682 research outputs found
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Using 3D epigenomic maps of primary olfactory neuronal cells from living individuals to understand gene regulation
As part of PsychENCODE, we developed a three-dimensional (3D) epigenomic map of primary cultured neuronal cells derived from olfactory neuroepithelium (CNON). We mapped topologically associating domains and high-resolution chromatin interactions using Hi-C and identified regulatory elements using chromatin immunoprecipitation and nucleosome positioning assays. Using epigenomic datasets from biopsies of 63 living individuals, we found that epigenetic marks at distal regulatory elements are more variable than marks at proximal regulatory elements. By integrating genotype and metadata, we identified enhancers that have different levels corresponding to differences in genetic variation, gender, smoking, and schizophrenia. Motif searches revealed that many CNON enhancers are bound by neuronal-related transcription factors. Last, we combined 3D epigenomic maps and gene expression profiles to predict enhancer-target gene interactions on a genome-wide scale. This study not only provides a framework for understanding individual epigenetic variation using a primary cell model system but also contributes valuable data resources for epigenomic studies of neuronal epithelium
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
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
The Type 2 Diabetes Knowledge Portal: an open access genetic resource dedicated to type 2 diabetes and related traits
Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results
Aspergillus fumigatus Can Display Persistence to the Fungicidal Drug Voriconazole
Aspergillus fumigatus is a filamentous fungus that can infect the lungs of patients with immunosuppression and/or underlying lung diseases. The mortality associated with chronic and invasive aspergillosis infections remain very high, despite availability of antifungal treatments. In the last decade, there has been a worrisome emergence and spread of resistance to the first-line antifungals, the azoles. The mortality caused by resistant isolates is even higher, and patient management is complicated as the therapeutic options are reduced. Nevertheless, treatment failure is also common in patients infected with azole-susceptible isolates, which can be due to several non-mutually exclusive reasons, such as poor drug absorption. In addition, the phenomena of tolerance or persistence, where susceptible pathogens can survive the action of an antimicrobial for extended periods, have been associated with treatment failure in bacterial infections, and their occurrence in fungal infections already proposed. Here, we demonstrate that some isolates of A. fumigatus display persistence to voriconazole. A subpopulation of the persister isolates can survive for extended periods and even grow at low rates in the presence of supra-MIC of voriconazole and seemingly other azoles. Persistence cannot be eradicated with adjuvant drugs or antifungal combinations and seemed to reduce the efficacy of treatment for certain individuals in a Galleria mellonella model of infection. Furthermore, persistence implies a distinct transcriptional profile, demonstrating that it is an active response. We propose that azole persistence might be a relevant and underestimated factor that could influence the outcome of infection in human aspergillosis. Importance: The phenomena of antibacterial tolerance and persistence, where pathogenic microbes can survive for extended periods in the presence of cidal drug concentrations, have received significant attention in the last decade. Several mechanisms of action have been elucidated, and their relevance for treatment failure in bacterial infections demonstrated. In contrast, our knowledge of antifungal tolerance and, in particular, persistence is still very limited. In this study, we have characterized the response of the prominent fungal pathogen Aspergillus fumigatus to the first-line therapy antifungal voriconazole. We comprehensively show that some isolates display persistence to this fungicidal antifungal and propose various potential mechanisms of action. In addition, using an alternative model of infection, we provide initial evidence to suggest that persistence may cause treatment failure in some individuals. Therefore, we propose that azole persistence is an important factor to consider and further investigate in A. fumigatus.J.A. is funded by an Atracción de Talento Modalidad 1 (020-T1/BMD-200) contract of the Madrid Regional Government. J.S. has been funded by a BSAC Scholarship (bsac-2016-0049). C.V. was funded by FAPESP (2108/00715-3 and 2020/01131-5). G.H.G. hasbeen funded by FAPESP (2016/07870-9 and 2021/04977-5), CNPq (301058/2019-9 and404735/2018-5) and by the NIH/NIAID (grant R01AI153356). S.G. was cofunded by the NIHR Manchester Research Centre and the Fungal Infection Trust.S
A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data
The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines.
One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials.
In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications.
Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains
Dysregulation of Non-Coding RNAs: Roles of miRNAs and lncRNAs in the Pathogenesis of Multiple Myeloma
The dysregulation of non-coding RNAs (ncRNAs), specifically microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), leads to the development and advancement of multiple myeloma (MM). miRNAs, in particular, are paramount in post-transcriptional gene regulation, promoting mRNA degradation and translational inhibition. As a result, miRNAs can serve as oncogenes or tumor suppressors depending on the target genes. In MM, miRNA disruption could result in abnormal gene expression responsible for cell growth, apoptosis, and other biological processes pertinent to cancer development. The dysregulated miRNAs inhibit the activity of tumor suppressor genes, contributing to disease progression. Nonetheless, several miRNAs are downregulated in MM and have been identified as gene regulators implicated in extracellular matrix remodeling and cell adhesion. miRNA depletion potentially facilitates the tumor advancement and resistance of therapeutic drugs. Additionally, lncRNAs are key regulators of numerous cellular processes, such as gene expression, chromatin remodeling, protein trafficking, and recently linked MM development. The lncRNAs are uniquely expressed and influence gene expression that supports MM growth, in addition to facilitating cellular proliferation and viability via multiple molecular pathways. miRNA and lncRNA alterations potentially result in anomalous gene expression and interfere with the regular functioning of MM. Thus, this review aims to highlight the dysregulation of these ncRNAs, which engender novel therapeutic modalities for the treatment of MM.</p
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Investigation of the clinical utility of two potential pro-oncogenic genes in prostate cancer and breast cancer
The Identification of novel and specific biomarkers is crucial to diagnosis, and prognosis, in patients with prostate and breast cancer. Because cancer therapies have side effects in patients, discovering and potentially targeting specific biomarkers could promote the use of personalised approach for a more effective treatment.
Firstly, we have focused on the development of a monoclonal antibody-drug-based therapy, targeting prostate cancer stem cells (PCSCs), using a monoclonal antibody (mAb) previously generated in our laboratory against human endothelial protein C receptor (EPCR). PCSCs were isolated using lentivirus expressing the enhanced green fluorescent protein (EGFP) under NANOG-promoter generating two populations NANOG-EGFP+ and NANOG-EGFP- and analysed for EPCR expression.
No significant difference was observed in the expression of EPCR between NANOG-EGFP+ and NANOGEGFP- cell populations. A lack of conclusive correlation was observed between EPCR deficient cells with epithelial-mesenchymal transition (EMT) markers, cancer stem cells (CSCs), and stem cell markers. Finally, Gene Expression Profiling Interactive Analysis (GEPIA) was used to look at the tissue expression in normal and tumour tissue, showing high expression of EPCR in endothelial cells. Finally, based tissue expression profiling, EPCR is not a suitable candidate for antibody targeting as it would lead to off-target effects in multiple tissues, therefore no further experiments were designed using EPCR as a target biomarker.
Following this, a feasible study on the effect of Sperm-Associated Antigen 5 (SPAG5) chemoresistance and cancer progression in prostate and breast cancer was performed. The transcriptome and proteome of SPAG5 deficient were investigated in triple-negative breast cancer (TBC) MDA-MB-231 and androgenindependent prostate cancer DU145 cell lines, by RNA-sequencing and mass spectrometry (MS) analysis. Transcriptome was performed and a total of 2,201 differentially expressed genes (DEGs) in MDA-MB-231 SPAG5 deficient cells, while 907 DEGs DU145 SPAG5 deficient cells, versus control empty vector pLKO.1 cells, were identified. No significant differences in the cell cycle were observed in Doxorubicin and Epirubicin treatment DU145 and MDA-MB-231 SPAG5 deficient cells versus controls.
A list of the most statistically significant genes upregulated and downregulated was taken forward for verification for common and unique pathways, through free available online resources such as METASCAPE, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO). Using StatsPro free online sources proteomics analysis generated 230 differentially expressed proteins (DEPs) in MDA-MB- 231 SPAG5 deficient cells and 65 DEPs DU145 SPAG5 deficient versus control cells. Protein-protein interaction (PPI) network using Cytoscape has been conducted for enrichment KEGG analysis.
Cross-over data from MS and RNAseq upregulated and downregulated genes in MDA-MB-231 and DU145 SPAG5 deficient were compared to in silico data from cBioPortal tool. Interestingly, positive correlation was observed in genes involved in cell cycle, but also in genes involved in catalyse and biosynthesis of cholesterol.
Collectively those data offer a wider insight into the association of SPAG5 in cancer progression and its potential role not only in pathways involved in cell cycle but also how in lipid metabolism in cancer
Digital Literacy Education in Welsh Primary and Secondary Schools from the 1960s to the Present
Digital technologies are imbued with ideologies that impact culture and society. These technologies are ubiquitous, pervasive, and central to how people communicate, consume information, and orchestrate their lives. Therefore, for people to fully understand the impact and influence of these technologies on their lives and engage with them and the digital environment in a critically informed way - digital literacy is an absolute and necessary requirement. However, we are not seeing digital literacy as standard. This study assesses: (1) Whether students are being sufficiently educated about how digital technologies use and affect them in a social, cultural, and ethical capacity; (2) Whether the programme content of digital literacy education (DLE) is primarily driven by neo-liberal economically driven government policies; and (3) How much influence private neo-liberal capitalistic enterprises have in determining the educational agenda of DLE? Qualitative data was collected via three focus group interviews and twenty-six semi-structured interviews which explored students, educational professionals, and government officials’ views of DLE in Wales. The data was thematically coded using critical discourse analysis, and analysed using theories developed in Herbert Marcuse’s 1964 publication One-Dimensional Man. The results indicated that DLE educational policy has broadened to include knowledge that extends beyond the teaching of purely mechanistic skills. However, a variety of factors were identified that impede their implementation. Additionally, it is argued that students’ mechanistic digital skills have been declining since the introduction of touch screen technologies into primary and secondary schools. Findings also indicated that educators main DLE focus was on preparing students for employment purposes, and the influence private neo-liberal capitalistic enterprises have in determining not only the educational agenda of DLE, but education in general is profound, and has accelerated exponentially since the COVID-19 imposed lockdowns
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