Pungent general anesthetics activate transient receptor potential-A1 to produce hyperalgesia and neurogenic bronchoconstriction

BACKGROUND: Volatile anesthetics such as isoflurane and halothane have been in clinical use for many years and represent the group of drugs most commonly used to maintain general anesthesia. However, despite their widespread use, the molecular mechanisms by which these drugs exert their effects are not completely understood. Recently, a seemingly paradoxical effect of general anesthetics has been identified: the activation of peripheral nociceptors by irritant anesthetics. This mechanism may explain the hyperalgesic actions of inhaled anesthetics and their adverse effects in the airways. METHODS: To test the hypothesis that irritant inhaled anesthetics activate the excitatory ion-channel transient receptor potential (TRP)-A1 and thereby contribute to hyperalgesia and irritant airway effects, we used the measurement of intracellular calcium concentration in isolated cells in culture. For our functional experiments, we used models of isolated guinea pig bronchi to measure bronchoconstriction and withdrawal threshold to mechanical stimulation with von Frey filaments in mice. RESULTS: Irritant inhaled anesthetics activate TRPA1 expressed in human embryonic kidney cells and in nociceptive neurons. Isoflurane induces mechanical hyperalgesia in mice by a TRPA1-dependent mechanism. Isoflurane also induces TRPA1-dependent constriction of isolated bronchi. Nonirritant anesthetics do not activate TRPA1 and fail to produce hyperalgesia and bronchial constriction. CONCLUSIONS: General anesthetics induce a reversible loss of consciousness and render the patient unresponsive to painful stimuli. However, they also produce excitatory effects such as airway irritation and they contribute to postoperative pain. Activation of TRPA1 may contribute to these adverse effects, a hypothesis that remains to be tested in the clinical setting.

Metabifurcation analysis of a mean field model of the cortex

Mean field models (MFMs) of cortical tissue incorporate salient, average features of neural masses in order to model activity at the population level, thereby linking microscopic physiology to macroscopic observations, e.g., with the electroencephalogram (EEG). One of the common aspects of MFM descriptions is the presence of a high-dimensional parameter space capturing neurobiological attributes deemed relevant to the brain dynamics of interest. We study the physiological parameter space of a MFM of electrocortical activity and discover robust correlations between physiological attributes of the model cortex and its dynamical features. These correlations are revealed by the study of bifurcation plots, which show that the model responses to changes in inhibition belong to two archetypal categories or “families”. After investigating and characterizing them in depth, we discuss their essential differences in terms of four important aspects: power responses with respect to the modeled action of anesthetics, reaction to exogenous stimuli such as thalamic input, and distributions of model parameters and oscillatory repertoires when inhibition is enhanced. Furthermore, while the complexity of sustained periodic orbits differs significantly between families, we are able to show how metamorphoses between the families can be brought about by exogenous stimuli. We here unveil links between measurable physiological attributes of the brain and dynamical patterns that are not accessible by linear methods. They instead emerge when the nonlinear structure of parameter space is partitioned according to bifurcation responses. We call this general method “metabifurcation analysis”. The partitioning cannot be achieved by the investigation of only a small number of parameter sets and is instead the result of an automated bifurcation analysis of a representative sample of 73,454 physiologically admissible parameter sets. Our approach generalizes straightforwardly and is well suited to probing the dynamics of other models with large and complex parameter spaces.