Computational analysis of determinants of dopamine (DA) dysfunction in DA nerve terminals

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Mathematical Biology Models of Parkinson's Disease

Parkinsons disease (PD) is a progressive neurodegenerative disease with substantial and growing socio-economic burden. In this multifactorial disease, aging, environmental, and genetic factors contribute to neurodegeneration and dopamine (DA) deficiency in the brain. Treatments aimed at DA restoration provide symptomatic relief, however, no disease modifying treatments are available, and PD remains incurable to date. Mathematical modeling can help understand such complex multifactorial neurological diseases. We review mathematical modeling efforts in PD with a focus on mechanistic models of pathogenic processes. We consider models of α-synuclein (Asyn) aggregation, feedbacks among Asyn, DA, and mitochondria and proteolytic systems, as well as pathology propagation through the brain. We hope that critical understanding of existing literature will pave the way to the development of quantitative systems pharmacology models to aid PD drug discovery and development.

The influence of dopaminergic system inhibition on biosynthesis of gonadotrophin-releasing hormone (GnRH) and GnRH receptor in anoestrous sheep; hierarchical role of kisspeptin and RFamide-related peptide-3 (RFRP-3)

This study aimed to explain how prolonged inhibition of central dopaminergic activity affects the cellular processes governing gonadotrophin-releasing hormone (GnRH) and LH secretion in anoestrous sheep. For this purpose, the study included two experimental approaches: first, we investigated the effect of infusion of sulpiride, a dopaminergic D2 receptor antagonist (D2R), on GnRH and GnRH receptor (GnRHR) biosynthesis in the hypothalamus and on GnRHR in the anterior pituitary using an immunoassay. This analysis was supplemented by analysis of plasma LH levels by radioimmunoassay. Second, we used real-time polymerase chain reaction to analyse the influence of sulpiride on the levels of kisspeptin (Kiss1) mRNA in the preoptic area and ventromedial hypothalamus including arcuate nucleus (VMH/ARC), and RFamide-related peptide-3 (RFRP-3) mRNA in the paraventricular nucleus (PVN) and dorsomedial hypothalamic nucleus. Sulpiride significantly increased plasma LH concentration and the levels of GnRH and GnRHR in the hypothalamic–pituitary unit. The abolition of dopaminergic activity resulted in a significant increase in transcript level of Kiss1 in VMH/ARC and a decrease of RFRP-3 in PVN. The study demonstrates that dopaminergic neurotransmission through D2R is involved in the regulatory pathways of GnRH and GnRHR biosynthesis in the hypothalamic–pituitary unit of anoestrous sheep, conceivably via mechanisms in which Kiss1 and RFRP-3 participate.

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.

A constraint-based modelling approach to metabolic dysfunction in Parkinson's disease

One of the hallmarks of sporadic Parkinson's disease is degeneration of dopaminergic neurons in the pars compacta of the substantia nigra. The aetiopathogenesis of this degeneration is still not fully understood, with dysfunction of many biochemical pathways in different subsystems suggested to be involved. Recent advances in constraint-based modelling approaches hold great potential to systematically examine the relative contribution of dysfunction in disparate pathways to dopaminergic neuronal degeneration, but few studies have employed these methods in Parkinson's disease research. Therefore, this review outlines a framework for future constraint-based modelling of dopaminergic neuronal metabolism to decipher the multi-factorial mechanisms underlying the neuronal pathology of Parkinson's disease.

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.

Metabolomics and in-silico analysis reveal critical energy deregulations in animal models of Parkinson's disease

Parkinson's disease (PD) is a multifactorial disease known to result from a variety of factors. Although age is the principal risk factor, other etiological mechanisms have been identified, including gene mutations and exposure to toxins. Deregulation of energy metabolism, mostly through the loss of complex I efficiency, is involved in disease progression in both the genetic and sporadic forms of the disease. In this study, we investigated energy deregulation in the cerebral tissue of animal models (genetic and toxin induced) of PD using an approach that combines metabolomics and mathematical modelling. In a first step, quantitative measurements of energy-related metabolites in mouse brain slices revealed most affected pathways. A genetic model of PD, the Park2 knockout, was compared to the effect of CCCP, a mitochondrial uncoupler [corrected]. Model simulated and experimental results revealed a significant and sustained decrease in ATP after CCCP exposure, but not in the genetic mice model. In support to data analysis, a mathematical model of the relevant metabolic pathways was developed and calibrated onto experimental data. In this work, we show that a short-term stress response in nucleotide scavenging is most probably induced by the toxin exposure. In turn, the robustness of energy-related pathways in the model explains how genetic perturbations, at least in young animals, are not sufficient to induce significant changes at the metabolite level.

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.

Canonical modeling of the multi-scale regulation of the heat stress response in yeast

Heat is one of the most fundamental and ancient environmental stresses, and response mechanisms are found in prokaryotes and shared among most eukaryotes. In the budding yeast Saccharomyces cerevisiae, the heat stress response involves coordinated changes at all biological levels, from gene expression to protein and metabolite abundances, and to temporary adjustments in physiology. Due to its integrative multi-level-multi-scale nature, heat adaptation constitutes a complex dynamic process, which has forced most experimental and modeling analyses in the past to focus on just one or a few of its aspects. Here we review the basic components of the heat stress response in yeast and outline what has been done, and what needs to be done, to merge the available information into computational structures that permit comprehensive diagnostics, interrogation, and interpretation. We illustrate the process in particular with the coordination of two metabolic responses, namely the dramatic accumulation of the protective disaccharide trehalose and the substantial change in the profile of sphingolipids, which in turn affect gene expression. The proposed methods primarily use differential equations in the canonical modeling framework of Biochemical Systems Theory (BST), which permits the relatively easy construction of coarse, initial models even in systems that are incompletely characterized.

An integrated genomic analysis of gene-function correlation on schizophrenia susceptibility genes

Schizophrenia is a highly complex inheritable disease characterized by numerous genetic susceptibility elements, each contributing a modest increase in risk for the disease. Although numerous linkage or association studies have identified a large set of schizophrenia-associated loci, many are controversial. In addition, only a small portion of these loci overlaps with the large cumulative pool of genes that have shown changes of expression in schizophrenia. Here, we applied a genomic gene-function approach to identify susceptibility loci that show direct effect on gene expression, leading to functional abnormalities in schizophrenia. We carried out an integrated analysis by cross-examination of the literature-based susceptibility loci with the schizophrenia-associated expression gene list obtained from our previous microarray study (Journal of Human Genetics (2009) 54: 665-75) using bioinformatic tools, followed by confirmation of gene expression changes using qPCR. We found nine genes (CHGB, SLC18A2, SLC25A27, ESD, C4A/C4B, TCP1, CHL1 and CTNNA2) demonstrate gene-function correlation involving: synapse and neurotransmission; energy metabolism and defense mechanisms; and molecular chaperone and cytoskeleton. Our findings further support the roles of these genes in genetic influence and functional consequences on the development of schizophrenia. It is interesting to note that four of the nine genes are located on chromosome 6, suggesting a special chromosomal vulnerability in schizophrenia.

MicroRNA-210: a unique and pleiotropic hypoxamir

Inadequate oxygen availability or hypoxia induces a complex and still incompletely understood set of adaptations that influence cellular survival and function. Many of these adaptations are directly controlled by a master transcription factor, hypoxia inducible factor-alpha (HIF-α). In response to hypoxia, HIF-α levels increase and directly induce the transcription of > 100 genes, influencing functions ranging from metabolism, survival, proliferation, migration, to angiogenesis, among others. Recently, it has been demonstrated that a specific set of microRNA molecules are upregulated by hypoxia, which we denote here as "hypoxamirs." In particular, the HIF-responsive hypoxamir microRNA-210 (miR-210) is a unique microRNA that is evolutionarily conserved and ubiquitously expressed in hypoxic cell and tissue types. A number of direct targets of miR-210 have been identified by in silico, transcriptional, and biochemical methods, a subset of which have been extensively validated. As a result, miR-210 has been mechanistically linked to the control of a wide range of cellular responses known to influence normal developmental physiology as well as a number of hypoxia-dependent disease states, including tissue ischemia, inflammation, and tumorigenesis. Thus, reflecting the pleiotropic actions of HIF-α, miR-210 appears to function as a "master microRNA" relevant for the control of diverse functions in the hypoxic state.