843 research outputs found

    Fibronectin rescues estrogen receptor α from lysosomal degradation in breast cancer cells

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    Estrogen receptor α (ERα) is expressed in tissues as diverse as brains and mammary glands. In breast cancer, ERα is a key regulator of tumor progression. Therefore, understanding what activates ERα is critical for cancer treatment in particular and cell biology in general. Using biochemical approaches and superresolution microscopy, we show that estrogen drives membrane ERα into endosomes in breast cancer cells and that its fate is determined by the presence of fibronectin (FN) in the extracellular matrix; it is trafficked to lysosomes in the absence of FN and avoids the lysosomal compartment in its presence. In this context, FN prolongs ERα half-life and strengthens its transcriptional activity. We show that ERα is associated with β1-integrin at the membrane, and this integrin follows the same endocytosis and subcellular trafficking pathway triggered by estrogen. Moreover, ERα+ vesicles are present within human breast tissues, and colocalization with β1-integrin is detected primarily in tumors. Our work unravels a key, clinically relevant mechanism of microenvironmental regulation of ERα signaling.Fil: Sampayo, Rocío Guadalupe. Universidad Nacional de San Martin. Instituto de Nanosistemas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Toscani, Andrés Martin. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Rubashkin, Matthew G.. University of California; Estados UnidosFil: Thi, Kate. Lawrence Berkeley National Laboratory; Estados UnidosFil: Masullo, Luciano Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Violi, Ianina Lucila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Lakins, Jonathon N.. University of California; Estados UnidosFil: Caceres, Alfredo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Hines, William C.. Lawrence Berkeley National Laboratory; Estados UnidosFil: Coluccio Leskow, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Nacional de Luján; ArgentinaFil: Stefani, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Chialvo, Dante Renato. Universidad de Buenos Aires; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología. Centro Internacional de Estudios Avanzados; ArgentinaFil: Bissell, Mina J.. Lawrence Berkeley National Laboratory; Estados UnidosFil: Weaver, Valerie M.. University of California; Estados UnidosFil: Simian, Marina. Universidad Nacional de San Martin. Instituto de Nanosistemas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Oncología "Ángel H. Roffo"; Argentin

    Retargeted adenoviruses for radiation-guided gene delivery

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    The combination of radiation with radiosensitizing gene delivery or oncolytic viruses promises to provide an advantage that could improve the therapeutic results for glioblastoma. X-rays can induce significant molecular changes in cancer cells. We isolated the GIRLRG peptide that binds to radiation-inducible 78 kDa glucose-regulated protein (GRP78), which is overexpressed on the plasma membranes of irradiated cancer cells and tumor-associated microvascular endothelial cells. The goal of our study was to improve tumor-specific adenovirus-mediated gene delivery by selectively targeting the adenovirus binding to this radiation-inducible protein. We employed an adenoviral fiber replacement approach to conduct a study of the targeting utility of GRP78-binding peptide. We have developed fiber-modified adenoviruses encoding the GRP78-binding peptide inserted into the fiber-fibritin. We have evaluated the reporter gene expression of fiber-modified adenoviruses in vitro using a panel of glioma cells and a human D54MG tumor xenograft model. The obtained results demonstrated that employment of the GRP78-binding peptide resulted in increased gene expression in irradiated tumors following infection with fiber-modified adenoviruses, compared with untreated tumor cells. These studies demonstrate the feasibility of adenoviral retargeting using the GRP78-binding peptide that selectively recognizes tumor cells responding to radiation treatment

    miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients

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    PURPOSE: The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. METHODS: A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan-Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs. RESULTS: All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower . We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR-29c and miR-101. CONCLUSIONS: In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer

    A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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    The molecular and cellular origin of human prostate cancer

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    Prostate cancer is the most commonly diagnosed male malignancy. Despite compelling epidemiology, there are no definitive aetiological clues linking development to frequency. Pre-malignancies such as proliferative inflammatory atrophy (PIA) and prostatic intraepithelial neoplasia (PIN) yield insights into the initiating events of prostate cancer, as they supply a background "field" for further transformation. An inflammatory aetiology, linked to recurrent prostatitis, and heterologous signalling from reactive stroma and infiltrating immune cells may result in cytokine addiction of cancer cells, including a tumour-initiating population also known as cancer stem cells (CSCs). In prostate tumours, the background mutational rate is rarely exceeded, but genetic change via profound sporadic chromosomal rearrangements results in copy number variations and aberrant gene expression. In cancer, dysfunctional differentiation is imposed upon the normal epithelial lineage, with disruption/disappearance of the basement membrane, loss of the contiguous basal cell layer and expansion of the luminal population. An initiating role for androgen receptor (AR) is attractive, due to the luminal phenotype of the tumours, but alternatively a pool of CSCs, which express little or no AR, has also been demonstrated. Indolent and aggressive tumours may also arise from different stem or progenitor cells. Castrate resistant prostate cancer (CRPC) remains the inevitable final stage of disease following treatment. Time-limited effectiveness of second-generation anti-androgens, and the appearance of an AR-neuroendocrine phenotype imply that metastatic disease is reliant upon the plasticity of the CSC population, and indeed CSC gene expression profiles are most closely related to those identified in CRPCs

    Conversion of Central Subfield Thickness Measurements of Diabetic Macular Edema Across Cirrus and Spectralis Optical Coherence Tomography Instruments

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    Purpose: Develop equations to convert Cirrus central subfield thickness (CST) to Spectralis CST equivalents and vice versa in eyes with diabetic macular edema (DME). Methods: The DRCR Retina Network Protocol O data were split randomly to train (70% sample) and validate (30% sample) conversion equations. Data from an independent study (CADME) also validated the equations. Bland-Altman 95% limits of agreement between predicted and observed values evaluated the equations. Results: Protocol O included 374 CST scan pairs from 187 eyes (107 participants). The CADME study included 150 scan pairs of 37 eyes (37 participants). Proposed conversion equations are Spectralis = 40.78 + 0.95 × Cirrus and Cirrus = 1.82 + 0.94 × Spectralis regardless of age, sex, or CST. Predicted values were within 10% of observed values in 101 (90%) of Spectralis and 99 (88%) of Cirrus scans in the validation data; and in 136 (91%) of the Spectralis and 148 (99%) of the Cirrus scans in the CADME data. Adjusting for within-eye correlations, 95% of conversions are estimated to be within 17% (95% confidence interval, 14%-21%) of CST on Spectralis and within 22% (95% confidence interval, 18%-28%) of CST on Cirrus. Conclusions: Conversion equations developed in this study allow the harmonization of CST measurements for eyes with DME using a mix of current Cirrus and Spectralis device images. Translational Relevance: The CSTs measured on Cirrus and Spectralis devices are not directly comparable owing to outer boundary segmentation differences. Converting CST values across spectral domain optical coherence tomography instruments should benefit both clinical research and standard care efforts

    Large-Scale Pretrained Model for Self-Supervised Music Audio Representation Learning

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    Self-supervised learning technique is an under-explored topic for music audio due to the challenge of designing an appropriate training paradigm. We hence propose MAP-MERT, a large-scale music audio pre-trained model for general music understanding. We achieve performance that is comparable to the state-of-the-art pre-trained model Jukebox using less than 2% of parameters

    Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity Networks

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    Resting-state functional magnetic resonance imaging (R-fMRI) is dynamic in nature since neural activities constantly change over the time and are dominated by repeating brief activations and deactivations involving many brain regions. Each region participates in multiple brain functions and is part of various functionally distinct but spatially overlapping networks. Functional connectivity computed as correlations over the entire time series always overlooks inter-region interactions that often occur repeatedly and dynamically in time, limiting its application to disease diagnosis
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