Computational modelling of molecular nexopathies

Neurodegenerative diseases are an ever-increasing health problem, requiring substantial human and financial resources. They are caused by pathogenic proteins, which accumulate and spread in the brain's neural network, causing neuronal loss and brain atrophy. However, the mechanisms that govern pathogenic protein accumulation, spread, and toxic effects are still poorly understood, and many competing hypotheses regarding them have been presented by researchers. A better understanding of these mechanisms can help inform which hypotheses are more likely to be true, improve prognosis tools, and assist in drug development. Clinically, brain atrophy follows specific spatiotemporal patterns in each neurodegenerative disease, and each disease is linked to specific pathogenic proteins. This observation led to the `molecular nexopathies' hypothesis, which states that clinical phenotypes can be predicted if the specific pathogenic protein variant and the neural network characteristics are both known. However, little computational work has been done that links pathogenic protein mechanisms, the brain's neural network, and clinical phenotypes. In this thesis, I developed computational models for a variety of hypotheses regarding pathogenic protein mechanisms of accumulation, spread, and toxic effects on the brain, which occur at the neuronal scale, while linking them to neuroimaging data, which is acquired at the brain scale. After running simulations with the modelled mechanisms within a neural network, I compared simulation results over time against empirical data for Alzheimer's disease and three genetic variants of frontotemporal dementia. For each disease, the model that best fitted its atrophy progression was found, discovering differences among diseases with regards to what degree each mechanism played a role. I also analysed how each mechanism affected disease progression, discovered each disease's seed location, and found mechanisms that showed potential as candidate targets for therapies, in particular, increasing the firing frequency of neurons

Predictive cognition in dementia: the case of music

The clinical complexity and pathological diversity of neurodegenerative diseases impose immense challenges for diagnosis and the design of rational interventions. To address these challenges, there is a need to identify new paradigms and biomarkers that capture shared pathophysiological processes and can be applied across a range of diseases. One core paradigm of brain function is predictive coding: the processes by which the brain establishes predictions and uses them to minimise prediction errors represented as the difference between predictions and actual sensory inputs. The processes involved in processing unexpected events and responding appropriately are vulnerable in common dementias but difficult to characterise. In my PhD work, I have exploited key properties of music – its universality, ecological relevance and structural regularity – to model and assess predictive cognition in patients representing major syndromes of frontotemporal dementia – non-fluent variant PPA (nfvPPA), semantic-variant PPA (svPPA) and behavioural-variant FTD (bvFTD) - and Alzheimer’s disease relative to healthy older individuals. In my first experiment, I presented patients with well-known melodies containing no deviants or one of three types of deviant - acoustic (white-noise burst), syntactic (key-violating pitch change) or semantic (key-preserving pitch change). I assessed accuracy detecting melodic deviants and simultaneously-recorded pupillary responses to these deviants. I used voxel-based morphometry to define neuroanatomical substrates for the behavioural and autonomic processing of these different types of deviants, and identified a posterior temporo-parietal network for detection of basic acoustic deviants and a more anterior fronto-temporo-striatal network for detection of syntactic pitch deviants. In my second chapter, I investigated the ability of patients to track the statistical structure of the same musical stimuli, using a computational model of the information dynamics of music to calculate the information-content of deviants (unexpectedness) and entropy of melodies (uncertainty). I related these information-theoretic metrics to performance for detection of deviants and to ‘evoked’ and ‘integrative’ pupil reactivity to deviants and melodies respectively and found neuroanatomical correlates in bilateral dorsal and ventral striatum, hippocampus, superior temporal gyri, right temporal pole and left inferior frontal gyrus. Together, chapters 3 and 4 revealed new hypotheses about the way FTD and AD pathologies disrupt the integration of predictive errors with predictions: a retained ability of AD patients to detect deviants at all levels of the hierarchy with a preserved autonomic sensitivity to information-theoretic properties of musical stimuli; a generalized impairment of surprise detection and statistical tracking of musical information at both a cognitive and autonomic levels for svPPA patients underlying a diminished precision of predictions; the exact mirror profile of svPPA patients in nfvPPA patients with an abnormally high rate of false-alarms with up-regulated pupillary reactivity to deviants, interpreted as over-precise or inflexible predictions accompanied with normal cognitive and autonomic probabilistic tracking of information; an impaired behavioural and autonomic reactivity to unexpected events with a retained reactivity to environmental uncertainty in bvFTD patients. Chapters 5 and 6 assessed the status of reward prediction error processing and updating via actions in bvFTD. I created pleasant and aversive musical stimuli by manipulating chord progressions and used a classic reinforcement-learning paradigm which asked participants to choose the visual cue with the highest probability of obtaining a musical ‘reward’. bvFTD patients showed reduced sensitivity to the consequence of an action and lower learning rate in response to aversive stimuli compared to reward. These results correlated with neuroanatomical substrates in ventral and dorsal attention networks, dorsal striatum, parahippocampal gyrus and temporo-parietal junction. Deficits were governed by the level of environmental uncertainty with normal learning dynamics in a structured and binarized environment but exacerbated deficits in noisier environments. Impaired choice accuracy in noisy environments correlated with measures of ritualistic and compulsive behavioural changes and abnormally reduced learning dynamics correlated with behavioural changes related to empathy and theory-of-mind. Together, these experiments represent the most comprehensive attempt to date to define the way neurodegenerative pathologies disrupts the perceptual, behavioural and physiological encoding of unexpected events in predictive coding terms

Modeling and inference of spatio-temporal protein dynamics across brain networks

Models of misfolded proteins (MP) aim at discovering the bio-mechanical propagation properties of neurological diseases (ND) by identifying plausible associated dynamical systems. Solving these systems along the full disease trajectory is usually challenging, due to the lack of a well defined time axis for the pathology. This issue is addressed by disease progression models (DPM) where long-term progression trajectories are estimated via time reparametrization of individual observations. However, due to their loose assumptions on the dynamics, DPM do not provide insights on the bio-mechanical properties of MP propagation. Here we propose a unified model of spatio-temporal protein dynamics based on the joint estimation of long-term MP dynamics and time reparameterization of individuals observations. The model is expressed within a Gaussian Process (GP) regression setting, where constraints on the MP dynamics are imposed through non--linear dynamical systems. We use stochastic variational inference on both GP and dynamical system parameters for scalable inference and uncertainty quantification of the trajectories. Experiments on simulated data show that our model accurately recovers prescribed rates along graph dynamics and precisely reconstructs the underlying progression. When applied to brain imaging data our model allows the bio-mechanical interpretation of amyloid deposition in Alzheimer's disease, leading to plausible simulations of MP propagation, and achieving accurate predictions of individual MP deposition in unseen data

Prion disease: experimental models and reality

The understanding of the pathogenesis and mechanisms of diseases requires a multidisciplinary approach, involving clinical observation, correlation to pathological processes, and modelling of disease mechanisms. It is an inherent challenge, and arguably impossible to generate model systems that can faithfully recapitulate all aspects of human disease. It is, therefore, important to be aware of the potentials and also the limitations of specific model systems. Model systems are usually designed to recapitulate only specific aspects of the disease, such as a pathological phenotype, a pathomechanism, or to test a hypothesis. Here, we evaluate and discuss model systems that were generated to understand clinical, pathological, genetic, biochemical, and epidemiological aspects of prion diseases. Whilst clinical research and studies on human tissue are an essential component of prion research, much of the understanding of the mechanisms governing transmission, replication, and toxicity comes from in vitro and in vivo studies. As with other neurodegenerative diseases caused by protein misfolding, the pathogenesis of prion disease is complex, full of conundra and contradictions. We will give here a historical overview of the use of models of prion disease, how they have evolved alongside the scientific questions, and how advancements in technologies have pushed the boundaries of our understanding of prion biology

An integrated mathematical model of cellular cholesterol biosynthesis and lipoprotein metabolism

Cholesterol regulation is an important aspect of human health. In this work we bring together and extend two recent mathematical models describing cholesterol biosynthesis and lipoprotein endocytosis to create an integrated model of lipoprotein metabolism in the context of a single hepatocyte. The integrated model includes a description of low density lipoprotein (LDL) receptor and cholesterol synthesis, delipidation of very low density lipoproteins (VLDLs) to LDLs and subsequent lipoprotein endocytosis. Model analysis shows that cholesterol biosynthesis produces the majority of intracellular cholesterol. The availability of free receptors does not greatly effect the concentration of intracellular cholesterol, but has a detrimental effect on extracellular VLDL and LDL levels. We test our model by considering its ability to reproduce the known biology of Familial Hypercholesterolaemia and statin therapy. In each case the model reproduces the known biological behaviour. Quantitative differences in response to statin therapy are discussed in the context of the need to extend the work to a more {\it in vivo} setting via the incorporation of more dietary lipoprotein related processes and the need for further testing and parameterisation of {\it in silico} models of lipoprotein metabolism

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits

Highlighting the effect of amyloid beta assemblies on the mechanical properties and conformational stability of cell membrane

Alzheimer disease (AD) is the most common cause of dementia, characterized by a progressive decline in cognitive function due to the abnormal aggregation and deposition of Amyloid beta (Aβ) fibrils in the brain of patients. In this context, the molecular mechanisms of protein misfolding and aggregation that are known to induce significant biophysical alterations in cells, including destabilization of plasma membranes, remain partially unclear. Physical interaction between the Aβ assemblies and the membrane leads to the disruption of the cell membrane in multiple ways including, surface carpeting, generation of transmembrane channels and detergent-like membrane dissolution. Understanding the impact of amyloidogenic protein in different stages of aggregation with the plasma membrane, plays a crucial role to fully elucidate the pathological mechanisms of AD. Within this framework, computer simulations represent a powerful tool able to shed lights on the interactions governing the structural influence of Aβ proteins on biological membrane. In this study, molecular dynamics (MD) simulations have been performed in order to characterize how POPC bilayer conformational and mechanical properties are affected by the interaction with Aβ11-42 peptide, oligomer and fibril

Mutations in SLC12A5 in epilepsy of infancy with migrating focal seizures

The potassium-chloride co-transporter KCC2, encoded by SLC12A5, plays a fundamental role in fast synaptic inhibition by maintaining a hyperpolarizing gradient for chloride ions. KCC2 dysfunction has been implicated in human epilepsy, but to date, no monogenic KCC2-related epilepsy disorders have been described. Here we show recessive loss-of-function SLC12A5 mutations in patients with a severe infantile-onset pharmacoresistant epilepsy syndrome, epilepsy of infancy with migrating focal seizures (EIMFS). Decreased KCC2 surface expression, reduced protein glycosylation and impaired chloride extrusion contribute to loss of KCC2 activity, thereby impairing normal synaptic inhibition and promoting neuronal excitability in this early-onset epileptic encephalopathy

Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany

This volume consists of a collection of conference abstracts