1. Investigación

El cáncer de ovario (CO) es la quinta causa de mortalidad entre las mujeres y la principal causa de muerte atribuida a los cánceres ginecológicos en los países desarrollados. Esta malignidad se caracteriza por diseminarse a través de la cavidad abdominal en lugar de hacerlo a través de los sistemas linfático o vascular. Las pacientes con CO a menudo se diagnostican en estadios avanzados, lo que resulta en altas tasas de mortalidad. Además, las herramientas de detección actuales tienen una sensibilidad y especificidad limitadas, especialmente en etapas tempranas. Por lo tanto, la identificación de biomarcadores específicos es crucial para un diagnóstico temprano y un tratamiento eficaz. Las vesículas extracelulares tumorales de pequeño tamaño, y entre ellas los exosomas, son capaces de modificar el microambiente tumoral y promover la progresión tumoral. Estas vesículas son secretadas a partir de cualquier tipo celular al espacio extracelular y acumulan una amplia variedad de biomoléculas que pueden modificar la fisiología de las células huésped que las internalizan. Por lo tanto, la secreción de tales vesículas en el fluido peritoneal podría ser un factor determinante en la diseminación y comportamiento de esta enfermedad. La presente tesis doctoral se desarrolló a partir de un estudio observacional prospectivo que evalúa el impacto de los exosomas derivados del fluido peritoneal (Exo-DFP) en aspectos clínicos del CO. Para ello, se recolectaron muestras de fluidos peritoneales de un total de 94 pacientes, que incluyeron 65 pacientes con CO, divididas en dos cohortes, que se sometieron a una cirugía diagnóstica o citorreductora, y 29 pacientes no oncológicas, como controles, que se sometieron a cirugía abdominal por condiciones ginecológicas benignas. La extracción sistemática de los Exo-DFP a partir de muestras quirúrgicas permitió observar diferencias cuantitativas y cualitativas significativas asociadas con el diagnóstico del CO, histología, etapa de la enfermedad y quimiosensibilidad al platino. El análisis del perfil proteómico de estos Exo-DFP condujo a la identificación de vías moleculares y proteínas de interés y a la validación biológica de S100A4 y STX5. Además, el análisis no supervisado de los perfiles proteómicos de los Exo-DFP en casos con CO seroso de alto grado reveló dos clústeres con diferentes resultados en términos de supervivencia global. En resumen, la caracterización exhaustiva del contenido de los Exo-DFP proporciona información pronóstica valiosa con posibles implicaciones para el manejo clínico del CO.

The prevalence and severity of allergic diseases have increased over the last 30 years. Understanding the mechanisms responsible for these diseases is a major challenge in current allergology, as it is crucial for the transition towards precision medicine, which encompasses predictive, preventive, and personalized strategies. The urge to identify predictive biomarkers of allergy at early stages of life is crucial, especially in the context of major allergic diseases such as food allergy and atopic dermatitis. Identifying these biomarkers could enhance our understanding of the immature immune responses, improve allergy handling at early ages and pave the way for preventive and therapeutic approaches. This minireview aims to explore the relevance of three biomarker categories (proteome, microbiome, and metabolome) in early life. First, levels of some proteins emerge as potential indicators of mucosal health and metabolic status in certain allergic diseases. Second, bacterial taxonomy provides insight into the composition of the microbiota through high-throughput sequencing methods. Finally, metabolites, representing the end products of bacterial and host metabolic activity, serve as early indicators of changes in microbiota and host metabolism. This information could help to develop an extensive identification of biomarkers in AD and FA and their potential in translational personalized medicine in early life.

Multiple sclerosis (MS) is a demyelinating and neurodegenerative disease of the central nervous system. It affects young people, and a considerable percentage of patients need the help of a wheelchair in 15 years of evolution. Currently, there is not a specific technique for the diagnosis of MS. The detection of oligoclonal IgG bands (OIgGBs) is the most sensitive assay for it, but it is not standardizable, only reference laboratories develop it, and uses cerebrospinal fluid. To obtain this sample, a lumbar puncture is necessary, an invasive proceeding with important side effects. It is important to develop and implement standard assays to obtain a rapid diagnosis because the earlier the treatment, the better the evolution of the disease. There are numerous modifying disease therapies, which delay the progression of the disease, but they have important side effects, and a considerable percentage of patients give up the treatment. In addition, around 40% of MS patients do not respond to the therapy and the disease progresses. Numerous researches have been focused on the characterization of predictive biomarkers of response to treatment, in order to help physicians to decide when to change to a second-line treatment, and then the best therapeutic option. Here, we review the new biomarkers for the diagnosis and response to treatment in MS. We draw attention in a new assay, the detection of serum IgM to phosphatidylcholine, that showed a similar sensitivity as OIgGBs and predicts the response to disease modifying treatments.

While anti-TNF therapies are effective against psoriasis, 30%–50% of patients do not show an adequate response to these rugs. Different candidate-gene pharmacogenetics studies have identified single nucleotide polymorphisms that may predict anti-TNF drugs response in psoriasis. Nevertheless, only one paper has undertaken a pharmacogenomic approach failing to find significant biomarkers of biological drug response along the whole genome. Furthermore, most of the pharmacogenetic candidate biomarkers identified previously have not been confirmed in a different cohort of patients. The objective of this study was to find biomarkers that could predict anti-TNF drugs response along the whole genome and validate biomarkers identified previously. A genome-wide association study (GWAS) was performed using the Human Omni Express-8 v1.2 Beadchips in 243 psoriasis patients treated with anti-TNF drugs. This study was multicentric and did not interfere with clinical practice. Associations between single nucleotide polymorphisms (SNP) and PASI75 (a 75% reduction with respect to baseline PASI) at 3 months were evaluated. Imputation was performed using SNPs with R2 > 0.7. There were two SNPs located in NPFFR2 that were close to the significant threshold of 5 × 10−8. These data suggest that NPFFR2 might be associated with anti-TNF drug response. However, further studies involving a larger cohort of patients are needed in order to confirm these results.

The incidence of obesity and its related disorders has increased dramatically in recent years and has become a pandemic. Adipose tissue is a crucial regulator of these diseases due to its endocrine capacity. Thus, understanding adipose tissue metabolism is essential to finding new effective therapeutic approaches. The “omic” revolution has identified new concepts about the complexity of the signaling pathways involved in the pathophysiology of adipose tissue-associated disorders. Specifically, advances in transcriptomics have allowed its application in clinical practice and primary or secondary prevention. Long non-coding RNAs (lncRNAs) have emerged as critical regulators of adipose tissue since they can modulate gene expression at the epigenetic, transcriptional, and post-transcriptional levels. They interact with DNA, RNA, protein complexes, other noncoding RNAs, and microRNAs to regulate a wide range of physiological and pathological processes. Here, we review the emerging field of lncRNAs, including how they regulate adipose tissue biology, and discuss circulating lncRNAs, which may represent a turning point in the diagnosis and treatment of adipose tissue-associated disorders. We also highlight potential biomarkers of obesity and diabetes that could be considered as therapeutic targets.

Metabolites are the final products of the metabolism and are, therefore, directly related to phenotype. They constitute the metabolome of an organism. The wide diversity of the physicochemical properties of the metabolites in terms of molecular weight, concentration, polarity, volatility, solubility, pKa, and charge makes their analysis a remarkable challenge. With over 220,000 metabolites recorded in the HMDB database, there is no single analytical technique capable of analyzing all of them. Therefore, multiple analytical platforms are required to obtain a comprehensive picture of the metabolome. Among these platforms, mass spectrometry (MS)-based analytical techniques are among the most widely used. Capillary electrophoresis (CE) coupled to MS has been employed to analyze polar/ionic metabolites. Although this technique is not widely used, it has demonstrated unique capabilities for the detection of polar and ionic metabolites that are an essential part of the metabolome and are not usually detected by other techniques. This review highlights the role of CE-MS in untargeted metabolomics, particularly in comparison to the hydrophilic interaction chromatography (HILIC) separation mode. Additionally, we discuss the metabolomics workflow in CE-MS for untargeted metabolomics, including sample treatment and analysis, data treatment, and metabolite annotation. We notably present the annotation tools developed explicitly for CE-MS, as well as some computational alternatives, in-house libraries of relative migration times, effective mobility, MS/MS fragmentation, in-source fragmentation, and the CEU Mass Mediator online tool. Finally, we mention future perspectives of this technique, such as single cell-CE and ion mobility (IM)-MS. Overall, this review shows the important role of CE-MS in the studies of untargeted analysis published in the last five years to approach human diseases.

El asma puede presentar múltiples fenotipos, como el asma alérgico o no alérgico. Su tratamiento es complejo, existiendo pacientes graves que no responden a las medicaciones actualmente disponibles, sufren exacerbaciones frecuentes y presentan comorbilidades como la poliposis nasosinusal. La estratificación de pacientes mediante el uso de biomarcadores permitiría mejorar el tratamiento y descubrir nuevas dianas terapéuticas. Para identificar biomarcadores de estratificación por gravedad, se estudiaron pacientes asmáticos alérgicos estratificados por gravedad utilizando metabolómica y proteómica. Los pacientes con asma grave no controlado presentaron una activación característica de las rutas del ácido araquidónico, la fosfolipasa A2, y la respuesta Th2. Además, para evaluar la contribución del fenotipo alérgico al asma, se realizó un análisis metabolómico de pacientes graves no controlados con y sin alergia. Los pacientes asmáticos alérgicos mostraron una activación de la ruta de la fosfolipasa A2 y una alteración en el perfil de ácidos biliares. Por último, para conocer el papel sistémico y local de la alergia en la poliposis nasal, se estudiaron pacientes con poliposis con y sin alergia utilizando metabolómica de suero y tejido y análisis histológicos. Los pacientes alérgicos presentaron niveles reducidos de lisofosfolípidos, bilirrubina y cortisol; y un mayor número de eosinófilos en sus pólipos.

In the last decades have emerged new technological platforms that allow evaluation of genes, transcripts, proteins, or metabolites of a living being, so-called omics sciences. More importantly, new technics for their integration have provided access to a complete set of information of the current conditions and features of a specific biological sample in a precise moment. Thus, omic sciences are now considered an essential tool for patient stratification in base to their severity, to understand disease progression and to identify new biomarkers. Severe patients, that are out of control, provide an excellent model to understand disease evolution and to identify new intervention and biomarkers strategies. Here we discuss the use of metabolomics to understand severity in allergic diseases in a strategy that opens new insights as well as identify new biological systems relevant for allergy progression. Metabolomics strategies are based in parallel evaluation of different allergy severity models by mean of untargeted analysis that allows the identification of potential biomarkers. Overlapping of different biomarkers in multiple models, provides information of general as well as specific biological systems involved in each model. Later a selected panel of biomarkers will be used in a target method to explore the diagnosis potential to stratify allergic patients.

Metabolomics, understood as a data driven strategy trying to find markers of a situation under study without a priori hypothesis, has rapidly caught the attention and evolved from the simple pattern recognition strategy, which was a great innovation at its origins, to the interest for the final identification of markers responsible for class separation, i.e., from data to knowledge. Due to differences in physico-chemical properties and concentrations of the metabolites, but also due to differences in matrix properties, crossplatform approaches are proving to increase the capability of information. Once more techniques do not compete. This is the scene where capillary electrophoresis (CE) has its niche to provide information mainly on polar or ionic compounds in biological fluids. General advantages and disadvantages of CE for sample fingerprinting will be discussed and methods will be classified depending on the detection system (UV or MS) as this strongly affects all the conditions. Recent developments will be presented in different biological fluids, although urine is without a doubt the preferred sample for CE analysis.

Los trastornos adictivos son enfermedades crónicas que ocurren en la confluencia de tres categorías de factores: biológicos, socio-económico-psicológicos y farmacológicos. Está científicamente aceptado que más del 50% de la causalidad adictiva puede atribuirse al sustrato biológico, por lo que la disponibilidad de biomarcadores convenientemente validados que puedan asociarse con distintos aspectos de los trastornos adictivos puede resultar de gran utilidad para mejorar la prevención, diagnóstico y tratamiento de estas enfermedades. En este contexto, nuestro equipo de investigación ha participado previamente en la identificación de clusterina, midkina, HRH3 y CART como biomarcadores potencialmente asociados a las adicciones. El objetivo del presente trabajo ha sido el de avanzar en la validación de estos mismos biomarcadores confrontando los perfiles adictivos de casi 1.400 sujetos con medidas fenotípicas y genotípicas relevantes. Entre los perfiles adictivos, destacan tanto el binomio caso/control como el nivel de trastorno adictivo en los casos, considerando para ello adictos al tabaco, cocaína y alcohol. Entre las medidas fenotípicas están las concentraciones de las biomoléculas en saliva y suero, y entre las medidas genotípicas, la frecuencia de polimorfismos de un solo nucleótido (SNP) en los genes que codifican para las mismas (CLU, MDK, HRH3 y CARTPT).