mGluR7 Genetics and Alcohol: Intersection Yields Clues for Addiction

Development of addiction to alcohol or other substances can be attributed in part to exposure-dependent modifications at synaptic efficacy leading to an organism which functions at an altered homeostatic setpoint. Genetic factors may also influence setpoints and the stability of the homeostatic system of an organism. Quantitative genetic analysis of voluntary alcohol drinking, and mapping of the involved genes in the quasi-congenic Recombinant QTL Introgression strain system, identified Eac2 as a Quantitative Trait Locus (QTL) on mouse chromosome 6 which explained 18% of the variance with an effect size of 2.09 g/kg/day alcohol consumption, and Grm7 as a quantitative trait gene underlying Eac2 [Vadasz et al. in Neurochem Res 32:1099–1112, 100, Genomics 90:690–702, 102]. In earlier studies, the product of Grm7 mGluR7, a G protein-coupled receptor, has been implicated in stress systems [Mitsukawa et al. in Proc Natl Acad Sci USA 102:18712–18717, 63], anxiety-like behaviors [Cryan et al. in Eur J Neurosci 17:2409–2417, 14], memory [Holscher et al. in Learn Mem 12:450–455, 26], and psychiatric disorders (e.g., [Mick et al. in Am J Med Genet B Neuropsychiatr Genet 147B:1412–1418, 61; Ohtsuki et al. in Schizophr Res 101:9–16, 72; Pergadia et al. in Paper presented at the 38th Annual Meeting of the Behavior Genetics Association, Louisville, Kentucky, USA, 76]. Here, in experiments with mice, we show that (1) Grm7 knockout mice express increased alcohol consumption, (2) sub-congenic, and congenic mice carrying a Grm7 variant characterized by higher Grm7 mRNA drink less alcohol, and show a tendency for higher circadian dark phase motor activity in a wheel running paradigm, respectively, and (3) there are significant genetic differences in Grm7 mRNA abundance in the mouse brain between congenic and background mice identifying brain areas whose function is implicated in addiction related processes. We hypothesize that metabotropic glutamate receptors may function as regulators of homeostasis, and Grm7 (mGluR7) is involved in multiple processes (including stress, circadian activity, reward control, memory, etc.) which interact with substance use and the development of addiction. In conclusion, we suggest that mGluR7 is a significant new therapeutic target in addiction and related neurobehavioral disorders.     

Shaping bursting by electrical coupling and noise

Gap-junctional coupling is an important way of communication between neurons and other excitable cells. Strong electrical coupling synchronizes activity across cell ensembles. Surprisingly, in the presence of noise synchronous oscillations generated by an electrically coupled network may differ qualitatively from the oscillations produced by uncoupled individual cells forming the network. A prominent example of such behavior is the synchronized bursting in islets of Langerhans formed by pancreatic β-cells, which in isolation are known to exhibit irregular spiking (Sherman and Rinzel, Biophys J 54:411–425, 1988; Sherman and Rinzel, Biophys J 59:547–559, 1991). At the heart of this intriguing phenomenon lies denoising, a remarkable ability of electrical coupling to diminish the effects of noise acting on individual cells. In this paper, building on an earlier analysis of denoising in networks of integrate-and-fire neurons (Medvedev, Neural Comput 21 (11):3057–3078, 2009) and our recent study of spontaneous activity in a closely related model of the Locus Coeruleus network (Medvedev and Zhuravytska, The geometry of spontaneous spiking in neuronal networks, submitted, 2012), we derive quantitative estimates characterizing denoising in electrically coupled networks of conductance-based models of square wave bursting cells. Our analysis reveals the interplay of the intrinsic properties of the individual cells and network topology and their respective contributions to this important effect. In particular, we show that networks on graphs with large algebraic connectivity (Fiedler, Czech Math J 23(98):298–305, 1973) or small total effective resistance (Bollobas, Modern graph theory, Graduate Texts in Mathematics, vol. 184, Springer, New York, 1998) are better equipped for implementing denoising. As a by-product of the analysis of denoising, we analytically estimate the rate with which trajectories converge to the synchronization subspace and the stability of the latter to random perturbations. These estimates reveal the role of the network topology in synchronization. The analysis is complemented by numerical simulations of electrically coupled conductance-based networks. Taken together, these results explain the mechanisms underlying synchronization and denoising in an important class of biological models.     

Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis

Understanding how dynamical responses of biological networks are constrained by underlying network topology is one of the fundamental goals of systems biology. Here we employ monotone systems theory to formulate a theorem stating necessary conditions for non-monotonic time-response of a biochemical network to a monotonic stimulus. We apply this theorem to analyze the non-monotonic dynamics of the σ^B-regulated glyoxylate shunt gene expression in Mycobacterium tuberculosis cells exposed to hypoxia. We first demonstrate that the known network structure is inconsistent with observed dynamics. To resolve this inconsistency we employ the formulated theorem, modeling simulations and optimization along with follow-up dynamic experimental measurements. We show a requirement for post-translational modulation of σ^B activity in order to reconcile the network dynamics with its topology. The results of this analysis make testable experimental predictions and demonstrate wider applicability of the developed methodology to a wide class of biological systems.     

Mapping of <Emphasis Type="Italic">taiep</Emphasis> rat phenotype to rat Chromosome 9

The taiep mutant rat was first described in a colony of Sprague-Dawley rats at the University of Puebla in 1989, with an autosomal recessive inherited pattern. taiep is an acronym for the progressive neurologic deficits that the rat develops, i.e., t = trembling (3–4 weeks), a = ataxia (at 4 months), i = immobility (5–6 months), e = epilepsy (5–6 months), and p = paresis (7 months onwards). Thus, mutant rats are first identified by a tremor at 3–4 weeks of age that is followed by a progressive neurological worsening (Holmgren et al. 1989; Lunn et al. 1997). The cause of the neurological symptoms is an early failure of normal myelination of the central nervous system (CNS) followed by progressive demyelination of certain CNS tracts (Lunn et al. 1997). We have been exploring the underlying pathophysiology of the mutant and have determined that the myelin defect results from the progressive accumulation of microtubules in oligodendrocytes, the myelin-producing cells of the CNS (Song et al. 1999). Microtubules are the major component of the cytoskeleton of this and many other cells of the body, and microtubule-based transport of protein and mRNA is essential for normal cell function. There is no direct human counterpart of the taiep rat. Nonetheless, providing an understanding of the control of microtubule dynamics in the oligodendrocyte will be highly relevant to our knowledge of the cell biology of the myelinating cell of the CNS. This information is of great relevance to the function of the cell in human myelin disorders and in experimental remyelination. As the taiep rat apparently has a primary disorder in the oligodendrocyte cytoskeleton, it is an ideal model in which to study this process. This information may also be a key to the complete understanding of the mechanism of microtubule assembly/disassembly in many cell types.     

Perceptual learning with perceptions

In this work we present an approach to understand neuronal mechanisms underlying perceptual learning. Experimental results achieved with stimulus patterns of coherently moving dots are considered to build a simple neuronal model. The design of the model is made transparent and underlying behavioral assumptions made explicit. The key aspect of the suggested neuronal model is the learning algorithm used: We evaluated an implementation of Hebbian learning and are thus able to provide a straight-forward model capable to explain the neuronal dynamics underlying perceptual learning. Moreover, the simulation results suggest a very simple explanation for the aspect of “sub-threshold” learning (Watanabe et al. in Nature 413:844–884, 2001) as well as the relearning of motion discrimination after damage to primary visual cortex as recently reported (Huxlin et al. in J Neurosci 29:3981–3991, 2009) and at least indicate that perceptual learning might only occur when accompanied by conscious percepts.     

Encoding of brain state changes in local field potentials modulated by motor behaviors

Local field potentials (LFPs) measure aggregate neural activity resulting from the coordinated firing of neurons within a local network. We hypothesized that state parameters associated with the underlying brain dynamics may be encoded in LFPs but may not be directly measurable in the signal temporal and spectral contents. Using the Kalman filter we estimated latent state changes in LFPs recorded in monkey motor cortical areas during the execution of a visually instructed reaching task, under different applied force conditions. Prior to the estimation, matched filtering was performed to decouple behavior-relevant signals (Stamoulis and Richardson, J Comput Neurosci, 2009) from unrelated background oscillations. State changes associated with baseline oscillations appeared insignificant. In contrast, state changes estimated from LFP components associated with the execution of movement were significant. Approximately direction-invariant state vectors were consistently observed. Their patterns appeared invariant also to force field conditions, with a peak in the first 200 ms of the movement interval, but exponentially decreasing to the zero state approximately 200 ms from movement onset, also the time at which movement velocity reached its peak. Thus, state appeared to be modulated by the dynamics of movement but neither by movement direction nor by the mechanical environment. Finally, we compared state vectors estimated using the Kalman filter to the basis functions obtained through Principal Component Analysis. The pattern of the estimated state vector was very similar to that of the first PCA component, further suggesting that LFPs may directly encode brain state fluctuations associated with the dynamics of behavior.     

Resetting biological oscillators—A stochastic approach

We present a stochastic approach to phase-resetting of an ensemble of oscillators. In order to describe stimulation-induced dynamical phenomena we develop a stochastic model which consists of an ensemble of phase oscillators interacting via random forces. Every single oscillator is submitted to a phase stimulus. The ensemble's dynamics is determined by a Fokker-Planck equation. The stationary states are calculated explicitly, whereas the transients are analysed numerically. If the stimulus of a given (non-vanishing) intensity is administered at a critical initial cluster phase for a critical duration T _crit the ensemble's synchronized oscillation is annihilated. A transition from type 1 resetting to type 0 resetting occurs when the stimulation duration exceeds T _crit. Stimulation causes a shift of the mean frequency of every single oscillator. This frequency shift is explicitly calculated by deriving the mean first passage time. The model shows that there is a subcritical intensity which is connected with an enhanced vulnerability to stimulation. The desynchronized states, the so-called black holes, are typically associated with a double peak in the ensemble's phase distribution. This is important for analysing experimental data because simple peak-detection algorithms are not able to extract the underlying dynamics.Our results are discussed from the experimentator's point of view so that the insights derived from our model can improve data analysis and design of stimulation experiments.     

Bioactive peptide design using the Resonant Recognition Model

With a large number of DNA and protein sequences already known, the crucial question is to find out how the biological function of these macromolecules is "written" in the sequence of nucleotides or amino acids. Biological processes in any living organism are based on selective interactions between particular bio-molecules, mostly proteins. The rules governing the coding of a protein's biological function, i.e. its ability to selectively interact with other molecules, are still not elucidated. In addition, with the rapid accumulation of databases of protein primary structures, there is an urgent need for theoretical approaches that are capable of analysing protein structure-function relationships. The Resonant Recognition Model (RRM) [1, 2] is one attempt to identify the selectivity of protein interactions within the amino acid sequence. The RRM [1, 2] is a physico-mathematical approach that interprets protein sequence linear information using digital signal processing methods. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein's biological activity. The RRM concept is based on the finding that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM approach to predict the amino acids in the protein sequence, which predominantly contribute to this frequency and thus, to the observed function, as well as to design de novo peptides having the desired periodicities. As was shown in our previous studies of fibroblast growth factor (FGF) peptidic antagonists [2, 3] and human immunodeficiency virus (HIV) envelope agonists [2, 4], such de novo designed peptides express desired biological function. This study utilises the RRM computational approach to the analysis of oncogene and proto-oncogene proteins. The results obtained have shown that the RRM is capable of identifying the differences between the oncogenic and proto-oncogenic proteins with the possibility of identifying the "cancer-causing" features within their protein primary structure. In addition, the rational design of bioactive peptide analogues displaying oncogenic or proto-oncogenic-like activity is presented here.     

Foot differentiation and genomic plasticity in Hydra: lessons from the PPOD gene family

In Hydra, developmental processes are permanently active to maintain a simple body plan consisting of a two-layered, radially symmetrical tube with two differentiated structures, head and foot. Foot formation is a dynamic process and includes terminal differentiation of gastric epithelial cells into mucous secreting basal disc cells. A well-established marker for this highly specialized cell type is a locally expressed peroxidase (Hoffmeister et al. 1985). Based on the foot-specific peroxidase activity, the gene PPOD1 has been identified (Hoffmeister-Ullerich et al. 2002). Unexpectedly, this approach led to the identification of a second gene, PPOD2, with high sequence similarity to PPOD1 but a strikingly different expression pattern. Here, we characterize PPOD2 in more detail and show that both genes, PPOD1 and PPOD2, are members of a gene family with differential complexity and expression patterns in different Hydra species. At the genomic level, differences in gene number and structure within the PPOD gene family, even among closely related species, support a recently proposed phylogeny of the genus Hydra and point to unexpected genomic plasticity within closely related species of this ancient metazoan taxon. Electronic supplementary material Supplementary material is available in the online version of this article at     

Therapeutic Processes and Perceived Helpfulness of <Emphasis Type="Italic">Dang-Ki</Emphasis> (Chinese Shamanism) from the Symbolic Healing Perspective

This study focuses on the therapeutic process and perceived helpfulness of dang-ki, a form of Chinese shamanistic healing, in Singapore. It aims to understand the healing symbols employed in dang-ki, whether or not patients find them helpful and whether their perceived helpfulness can be explained by the symbolic healing model (Dow, Am Anthropol 88(1):56–69, 1986; Levi-Strauss, Structural anthropology. Basic Books, New York, 1963). Although many researchers have applied this model to explain the efficacy of shamanistic healings, they did not directly provide empirical support. Furthermore, the therapeutic process of a shared clinical reality as proposed by the model may be achievable in small-scale traditional societies that are culturally more homogeneous than in contemporary societies that are culturally more diversified due to globalization and immigration. Patients may hold multidimensional health belief systems, as biomedicine and alternative healing systems coexist. Thus, it would be interesting to see the relevance and applicability of the symbolic healing model to shamanistic healing in contemporary societies. In this study, ethnographic interviews were conducted with 21 patients over three stages: immediately before and after the healing and approximately 1 month later. The dang-ki healing symbols were identified by observing the healing sessions with video recording. Results show that dang-kis normally applied more than one method to treat a given problem. These methods included words, talismans and physical manipulations. Overall, 11 patients perceived their consultations as helpful, 4 perceived their consultations as helpful but were unable to follow all recommendations, 5 were not sure of the outcome because they had yet to see any concrete results and only 1 patient considered his consultation unhelpful. Although the symbolic healing model provides a useful framework to understand perceived helpfulness, processes such as enactment of a common meaning system and symbolic transformation are complex and dynamic, and may be carried over several healing sessions.     

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

How is cognitive task behavior generated by brain network interactions? This study describes a novel network modeling approach and applies it to source electroencephalography data. The model accurately predicts future information dynamics underlying behavior and (via simulated lesioning) suggests a role for cognitive control networks as key drivers of response information flow.     

A QbD Case Study: Bayesian Prediction of Lyophilization Cycle Parameters

As stipulated by ICH Q8 R2 (1), prediction of critical process parameters based on process modeling is a part of enhanced, quality by design approach to product development. In this work, we discuss a Bayesian model for the prediction of primary drying phase duration. The model is based on the premise that resistance to dry layer mass transfer is product specific, and is a function of nucleation temperature. The predicted duration of primary drying was experimentally verified on the lab scale lyophilizer. It is suggested that the model be used during scale-up activities in order to minimize trial and error and reduce costs associated with expensive large scale experiments. The proposed approach extends the work of Searles et al. (2) by adding a Bayesian treatment to primary drying modeling.     

Progress in erigonine spider phylogeny—the <Emphasis Type="Italic">Savignia</Emphasis>-group is not monophyletic (Araneae: Linyphiidae)

We present the most inclusive study on the higher-level phylogeny of erigonine spiders, including about 30% of all erigonine genera. By expanding the previously most comprehensive analysis (Miller and Hormiga Cladistics 20:385–442, 2004) we tested the robustness of its results to the addition of closely related taxa, and also the monophyly of the Savignia-group defined by Millidge (Bulletin of the British Arachnological Society 4:1–60, 1977). The character matrix was expanded by adding 18 newly scored species in 15 genera, and also includes all species scored by other authors. This adds up to 98 species in 91 erigonine genera plus 13 outgroup taxa. The parsimony analysis led to eight fully resolved most parsimonious trees (L=1084). The topology concerning the taxa basal to the ‘distal erigonines’ remained unchanged, and the latter clade still shares 67% of all nodes with the original analysis. The Savignia-group is not monophyletic at genus level with respect to Saloca diceros and Alioranus pastoralis, and the same applies at species level in Diplocephalus and Erigonella. From the Savignia-group, Glyphesis servulus, Diplocephalus cristatus, Savignia frontata, and two representatives each of Erigonella, Dicymbium and Araeoncus combine to form a monophyletic clade.     

Capacity analysis in multi-state synaptic models: a retrieval probability perspective

We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485–493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599–611, 2005; Leibold and Kempter, Cereb Cortex 18:67–77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.     

Dynamics of the sit-to-stand movement

The strategies of the sit-to-stand movement are investigated by describing the movement in terms of the topology of an associated phase diagram. Kinematic constraints are applied to describe movement sequences, thus reducing the dimension of the phase space. This dimensional reduction allows us to apply theorems of topological dynamics for two-dimensional systems to arrive at a classification of six possible movement strategies, distinguished by the topology of their corresponding phase portrait. Since movement is treated in terms of topological structure rather than specific trajectories, individual variations are automatically included, and the approach is by nature model independent. Pathological movement is investigated, and this method clarifies how subtle abnormalities in movement lead to difficulties in achieving a stable stance upon rising from a seated position. This article was processed by the author using the LAT_EX style file pljour2 from Springer-Verlag.     

Endocrine modulation of a pheromone-responsive gene in the honey bee brain

Pheromones cause dramatic changes in behavior and physiology, and are critical for honey bee colony organization. Queen mandibular pheromone (QMP) regulates multiple behaviors in worker bees (Slessor et al. in J Chem Ecol 31(11):2731–2745, 2005). We also identified genes whose brain expression levels were altered by exposure to QMP (Grozinger et al. in Proc Natl Acad Sci USA 100(Suppl 2):14519–14525, 2003). Krüppel-homolog 1 (Kr-h1) RNA levels were significantly downregulated by QMP, and were higher in foragers than in nurses (Whitfield et al. in Science 302(5643):296–299, 2003). Here we report on results of behavioral and pharmacological experiments that characterize factors regulating expression of Kr-h1. Foragers have higher brain levels of Kr-h1 than in-hive bees, regardless of age and pheromone exposure. Furthermore, forager Kr-h1 levels were not affected by QMP. Since the onset of foraging is caused, in part, by increasing juvenile hormone blood titers and brain octopamine levels, we investigated the effects of octopamine and methoprene (a juvenile hormone analog) on Kr-h1 expression. Methoprene produced a marginal (not significant) increase in Kr-h1 expression, but Kr-h1 brain levels in methoprene-treated bees were no longer downregulated by QMP. Octopamine did not modulate Kr-h1 expression. Our results demonstrate that the gene expression response to QMP is not hard-wired in the brain but is instead dependent on worker behavioral state.     

Dynamic formation of oriented patches in chondrocyte cell cultures

Growth factors have a significant impact not only on the growth dynamics but also on the phenotype of chondrocytes (Barbero et al. in J. Cell. Phys. 204:830–838, 2005). In particular, as chondrocytes approach confluence, the cells tend to align and form coherent patches. Starting from a mathematical model for fibroblast populations at equilibrium (Mogilner et al. in Physica D 89:346–367, 1996), a dynamic continuum model with logistic growth is developed. Both linear stability analysis and numerical solutions of the time-dependent nonlinear integro-partial differential equation are used to identify the key parameters that lead to pattern formation in the model. The numerical results are compared quantitatively to experimental data by extracting statistical information on orientation, density and patch size through Gabor filters.     

A statistical light scattering approach to separating fast and slow dynamics

Light scattering is a powerful technique to study the structural and dynamical properties of biomolecular systems or other soft materials such as polymeric solutions and blends or gels. An important application of this technique is the study of the kinetics of formation of supramolecular structures. However, in such cases, the system under study is rapidly changing, and consequently the integration time for each measurement is limited. In order to overcome this difficulty, a statistical approach has been developed based on the analysis of the scattered light intensity distribution (Manno et al. 2006, 2004). Indeed the intensity distribution depends upon the ratio between the integration time of each measurement and the coherence time of scattered radiation. This method has been applied to protein aggregation (Manno et al. 2006) and to sol-gel transition (Manno et al. 2004), to obtain information on the heterogeneity of morphological and dynamical features during such processes. In the present work, we accurately test the validity of this approach by analyzing the statistical properties of the light scattered by a model system: a solution of polystyrene spherical macromolecules of different sizes. Each molecular size is related to a given diffusion coefficient and to a given coherence time of the scattered intensity. The effect of changing the experimental integration time is systematically investigated. A mixture of particles of two different sizes is also analyzed to test the validity and robustness of the model based on the convolution of a gaussian with an exponential distribution. Proceedings of the XVIII Congress of the Italian Society of Pure and Applied Biophysics (SIBPA), Palermo, Sicily, September 2006.