GABAA receptors as molecular targets of general anesthetics: identification of binding sites provides clues to allosteric modulation

PurposeThe purpose of this review is to summarize current knowledge of detailed biochemical evidence for the role of γ-aminobutyric acid type A receptors (GABA(A)-Rs) in the mechanisms of general anesthesia.Principal findingsWith the knowledge that all general anesthetics positively modulate GABA(A)-R-mediated inhibitory transmission, site-directed mutagenesis comparing sequences of GABA(A)-R subunits of varying sensitivity led to identification of amino acid residues in the transmembrane domain that are critical for the drug actions in vitro. Using a photo incorporable analogue of the general anesthetic, R(+)etomidate, we identified two transmembrane amino acids that were affinity labelled in purified bovine brain GABA(A)-R. Homology protein structural modelling positions these two residues, αM1-11' and βM3-4', close to each other in a single type of intersubunit etomidate binding pocket at the β/α interface. This position would be appropriate for modulation of agonist channel gating. Overall, available information suggests that these two etomidate binding residues are allosterically coupled to sites of action of steroids, barbiturates, volatile agents, and propofol, but not alcohols. Residue α/βM2-15' is probably not a binding site but allosterically coupled to action of volatile agents, alcohols, and intravenous agents, and α/βM1-(-2') is coupled to action of intravenous agents.ConclusionsEstablishment of a coherent and consistent structural model of the GABA(A)-R lends support to the conclusion that general anesthetics can modulate function by binding to appropriate domains on the protein. Genetic engineering of mice with mutation in some of these GABA(A)-R residues are insensitive to general anesthetics in vivo, suggesting that further analysis of these domains could lead to development of more potent and specific drugs

Locomotion disorders and skin and claw lesions in gestating sows housed in dynamic versus static groups

Lameness and lesions to the skin and claws of sows in group housing are commonly occurring indicators of reduced welfare. Typically, these problems are more common in group housing than in individual housing systems. Group management type (dynamic versus static) and stage of gestation influence the behavior of the animals, which in turn influences the occurrence of these problems. The present study compared prevalence, incidence and mean scores of lameness and skin and claw lesions in static versus dynamic group housed sows at different stages of gestation during three consecutive reproductive cycles. A total of 10 Belgian sow herds were monitored; 5 in which dynamic groups and 5 in which static groups were utilized. All sows were visually assessed for lameness and skin lesions three times per cycle and the claws of the hind limbs were assessed once per cycle. Lameness and claw lesions were assessed using visual analogue scales. Static groups, in comparison with dynamic groups, demonstrated lower lameness scores (P<0.05) and decreased skin lesion prevalence (24.9 vs. 47.3%, P<0.05) at the end of gestation. There was no difference between treatment group regarding claw lesion prevalence with 75.5% of sows demonstrating claw lesions regardless of group management. Prevalences of lameness (22.4 vs. 8.9%, P<0.05) and skin lesions (46.6 vs. 4.4%, P<0.05) were highest during the group-housed phase compared to the individually housed phases. Although the prevalence of lameness and skin lesions did not differ three days after grouping versus at the end of the group-housing phase, their incidence peaked during the first three days after moving from the insemination stalls to the group. In conclusion, the first three days after grouping was the most risky period for lameness incidence, but there was no significant difference between static or dynamic group management

Inference for stochastic chemical kinetics using moment equations and system size expansion

Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity

LNA++: Linear noise approximation with first and second order sensitivities.

The linear noise approximation (LNA) provides an approximate description of the statistical moments of stochastic chemical reaction networks (CRNs). LNA is a commonly used modeling paradigm describing the probability distribution of systems of biochemical species in the intracellular environment. Unlike exact formulations, the LNA remains computationally feasible even for CRNs with many reactions. The tractability of the LNA makes it a common choice for inference of unknown chemical reaction parameters. However, this task is impeded by a lack of suitable inference tools for arbitrary CRN models. In particular, no available tool provides temporal cross-correlations, parameter sensitivities and efficient numerical integration. In this manuscript we present LNA++, which allows for fast derivation and simulation of the LNA including the computation of means, covariances, and temporal cross-covariances. For efficient parameter estimation and uncertainty analysis, LNA++ implements first and second order sensitivity equations. Interfaces are provided for easy integration with Matlab and Python. Implementation and availability: LNA++ is implemented as a combination of C/C++, Matlab and Python scripts. Code base and the release used for this publication are available on GitHub (https://github.com/ICB-DCM/LNAplusplus ) and Zenodo (https://doi.org/10.5281/zenodo.1287771 )

Cocaine-evoked synaptic plasticity: persistence in the VTA triggers adaptations in the NAc

Addictive drugs hijack mechanisms of learning and memory that normally underlie reinforcement of natural rewards and induce synaptic plasticity of glutamatergic transmission in the mesolimbic dopamine (DA) system. In the ventral tegmental area (VTA), a single exposure to cocaine efficiently triggers NMDA receptor-dependent synaptic plasticity in DA neurons, whereas plasticity in the nucleus accumbens (NAc) occurs only after repeated injections. Whether these two forms of plasticity are independent or hierarchically organized remains unknown. We combined ex vivo electrophysiology in acute brain slices with behavioral assays modeling drug relapse in mice and found that the duration of the cocaine-evoked synaptic plasticity in the VTA is gated by mGluR1. Overriding mGluR1 in vivo made the potentiation in the VTA persistent. This led to synaptic plasticity in the NAc, which contributes to cocaine-seeking behavior after protracted withdrawal. Impaired mGluR1 function in vulnerable individuals could represent a first step in the recruitment of the neuronal network that underlies drug addiction