithmetic tasks.ResultsMultimodal training produced better latent representation in terms of clustering quality, which is consistent with previous findings on multimodal learning in deep neural networks. Moreover, the representations learned using multimodal information exhibited superior performance in arithmetic tasks.DiscussionOur novel findings experimentally demonstrate that changes in acquired latent representations through multimodal association learning are directly related to cognitive functions, including mathematical skills. This supports the possibility that multimodal learning using deep neural network models may offer novel insights into higher cognitive functions

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

    Get PDF
    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

    Undergraduate Catalog of Studies, 2022-2023

    Get PDF
    • …
    corecore