Computational Modeling of Synaptic Neurotransmission as a Tool for Assessing Dopamine Hypotheses of Schizophrenia

Selenium deficiency is associated with disease severity, disrupted reward processing, and increased suicide risk in patients with Anorexia Nervosa

BACKGROUND & AIMS

Patients with Anorexia Nervosa (AN) present many nutritional deficiencies (macro- and often also micro-nutrients), possibly explained by their inadequate food intake. Previous studies reported that selenium (Se) deficiency is common in the general population. As Se can be easily added as a supplement, the goal of this study was to evaluate the clinical impact of Se deficiency in patients with AN.

METHODS

This cross-sectional study concerned 153 patients with AN (92.9% women) followed at the Eating Disorder Unit of Lapeyronie Academic Hospital, Montpellier, France. Patients underwent an extensive neuropsychological assessment, and completed validated questionnaires. Blood samples were collected for Se quantification. Results were compared with the t-test, Mann-Whitney U, and Chi square tests, and univariate linear and multivariate logistic regression models.

RESULTS

Se plasma levels were below the cut-off of 80 µg/L in 53.6% (N = 82) of patients. AN onset was earlier in patients with Se deficiency, (p = .005), whereas disease duration was comparable between groups (p = .77). General eating disorder symptomatology in the past 28 days (Eating Disorder Examination Questionnaire) was more severe in patients with Se deficiency (p = .010). The suicide risk (MINI International Neuropsychiatric Evaluation) tended to be higher (p = .037), and suicide attempt history was more frequent (28.39% vs 9.85%, p = .004) in patients with low Se levels. Se plasma concentration was negatively correlated with the performance in the temporal delayed discounting task (p = .006).

CONCLUSIONS

Our findings suggest that in patients with AN, Se plasma concentration might be implicated in disease severity and suicide risk. The finding that Se deficiency in patients with AN was associated only with reward-related processes, but not with other psychological functions suggests the selective involvement of dopamine-related pathways. Our results suggest that it might be useful to monitor the plasma micronutrient profile in patients with AN. Future studies should determine whether Se supplementation in AN might improve clinical outcomes.

DOPA Homeostasis by Dopamine: A Control-Theoretic View

Dopamine (DA) is an important signal mediator in the brain as well as in the periphery. The term "dopamine homeostasis" occasionally found in the literature refers to the fact that abnormal DA levels can be associated with a variety of neuropsychiatric disorders. An analysis of the negative feedback inhibition of tyrosine hydroxylase (TH) by DA indicates, with support from the experimental data, that the TH-DA negative feedback loop has developed to exhibit 3,4-dihydroxyphenylalanine (DOPA) homeostasis by using DA as a derepression regulator. DA levels generally decline when DOPA is removed, for example, by increased oxidative stress. Robust DOPA regulation by DA further implies that maximum vesicular DA levels are established, which appear necessary for a reliable translation of neural activity into a corresponding chemical transmitter signal. An uncontrolled continuous rise (windup) in DA occurs when Levodopa treatment exceeds a critical dose. Increased oxidative stress leads to the successive breakdown of DOPA homeostasis and to a corresponding reduction in DA levels. To keep DOPA regulation robust, the vesicular DA loading requires close to zero-order kinetics combined with a sufficiently high compensatory flux provided by TH. The protection of DOPA and DA due to a channeling complex is discussed.

Medicinal chemistry strategies for the development of phosphodiesterase 10A (PDE10A) inhibitors - An update of recent progress

Phosphodiesterase 10A is a member of Phosphodiesterase (PDE)-superfamily of the enzyme which is responsible for hydrolysis of cAMP and cGMP to their inactive forms 5'-AMP and 5'-GMP, respectively. PDE10A is highly expressed in the brain, particularly in the putamen and caudate nucleus. PDE10A plays an important role in the regulation of localization, duration, and amplitude of the cyclic nucleotide signalling within the subcellular domain of these regions, and thereby modulation of PDE10A enzyme can give rise to a new therapeutic approach in the treatment of schizophrenia and other neurodegenerative disorders. Limitation of the conventional therapy of schizophrenia forced the pharmaceutical industry to move their efforts to develop a novel treatment approach with reduced side effects. In the past decade, considerable developments have been made in pursuit of PDE10A centric antipsychotic agents by several pharmaceutical industries due to the distribution of PDE10A in the brain and the ability of PDE10A inhibitors to mimic the effect of D2 antagonists and D1 agonists. However, no selective PDE10A inhibitor is currently available in the market for the treatment of schizophrenia. The present compilation concisely describes the role of PDE10A inhibitors in the therapy of neurodegenerative disorders mainly in psychosis, the structure of PDE10A enzyme, key interaction of different PDE10A inhibitors with human PDE10A enzyme and recent medicinal chemistry developments in designing of safe and effective PDE10A inhibitors for the treatment of schizophrenia. The present compilation also provides useful information and future direction to bring further improvements in the discovery of PDE10A inhibitors.

Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity

Precision medicine considers an individual’s unique physiological characteristics as strongly influential in disease vulnerability and in response to specific therapies. Predicting an individual’s susceptibility to developing an illness, making an accurate diagnosis, maximizing therapeutic effects, and minimizing adverse effects for treatment are essential in precision medicine. We introduced model-based precision medicine optimization approaches, including pathogenesis, biomarker detection, and drug target discovery, for treating presynaptic dopamine overactivity. Three classes of one-hit and two-hit enzyme defects were detected as the causes of disease states by the optimization approach of pathogenesis. The cluster analysis and support vector machine was used to detect optimal biomarkers in order to discriminate the accurate etiology from three classes of disease states. Finally, the fuzzy decision-making method was employed to discover common and specific drug targets for each classified disease state. We observed that more accurate diagnoses achieved higher satisfaction grades and dosed fewer enzyme targets to treat the disease. Furthermore, satisfaction grades for common drugs were lower than for specific ones, but common drugs could simultaneously treat several disease states that had different etiologies.

A heuristic model for working memory deficit in schizophrenia

BACKGROUND

The life of schizophrenia patients is severely affected by deficits in working memory. In various brain regions, the reciprocal interactions between excitatory glutamatergic neurons and inhibitory GABAergic neurons are crucial. Other neurotransmitters, in particular dopamine, serotonin, acetylcholine, and norepinephrine, modulate the local balance between glutamate and GABA and therefore regulate the function of brain regions. Persistent alterations in the balances between the neurotransmitters can result in working memory deficits.

METHODS

Here we present a heuristic computational model that accounts for interactions among neurotransmitters across various brain regions. The model is based on the concept of a neurochemical interaction matrix at the biochemical level and combines this matrix with a mobile model representing physiological dynamic balances among neurotransmitter systems associated with working memory.

RESULTS

The comparison of clinical and simulation results demonstrates that the model output is qualitatively very consistent with the available data. In addition, the model captured how perturbations migrated through different neurotransmitters and brain regions. Results showed that chronic administration of ketamine can cause a variety of imbalances, and application of an antagonist of the D2 receptor in PFC can also induce imbalances but in a very different manner.

CONCLUSIONS

The heuristic computational model permits a variety of assessments of genetic, biochemical, and pharmacological perturbations and serves as an intuitive tool for explaining clinical and biological observations.

GENERAL SIGNIFICANCE

The heuristic model is more intuitive than biophysically detailed models. It can serve as an important tool for interdisciplinary communication and even for psychiatric education of patients and relatives. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Computational neuropsychiatry - schizophrenia as a cognitive brain network disorder

Computational modeling of functional brain networks in fMRI data has advanced the understanding of higher cognitive function. It is hypothesized that functional networks mediating higher cognitive processes are disrupted in people with schizophrenia. In this article, we review studies that applied measures of functional and effective connectivity to fMRI data during cognitive tasks, in particular working memory fMRI studies. We provide a conceptual summary of the main findings in fMRI data and their relationship with neurotransmitter systems, which are known to be altered in individuals with schizophrenia. We consider possible developments in computational neuropsychiatry, which are likely to further our understanding of how key functional networks are altered in schizophrenia.

Rotenone and paraquat perturb dopamine metabolism: A computational analysis of pesticide toxicity

Pesticides, such as rotenone and paraquat, are suspected in the pathogenesis of Parkinson's disease (PD), whose hallmark is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Thus, compounds expected to play a role in the pathogenesis of PD will likely impact the function of dopaminergic neurons. To explore the relationship between pesticide exposure and dopaminergic toxicity, we developed a custom-tailored mathematical model of dopamine metabolism and utilized it to infer potential mechanisms underlying the toxicity of rotenone and paraquat, asking how these pesticides perturb specific processes. We performed two types of analyses, which are conceptually different and complement each other. The first analysis, a purely algebraic reverse engineering approach, analytically and deterministically computes the altered profile of enzyme activities that characterize the effects of a pesticide. The second method consists of large-scale Monte Carlo simulations that statistically reveal possible mechanisms of pesticides. The results from the reverse engineering approach show that rotenone and paraquat exposures lead to distinctly different flux perturbations. Rotenone seems to affect all fluxes associated with dopamine compartmentalization, whereas paraquat exposure perturbs fluxes associated with dopamine and its breakdown metabolites. The statistical results of the Monte-Carlo analysis suggest several specific mechanisms. The findings are interesting, because no a priori assumptions are made regarding specific pesticide actions, and all parameters characterizing the processes in the dopamine model are treated in an unbiased manner. Our results show how approaches from computational systems biology can help identify mechanisms underlying the toxicity of pesticide exposure.

Biochemical Systems Theory: A Review

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. This paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. The paper is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.

Systems biology, bioinformatics, and biomarkers in neuropsychiatry

Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

Systems biology in psychiatric research: from complex data sets over wiring diagrams to computer simulations

The classification of psychiatric disorders has always been a problem in clinical settings. The present debate about the major systems in clinical practice, DSM-IV and ICD-10, has resulted in attempts to improve and replace those schemes by some that include more endophenotypic and molecular features. However, these disorders not only require more precise diagnostic tools, but also have to be viewed more extensively in their dynamic behaviors, which require more precise data sets related to their origins and developments. This enormous challenge in brain research has to be approached on different levels of the biological system by new methods, including improvements in electroencephalography, brain imaging, and molecular biology. All these methods entail accumulations of large data sets that become more and more difficult to interpret. In particular, on the molecular level, there is an apparent need to use highly sophisticated computer programs to tackle these problems. Evidently, only interdisciplinary work among mathematicians, physicists, biologists, and clinicians can further improve our understanding of complex diseases of the brain.

Effects of amphetamine on subcellular distribution of dopamine and DOPAC

Amphetamine effects on distribution of dopamine, 3,4-dihydroxyphenylacetic acid (DOPAC), and amphetamine in vesicular, cytosolic, and extracellular compartments associated with a striatal varicosity were estimated through use of a computer simulation model. In addition, contribution to overall effects of amphetamine by each of five actions--transport by dopamine transporter (DAT), transport by vesicular monoamine transporter, stimulation of reverse transport, inhibition of monoamine oxidase (MAO), and slowing of dopamine cell firing rate--were evaluated. Amphetamine enters a varicosity almost entirely by DAT and accumulates to very high levels within the varicosity. Both reverse transport by DAT and passive diffusion contribute to continual amphetamine egress across the plasma membrane. Amphetamine enters storage vesicles by both transport and diffusion. The transport portion competes with dopamine storage, resulting in redistribution of approximately half of dopamine from vesicles to cytosol. The high concentration of amphetamine in the cytosol inhibits MAO, protecting cytosolic dopamine. A very small fraction of cytosolic dopamine is moved to extracellular compartment via reverse transport by DAT. The amount of dopamine moved by reverse transport is limited because of competition by very high cytosolic levels of amphetamine. In the presence of amphetamine, rate of dopamine transfer to extracellular compartment is less than control; however, high levels of extracellular dopamine are maintained because amphetamine occupies the DAT, thus limiting dopamine reuptake. Simulation output from a model using exchange-diffusion mechanism of reverse transport does not match all published data that were simulated, suggesting that inward transport of a substrate is not required to initiate reverse transport.

A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior

The neural mechanisms underlying schizophrenic behavior are unknown and very difficult to investigate experimentally, although a few experimental and modeling studies suggested possible causes for some of the typical psychotic symptoms related to this disease. The brain region most involved in these processes seems to be the hippocampus, because of its critical role in establishing memories for objects or events in the context in which they occur. In particular, a hypofunction of the N-methyl-D-aspartate (NMDA) component of the synaptic input on the distal dendrites of CA1 pyramidal neurons has been suggested to play an important role for the emergence of schizophrenic behavior. Modeling studies have investigated this issue at the network and cellular level. Here, starting from the experimentally supported assumption that hippocampal neurons are very specific, sparse, and invariant in their firing, we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a context-dependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for feature recognition, leading to a distorted perception of objects.