Background: Histamine (HA), a small molecule that is synthesized from the amino acid histidine, plays an important role in the immune system where it is associated with allergies, inflammation, and T-cell regulation. In the brain, histamine is stored in mast cells and other non-neuronal cells and also acts as a neurotransmitter. The histamine neuron cell bodies are in the tuberomammillary (TM) nucleus of the hypothalamus and these neurons send projections throughout the central nervous system (CNS), in particular to the cerebral cortex, amygdala, basal ganglia, hippocampus, thalamus, retina, and spinal cord. HA neurons make few synapses, but release HA from the cell bodies and from varicosities when the neurons fire. Thus the HA neural system seems to modulate and control the HA concentration in projection regions. It is known that high HA levels in the extracellular space inhibit serotonin release, so HA may play a role in the etiology of depression.
Results: We compare model predictions to classical physiological experiments on HA half-life, the concentration of brain HA after histidine loading, and brain HA after histidine is dramatically increased or decreased in the diet. The model predictions are also consistent with in vivo experiments in which extracellular HA is measured, using Fast Scan Cyclic Voltammetry, in the premammillary nucleus (PM) after a 2 s antidromic stimulation of the TM, both without and in the presence of the H 3 autoreceptor antagonist thioperamide. We show that the model predicts well the temporal behavior of HA in the extracellular space over 30 s in both experiments.
Conclusions: Our ability to measure in vivo histamine dynamics in the extracellular space after stimulation presents a real opportunity to understand brain function and control. The observed extracellular dynamics depends on synthesis, storage, neuronal firing, release, reuptake, glial cells, and control by autoreceptors, as well as the behavioral state of the animal (for example, depression) or the presence of neuroinflammation. In this complicated situation, the mathematical model will be useful for interpreting data and conducting in silico experiments to understand causal mechanisms. And, better understanding can suggest new therapeutic drug targets.