Control of neurulation by the nucleosome assembly protein-1-like 2.

Neurulation is a complex process of histogenesis involving the precise temporal and spatial organization of gene expression. Genes influencing neurulation include proneural genes determining primary cell fate, neurogenic genes involved in lateral inhibition pathways and genes controlling the frequency of mitotic events. This is reflected in the aetiology and genetics of human and mouse neural tube defects, which are of both multifactorial and multigenic origin. The X-linked gene Nap1l2, specifically expressed in neurons, encodes a protein that is highly similar to the nucleosome assembly (NAP) and SET proteins. We inactivated Nap1l2 in mice by gene targeting, leading to embryonic lethality from mid-gestation onwards. Surviving mutant chimaeric embryos showed extensive surface ectoderm defects as well as the presence of open neural tubes and exposed brains similar to those observed in human spina bifida and anencephaly. These defects correlated with an overproduction of neuronal precursor cells. Protein expression studies showed that the Nap1l2 protein binds to condensing chromatin during S phase and in apoptotic cells, but remained cytoplasmic during G1 phase. Nap1l2 therefore likely represents a class of tissue-specific factors interacting with chromatin to regulate neuronal cell proliferation.

Molecular model of a lattice of signalling proteins involved in bacterial chemotaxis.

Coliform bacteria detect chemical attractants by means of a membrane-associated cluster of receptors and signalling molecules. We have used recently determined molecular structures, in conjunction with plastic models generated by three-dimensional printer technology, to predict how the proteins of the complex are arranged in relation to the plasma membrane. The proposed structure is a regular two-dimensional lattice in which the cytoplasmic ends of chemotactic-receptor dimers are inserted into a hexagonal array of CheA and CheW molecules. This structure creates separate compartments for adaptation and downstream signalling, and indicates a possible basis for the spread of activity within the cluster.

Systems Oncology: Bridging Pancreatic and Castrate Resistant Prostate Cancer.

Large investments by pharmaceutical companies in the development of new antineoplastic drugs have not been resulting in adequate advances of new therapies. Despite the introduction of new methods, technologies, translational medicine and bioinformatics, the usage of collected knowledge is unsatisfactory. In this paper, using examples of pancreatic ductal adenocarcinoma (PaC) and castrate-resistant prostate cancer (CRPC), we proposed a concept showing that, in order to improve applicability of current knowledge in oncology, the re-clustering of clinical and scientific data is crucial. Such an approach, based on systems oncology, would include bridging of data on biomarkers and pathways between different cancer types. Proposed concept would introduce a new matrix, which enables combining of already approved therapies between cancer types. Paper provides a (a) detailed analysis of similarities in mechanisms of etiology and progression between PaC and CRPC, (b) diabetes as common hallmark of both cancer types and (c) knowledge gaps and directions of future investigations. Proposed horizontal and vertical matrix in cancer profiling has potency to improve current antineoplastic therapy efficacy. Systems biology map using Systems Biology Graphical Notation Language is used for summarizing complex interactions and similarities of mechanisms in biology of PaC and CRPC.

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions.

Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive dysfunction and loss of neurons. Here, we distil and discuss the current state of modeling in the area of neurodegeneration, and objectively compare the gaps between existing clinical knowledge and the mechanistic understanding of the major pathological processes implicated in neurodegenerative disorders. We also discuss new directions in the field of neurodegeneration that hold potential for furthering therapeutic interventions and strategies.

LGICdb: the ligand-gated ion channel database.

Ligand-Gated Ion Channels (LGIC) are polymeric transmembrane proteins involved in the fast response to numerous neurotransmitters. All these receptors are formed by homologous subunits and the last two decades revealed an unexpected wealth of genes coding for these subunits. The Ligand-Gated Ion Channel database (LGICdb) has been developed to handle this increasing amount of data. The database aims to provide only one entry for each gene, containing annotated nucleic acid and protein sequences. The repository is carefully structured and the entries can be retrieved by various criteria. In addition to the sequences, the LGICdb provides multiple sequence alignments, phylogenetic analyses and atomic coordinates when available. The database is accessible via the World Wide Web (http://www.pasteur.fr/recherche/banques/LGIC /LGIC.html), where it is continuously updated. The version 16 (September 2000) available for download contained 333 entries covering 34 species.

The promises of quantitative systems pharmacology modelling for drug development.

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Conformational spread in a ring of proteins: a stochastic approach to allostery.

We recently suggested that the sensitivity and range of a cluster of membrane receptors in bacteria would be enhanced by cooperative interactions between neighbouring proteins. Here, we examine the consequences of this "conformational spread" mechanism for an idealised one-dimensional system comprising a closed ring of identical allosteric protomers (protein molecules, or a group of protein domains operating as a unit). We show analytically and by means of Monte Carlo simulations that a ring of allosteric protomers can exhibit a switch-like response to changes in ligand concentration. We derive expressions for the sensitivity and cooperativity of switching and show that the maximum sensitivity is proportional to the number of protomers in the ring. A ring of this kind can reproduce the sensitivity and kinetics of the switch complex of a bacterial flagellar motor, for example, which is based on a ring of 34 FliM proteins. We also compare smaller rings of conformationally coupled protomers to classical allosteric proteins such as haemoglobin and show that the canonical MWC and KNF models arise naturally as limiting cases. Conformational spread appears to be a natural extension of the familiar mechanism of allostery: a physically realistic mechanism that should apply widely to many structures built from protein molecules.

BioModels: Content, Features, Functionality, and Use.

BioModels is a reference repository hosting mathematical models that describe the dynamic interactions of biological components at various scales. The resource provides access to over 1,200 models described in literature and over 140,000 models automatically generated from pathway resources. Most model components are cross-linked with external resources to facilitate interoperability. A large proportion of models are manually curated to ensure reproducibility of simulation results. This tutorial presents BioModels' content, features, functionality, and usage.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Promoter elements conferring neuron-specific expression of the beta 2-subunit of the neuronal nicotinic acetylcholine receptor studied in vitro and in transgenic mice.

Several genes encoding subunits of the neuronal nicotinic acetylcholine receptors have been cloned and regulatory elements involved in the transcription of the alpha 2 and alpha 7-subunit genes have been described. Yet, the detailed mechanisms governing the neuron-specific transcription and the spatio-temporal expression pattern of these genes remain largely uninvestigated. The beta 2-subunit is the most widely expressed neuronal nicotinic receptor subunit in the nervous system. We have studied the structural and regulatory properties of the 5' sequence of this gene. A fragment of 1163 bp of upstream sequence is sufficient to drive the cell-specific transcription of a reporter gene in both transient transfection assays and in transgenic mice. Deletion analysis and site-directed mutagenesis of this promoter reveal two negative elements and one positive element. The positively-acting sequence includes one functional E-box. One of the repressor elements is located in the transcribed region and is the NRSE/RE1 sequence already described in promoters of neuronal genes. In this paper, we describe the neuron-specific promoter of the gene encoding the neuronal nicotinic acetylcholine receptor beta 2-subunit.

Involvement of alpha6 nicotinic receptor subunit in nicotine-elicited locomotion, demonstrated by in vivo antisense oligonucleotide infusion.

Enhanced locomotion in a habituated environment is a well documented effect of nicotine mediated by the mesotelencephalic dopaminergic system. The nicotinic receptor subunit alpha6 is, among other subunits, strongly expressed in the dopaminergic neurons of the mesencephalon. To examine the functional role of this subunit, we inhibited its expression in vivo using antisense oligonucleotides. In vitro treatments of embryonic mesencephalic neuron cultures demonstrated that the alpha6 antisense oligonucleotides caused a marked decrease in the level of alpha6 subunit protein. In vivo, 1 week infusion of alpha6 antisense oligonucleotides by osmotic mini-pump reduced the effect of nicotine on locomotor activity in habituated environment by 70%. These data support the notion that the effects of nicotine on the dopaminergic system involve alpha6 subunit containing nAChRs.

A single (-)-nicotine injection causes change with a time delay in the affinity of striatal D2 receptors for antagonist, but not for agonist, nor in the D2 receptor mRNA levels in the rat substantia nigra.

The in vitro and in vivo effects of (-)-nicotine on dopamine D2 receptors in the rat neostriatum have been studied using biochemical binding, in situ hybridization and immunocytochemistry. A single i.p. injection (1 mg/kg) of (-)-nicotine resulted in a reduction of the KD value of the D2 antagonist [3H]raclopride binding sites in rat neostriatal membrane preparations at 12 h without any significant change in the Bmax value. This action of (-)-nicotine was counteracted by pretreatment 15 min earlier with the nicotine antagonist mecamylamine (1 mg/kg, i.p.). However, the KD and the Bmax values of the D2 agonist [3H]NPA binding sites in the rat neostriatal membrane preparations were not significantly affected 0.5-48 h after a single i.p. injection with 1 mg/kg of (-)-nicotine. No significant change in neostriatal D2 receptor mRNA levels was observed at any time interval after the (-)-nicotine injection. No significant change was observed in tyrosine hydroxylase (TH) immunoreactivity in either the substantia nigra or the neostriatum, nor in nigral TH mRNA levels during the time interval studied (4-24 h posttreatment). Furthermore, addition of low (10 nM) or high (1 microM) concentrations of (-)-nicotine in vitro to rat neostriatal membranes did not alter the characteristics of [3H]raclopride or [3H]NPA binding. These results indicate that a single (-)-nicotine injection can produce a selective and delayed increase in the affinity of D2 receptors for the antagonist, but not for the agonist without modifying the levels of D2 receptor mRNA, probably via the activation of central nicotinic receptors.

The impact of mathematical modeling on the understanding of diabetes and related complications.

Diabetes is a chronic and complex multifactorial disease caused by persistent hyperglycemia and for which underlying pathogenesis is still not completely understood. The mathematical modeling of glucose homeostasis, diabetic condition, and its associated complications is rapidly growing and provides new insights into the underlying mechanisms involved. Here, we discuss contributions to the diabetes modeling field over the past five decades, highlighting the areas where more focused research is required.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e54; doi:10.1038/psp.2013.30; advance online publication 10 July 2013.

The Input Signal Step Function (ISSF), a standard method to encode input signals in SBML models with software support, applied to circadian clock models.

Time-dependent light input is an important feature of computational models of the circadian clock. However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology.

SysBioMed report: advancing systems biology for medical applications.

The following report selects and summarises some of the conclusions and recommendations generated throughout a series of workshops and discussions that have lead to the publication of the Science Policy Briefing (SPB) Nr. 35, published by the European Science Foundation. (Large parts of the present text are directly based on the ESF SPB. Detailed recommendations with regard to specific application areas are not given here but can be found in the SPB. Issues related to mathematical modelling, including training and the need for an infrastructure supporting modelling are discussed in greater detail in the present text.)The numerous reports and publications about the advances within the rapidly growing field of systems biology have led to a plethora of alternative definitions for key concepts. Here, with 'mathematical modelling' the authors refer to the modelling and simulation of subcellular, cellular and macro-scale phenomena, using primarily methods from dynamical systems theory. The aim of such models is encoding and testing hypotheses about mechanisms underlying the functioning of cells. Typical examples are models for molecular networks, where the behaviour of cells is expressed in terms of quantitative changes in the levels of transcripts and gene products. Bioinformatics provides essential complementary tools, including procedures for pattern recognition, machine learning, statistical modelling (testing for differences, searching for associations and correlations) and secondary data extracted from databases.Dynamical systems theory is the natural language to investigate complex biological systems demonstrating nonlinear spatio-temporal behaviour. However, the generation of experimental data suitable to parameterise, calibrate and validate such models is often time consuming and expensive or not even possible with the technology available today. In our report, we use the term 'computational model' when mathematical models are complemented with information generated from bioinformatics resources. Hence, 'the model' is, in reality, an integrated collection of data and models from various (possibly heterogeneous) sources. The present report focuses on a selection of topics, which were identified as appropriate case studies for medical systems biology, and adopts a particular perspective which the authors consider important. We strongly believe that mathematical modelling represents a natural language with which to integrate data at various levels and, in doing so, to provide insight into complex diseases: 1. Modelling necessitates the statement of explicit hypotheses, a process which often enhances comprehension of the biological system and can uncover critical points where understanding is lacking. 2. Simulations can reveal hidden patterns and/or counter-intuitive mechanisms in complex systems. 3. Theoretical thinking and mathematical modelling constitute powerful tools to integrate and make sense of the biological and clinical information being generated and, more importantly, to generate new hypotheses that can then be tested in the laboratory.Medical Systems Biology projects carried out recently across Europe have revealed a need for action: 4. While the need for mathematical modelling and interdisciplinary collaborations is becoming widely recognised in the biological sciences, with substantial implications for the training and research funding mechanisms within this area, the medical sciences have yet to follow this lead. 5. To achieve major breakthroughs in Medical Systems Biology, existing academic funding schemes for large-scale projects need to be reconsidered. 6. The hesitant stance of the pharmaceutical industry towards major investment in systems biology research has to be addressed. 7. Leading medical journals should be encouraged to promote mathematical modelling.

Neurological disease: are systems approaches the way forward?

Most neurological diseases are multifactorial diseases, where environmental conditions combine with genetic background or somatic mutations to trigger a pathological state. In the case of Parkinson's Disease and Schizophrenia, recent research revealed that susceptibility genes coded for proteins involved at different steps of specific metabolic networks and cellular processes. Comprehension of the pathology of those diseases is therefore very likely to benefit from Systems approaches. This is also true of their symptomatology, affecting neurological systems at molecular, cellular, and microcircuit levels.

MELTING, computing the melting temperature of nucleic acid duplex.

UNLABELLED: MELTING computes the enthalpy and entropy of an oligonucleotide duplex helix-coil transition, and then its melting temperature. The program uses the method of nearest-neighbours. The set of thermodynamic parameters can be easily customized. The program provides several correction methods for the concentration of salt. MELTING is a free program, available at no cost and open-source. Perl scripts are provided to show how MELTING can be used to construct more ambitious programs. AVAILABILITY: MELTING is available for several platforms (http://www.pasteur.fr/recherche/unites/neubiomol/meltinghome.html) and is accessible via a www server (http://bioweb.pasteur.fr/seqanal/interfaces/melting.html). CONTACT: nl223@cus.cam.ac.uk

Molecular evolution of the nicotinic acetylcholine receptor: an example of multigene family in excitable cells.

An extensive phylogenetic analysis of the nicotinic-acetylcholine-receptor subunit gene family has been performed by cladistic and phenetic methods. The conserved parts of amino acid sequences have been analyzed by CLUSTAL V and PHYLIP software. The structure of the genes was also taken in consideration. The results show that a first gene duplication may have occurred before the appearance of Bilateria. Three subfamilies then appeared: I--the neuronal alpha-bungarotoxin binding-site subunits (alpha 7, alpha 8); III--the neuronal nicotinic subunits (alpha 2-alpha 6, beta 2-beta 4), which also contain the muscle acetylcholine-binding subunit (alpha 1); and IV--the muscle non-alpha subunits (beta 1, gamma, delta, epsilon). The Insecta subunits (subfamily II) could be orthologous to family III and IV. Several tissular switches of expression from neuron to muscle and the converse can be inferred from the extant expression of subunits and the reconstructed trees. The diversification of the neuronal nicotinic subfamily begins in the stem lineage of chordates, the last duplications occurring shortly before the onset of the mammalian lineage. Such evolution parallels the increase in complexity of the cholinergic systems.

The Ligand Gated Ion Channel Database.

The ligand gated ion channels (LGICs) are ionotropic receptors to neurotransmitters. Their physiological effect is carried out by the opening of an ionic channel upon binding of a particular neurotransmitter. These LGICs constitute superfamilies of receptors formed by homologous subunits. A database has been developed to handle the growing wealth of cloned subunits. This database contains nucleic acid sequences, protein sequences, as well as multiple sequence alignments and phylogenetic studies. This database is accessible via the worldwide web (http://www.pasteur.fr/units/neubiomol/LGIC.h tml), where it is continuously updated. A downloadable version is also available [currently v0.1 (98.06)].