1,141 research outputs found

    Metabolomic profile of neuroendocrine tumors (NETs) identifies methionine, porphyrin and tryptophan metabolism as key dysregulated pathways associated with patient survival

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    Objective: Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways.Design and Methods: Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA).Results: Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs.Conclusions: We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients

    Metabolomics Unraveling the Biochemical Insight of High Altitude Diseases and Sepsis A Narrative Review

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    High altitude diseases and sepsis may seem distinct at first glance, but there are underlying physiological similarities that lie in their responses to hypoxia, tissue dysfunction, inflammation, and multi-organ failure conditions. Understanding these commonalities can help medical professionals draw parallels between them and apply relevant knowledge to improve patient care and treatment.In this direction,a literature review of metabolomics-based studies has been done for high-altitude diseases and sepsis, and the panel of common disease-related metabolic markers and associated pathways areunraveled. Themetabolic pathways found dysregulated in both conditions are amino acid metabolism, lipid metabolism, energy metabolism, inflammatory response-related metabolism, bile acid metabolism, and purine and pyrimidine metabolism

    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

    Interindividualidad asociada al metabolismo de polifenoles por la microbiota intestinal : nuevos metabotipos y sus agrupaciones, metabolitos y bacterias, y posibles implicaciones en salud

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    Los (poli)fenoles son compuestos bioactivos presentes en los alimentos vegetales. Aunque diversos estudios clínicos y de intervención han evidenciado efectos preventivos de dietas ricas en (poli)fenoles, como la dieta mediterránea, frente a enfermedades cardiometabólicas, inflamatorias y neurodegenerativas, estos efectos no se producen en todos los individuos por igual, fundamentalmente por la variabilidad interindividual en su metabolización. Uno de los principales causantes de dicha variabilidad en el metabolismo y bioactividad de los (poli)fenoles es la composición y funcionalidad de la microbiota intestinal (MI), distinta en cada individuo. Los individuos pueden agruparse según “metabotipos”, es decir, por la capacidad diferencial de su MI para metabolizar algunos (poli)fenoles como las isoflavonas (soja) hasta equol (“productores o no de equol”) y los elagitaninos (ETs) (granada, bayas y nueces) hasta urolitinas (UROs) (“metabotipos de UROs: UMA, UMB (según UROs producidas) o UM0 (no productores)”. También se han identificado algunas bacterias metabolizadoras de estos (poli)fenoles como ciertas especies de Adlercreutzia y Slackia, productoras de equol, y de Gordonibacter y Ellagibacter, productoras de algunas UROs. La bioactividad de los (poli)fenoles también está condicionada por su baja biodisponibilidad, pues además del metabolismo bacteriano, también son metabolizados en el organismo hasta conjugados de fase-II como sulfatos y glucurónidos, mucho menos bioactivos que sus precursores. En esta Tesis Doctoral, los objetivos incluyeron investigar la existencia de nuevos metabotipos y metabolitos de (poli)fenoles relacionados con el metabolismo microbiano, nuevas bacterias implicadas en las transformaciones de las UROs y su uso para la replicación in vivo de los metabotipos de UROs. También, se exploró la presencia de diferentes combinaciones de metabotipos en un mismo individuo, su prevalencia, la MI asociada, su funcionalidad y sus distintivas redes microbianas. Además, se investigó el uso de exosomas de leche (EXOs-L) para encapsular y vehiculizar resveratrol (RSV) y curcumina (CUR) hasta tejidos sistémicos y evaluar el efecto anticancerígeno con las concentraciones detectadas. Por último, se exploró la posible encapsulación y transporte de (poli)fenoles y/o sus metabolitos en vesículas extracelulares con exosomas (E-EVs) humanos. Se realizaron 2 estudios de intervención humana, uno de estratificación de metabotipos y efecto en la MI (7 días), y otro farmacocinético (transporte en E-EVs). En ambos, los participantes consumieron cápsulas con extractos ricos en (poli)fenoles. También, 2 estudios en animales: en uno, las ratas consumieron dos fuentes de (poli)fenoles y bacterias productoras de UROs y en el otro, RSV y CUR encapsulados en EXOs-L. También se realizó 1 estudio in vitro con cultivos bacterianos y otro en modelos celulares de cáncer de mama. Se analizaron muestras de sangre, exosomas, orina, heces, e incubaciones in vitro mediante metabolómica (HPLC-DAD-ESI-Q-MS, UPLC-ESI-QTOF-MS y GC-MS), análisis de la MI por secuenciación (16S ARNr) y reacción en cadena de la polimerasa a tiempo real (qPCR), y análisis bioinformáticos y estadísticos, entre otras técnicas y enfoques. Los principales resultados obtenidos han sido: identificación de un nuevo metabolito (4-hidroxidibencilo) derivado del metabolismo microbiano de RSV; nuevos metabotipos de la MI asociados al metabolismo de RSV (“productores o no de lunularina”); identificación de 10 agrupaciones diferentes de los metabotipos, y caracterización de su MI asociada; identificación de la nueva Uro-G, y aislamiento e identificación de nuevas bacterias implicadas en el metabolismo de UROs (varias especies de Enterocloster), y caracterización de consorcios bacterianos responsables de los UMs; replicación in vivo del perfil de producción de UROs, administrando dichos consorcios y evaluando su seguridad; uso de EXOs-L cargados con RSV y CUR, aumentando su biodisponibilidad y actividad anticancerígena; y finalmente, identificación de E-EVs humanos como transportadores de RSV y sus metabolitos en sangre. Estos resultados contribuyen de forma pionera a identificar estrategias personalizadas, dirigidas a mejorar los efectos saludables de los (poli)fenoles.(Poly)phenols are bioactive compounds present in plant-foods. Clinical and intervention studies have shown the preventive effects of (poly)phenol-rich diets, such as the Mediterranean diet, against cardiometabolic, inflammatory, and neurodegenerative diseases. However, these effects do not occur equally in all individuals, mainly due to the interindividual variability in the metabolism of (poly)phenols. One of the primary factors contributing to the variability in the metabolism and bioactivity of (poly)phenols is the composition and functionality of the gut microbiota (GM), which varies among individuals. Individuals can be grouped according to “metabotypes”, based on their GM's differential capacity to metabolize certain (poly)phenols, such as isoflavones (soy) to equol (“equol-producers or non-producers”) and ellagitannins (pomegranate, berries, and nuts) to urolithins (UROs) (“URO metabotypes”: UMA, UMB (according to UROs produced) or UM0 (UROs non-producers). Some bacteria that metabolize these (poly)phenols have also been identified, such as certain species of Adlercreutzia and Slackia, which produce equol, and Gordonibacter and Ellagibacter, which produce some UROs. The bioactivity of (poly)phenols is also conditioned by their low bioavailability since, in addition to bacterial metabolism, they are also metabolized by the body’s cells into phase-II conjugates such as sulfates and glucuronides, which exhibit much lower activity than their precursors. In this Doctoral Thesis, our objectives included to investigating the existence of new metabotypes and metabolites of (poly)phenols related to microbial metabolism, new bacteria involved in UROs metabolism, and their use for in vivo replication of UROs metabotypes. Also, the presence of different combinations of metabotypes within the same individual, their prevalence in the population, their associated GM composition, functionality, and distinctive microbial networks were explored. In addition, we investigated the use of milk exosomes (EXOs-L) to encapsulate and transport resveratrol (RSV) and curcumin (CUR) to systemic tissues, and evaluated their anticancer effects at detected concentrations. Finally, the possible encapsulation and transport of (poly)phenols and(or) their metabolites in human exosome-containing EVs (E-EVs) were also explored. Human, animal, and in vitro studies were carried out to achieve these goals. Two human intervention studies were performed: stratification of metabotypes and GM modulation (7 days), as well as a pharmacokinetic study (transport in E-EVs). The participants consumed capsules containing (poly)phenol-rich plant extracts in both trials. Two animal studies: one where rats consumed two sources of (poly)phenols and URO-producing bacteria, and another, where they were administered with RSV and CUR, encapsulated in EXOs-L. Finally, one in vitro study with bacterial cultures and another with breast cancer cell models were also performed. Blood samples, exosomes, urine, feces, and in vitro incubations were analyzed using metabolomics (HPLC-DAD-ESI-Q-MS, UPLC-ESI-QTOF-MS, and GC-MS), GM analysis by genomic sequencing (16S rRNA) and real-time polymerase chain reaction (qPCR), as well as bioinformatics and statistical analysis were applied, among other techniques and approaches. The main findings were: identification of a new metabolite (4-hydroxydibenzyl) derived from the gut microbial metabolism of RSV; new GM metabotypes associated with RSV metabolism (“lunularin-producers or non-producers”); identification of 10 different clusters of existing metabotypes, as well as their distinctive associated GM; identification of the new Uro-G, and the isolation and identification of new bacteria (several Enterocloster species) involved in the metabolism of UROs and characterization of bacterial consortia responsible for UMs; in vivo replication of the metabolic profile of UROs production using said consortia and evaluation of their safety; use of EXOs-L loaded with RSV and CUR, increasing their bioavailability and anticancer activity; and finally, identification of human E-EVs as new carriers of RSV and its metabolites through the bloodstream. The research outcomes of this Thesis contribute in a pioneering way to identify personalized strategies aimed at improving the health effects of (poly)phenols

    30th European Congress on Obesity (ECO 2023)

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

    KODAMA exploratory analysis in metabolic phenotyping

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    KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research

    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

    Exploring the gut microbiota of breast cancer patients

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    Host-associated microbial communities play a key role in health and disease, and more recently there has been a growing appreciation for how particular microbes and microbial ‘signatures’ are associated with different cancers. However, breast cancer remains an understudied cancer type, and there is a pressing need to define, if and how, the gut microbiota maybe be linked to disease progression and treatment outcomes. To investigate the gut microbiota and breast cancer, two clinical cohorts were profiled (using a range of sequencing and bioinformatics approaches) and additional mechanistic in vitro and in vivo studies were also undertaken. First, a local Norfolk cohort was established – BEAM, with the aim of longitudinally profiling newly diagnosed breast cancer patients (1 control and 35 breast cancer patients, as of 30 June 2023), however study recruitment was severely impacted due to the SARS-Cov-2 pandemic. My initial analysis indicated no significant shifts in microbiome profiles in the limited number of patients profiled, however I was able to establish a large culture collection through untargeted culturing. I obtained 298 strains from 50 different species which were whole genome sequenced and phylogenetically characterised. This work also led to the discovery and detailed description of one novel genus and one novel species - Allocoprobacillus halotolerans gen. nov., sp. nov and Coprobacter tertius sp. nov. Concurrent to BEAM, the oral and gut microbiota samples from a phase 2a clinical trial (KELLY) that had been completed were processed, sequenced, and analysed which led to the creation of the CALADRIO study. The KELLY trial had one arm where all patients received treatment, a chemotherapeutic and immunotherapeutic. Overall, treatment did not cause significant gut or oral microbiota perturbations, which is usually indicative of drug-related microbiota toxicity. Differential analysis indicated that clinical benefit was driven, in part, by gut-associated Bacteroides fragilis. Further in vitro studies indicated a product present in the cell-free supernatant of B. fragilis led to greater cellular stress in breast cancer cells, but it did not result in complete cell death. Bifidobacterium, generally considered a beneficial gut-associated bacterium, was consistently in the top ten most abundant genera of the gut microbiota in the BEAM and CALADRIO study. Thus, to define if Bifidobacterium was mechanistically associated with breast cancer outcomes, a Bifidobacterium longum subsp. longum isolate was selected and used as a live oral supplementation in a murine breast cancer model that was also treated with chemotherapy (cyclophosphamide). Oral supplementation resulted in larger primary tumours than cyclophosphamide alone suggesting that oral supplementation interfered with treatment efficacy. Genomic screening of the isolate showed that it possessed aldehyde dehydrogenase which is known to inactivate cyclophosphamide. These data allowed me to explore how the gut microbiota of breast cancer patients may link to treatment outcomes and indicated both positive (e.g., B. fragilis) and negative (e.g., B. longum subsp. longum) impacts. Translating it into the clinic, such findings could provide avenues for improving efficacy of anti-cancer therapeutics. To test these further in vivo studies could be conducted to determine how candidate bacterial strains could influence the immune system in the context of breast cancer and building on those findings in vitro studies would investigate the intricacies of the gut-immune axis. Overall, my thesis outputs highlight the complex interactions between the microbiota and their host, and suggest new avenues for biomarker and therapy development, particularly in breast cancer
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