In-vivo brain neuroimaging provides a gateway for integrating biological and clinical biomarkers of Alzheimer's disease.

PURPOSE OF REVIEW: Only 5% of the Alzheimer's cases are explained by genetic mutations, whereas the remaining 95% are sporadic. The pathophysiological mechanisms underlying sporadic Alzheimer's disease are not well understood, suggesting a complex multifactorial cause. This review summarizes the recent findings on research aiming to show how biomarkers can be used for revealing the underlying mechanisms of preclinical stage Alzheimer's disease and help in their diagnosis. RECENT FINDINGS: The undisputed successful publicly accessible repositories provide longitudinal brain images, clinical, genetic and proteomic information of Alzheimer's disease. By combining with increasingly sophisticated data analysis methods, it is a great opportunity for searching new biomarkers. Innovative studies validated theoretical models of disease progression demonstrating the sequential ordering of well-established biomarkers. Novel observations shed light on the interaction between biomarkers to confirm that disease progression is related to multiple pathological factors. A typical example is the tau-associated neuronal toxicity that can be additionally potentiated by amyloid β peptides. To increase further the complexity, studies report specific impact of common genetic variants that can be traced from childhood through middle age up to the symptomatic onset of Alzheimer's disease. SUMMARY: The discovery of efficient therapies to prevent the disease or modify the progression of disease requires a more thorough understanding of the underlying biological processes. Neuroimaging, genetic and proteomic biomarkers for Alzheimer's disease are critically discussed and proposed to be included in clinical descriptions and diagnostic guidelines.

The paracaspase MALT1: biological function and potential for therapeutic inhibition.

The paracaspase MALT1 has a central role in the activation of lymphocytes and other immune cells including myeloid cells, mast cells and NK cells. MALT1 activity is required not only for the immune response, but also for the development of natural Treg cells that keep the immune response in check. Exaggerated MALT1 activity has been associated with the development of lymphoid malignancies, and recently developed MALT1 inhibitors show promising anti-tumor effects in xenograft models of diffuse large B cell lymphoma. In this review, we provide an overview of the present understanding of MALT1's function, and discuss possibilities for its therapeutic targeting based on recently developed inhibitors and animal models.

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

In diffusion MRI, traditional tractography algorithms do not recover truly quantitative tractograms and the structural connectivity has to be estimated indirectly by counting the number of fiber tracts or averaging scalar maps along them. Recently, global and efficient methods have emerged to estimate more quantitative tractograms by combining tractography with local models for the diffusion signal, like the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework. In this abstract, we show the importance of using both (i) proper multi-compartment diffusion models and (ii) adequate multi-shell acquisitions, in order to evaluate the accuracy and the biological plausibility of the tractograms.

Evaluation of drug-induced neurotoxicity based on metabolomics, proteomics and electrical activity measurements in complementary CNS in vitro models.

The present study was performed in an attempt to develop an in vitro integrated testing strategy (ITS) to evaluate drug-induced neurotoxicity. A number of endpoints were analyzed using two complementary brain cell culture models and an in vitro blood-brain barrier (BBB) model after single and repeated exposure treatments with selected drugs that covered the major biological, pharmacological and neuro-toxicological responses. Furthermore, four drugs (diazepam, cyclosporine A, chlorpromazine and amiodarone) were tested more in depth as representatives of different classes of neurotoxicants, inducing toxicity through different pathways of toxicity. The developed in vitro BBB model allowed detection of toxic effects at the level of BBB and evaluation of drug transport through the barrier for predicting free brain concentrations of the studied drugs. The measurement of neuronal electrical activity was found to be a sensitive tool to predict the neuroactivity and neurotoxicity of drugs after acute exposure. The histotypic 3D re-aggregating brain cell cultures, containing all brain cell types, were found to be well suited for OMICs analyses after both acute and long term treatment. The obtained data suggest that an in vitro ITS based on the information obtained from BBB studies and combined with metabolomics, proteomics and neuronal electrical activity measurements performed in stable in vitro neuronal cell culture systems, has high potential to improve current in vitro drug-induced neurotoxicity evaluation.

On the Rule of Mixtures for Predicting Stress-Softening and Residual Strain Effects in Biological Tissues and Biocompatible Materials

In this work, we use the rule of mixtures to develop an equivalent material model in which the total strain energy density is split into the isotropic part related to the matrix component and the anisotropic energy contribution related to the fiber effects. For the isotropic energy part, we select the amended non-Gaussian strain energy density model, while the energy fiber effects are added by considering the equivalent anisotropic volumetric fraction contribution, as well as the isotropized representation form of the eight-chain energy model that accounts for the material anisotropic effects. Furthermore, our proposed material model uses a phenomenological non-monotonous softening function that predicts stress softening effects and has an energy term, derived from the pseudo-elasticity theory, that accounts for residual strain deformations. The model’s theoretical predictions are compared with experimental data collected from human vaginal tissues, mice skin, poly(glycolide-co-caprolactone) (PGC25 3-0) and polypropylene suture materials and tracheal and brain human tissues. In all cases examined here, our equivalent material model closely follows stress-softening and residual strain effects exhibited by experimental data

The Role of the Delay Time in the Modeling of Biological Range Expansions

The time interval between successive migrations of biological species causes a delay time in the reaction-diffusion equations describing their space-time dynamics. This lowers the predicted speed of the waves of advance, as compared to classical models. It has been shown that this delay-time effect improves the modeling of human range expansions. Here, we demonstrate that it can also be important for other species. We present two new examples where the predictions of the time-delayed and the classical (Fisher) approaches are compared to experimental data. No free or adjustable parameters are used. We show that the importance of the delay effect depends on the dimensionless product of the initial growth rate and the delay time. We argue that the delay effect should be taken into account in the modeling of range expansions for biological species

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

A Farewell to Flat Biology. Three-dimensional Cell Culture Models in Cancer Drug Target Identification and Validation

Cells of epithelial origin, e.g. from breast and prostate cancers, effectively differentiate into complex multicellular structures when cultured in three-dimensions (3D) instead of conventional two-dimensional (2D) adherent surfaces. The spectrum of different organotypic morphologies is highly dependent on the culture environment that can be either non-adherent or scaffold-based. When embedded in physiological extracellular matrices (ECMs), such as laminin-rich basement membrane extracts, normal epithelial cells differentiate into acinar spheroids reminiscent of glandular ductal structures. Transformed cancer cells, in contrast, typically fail to undergo acinar morphogenic patterns, forming poorly differentiated or invasive multicellular structures. The 3D cancer spheroids are widely accepted to better recapitulate various tumorigenic processes and drug responses. So far, however, 3D models have been employed predominantly in the Academia, whereas the pharmaceutical industry has yet to adopt a more widely and routine use. This is mainly due to poor characterisation of cell models, lack of standardised workflows and high throughput cell culture platforms, and the availability of proper readout and quantification tools. In this thesis, a complete workflow has been established entailing well-characterised 3D cell culture models for prostate cancer, a standardised 3D cell culture routine based on high-throughput-ready platform, automated image acquisition with concomitant morphometric image analysis, and data visualisation, in order to enable large-scale high-content screens. Our integrated suite of software and statistical analysis tools were optimised and validated using a comprehensive panel of prostate cancer cell lines and 3D models. The tools quantify multiple key cancer-relevant morphological features, ranging from cancer cell invasion through multicellular differentiation to growth, and detect dynamic changes both in morphology and function, such as cell death and apoptosis, in response to experimental perturbations including RNA interference and small molecule inhibitors. Our panel of cell lines included many non-transformed and most currently available classic prostate cancer cell lines, which were characterised for their morphogenetic properties in 3D laminin-rich ECM. The phenotypes and gene expression profiles were evaluated concerning their relevance for pre-clinical drug discovery, disease modelling and basic research. In addition, a spontaneous model for invasive transformation was discovered, displaying a highdegree of epithelial plasticity. This plasticity is mediated by an abundant bioactive serum lipid, lysophosphatidic acid (LPA), and its receptor LPAR1. The invasive transformation was caused by abrupt cytoskeletal rearrangement through impaired G protein alpha 12/13 and RhoA/ROCK, and mediated by upregulated adenylyl cyclase/cyclic AMP (cAMP)/protein kinase A, and Rac/ PAK pathways. The spontaneous invasion model tangibly exemplifies the biological relevance of organotypic cell culture models. Overall, this thesis work underlines the power of novel morphometric screening tools in drug discovery.

Animal models of anxiety: an ethological perspective

In the field of anxiety research, animal models are used as screening tools in the search for compounds with therapeutic potential and as simulations for research on mechanisms underlying emotional behaviour. However, a solely pharmacological approach to the validation of such tests has resulted in distinct problems with their applicability to systems other than those involving the benzodiazepine/GABAA receptor complex. In this context, recent developments in our understanding of mammalian defensive behaviour have not only prompted the development of new models but also attempts to refine existing ones. The present review focuses on the application of ethological techniques to one of the most widely used animal models of anxiety, the elevated plus-maze paradigm. This fresh approach to an established test has revealed a hitherto unrecognized multidimensionality to plus-maze behaviour and, as it yields comprehensive behavioural profiles, has many advantages over conventional methodology. This assertion is supported by reference to recent work on the effects of diverse manipulations including psychosocial stress, benzodiazepines, GABA receptor ligands, neurosteroids, 5-HT1A receptor ligands, and panicolytic/panicogenic agents. On the basis of this review, it is suggested that other models of anxiety may well benefit from greater attention to behavioural detail

Anticonvulsant and proconvulsant roles of nitric oxide in experimental epilepsy models

The effect of acute (120 mg/kg) and chronic (25 mg/kg, twice a day, for 4 days) intraperitonial injection of the nitric oxide (NO) synthase (NOS) inhibitor NG-nitro-L-arginine (L-NOARG) was evaluated on seizure induction by drugs such as pilocarpine and pentylenetetrazole (PTZ) and by sound stimulation of audiogenic seizure-resistant (R) and audiogenic seizure-susceptible (S) rats. Seizures were elicited by a subconvulsant dose of pilocarpine (100 mg/kg) only after NOS inhibition. NOS inhibition also simultaneously potentiated the severity of PTZ-induced limbic seizures (60 mg/kg) and protected against PTZ-induced tonic seizures (80 mg/kg). The audiogenic seizure susceptibility of S or R rats did not change after similar treatments. In conclusion, proconvulsant effects of NOS inhibition are suggested to occur in the pilocarpine model and in the limbic components of PTZ-induced seizures, while an anticonvulsant role is suggested for the tonic seizures induced by higher doses of PTZ, revealing inhibitor-specific interactions with convulsant dose and also confirming the hypothesis that the effects of NOS inhibitors vary with the model of seizure

Antinociceptive effect of intrathecal neostigmine evaluated in rats by two different pain models

The analgesic efficacy of cholinergic agonists and anticholinesterase agents has been widely recognized. The analgesic effect obtained by activating cholinergic mechanisms, however, seems to depend on the experimental pain model utilized for its evaluation. The antinociceptive effect of intraspinal neostigmine was examined in rats submitted concurrently to the tail flick and formalin tests. Neostigmine (8.25 and 16.5 nmol) produced a dose-dependent antinociceptive effect in the tail flick test (a model of phasic pain) and reduced the first phase (phasic pain) of the animal response to formalin also in a dose-dependent manner. The second phase (tonic pain) of the response to formalin, however, was slightly reduced after a longer period of time only by the higher dose of the anticholinesterase. The effect of neostigmine was not significantly different when the drug was injected into rats submitted exclusively to the tail flick test. The second phase of the animal response to formalin was slightly reduced by neostigmine (8.25 nmol) and strongly inhibited by the higher dose of the anticholinesterase when injection was made after the first phase. We conclude that phasic and tonic pain can both be controlled by high doses of neostigmine. In addition, we show that inhibition by a lower dose of neostigmine of the formalin-induced phasic pain did not prevent the subsequent occurrence of tonic pain produced by the irritant

The use of non-human primates as animal models for the study of hepatitis viruses

Hepatitis viruses belong to different families and have in common a striking hepatotropism and restrictions for propagation in cell culture. The transmissibility of hepatitis is in great part limited to non-human primates. Enterically transmitted hepatitis viruses (hepatitis A virus and hepatitis E virus) can induce hepatitis in a number of Old World and New World monkey species, while the host range of non-human primates susceptible to hepatitis viruses transmitted by the parenteral route (hepatitis B virus, hepatitis C virus and hepatitis delta virus) is restricted to few species of Old World monkeys, especially the chimpanzee. Experimental studies on non-human primates have provided an invaluable source of information regarding the biology and pathogenesis of these viruses, and represent a still indispensable tool for vaccine and drug testing.

Transgenic animal models for the functional analysis of vasoactive peptides

The interplay of vasoactive peptide systems is an essential determinant of blood pressure regulation in mammals. While the endothelin and the renin-angiotensin systems raise blood pressure by inducing vasoconstriction and sodium retention, the kallikrein-kinin and the natriuretic-peptide systems reduce arterial pressure by eliciting vasodilatation and natriuresis. Transgenic technology has proven to be very useful for the functional analysis of vasoactive peptide systems. As an outstanding example, transgenic rats overexpressing the mouse Ren-2 renin gene in several tissues become extremely hypertensive. Several other transgenic rat and mouse strains with genetic modifications of components of the renin-angiotensin system have been developed in the past decade. Moreover, in recent years gene-targeting technology was employed to produce mouse strains lacking these proteins. The established animal models as well as the main insights gained by their analysis are summarized in this review.

The relationship between anxiety and depression in animal models: a study using the forced swimming test and elevated plus-maze

The present study evaluated the correlation between the behavior of mice in the forced swimming test (FST) and in the elevated plus-maze (PM). The effect of the order of the experiments, i.e., the influence of the first test (FST or PM) on mouse behavior in the second test (PM or FST, respectively) was compared to handled animals (HAND). The execution of FST one week before the plus-maze (FST-PM, N = 10), in comparison to mice that were only handled (HAND-PM, N = 10) in week 1, decreased % open entries (HAND-PM: 33.6 ± 2.9; FST-PM: 20.0 ± 3.9; mean ± SEM; P<0.02) and % open time (HAND-PM: 18.9 ± 3.3; FST-PM: 9.0 ± 1.9; P<0.03), suggesting an anxiogenic effect. No significant effect was seen in the number of closed arm entries (FST-PM: 9.5 (7.0-11.0); HAND-PM: 10.0 (4.0-14.5), median (interquartile range); U = 46.5; P>0.10). A prior test in the plus-maze (PM-FST) did not change % immobility time in the FST when compared to the HAND-FST group (HAND-FST: 57.7 ± 3.9; PM-FST: 65.7 ± 3.2; mean ± SEM; P>0.10). Since these data suggest that there is an order effect, the correlation was evaluated separately with each test sequence: FST-PM (N = 20) and PM-FST (N = 18). There was no significant correlation between % immobility time in the FST and plus-maze indexes (% time and entries in open arms) in any test sequence (r: -0.07 to 0.18). These data suggest that mouse behavior in the elevated plus-maze is not related to behavior in the forced swimming test and that a forced swimming test before the plus-maze has an anxiogenic effect even after a one-week interval.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.