Targeting mGlu5 metabotropic glutamate receptors in the treatment of cognitive dysfunction in a mouse model of phenylketonuria

We studied group-I metabotropic glutamate (mGlu) receptors in Pah(enu2) (ENU2) mice, which mimic the genetics and neurobiology of human phenylketonuria (PKU), a metabolic disorder characterized, if untreated, by autism, and intellectual disability (ID). Male ENU2 mice showed increased mGlu5 receptor protein levels in the hippocampus and corpus striatum (but not in the prefrontal cortex) whereas the transcript of the mGlu5 receptor was unchanged. No changes in mGlu1 receptor mRNA and protein levels were found in any of the three brain regions of ENU2 mice. We extended the analysis to Homer proteins, which act as scaffolds by linking mGlu1 and mGlu5 receptors to effector proteins. Expression of the long isoforms of Homer was significantly reduced in the hippocampus of ENU2 mice, whereas levels of the short Homer isoform (Homer 1a) were unchanged. mGlu5 receptors were less associated to immunoprecipitated Homer in the hippocampus of ENU2 mice. The lack of mGlu5 receptor-mediated long-term depression (LTD) in wild-type mice (of BTBR strain) precluded the analysis of hippocampal synaptic plasticity in ENU2 mice. We therefore performed a behavioral analysis to examine whether pharmacological blockade of mGlu5 receptors could correct behavioral abnormalities in ENU2 mice. Using the same apparatus we sequentially assessed locomotor activity, object exploration, and spatial object recognition (spatial novelty test) after displacing some of the objects from their original position in the arena. Systemic treatment with the mGlu5 receptor antagonist, MPEP (20 mg/kg, i.p.), had a striking effect in the spatial novelty test by substantially increasing the time spent in exploring the displaced objects in ENU2 mice (but not in wild-type mice). These suggest a role for mGlu5 receptors in the pathophysiology of ID in PKU and suggest that, also in adult untreated animals, cognitive dysfunction may be improved by targeting these receptors with an appropriate therapy

Antidepressant activity of fingolimod in mice

Recent findings indicate that fingolimod, the first oral drug approved for the treatment of multiple sclerosis (MS), acts as a direct inhibitor of histone deacetylases (HDACs) and enhances the production of brain-derived neurotrophic factor (BDNF) in the CNS. Both mechanisms are relevant to the pathophysiology and treatment of major depression. We examined the antidepressant activity of fingolimod in mice subjected to chronic unpredictable stress (CUS), a model of reactive depression endowed with face and pharmacological validity. Chronic treatment with fingolimod (3\ua0mg\ua0kg(-1), i.p., once a day for 4\ua0weeks) reduced the immobility time in the forced swim test (FST) in a large proportion of CUS mice. This treatment also caused anxiogenic-like effects in the social interaction test without affecting anxiety-like behavior in the elevated plus maze or spatial learning in the water maze. CUS mice showed reduced BDNF levels and enhanced HDAC2 levels in the hippocampus. These changes were reversed by fingolimod exclusively in mice that showed a behavioral response to the drug in the FST. Fingolimod treatment also enhanced H3 histone K14-acetylation and adult neurogenesis in the hippocampus of CUS mice. Fingolimod did not affect most of the parameters we have tested in unstressed control mice. The antidepressant-like activity of fingolimod was confirmed in mice chronically treated with corticosterone. These findings show for the first time that fingolimod exerts antidepressant-like effect acting in a "disease-dependent" manner, and raise the interesting possibility that the drug could relieve depressive symptoms in MS patients independently of its disease-modifying effect on MS

Antidepressant activity of fingolimod in mice

Recent findings indicate that fingolimod, the first oral drug approved for the treatment of multiple sclerosis (MS), acts as a direct inhibitor of histone deacetylases (HDACs) and enhances the production of brain-derived neurotrophic factor (BDNF) in the CNS. Both mechanisms are relevant to the pathophysiology and treatment of major depression. We examined the antidepressant activity of fingolimod in mice subjected to chronic unpredictable stress (CUS), a model of reactive depression endowed with face and pharmacological validity. Chronic treatment with fingolimod (3\ua0mg\ua0kg(-1), i.p., once a day for 4\ua0weeks) reduced the immobility time in the forced swim test (FST) in a large proportion of CUS mice. This treatment also caused anxiogenic-like effects in the social interaction test without affecting anxiety-like behavior in the elevated plus maze or spatial learning in the water maze. CUS mice showed reduced BDNF levels and enhanced HDAC2 levels in the hippocampus. These changes were reversed by fingolimod exclusively in mice that showed a behavioral response to the drug in the FST. Fingolimod treatment also enhanced H3 histone K14-acetylation and adult neurogenesis in the hippocampus of CUS mice. Fingolimod did not affect most of the parameters we have tested in unstressed control mice. The antidepressant-like activity of fingolimod was confirmed in mice chronically treated with corticosterone. These findings show for the first time that fingolimod exerts antidepressant-like effect acting in a "disease-dependent" manner, and raise the interesting possibility that the drug could relieve depressive symptoms in MS patients independently of its disease-modifying effect on MS

Interest rates calibration with a CIR model

The purpose of this paper is to model interest rates from observed financial market data through a new approach to the Cox–Ingersoll–Ross (CIR) model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek framework. However, there are a number of issues in describing interest rate dynamics within the CIR framework on which focus should be placed. Therefore, a new methodology has been proposed that allows forecasting future expected interest rates from observed financial market data by preserving the structure of the original CIR model, even with negative interest rates. The performance of the new approach, tested on monthly-recorded interest rates data, provides a good fit to current data for different term structures. Design/methodology/approach To ensure a fitting close to current interest rates, the innovative step in the proposed procedure consists in partitioning the entire available market data sample, usually showing a mixture of probability distributions of the same type, in a suitable number of sub-sample having a normal/gamma distribution. An appropriate translation of market interest rates to positive values has been introduced to overcome the issue of negative/near-to-zero values. Then, the CIR model parameters have been calibrated to the shifted market interest rates and simulated the expected values of interest rates by a Monte Carlo discretization scheme. We have analysed the empirical performance of the proposed methodology for two different monthly-recorded EUR data samples in a money market and a long-term data set, respectively. Findings Better results are shown in terms of the root mean square error when a segmentation of the data sample in normally distributed sub-samples is considered. After assessing the accuracy of the proposed procedure, the implemented algorithm was applied to forecast next-month expected interest rates over a historical period of 12 months (fixed window). Through an error analysis, it was observed that our algorithm provides a better fitting of the predicted expected interest rates to market data than the exponentially weighted moving average model. A further confirmation of the efficiency of the proposed algorithm and of the quality of the calibration of the CIR parameters to the observed market interest rates is given by applying the proposed forecasting technique. Originality/value This paper has the objective of modelling interest rates from observed financial market data through a new approach to the CIR model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek model (Section 2). However, there are a number of issues in describing short-term interest rate dynamics within the CIR framework on which focus should be placed. A new methodology has been proposed that allows us to forecast future expected short-term interest rates from observed financial market data by preserving the structure of the original CIR model. The performance of the new approach, tested on monthly data, provides a good fit for different term structures. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, clustered volatility and skewed tails), as well as the new ones added by the current market environment (particularly the need to model a downward trend to negative interest rates

Forecasting interest rates through Vasicek and CIR models: a partitioning approach

The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates (for each maturity) based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expected rates at a given maturity, consists in an appropriate partitioning of the data sample. This allows capturing all the statistically significant time changes in volatility of interest rates, thus giving an account of jumps in market dynamics. The performance of the new approach is carried out for different term structures and is tested for both models. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, volatility clustering, skewed tails, etc.) as well as the new ones added by the current market environment characterized by low to negative interest rates

Cigarette smoke inhibits adenine nucleotide hydrolysis by human platelets

Cigarette smoking is a recognized risk factor for cardiovascular diseases and has been implicated in the pathogenesis of atherosclerosis and thrombotic events. In athero-thrombotic diseases, the extracellular adenine nucleotides play an important role by triggering a range of effects such as the recruitment and activation of platelets, endothelial cell activation and vasoconstriction. NTPDase, a plasma membrane-bound enzyme, is the most relevant enzyme involved in the hydrolysis of extracellular tri- and di-phosphate nucleotides to adenosine monophosphate, which is further degraded by 5′ectonucleotidase to the anti-thrombotic and anti-inflammatory mediator adenosine. Thus, the preserved activity of these enzymes, regulating the extracellular concentrations of nucleotides, is critical in thromboregulatory functions. In the present in vitro study, performed on human platelets suspended in undiluted or diluted aqueous cigarette smoke extract (aCSE), we demonstrated that undiluted and 1 : 2 diluted aCSE is able to significantly reduce ADP hydrolysis (-24% and 12%, respectively) by intact human platelets. ATP degradation was also reduced (-31%) by undiluted aCSE. Conversely, aCSE did not alter platelet AMP hydrolysis. Results obtained by using N-acetylcysteine, a thiol-containing antioxidant, suggest that stable oxidants present in aCSE are responsible for the platelet NTPDase inhibition induced by aCSE. The decreased adenine nucleotide degradation could play a significant role in the extensive platelet activation and vascular inflammation observed in chronic smokers. © 2008 Informa UK Ltd