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Background: Nasal congestion in allergic rhinitis (AR) is caused by vascular hyperpermeability and vascular relaxation of the nasal mucosa. We previously detected high levels of a lipoxygenation metabolite of dihomogammalinolenic acid, 15-hydroxy-8Z,11Z,13E-eicosatrienoic acid (15-HETrE) in the nasal lavage fluid of AR model mice. Here, we investigated the effects of 15-HETrE on vascular functions associated with nasal congestion. Methods: We measured 15-HETrE levels in the nasal lavage fluid of ovalbumin-induced AR model mice and nasal discharge of patients with AR. We also assessed nasal congestion and vascular relaxation in mice. Vascular contractility was investigated using isolated mouse aortas. Results: Five ovalbumin challenges increased 15-HETrE levels in AR model mice. 15-HETrE was also detected in patients who exhibiting AR-related symptoms. Intranasal administration of 15-HETrE elicited dyspnea-related behavior and decreased the nasal cavity volume in mice. Miles assay and whole-mount immunostaining revealed that 15-HETrE administration caused vascular hyperpermeability and relaxation of the nasal mucosa. Intravital imaging demonstrated that 15-HETrE relaxed the ear vessels that were precontracted via thromboxane receptor stimulation. Moreover, 15-HETrE dilated the isolated mouse aortas, and this effect was attenuated by K+ channel inhibitors and prostaglandin D2 (DP) and prostacyclin (IP) receptor antagonists. Additionally, vasodilatory effects of 15-HETrE were accompanied by an increase in intracellular cAMP levels. Conclusions: Our results indicate that 15-HETrE, whose levels are elevated in the nasal cavity upon AR, can be a novel lipid mediator that exacerbates nasal congestion. Moreover, it can stimulate DP and IP receptors and downstream K+ channels to dilate the nasal mucosal vasculature.

Although the appropriate evaluation of mouse behavior is crucial in pharmacological research, most current methods focus on single mouse behavior under light conditions, owing to the limitations of human observation and experimental tools. In this study, we aimed to develop a novel marker-less tracking method for multiple mice with top-view videos using deep-learning-based techniques. The following stepwise method was introduced: (i) detection of mouse contours, (ii) assignment of identifiers (IDs) to each mouse, and (iii) correction of mis-predictions. The behavior of C57BL/6 mice was recorded in an open-field arena, and the mouse contours were manually annotated for hundreds of frame images. Then, we trained the mask regional convolutional neural network (Mask R-CNN) with all annotated images. The mouse contours predicted by the trained model in each frame were assigned to IDs by calculating the similarities of every mouse pair between frames. After assigning IDs, correction steps were applied to remove the predictive errors semi-automatically. The established method could accurately predict two to four mice for first-look videos recorded under light conditions. The method could also be applied to videos recorded under dark conditions, extending our ability to accurately observe and analyze the sociality of nocturnal mice. This technology would enable a new approach to understand mouse sociality and advance the pharmacological research.