Molecular mechanisms underlying deficient neurogenesis in Alzheimer’s disease

La neurogenèse est présente, dans le cerveau adulte, dans la zone sous-ventriculaire (ZSV) encadrant les ventricules latéraux et dans le gyrus dentelé (GD) de l’hippocampe, permettant l’apprentissage, la mémoire et la fonction olfactive. Ces micro-environnements possèdent des signaux contrôlant l’auto-renouvellement des cellules souches neurales (CSN), leur prolifération, leur destin et leur différenciation. Or, lors du vieillissement, les capacités régénératives et homéostatiques et la neurogenèse déclinent. Les patients atteints de la maladie d’Alzheimer (MA), comme le modèle animal reproduisant cette maladie (3xTg-AD), montrent une accélération des phénotypes liés au vieillissement dont une diminution de la neurogenèse. Notre hypothèse est que la découverte des mécanismes affectant la neurogenèse, lors du vieillissement et de la MA, pourrait fournir de nouvelles cibles thérapeutiques pour prévenir le déclin cognitif. Les études sur l’âge d’apparition et les mécanismes altérant la neurogenèse dans la MA sont contrastées et nous ont guidé vers deux études. L’examen des changements dans les étapes de la neurogenèse lors du vieillissement et du développement de la neuropathologie. Nous avons étudié la ZSV, les bulbes olfactifs et le GD de souris femelles de 11 et 18 mois, et l’apparition des deux pathologies associées à la MA : les plaques amyloïdes et les enchevêtrements neurofibrillaires. Nous avons découvert que les souris 3xTg-AD possèdent moins de cellules en prolifération, de progéniteurs et de neuroblastes, induisant une diminution de l’intégration de nouvelles cellules dans le GD et les bulbes olfactifs. Notons que le taux de neurogenèse chez ces souris de 11 mois est similaire à celui des souris de phénotype sauvage de 18 mois, indiquant une accélération des changements liés au vieillissement dans la MA. Dans la ZSV, nous avons aussi démontré une accumulation de gouttelettes lipidiques, suggérant des changements dans l’organisation et le métabolisme de la niche. Enfin, nous avons démontré que le déficit de la neurogenèse apparait lors des premières étapes de la MA, avant l’apparition des plaques amyloïdes et des enchevêtrements neurofibrillaires. A l’examen des mécanismes inhibant la neurogenèse lors de la MA, nous voyons que chez des souris de 5 mois, le déficit de la neurogenèse dans la ZSV et le GD est corrélé avec l’accumulation de lipides, qui coïncide avec l’apparition du déclin cognitif. Nous avons aussi découvert que dans le cerveau humain de patients atteints de la MA et dans les 3xTg-AD, des gouttelettes lipidiques s’accumulaient dans les cellules épendymaires, représentant le principal soutien des CSN de la niche. Ces lipides sont des triglycérides enrichis en acide oléique qui proviennent de la niche et pas d’une défaillance du système périphérique. De plus, l’infusion locale d’acide oléique chez des souris de phénotype sauvage permet de reproduire l’accumulation de triglycérides dans les cellules épendymaires, comme dans la MA. Ces gouttelettes induisent un dérèglement de la voie de signalisation Akt-FoxO3 dans les CSN, menant à l’inhibition de leur activation in vitro et in vivo. Ces résultats permettent une meilleure compréhension de la régulation de la neurogenèse par le métabolisme lipidique. Nous avons démontré un nouveau mécanisme par lequel l’accumulation des lipides dans la ZSV induit une inhibition des capacités de prolifération et de régénération des CSN lors de la MA. Les travaux futurs permettront de comprendre comment et pourquoi le métabolisme lipidique du cerveau est altéré dans la MA, ce qui pourrait offrir de nouvelles voies thérapeutiques pour la prévention et la régénération.

The 'direct costs' of livestock disease: The development of a system of models for the analysis of 30 endemic livestock diseases in Great Britain

The 'direct costs' attributable to 30 different endemic diseases of farm animals in Great Britain are estimated using a standardised method to construct a simple model for each disease that includes consideration of disease prevention and treatment costs. The models so far developed provide a basis for further analyses including cost-benefit analyses for the economic assessment of disease control options. The approach used reflects the inherent livestock disease information constraints, which limit the application of other economic analytical methods. It is a practical and transparent approach that is relatively easily communicated to veterinary scientists and policy makers. The next step is to develop the approach by incorporating wider economic considerations into the analyses in a way that will demonstrate to policy makers and others the importance of an economic perspective to livestock disease issues.

The role of animal nutrition in improving the nutritive value of animal-derived foods in relation to chronic disease

Foods derived from animals are an important source of nutrients in the diet; for example, milk and meat together provide about 60 and 55% of the dietary intake of Ca and protein respectively in the UK. However, certain aspects of some animal-derived foods, particularly their fat and saturated fatty acid (SFA) contents, have led to concerns that these foods substantially contribute to the risk of CVD, the metabolic syndrome and other chronic diseases. In most parts of Europe dairy products are the greatest single dietary source of SFA. The fatty acid composition of various animal-derived foods is, however, not constant and can, in many cases, be enhanced by animal nutrition. In particular, milk fat with reduced concentrations of the C12-16 SFA and an increased concentration of 18:1 MUFA is achievable, although enrichment with very-long-chain n-3 PUFA is much less efficient. However, there is now evidence that some animal-derived foods (notably milk products) contain compounds that may actively promote long-term health, and research is urgently required to fully characterise the benefits associated with the consumption of these compounds and to understand how the levels in natural foods can be enhanced. It is also vital that the beneficial effects are not inadvertently destroyed in the process of reducing the concentrations of SFA. In the future the role of animal nutrition in creating foods closer to the optimum composition for long-term human health is likely to become increasingly important, but production of such foods on a scale that will substantially affect national diets will require political and financial incentives and great changes in the animal production industry.

Use and abuse of mathematical models: an illustration from the 2001 foot and mouth disease epidemic in the United Kingdom

Foot and mouth disease (FMD) is a major threat, not only to countries whose economies rely on agricultural exports, but also to industrialised countries that maintain a healthy domestic livestock industry by eliminating major infectious diseases from their livestock populations. Traditional methods of controlling diseases such as FMD require the rapid detection and slaughter of infected animals, and any susceptible animals with which they may have been in contact, either directly or indirectly. During the 2001 epidemic of FMD in the United Kingdom (UK), this approach was supplemented by a culling policy driven by unvalidated predictive models. The epidemic and its control resulted in the death of approximately ten million animals, public disgust with the magnitude of the slaughter, and political resolve to adopt alternative options, notably including vaccination, to control any future epidemics. The UK experience provides a salutary warning of how models can be abused in the interests of scientific opportunism.

Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

Porcine models for the metabolic syndrome, digestive and bone disorders: a general overview

The aim of this review article is to provide an overview of the role of pigs as a biomedical model for humans. The usefulness and limitations of porcine models have been discussed in terms of metabolic, cardiovascular, digestive and bone diseases in humans. Domestic pigs and minipigs are the main categories of pigs used as biomedical models. One drawback of minipigs is that they are in short supply and expensive compared with domestic pigs, which in contrast cost more to house, feed and medicate. Different porcine breeds show different responses to the induction of specific diseases. For example, ossabaw minipigs provide a better model than Yucatan for the metabolic syndrome as they exhibit obesity, insulin resistance and hypertension, all of which are absent in the Yucatan. Similar metabolic/physiological differences exist between domestic breeds (e.g. Meishan v. Pietrain). The modern commercial (e.g. Large White) domestic pig has been the preferred model for developmental programming due to the 2- to 3-fold variation in body weight among littermates providing a natural form of foetal growth retardation not observed in ancient (e.g. Meishan) domestic breeds. Pigs have been increasingly used to study chronic ischaemia, therapeutic angiogenesis, hypertrophic cardiomyopathy and abdominal aortic aneurysm as their coronary anatomy and physiology are similar to humans. Type 1 and II diabetes can be induced in swine using dietary regimes and/or administration of streptozotocin. Pigs are a good and extensively used model for specific nutritional studies as their protein and lipid metabolism is comparable with humans, although pigs are not as sensitive to protein restriction as rodents. Neonatal and weanling pigs have been used to examine the pathophysiology and prevention/treatment of microbial-associated diseases and immune system disorders. A porcine model mimicking various degrees of prematurity in infants receiving total parenteral nutrition has been established to investigate gut development, amino acid metabolism and non-alcoholic fatty liver disease. Endoscopic therapeutic methods for upper gastrointestinal tract bleeding are being developed. Bone remodelling cycle in pigs is histologically more similar to humans than that of rats or mice, and is used to examine the relationship between menopause and osteoporosis. Work has also been conducted on dental implants in pigs to consider loading; however with caution as porcine bone remodels slightly faster than human bone. We conclude that pigs are a valuable translational model to bridge the gap between classical rodent models and humans in developing new therapies to aid human health.

Cannabidiol exerts anti-convulsant effects in animal models of temporal lobe and partial seizures

Cannabis sativa has been associated with contradictory effects upon seizure states despite its medicinal use by numerous people with epilepsy. We have recently shown that the phytocannabinoid cannabidiol (CBD) reduces seizure severity and lethality in the well-established in vivo model of pentylenetetrazoleinduced generalised seizures, suggesting that earlier, small-scale clinical trials examining CBD effects in people with epilepsy warrant renewed attention. Here, we report the effects of pure CBD (1, 10 and 100 mg/kg) in two other established rodent seizure models, the acute pilocarpine model of temporal lobe seizure and the penicillin model of partial seizure. Seizure activity was video recorded and scored offline using model-specific seizure severity scales. In the pilocarpine model CBD (all doses) significantly reduced the percentage of animals experiencing the most severe seizures. In the penicillin model, CBD (�10 mg/kg) significantly decreased the percentage mortality as a result of seizures; CBD (all doses) also decreased the percentage of animals experiencing the most severe tonic–clonic seizures. These results extend the anticonvulsant profile of CBD; when combined with a reported absence of psychoactive effects, this evidence strongly supports CBD as a therapeutic candidate for a diverse range of human epilepsies.

Representing the acquisition and use of energy by individuals in agent-based models of animal populations

Summary 1. Agent-based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use in ABMs should be constructed. 2. Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically. 3. Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate. 4. If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern-oriented modelling, artificial evolution or from allometric equations. 5. The development of ABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. Such ABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests. Keywords: bioenergetics; energy budget; individual-based models; population dynamics.

Animal disease and the limits of local knowledge: dealing with ticks and tick-borne diseases in South Africa

Some proponents of local knowledge, such as Sillitoe (2010), have expressed second thoughts about its capacity to effect development on the ‘revolutionary’ scale once predicted. Our argument in this article follows a similar route. Recent research into the management of livestock in South Africa makes clear that rural African livestock farmers experience uncertainty in relation to the control of stock diseases. State provision of veterinary services has been significantly reduced over the past decade. Both white and African livestock owners are to a greater extent left to their own devices. In some areas of animal disease management, African livestock owners have recourse to tried-and-tested local remedies, which are largely plant-based. But especially in the critical sphere of tick control, efficacious treatments are less evident, and livestock owners struggle to find adequate solutions to high tickloads. This is particularly important in South Africa in the early twenty-first century because land reform and the freedom to purchase land in the post-apartheid context affords African stockowners opportunities to expand livestock holdings. Our research suggests that the limits of local knowledge in dealing with ticks is one of the central problems faced by African livestock owners. We judge this not only in relation to efficacy but also the perceptions of livestock owners themselves. While confidence and practice varies, and there is increasing resort of chemical acaricides we were struck by the uncertainty of livestock owners over the best strategies.

Transcriptional dysregulation of coding and non-coding genes in cellular models of Huntington's disease

HD (Huntington's disease) is a late onset heritable neurodegenerative disorder that is characterized by neuronal dysfunction and death, particularly in the cerebral cortex and medium spiny neurons of the striatum. This is followed by progressive chorea, dementia and emotional dysfunction, eventually resulting in death. HD is caused by an expanded CAG repeat in the first exon of the HD gene that results in an abnormally elongated polyQ (polyglutamine) tract in its protein product, Htt (Huntingtin). Wild-type Htt is largely cytoplasmic; however, in HD, proteolytic N-terminal fragments of Htt form insoluble deposits in both the cytoplasm and nucleus, provoking the idea that mutHtt (mutant Htt) causes transcriptional dysfunction. While a number of specific transcription factors and co-factors have been proposed as mediators of mutHtt toxicity, the causal relationship between these Htt/transcription factor interactions and HD pathology remains unknown. Previous work has highlighted REST [RE1 (repressor element 1)-silencing transcription factor] as one such transcription factor. REST is a master regulator of neuronal genes, repressing their expression. Many of its direct target genes are known or suspected to have a role in HD pathogenesis, including BDNF (brain-derived neurotrophic factor). Recent evidence has also shown that REST regulates transcription of regulatory miRNAs (microRNAs), many of which are known to regulate neuronal gene expression and are dysregulated in HD. Thus repression of miRNAs constitutes a second, indirect mechanism by which REST can alter the neuronal transcriptome in HD. We will describe the evidence that disruption to the REST regulon brought about by a loss of interaction between REST and mutHtt may be a key contributory factor in the widespread dysregulation of gene expression in HD.

Animal infectious disease and climate change: a selected review

It is indisputable that climate is an important factor in many livestock diseases. Nevertheless, our knowledge of the impact of climate change on livestock infectious diseases is much less certain. Therefore, the aim of the article is to conduct a systematic review of the literature on the topic utilizing available retrospective data and information. Across a corpus of 175 formal publications, limited empirical evidence was offered to underpin many of the main arguments. The literature reviewed was highly polarized and often inconsistent regarding what the future may hold. Historical explorations were rare. However, identifying past drivers to livestock disease may not fully capture the extent that new and unknown drivers will influence future change. As such, our current predictive capacity is low. We offer a number of recommendations to strengthen this capacity in the coming years. We conclude that our current approach to research on the topic is limiting and unlikely to yield sufficient, actionable evidence to inform future praxis. Therefore, we argue for the creation of a reflexive, knowledge-based system, underpinned by a collective intelligence framework to support the drawing of inferences across the literature.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.