Cannabinoid addiction: behavioral models and neural correlates

The use of cannabis sativa preparations as recreational drugs can be traced back to the earliest civilizations. However, animal models of cannabinoid addiction allowing the exploration of neural correlates of cannabinoid abuse have been developed only recently. We review these models and the role of the CB1 cannabinoid receptor, the main target of natural cannabinoids, and its interaction with opioid and dopamine transmission in reward circuits. Extensive reviews on the molecular basis of cannabinoid action are available elsewhere (Piomelli et al., 2000;Schlicker and Kathmann, 2001).

Adipose tissue integrity as a prerequisite for systemic energy balance: a critical role for peroxisome proliferator-activated receptor gamma.

Peroxisome proliferator-activated receptor gamma (PPARgamma) is an essential regulator of adipocyte differentiation, maintenance, and survival. Deregulations of its functions are associated with metabolic diseases. We show here that deletion of one PPARgamma allele not only affected lipid storage but, more surprisingly, also the expression of genes involved in glucose uptake and utilization, the pentose phosphate pathway, fatty acid synthesis, lipolysis, and glycerol export as well as in IR/IGF-1 signaling. These deregulations led to reduced circulating adiponectin levels and an energy crisis in the WAT, reflected in a decrease to nearly half of its intracellular ATP content. In addition, there was a decrease in the metabolic rate and physical activity of the PPARgamma(+/-) mice, which was abolished by thiazolidinedione treatment, thereby linking regulation of the metabolic rate and physical activity to PPARgamma. It is likely that the PPARgamma(+/-) phenotype was due to the observed WAT dysfunction, since the gene expression profiles associated with metabolic pathways were not affected either in the liver or the skeletal muscle. These findings highlight novel roles of PPARgamma in the adipose tissue and underscore the multifaceted action of this receptor in the functional fine tuning of a tissue that is crucial for maintaining the organism in good health.

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling.

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.

Dispensable role of myeloid differentiation primary response gene 88 (MyD88) and MyD88-dependent toll-like receptors (TLRs) in a murine model of osteoarthritis.

OBJECTIVES: The aim of our study was to evaluate the role of cell-membrane expressed TLRs and the signaling molecule MyD88 in a murine model of OA induced by knee menisectomy (surgical partial removal of the medial meniscus [MNX]). METHODS: OA was induced in 8-10weeks old C57Bl/6 wild-type (WT) female (n=7) mice and in knockout (KO) TLR-1 (n=7), -2 (n=8), -4 (n=9) -6 (n=5), MyD88 (n=8) mice by medial menisectomy, using the sham-operated contralateral knee as a control. Cartilage destruction and synovial inflammation were evaluated by knee joint histology using the OARSI scoring method. Apoptotic chondrocytes and cartilage metabolism (collagen II synthesis and MMP-mediated aggrecan degradation) were analyzed using immunohistochemistry. RESULTS: Operated knees exhibited OA features at 8weeks post-surgery compared to sham-operated ones. In menisectomized TLR-1, -2, -4, and -6 deficient mice, cartilage lesions, synovial inflammation and cartilage metabolism were similar to that in operated WT mice. Accordingly, using the same approach, we found no significant protection in MyD88-deficient mice in terms of OA progression as compared to WT littermates. CONCLUSIONS: Deficiency of TLRs or their signalling molecule MyD88 did not impact on the severity of experimental OA. Our results demonstrate that MyD88-dependent TLRs are not involved in this murine OA model. Moreover, the dispensable role of MyD88, which is also an adaptor for IL-1 receptor signaling, suggests that IL-1 is not a key mediator in the development of OA. This latter hypothesis is strengthened by the lack of efficiency of IL-1β antagonist in the treatment of OA.

Role of islet microRNAs in diabetes: which model for which question?

MicroRNAs are important regulators of gene expression. The vast majority of the cells in our body rely on hundreds of these tiny non-coding RNA molecules to precisely adjust their protein repertoire and faithfully accomplish their tasks. Indeed, alterations in the microRNA profile can lead to cellular dysfunction that favours the appearance of several diseases. A specific set of microRNAs plays a crucial role in pancreatic beta cell differentiation and is essential for the fine-tuning of insulin secretion and for compensatory beta cell mass expansion in response to insulin resistance. Recently, several independent studies reported alterations in microRNA levels in the islets of animal models of diabetes and in islets isolated from diabetic patients. Surprisingly, many of the changes in microRNA expression observed in animal models of diabetes were not detected in the islets of diabetic patients and vice versa. These findings are unlikely to merely reflect species differences because microRNAs are highly conserved in mammals. These puzzling results are most probably explained by fundamental differences in the experimental approaches which selectively highlight the microRNAs directly contributing to diabetes development, the microRNAs predisposing individuals to the disease or the microRNAs displaying expression changes subsequent to the development of diabetes. In this review we will highlight the suitability of the different models for addressing each of these questions and propose future strategies that should allow us to obtain a better understanding of the contribution of microRNAs to the development of diabetes mellitus in humans.

The role of the T-cell receptor (TCR) in alpha beta/gamma delta lineage commitment: clues from intracellular TCR staining.

T cells belong to two mutually exclusive lineages expressing either alpha beta or gamma delta T-cell receptors (TCR). Although alpha beta and gamma delta cells are known to share a common precursor the role of TCR rearrangement and specificity in the lineage commitment process is controversial. Instructive lineage commitment models endow the alpha beta or gamma delta TCR with a deterministic role in lineage choice, whereas separate lineage models invoke TCR-independent lineage commitment followed by TCR-dependent selection and maturation of alpha beta and gamma delta cells. Here we review the published data pertaining to the role of the TCR in alpha beta/gamma delta lineage commitment and provide some additional information obtained from recent intracellular TCR staining studies. We conclude that a variant of the separate lineage model is best able to accommodate all of the available experimental results.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Communication, Coordination, and Efficiency in Evolutionary One-population Models

We analyze the role of commitment in pre-play communication for ensuring efficient evolutionarily stable outcomes in coordination games. All players are a priori identical as they are drawn from the same population. In games where efficient outcomes can be reached by players coordinating on the same action we find commitment to be necessary to enforce efficiency. In games where efficienct outcomes only result from play of different actions, communication without commitment is most effective although efficiency can no longer be guaranteed. Only when there are many messages then inefficient outcomes are negligible as their basins of attraction become very small.

Co-evolution between sociality and dispersal: The role of synergistic cooperative benefits.

Explaining the evolution of sociality is challenging because social individuals face disadvantages that must be balanced by intrinsic benefits of living in a group. One potential route towards the evolution of sociality may emerge from the avoidance of dispersal, which can be risky in some environments. Although early studies found that local competition may cancel the benefits of cooperation in viscous populations, subsequent studies have identified conditions, such as the presence of kin recognition or specific demographic conditions, under which altruism will still spread. Most of these studies assume that the costs of cooperating outweigh the direct benefits (strong altruism). In nature, however, many organisms gain synergistic benefits from group living, which may counterbalance even costly altruistic behaviours. Here, we use an individual based model to investigate how dispersal and social behaviour co-evolve when social behaviours result in synergistic benefits that counterbalance the relative cost of altruism to a greater extent than assumed in previous models. When the cost of cooperation is high, selection for sociality responds strongly to the cost of dispersal. In particular, cooperation can begin to spread in a population when higher cooperation levels become correlated with lower dispersal tendencies within individuals. In contrast, less costly social behaviours are less sensitive to the cost of dispersal. In line with previous studies, we find that mechanisms of global population control also affect this relationship: when whole patches (groups) go extinct each generation, selection favours a relatively high dispersal propensity, and social behaviours evolve only when they are not very costly. If random individuals within groups experience mortality each generation to maintain a global carrying capacity, on the other hand, social behaviours spread and dispersal is reduced, even when the latter is not costly.

Essential role of platelet activation via protease activated receptor 4 in tissue factor-initiated inflammation.

INTRODUCTION: Tissue factor (TF) activation of the coagulation proteases enhances inflammation in animal models of arthritis and endotoxemia, but the mechanism of this effect is not yet fully understood - in particular, whether this is primarily due to fibrin formation or through activation of protease activated receptors (PARs). METHODS: We induced extravascular inflammation by injection of recombinant soluble murine TF (sTF1-219) in the hind paw. The effects of thrombin inhibition, fibrinogen and platelet depletion were evaluated, as well as the effects of PAR deficiency using knockout mice deficient for each of the PARs. RESULTS: Injection of soluble TF provoked a rapid onset of paw swelling. Inflammation was confirmed histologically and by increased serum IL-6 levels. Inflammation was significantly reduced by depletion of fibrinogen (P < 0.05) or platelets (P = 0.015), and by treatment with hirudin (P = 0.04) or an inhibitor of activated factor VII (P < 0.001) compared with controls. PAR-4-deficient mice exhibited significantly reduced paw swelling (P = 0.003). In contrast, a deficiency in either PAR-1, PAR-2 or PAR-3 did not affect the inflammatory response to soluble TF injection. CONCLUSION: Our results show that soluble TF induces acute inflammation through a thrombin-dependent pathway and both fibrin deposition and platelet activation are essential steps in this process. The activation of PAR-4 on platelets is crucial and the other PARs do not play a major role in soluble TF-induced inflammation.

The role of primary care in the prevention and control of healthcare associated infections

Little research has been conducted to date on the role of primary health care (PHC) in the prevention of healthcare associated infections (HCAIs). The present article is a theoretical study of the principle of primum non nocere and aims to promote reflection on the role of PHC in HCAI prevention with emphasis on practical recommendations. The indirect and direct roles of PHC in HCAI prevention are debated in light of this guiding principle. With respect to the indirect role of PHC, we discuss the issues of hospital-centrism and ambulatory care-sensitive conditions. The article outlines a number of challenges faced by health services related to PHC’s direct role in HCAI prevention, highlights seven key components of HCAI prevention programmes within the PHC sphere and provides practical recommendations for HCAI control and prevention.


Constitutively active mutants of the alpha 1B-adrenergic receptor: role of highly conserved polar amino acids in receptor activation.

Site-directed mutagenesis and molecular dynamics simulations of the alpha 1B-adrenergic receptor (AR) were combined to explore the potential molecular changes correlated with the transition from R (inactive state) to R (active state). Using molecular dynamics analysis we compared the structural/dynamic features of constitutively active mutants with those of the wild type and of an inactive alpha 1B-AR to build a theoretical model which defines the essential features of R and R. The results of site-directed mutagenesis were in striking agreement with the predictions of the model supporting the following hypothesis. (i) The equilibrium between R and R depends on the equilibrium between the deprotonated and protonated forms, respectively, of D142 of the DRY motif. In fact, replacement of D142 with alanine confers high constitutive activity to the alpha 1B-AR. (ii) The shift of R143 of the DRY sequence out of a conserved 'polar pocket' formed by N63, D91, N344 and Y348 is a feature common to all the active structures, suggesting that the role of R143 is fundamental for mediating receptor activation. Disruption of these intramolecular interactions by replacing N63 with alanine constitutively activates the alpha 1B-AR. Our findings might provide interesting generalities about the activation process of G protein-coupled receptors.

The pathogenic role of tissue-resident immune cells in psoriasis.

Psoriasis is a common chronic inflammatory skin disease, the study of which might also be of considerable value to the understanding of other inflammatory and autoimmune-type diseases, such as rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis and diabetes mellitus. There is clear evidence that T cells and dendritic cells have a central role in psoriasis. Based on recent data from humans and animal models, we propose that a psoriasis lesion can be triggered and sustained by the local network of skin-resident immune cells. This concept focuses attention on local, rather than systemic, components of the immune system for rationalized therapeutic approaches of psoriasis and possibly also other chronic inflammatory diseases.

The role of role uncertainty in modified dictator games

We compare behavior in modified dictator games with and without role uncertainty. Subjectschoose between a selfish action, a costly surplus creating action (altruistic behavior) and acostly surplus destroying action (spiteful behavior). While costly surplus creating actions are themost frequent under role uncertainty (64%), selfish actions become the most frequent withoutrole uncertainty (69%). Also, the frequency of surplus destroying choices is negligible with roleuncertainty (1%) but not so without it (11%). A classification of subjects into four differenttypes of interdependent preferences (Selfish, Social Welfare maximizing, Inequity Averse andCompetitive) shows that the use of role uncertainty overestimates the prevalence of SocialWelfare maximizing preferences in the subject population (from 74% with role uncertainty to21% without it) and underestimates Selfish and Inequity Averse preferences. An additionaltreatment, in which subjects undertake an understanding test before participating in theexperiment with role uncertainty, shows that the vast majority of subjects (93%) correctlyunderstand the payoff mechanism with role uncertainty, but yet surplus creating actions weremost frequent. Our results warn against the use of role uncertainty in experiments that aim tomeasure the prevalence of interdependent preferences.

Two-sided learning in New Keynesian models: Dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank(two-sided learning) in a New Keynesian framework in which both sides of the economyhave asymmetric and imperfect knowledge about the true data generating process. Weassume that all agents employ the data that they observe (which may be distinct fordifferent sets of agents) to form beliefs about unknown aspects of the true model ofthe economy, use their beliefs to decide on actions, and revise these beliefs througha statistical learning algorithm as new information becomes available. We study theshort-run dynamics of our model and derive its policy recommendations, particularlywith respect to central bank communications. We demonstrate that two-sided learningcan generate substantial increases in volatility and persistence, and alter the behaviorof the variables in the model in a significant way. Our simulations do not convergeto a symmetric rational expectations equilibrium and we highlight one source thatinvalidates the convergence results of Marcet and Sargent (1989). Finally, we identifya novel aspect of central bank communication in models of learning: communicationcan be harmful if the central bank's model is substantially mis-specified.