Optimisation of algorithms to compute information theoretic indexes

Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document. Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document.

Mild cognitive impairment subtypes: An MEG study

Previous studies of the dementia continuum have characterized the early disruption of the brain oscillatory activity at the stage of Mild cognitive impairment (MCI). Reduction in power in posterior regions in the alpha band has been one of the landmarks of the Alzheimer Disease accompanied by the anteriorization of the theta band power. However, little is known about the neurophysiological differences between single and multidomain MCI patients.Our goal is to study the differences in oscillatory magnetic activity between amnestic single and multidomain MCI. This will allow us to test whether the effect of the impairment in a single cognitive domain or in a more widespread functional impairment can be reflected in specific neurophysiological profiles.

Large scale multiphysics modeling of neurites

Situado en el límite entre Ingeniería, Informática y Biología, la mecánica computacional de las neuronas aparece como un nuevo campo interdisciplinar que potencialmente puede ser capaz de abordar problemas clínicos desde una perspectiva diferente. Este campo es multiescala por naturaleza, yendo desde la nanoescala (como, por ejemplo, los dímeros de tubulina) a la macroescala (como, por ejemplo, el tejido cerebral), y tiene como objetivo abordar problemas que son complejos, y algunas veces imposibles, de estudiar con medios experimentales. La modelización computacional ha sido ampliamente empleada en aplicaciones Neurocientíficas tan diversas como el crecimiento neuronal o la propagación de los potenciales de acción compuestos. Sin embargo, en la mayoría de los enfoques de modelización hechos hasta ahora, la interacción entre la célula y el medio/estímulo que la rodea ha sido muy poco explorada. A pesar de la tremenda importancia de esa relación en algunos desafíos médicos—como, por ejemplo, lesiones traumáticas en el cerebro, cáncer, la enfermedad del Alzheimer—un puente que relacione las propiedades electrofisiológicas-químicas y mecánicas desde la escala molecular al nivel celular todavía no existe. Con ese objetivo, esta investigación propone un marco computacional multiescala particularizado para dos escenarios respresentativos: el crecimiento del axón y el acomplamiento electrofisiológicomecánico de las neuritas. En el primer caso, se explora la relación entre los constituyentes moleculares del axón durante su crecimiento y sus propiedades mecánicas resultantes, mientras que en el último, un estímulo mecánico provoca deficiencias funcionales a nivel celular como consecuencia de sus alteraciones electrofisiológicas-químicas. La modelización computacional empleada en este trabajo es el método de las diferencias finitas, y es implementada en un nuevo programa llamado Neurite. Aunque el método de los elementos finitos es también explorado en parte de esta investigación, el método de las diferencias finitas tiene la flexibilidad y versatilidad necesaria para implementar mode los biológicos, así como la simplicidad matemática para extenderlos a simulaciones a gran escala con un coste computacional bajo. Centrándose primero en el efecto de las propiedades electrofisiológicas-químicas sobre las propiedades mecánicas, una versión adaptada de Neurite es desarrollada para simular la polimerización de los microtúbulos en el crecimiento del axón y proporcionar las propiedades mecánicas como función de la ocupación de los microtúbulos. Después de calibrar el modelo de crecimiento del axón frente a resultados experimentales disponibles en la literatura, las características mecánicas pueden ser evaluadas durante la simulación. Las propiedades mecánicas del axón muestran variaciones dramáticas en la punta de éste, donde el cono de crecimiento soporta las señales químicas y mecánicas. Bansándose en el conocimiento ganado con el modelo de diferencias finitas, y con el objetivo de ir de 1D a 3D, este esquema preliminar pero de una naturaleza innovadora allana el camino a futuros estudios con el método de los elementos finitos. Centrándose finalmente en el efecto de las propiedades mecánicas sobre las propiedades electrofisiológicas- químicas, Neurite es empleado para relacionar las cargas mecánicas macroscópicas con las deformaciones y velocidades de deformación a escala microscópica, y simular la propagación de la señal eléctrica en las neuritas bajo carga mecánica. Las simulaciones fueron calibradas con resultados experimentales publicados en la literatura, proporcionando, por tanto, un modelo capaz de predecir las alteraciones de las funciones electrofisiológicas neuronales bajo cargas externas dañinas, y uniendo lesiones mecánicas con las correspondientes deficiencias funcionales. Para abordar simulaciones a gran escala, aunque otras arquitecturas avanzadas basadas en muchos núcleos integrados (MICs) fueron consideradas, los solvers explícito e implícito se implementaron en unidades de procesamiento central (CPU) y unidades de procesamiento gráfico (GPUs). Estudios de escalabilidad fueron llevados acabo para ambas implementaciones mostrando resultados prometedores para casos de simulaciones extremadamente grandes con GPUs. Esta tesis abre la vía para futuros modelos mecánicos con el objetivo de unir las propiedades electrofisiológicas-químicas con las propiedades mecánicas. El objetivo general es mejorar el conocimiento de las comunidades médicas y de bioingeniería sobre la mecánica de las neuronas y las deficiencias funcionales que aparecen de los daños producidos por traumatismos mecánicos, como lesiones traumáticas en el cerebro, o enfermedades neurodegenerativas como la enfermedad del Alzheimer. ABSTRACT Sitting at the interface between Engineering, Computer Science and Biology, Computational Neuron Mechanics appears as a new interdisciplinary field potentially able to tackle clinical problems from a new perspective. This field is multiscale by nature, ranging from the nanoscale (e.g., tubulin dimers) to the macroscale (e.g., brain tissue), and aims at tackling problems that are complex, and sometime impossible, to study through experimental means. Computational modeling has been widely used in different Neuroscience applications as diverse as neuronal growth or compound action potential propagation. However, in the majority of the modeling approaches done in this field to date, the interactions between the cell and its surrounding media/stimulus have been rarely explored. Despite of the tremendous importance of such relationship in several medical challenges—e.g., traumatic brain injury (TBI), cancer, Alzheimer’s disease (AD)—a bridge between electrophysiological-chemical and mechanical properties of neurons from the molecular scale to the cell level is still lacking. To this end, this research proposes a multiscale computational framework particularized for two representative scenarios: axon growth and electrophysiological-mechanical coupling of neurites. In the former case, the relation between the molecular constituents of the axon during its growth and its resulting mechanical properties is explored, whereas in the latter, a mechanical stimulus provokes functional deficits at cell level as a consequence of its electrophysiological-chemical alterations. The computational modeling approach chosen in this work is the finite difference method (FDM), and was implemented in a new program called Neurite. Although the finite element method (FEM) is also explored as part of this research, the FDM provides the necessary flexibility and versatility to implement biological models, as well as the mathematical simplicity to extend them to large scale simulations with a low computational cost. Focusing first on the effect of electrophysiological-chemical properties on the mechanical proper ties, an adaptation of Neurite was developed to simulate microtubule polymerization in axonal growth and provide the axon mechanical properties as a function of microtubule occupancy. After calibrating the axon growth model against experimental results available in the literature, the mechanical characteristics can be tracked during the simulation. The axon mechanical properties show dramatic variations at the tip of the axon, where the growth cone supports the chemical and mechanical signaling. Based on the knowledge gained from the FDM scheme, and in order to go from 1D to 3D, this preliminary yet novel scheme paves the road for future studies with FEM. Focusing then on the effect of mechanical properties on the electrophysiological-chemical properties, Neurite was used to relate macroscopic mechanical loading to microscopic strains and strain rates, and simulate the electrical signal propagation along neurites under mechanical loading. The simulations were calibrated against experimental results published in the literature, thus providing a model able to predict the alteration of neuronal electrophysiological function under external damaging load, and linking mechanical injuries to subsequent acute functional deficits. To undertake large scale simulations, although other state-of-the-art architectures based on many integrated cores (MICs) were considered, the explicit and implicit solvers were implemented for central processing units (CPUs) and graphics processing units (GPUs). Scalability studies were done for both implementations showing promising results for extremely large scale simulations with GPUs. This thesis opens the avenue for future mechanical modeling approaches aimed at linking electrophysiological- chemical properties to mechanical properties. Its overarching goal is to enhance the bioengineering and medical communities knowledge on neuronal mechanics and functional deficits arising from damages produced by direct mechanical insults, such as TBI, or neurodegenerative evolving illness, such as AD.

Neurotrophic factors [activity-dependent neurotrophic factor (ADNF) and basic fibroblast growth factor (bFGF)] interrupt excitotoxic neurodegenerative cascades promoted by a PS1 mutation

Although an excitotoxic mechanism of neuronal injury has been proposed to play a role in chronic neurodegenerative disorders such as Alzheimer’s disease, and neurotrophic factors have been put forward as potential therapeutic agents, direct evidence is lacking. Taking advantage of the fact that mutations in the presenilin-1 (PS1) gene are causally linked to many cases of early-onset inherited Alzheimer’s disease, we generated PS1 mutant knock-in mice and directly tested the excitotoxic and neurotrophic hypotheses of Alzheimer’s disease. Primary hippocampal neurons from PS1 mutant knock-in mice exhibited increased production of amyloid β-peptide 42/43 and increased vulnerability to excitotoxicity, which occurred in a gene dosage-dependent manner. Neurons expressing mutant PS1 exhibited enhanced calcium responses to glutamate and increased oxyradical production and mitochondrial dysfunction. Pretreatment with either basic fibroblast growth factor or activity-dependent neurotrophic factor protected neurons expressing mutant PS1 against excitotoxicity. Both basic fibroblast growth factor and activity-dependent neurotrophic factor stabilized intracellular calcium levels and abrogated the increased oxyradical production and mitochondrial dysfunction otherwise caused by the PS1 mutation. Our data indicate that neurotrophic factors can interrupt excitotoxic neurodegenerative cascades promoted by PS1 mutations.

Mapping the active site in vasoactive intestinal peptide to a core of four amino acids: Neuroprotective drug design

The understanding of the molecular mechanisms leading to peptide action entails the identification of a core active site. The major 28-aa neuropeptide, vasoactive intestinal peptide (VIP), provides neuroprotection. A lipophilic derivative with a stearyl moiety at the N-terminal and norleucine residue replacing the Met-17 was 100-fold more potent than VIP in promoting neuronal survival, acting at femtomolar–picomolar concentration. To identify the active site in VIP, over 50 related fragments containing an N-terminal stearic acid attachment and an amidated C terminus were designed, synthesized, and tested for neuroprotective properties. Stearyl-Lys-Lys-Tyr-Leu-NH2 (derived from the C terminus of VIP and the related peptide, pituitary adenylate cyclase activating peptide) captured the neurotrophic effects offered by the entire 28-aa parent lipophilic derivative and protected against β-amyloid toxicity in vitro. Furthermore, the 4-aa lipophilic peptide recognized VIP-binding sites and enhanced choline acetyltransferase activity as well as cognitive functions in Alzheimer’s disease-related in vivo models. Biodistribution studies following intranasal administration of radiolabeled peptide demonstrated intact peptide in the brain 30 min after administration. Thus, lipophilic peptide fragments offer bioavailability and stability, providing lead compounds for drug design against neurodegenerative diseases.

Mutation in the tau gene in familial multiple system tauopathy with presenile dementia

Familial multiple system tauopathy with presenile dementia (MSTD) is a neurodegenerative disease with an abundant filamentous tau protein pathology. It belongs to the group of familial frontotemporal dementias with Parkinsonism linked to chromosome 17 (FTDP-17), a major class of inherited dementing disorders whose genetic basis is unknown. We now report a G to A transition in the intron following exon 10 of the gene for microtubule-associated protein tau in familial MSTD. The mutation is located at the 3′ neighboring nucleotide of the GT splice-donor site and disrupts a predicted stem-loop structure. We also report an abnormal preponderance of soluble tau protein isoforms with four microtubule-binding repeats over isoforms with three repeats in familial MSTD. This most likely accounts for our previous finding that sarkosyl-insoluble tau protein extracted from the filamentous deposits in familial MSTD consists only of tau isoforms with four repeats. These findings reveal that a departure from the normal ratio of four-repeat to three-repeat tau isoforms leads to the formation of abnormal tau filaments. The results show that dysregulation of tau protein production can cause neurodegeneration and imply that the FTDP-17 gene is the tau gene. This work has major implications for Alzheimer’s disease and other tauopathies.

Additional evidence for an eight-transmembrane-domain topology for Caenorhabditis elegans and human presenilins

Presenilins have been implicated in the genesis of Alzheimer’s disease and in facilitating LIN-12/Notch activity during development. All presenilins have multiple hydrophobic regions that could theoretically span a membrane, and a description of the membrane topology is a crucial step toward deducing the mechanism of presenilin function. Previously, we proposed an eight-transmembrane-domain model for presenilin, based on studies of the Caenorhabditis elegans SEL-12 presenilin. Here, we describe experiments that support the view that two of the hydrophobic regions of SEL-12 function as the seventh and eighth transmembrane domains. Furthermore, we have shown that human presenilin 1 behaves like SEL-12 presenilin when analyzed by our methods. Our results provide additional experimental support for the eight-transmembrane-domain model of presenilin topology.

Temperature dependence of amyloid β-protein fibrillization

Fibrillogenesis of the amyloid β-protein (Aβ) is believed to play a central role in the pathogenesis of Alzheimer’s disease. Previous studies of the kinetics of Aβ fibrillogenesis showed that the rate of fibril elongation is proportional to the concentration of monomers. We report here the study of the temperature dependence of the Aβ fibril elongation rate constant, ke, in 0.1 M HCl. The rate of fibril elongation was measured at Aβ monomer concentrations ranging from 50 to 400 μM and at temperatures from 4°C to 40°C. Over this temperature range, ke increases by two orders of magnitude. The temperature dependence of ke follows the Arrhenius law, ke = A exp (−EA/kT). The preexponential factor A and the activation energy EA are ≈6 × 1018 liter/(mol·sec) and 23 kcal/mol, respectively. Such a high value of EA suggests that significant conformational changes are associated with the binding of Aβ monomers to fibril ends.

Rapid treadmilling of brain microtubules free of microtubule-associated proteins in vitro and its suppression by tau

We have determined the treadmilling rate of brain microtubules (MTs) free of MT-associated proteins (MAPs) at polymer mass steady state in vitro by using [3H]GTP-exchange. We developed buffer conditions that suppressed dynamic instability behavior by ≈10-fold to minimize the contribution of dynamic instability to total tubulin-GTP exchange. The MTs treadmilled rapidly under the suppressed dynamic instability conditions, at a minimum rate of 0.2 μm/min. Thus, rapid treadmilling is an intrinsic property of MAP-free MTs. Further, we show that tau, an axonal stabilizing MAP involved in Alzheimer’s disease, strongly suppresses the treadmilling rate. These results indicate that tau’s function in axons might involve suppression of axonal MT treadmilling. We describe mathematically how treadmilling and dynamic instability are mechanistically distinct MT behaviors. Finally, we present a model that explains how small changes in the critical tubulin subunit concentration at MT minus ends, caused by intrinsic differences in rate constants or regulatory proteins, could produce large changes in the treadmilling rate.

Neuronal human BACE1 knock-in induces systemic diabetes in mice

Funding We would like to thank R. Simcox, Romex Oilfield Chemicals, for financial support for KP, and acknowledge additional contributions from the Scottish Alzheimer’s Research UK network for the lipidomics work. The College of Life Science and Medicine, University of Aberdeen, sponsored the imaging study. MD was funded by British Heart Foundation and Diabetes UK; NM was funded by a British Heart Foundation Intermediate Fellowship; KS was funded by a European Foundation for the Study of Diabetes/Lilly programme grant; and RD was funded by an Institute of Medical Sciences PhD studentship.

Inhibiting transthyretin conformational changes that lead to amyloid fibril formation

Insoluble protein fibrils resulting from the self-assembly of a conformational intermediate are implicated as the causative agent in several severe human amyloid diseases, including Alzheimer’s disease, familial amyloid polyneuropathy, and senile systemic amyloidosis. The latter two diseases are associated with transthyretin (TTR) amyloid fibrils, which appear to form in the acidic partial denaturing environment of the lysosome. Here we demonstrate that flufenamic acid (Flu) inhibits the conformational changes of TTR associated with amyloid fibril formation. The crystal structure of TTR complexed with Flu demonstrates that Flu mediates intersubunit hydrophobic interactions and intersubunit hydrogen bonds that stabilize the normal tetrameric fold of TTR. A small-molecule inhibitor that stabilizes the normal conformation of a protein is desirable as a possible approach to treat amyloid diseases. Molecules such as Flu also provide the means to rigorously test the amyloid hypothesis, i.e., the apparent causative role of amyloid fibrils in amyloid disease.

Axon pathology in Parkinson’s disease and Lewy body dementia hippocampus contains α-, β-, and γ-synuclein

Pathogenic α-synuclein (αS) gene mutations occur in rare familial Parkinson’s disease (PD) kindreds, and wild-type αS is a major component of Lewy bodies (LBs) in sporadic PD, dementia with LBs (DLB), and the LB variant of Alzheimer’s disease, but β-synuclein (βS) and γ-synuclein (γS) have not yet been implicated in neurological disorders. Here we show that in PD and DLB, but not normal brains, antibodies to αS and βS reveal novel presynaptic axon terminal pathology in the hippocampal dentate, hilar, and CA2/3 regions, whereas antibodies to γS detect previously unrecognized axonal spheroid-like lesions in the hippocampal dentate molecular layer. The aggregation of other synaptic proteins and synaptic vesicle-like structures in the αS- and βS-labeled hilar dystrophic neurites suggests that synaptic dysfunction may result from these lesions. Our findings broaden the concept of neurodegenerative “synucleinopathies” by implicating βS and γS, in addition to αS, in the onset/progression of PD and DLB.

Neuronal overexpression of mutant amyloid precursor protein results in prominent deposition of cerebrovascular amyloid

Transgenic mice that overexpress mutant human amyloid precursor protein (APP) exhibit one hallmark of Alzheimer’s disease pathology, namely the extracellular deposition of amyloid plaques. Here, we describe significant deposition of amyloid β (Aβ) in the cerebral vasculature [cerebral amyloid angiopathy (CAA)] in aging APP23 mice that had striking similarities to that observed in human aging and Alzheimer’s disease. Amyloid deposition occurred preferentially in arterioles and capillaries and within individual vessels showed a wide heterogeneity (ranging from a thin ring of amyloid in the vessel wall to large plaque-like extrusions into the neuropil). CAA was associated with local neuron loss, synaptic abnormalities, microglial activation, and microhemorrhage. Although several factors may contribute to CAA in humans, the neuronal origin of transgenic APP, high levels of Aβ in cerebrospinal fluid, and regional localization of CAA in APP23 mice suggest transport and drainage pathways rather than local production or blood uptake of Aβ as a primary mechanism underlying cerebrovascular amyloid formation. APP23 mice on an App-null background developed a similar degree of both plaques and CAA, providing further evidence that a neuronal source of APP/Aβ is sufficient to induce cerebrovascular amyloid and associated neurodegeneration.

PAT1, a microtubule-interacting protein, recognizes the basolateral sorting signal of amyloid precursor protein

In epithelial cells, sorting of membrane proteins to the basolateral surface depends on the presence of a basolateral sorting signal (BaSS) in their cytoplasmic domain. Amyloid precursor protein (APP), a basolateral protein implicated in the pathogenesis of Alzheimer’s disease, contains a tyrosine-based BaSS, and mutation of the tyrosine residue results in nonpolarized transport of APP. Here we report identification of a protein, termed PAT1 (protein interacting with APP tail 1), that interacts with the APP-BaSS but binds poorly when the critical tyrosine is mutated and does not bind the tyrosine-based endocytic signal of APP. PAT1 shows homology to kinesin light chain, which is a component of the plus-end directed microtubule-based motor involved in transporting membrane proteins to the basolateral surface. PAT1, a cytoplasmic protein, associates with membranes, cofractionates with APP-containing vesicles, and binds microtubules in a nucleotide-sensitive manner. Cotransfection of PAT1 with a reporter protein shows that PAT1 is functionally linked with intracellular transport of APP. We propose that PAT1 is involved in the translocation of APP along microtubules toward the cell surface.

Transgenic Drosophila expressing human amyloid precursor protein show γ-secretase activity and a blistered-wing phenotype

The importance of the amyloid precursor protein (APP) in the pathogenesis of Alzheimer’s disease (AD) became apparent through the identification of distinct mutations in the APP gene, causing early onset familial AD with the accumulation of a 4-kDa peptide fragment (βA4) in amyloid plaques and vascular deposits. However, the physiological role of APP is still unclear. In this work, Drosophila melanogaster is used as a model system to analyze the function of APP by expressing wild-type and various mutant forms of human APP in fly tissue culture cells as well as in transgenic fly lines. After expression of full-length APP forms, secretion of APP but not of βA4 was observed in both systems. By using SPA4CT, a short APP form in which the signal peptide was fused directly to the βA4 region, transmembrane domain, and cytoplasmic tail, we observed βA4 release in flies and fly-tissue culture cells. Consequently, we showed a γ-secretase activity in flies. Interestingly, transgenic flies expressing full-length forms of APP have a blistered-wing phenotype. As the wing is composed of interacting dorsal and ventral epithelial cell layers, this phenotype suggests that human APP expression interferes with cell adhesion/signaling pathways in Drosophila, independently of βA4 generation.