A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype

<p>Background The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question.</p> <p>Results In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases.</p> <p>Conclusions With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.</p&gt

Valoración de la calidad de los datos arqueológicos a través de la gestión de su vaguedad. Aplicación al estudio del poblamiento tardorromano

Resumen. El presente trabajo, que constituye parte de la tesis doctoral de la primera autora, plantea una propuesta de medición de la calidad de los datos arqueológicos a través de la gestión de la vaguedad, concretamente de tres variables: incertidumbre, imprecisión e inexactitud. Si tenemos presente que el dato arqueológico es parcial desde su origen, valorar ese grado de parcialidad debería ser uno de los objetivos desde la obtención de los datos en las intervenciones arqueológicas y durante todo el proceso de investigación, con el objetivo de que nuestros resultados muestren las debilidades y fortalezas de nuestros datos y que los resultados de los análisis posteriores sean lo más honestos posibles con el lector. Aplicaremos la metodología propuesta a un caso de estudio centrado en el análisis de las dinámicas de poblamiento durante la Antigüedad Tardía, valorando la calidad de los datos como fase previa al análisis pero también como parte de los propios cálculos estadísticos.Abstract. The present work, which is part of the PhD of the first author, proposes a measurement of the quality of archaeological data through the management of vagueness, specifically of three variables: uncertainty, imprecision and inaccuracy. If we bear in mind that archaeological data is partial from its origin, assessing its degree of partiality should be one of the objectives from the time the data is obtained in the archaeological interventions and during the whole research process, with the aim that our results show the weaknesses and strengths of our data and are as honest as possible. We will apply the proposed methodology to a case study focused on the analysis of the dynamics of settlement during Late Antiquity, assessing the quality of the data as a preliminary phase to its statistical analysis

Fréquentations des grottes durant l’époque romaine: le cas de la Navarre

El estudio de la ocupación de las cuevas ha copado el interés de los investigadores desde el siglo XIX. Sin embargo, han sido los niveles prehistóricos los que han gozado de un mayor protagonismo, dejando en un segundo plano los vestigios postpaleolíticos. Si bien en los últimos años se está aumentando el número de publicaciones sobre la frecuentación de las cuevas en época romana y medieval, en Navarra es un tema muy poco estudiado. Es por ello que el siguiente artículo pretende cubrir, al menos en parte, ese vacío historiográfico. Así, en este texto, presentamos una primera aproximación al estudio de la frecuentación de las cuevas en época romana en Navarra.The study of the caves occupation has cornered the interest of the researchers since the 19th century. Nevertheless, their prehistoric levels are which have been the biggest interest, leaving in a background the post‐Paleolithic vestiges. Although, the last years, the number of publications about the frequentness of the caves in roman and medieval epoch has increased. However, in Navarre it is a topic very slightly studied. The following article tries to cover this emptiness of the research. This text is our first approximation to the study about the frequentness of the caves in roman epoch in Navarre.

Prevention of diabetes in overweight/obese children through a family based intervention program including supervised exercise (PREDIKID project): study protocol for a randomized controlled trial

Background: The global pandemic of obesity has led to an increased risk for prediabetes and type-2 diabetes (T2D). The aims of the current project are: (1) to evaluate the effect of a 22-week family based intervention program, including supervised exercise, on insulin resistance syndrome (IRS) risk in children with a high risk of developing T2D and (2) to identify the profile of microRNA in circulating exosomes and in peripheral blood mononuclear cells in children with a high risk of developing T2D and its response to a multidisciplinary intervention program including exercise. Methods: A total of 84 children, aged 8-12 years, with a high risk of T2D will be included and randomly assigned to control (N = 42) or intervention (N = 42) groups. The control group will receive a family based lifestyle education and psycho-educational program (2 days/month), while the intervention group will attend the same lifestyle education and psycho-educational program plus the exercise program (3 days/week, 90 min per session including warm-up, moderate to vigorous aerobic activities, and strength exercises). The following measurements will be evaluated at baseline prior to randomization and after the intervention: fasting insulin, glucose and hemoglobin A1c; body composition (dual-energy X-ray absorptiometry); ectopic fat (magnetic resonance imaging); microRNA expression in circulating exosomes and in peripheral blood mononuclear cells (MiSeq; Illumina); cardiorespiratory fitness (cardiopulmonary exercise testing); dietary habits and physical activity (accelerometry). Discussion: Prevention and identification of children with a high risk of developing T2D could help to improve their cardiovascular health and to reduce the comorbidities associated with obesity.The Spanish Ministry of Industry and Competitiveness (DEP2016-78377-R), by “Fondos Estructurales de la Unión Europea (FEDER), Una manera de hacer Europa.” and by the University of the Basque Country (GIU14/21). This work was also supported by grants from Spanish Ministry of Economy and Competitiveness (RYC-2010-05957; RYC- 2011-09011), Spanish Ministry of Education, Culture and Sports (FPU14/ 03329) and by the Education, Linguistic Policy and Culture Department of the Government of the Basque Country (PRE_2016_1_0057)

Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries

<p>Abstract</p> <p>Background</p> <p>The use of hospital discharge administrative data (HDAD) has been recommended for automating, improving, even substituting, population-based cancer registries. The frequency of false positive and false negative cases recommends local validation.</p> <p>Methods</p> <p>The aim of this study was to detect newly diagnosed, false positive and false negative cases of cancer from hospital discharge claims, using four Spanish population-based cancer registries as the gold standard. Prostate cancer was used as a case study.</p> <p>Results</p> <p>A total of 2286 incident cases of prostate cancer registered in 2000 were used for validation. In the most sensitive algorithm (that using five diagnostic codes), estimates for Sensitivity ranged from 14.5% (CI95% 10.3-19.6) to 45.7% (CI95% 41.4-50.1). In the most predictive algorithm (that using five diagnostic and five surgical codes) Positive Predictive Value estimates ranged from 55.9% (CI95% 42.4-68.8) to 74.3% (CI95% 67.0-80.6). The most frequent reason for false positive cases was the number of prevalent cases inadequately considered as newly diagnosed cancers, ranging from 61.1% to 82.3% of false positive cases. The most frequent reason for false negative cases was related to the number of cases not attended in hospital settings. In this case, figures ranged from 34.4% to 69.7% of false negative cases, in the most predictive algorithm.</p> <p>Conclusions</p> <p>HDAD might be a helpful tool for cancer registries to reach their goals. The findings suggest that, for automating cancer registries, algorithms combining diagnoses and procedures are the best option. However, for cancer surveillance purposes, in those cancers like prostate cancer in which care is not only hospital-based, combining inpatient and outpatient information will be required.</p

Novel metabolic network reconstruction algorithms for -omics data integration and in-silico gene essentiality analysis in cancer.

In order to defeat cancer, we need to understand its biology. The study of metabolism is an active area of research in cancer nowadays. Some Systems Biology techniques used for analyzing cancer metabolism contextualize prior biological knowledge with experimental data before further analysis aimed at finding weak points in cancer cells is conducted. Not only cancer but also bacterial communities living in our bodies have an impact on our health, hence methods for their study are also needed. The purpose of this doctoral thesis is to improve automated network reconstruction techniques and apply them to obtain new insights from experimental data and find essential genes for cancer survival. It also aims to find new methods that can be used to integrate experimental data and improve the prediction accuracy of therapeutic targets. This thesis introduces two novel fast network reconstruction algorithms. One of them is focused on bacterial communities and the use of metaproteomic and taxonomic data. The other is focused on gene expression data coming from cancer samples. The latter algorithm allows us to evaluate a current in-silico approach used for finding essential metabolic genes against experimentally obtained high-throughput gene essentiality data. Finally, a new method that answers the question of what other reactions in a metabolic network make a given one essential is developed, opening the possibility to new methods of integrating experimental data with metabolic networks.Para conseguir derrotar al cáncer, es necesario entender su biología. Hoy en día, el estudio del metabolismo constituye un área de investigación muy activa en cáncer. Algunas técnicas de Biología de Sistemas, empleadas para analizar el metabolismo del cáncer, permiten contextualizar el conocimiento biológico disponible con datos experimentales antes de realizar otros tipos de análisis dirigidos a encontrar puntos débiles en las células cancerosas. No sólo el cáncer, sino también las comunidades bacterianas que viven en nuestros cuerpos tienen un impacto en nuestra salud y, por tanto, también necesitan de estos métodos para su estudio. El propósito de esta tesis consiste en mejorar las técnicas de reconstrucción automática de redes metabólicas, y aplicarlas para extraer nuevos conocimientos a partir de datos experimentales, así como para encontrar qué genes son esenciales para la supervivencia del cáncer. También tiene como objetivo encontrar nuevos métodos que puedan ser empleados para integrar datos experimentales y mejorar la precisión en la predicción de dianas terapéuticas. Esta tesis introduce dos nuevos algoritmos para la reconstrucción de redes metabólicas. Uno de ellos está centrado en comunidades bacterianas y el uso de datos de metaproteómica y taxonomías. El otro está centrado en datos de expresión génica procedentes de muestras cancerosas. Este último algoritmo nos permite evaluar una técnica in-silico usada en la actualidad para la búsqueda de genes esenciales metabólicos contra datos experimentales de alto rendimiento acerca de la esencialidad de los genes. Por último, desarrollamos un nuevo método que responde a la pregunta sobre qué reacciones en una red metabólica es necesario bloquear para que otra reacción concreta se convierta en esencial, abriendo la posibilidad de desarrollar nuevos métodos para la integración de datos experimentales con redes metabólicas