Publications

by Keyword: Tau


By year:[ 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 ]

Matamoros-Angles, A., Gayosso, L. M., Richaud-Patin, Y., Di Domenico, A., Vergara, C., Hervera, A., Sousa, A., Fernández-Borges, N., Consiglio, A., Gavín, R., López de Maturana, R., Ferrer, I., López de Munain, A., Raya, A., Castilla, J., Sánchez-Pernaute, R., Del Río, J. A., (2017). iPS cell cultures from a Gerstmann-Sträussler-Scheinker patient with the Y218N PRNP mutation recapitulate tau pathology Molecular Neurobiology online

Gerstmann-Sträussler-Scheinker (GSS) syndrome is a fatal autosomal dominant neurodegenerative prionopathy clinically characterized by ataxia, spastic paraparesis, extrapyramidal signs and dementia. In some GSS familiar cases carrying point mutations in the PRNP gene, patients also showed comorbid tauopathy leading to mixed pathologies. In this study we developed an induced pluripotent stem (iPS) cell model derived from fibroblasts of a GSS patient harboring the Y218N PRNP mutation, as well as an age-matched healthy control. This particular PRNP mutation is unique with very few described cases. One of the cases presented neurofibrillary degeneration with relevant Tau hyperphosphorylation. Y218N iPS-derived cultures showed relevant astrogliosis, increased phospho-Tau, altered microtubule-associated transport and cell death. However, they failed to generate proteinase K-resistant prion. In this study we set out to test, for the first time, whether iPS cell-derived neurons could be used to investigate the appearance of disease-related phenotypes (i.e, tauopathy) identified in the GSS patient.

Keywords: Cellular prion protein, Gerstmann-Sträussler-Scheinker, Induced pluripotent stem cells, Tau


Vergara, C., Ordóñez-Gutiérrez, L., Wandosell, F., Ferrer, I., del Río, J. A., Gavín, R., (2015). Role of PrPC expression in tau protein levels and phosphorylation in alzheimer's disease evolution Molecular Neurobiology 51, (3), 1206-1220

Alzheimer's disease (AD) is characterized by the presence of amyloid plaques mainly consisting of hydrophobic β-amyloid peptide (Aβ) aggregates and neurofibrillary tangles (NFTs) composed principally of hyperphosphorylated tau. Aβ oligomers have been described as the earliest effectors to negatively affect synaptic structure and plasticity in the affected brains, and cellular prion protein (PrPC) has been proposed as receptor for these oligomers. The most widely accepted theory holds that the toxic effects of Aβ are upstream of change in tau, a neuronal microtubule-associated protein that promotes the polymerization and stabilization of microtubules. However, tau is considered decisive for the progression of neurodegeneration, and, indeed, tau pathology correlates well with clinical symptoms such as dementia. Different pathways can lead to abnormal phosphorylation, and, as a consequence, tau aggregates into paired helical filaments (PHF) and later on into NFTs. Reported data suggest a regulatory tendency of PrPC expression in the development of AD, and a putative relationship between PrPC and tau processing is emerging. However, the role of tau/PrPC interaction in AD is poorly understood. In this study, we show increased susceptibility to Aβ-derived diffusible ligands (ADDLs) in neuronal primary cultures from PrPC knockout mice, compared to wild-type, which correlates with increased tau expression. Moreover, we found increased PrPC expression that paralleled with tau at early ages in an AD murine model and in early Braak stages of AD in affected individuals. Taken together, these results suggest a protective role for PrPC in AD by downregulating tau expression, and they point to this protein as being crucial in the molecular events that lead to neurodegeneration in AD.

Keywords: Aβ oligomers, Alzheimer's disease, Cellular prion protein, Microtubule-associated protein tau


Giraldo, B. F., Chaparro, J. A., Caminal, P., Benito, S., (2013). Characterization of the respiratory pattern variability of patients with different pressure support levels Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 3849-3852

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation


Giraldo, B.F., Gaspar, B.W., Caminal, P., Benito, S., (2012). Analysis of roots in ARMA model for the classification of patients on weaning trials Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 698-701

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials


Chaparro, J.A., Giraldo, B.F., Caminal, P., Benito, S., (2012). Performance of respiratory pattern parameters in classifiers for predict weaning process Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 4349-4352

Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (TI), expiratory time (TE), breathing cycle duration (TTot), tidal volume (VT), inspiratory fraction (TI/TTot), half inspired flow (VT/TI), and rapid shallow index (f/VT), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers


Guix, F. X., Ill-Raga, G., Bravo, R., Nakaya, T., de Fabritiis, G., Coma, M., Miscione, G. P., Villa-Freixa, J., Suzuki, T., Fernàndez-Busquets, X., Valverde, M. A., de Strooper, B., Munoz, F. J., (2009). Amyloid-dependent triosephosphate isomerase nitrotyrosination induces glycation and tau fibrillation Brain 132, (5), 1335-1345

Alzheimer's disease neuropathology is characterized by neuronal death, amyloid beta-peptide deposits and neurofibrillary tangles composed of paired helical filaments of tau protein. Although crucial for our understanding of the pathogenesis of Alzheimer's disease, the molecular mechanisms linking amyloid beta-peptide and paired helical filaments remain unknown. Here, we show that amyloid beta-peptide-induced nitro-oxidative damage promotes the nitrotyrosination of the glycolytic enzyme triosephosphate isomerase in human neuroblastoma cells. Consequently, nitro-triosephosphate isomerase was found to be present in brain slides from double transgenic mice overexpressing human amyloid precursor protein and presenilin 1, and in Alzheimer's disease patients. Higher levels of nitro-triosephosphate isomerase (P < 0.05) were detected, by Western blot, in immunoprecipitates from hippocampus (9 individuals) and frontal cortex (13 individuals) of Alzheimer's disease patients, compared with healthy subjects (4 and 9 individuals, respectively). Triosephosphate isomerase nitrotyrosination decreases the glycolytic flow. Moreover, during its isomerase activity, it triggers the production of the highly neurotoxic methylglyoxal (n = 4; P < 0.05). The bioinformatics simulation of the nitration of tyrosines 164 and 208, close to the catalytic centre, fits with a reduced isomerase activity. Human embryonic kidney (HEK) cells overexpressing double mutant triosephosphate isomerase (Tyr164 and 208 by Phe164 and 208) showed high methylglyoxal production. This finding correlates with the widespread glycation immunostaining in Alzheimer's disease cortex and hippocampus from double transgenic mice overexpressing amyloid precursor protein and presenilin 1. Furthermore, nitro-triosephosphate isomerase formed large beta-sheet aggregates in vitro and in vivo, as demonstrated by turbidometric analysis and electron microscopy. Transmission electron microscopy (TEM) and atomic force microscopy studies have demonstrated that nitro-triosephosphate isomerase binds tau monomers and induces tau aggregation to form paired helical filaments, the characteristic intracellular hallmark of Alzheimer's disease brains. Our results link oxidative stress, the main etiopathogenic mechanism in sporadic Alzheimer's disease, via the production of peroxynitrite and nitrotyrosination of triosephosphate isomerase, to amyloid beta-peptide-induced toxicity and tau pathology.

Keywords: Alzheimer's disease, Amyloid β-peptide, Tau protein, Triosephosphate isomerase, Peroxynitrite


Comments are closed