405 research outputs found

    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

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Genetic basis and adaptive relevance of drought response in Cape Verde Arabidopsis

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    Climate change is predicted to impact precipitation patterns, leading to shorter growing seasons and increased susceptibility to drought in many regions worldwide. These changes significantly threaten plant populations and may result in ecosystem desertification. Understanding the mechanisms enabling species to adapt to such changes is crucial for effective conservation strategies and developing resilient crop varieties. Plants cope with drought through various strategies, including avoidance, escape, and drought tolerance, which can be canalized or plastic, depending on the genetic and environmental context. Understanding the balance between canalization and plasticity is essential for predicting plant responses to future climate change. Here, we investigated the genetic architecture of drought adaptation in natural Cape Verdean Arabidopsis thaliana populations. In Chapter One, we reviewed the impact of climate change on precipitation patterns and its consequences on plant populations, including increased susceptibility to drought and extinction risk. We discussed various strategies plants employ to cope with drought, such as avoidance, escape, and drought tolerance. We also discussed the importance of understanding the balance between canalization and plasticity for predicting plant responses to future climate changes. We also highlighted the significance of genetic adaptations in enabling species to adapt and persist in rapidly changing environments and the potential insights gained from studying A. thaliana populations on the Cape Verde Islands (CVI), which have experienced rapid adaptation and evolutionary rescue in response to drought-prone climates. In Chapter Two, we investigated the evolution of stomatal conductance and water use efficiency (WUE) in an A. thaliana population that colonized an island with a montane cloud scrubland ecosystem characterized by seasonal drought and fog-based precipitation. We found that stomatal conductance increases and WUE decreases in the colonizing population relative to its closest outgroup population from temperate North Africa. Genome-wide association mapping revealed a polygenic basis of trait variation, with a substantial contribution from a nonsynonymous SNP in MAP KINASE 12 (MPK12 G53R), which explains 35% of the phenotypic variance in WUE in the island population. Furthermore, we reconstructed the spatially-explicit evolutionary history of MPK12 53R on the island and demonstrated that this allele increased in frequency due to positive selection as A. thaliana expanded into harsher regions of the island. The findings showed how adaptation shaped quantitative eco-physiological traits in a new precipitation regime defined by low rainfall and high humidity. In Chapter Three, we examined the genetic architecture of variation in growth rate, leaf color, and stomatal patterning in response to precisely controlled water conditions among CVI A. thaliana populations. Genome-wide association mapping analyses revealed that moderately complex genetic architectures with roles for several major effect variants underlie variation in these traits. Furthermore, we found that several identified genes through genetic mapping have pleiotropic functions for complex traits underlying drought stress, highlighting the intricate nature of plant adaptation to these challenging conditions. In conclusion, this work presents a comprehensive analysis of the mechanisms and genetic basis of plant adaptation to drought stress, focusing on the natural A. thaliana populations in the CVI islands. Understanding these mechanisms is critical for predicting species distribution and adaptive responses to drought stress. Furthermore, our findings expand our knowledge of how drought adaptation results from numerous genetic variants, suggesting polygenic adaptation, and reveal that new mutations arise frequently enough to potentially facilitate rapid adaptation in colonizing populations. Lastly, these findings enrich our understanding of plant responses to drought and provide valuable insights for developing effective conservation strategies and resilient crop varieties

    Prognostic research:Methodological aspects and applications in acute care

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    Investigating the metabolomics of treatment response in patients with inflammatory rheumatic diseases

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    Background: Rheumatic and musculoskeletal diseases (RMDs) are autoimmune-mediated chronic diseases affecting the joints around the body, involving an inappropriate immune response being launched against the tissues of the joint. These devastating diseases include rheumatoid arthritis (RA) and psoriatic arthritis (PsA). If insufficiently managed – or indeed in severe cases – these diseases can substantially impact a patient’s quality of life, leading to joint damage, dysfunction, and disability. However, numerous treatments exist for these diseases that control the immune-mediated factors driving disease, described as disease modifying anti-rheumatic drugs (DMARDs). Despite the success of these drugs for patients in achieving remission, they are not effective in all patients, and those who do not respond well to first-line treatments will typically be given an alternative drug on a trial-and-error basis until they respond successfully. Given the rapid and irreversible damage these diseases can induce even in the early stages, the need for early and aggressive treatment is fundamental for reaching a good outcome for the patient. Biomarkers can be employed to identify the most suitable drug to administer on a patient-to-patient basis, using these to predict who will respond to which drug. Incorporating biomarkers into the clinical management of these diseases is expected to be fundamental for precision medicine. These may come from multiple molecular sources. For example, currently used biomarkers include autoantibodies while this project primarily focuses on discovering biomarkers from the metabolome. Methodology: This project involved the secondary analyses of metabolomic and transcriptomic datasets generated from patients enrolled on multiple clinical studies. These include data from the Targeting Synovitis in Early Rheumatoid Arthritis (TaSER) (n=72), Treatment in the Rotterdam Early Arthritis Cohort (tREACH) (n=82), Characterising the Centralised Pain Phenotype in Chronic Rheumatic Disease (CENTAUR) (n=50) and Mayo Clinic - Hur et al. (2021) (n=64) – cohorts. The metabolic findings' translatability across cohorts was evaluated by incorporating datasets from various regions, including the United Kingdom, the Netherlands, and the United States of America. These multi-omic datasets were analysed using an in-house workflow developed throughout this project’s duration, involving the use of the R environment to perform exploratory data analysis, supervised machine learning and an investigation of the biological relevance of the findings. Other methods were also employed, notably an exploration and evaluation of data integration methods. Supervised machine learning was included to generate molecular profiles of treatment responses from multiple datasets. Doing so showed the value of combining multiple weakly-associated analytes in a model that could predict patient responses. However, an important component, the validation of these models, could not be performed in this work, although suggestions were made throughout of possible next steps. Results and Discussion: The analysis of the TaSER metabolomic data showed metabolites associated with methotrexate response after 3 months of treatment. Tryptophan and argininerelated metabolites were included in the metabolic model predictive of the 3-month response. While the model was not directly validated using subsequent datasets, including the tREACH and Mayo Clinic cohorts, additional features from these pathways were associated with treatment response. Included across cohorts were several tryptophan metabolites, including those derived from indole. Since these are largely produced via the gut microbiome it was suggested that the gut microbiome may influence the effectiveness of RMD treatments. Since RA and PsA were considered in this work as two archetypal RMDs, part of the project intended to investigate whether there were shared metabolic features found in association to treatment response in both diseases. These common metabolites were not clearly identified, although arginine-related metabolites were observed in models generated from the TaSER and CENTAUR cohorts in association with response to treatment in both conditions. Owing to the limitations of the untargeted metabolomic approach, this work was expected to provide an initial step in understanding the involvement of arginine and tryptophan related pathways in influencing treatment response in RMDs. Not performed in this work, it was expected that targeted metabolomics would provide clearer insights into these metabolites, providing absolute quantification with the identification of these features of interest in the patient samples. It was expected that expanding the cohort sizes and incorporating other omics platforms would provide a greater understanding of the mechanisms of the resolution of RMDs and inform future therapeutic targets. An important output from this project was the analytical pipeline developed and employed throughout for the omics analysis to inform biomarker discovery. Later work will involve generating a package in the R environment called markerHuntR. The R scripts for the functions with example datasets can be found at https://github.com/cambest202/markerHuntR.git. It is anticipated that the package will soon be described in more detail in a publication. The package will be available for researchers familiar with R to perform similar analyses as those described in this work

    El microbioma del olivo y su papel en la respuesta de la planta a la Verticilosis causada por Verticillium dahliae: factores determinantes y modificadores

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    Olive tree (Olea europaea subsp. europaea) is one of the oldest cultivated trees in the world and plays a critical role in the sustainability of Mediterranean ecosystems. As an agricultural system with important economic, sociological and environmental roles its cultivation should be maintained and preserved. However, nowadays, the health of the olive groves is seriously threatened by a notable increase, both in incidence and severity of diseases caused by various pathogens which are affecting its growth and production. Among olive diseases, those caused by the vascular plant pathogenic bacterium Xylella fastidiosa (in particular from subspecies multiplex and pauca) and the soilborne vascular fungus Verticillium dahliae are, without a doubt, global threats to olive production worldwide. The most practical and economically efficient method for the management of olive vascular diseases is the use of resistant cultivars. However, most olive cultivars widely grown in the Mediterranean Basin including Spain, are moderate to highly susceptible to the most virulent strains of these pathogens, which include V. dahliae defoliating (D) pathotype or X. fastidiosa subsp. pauca ST53. Thus, an integrated management strategy is recommended to prevent the spread and reduce the incidence and severity of those pathogens. This approach combines preventive and palliative measures to mitigate the development of the disease among which the exploitation of the beneficial plant-associated microbiome by using biological control agents might represent a long-term sustainable and environmentally friendly strategy. In nature, healthy plants live in permanent association and interact with a myriad of microorganisms, collectively called the plant microbiome, which are known to play essential roles in plant health. Thus, endophytes, known as bacteria and fungi that live within plants where they establish nonpathogenic relationships with their hosts, can control the growth of pathogens through inter-microbial interactions and by stimulating host plant immunity. The potential role that microbial communities may play in the resistant response to olive vascular pathogens has been overlooked and remains unexplored to date. Therefore, a thorough knowledge of the microbial communities inhabiting the xylem vessels of olive trees may be crucial for understanding their potential influence on the healthy growth of this tree as well as on the resistance shown by specific olive genotypes against vascular plant pathogens. This Doctoral Thesis has been focused on the characterization of the microbial communities inhabiting the xylem vessels of the olive tree, optimizing the methodological approaches for its study and cultivation, determining the main biotic and abiotic factors that determine and modify the structure, diversity and the interactions existing among the members of these microbial communities. For this, as a first step, a Next Generation Sequencing (NGS) protocol was optimized for analysis of xylem-associated microbial communities, which included the evaluation of: i) the procedure for extracting the microbiome when using xylem sap or xylem tissue, ii) the influence of the DNA extraction kits (Chapter II), iii) the choice of PCR primers targeting 16S rRNA (Chapter III). In Chapter II it was shown the significant effect that the DNA extraction protocol has on xylem sap bacterial community assessment. Thus, significant differences in the alpha (Richness) and beta (UniFrac distances) diversity measures of xylem-inhabiting bacterial communities were found among 12 DNA extraction kits, which could be clustered in four groups. Although the core number of taxa detected by all DNA extraction kits included four phyla, seven classes, 12 orders, 21 families, and 14 genera, some specific taxa, particularly those identified at low frequency, were only detected by some DNA extraction kits. The most accurate recovery and assessment of a bacterial mock community artificially inoculated on sap samples was generated when using the PowerPlant and PowerSoil DNA extraction kits. Chapter III addressed another important drawback in metabarcoding studies, such is the primer choice for amplification of 16S rRNA. For that, four PCR-primer pairs targeting a different región of the 16S rRNA were compared for their efficacy to avoid the co-amplification of mitochondria and chloroplast plant rRNA. The highest yields of mitochondria and chloroplast reads were obtained when using xylem woody chips and the PCR1-799F/1062R (76.05%) and PCR3-967F/1391R (99.96%) primer pairs. On the contrary, the PCR2-799F/1115R and PCR4-799F/1193R primer pairs showed the lowest mitochondria 16S rRNA amplification (76% of reads). Among the genera identified using NGS, 14 (41.2%) were also recovered in the culture collection, whereas 20 (58.8%) of the isolated bacteria were not detected by the NGS approach. In Chapter VI, we evaluated six different broth media (SXM, XVM2, XF26, PD3, 3G10R and XDM2) mimicking xylem sap composition for their ability to sustain growth of olive xylem-inhabiting bacteria. A total of 66 olive xylem-inhabiting bacterial genera could be cultured in vitro, of which 28 (42.4%) were previously described as endophytes of the plant stem in other studies; but 38 of them, were described, for the first time, as cultivable plant-endophytic bacteria. Alpha and beta-diversity measures of bacterial communities developed during cultivation indicated that the main differences were due to the broth media used, followed by the olive genotype from which the xylem sap was extracted, with no effect (Richness and Shannon alpha-diversity) or a minor effect (UniFrac beta-diversity distance) of incubation time. PD3 was the medium that best supported bacterial growth but enriched for the lowest number of bacterial amplicon sequence variants (ASVs); whereas XVM2 medium showed the highest number of ASVs detected when using sap extracted from “Picual” and “Arbequina” genotypes (258 in both), followed by 3G10R (244) in “Picual” sap and XDM2 (244) in “Arbequina” sap. These culture media can facilitate the in vitro cultivation of synthetic microbial communities that can be later used to modify the plant xylem microbiome to enhance plant resilience to vascular pathogens. Additionally, this Doctoral Thesis elucidated whether in vitro olive propagation may alter the diversity and composition of the xylem-inhabiting microbiome and if those changes may modify the resistance response that a wild olive clone shows to the highly virulent D pathotype of V. dahliae (Chapter VII). Results from this chapter indicated that although there were differences in microbial communities among the different plant propagation methods, most substantial changes occurred when plants were inoculated with V. dahliae, regardless of whether the infection process of the stem took place. Thus, a significant increase in the diversity of bacterial communities occurred when the pathogen was present in the soil. Furthermore, it was noticeable that olive plants multiplied under in vitro conditions developed a susceptible reaction to D V. dahliae, characterized by severe wilting symptoms and a 100% of stem vascular colonization. Moreover, those in vitro propagated plants showed an altered xylem microbiome with a decrease in total OTU numbers as compared to that of plants multiplied under non-aseptic conditions. Pseudomonas spp. appeared as the most predominant bacterial group in micropropagated plants and Anoxybacillus was revealed as a keystone bacterium in V. dahliae-inoculated plants, irrespective of their propagation process. Our results showed a breakdown of resistance to V. dahliae in a wild olive genotype that could potentially be related to a modification of its xylem microbiome. These results have contributed to expand our knowledge of the role of indigenous xylem-associated microorganisms on host resistance, which can be of use to fight against the main vascular diseases of olive. Finally, this Doctoral Thesis characterized the structure and diversity of the olive microbiome under natural conditions and their potential determinant and modifying factors including plant-associated host factors such as plant niche and olive genotype, and the influence of environment including climate, soil and agronomic conditions of the orchard and the season of sampling (Chapter VIII). This chapter resulted in the identification of a total of 7,132 bacterial ASVs, distributed in 28 phyla and 3,469 genera, whereas 1,356 ASVs were identified for fungal communities that were composed of 10 phyla and 714 genera. Proteobacteria was the most abundant bacterial phylum (45.21%) followed by Actinobacteriota (25.22%); whereas Pseudomonas and Sphingomonas (7.37% and 5.11%, respectively) were the dominant genera. For fungal communities, Ascomycota (87.81%) followed by Basidiomycota (9.02%) were the most abundant phyla; whereas Aureobasidium and a member of the order Saccharomycetales (18.54% and 15.00%, respectively) were the dominant fungal genera. Alpha diversity showed main significant differences for the Richness and Shannon indexes according to the plant niche, both for bacterial and fungal communities. Similarly, ANOSIM analysis of beta diversity weighted UniFrac distances indicated a significant main effect of the plant niche, followed by the field location and season of sampling, with a minor effect of the olive genotype. Network analysis identified co-presence or mutual exclusion associations between the above- and below-ground compartments of olive trees. Interestingly, specific ASVs were identified showing different relative number of positive and negative associations with other ASVs in the network analysis within each studied factor. Our results are pioneer in describing the olive holobiont and its main shaping factors including the plant niche, environmental conditions (soil physico-chemical properties, climate and seasonality) and host genotype. These results will contribute to facilitate the exploration and selection of specific keystone microorganisms that can live in close association with olive under a range of environmental/agronomic conditions and could be ideal targets for the design of biofertilizers, biostimulants and biocontrol agents for management of olive diseases. To conclude, this Doctoral Thesis has settled the methodological approaches to unravel the biotic and abiotic factors that affect the xylem microbial communities and have characterized some members of the core xylem microbiome, establishing the basis to isolate and culture them. These isolated microorganisms could be used to produce a consortium of xylem-inhabiting microorganisms that can be artificially inoculated into xylem vessels of olive plantlets to modify their native xylem microbiome to obtain plants more resilient to infection by xylem-inhabiting pathogens or to enhance olive plant physiology and growth.El olivo (Olea europaea subsp. europaea) es uno de los árboles cultivados más antiguos del mundo y desempeña un papel fundamental en la sostenibilidad de los ecosistemas mediterráneos. Al ser un sistema agrícola con importantes funciones económicas, sociales y ambientales, su cultivo debe mantenerse y preservarse. Sin embargo, en la actualidad, la salud de los olivares se está viendo seriamente amenazada por un notable incremento, tanto en incidencia como en severidad, de enfermedades causadas por diversos patógenos que están afectando tanto a su desarrollo como producción. Entre las enfermedades del olivo, las causadas por la bacteria patógena Xylella fastidiosa (en particular de las subespecies multiplex y pauca) y el hongo vascular del suelo Verticillium dahliae son, sin duda, las principales amenazas globales para la producción del olivar a nivel mundial. El método más practico y económicamente eficiente para el manejo de las enfermedades vasculares del olivo es el uso de cultivares resistentes. Sin embargo, la mayoría de los cultivares de olivo más ampliamente utilizados en la cuenca mediterránea, incluida España, presentan una reacción de moderada a altamente susceptible a las variantes más virulentas de estos patógenos, que incluyen el patotipo Defoliante (D) en V. dahliae o la subespecie pauca ST53 en X. fastidiosa. Por lo tanto, es necesaria una estrategia de gestión integrada para poder prevenir la propagación y reducir la incidencia y severidad de esos patógenos. Este enfoque debe combinar medidas preventivas y paliativas para mitigar el desarrollo de la enfermedad, entre las que se incluye la explotación del microbioma beneficioso asociado a las plantas mediante el uso de agentes de control biológico que puede representar una estrategia sostenible y respetuosa con el medio ambiente a largo plazo. En la naturaleza, las plantas sanas viven en asociación permanente e interactúan con una gran variedad de microorganismos, denominados microbioma vegetal, que desempeña funciones esenciales en la salud de las plantas. Así, los microorganismos endofitos, entre los que se incluyen las bacterias y hongos que viven en el interior de las plantas estableciendo relaciones no patogénicas con su huésped, pueden controlar el crecimiento de patógenos a través de interacciones inter-microbianas y al estimular la inmunidad de la planta huésped. El potencial papel que pueden desempeñar las comunidades microbianas en la respuesta de resistencia en olivo a patógenos vasculares no ha sido considerada y permanece inexplorado. Por tanto, un mejor conocimiento de las comunidades microbianas que habitan en los vasos del xilema del olivo puede ser crucial para comprender su influencia potencial en el crecimiento saludable de la planta, así como en la resistencia que muestran genotipos específicos de olivo frente a patógenos vasculares. Esta Tesis Doctoral se ha centrado en la caracterización de las comunidades microbianas que habitan el xilema del olivo optimizando los enfoques metodológicos para su estudio y determinando el efecto de los principales factores bióticos y abióticos que son claves en la configuración de su estructura, diversidad y las interacciones existentes entre los componentes del microbioma. Para ello, como primer paso, se optimizo un protocolo de secuenciación masiva (NGS) para el análisis de comunidades microbianas asociadas al xilema, que incluía la evaluación de: i) el procedimiento de extracción del microbioma cuando se utiliza savia o tejido xilemático, ii) la influencia de los kits de extracción de ADN (Capítulo II) y iii) la elección de los cebadores de PCR dirigidos al ARNr 16S (Capítulo III). En el Capítulo II se mostró el efecto significativo del protocolo de extracción de ADN en la evaluación de la comunidad bacteriana de la savia del xilema. Así, se encontraron diferencias significativas en los valores de diversidad alfa (riqueza) y beta (distancias UniFrac) de las comunidades bacterianas que habitan en el xilema entre 12 kits de extracción de ADN, que pudieron agruparse en cuatro grupos. Aunque el número principal de taxones detectados por todos los kits de extracción de ADN incluía cuatro filos, siete clases, 12 órdenes, 21 familias y 14 géneros, algunos taxones, en particular los identificados con baja frecuencia, solo se detectaron con algunos de los kits de extracción de ADN. La recuperación y evaluación más precisa de una comunidad bacteriana inoculada artificialmente en muestras de savia se generó al utilizar los kits de extracción de ADN PowerPlant y PowerSoil. El Capítulo III abordo otro aspecto importante en los estudios de secuenciación masiva, como es la elección del cebador para la amplificación del ARNr 16S. Para ello, se compararon cuatro pares de cebadores de PCR dirigidos a diferentes regiones del ARNr por su eficacia para evitar la coamplificación de ARNr de mitocondrias y cloroplastos de la planta. Las amplificaciones más altas de secuencias de mitocondrias y cloroplastos se obtuvieron cuando se utilizó tejido xilemático con los pares de cebadores PCR1-799F/1062R (76,05 %) y PCR3-967F/1391R (99,96 %). Por el contrario, los pares de cebadores PCR2-799F/1115R y PCR4-799F/1193R mostraron la menor amplificación de ARNr 16S mitocondrial (<27,48 %), ausencia de secuencias de cloroplastos y el mayor número de unidades taxonómicas operacionales (OTU) bacterianas identificadas (es decir, 254 y 266, respectivamente). Curiosamente, solo 73 de 172 y 46 de 181 géneros fueron comunes a la savia y el tejido del xilema después de la amplificación con los cebadores PCR2 o PCR4, respectivamente, lo que indica un fuerte sesgo en la caracterización de las comunidades bacterianas según los cebadores utilizados. Estos resultados ofrecieron la hoja de ruta para diseñar una estrategia optimizada en la selección del kit de extracción de ADN y la pareja de cebadores de PCR más adecuados para la evaluación de las comunidades bacterianas en olivo junto con una línea de comandos bioinformáticos precisos que pueden ser utilizados para una descripción precisa de las comunidades bacterianas presentes en los vasos xilemáticos u otros nichos de plantas. En el Capítulo IV, se llevó a cabo la caracterización de la composición ionómica y metabolómica de la savia para la identificación de los requerimientos nutricionales potenciales de los microorganismos limitados al xilema. También se analizó el efecto de la edad y el genotipo de la planta sobre la composición bacteriana y química de la savia en olivo. Los resultados mostraron que la savia del olivo incluía un alto contenido de azucares (54,35%), alcoholes (28,85%), aminoácidos (8,01%), ácidos orgánicos (7,68%) y osmolitos (1,12%). Sin embargo, este perfil metabolómico vario en función de la edad y el genotipo de la planta. Los niveles de glucosa, fructosa, sacarosa y manitol, colina, B y PO4 3− fueron significativamente mayores en arboles adultos que en plantones para ambos genotipos de olivo, mientras que los contenidos de NO3 − y Rb mostraron un comportamiento opuesto. Por otro lado, los niveles de ácido aspártico, fenilalanina y Na fueron significativamente más altos en “Picual” que en “Arbequina”, y ocurrió lo contrario para Fe, pero solo para arboles adultos. El conocimiento de la composición química de la savia podrá conducir a una mejor comprensión de los complejos requisitos nutricionales de los microorganismos que habitan el xilema del olivo, incluidos los patógenos vasculares y sus posibles antagonistas, lo que puede permitir un mejor diseño y optimización de los medios de cultivo artificiales para el cultivo del microbioma del olivo. Otros aspectos relevantes de esta Tesis Doctoral incluyeron la comparación de enfoques cultivo dependientes e independientes para el análisis de la microbiota del xilema, incluido el aislamiento y cultivo in vitro del núcleo central de bacterias

    Genetic architecture of glycomic and lipidomic phenotypes in isolated populations

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    Understanding how genetics contributes to the variation of complex traits and diseases is one of the key objectives of current medical studies. To date, a large portion of this genetic variation still needs to be identified, especially considering the contribution of low-frequency and rare variants. Omics data, such as proteomics and metabolomics, are extensively employed in genetic association studies as ‘proxies’ for traits or diseases of interest. They are regarded as “intermediate” traits: measurable manifestations of more complex phenotypes (e.g., cholesterol levels for cardiovascular diseases), often more strongly associated with genetic variation and having a clearer functional link than the endpoint or disease of interest. Accordingly, the genetics of omics have the potential to offer insights into relevant biological mechanisms and pathways and point to new drug targets or diagnostic biomarkers. The main goal of this thesis is to expand the current knowledge about the genetic architecture of protein glycomics and bile acid lipidomics, two under-studied omic traits, but which are involved in several common diseases. First, in Chapter 2 I compared genetic regulation of glycosylation of two different proteins, transferrin and immunoglobulin G (IgG). By performing a genome-wide association study (GWAS) of ~2000 European samples, I identified 10 loci significantly associated with transferrin glycosylation, 9 of which were previously not reported as being related with the glycosylation of this protein. Comparing these with IgG glycosylation-associated genes, I noted both protein-specific and shared associations. These shared associations are likely regulated by different causal variants, suggesting that glycosylation of transferrin and IgG is genetically regulated by both shared and protein-specific mechanisms. Next, in Chapter 3 I investigated the effect of rare (MAF<5%) predicted loss-of-function (pLOF) and missense variants on the glycome of transferrin and IgG in ~3000 samples of European ancestry. Using multiple gene-based aggregation tests, I identified 16 significant gene-based associations for transferrin and 32 for IgG glycan traits,located in 6 genes already known to have a biological link to protein glycosylation but also in 2 genes which have not been previously reported. Finally, in Chapter 4 I applied a similar approach to bile acid lipidomics, exploring the genetic contribution of both common and rare variants. Despite more than double the sample size (N = ~5000) compared to protein glycomics analysis, I identified only 2 loci, near the SLCO1B1 and PRKG1 genes, significantly associated with bile acid traits., for which I noted a sex-specific effect. Further, I found 3 rare variant gene-based associations, in genes not previously reported as associated with bile acid levels. While the biological mechanisms linking these genes to levels of bile acid is not immediately clear, there is evidence in the literature of their involvement in bile acid synthesis and secretion and in liver diseases. In summary, in my thesis I describe the genetic architecture of the protein glycome and the bile acid lipidome: the former has a higher genetic component, while the latter is largely influenced by environmental factors (e.g., sex, diet, gut flora). Despite the limited sample size, we were able to describe rare variant associations, demonstrating that isolated populations represent a useful strategy to increase statistical power. However, additional statistical power is needed to identify the possible effect of protein glycome and bile acid lipidome on complex disease. A clearer understanding of the genetic architecture of omics traits is crucial to develop informed disease screening tests, to improve disease diagnosis and prognosis, and finally to design innovative and more customised treatment strategies to enhance human health
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