Multifunctional ligands for application in Alzheimer’s Disease: molecular modelling and biological evaluation

Projecte de recerca elaborat a partir d’una estada a la University of British Columbia, Canadà, entre 2010 i 2012 La malaltia d'Alzheimer (MA) representa avui la forma més comuna de demència en la població envellida. Malgrat fa 100 anys que va ser descoberta, encara avui no existeix cap tractament preventiu i/o curatiu ni cap agent de diagnòstic que permeti valorar quantitativament l'evolució d'aquesta malaltia. L'objectiu en el que s'emmarca aquest treball és contribuir a aportar solucions al problema de la manca d'agents terapèutics i de diagnosi, unívocs i rigorosos, per a la MA. Des del camp de la química bioinorgànica és fàcil fixar-se en l'excessiva concentració d'ions Zn(II) i Cu(II) en els cervells de malalts de MA, plantejar-se la seva utilització com a dianes terapèutica i, en conseqüència, cercar agents quelants que evitin la formació de plaques senils o contribueixin a la seva dissolució. Si bé aquest va ser el punt de partida d’aquest projecte, els múltiples factors implicats en la patogènesi de la MA fan que el clàssic paradigma d’ ¨una molècula, una diana¨ limiti la capacitat de la molècula de combatre aquesta malaltia tan complexa. Per tant, un esforç considerable s’ha dedicat al disseny d’agentsmultifuncionals que combatin els múltiples factors que caracteritzen el desenvolupament de la MA. En el present treball s’han dissenyat agents multifuncionals inspirats en dos esquelets moleculars ben establers i coneguts en el camp de la química medicinal: la tioflavina-T (ThT) i la deferiprona (DFP). La utilització de tècniques in silico que inclouen càlculs farmacocinètics i modelatge molecular ha estat un procés cabdal per a l’avaluació dels millors candidats en base als següents requeriments: (a) compliment de determinades propietats farmacocinètiques que estableixin el seu possible ús com a fàrmac (b) hidrofobicitat adequada per travessar la BBB i (c) interacció amb el pèptid Aen solució.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

Methylglyoxal Produced by Amyloid- Peptide-Induced Nitrotyrosination of Triosephosphate Isomerase Triggers Neuronal Death in Alzheimer’s Disease

Amyloid-β peptide (Aβ) aggregates induce nitro-oxidative stress, contributing to the characteristic neurodegeneration found in Alzheimer's disease (AD). One of the most strongly nitrotyrosinated proteins in AD is the triosephosphate isomerase (TPI) enzyme which regulates glycolytic flow, and its efficiency decreased when it is nitrotyrosinated. The main aims of this study were to analyze the impact of TPI nitrotyrosination on cell viability and to identify the mechanism behind this effect. In human neuroblastoma cells (SH-SY5Y), we evaluated the effects of Aβ42 oligomers on TPI nitrotyrosination. We found an increased production of methylglyoxal (MG), a toxic byproduct of the inefficient nitro-TPI function. The proapoptotic effects of Aβ42 oligomers, such as decreasing the protective Bcl2 and increasing the proapoptotic caspase-3 and Bax, were prevented with a MG chelator. Moreover, we used a double mutant TPI (Y165F and Y209F) to mimic nitrosative modifications due to Aβ action. Neuroblastoma cells transfected with the double mutant TPI consistently triggered MG production and a decrease in cell viability due to apoptotic mechanisms. Our data show for the first time that MG is playing a key role in the neuronal death induced by Aβ oligomers. This occurs because of TPI nitrotyrosination, which affects both tyrosines associated with the catalytic center.

Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group di®erences and within-subject variability. We found that ICA diminished Leave-One- Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group di®erence. More interestingly, ICA reduced the inter-subject variability within each group (¾ = 2:54 in the ± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer- roni correction). Additionally, we present a method to limit the impact of human error (' 13:8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These ¯ndings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease

Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients and healthy subjects is increased using the proposed ratio.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Funcions de les molècules associades a la mielina i la proteïna priònica cel•lular (PrPc) en neurodegeneració (Malaltia d’Alzheimer) i neuroinflamació (Esclerosi Múltiple)

Les proteïnes associades a la mielina (MAIS), Nogo-A, MAG i OMgp, són molècules que presenten una capacitat inhibitòria molt important per el recreixement axonal i la neuroreparació després de lesió. No obstant des de fa anys les seves funcions han estat ampliades i s’han involucrat en diferents processos degeneratius del sistema nerviós o en processos neuroinflamatoris del sistema nerviós central i el perifèric com ara l'Escleresi Múltiple (MS). La base neurobiològica d’indicadors moleculars que són responsables del dany axonal en MS segueixen sense estar plenament descrits. Recentment s’ha publicat que el mecanisme de senyalització Nogo-A pot regir els primers canvis de la desmielinització immunomediada del sistema nerviós central en el model animal de MS, l’encefalomielitis autoimmune experimental (EAE). De la mateixa forma la proteïna priònica cel•lular és una proteïna que s’ha associat majoritàriament a malalties espongiformes, però que recentment s’ha vinculat (no sense controvèrsia) amb la seva possible relació amb la Malaltia d'Alzheimer (AD), ja que seria capaç de reclutar els oligòmers d’Aβ (ADDLs), els quals correlacionen millor amb el grau de demència, i amb els que interacciona directament, actuant així com un possible mediador de la fosforilació de tau en la malaltia. No obstant, les funcions de les MAIS i de la PrPc en aquests models de la malaltia no estan clarament definits i, per altra banda, es desconeixen els mecanismes de senyalització implicats, no descartant de forma clara el component neural i l’immune.

Vitamin E but not 17B-estradiol protect against vascular toxicity induced by B-amyloid wild type and the Dutch amyploid variant

Amyloid β-peptide (Aβ) fibril deposition on cerebral vessels produces cerebral amyloid angiopathy that appears in the majority of Alzheimer's disease patients. An early onset of a cerebral amyloid angiopathy variant called hereditary cerebral hemorrhage with amyloidosis of the Dutch type is caused by a point mutation in Aβ yielding AβGlu22→Gln. The present study addresses the effect of amyloid fibrils from both wild-type and mutated Aβ on vascular cells, as well as the putative protective role of antioxidants on amyloid angiopathy. For this purpose, we studied the cytotoxicity induced by Aβ1–40 Glu22→Gln and Aβ1–40 wild-type fibrils on human venule endothelial cells and rat aorta smooth muscle cells. We observed that AβGlu22→Gln fibrils are more toxic for vascular cells than the wild-type fibrils. We also evaluated the cytotoxicity of Aβ fibrils bound with acetylcholinesterase (AChE), a common component of amyloid deposits. Aβ1–40 wild-type–AChE fibrillar complexes, similar to neuronal cells, resulted in an increased toxicity on vascular cells. Previous reports showing that antioxidants are able to reduce the toxicity of Aβ fibrils on neuronal cells prompted us to test the effect of vitamin E, vitamin C, and 17β-estradiol on vascular damage induced by Aβwild-type and AβGlu22→Gln. Our data indicate that vitamin E attenuated significantly the Aβ-mediated cytotoxicity on vascular cells, although 17β-estradiol and vitamin C failed to inhibit the cytotoxicity induced by Aβ fibrils.

Stable complexes involving acetylcholinesterase and amyloid-ß-peptide change the biochemical properties of the enzyme and increase the neurotoxicity of Alzheimer's fibrils

Brain acetylcholinesterase (AChE) forms stable complexes with amyloid-beta peptide (Abeta) during its assembly into filaments, in agreement with its colocalization with the Abeta deposits of Alzheimer's brain. The association of the enzyme with nascent Abeta aggregates occurs as early as after 30 min of incubation. Analysis of the catalytic activity of the AChE incorporated into these complexes shows an anomalous behavior reminiscent of the AChE associated with senile plaques, which includes a resistance to low pH, high substrate concentrations, and lower sensitivity to AChE inhibitors. Furthermore, the toxicity of the AChE-amyloid complexes is higher than that of the Abeta aggregates alone. Thus, in addition to its possible role as a heterogeneous nucleator during amyloid formation, AChE, by forming such stable complexes, may increase the neurotoxicity of Abeta fibrils and thus may determine the selective neuronal loss observed in Alzheimer's brain.