Analysis of abnormalities in purine metabolism leading to gout and to neurological dysfunctions in man

Spermidine attenuates monocrotaline-induced pulmonary arterial hypertension in rats by inhibiting purine metabolism and polyamine synthesis-associated vascular remodeling

Ensuring the homeostatic integrity of pulmonary artery endothelial cells (PAECs) is essential for combatting pulmonary arterial hypertension (PAH), as it equips the cells to withstand microenvironmental challenges. Spermidine (SPD), a potent facilitator of autophagy, has been identified as a significant contributor to PAECs function and survival. Despite SPD's observed benefits, a comprehensive understanding of its protective mechanisms has remained elusive. Through an integrated approach combining metabolomics and molecular biology, this study uncovers the molecular pathways employed by SPD in mitigating PAH induced by monocrotaline (MCT) in a Sprague-Dawley rat model. The study demonstrates that SPD administration (5 mg/kg/day) significantly corrects right ventricular impairment and pathological changes in pulmonary tissues following MCT exposure (60 mg/kg). Metabolomic profiling identified a purine metabolism disorder in MCT-treated rats, which SPD effectively normalized, conferring a protective effect against PAH progression. Subsequent in vitro analysis showed that SPD (0.8 mM) reduces oxidative stress and apoptosis in PAECs challenged with Dehydromonocrotaline (MCTP, 50 μM), likely by downregulating purine nucleoside phosphorylase (PNP) and modulating polyamine biosynthesis through alterations in S-adenosylmethionine decarboxylase (AMD1) expression and the subsequent production of decarboxylated S-adenosylmethionine (dcSAM). These findings advocate SPD's dual inhibitory effect on PNP and AMD1 as a novel strategy to conserve cellular ATP and alleviate oxidative injuries, thus providing a foundation for SPD's potential therapeutic application in PAH treatment.

Inborn errors of purine and pyrimidine metabolism: A guide to diagnosis

Inborn errors of purine and pyrimidine (P/P) metabolism are under-reported and rarely mentioned in the general literature or in clinical practice, as well as in reviews dedicated to other inborn errors of metabolism (IEMs). However, their diagnosis is important because genetic counseling can be provided and, in some cases, specific treatment exists that may slow or even reverse clinical signs. The purpose of this review is to provide a practical guideline on the suspicion and investigation of inborn errors of P/P metabolism. Failure of a physician to recognize the presence of these disorders may be devastating for affected infants and children because of its permanent effects in the patient, and for their parents because of implications for future offspring. Diagnosis is crucial because genetic counseling can be provided and, in some cases, specific treatment can be offered that may slow or even reverse clinical symptoms. This review highlights the risk factors in the history, the important examination findings, and the appropriate biochemical investigation of the child. Herein we describe the approach to the diagnosis of P/P disorders and emphasize clinical situations in which physicians should consider these diseases as diagnostic possibilities.

Hypoxanthine-guanine phosphoribosyltransferase is activated via positive cooperativity between guanine and IMP

Hypoxanthine-guanine phosphoribosyltransferase (HGPRT) is a key enzyme in the purine salvage pathway. Here, the reverse reaction of HGPRT from the thermophilic bacterium Hungateiclostridium thermocellum was studied in the presence of IMP and pyrophosphate. As for the human enzyme, the bacterial HGPRT was activated by guanine. Furthermore, guanine was found to operate as both an activator and an inhibitor. Intriguingly, within the concentration range of guanine where it functions as the activator, the K_m value for IMP was not influenced by guanine. Consequently, guanine was found to noncompetitively activate the reverse reaction toward IMP. Here, we propose a reaction scheme that explains the activation mechanism in which the enzyme forms a chimeric oligomer bound to both IMP and guanine.

Serum lipidomics reveals distinct metabolic profiles for asymptomatic hyperuricemic and gout patients

OBJECTIVES

This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and find potential underlying pathological mechanisms therefrom.

METHODS

Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as center 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as center 2 (external validation set) including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by UHPLC-Q Exactive MS. Differential metabolites were identifed by both variable importance in the projection ≥ 1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted p ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis.

RESULTS

In the discovery set, a total of 245 and 150 metabolites respectively were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included DAG, TAG, PC, PE, PI, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under ROC curve was 0.8288, the sensitivity was 82%, the specificity was 78% at a 95% confidence interval from 0.747-0.9106. In internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while 84.03% in external validation set.

CONCLUSION

We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients.

Physiology, Development, and Disease Modeling in the Drosophila Excretory System

The insect excretory system contains two organ systems acting in concert: the Malpighian tubules and the hindgut perform essential roles in excretion and ionic and osmotic homeostasis. For over 350 years, these two organs have fascinated biologists as a model of organ structure and function. As part of a recent surge in interest, research on the Malpighian tubules and hindgut of Drosophila have uncovered important paradigms of organ physiology and development. Further, many human disease processes can be modeled in these organs. Here, focusing on discoveries in the past 10 years, we provide an overview of the anatomy and physiology of the Drosophila excretory system. We describe the major developmental events that build these organs during embryogenesis, remodel them during metamorphosis, and repair them following injury. Finally, we highlight the use of the Malpighian tubules and hindgut as accessible models of human disease biology. The Malpighian tubule is a particularly excellent model to study rapid fluid transport, neuroendocrine control of renal function, and modeling of numerous human renal conditions such as kidney stones, while the hindgut provides an outstanding model for processes such as the role of cell chirality in development, nonstem cell-based injury repair, cancer-promoting processes, and communication between the intestine and nervous system.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

Combinational Biomarkers for Atrial Fibrillation Derived from Atrial Appendage and Plasma Metabolomics Analysis

Atrial fibrillation (AF) is one of the most common types of arrhythmias and often leads to clinical complications. The objectives of this study were to offer insights into the metabolites of AF and to determine biomarkers for AF diagnosis or prediction. Sixty atrial appendage samples (AF group: 30; non-AF group: 30) and 163 plasma samples (AF group: 48; non-AF group: 115) from 49 AF patients and 116 non-AF patients were subjected to liquid chromatography positive ion electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) metabolomics analysis. Consequently, 24 metabolites in atrial appendage samples and 24 metabolites in plasma samples were found to reflect metabolic differences between AF and non-AF patients (variable importance in projection (VIP) ≥ 1, P ≤ 0.05). Five identical metabolites including creatinine, D-glutamic acid, choline, hypoxanthine, and niacinamide (VIP ≥ 1.5, P < 0.01, FDR < 0.05) in atrial appendage and plasma samples were considered prominent features of AF patients, and the D-glutamine and D-glutamate metabolic pathway was also identified as a feature of AF patients. Finally, in plasma samples, the combination of D-glutamic acid, creatinine, and choline had an AUC value of 0.927 (95% CI: 0.875-0.979, P < 0.001) and displayed 90.5% sensitivity and 83.3% specificity; this group of metabolites was thus defined as a combinational biomarker for the recognition of AF and non-AF patients.

Strategies for Comparing Metabolic Profiles: Implications for the Inference of Biochemical Mechanisms from Metabolomics Data

BACKGROUND

Large amounts of metabolomics data have been accumulated in recent years and await analysis. Previously, we had developed a systems biology approach to infer biochemical mechanisms underlying metabolic alterations observed in cancers and other diseases. The method utilized the typical Euclidean distance for comparing metabolic profiles. Here, we ask whether any of the numerous alternative metrics might serve this purpose better.

METHODS AND FINDINGS

We used enzymatic alterations in purine metabolism that were measured in human renal cell carcinoma to test various metrics with the goal of identifying the best metrics for discerning metabolic profiles of healthy and diseased individuals. The results showed that several metrics have similarly good performance, but that some are unsuited for comparisons of metabolic profiles. Furthermore, the results suggest that relative changes in metabolite levels, which reduce bias toward large metabolite concentrations, are better suited for comparisons of metabolic profiles than absolute changes. Finally, we demonstrate that a sequential search for enzymatic alterations, ranked by importance, is not always valid.

CONCLUSIONS

We identified metrics that are appropriate for comparisons of metabolic profiles. In addition, we constructed strategic guidelines for the algorithmic identification of biochemical mechanisms from metabolomics data.

Metabolic modeling helps interpret transcriptomic changes during malaria

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Inference of cancer mechanisms through computational systems analysis

Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite generic; its efficacy is demonstrated by the analysis of changes in purine metabolism of human renal cell carcinoma. The method and results are essentially unbiased and tolerate noise in the data well. The proposed method correctly identified and accurately quantified primary enzymatic alterations in cancer, and these account for over 80% of the metabolic alterations in the investigated carcinoma. Interestingly, the two primary action sites are not the most sensitive reaction steps in purine metabolism, which implies that sensitivity analysis is not a valid approach for identifying cancer targets. The proposed method exhibits statistically high precision and robustness even for analyses of moderately incomplete metabolomics data. By permitting analyses of individual metabolic profiles, the method may become a tool of personalized precision medicine.

Tuna extract reduces serum uric acid in gout-free subjects with insignificantly high serum uric acid: A randomized controlled trial

Long-term reduction of serum urate levels is vital in the treatment of gout. However, it is difficult to convince gout-free individuals of the necessity of treatment as few appropriate over-the-counter remedies and dietary supplements are available. Therefore, the present study aimed to investigate the antihyperuricemic efficacy and safety of a tuna extract containing the imidazole compounds to evaluate its potential as a functional food ingredient. A randomized, 4-week, double-blind, placebo-controlled study was conducted. A total of 48 male gout-free subjects with insignificantly high serum uric acid were randomly assigned to low- and high-dose tuna extract groups or a placebo group. The efficacy of the extract was assessed by measuring serum uric acid levels. Furthermore, a safety assessment was performed by physical parameters, hematology, blood biochemistry and urinalysis. The results indicated that the uric acid level was decreased at week 4 during the intervention in the tuna extract groups (low and high dose, -0.23 and -0.34 mg/dl, respectively) compared to the placebo group (-0.07 mg/dl). At week 4 after the intervention, a significant reduction in uric acid levels (-0.41 mg/dl; P<0.05) was observed in the high-dose tuna extract group compared with the placebo group (+0.11 mg/dl). No dose-related adverse events were observed during and following the intervention. Therefore, the present results suggest that oral administration of tuna extract containing the imidazole compounds has hypouricemic activity with no undesirable side effects.

Plasma Hypoxanthine-Guanine Phosphoribosyl Transferase Activity in Bottlenose Dolphins Contributes to Avoiding Accumulation of Non-recyclable Purines

Marine mammals are exposed to ischemia/reperfusion and hypoxia/reoxygenation during diving. During oxygen deprivation, adenosine triphosphate (ATP) breakdown implies purine metabolite accumulation, which in humans is associated with pathological conditions. Purine recycling in seals increases in response to prolonged fasting and ischemia. Concentrations of metabolites and activities of key enzymes in purine metabolism were examined in plasma and red blood cells from bottlenose dolphins (Tursiops truncatus) and humans. Hypoxanthine and inosine monophosphate concentrations were higher in plasma from dolphins than humans. Plasma hypoxanthine-guanine phosphoribosyl transferase (HGPRT) activity in dolphins suggests an elevated purine recycling rate, and a mechanism for avoiding accumulation of non-recyclable purines (xanthine and uric acid). Red blood cell concentrations of hypoxanthine, adenosine diphosphate, ATP and guanosine triphosphate were lower in dolphins than in humans; adenosine monophosphate and nicotinamide adenine dinucleotide concentrations were higher in dolphins. HGPRT activity in red blood cells was higher in humans than in dolphins. The lower concentrations of purine catabolism and recycling by-products in plasma from dolphins could be beneficial in providing substrates for recovery of ATP depleted during diving or vigorous swimming. These results suggest that purine salvage in dolphins could be a mechanism for delivering nucleotide precursors to tissues with high ATP and guanosine triphosphate requirements.

Do clinical features of Lesch-Nyhan disease correlate more closely with hypoxanthine or guanine recycling?

Lesch-Nyhan disease (LND) is a rare, X-linked recessive neurodevelopmental disorder caused by deficiency of hypoxanthine-guanine phosphoribosyltransferase (HGprt), an enzyme in the purine salvage pathway. HGprt has two functions; it recycles hypoxanthine and guanine. Which of these two functions is more relevant for pathogenesis is unclear because some evidence points to hypoxanthine recycling, but other evidence points to guanine recycling. In this study, we selectively assayed hypoxanthine (Hprt) and guanine (Gprt) recycling in skin fibroblasts from 17 persons with LND, 11 with an attenuated variant of the disease (LNV), and 19 age-, sex-, and race-matched healthy controls (HC). Activity levels of both enzymes differed across groups (p < 0.0001), but only Gprt distinguished patients with LND from those with LNV (p < 0.05). Gprt also showed slightly stronger correlations than Hprt with 13 of 14 measures of the clinical phenotype, including the severity of dystonia, cognitive impairment, and behavioral abnormalities. These findings suggest that loss of guanine recycling might be more closely linked to the LND/LNV phenotype than loss of hypoxanthine recycling.

MicroRNAs: tools of mechanistic insights and biological therapeutics discovery for the rare neurogenetic syndrome Lesch-Nyhan disease (LND)

MicroRNAs (miRNAs) are small regulatory RNAs that modulate the translation of mRNA. They have emerged over the past few years as indispensable entities in the transcriptional regulation of genes. Their discovery has added additional layers of complexity to regulatory networks that control cellular homeostasis. Also, their dysregulated pattern of expression is now well demonstrated in myriad diseases and pathogenic processes. In the current review, we highlight the role of miRNAs in Lesch-Nyhan disease (LND), a rare neurogenetic syndrome caused by mutations in the purine metabolic gene encoding the hypoxanthine-guanine phosphoribosyltransferase (HPRT) enzyme. We describe how experimental and biocomputational approaches have helped to unravel genetic and signaling pathways that provide mechanistic understanding of some of the molecular and cellular basis of this ill-defined neurogenetic disorder. Through miRNA-based target predictions, we have identified signaling pathways that may be of significance in guiding biological therapeutic discovery for this incurable neurological disorder. We also propose a model to explain how a gene such as HPRT, mostly known for its housekeeping metabolic functions, can have pleiotropic effects on disparate genes and signal transduction pathways. Our hypothetical model suggests that HPRT mRNA transcripts may be acting as competitive endogenous RNAs (ceRNAs) intertwined in multiregulatory cross talk between key neural transcripts and miRNAs. Overall, this approach of using miRNA-based genomic approaches to elucidate the molecular and cellular basis of LND and guide biological target identification might be applicable to other ill-defined rare inborn-error metabolic diseases.

New targets for drug discovery against malaria

A mathematical model which predicts the intraerythrocytic stages of Plasmodium falciparum infection was developed using data from malaria-infected mice. Variables selected accounted for levels of healthy red blood cells, merozoite (Plasmodium asexual phase) infected red blood cells, gametocyte (Plasmodium sexual phase) infected red blood cells and a phenomenological variable which accounts for the mean activity of the immune system of the host. The model built was able to reproduce the behavior of three different scenarios of malaria. It predicts the later dynamics of malaria-infected humans well after the first peak of parasitemia, the qualitative response of malaria-infected monkeys to vaccination and the changes observed in malaria-infected mice when they are treated with antimalarial drugs. The mathematical model was used to identify new targets to be focused on drug design. Optimization methodologies were applied to identify five targets for minimizing the parasite load; four of the targets thus identified have never before been taken into account in drug design. The potential targets include: 1) increasing the death rate of the gametocytes, 2) decreasing the invasion rate of the red blood cells by the merozoites, 3) increasing the transformation of merozoites into gametocytes, 4) decreasing the activation of the immune system by the gametocytes, and finally 5) a combination of the previous target with decreasing the recycling rate of the red blood cells. The first target is already used in current therapies, whereas the remainders are proposals for potential new targets. Furthermore, the combined target (the simultaneous decrease of the activation of IS by gRBC and the decrease of the influence of IS on the recycling of hRBC) is interesting, since this combination does not affect the parasite directly. Thus, it is not expected to generate selective pressure on the parasites, which means that it would not produce resistance in Plasmodium.

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.

Genotype-phenotype correlations in Lesch-Nyhan disease: moving beyond the gene

Lesch-Nyhan disease and its attenuated variants are caused by mutations in the HPRT1 gene, which encodes the purine recycling enzyme hypoxanthine-guanine phosphoribosyltransferase. The mutations are heterogeneous, with more than 400 different mutations already documented. Prior efforts to correlate variations in the clinical phenotype with different mutations have suggested that milder phenotypes typically are associated with mutants that permit some residual enzyme function, whereas the most severe phenotype is associated with null mutants. However, multiple exceptions to this concept have been reported. In the current studies 44 HPRT1 mutations associated with a wide spectrum of clinical phenotypes were reconstructed by site-directed mutagenesis, the mutant enzymes were expressed in vitro and purified, and their kinetic properties were examined toward their substrates hypoxanthine, guanine, and phosphoribosylpyrophosphate. The results provide strong evidence for a correlation between disease severity and residual catalytic activity of the enzyme (k(cat)) toward each of its substrates as well as several mechanisms that result in exceptions to this correlation. There was no correlation between disease severity and the affinity of the enzyme for its substrates (K(m)). These studies provide a valuable model for understanding general principles of genotype-phenotype correlations in human disease, as the mechanisms involved are applicable to many other disorders.

Two-stage flux balance analysis of metabolic networks for drug target identification

Background

Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.

Results

In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.

Conclusions

Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.

Modelling the effects of inhibitors of guanine nucleotide synthesis: implications for studies of cellular differentiation pathways

Mizoribine induces the differentiation of promyelocytes by an unknown mechanism that relies on compromised guanine nucleotide synthesis. I have found that mizoribine also perturbs adenosine nucleotide levels in HL-60 promyelocytes, particularly ATP. To reconcile these observations with the known actions of mizoribine I have adapted an existing model of human purine metabolism composed as an S-system familiar from Biochemical Systems Theory. Mizoribine's actions were then simulated and compared with experimental data.

Recent developments in parameter estimation and structure identification of biochemical and genomic systems

The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.

Evidence of a new phosphoryl transfer system in nucleotide metabolism

Crude rat liver extract showed AMP-AMP phosphotransferase activity which, on purification, was ascribed to a novel interaction between adenylate kinase, also known as myokinase (EC 2.7.4.3), and adenosine kinase (EC 2.7.1.20). The activity was duplicated using the same enzymes purified from recombinant sources. The reaction requires physical contact between myokinase and adenosine kinase, and the net reaction is aided by the presence of adenosine deaminase (EC 3.5.4.4), which fills the gap in the energy balance of the phosphoryl transfer and shifts the equilibrium towards ADP and inosine synthesis. The proposed mechanism involves the association of adenosine kinase and myokinase through non-covalent, transient interactions that induce slight conformational changes in the active site of myokinase, bringing two already bound molecules of AMP together for phosphoryl transfer to form ADP. The proposed mechanism suggests a physiological role for the enzymes and for the AMP-AMP phosphotransferase reaction under conditions of extreme energy drain (such as hypoxia or temporary anoxia, as in cancer tissues) when the enzymes cannot display their conventional activity because of substrate deficiency.

A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view

Modern advances in molecular biology have produced enormous amounts of data characterizing physiological and disease states in cells and organisms. While bioinformatics has facilitated the organizing and mining of these data, it is the task of systems biology to merge the available information into dynamic, explanatory and predictive models. This article takes a step into this direction. It proposes a conceptual approach toward formalizing health and disease and illustrates it in the context of inflammation and preconditioning. Instead of defining health and disease states, the emphasis is on simplexes in a high-dimensional biomarker space. These simplexes are bounded by physiological constraints and permit the quantitative characterization of personalized health trajectories, health risk profiles that change with age, and the efficacy of different treatment options. The article mainly focuses on concepts but also briefly describes how the proposed concepts might be formulated rigorously within a mathematical framework.

Detection of potential enzyme targets by metabolic modelling and optimization: Application to a simple enzymopathy

MOTIVATION

A very promising approach in drug discovery involves the integration of available biomedical data through mathematical modelling and data mining. We have developed a method called optimization program for drug discovery (OPDD) that allows new enzyme targets to be identified in enzymopathies through the integration of metabolic models and biomedical data in a mathematical optimization program. The method involves four steps: (i) collection of the necessary information about the metabolic system and disease; (ii) translation of the information into mathematical terms; (iii) computation of the optimization programs prioritizing the solutions that propose the inhibition of a reduced number of enzymes and (iv) application of additional biomedical criteria to select and classify the solutions. Each solution consists of a set of predicted values for metabolites, initial substrates and enzyme activities, which describe a biologically acceptable steady state of the system that shifts the pathologic state towards a healthy state.

RESULTS

The OPDD was used to detect target enzymes in an enzymopathy, the human hyperuricemia. An existing S-system model and bibliographic information about the disease were used. The method detected six single-target enzyme solutions involving dietary modification, one of them coinciding with the conventional clinical treatment using allopurinol. The OPDD detected a large number of possible solutions involving two enzyme targets. All except one contained one of the previously detected six enzyme targets. The purpose of this work was not to obtain solutions for direct clinical implementation but to illustrate how increasing levels of biomedical information can be integrated together with mathematical models in drug discovery.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cooperativity and saturation in biochemical networks: A saturable formalism using Taylor series approximations

Cooperative and saturable systems are common in molecular biology. Nevertheless, common canonical formalisms for kinetic modeling that are theoretically well justified do not have a saturable form. Modeling and fitting data from saturable systems are widely done using Hill-like equations. In practice, there is no theoretical justification for the generalized use of these equations, other than their ability to fit experimental data. Thus it is important to find a canonical formalism that is (a) theoretically well supported, (b) has a saturable functional form, and (c) can be justifiably applicable to any biochemical network. Here we derive such a formalism using Taylor approximations in a special transformation space defined by power-inverses and logarithms of power-inverses. This formalism is generalized for processes with n-variables, leading to a useful mathematical representation for molecular biology: the Saturable and Cooperative Formalism (SC formalism). This formalism provides an appropriate representation that can be used for modeling processes with cooperativity and saturation. We also show that the Hill equation can be seen as a special case within this formalism. Parameter estimation for the SC formalism requires information that is also necessary to build Power-Law models, Metabolic Control Analysis descriptions or (log)linear and Lin-log models. In addition, the saturation fraction of the relevant processes at the operating point needs to be considered. The practical use of the SC formalism for modeling is illustrated with a few examples. Similar models are built using different formalisms and compared to emphasize advantages and limitations of the different approaches.

Biological systems modeling and analysis: a biomolecular technique of the twenty-first century

It is proposed that computational systems biology should be considered a biomolecular technique of the twenty-first century, because it complements experimental biology and bioinformatics in unique ways that will eventually lead to insights and a depth of understanding not achievable without systems approaches. This article begins with a summary of traditional and novel modeling techniques. In the second part, it proposes concept map modeling as a useful link between experimental biology and biological systems modeling and analysis. Concept map modeling requires the collaboration between biologist and modeler. The biologist designs a regulated connectivity diagram of processes comprising a biological system and also provides semi-quantitative information on stimuli and measured or expected responses of the system. The modeler converts this information through methods of forward and inverse modeling into a mathematical construct that can be used for simulations and to generate and test new hypotheses. The biologist and the modeler collaboratively interpret the results and devise improved concept maps. The third part of the article describes software, BST-Box, supporting the various modeling activities.

Association of adenosine deaminase polymorphism with mild mental retardation

The etiology of mild mental retardation remains undefined in about 60% of cases. Even though the causes of mild mental retardation are likely to be heterogeneous, the evidence for genetic involvement is increasing, along with the development of specific diagnostic techniques. To improve our understanding of the genetic basis of mild mental retardation, we explored the role of polymorphisms of adenosine deaminase, an enzyme that is supposed to act as a neuroregulatory protein. To this end, we conducted an association study comparing children with mild mental retardation of unknown origin with two groups of controls: (1) apparently healthy children and (2) children with moderate or severe mental retardation of known etiology. Overall, 338 participants were enrolled in the study. Cases (ie, 80 children) were more likely than controls (ie, 153 healthy children and 105 children with moderate or severe mental retardation) to have the low-activity ADA-Asn 8 (ADA(1) *2) polymorphism (P < .05) and to present the ADA(1) *2/ ADA(2) *1 haplotype. No significant differences were found with respect to adenosine deaminase polymorphisms when comparing the group with moderate or severe mental retardation of known causes and healthy controls. In conclusion, our findings suggest a possible role for a low-activity genotype (ADA-8Asn) (ADA(1) *2) of adenosine deaminase in the pathogenesis of mild mental retardation.

Metabolic modeling: a tool of drug discovery in the post-genomic era

The Human Genome Project, DNA microarrays, proteome chips and metabolic profiles, among others, are generating unprecedented amounts of valuable information about the genetic and metabolic responses of organisms to stimuli. This wealth of information poses a great scientific challenge, namely the development of novel, effective methods for functional analysis and interpretation. It is proposed here that biomathematical systems models must accompany data generation and management. A particularly effective framework for this purpose is canonical modeling based on Biochemical Systems Theory. The key concept of this theory is the formulation of biological phenomena as systems of differential equations, in which all processes are represented as products of power-law functions.

Renal regulation of plasma total antioxidant capacity

Hyperuricemia has been labeled both a risk factor and marker for cardiovascular pathology in addition to being associated with gout and kidney disease. Uric acid in vitro acts as a potent antioxidant capable of scavenging hydroxy radicals and peroxynitrite and reacting with nitric oxide. Some clinical studies have provided evidence that, in vivo, uric acid is oxidized under conditions associated with high oxidant stress and may spare other antioxidants such as ascorbic acid. The plasma level of uric acid is controlled by the rates of production and excretion or degradation of uric acid. Under most circumstances, it is the renal clearance of uric acid which primarily determines the plasma concentration. Many factors of exogenous and endogenous origin can influence renal tubular absorption and secretion of uric acid. We suggest that renal urate clearance is not haphazard but regulated by an unknown signal that is issued in response to the level of oxidative stress. Since much cardiovascular pathology is now believed to have an inflammatory component and is associated with enhanced production of free radicals, the accompanying hyperuricemia may be viewed as a compensatory response of potential benefit.

Neurotransmitter changes in the pathophysiology of Lesch-Nyhan syndrome

The neurological symptoms of Lesch-Nyhan syndrome (LNS) are assumed to result from the neurotransmitter changes in this disorder. Among them, the dopaminergic system is believed to play a role in the self-injurious behavior through receptor supersensitivity. However, the precise mechanism underlying the dopamine supersensitivity remains unclear. An increased serotonergic action in the striatum may be crucial for the appearance of self-injurious behavior, and pharmacological evidence suggests the efficacy of serotonin agonists/antagonists for the treatment of the self-mutilation in LNS.

Analysis and prediction of the effect of uncertain boundary values in modeling a metabolic pathway

The integration of large quantities of biological information into mathematical models of cell metabolism provides a way for quantitatively evaluating the effect of parameter changes on simultaneous, coupled, and, often, counteracting processes. From a practical point of view, the validity of the model's predictions would critically depend on its quality. Among others, one of the critical steps that may compromise this quality is to decide which are the boundaries of the model. That is, we must decide which metabolites are assumed to be constants, and which fluxes are considered to be the inputs and outputs of the system. In this article, we analyze the effect of the experimental uncertainty on these variables on the system's characterization. Using a previously defined model of glucose fermentation in Saccharomyces cerevisiae, we characterize the effect of the uncertainty on some key variables commonly considered to be constants in many models of glucose metabolism, i.e., the intracellular pH and the pool of nucleotides. Without considering if this variability corresponds to a possible true physiological phenomenon, the goal of this article is to illustrate how this uncertainty may result in an important variability in the systemic responses predicted by the model. To characterize this variability, we analyze the utility and limitations of computing the sensitivities of logarithmic-gains (control coefficients) to the boundary parameters. With the exception of some special cases, our analysis shows that these sensitivities are good indicators of the dependence of the model systemic behavior on the parameters of interest.

Lesch-Nyhan disease and the basal ganglia

The purpose of this review is to summarize emerging evidence that the neurobehavioral features of Lesch-Nyhan disease (LND), a developmental disorder caused by congenital deficiency of the purine salvage enzyme hypoxanthine-guanine phosphoribosyltransferase (HPRT), may be attributable to dysfunction of the basal ganglia. Affected individuals have severe motor disability described by prominent extrapyramidal features that are characteristic of dysfunction of the motor circuits of the basal ganglia. They also display disturbances of ocular motility, cognition, and behavioral control that may reflect disruption of other circuits of the basal ganglia. Though neuropathologic studies of autopsy specimens have revealed no obvious neuroanatomical abnormalities in LND, neurochemical studies have demonstrated 60-90% reductions in the dopamine content of the basal ganglia. In addition, recent PET studies have documented significant reductions in dopamine transporters and [18F]fluorodopa uptake in the basal ganglia. These findings support the proposal that many of the neurobehavioral features of LND might be related to dysfunction of the basal ganglia.