Reprogramación celular y cáncer

máticas hasta el estado pluripotente. El estudio de este proceso aporta información muy valiosa sobre la identidad celular y resulta de gran utilidad como herramienta para la investigación biomédica de distintas patologías. Los procesos de reprogramación celular y transformación oncogénica comparten muchas similitudes. Sin embargo, las nuevas identidades celulares establecidas mediante estos procesos son incompatibles entre sí. Las células tumorales presentan barreras, todavía no identificadas, que se oponen a la reprogramación. La identificación de estas barreras podría aportar nuevos aspectos acerca de la identidad tumoral. Además, la reprogramación de estas células generaría valiosos modelos para la investigación oncológica. En este trabajo analizamos el efecto de la activación oncogénica sobre la reprogramación celular. Nuestros resultados muestran cómo la activación oncogénica combinada con los factores de reprogramación, en un contexto no tumoral, favorece la generación de células iPS mediante el establecimiento de un estado más plástico, tanto in vitro como in vivo. Por el contrario, cuando esta misma activación oncogénica se produce en un contexto que permite la adquisición del estado transformado, se bloquea la reprogramación celular al imponerse nuevas barreras características de las células tumorales. Estos resultados indican que el establecimiento de una nueva identidad celular transformada genera un estado que es incompatible con la reprogramación. La expresión de los factores de reprogramación en estas células pone en riesgo esta nueva identidad, lo que provoca una disminución de la capacidad tumorogénica. En conjunto, esta tesis muestra el efecto diferencial de la activación oncogénica sobre el proceso de reprogramación en función del contexto en el que se produzca

Adult Sox2+ stem cell exhaustion in mice results in cellular senescence and premature aging

Aging is characterized by a gradual functional decline of tissues with age. Adult stem and progenitor cells are responsible for tissue maintenance, repair, and regeneration, but during aging, this population of cells is decreased or its activity is reduced, compromising tissue integrity and causing pathologies that increase vulnerability, and ultimately lead to death. The causes of stem cell exhaustion during aging are not clear, and whether a reduction in stem cell function is a cause or a consequence of aging remains unresolved. Here, we took advantage of a mouse model of induced adult Sox2+ stem cell depletion to address whether accelerated stem cell depletion can promote premature aging. After a short period of partial repetitive depletion of this adult stem cell population in mice, we observed increased kyphosis and hair graying, and reduced fat mass, all of them signs of premature aging. It is interesting that cellular senescence was identified in kidney after this partial repetitive Sox2+ cell depletion. To confirm these observations, we performed a prolonged protocol of partial repetitive depletion of Sox2+ cells, forcing regeneration from the remaining Sox2+ cells, thereby causing their exhaustion. Senescence specific staining and the analysis of the expression of genetic markers clearly corroborated that adult stem cell exhaustion can lead to cellular senescence induction and premature agingWork in the laboratory of M.C. is funded by an ISCIII and EU‐FEDER grant (PI14/00554)S

Context-dependent impact of RAS oncogene expression on cellular reprogramming to pluripotency

Induction of pluripotency in somatic cells with defined genetic factors has been successfully used to investigate the mechanisms of disease initiation and progression. Cellular reprogramming and oncogenic transformation share common features; both involve undergoing a dramatic change in cell identity, and immortalization is a key step for cancer progression that enhances reprogramming. However, there are very few examples of complete successful reprogramming of tumor cells. Here we address the effect of expressing an active oncogene, RAS, on the process of reprogramming and found that, while combined expression with reprogramming factors enhanced dedifferentiation, expression within the context of neoplastic transformation impaired reprogramming. RAS induces expression changes that promote loss of cell identity and acquisition of stemness in a paracrine manner and these changes result in reprogramming when combined with reprogramming factors. When cells carry cooperating oncogenic defects, RAS drives cells into an incompatible cellular fate of malignancy.A.F. is an FPU predoctoral fellow from MECD; P.P. and J.M.V. are predoctoral fellows from Xunta de Galicia; F.T.-M. is a postdoctoral fellow from CONACYT (cvu 268632). M.C. is a ‘‘Miguel Servet II’’ investigator (CPII16/00015). Work in the laboratory of M.C. is funded by an ISCIII and EU-FEDER grant (PI14/00554). Work in the laboratory of A.V. is funded by Xunta de Galicia (ED431B 2016) and MINECO (MAT2017-89678-R; cofinanced with FEDER Funds)S

Circulating Tumor Cells Characterization Revealed TIMP1 as a Potential Therapeutic Target in Ovarian Cancer

Background: Recent studies showed a relevant role of hematogenous spread in ovarian cancer and the interest of circulating tumor cells (CTCs) monitoring as a prognosis marker. The aim of the present study was the characterization of CTCs from ovarian cancer patients, paying special attention to cell plasticity characteristics to better understand the biology of these cells. Methods: CTCs isolation was carried out in 38 patients with advanced high-grade serous ovarian cancer using in parallel CellSearch and an alternative EpCAM-based immunoisolation followed by RT-qPCR analysis to characterize these cells. Results: Epithelial CTCs were found in 21% of patients, being their presence higher in patients with extraperitoneal metastasis. Importantly, this population was characterized by the expression of epithelial markers as MUC1 and CK19, but also by genes associated with mesenchymal and more malignant features as TIMP1, CXCR4 and the stem markers CD24 and CD44. In addition, we evidenced the relevance of TIMP1 expression to promote tumor proliferation, suggesting its interest as a therapeutic target. Conclusions: Overall, we evidenced the utility of the molecular characterization of EpCAM+ CTCs from advanced ovarian cancer patients to identify biomarkers with potential applicability for disseminated disease detection and as therapeutic targets such as TIMP1Part of this research was supported by CIBERONC funds (CB16/12/00328)S

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algo-rithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits.Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12).The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non -treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables.Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified.The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-alpha levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58).In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-alpha (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption.Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Science, Innovation and Universities (PI15/00662, PI15/0039, PI15/00204, PI19/01040), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/Generalitat de Catalunya for institutional support. Work partially supported by Biobank HUB-ICO-IDIBELL, integrated in the Spanish Biobank Network and funded by Instituto de Salud Carlos III (PT17/0015/0024) and by Xarxa Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. YSC work is supported by the FPI predoctoral grant (FPI 2016/17) from Universidad Autonoma de Madrid. VS received an Intensification of the Research Activity Grant from the Instituto de Salud Carlos III (INT21/00055) during 202

Identification and characterization of Cardiac Glycosides as senolytic compounds

Compounds with specific cytotoxic activity in senescent cells, or senolytics, support the causal involvement of senescence in aging and offer therapeutic interventions. Here we report the identification of Cardiac Glycosides (CGs) as a family of compounds with senolytic activity. CGs, by targeting the Na+/K+ATPase pump, cause a disbalanced electrochemical gradient within the cell causing depolarization and acidification. Senescent cells present a slightly depolarized plasma membrane and higher concentrations of H+, making them more susceptible to the action of CGs. These vulnerabilities can be exploited for therapeutic purposes as evidenced by the in vivo eradication of tumors xenografted in mice after treatment with the combination of a senogenic and a senolytic drug. The senolytic effect of CGs is also effective in the elimination of senescence-induced lung fibrosis. This experimental approach allows the identification of compounds with senolytic activity that could potentially be used to develop effective treatments against age-related diseases.We thank Matthias Drosten, Alejo Efeyan and Sean Morrison for plasmids. F.T-M. is a postdoctoral fellow from CONACYT (cvu 268632); P.P. is a predoctoral fellow from Xunta de Galicia; M.C. is a "Miguel Servet II" investigator (CPII16/00015). P.P.-R. receives support from a program by the Deputacion de Coruna (BINV-CS/2019). Work in the laboratory of M.C. is funded by grant RTI2018-095818-B-100 (MCIU/AEI/FEDER, UE). P.J.F.-M. is funded by the IMDEA Food Institute, the Ramon Areces Foundation, (CIVP18A3891), and a Ramon y Cajal Award (MICINN) (RYC-2017-22335). M.P.I. is funded by Talento Modalidad-1 Program Grant, Madrid Regional Government (#2018-T1/BIO-11262). F.P. was funded by a Long Term EMBO Fellowship (ALTF-358-2017) and F.H-G. was funded by the PhD4MD Programme of the IRB, Hospital Clinic and IDIBAPS. Work in the laboratory of M.S. was funded by the IRB and by grants from the Spanish Ministry of Economy co-funded by the European Regional Development Fund (ERDF) (SAF2013-48256-R), the European Research Council (ERC-2014-AdG/669622), and "laCaixa" Foundation.S

Perfusion-weighted software written in Python for DSC-MRI analysis

IntroductionDynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes.MethodsThe DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood–brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature.ResultsA total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland–Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF.ConclusionAn open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented

Extracellular Vesicles’ Genetic Cargo as Noninvasive Biomarkers in Cancer: A Pilot Study Using ExoGAG Technology

The two most developed biomarkers in liquid biopsy (LB)—circulating tumor cells and circulating tumor DNA—have been joined by the analysis of extracellular vesicles (EVs). EVs are lipid-bilayer enclosed structures released by all cell types containing a variety of molecules, including DNA, mRNA and miRNA. However, fast, efficient and a high degree of purity isolation technologies are necessary for their clinical routine implementation. In this work, the use of ExoGAG, a new easy-to-use EV isolation technology, was validated for the isolation of EVs from plasma and urine samples. After demonstrating its efficiency, an analysis of the genetic material contained in the EVs was carried out. Firstly, the sensitivity of the detection of point mutations in DNA from plasma EVs isolated by ExoGAG was analyzed. Then, a pilot study of mRNA expression using the nCounter NanoString platform in EV-mRNA from a healthy donor, a benign prostate hyperplasia patient and metastatic prostate cancer patient plasma and urine samples was performed, identifying the prostate cancer pathway as one of the main ones. This work provides evidence for the value of using ExoGAG for the isolation of EVs from plasma and urine samples, enabling downstream applications of the analysis of their genetic cargo

Extracellular Vesicles’ Genetic Cargo as Noninvasive Biomarkers in Cancer: A Pilot Study Using ExoGAG Technology

The two most developed biomarkers in liquid biopsy (LB)—circulating tumor cells and circulating tumor DNA—have been joined by the analysis of extracellular vesicles (EVs). EVs are lipid-bilayer enclosed structures released by all cell types containing a variety of molecules, including DNA, mRNA and miRNA. However, fast, efficient and a high degree of purity isolation technologies are necessary for their clinical routine implementation. In this work, the use of ExoGAG, a new easy-to-use EV isolation technology, was validated for the isolation of EVs from plasma and urine samples. After demonstrating its efficiency, an analysis of the genetic material contained in the EVs was carried out. Firstly, the sensitivity of the detection of point mutations in DNA from plasma EVs isolated by ExoGAG was analyzed. Then, a pilot study of mRNA expression using the nCounter NanoString platform in EV-mRNA from a healthy donor, a benign prostate hyperplasia patient and metastatic prostate cancer patient plasma and urine samples was performed, identifying the prostate cancer pathway as one of the main ones. This work provides evidence for the value of using ExoGAG for the isolation of EVs from plasma and urine samples, enabling downstream applications of the analysis of their genetic cargo